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+Amidst several widely publicized suicides among adolescents with a minority sexual orientation in the past year and a half [1], there has been a national conversation about what can be done to reduce and prevent suicides among lesbian, gay, and bisexual (LGB) youths. Within this context, several individuals have initiated court cases against school districts whose policies may
+have harmed LGB students by their failure to adopt policies that protect LGB youths [2], including inclusive anti-bullying policies [3]. Although social science data are frequently used in court cases involving issues related to sexual orientation [4,5], there is currently a paucity of research examining the associations between anti-bullying policies and mental health outcomes for LGB students upon which to inform policy recommendations. The goal of the present study was to address this gap in the literature.
+Evaluating the associations between anti-bullying policies and LGB youths’ mental health has important implications for etiologic and prevention research. Population-based studies of adolescents in the United States have consistently shown that LGB youths’ rates of suicide attempts are between two and seven times higher than those of their heterosexual peers [6]. Although
+these disparities are well-documented, there is comparably less research on the processes that create risk for, or protection against, suicide attempts among LGB youths. Consequently, establishing associations between anti-bullying policies and reduced risk of suicide attempts among LGB youths would provide critical information on social and contextual protective factors within this population and aid in public health intervention efforts.
+Recent research has shown that social policies negatively targeting gays and lesbians, including constitutional amendments banning same-sex marriage [7] and the absence of employment nondiscrimination acts [8], are robust predictors of psychiatric morbidity among LGB adults. Whereas negative social policies appear to increase risk for psychopathology in LGB populations, supportive policies and programs may protect LGB individuals against the development of mental health problems [9,10]. For instance, LGB youths who attend schools with Gay-Straight Alliances report less suicidality than youths who attend schools without these programs [11]. These empirical findings are consistent with ecosocial [12] and ecological systems [13] theories, both of which highlight the importance of broad social and contextual influences, including family, school, and neighborhood factors, on health and development. Thus, several lines of evidence suggest that inclusive anti-bullying policies may be associated with reduced prevalence of suicide attempts among LGB youths [7—11]. The current study tested this hypothesis by evaluating whether LGB students living in counties with a greater proportion of school districts with inclusive antibullying policies have a lower risk of suicide attempts.
+Methods
+Sample and setting
+We obtained data from the Oregon Healthy Teens (OHT) study. Annual OHT surveys are administered to more than one third of Oregon’s eighth- and 11th-grade students attending public schools. Each year, a random sample of districts within counties and schools within districts is selected. Participating students came from 34 counties (no respondents were sampled in the remaining two counties in Oregon). The questionnaire was available in both English and Spanish. All participants were assured that the survey is anonymous and voluntary, and parents provided passive consent for their children to participate. For the current study, we pooled data from the years 2006 (when sexual orientation was first assessed) to 2008 (the most recently available data), to increase the sample size of LGB participants. Sampling for the 2007—2008 years was conducted so that each school would be asked to participate as part of the state sample once in the 2-year period, minimizing the likelihood that the same schools were sampled in multiple years. In 2008, 75.4% of the eighth- and 11th-grade students in participating schools completed the OHT survey.
+Measures
+Demographic variables including sex and race/ethnicity were obtained via self-report. Sexual orientation, which is only assessed in the survey of 11th graders, was measured with a single item asking respondents to indicate “which of the following best describes you.” Four response options were given: (1) heterosexual (straight); (2) gay or lesbian;
+(3) bisexual, and (4) not sure. Of the 33,714 original OHT respondents, 30,439 (90.3%) self-identified as heterosexual, 301 (.9%) self-identified as gay or lesbian, and 1,112 (3.3%) selfidentified as bisexual. We excluded from analyses participants who indicated that they were “not sure” about their sexual orientation (n = 653; 1.9%), which is consistent with previous studies [14]. An additional 1,209 respondents did not complete the sexual orientation item, and were also excluded. Consequently, the final sample size was 31,852. The sociodemographic characteristics of the LGB sample in the OHT study are provided in a previous report [9].
+Independent variable
+We obtained data on school anti-bullying policies at the district level from the Oregon Department of Education. We analyzed school district websites and high school student handbooks for 197 school districts. If we were not able to obtain policy information from this search (31 school districts), we contacted the individual school district to request this information. Of the 197 districts in Oregon, we were not able to obtain information for 18 districts, which we coded as missing. The missing data were largely clustered within four counties: Of the 36 counties in Oregon, 60% (21 counties) had no missing district data, 31% (11 counties) had only one or two districts with missing data, and 11% (four counties) had more than half of districts with missing data. We conducted sensitivity analyses by removing respondents from the four counties with the most missing data. The magnitude of the results remained unchanged when we removed these counties from the analyses, so the current report included all counties in the analyses.
+We first coded school district websites and student handbooks for whether the districts had any anti-bullying policies (these policies had to specifically mention bullying; harassment and antidiscrimination policies were not included in this category). Next, we coded the policies to indicate whether they contained an enumerated list of groups specifically covered by the policy, and finally, whether the enumerated list included sexual orientation. Policies had to include the phrase “sexual orientation” (e.g., in a list of protected class statuses) to be considered to protect LGB youth. Thus, these data made it possible to differentiate among (1) the absence of anti-bullying policies; (2) the presence of anti-bullying policies including specific categories (e.g., gender, race, religion), but not sexual orientation (which are hereafter referred to as “restrictive antibullying policies” [This category includes districts with antibullying policies but no enumeration of specific protected groups, as well as districts with anti-bullying policies with enumeration of groups, but no mention of sexual orientation]); and (3) anti-bullying policies that were inclusive of sexual orientation (which are hereafter referred to as “inclusive antibullying policies”).
+Because information on location of residence was available only at the county level, we aggregated the measures of antibullying policies from the district to the county level by dividing the number of school districts with anti-bullying policies by the total number of school districts in the county. We created variables of the proportion of school districts that had restrictive and inclusive anti-bullying policies within each of the Oregon counties. Of the school districts with available data, 7% had no anti-bullying policies; among districts with anti-bullying policies, 37% did not include sexual orientation as a protected
+class status. Of the counties with available data, 15% had no districts with inclusive anti-bullying policies; 18% had fewer than half of their school districts with inclusive policies; and only 15% of the counties had 100% of their school districts with inclusive policies.
+Outcome variable
+Participants were asked the number of times they attempted suicide during the past 12 months. Given the non-normal distribution, we examined suicide attempts as a dichotomous outcome. The suicide question used in the OHT was based on a measure from the Youth Risk Behavior Surveillance Survey, which showed excellent test—retest reliability (k = 76.4) [15,16].
+Covariates
+We were interested in examining whether anti-bullying policies were associated with reduced risk of suicide attempts after controlling for exposure to peer victimization, a risk factor for suicide attempts among sexual minority adolescents [17,18]. Exposure to peer victimization was assessed by asking participants, “During the last 30 days, have you been harassed at school (or on the way to or from school)?” This item had a “yes” or “no” response option.
+Statistical analysis
+The analytic strategy consisted of four steps corresponding to the four study aims. First, we calculated differences in suicide attempts and risk factors between LGB and heterosexual youth using basic descriptive cross-tabulations. Second, we tested whether the effect of inclusive anti-bullying policies on suicide attempts varies by sexual orientation. For this aim, we divided the inclusive anti-bullying policy into tertiles based on the distribution in the data. Third, we examined whether inclusive anti-bullying policies were significantly associated with suicide attempts among LGB youth after adjusting for individual-level risk factors (sociodemographic characteristics and peer victimization). For this aim, we entered inclusive anti-bullying policies as a continuous variable, with larger values indicating a higher proportion of districts with inclusive anti-bullying policies within the county. For the second and third study aims, we used Generalized Estimating Equations, a method developed for handling clustered data, in which the observations within each cluster are correlated with each other [19]. Given that OHT respondents were nested within their county of residence, we used Generalized Estimating Equations to account for the correlations among observations from each individual within the same county. Fourth, we repeated the second and third study
+aims to determine whether the presence of any anti-bullying policies (i.e., restrictive policies) buffered LGB youth against risk of suicide attempts, or whether these protective effects were only observed for policies that specifically include sexual orientation (i.e., inclusive policies). These analyses therefore tested the specificity of the protective effects of inclusive antibullying policies on rates of suicide attempts among LGB youth.
+Recent research that has disaggregated bisexuals from gay and lesbian youths has shown that bisexual adolescents are more likely to attempt suicide than gay and lesbian youths [20]; consequently, we separated these groups in all analyses. Given the relatively small number of lesbian and gay participants, we did not stratify analyses by sex. Statistical significance was set at a = .05.
+Results
+Lesbian, gay, and bisexual respondents were significantly more likely to have attempted suicide in the past 12 months than heterosexuals (%2 = 109.1; degrees of freedom = 2; p < .001). Approximately 21% of lesbian and gay youths and 23% of bisexual youths reported attempting suicide at least once in the previous 12 months, compared with 4.3% of their heterosexual peers. Lesbian, gay, and bisexual adolescents were also more likely to report past-30-day peer victimization (lesbian and gay: 60.2%; bisexual: 56.7%; heterosexual: 28.8%), compared with heterosexual youths. These group differences in peer victimization were statistically significant: %2 = 175.4; degrees of freedom = 2; p < .001.
+Associations between inclusive anti-bullying policies and suicide attempts
+We divided the inclusive anti-bullying policies into tertiles ranging from least inclusive (i.e., counties with the smallest proportion of school districts with inclusive policies) to most inclusive (i.e., counties with the largest proportion of school districts with inclusive policies). We examined the prevalence of suicide attempts within each tertile for the three different sexual orientation groups (lesbian/gay, bisexual, and heterosexual).
+Among lesbian and gay youths, the risk of suicide attempts was lowest in counties that had the greatest proportion of school districts with inclusive policies (Table 1). The proportion of lesbian and gay respondents attempting past-year suicide within the tertiles was as follows: most inclusive (16.67%); medium (19.05%); and least inclusive (31.08%). Lesbian and gay youths living in the least inclusive counties were 2.25 times (95% confidence interval [CI], 1.13—4.49) more likely to have attempted suicide in the past year compared with those in the most inclusive counties.
+In contrast, we did not observe this pattern for the bisexual or heterosexual youths. Among bisexual youths living in the most inclusive counties, 20.76% attempted suicide in the past year, compared with 25.65% in the medium and 22.11% in the least inclusive counties. Bisexual youths living in the least inclusive counties were not more likely to attempt suicide than those living in the most inclusive counties (odds ratio [OR] = 1.08; 95% CI, .75—1.56). Similarly, the proportion of heterosexual respondents attempting suicide was nearly identical across tertiles: least inclusive (4.72%); medium (3.77%); and most inclusive (4.45%). Heterosexual youths were no more likely to attempt suicide in the least inclusive compared with the most inclusive counties (OR = 1.06; 95% CI, .93—1.22).
+Having documented a protective effect of inclusive antibullying policies only among lesbian and gay youths, we next tested whether there was an association between inclusive antibullying policies and suicide attempts over and above peer victimization experiences (Table 2). In the full sample, peer victimization was significantly more likely to occur in the least inclusive (31.59%) compared with the most inclusive (29.69%) counties (Wald F = 4.44; p = .01). Even after adjusting for peer victimization and sociodemographic characteristics (sex and race/ethnicity), a higher proportion of districts with inclusive anti-bullying policies was associated with reduced risk for suicide attempts among lesbian and gay youths (OR = 0.18; 95% CI, .03—.92).
+Tests of specificity
+We conducted follow-up analyses to determine whether these effects were specific to inclusive anti-bullying policies. Results indicated that having any anti-bullying policy (i.e., restrictive policies that did not include sexual orientation as a protected class status) did not protect lesbian and gay youths from attempting suicide. The proportion of gay and lesbian respondents attempting suicide did not differ between the low-and high-inclusion categories: 21.56% and 20.00%, respectively. Moreover, after controlling for other established risk factors for suicide attempts (Table 3), restrictive anti-bullying policies did not buffer lesbian and gay youths against attempting suicide (OR = .38; 95% CI, .02—7.33).
+Discussion
+Suicide is the third leading cause of death among youths aged 15—24 years [21], and studies have consistently documented
+sexual orientation—related disparities in suicide attempts among adolescents [6,22]. However, the prevalence of suicide attempts among LGB youths does not appear to be invariant across social context. For instance, a recent study found that the risk of suicide attempts was 20% higher among LGB youths living in communities characterized by lower support for gays and lesbians (e.g., counties with a lower density of same-sex couples and fewer schools with protective policies), compared with LGB youths living in more supportive communities [9]. In addition, data from the pooled 2001—2009 Youth Risk Behavior Surveillance Survey studies showed that, across 13 states and cities that included a measure of sexual identity, rates of past-year suicide attempts among gay and lesbian youths ranged from a low of 15.1% to a high of 34.3%, over a twofold difference [23]. This geographic variation in the prevalence of suicide attempts among lesbian and gay adolescents suggests that social and contextual factors likely contribute to sexual orientation disparities in suicide attempts. The current study examined school policies, and in particular inclusive anti-bullying policies, as one social/contex-tual factor that may lower the risk of suicide attempts among LGB adolescents. We highlight four key findings below.
+First, as the proportion of school districts that adopted inclusive anti-bullying policies increased, rates of past-year suicide attempts among lesbian and gay youths decreased. Whereas 31% of lesbian and gay adolescents attempted suicide in counties where school districts were the least likely to adopt inclusive anti-bullying policies, only 17% attempted suicide in counties with the greatest proportion of school districts with inclusive policies. In models adjusted for established risk factors at the individual level (sex, race/ethnicity, and peer victimization), inclusive anti-bullying policies remained significantly associated with lower rates of suicide attempts among lesbian and gay youths.
+Second, peer victimization of all youth was also less likely to occur in counties with inclusive anti-bullying policies. These results not only suggest one potential mechanism linking inclusive anti-bullying policies to reduced risk of suicide attempts in lesbian and gay youth, but also demonstrate that policies protecting sexual minority adolescents may confer benefits for heterosexual youths as well [9].
+Third, the results documented specificity of the protective effects of inclusive anti-bullying policies to lesbian and gay youths. Inclusive anti-bullying policies did not reduce the risk of suicide attempts among bisexual youths. Recent studies that have disaggregated gay and lesbian from bisexual youths suggest one possible explanation for these results. This research has
+Data represent the Generalized Estimating Equations model predicting suicide attempts in the past 12 months. We entered restrictive anti-bullying policy policies as a continuous variable, ranging from 0 to 1.0. Higher values indicate a greater proportion of districts with inclusive anti-bullying policies. Sex: male = 0; female = 1. Race/ethnicity: non-white = 0; white = 1. Peer harassment (0 = no peer victimization in past 30 days).
+Abbreviations as in Table 1.
+illustrated that risk factors for mental health problems among bisexual youths are somewhat distinct from those for individuals with same-sex sexual orientations [24], which suggests that factors benefiting gay and lesbian youths do not always generalize to bisexual youths. Given the high rates of suicide attempts among bisexual youths observed in this study and others [20], the identification of social and contextual factors that protect bisexual youths from engaging in suicidal behaviors represents an important avenue for future inquiry. In addition, inclusive anti-bullying policies were not associated with a decreased risk for suicide attempts in the heterosexual sample. It is likely that these policies are more relevant to subgroups of heterosexual youths that are targets of bullying, such as the overweight or obese [25]. However, we did not code for other groups that were protected in these inclusive policies, which was beyond the scope of this study. This remains an important topic that can be examined in subsequent research with this sample.
+Fourth, the results documented specificity of the effects to inclusive anti-bullying policies. That is, policies had to include sexual orientation in the list of protected class statuses to be associated with significantly lower rates of suicide attempts among lesbian and gay youths. There was not sufficient evidence to indicate that restrictive anti-bullying policies (which did not enumerate sexual orientation) exerted a mental health benefit for lesbian and gay students. These results therefore suggest the importance of specifically including sexual orientation in antibullying policies that enumerate protected groups, to signal supportive and inclusive school environments for lesbian and gay youths. However, over three quarters of the school districts had restrictive anti-bullying policies; thus, most students, both LGB and heterosexual, were in districts with at least some antibullying policies. The limited range for this variable may have reduced our ability to detect significant results for the restrictive anti-bullying policies.
+This study had several limitations. The OHT survey assesses youths attending public schools. Results are therefore not generalizable to students attending private or alternative schools, or to adolescents who do not attend school. In addition, a quarter of school districts that were randomly selected declined to participate in the study. The OHT does not provide information on these school districts. Consequently, we cannot determine to what extent differential nonresponse by school district might affect the study’s results.
+In addition to issues of sampling, there are measurement limitations. In particular, the number of questions that can be included in large-scale surveys such as the OHT is necessarily limited, especially given the time constraints involved in
+administering questionnaires in classroom settings. Thus, in many cases, the OHT survey relied on single-item questions, including those for suicide attempts and peer victimization. Although the reliability of these measures has been well validated [15,16], future studies examining similar research questions would benefit from more detailed assessments of suicide attempts and associated risk factors.
+Our measure of school policies is also subject to a number of limitations. First, because the OHT study does not release information on the individual schools participating in the survey, it was not possible to obtain data on whether these policies were enforced in the schools. An important direction for future studies is to conduct detailed assessments of the extent to which school policies are consistent with daily practices. Second, school policies on bullying are determined at the district level; however, data had to be aggregated to the county, because participants’ residence was available only at this level of analysis. This approach could introduce potential error in the county variable; however, this would likely bias us toward the null, because we would not expect that misclassification is related to the proportion of students attempting suicide within the county. Consequently, these results are likely a conservative estimate of the association between anti-bullying policies and suicide attempts among lesbian and youth.
+A final study limitation is that the data are cross-sectional. Consequently, we are unable to determine whether antibullying policies are causally related to decreases in suicide attempts among lesbian and gay youth, or whether such policies are merely a marker of more supportive environments known to protect LGB youth [9]. Future studies with stronger research designs are needed to strengthen causal inferences regarding the effect of anti-bullying policies on LGB health. For instance, quasi-experimental designs can be used to compare rates of suicide attempts among LGB youth before and after inclusive antibullying policies are implemented.
+Despite these limitations, the current study has several methodological advantages for testing relationships between anti-bullying policies and suicide attempts. The large, population-based sample increases generalizability of the results and minimizes biases that may occur with convenience samples of LGB youths [26]. Moreover, unlike many previous studies [27], the LGB and heterosexual participants in the OHT study were recruited using identical sampling methods (i.e., through schools), which further diminished sampling biases [28]. An additional strength was the ability to document associations between social policies and mental health at geographic scales below the state level. Most studies that have examined the health consequences of policies targeting gays and lesbians have been conducted at the state level [7,8]. Because the OHT study released data at the county level, we were able to use measures of ecological environments that are more proximal to LGB youth.
+This study provides a significant contribution to the literature on social determinants of suicide attempts among sexual minority youths. In particular, the results indicate that the social environments in which lesbian and gay adolescents are embedded can shape their mental health, independent of individual-level characteristics. Schools are key social contexts in which important health and developmental processes unfold for adolescents [29]. In documenting associations between inclusive anti-bullying policies in schools and reduced risk of suicide attempts among lesbian and gay youth, this study lends further empirical support to the argument that social policies exert
+downstream health effects [30,31]. Consequently, altering negative social environments surrounding LGB youths through policylevel changes may ultimately lead to reductions in sexual orientation—related disparities in suicide attempts, an important public health priority [32].
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+INTRODUCTION
+Substance use disorders (SUD)—and opioid use disorders (OUD), in particular—are among the most stigmatized conditions in psychiatry and, indeed, throughout societies more generally [1-4]. Such stigma leads to fears of discrimination and negative repercussions that prevent or delay sufferers from seeking treatment leading to greater morbidity and mortality risk [5]. To help mitigate the
+negative personal and public health impact of stigma in relation to drug-related impairment, different medical terminology (e.g. ‘chronically relapsing brain disease’, ‘disorder’) has been adopted and deployed explicitly by US federal public health agencies [6] [e.g. National Institute on Drug Abuse (NIDA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), Substance Abuse and Mental Health Services Administration (SAMHSA)], the American Psychiatric Association (APA)
+1758 John F Kelly et al.
+[7] (e.g. substance use ‘disorder’ category in DSM-5) and prominent addiction-specific medical organizations [e.g. American Society of Addiction Medicine (ASMSA)] [8]. The belief is that greater emphasis on the brain-based and medical nature of drug-related impairment will reduce stigma. At the same time, others have vehemently objected to the over-medicalization of substance-related impairment [9-11], arguing that doing so may undermine personal agency and self-efficacy among sufferers, thereby reducing prognostic optimism and the likelihood that someone would initiate or persevere in salutary change efforts. While these issues remain hotly debated, there are no existing rigorous empirical data to inform the field about which terms may be optimal and under what circumstances.
+The choice of language and terminology used is particularly important with regard to drug-related impairment, because whether or not we are aware of it, the use of certain terms can perpetuate stigmatizing attitudes that influence the selection and effectiveness of our social and public health policies for addressing them [12-14]. In fact, rigorous scientific investigations have now shown that certain common terms in the field used to describe individuals suffering from chronic drug-related impairment (e.g. ‘substance abuser’) may actually induce explicit and implicit cognitive biases that result in a perceived need for punishment rather than treatment [15-17]. Such research has made it difficult to trivialize and dismiss the terminology debate as merely ‘semantics’ or a linguistic preference for ‘political correctness’.
+Whereas several terms are used somewhat interchangeably across federal and state public health agencies, we are not aware of any rigorous research that may inform and guide the choice regarding which terms may be optimal in describing the phenomena of drug-related impairment itself. There have been recent efforts to re-assert the notion of addiction as a ‘disease’ characterized by brain-related structural deficits and functional impairment [18] (e.g. ‘brain disease’) or as a chronically relapsing variant thereof (e.g. ‘chronically relapsing brain disease’). It has also been described more generally as simply a medical ‘disease’ or ‘illness’ without specific emphasis on the brain per se, and also as a ‘disorder’ as in the current fifth edition of the Diagnostic and Statistical Manual (DSM-5) of the American Psychiatric Association (AMA) [7]. It is also often referred to more generically as a ‘problem’ (e.g. a ‘drug problem’).
+With the exception of the latter, most, if not all, these terms are used explicitly with the intention of placing the responsibility for addressing these problems squarely within the broad realm of medicine, psychiatry and public health, and to reduce blame and associated self- and public stigma in order that more people will seek and stay engaged with treatment [16,19]. Little is known,
+however, about any differential impact of the use of each of these commonly used terms when applied to drug-related impairment on well-studied dimensions of stigma, such as perceptions of blame, danger, social distance, treatment need and prognostic optimism. Although it is challenging to assess the impact of stigma directly, randomized experimental designs have been conducted to assess differences in attitudes that result from differential exposure to certain terminology typically presented within a vignette (e.g. Kelly & Westerhoff, 2010). Using this type of study design, compelling evidence has emerged from the mental health field that emphasizing more biomedically oriented genetic explanations of causes of mental illness may reduce blame attributions, but increase prognostic pessimism and perceptions of dangerousness [20] and that emphasizing brain-based neurobiological explanations for mental illness actually may increase perceptions of dangerousness, desire for social distance and pessimism about people’s likelihood of recovering.
+Although untested, it is thus conceivable that describing drug-related impairment as a ‘chronically relapsing brain disease’, while intended to diminish self-blame and stigma, may similarly increase perceptions that someone is chronically volatile and dangerous, thereby increasing attitudes of social exclusion and reducing beliefs that the affected person can recover. Knowledge of such attitudes are important, particularly in the general population, as public opinion can exert pressure for greater investment in therapeutic versus punitive criminal justice approaches to addressing drug-related impairment at local and national levels.
+To this end, using a nationally representative sample of the US general population, this randomized study is intended to shed light upon this hotly debated issue of whether differential use of commonly used terms produce attitudinal differences across dimensions of stigma and perceptions governing the likelihood of recovery. Given the recent dramatic rise in opioid use disorder and overdose deaths in the United States and several other nations, in this study we test an example relating to opioid-related impairment, in particular. Specifically, the study tested whether (1) commonly used terminology differentially affected perceptions of stigma; (2) perceptions of stigma differed depending upon whether the opioid-impaired person being portrayed was depicted as a man or a woman; and (3) any observed stigma differences across terminology depended upon whether the portrayed opioid-impaired person was a man or a woman. It is hoped that this investigation will provide some empirical basis for the choice of terminology in our clinical practices with patients, families and colleagues, as well as in our broader public health and social policies and communication efforts.
+METHOD
+Participants
+A nationally representative sample of non-institutionalized adults in the United States was recruited in partnership with Ipsos, an internationally recognized survey company, to participate in this experimental study. Participants enrolled in Ipsos’ ‘KnowledgePanel’—the largest probability-based on-line panel assembled via addressbased sampling and representative of the United States pop-ulation—were screened for eligibility in this study. The KnowledgePanel uses address-based sampling (ABS) to randomly select individuals from 97% of all US households based on the US Postal Service’s Delivery Sequence File. If necessary, Ipsos provides individuals with a web-enabled computer and free internet service. Using this Ipsos is able to include households that (a) have unlisted telephone numbers, (b) do not have landline telephones, (c) are cellphone only, (d) do not have current internet access and (e) do not have devices to access the internet. This type of broad-scale sampling helps to redress socio-economic differences in landline telephone use and internet access. Ipsos’ population-based probability sampling approach has been vetted and validated in dozens of published studies in the medical and behavioral health fields (e.g. Journal of the American Medical Association, JAMA Internal Medicine, Journal of Consulting and Clinical Psychology). Eligible people were adults aged 18 years and older and English-speakers. In order to produce unbiased estimates of population parameters from these respondents, survey data are weighted to account for selection probabilities, non-response and under-coverage. The sample is weighted to geo-demographic benchmarks obtained from the US Census Bureau’s Current Population Survey (CPS), including gender, age, race/ ethnicity, education, census region, household income, home ownership and metropolitan area. The resulting weights are used as a measure of size, which is then applied using probability-proportional-to-size, to select studyspecific samples. After data are collected, design weights are adjusted to account for differential non-response using iterative proportional fitting (i.e. raking). Finally, outlier weights are trimmed and resulting weights are scaled to the total sample size of eligible participants.
+The survey was pre-tested over 30 days in December 2019-January 2020 to estimate time to completion and identify potential pitfalls in the survey to be addressed. The official survey was administered over 16 days in February 2020. Of the 5998 participants who were sampled, 3635 completed the survey (61% completion rate). This response rate is comparable to most other current nationally representative surveys [e.g. National Epidemiologic Survey on Alcohol and Related Condi-tions-III (NESARC-III), 60.1% [21]; the 2018 National Survey on Drug Use and Health (NSDUH), 58.3%] [5].
+Non-responders to the screening question were sent e-mail reminders on days 3, 7 and 11 of the survey period. The median time it took for participants to complete the survey was 8 minutes. Participants were able to refuse to respond to an item or skip it entirely. If they skipped the item, they were provided with a warning notification.
+Procedures
+Participants were randomized to receive one of 12 vignettes (six terms x gender) describing a person who had become increasingly involved with opioids and was currently receiving treatment wherein they were learning about the exact nature of their condition described in one of six different ways as: ‘a chronically relapsing brain disease’, ‘a brain disease’, ‘a disease’, ‘an illness’, ‘a disorder’ or ‘a problem’. Vignettes also depicted the person as either male or female, but used the same gender-neutral name (Alex’) making for a total of 12 randomized cells (approximately n = 300 participants per cell). The vignette used was as follows:
+Alex was having serious trouble at home and work because of (his/her) increasing opioid use. (He/She) is now in a treatment program where (he/she) is learning from staff that (his/her) drug use is best understood as a (chronically relapsing brain disease/brain disease/ disease/illness/disorder/problem) that often impacts multiple areas of one’ss life. Alex is committed to doing all that (he/she) can to ensure success following treatment. In the meantime, (he/she) has been asked by (his/her) counselor to think about what (he/she) has learned with regard to understanding (his/her) opioid use as a (chronically relapsing brain disease/brain disease/disease/illness/disorder/problem).
+Participants were asked to read their specific assigned vignette and then answered 2 7 stigma-related questions reliably clustered within five subscales (stigma-blame; social distance/exclusion; prognostic optimism, need for continu-ingcare, perceived danger; a = 0.70-0.83). All study procedures were approved by the Massachusetts General Hospital Partners HealthCare Institutional Review Board. The study was not pre-registered on a publicly available platform, and thus results should be considered exploratory.
+Measures
+Demographic characteristics
+Demographic data were derived from the Ipsos’ existing KnowledgePanel sample of respondents (collected prior to the survey), as well as from our survey data for variables not assessed by Ipsos. Regarding existing demographic data, participants reported the following: (a) age, (b) sex
+Addiction, 116, 1757-1767
+at birth, (c) level of education, (d) race/ethnicity, (e) marital status, (f) employment, (g) household income and (h) US census region.
+Stigma and attributions
+Twenty-seven questions covering multiple dimensions of stigma and attitudes towards opioid-related impairment were administered as part of the stigma and attribution assessment. The measure comprised five distinct scales, (a ranged from 0.70 to 0.83) including: (1) blame attribution (five items), (2) prognostic optimism (five items), (3) need for continuing care (three items), social distance (five items) and attribution (nine items; AQ-9 [22,23], a = 0.72). For each question, response options ranged from ‘strongly disagree’ to ‘strongly agree’ on a corresponding 1-6 scale. Apart from the AQ-9, most items were constructed as distinct scales for the purpose of the study by the first author and/or adapted from prior work [24]
+Statistical analysis
+We first described the distribution of demographic characteristics in our study population. In this sample we evaluated the internal consistency and construct validity of the 27-item stigma measure using exploratory factor analyses and Cronbach’s alpha coefficient. Exploratory factor analysis of all 2 7 items was used to identify stigma subscales. First, a principal components analysis was used to determine the number of factors to extract. We plotted the eigenvalues on a scree plot to identify an inflection point that corresponded to the number of factors that explained a sufficient proportion of the variance (i.e. approximately 5% or greater) in stigma. Factor loadings were estimated using orthogonal or oblique rotation depending upon whether factors displayed a low or moderate/high intercorrelation, respectively. We eliminated items with low item-total correlations as well as low factor loadings (X < 0.35) and high uniqueness. Total scores for each subscale were calculated as the sum of all retained items. Using unadjusted linear regression models, we estimated the mean difference (i.e. beta coefficient) in each of the final stigma subscales as a function of: (1) opioid-related impairment terminology, (2) gender of the vignette character and (3) gender of the vignette character stratified by opioid-related impairment terminology. In the opioid-related impairment terminology regression models, ‘chronically relapsing brain disease’ was included as the reference group. In the models examining stigma as a function of the gender of the vignette character in the full sample, and when stratified by opioid-related impairment terminology, female was the reference group. Given the potential for more subtle nuances to be obscured if a participant had a language other than English as a first language, we conducted a sensitivity analysis excluding
+all people who spoke a language other than English in their home to determine whether language fluency influenced the observed findings. All analyses were conducted in Stata version 14 and incorporated sampling weights.
+RESULTS
+Characteristics of study population
+The sample included 3635 adults living in the northeast (17.5%), Midwest (20.8%), South, (3 7.9%), or Western (23.8%) region of the United States. On average, participants were 47.8 years of age and most were female at birth (52.4%), non-Hispanic white (63.1%), married (54.9%), had at least some college education (61.0%), were working (59.6%) and reported an income > US$50 000 (68.3%; Table 1).
+Psychometrics of the stigma and attribution scales
+We extracted five factors based on the eigenvalues, scree plot and variance explained in the overall stigma construct (see Supporting information, Appendix S1). Of the 27 items, three were initially dropped due to low factor loadings and high uniqueness (items 1, 13, 15). Oblique rotation was used to estimate the factor loadings due to the moderate-high correlation of extracted factors. Low internal consistency of the ‘need for continuing care’ subscale resulted in the removal of two additional items displaying low item-total correlations (items 17 and 19; see Table 2 for list of all items). Final stigma subscale reliability coefficients are shown also in Table 2).
+Differences in stigma and attitudes toward opioid-related impairment in the US adult population as a function of terminology and gender
+Main effect of terminology
+Relative to participants who were randomized to receive the vignette describing the opioid-impaired person as having a ‘chronically relapsing brain disease’, participants whose vignette included any of the other five terms (brain disease, disease, illness, disorder, problem) reported significantly higher levels of blame attribution toward the individual with the opioid-related impairment (Table 3). We identified a relationship suggesting that blame attributions were highest for individuals with a ‘problem’ or ‘disease’, moderate blame attribution for individuals with an ‘illness’ or ‘disorder’ and lower levels of blame attribution for individuals with a ‘brain disease’ followed by the lowest levels of blame attribution for individuals with a ‘chronic relapsing brain disease’ (Fig. 1).
+Of note, participants who were randomized to receive the vignette describing the depicted person as having an opioid ‘problem’ were more likely to attribute more
+Addiction, 116, 1757-1767
+Pct = percent; CI = confidence interval.
+personal blame for the opioid impairment; however, at the same time they were more likely to view that same person more positively in terms of viewing them as being less dangerous, viewing that person as being more able to recover from their opioid impairment and less likely to need continuing care than those participants who were randomized to the term ‘chronically relapsing brain disease’. In addition to characters portrayed as having a ‘problem’, characters described as having a ‘disorder’ or ‘brain disease’ were also perceived as less likely to require continuing care relative to characters portrayed as having a ‘chronically relapsing brain disease’. We did not find any significant differences in perceptions of need for social distance as a function of the different vignette terms.
+Main effect of gender
+When comparing the effect of gender of the character portrayed in the vignette (collapsing across terminology), we found that, when the person with the opioid-related
+impairment was described as a male, participants attributed significantly less blame, but a desire for greater social distance and expressed higher levels of perceived dangerousness relative to participants who were randomized to a vignette with a female character exhibiting opioid-related impairment. We did not identify significant differences in prognostic optimism or need for continuing care between participants who were randomized to a vignette with a female versus male character with opioid-related impairment.
+Effect of gender by terminology
+We further examined whether gender modified the effects of exposure to the opioid-related impairment terms (or null effects) observed in the main effects analysis (Table 3; Fig. 2). We found that decreased blame attribution toward male characters relative to female characters with opioid-related impairment was observed only when the term ‘chronically relapsing brain disease’ was included in
+the vignette. Specifically, participants were less likely to attribute blame to males with a chronically relapsing brain disease relative to females with a chronically relapsing brain disease. We did not identify any significant differences in blame attribution by gender when other terms were included in the vignettes.
+We found that males with a brain disease were significantly less likely to be perceived to need continuing care relative to females with a brain disease. Across all terms, participants expressed a desire for increased levels of social distance from males relative to females exhibiting opioid-related impairment; however, this was only statistically significant when the opioid-related impairment was referred to as a ‘brain disease’, a ‘disease’ or an ‘illness’. Males with a ‘disease’ or ‘illness’ were also perceived as more dangerous relative to females whose opioid impairment was described as a ‘disease’ or an ‘illness’. Results of the sensitivity analysis restricting our sample to participants who reported English as the primary language spoken at home (84.7% of the sample) did not reveal notable differences in the pattern of effects of gender or term on the stigma and attribution subscales.
+DISCUSSION
+Using a large nationally representative sample of the US general population and a randomized design, our study examined the impact of exposure to different common terms used to describe someone suffering from opioid-related impairment (i.e. ‘chronically relapsing brain disease’, ‘brain disease’, ‘disease’, ‘illness’, ‘disorder’, ‘problem’) on perceptions of several dimensions of stigma (e.g. blame, dangerousness), treatment need and prognostic optimism. Findings were nuanced with differential effects observed across terminology, gender and dimensions of stigma.
+In terms of the main effect of terminology, perhaps the most notable finding was that whereas there were beneficial stigma-reducing effects observed for certain terms on certain stigma dimensions, there was not one clear single term that produced beneficial effects across all dimensions of stigma, treatment need and prognostic optimism. Specifically, exposure to the ‘chronically relapsing brain disease’ term was associated with the lowest levels of stigmatizing blame attributions; in fact, exposure to any other term was associated with a significant increase in stigmatizing blame although, intriguingly, the blame effect was related in a linear ordinal fashion with ‘problem’, resulting in the greatest stigmatizing blame attribution. In contrast, study participants who were exposed to the person described as having an opioid ‘problem’ compared to ‘chronically relapsing brain disease’ exhibited the strongest beliefs that the person could recover (Fig. 1), were less dangerous and less likely to require continuing care. These findings support the use of the ‘chronically relapsing brain disease’
+term to reduce stigmatizing blame, but simultaneously suggest that this may not be the best term to use to convey the more positive notion that someone with opioid-related impairment is approachable and can recover; in that case, the less medical and more generic, ‘problem’ term may be optimal.
+In terms of gender of the subject, compared to a man, a woman exhibiting opioid-related impairment was judged significantly more harshly—as more to blame. Conversely, when study participants were exposed to a male versus a female character, they seemed more afraid and rated both social distance and danger higher for a man than a woman with the same level of opioid-related impairment. It is perhaps expected that a man would be viewed as more dangerous and for people to want to stay further away from a man than a woman due to greater perceived aggression,
+but it is noteworthy that a woman was judged more harshly and more personally to blame for exhibiting opioid-related impairment than a man.
+Aspects of this pattern became clearer and more pronounced when examining the results of the stratified models. When described using the ‘chronically relapsing brain disease’ terminology women may be viewed as more personally responsible, suggesting a potentially harsher and less forgiving social stance against women—even when exhibiting the same level of opioid-related impairment. This may be a case of socially stereotyped exonerating expectations that ‘boys will be boys’ (i.e. ‘bad’ behavior is to be expected and is excusable) and that ‘girls should behave’, thereby implicitly assigning greater levels of expected pre-programmed externalizing behavior and impulsivity regarding male behavior. However, the pattern
+Addiction, 116, 1757-1767
+is complex, as although men may be viewed less harshly for exhibiting opioid-related impairment when described in that manner, they are more likely to be viewed as more dangerous and thus socially ostracized and excluded overall compared to women.
+Limitations
+Observed differences were small in absolute magnitude, and the extent to which such differences may translate into actual real-world differences in terms of behavior of the general population is not known; for example, whether this would mean voting for a particular policy measure or not (e.g. increased appropriation for treatment). The set of six terms used as the levels of the independent variable is highly applicable within a US English-speaking cultural context; applicability in other cultures could vary. It would be very helpful to know also what terms drug-impaired person themselves would regard as either helping or discouraging them. Future research should examine this. Also, we used opioid-related impairment in this study as a specific example of ‘drug-related’ impairment—we do not know the extent to which observed differences in the stigma dimensions would generalize to other substances. Also, although the focus here was to examine general (main) effects in response to certain commonly used terminology, this pattern of findings could be moderated by specific respondent characteristics (e.g. personal history of a substance problem), which is worthy of further investigation. Finally, we explored the covariance and internal consistency of items to identify meaningful subscales; however,
+this 2 7-item measure has not been previously validated. We summed the item scores within each subscale for this analysis to be consistent with the way these items have been previously scored, which may have also introduced measurement error. Further research exploring the measurement and criterion validity of these stigma subscales is needed to confirm their ability to assess substance use stigma and related attitudes.
+Implications for practice and policy
+In summary, findings suggest that there may not be one single recommended term that can be applied across the board to meet all desired clinical and public health goals when attempting to reduce stigma. Choice of terminology may depend on the purpose of communication: to reduce stigmatizing blame, the more biomedical ‘chronically relapsing brain disease’ terminology may be optimal; to increase prognostic optimism and decrease perceived danger and social exclusion of affected people’s use of non-medical terminology (e.g. ‘opioid problem’) may be optimal. Findings also suggest that women may be judged more harshly than men, possibly due to broad cultural sex-based stereotypes governing differential acceptability of opioid-related impairment; and men, overall, may have more difficulty being trusted and reintegrating into society due to greater fears that they present more danger.
+Declaration of interests
+We have no conflicts of interest to report. Dr. John Kelly has received funding from the United States national institutes
+of health (NIH) as well as the US substance abuse and mental health services administration (SAMHSA), state governments, and private foundations to conduct research on addiction and its treatment.
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+1.	Introduction
+In most parts of the world, suicide rates in older adults are generally higher than their younger counterparts (Conwell, 2014). In order to comprehend the severity of late-life suicides, not only the absolute suicide rates but also their relative suicide age ratios (i.e., suicide rate ratios between older adults versus the younger age groups) within the respective population should be considered as well. For example, according to the Global Burden of Disease (“GBD”) Study in 2015, suicide
+rates in the population aged 60 years and above in China, Austria and Ukraine were similar at around 28.0 per 100,000. However, due to greater variations in suicide rates among the younger age groups, the old to non-old suicide rate ratios of those three countries were 4.64, 2.29, and 1.30, respectively. Suicide age ratios vary significantly among countries and regions of the world, thus providing a new perspective to understand the meaning of suicide rates in old age across the globe. Greater understanding of the age patterns of suicides could result in potential preventive solutions (Snowdon et al., 2017). To the best of the
+authors’ knowledge, there are no existing studies that have explored the global variations of suicide age ratios and their associated factors.
+Families are valuable resources in not only providing caregiving but also imparting a sense of worth, lasting emotional ties, and human dignity to elders in their later years (Walsh, 2016). From a life course perspective, previous researchers have found that higher risks in late-life suicides are associated with the unique experiences of the elderly adapting to age-related challenges and family dysfunction (Chan et al., 2014; Chang et al., 2017; Duberstein et al., 2004; Park and Moon, 2016; Purcell et al., 2012; Rubenowitz et al., 2001; Van Orden et al., 2015). However, whether the socioeconomic status of older people within families is associated with suicide risks has never been properly examined. In this study, loss of socioeconomic status in the older adults within their families was measured by three critical constructs: i) loss of household headship, ii) loss of residential independence, and iii) loss of pension support.
+With regard to family status in a culture-based index, being the “head of household” indicates the importance of a family member which is related to the power to control and allocate the family's economic and social resources (Phua et al., 2001). Loss of family headship therefore represents an important life-stage transition associated with the fundamental questions of independence and authority that lend sociological meaning to the concept of old age (Gordon et al., 1981).
+Living with one's children reflects loss of independence, which is a valued condition. Owing to the stigma of dependency in the dominant culture, most older adults in good health prefer to maintain a separate household from their children, yet sustaining frequent contacts, reciprocal emotional ties and mutual support in a pattern aptly termed “intimacy at a distance” (Blenkner, 1965; Walsh, 2016). With the advancement of telecommunication and transportation technologies, high levels of geographic mobility in modern societies have significantly supported the aforementioned living arrangement (Phua et al., 2001). Nonetheless, the transition from independent living to co-residence with the younger generations in later life is common today, reflecting reduced autonomy of the older adults in family life.
+Financial independence is important for the older adults in keeping their authority in the family. For instance, those who are more financially independent will be consulted more frequently than those who are supported economically by their children (Williams et al., 1999). In other words, economically independent elders could play a considerable role in family decisions. The pension scheme, a well-known policy to maintain financial security, led to significant reductions in poverty rates among older adults (Lloyd-Sherlock et al., 2012). Moreover, pension receipt directly affects well-being of retired older adults with a low economic status (Ju et al., 2017). Earlier studies have demonstrated that low financial status in older adults may act as a stressor that exacerbates any ongoing deterioration in psychological well-being and contributes to suicide risk (Almeida et al., 2012; Duberstein et al., 2004).
+In this study, it is hypothesized that lower late-life socioeconomic status within families would significantly increase suicide rate among the older adults. In order to take into account different base rates among different countries, suicide age ratios are used instead. To be more specific, loss of household headship, dependence in residence, and receiving no pension might elevate the suicide age ratios. The aim of this study is firstly to examine the variability of suicide age ratios in the world, and secondly to illustrate the associations of suicide age ratios with potential socioeconomic factors including household headship, living arrangement, and whether in receipt of pension in later life.
+2.	Methods
+2.1.	Data and measures
+Suicide age ratio was measured by the old (>=60 years) to non-old (<60 years) suicide rate ratios, which was our dependent variable of
+primary interest. The suicide data in the year of 2015 were obtained from the Global Burden of Disease (“GBD”) Study (Global Burden of Disease Collaborative Network, 2016) and suicide mortality was identified by the International Classification of Diseases, 10th Revision (“ICD-10") codes X60-X84 (self-harm). Suicide age ratios for the 173 regions were computed in the world.
+In this study, lower socioeconomic status within family was conceptualized by three aspects: losing household headship, living dependently, and having no pension, which reflected the honorary, residential and economic status of older adults, respectively. Furthermore, based on the data available for consistent comparisons across the nations worldwide, we used three variables to measure older people's socioeconomic status: i) the percentages of households with heads aged 60 years and above, ii) the percentages of households with both older adults aged 60 and above and children under 15, and iii) the proportions of the population above retirement age receiving a pension.
+The percentages of households with the heads aged 60 years and above were obtained from the United Nations Report on Household Size and Composition Around the World 2017. The head of household was nominated by family members in the census or survey. Elderly headship rate is calculated by dividing the number of heads aged 60 years or over identified on the household roster of the census or survey by the total number of household heads (United Nations, 2017). The data were the latest available estimates (i.e. the data for the most recent years) between 1990 and 2015 for 141 regions and ranged from 12% in North Korea to 44% in Italy.
+The percentages of households with both older adults aged 60 and above and children under 15 were also obtained from the United Nations Report on Household Size and Composition Around the World 2017. It is calculated by dividing the number of households with at least one member under age 15 years and at least one member aged 60 years or over by the total number of households (United Nations, 2017). The data represented estimates from 1990 to 2015 for 125 regions and ranged from near 0% in Germany and the Netherlands to 34% in Gambia.
+The proportions of the population above retirement age receiving a pension were extracted from the United Nations Statistics Division. It is calculated by dividing the number of population above retirement age receiving a pension by the total number of population above retirement age (United Nations Statistics Division, Department of Economic and Social Affairs, 2017). Among the latest available data from 132 regions of the world during the period 2010-2016, the values on pension ranged from 0.93% in Myanmar to 100% in many European Countries.
+2.2.	Analytic strategies
+Suicidal rates for each country were age-standardized by the standard structure of the world population in 2015. In this study, the threshold of the older adults was 60 years and above. In order to take into account the respective suicide rates in each of the countries, suicide age ratios were calculated and a world map was constructed according to the different levels of suicide age ratios including <1.0, [1.0, 2.0), [2.0, 3.0), [3.0, 4.0), and >=4. Scatterplots of three exposure variables (i.e., household headship, living with descendants, and recipient of pension) and log-transformed suicide age ratios were presented in the figures in the Appendix (Appendix Figs. A1-A3).
+Forest plots were performed to assess whether elderly household headship, living with descendants, and recipient of pension moderated the worldwide patterns of suicide age ratios. Three exposure factors were not modeled as continuous variables as the relationships between them and suicide age ratios were not linear. In the moderation analyses, percentages of the elderly heads were modeled as a dichotomized variable by the first quarter point: the higher (>19%) versus the lower (<=19%). Similarly, regions were classified by median value into higher (>11%) versus lower (<=11%) percentages of late-life co-re-sidence of the elderly with their descendants. Percentages of the elderly
+receiving a pension above retirement age were also grouped as a dichotomized variable by median value: the higher (>73%) versus the lower (<=73%). It could be seen clearly that from the United Nations Report on Household Size and Composition around the World 2017, the vast majority of countries in Africa and Asia had the very low percentages of elderly headship less than the first quarter point at 19%. Unlike the population in these Africa and Asia countries shared with the common practice of multi-generational living arrangements, older adults in other countries prefer to maintain a separate household from their children, which leads to higher percentages of elderly headship than 19%. Therefore, on the cultural and empirical bases, the first quarter point is preferable to the median split as the cut-off for the household headship variable. Pooled suicide age ratios with 95% confidence interval of associations between the suicide age ratios and the potential affecting factors were calculated using the Comprehensive Meta-Analysis software program. The software program takes population size of each analyzed region into account. Total between-group variance (“Total Qb-) was then calculated to examine the differential moderation effect among the different subgroups. Gender-specific forest plots were also constructed to examine the variance of the moderation effect between men and women.
+Crude and adjusted regression analyses were then utilized to estimate the extent to which elderly household headship, living with descendants, and recipient of pension affect suicidal age ratios in the world. Stratified analyses were also performed based on gender-specific data. The outcome variable was log-transformed as according to the Kline's rule, i.e., skew index absolute value <3; kurtosis index absolute values <10 (Kline, 2005), the distribution of suicide age ratios was not normal. The countries with missing data on the independent variables were handled by the Listwise Deletion method and were not used in the analyses.
+3.	Results
+From Fig. 1, there were significant variations of suicide age ratios across different regions in the world. On the whole, the suicide age ratios were higher than 1.00 in most parts of world, indicating that worldwide, suicide rates in older adults were generally higher than the younger population. The highest old to non-old suicide rate ratios were found in the Western Pacific and African regions.
+Appendix Table 1 shows that there were strong correlations among the suicide age ratios and the potential factors. To be specific, higher suicide age ratio was significantly correlated with lower percentage of elderly household head (r =-0.36, P <0.01), higher percentage of coresidence of the elderly with their descendants (r = 0.37, P <0.01), and lower percentage of the population receiving a pension above retirement age (r =-0.51, P <0.01). Scatterplots of three exposure variables, i.e., household headship, living with descendants, recipient of pension, and log-transformed suicide age ratios were presented in Appendix Figs. A1-A3, respectively.
+Fig. 2 presents the forest plots of suicide age ratios between countries with the higher versus the lower percentages of household heads aged 60 and above. Regions with higher elderly headship percentages had the lower suicide age ratio (1.69), whereas regions with lower percentages of elderly headship had the higher suicide age ratio (2.73). There was a significant difference between the higher and lower subgroups (Qb = 7.57, P = 0.01). In terms of the gender-specific analyses, the impact of household headship on suicide age ratios was only found in men (ratios = 1.77 vs. 2.92, P = 0.02) but not in women (ratios = 2.10vs. 2.54, P = 0.55).
+Fig. 3 shows the forest plots of suicide age ratios among countries with higher versus lower percentages of co-residence of the elderly with their descendants. As to the overall population, regions with higher percentages of co-residence of the elderly with their descendants had the higher suicide age ratio (2.72), whereas regions with lower percentages of co-residence of the elderly with their descendants had lower suicide age ratio (1.39). There was a significant difference between the higher and the lower subgroups (Qb = 12.14, P<0.01). In addition, the impact of co-residence of the elderly with their descendants on suicide age ratios could be observed in men (ratios = 2.83 vs. 1.56, P = 0.01) but not in women (ratios = 1.88vs. 1.57, P = 0.16).
+Referring to the forest plots of suicide age ratios in countries with higher versus lower percentages of older adults receiving a pension (Fig. 4), regions with higher percentages of the elderly receiving a pension had lower old to non-old suicide rate ratios, whereas regions with lower percentages of the elderly receiving a pension had higher old to non-old suicide rate ratios. The significant impact of pension on suicide age ratios could be observed in the overall population (ratios = 1.42vs. 2.76, P<0.01), men (ratios = 1.56vs. 2.91, P<0.01), and women (ratios = 1.64vs. 2.66, P<0.01).
+As seen from Models 1-3 in Table 1, the crude regression analyses showed that the lower status of the elderly within a family in terms of the loss of household headship, dependent dwelling and having no pension, were significantly associated with higher suicide age ratios in overall population and both genders (P <0.01). Since the factors were correlated with each other, adjustments had also been made to the regression analyses. In the adjusted model (Model 4), receiving no pension remained to be a significant determinant for both overall population (P = 0.01) and men (P<0.01) but not for women (P = 0.29), and loss of household headship was only significant for men (P = 0.05) but not for either overall population (P = 0.22) or women (P = 0.55), whereas the elderly living with their descendants was no longer significant for either overall population (P = 0.60) or both genders (men: P = 0.72; women P = 0.11).
+4.	Discussion
+The present study reveals that worldwide variations in suicide age ratios were associated with constructs reflecting the socioeconomic status of the older adults within families. Relatively higher suicidal risks in later life were linked to loss of domestic headship/authority, living with their descendants, and receiving no pension. In the case of the absence of pension provision, it showed robust effects on higher suicide age ratios worldwide. The culture-based indicator of intra-family status revealed that household headship was more sensitive in men than in women. The impact of co-residence with the younger generations on suicide age ratios was however controlled by the elderly economic status and household headship.
+Many previous ecological studies have examined how certain factors such as mental health funding and mental health service provision (Shah and Bhat, 2008), life expectancy and markers of socioeconomic
+status and health care (Shah et al., 2008), and elderly dependency ratios (Shah et al., 2008) are specifically associated with suicide rates in later life but without considering the relative suicide rate ratios. The present findings suggested that certain socioeconomic factors may lead to higher suicide rates in older adults and consequently higher suicide age ratios. Based on the present research, for cross-national comparison, the ratio was more robust than the rate itself. Suicide age ratio was used as it was a better-chosen indicator to compare the prevalence rates of two specific population groups, namely, the older adults versus the non-older adults. In addition, as the quality of GBD suicide rates data was not so reliable in low- and middle-income countries, presuming that there is no differential underreporting by age, exploring suicide age ratios may be better able to address concerns about potential underreporting of absolute age-specific suicide rates.
+The family life cycle theory has placed the nuclear family as a group with its regular patterns of expansion, transition, and contraction (Mattessich and Hill, 1987). The present findings can well be understood from the family developmental perspective. Families in later life are facing the graying transitions and challenges. With the structural contraction of a family from a multi-generational household to an elderly couple or single parent, changes brought about by retirement, grandparenthood, illnesses, deaths, widowhood and so on, alter complex relationships within a household, often requiring family support, adjustments to losses, reorientations, and reorganizations (Walsh, 2016). Many disturbances such as mental problems are associated with losses in family adaptation and moving to the stages of “empty nest” and “aging families” such as loss of household headship, loss of independent residence and loss of financial security.
+This study is the first to detect that loss of household headship, the culture-based indicator of domestic status, was significant to the suicide age ratios in men but not in women. The potential explanation lies in the cultural construction of traditional gender roles in a family, namely, the patriarchal authority of masculinity, but there was no such cultural expectation for women. According to traditional culture, patriarchy was mainly based on the construction of the norm of the male as breadwinner (Seccombe, 1986). Other family members may also have difficulty with the retirement of the male head, accompanied by losses of his job-related status and social network (Walsh, 2016). The loss of the elderly male as the household head signified loss of authority, and with it his self-esteem, and replaced by his adult children within the family. The role of the male gender tends to emphasize greater levels of strength and independence, and reinforcement of this gender role often deters the males from seeking help in suicidal thoughts, feelings, as well as depression (Zhang, 2014). The present study suggests the need for further research on how to enhance the resilience of the males in later life and how to renegotiate their relationships to achieve a new balance with other family members after their loss of headship.
+This study further illustrates that the impact of the suicide risk of the elderly living with the younger generations was relatively mitigated by the recipient of pension and household headship of the elderly. Predominantly, recipient of pension showed robust effects on higher old to non-old suicide rate ratios in both men and women in the world. In other words, depending on the late-life status of the elderly, especially on whether in receipt of pension, living with their descendants has dual effects on the well-being of the elderly within the family. If the seniors have economic independence such as a pension, they do not have to become a financial burden on their children, and can even provide better grandparenthood, which in turn benefits their health. On the other hand, having no pension can significantly strain relationships with cohabited descendants. Therefore, those older adults who have lost their jobs and benefits should find new work or make contributions to the family such as housework and caring for the grandchildren, but they should always be aware of facing age discrimination. Owing to the stigma of dependency in the dominant culture, based on the Interpersonal Theory of Suicide, perceived burdensomeness could increase the suicide risks for the older adults (Jahn et al., 2011).
+According to previous studies, living arrangement for the elderly also yielded the most inconsistent and mixed effects on late-life suicidal risks (Chang et al., 2017). Thus, the present observation on the associations between the elderly living with their descendants and late-life suicide risk should be highly context dependent. For instance, living with children in the Chinese community is more than an indication of dependency, however, it is usually a cultural expectation that children take responsibility to support their older parents and show their filial piety. This seems to contradict the intention to measure loss of residential independence. Another problem is that our measurement of living arrangement would include households with only older adults and children, which indicates that grandparents supporting grandchildren with the absence of parents, in contrast to older adults with a loss of residential independence as intended to be measured as well. Moreover, as the Interpersonal Theory of Suicide posits, perceived burdensomeness and thwarted belonging are both powerful drivers of suicide in later life (Jahn et al., 2011). Hence, there should be more future studies to examine the effects of suicide on the older adults living with family members.
+The present ecological findings suggest that strategies to enhance the socioeconomic status of older adults may be important to prevent suicides in later life both within and across countries on a grand scale. At the base of a 5-tier health impact pyramid, interventions with the greatest potential impact are efforts to address the socioeconomic determinants of health (Frieden, 2010). The present findings provide important evidence to highlight the substrata role of socioeconomic factors in public health as well as late-life suicide prevention across countries. Although the exact mechanisms by which socioeconomic
+status exerts its effects are not always apparent, lower status such as the elderly losing the domestic headship/authority, dependently living with their descendants and being without receiving pension, could ostensibly increase exposure to environmental hazards (Wood, 2003). Moreover, it should be noted that social policies to enhance the late-life socioeconomic status are highly context dependent. For example, the present results revealed that higher suicide age ratios could be found in Western Pacific and African regions rather than other places in the world. According to the collected data, the proportions of the population above retirement age receiving a pension were especially low in most Western Pacific and African countries, as opposed to nearly 100% in most European countries. Therefore, in many middle- and low-income countries, priorities in social policies may include concentrating on alleviating late-life poverty and keeping financial security. By contrast, whereas in the well-off regions, eliminating socio-cultural ageism by education and legislation would be more imminent.
+Nonetheless, the present ecological findings could have important implications for suicide research and prevention on older adults at individual and family levels. Firstly, in future research and interventions, both qualitative and quantitative investigations need be made on how particular risk factors such as loss of headship, living with their descendants and receiving no pension increase the likelihood of older adults at some point displaying suicidal behaviors, and how protective factors such as family support build resilience against suicidal behaviors and thoughts. In addition, as is well known, the first driver of decreased suicide mortality is early detection of individuals at risk. With the benefit of this study, risk factors relating to lower domestic status such as loss of headship and receiving pension ought to be the main target of early detection efforts in the prevention of suicides in older adults. Thirdly, this study strongly highlights the gatekeeper role of family in late-life suicide prevention. Relational resilience can be strengthened as family members pull together to reshape the elders’ lives, plan their financial security, and explore new interests to provide meaning and satisfaction for them (Walsh, 2016).
+However, it is worth noting that this present study has several limitations. Firstly, there is the issue of the quality of the data on suicides, which is often lower in developing countries and may lead to underestimation of suicide deaths (WHO, 2014). Estimates from GBD for many countries, particularly locations in sub-Saharan Africa, have uncertain validity because there are limited vital registration data in these countries, and thus available data from a few neighboring countries may be used to impute the missing data, leading to similar estimates in these sub-Saharan African countries. Therefore, sensitivity analyses were conducted to check the robustness of our findings by excluding countries without vital registration as indicated in 2014 WHO report of suicide (WHO, 2014). The results of the sensitivity analyses showed that higher suicide age ratios were significantly found in countries with lower percentages of the elderly being heads of households (ratios = 1.50vs 2.62, Qb = 9.10, P = 0.003), higher percentages of co-residence of the elderly with their descendants (ratios = 2.50 vs 1.38, Qb = 8.77, P = 0.003), and lower percentages of the elderly receiving a pension (ratios=1.41 vs 2.58, Qb=10.27, P = 0.001). The findings of the sensitivity analyses were generally the same as the analyses with overall countries, which indicated the robustness of our findings. Secondly, as in prior GBD studies, the accuracy of the estimates depends on the availability of data for each age-sex-year-location. Due to delays in data reporting, estimates for more recent years rely on additional data and trends from prior years. Thus, the GBD data for 2015 in the present study may in some instances reflect rates from earlier years as well. Thirdly, due to the cross-sectional study design, caution should be exercised in the attribution of causal relationships. Fourthly, the cutoff point at 60 years old may have different implications in different countries where life expectancies and cultural formulations of ‘old age’ differ so much. Last but not least, it should be borne in mind that the three independent indices were the latest available estimates from 1990 to 2015 (United Nations, 2017), which
+had data deficiencies and limitations in validity. However, to date, these data are the best available data for consistent comparisons across the nations worldwide.
+5. Conclusion
+Socioeconomic factors have important impacts on public health as well as late-life suicide prevention. The present study suggests that a set of negative transitions of socioeconomic status that the older adults frequently experience, such as loss of the domestic headship, dependently living with their descendants, and receiving no pension, may lead to higher elderly suicide rates. The present ecological findings suggest that strategies to enhance the socioeconomic status of older adults may be important to prevent suicides in later life both within and across countries on a grand scale. Therefore, priority ought to be given to facilitate efforts of older adults, families, and societies to reposition their roles in a household, enhance financial independence, and explore new meanings and expectations in the elderly people's later life.
+Q. Chang, et al
+Journal of Affective Disorders 256 (2019) 618-626
+Aged 60+ and Children under 15 (%)
+Fig. A.2. Scatter plot of percentages of households with both older adults aged 60+ and children under 15 and log-transformed suicide age ratios.
+Fig. A.3. Scatter plot of the proportions of the population above retirement age receiving a pension and log-transformed suicide age ratios.
+625
+Q. Chang, et al
+Journal of Affective Disorders 256 (2019) 618-626
+Table A.1
+Pearson's correlations among the variables used in the analyses.
+Pearson's Correlation	1. Suicide age	2. Percentages of headship, 3. Percentages of co-residence of the elderly with both 4. Percentages of the population above ratios	60 + ,%s	their children and grandchildren,%s	retirement age receiving a pension,%s
+1 2 3 4	1.00 -0.36*	1.00 0.37*	-0.48*	1.00 -0.51*	0.59*	-0.55*	1.00
+■ P <0.05.
+References
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+Blenkner, M., 1965. Social work and family relationships in later life with some thoughts on filial maturity. In: E., Shanas, G.F., Streib (Eds.), Social structure and the family: generational relations. Englewood Cliffs, NJ: Prentice Hall, pp. 117-130.
+Chan, S.M.S., Chiu, F.K.H., Lam, C.W.L., Wong, S.M.C., Conwell, Y., 2014. A multidimensional risk factor model for suicide attempts in later life. Neuropsychiatr. Dis. Treat. 10.
+Chang, Q., Chan, C.H., Yip, P.S., 2017. A meta-analytic review on social relationships and suicidal ideation among older adults. Soc. Sci. Med. 191, 65-76. https://doi.org/10. 1016/j.socscimed.2017.09.003.
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+Phua, V.C., Kaufman, G., Park, K.S., 2001. Strategic adjustments of elderly Asian Americans: living arrangements and headship. J. Comp. Fam. Stud. 32, 263-281.
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+Rubenowitz, E., Waern, M., Wilhelmson, K., Allebeck, P., 2001. Life events and psychosocial factors in elderly suicides-a case-control study. Psychol. Med. 31 (07), 1193-1202.
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+Shah, A., Bhat, R., 2008. The relationship between elderly suicide rates and mental health funding, service provision and national policy: a cross-national study. Int.. Psychogeriatr. 20 (3), 605-615.
+Shah, A., Bhat, R., MacKenzie, S., Koen, C., 2008a. A cross-national study of the relationship between elderly suicide rates and life expectancy and markers of socioeconomic status and health care. Int. Psychogeriatr. 20 (2), 347-360.
+Shah, A., Padayatchi, M., Das, K., 2008b. The relationship between elderly suicide rates and elderly dependency ratios: a cross-national study using data from the WHO data bank. Int. Psychogeriatr. 20 (3), 596-604.
+Snowdon, J., Phillips, J., Zhong, B., Yamauchi, T., Chiu, H.F., Conwell, Y., 2017. Changes in age patterns of suicide in Australia, the United States, Japan and Hong Kong. J. Affect. Disord. 211, 12-19.
+United Nations, 2017. Department of Economic and Social Affairs, Population Division. Household Size and Composition Around the World 2017 -Data Booklet. http:// www.un.org/en/development/desa/population/publications/pdf/ageing/ household_size_and_composition_around_the_world_2017_data_booklet.pdf accessed August 10, 2018.
+United Nations Statistics Division, Department of Economic and Social Affairs (2017). Sustainable development goals indicators-proportion of population above retirement age receiving a pension. https://unstats.un.org/sdgs/indicators/database (accessed August 10, 2018).
+Van Orden, K.A., Wiktorsson, S., Duberstein, P., Berg, A.I., Fassberg, M.M., Waern, M., 2015. Reasons for attempted suicide in later life. Am. J. Geriatr. Psychiatry 23 (5), 536-544.
+Walsh, F., 2016. Families in later life: challenges, opportunities, and resilience. Expand. Fam. Life Cycle 261-277.
+Williams, L., Mehta, K., Lin, H.S., 1999. Intergenerational influence in Singapore and Taiwan: the role of the elderly in family decisions. J. Cross Cult. Gerontol. 14 (4), 291-322.
+Wood, D., 2003. Effect of child and family poverty on child health in the United States. Pediatrics 112 (Supplement 3), 707-711.
+World Health Organization (“WHO”), 2014. Preventing suicide: a Global Imperative. World Health Organization.
+Zhang, J., 2014. The gender ratio of Chinese suicide rates: an explanation in confucianism. Sex Roles 70 (3-4), 146-154.
+626
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+1.	Introduction
+Suicide is a phenomenon that bears a significant public health impact worldwide. Each year it is estimated that approximately 800,000 people die by suicide, ranking it as the second leading cause of death in 15-29 year olds globally (WHO, 2014). Though preventable, suicidal thoughts and behaviors are complex phenomena influenced by several interacting factors, including personal, social, psychological, cultural, biological, and environmental (Goldston et al., 2009; King et al., 2001; Mann, 2003; O'Connor, 2011). As such, there is no singular underlying explanation as to why a person may attempt suicide, resulting in a highly contextual and varied picture of the barriers and facilitators to help seeking.
+Recently, the Interpersonal Psychological Theory of Suicide (IPTS) (Joiner, 2005; Van Orden et al., 2010) was developed with the aim of providing a theoretical model of suicide behavior. The theory consolidates a broad range of suicide risk factors, and provides testable predictions of who will develop desire for suicide (i.e., ideation), and from these, who will go on to attempt. As such, the theory holds much promise in regards to bettering our understanding of how certain suicide risk factors interact, and where prevention and intervention efforts may be best focused.
+According to the IPTS, suicidal desire is caused by the simultaneous presence of two proximal, causal risk factors: (1) thwarted belongingness and (2) perceived burdensomeness, and hopelessness (i.e., “this will never change”) about these states (Joiner, 2005; Van Orden et al., 2010). Thwarted belongingness refers to the experience that one is alienated from friends, family, or other valued social circles. It is said to comprise of two facets, loneliness (i.e., “I feel disconnected from others”) and the absence of reciprocal care (i.e., “I have no one to turn to and I don't support others”). It is viewed as a dynamic cognitive-affective state that is influenced by inter and intrapersonal factors such as experiencing family conflict, living alone, possessing few social supports, and being prone to interpret others'behavior as rejection (Van Orden et al., 2010). Perceived burdensomeness, on the other hand, refers to the view that one's existence is a burden on friends, family members, and/or society, and comprises of two facets, self-hate (i.e., “I hate myself") and feelings of liability (i.e., “my death is worth more than my life to others”). Like thwarted belongingness, perceived burdensomeness is conceptualised as a dynamic cognitive affect state, where risk factors such as homelessness, unemployment, physical illness, and feelings of low-self-esteem and being unwanted are said to contribute to its development (Van Orden et al., 2010). Though it is hypothesized that experiencing either perceived burdensomeness or thwarted belongingness alone will elicit passive suicidal ideation, it is their
+interaction coupled with the view that they are stable and unchanging (i.e., hopelessness) that will cause active suicidal desire.
+The development from active suicidal desire to suicidal intent is said to only result through the presence of an additional third construct: (3) acquired capability. Acquired capability refers to one's ability to overcome the inherent drive for self-preservation and engage in lethal self-injury (Joiner, 2005). This is hypothesized as being possible due to a lowered fear of death resulting from repeated exposure and habituation to physically painful and/or fear-inducing experiences, and an elevated tolerance of physical pain. It is viewed as a continuous construct that accumulates over time, with risk factors such as family history of suicide, previous suicide attempt, exposure to combat, and childhood maltreatment contributing to its development (Ribeiro &Joiner, 2009; Van Orden et al., 2010). Thus, individuals who have high levels of all three constructs, thwarted belongingness, perceived burdensomeness, and acquired capability, are said to be at most risk for lethal suicidal behavior, as they possess both the desire for and capability to attempt suicide. See Fig. 1.
+Since the development of the IPTS in 2005, a growing body of research has emerged testing different aspects of the theory across a range of populations. In 2009, an article on the current status and future directions of the IPTS stated that the theory has stood up to 20 direct empirical tests, with results generally substantiating the theory's main predictions (Ribeiro &Joiner, 2009). Since then, two systematic reviews
+on the IPTS have been published, one reporting on the role of perceived burdensomeness on suicide-related behavior within clinical samples (Hill & Pettit, 2014), and another examining support for the IPTS from studies published between 2002 and 2011 (Wachtel &Teismann, 2013).
+In their systematic review of 27 empirical studies testing the association between perceived burdensomeness and suicide ideation, suicide attempts, or suicide within clinical samples, Hill & Pettit (2014) found perceived burdensomeness to have statistically significant bivariate associations with both suicide ideation and past suicide attempt. Perceived burdensomeness was also found to be a predictor of suicidal ideation beyond the effects of other well established risk factors, and played a role as both moderator and mediator between suicide-related behaviors and other risk and protective factors. The authors noted that the majority of studies conducted focused on the relationship between perceived burdensomeness and suicide ideation, with results highlighting the role of perceived burdensomeness as a potential route for suicide intervention in clinical populations. A limitation of this review, however, is that it focused exclusively on the role of perceived burdensomeness within clinical samples, to the exclusion of the theory's more critical interaction predictions and applicability within other sample types.
+The other systematic review, by Wachtel & Teismann (2013), was more comprehensive, in that it reviewed the results of 29 studies (published between 2002 and 2011) that examined support for all three interpersonal risk factors in relation to suicide-related behaviors. The authors found perceived burdensomeness, thwarted belongingness, and acquired capability to be associated with different facets of suicidality, concluding that there was a lack of studies investigating the interrelation of the theory's constructs. However, this review was published solely in German with its findings being inaccessible to non-German readers in the field. Additionally, the review was limited to articles published up to 2011, with a considerable proliferation of IPTS studies since that time.
+Thus, the aim of the present review was to provide the first English systematic review of the full set of predictions of the IPTS across multiple populations. To assess the predictive power of the IPTS constructs independently of the contribution of other major suicide risk factors, the review focused specifically on the results of studies that adjusted for the presence of other IPTS variables (i.e., thwarted belongingness, perceived burdensomeness, and acquired capability) and/or mental health-related measures (e.g., depression, anxiety, hopelessness) to provide a rigorous test of these predictions. In doing so, the current review aims to identify whether empirical research supports the theory, and to highlight critical gaps in the evidence base by reviewing what populations and what aspects of the theory have been most tested and supported.
+2.	Methods
+On the 8th of July 2015, the Medline and PsycInfo databases were electronically searched for English-language, human, peer reviewed articles published from January 2005 up to July 2015 using the search terms: “Interpersonal psychological OR interpersonal-psychological OR Joiner* OR thwarted belong* OR perceived burden* OR acquired capability AND suicid*.” With limits imposed, 315 records were identified through database searching, and two additional articles from reference list searches. After duplicates were removed, 207 records were screened by the primary author for relevance to the systematic review. Sixty-three articles were excluded based on content (i.e., articles that were topically unrelated), and type of publication (i.e., review and scale development articles). The remaining 144 articles were considered for full-text review.
+Full-text articles were coded by the primary author (JM) and one of three independent reviewers (PJB, ALC, JH). Potential discrepancies in double coding were resolved by reaching a joint consensus between the two authors, or by assent of a third author where consensus could not be reached. Articles were included in the systematic review if they met all of the following criteria: (i) included a direct predictor measure of IPTS components (i.e., either thwarted belongingness, perceived
+burdensomeness, or acquired capability), (ii) included a direct outcome measure of suicidal thoughts or behaviors (i.e., either suicide ideation, attempt, or a composite measure), and (iii) reported on original, quantitative data. The exclusion criteria were as follows: (i) the study did not adjust for the presence of other IPTS variables/and or mental health-related measures, (ii) the article was not in English, (iii) no original data were reported, (iv) the study was a case-control design, (v) the study was qualitative, (vi) the study was not published after 2005, and (vii) the study was not published in a peer-reviewed journal. In the case where analysis was repeated on the same samples across articles, the most comprehensive and/or recent article was chosen for analysis, with the other being excluded.
+In total, 58 articles, comprising of 66 studies, adhering to the inclusion and exclusion criteria were included in the present review (see Fig. 2). Where sufficient data was available, effect size estimates were calculated based on formulas from “Practical Meta-analysis” by Lipsey & Wilson (2001). Odds Ratios were converted to Cohen's d (Cohen, 1988) for comparability between continuous and dichotomous outcomes using formulas outlined by Hasselblad & Hedges (1995). According to Cohen (1988), an effect size of 0.20 is considered small, 0.50 moderate, and 0.80 large. Where an effect size was not calculable, analyses of results relied on number of tests significant, using an alpha level of p < 0.05. Due to the heterogeneity of the studies (range of settings), the lack of effect size data, and the insufficiency of available data on interaction effects, we were unable to conduct a meta-analysis.
+3.	Results
+A total of 66 studies were identified that tested the IPTS constructs in relation to suicide ideation or attempt (See Appendix A for study characteristics). In order to present the results categorically under either suicide ideation or suicide attempt, composite measures such as “suicide risk”, “suicide potential”, “suicide proneness”, “suicidal symptoms,” “suicide behavior”, “future likelihood of behavior”,and “suicidality” were classified under suicide ideation, as they all encompassed a measure of suicide ideation. Eleven studies were found to include a composite measure, operationalised by the measurement scale used. The most commonly used composite measurement scale was the 4-item Suicidal Behaviours Questionnaire Revised (SBQ-R; Osman et al., 2001). The SBQ-R comprises of 4 items that measure suicidal ideation and attempt (“Have you ever thought about or attempted to kill yourself’); suicide ideation in the past year (“How often have you thought about killing yourself in the past year”); communication of intent (“Have you ever told someone that you were going to commit suicide, or that you might do it”); and likelihood of future attempts (“How likely is it that you attempt suicide someday”). Other composite measures used were similar in that they comprised of items or subscales that combined current suicidal ideation, suicide plans and preparation, and communication or threats of suicide.
+Across the 66 studies, 206 tests adjusted for the presence of other IPTS variables (i.e., thwarted belongingness, perceived burdensomeness, and acquired capability) and/or mental health-related measures (e.g., depression, anxiety, hopelessness). The largest number of tests was on the main effect of perceived burdensomeness on suicide ideation (33.4%), followed by thwarted belongingness on suicide ideation (22.6%). Tests on the main effect of acquired capability on suicide attempt (4.3%), and the two-way (5.8%) and three-way interactions (3.3%) proposed by the IPTS were scant in comparison. Table 1 summarises the results of the adjusted tests across the various IPTS constructs.
+3.1.	Suicide ideation
+3.1.1.	IPTS critical interaction effect: Thwarted belongingness and perceived burdensomeness on suicide ideation
+Twelve tests of the interaction between thwarted belongingness and perceived burdensomeness on suicide ideation were found, 8 (66.6%) of
+which were significant, and 4 (33.3%) non-significant. Significant study sample sizes ranged from 115 to 6133, with a mean of 1033.4, and median of 239. Non-significant study sample sizes ranged from 60 to 293, with a mean of 147, and median of 88. Only two studies reported an effect size, with effect sizes ranging from 0.46 to 0.61, with a mean of 0.53, considered a moderate effect.
+The interaction of thwarted belongingness and perceived burdensomeness was found to predict suicide ideation across hospital, primary care, school, and community populations. In one of the largest studies testing this interaction in a community sample, Christensen, Batterham, Mackinnon, Donker, & Soubelet (2014) found that after adjusting for gender, age, and the IPTS main effects, the combination of high levels of thwarted belongingness and perceived burdensomeness significantly contributed to suicide ideation in a cross-sectional sample of 1167 participants aged between 32 and 38 years old. This effect was also observed in studies that used proxy measures, such as social support (proxy for thwarted belongingness) and mattering (proxy for perceived burdensomeness). In their study on 815 young adults, Joiner et al. (2009) found that those low in both mattering and family social support reported the highest levels of suicidal ideation, controlling for the effects of six-month and lifetime histories of depression.
+Some studies showed that the interaction between thwarted belongingness and perceived burdensomeness on suicide ideation was only significant at high levels of perceived burdensomeness (Van Orden, Witte, Gordon, Bender, &Joiner, 2008(1)), high levels of thwarted belongingness (Kleiman, Riskind, et al., 2014; O'Keefe et al., 2014), or by age group (Christensen, Batterham, Soubelet, & Mackinnon, 2013). In their community-based study of 6133 participants aged between 28 to 72 years of age, Christensen et al. (2013) found that the interaction between thwarted belongingness and perceived burdensomeness was significant in a model including the main effects of thwarted belongingness, perceived burdensomeness, hopelessness, and the two-way and three-way interactions between the constructs only when the analyses was stratified by age, as opposed to when
+analyzed in the full sample. Here, the interaction between thwarted belongingness and perceived burdensomeness became non-significant in the full sample when the three-way interaction between thwarted belongingness, perceived burdensomeness, and hopelessness was included, suggesting that hopelessness plays an important role as a suicide risk factor. Studies reporting on this interaction effect were typically limited by cross-sectional designs and focus on samples with low base rates of suicidal ideation.
+3.1.2.	IPTS main effect: Thwarted belongingness and suicidal ideation
+Fifty-five tests were conducted on the effect of thwarted belongingness on suicide ideation. Of these, 22 (40%) were significant, and 33 (60%) were non-significant. Sample sizes among significant studies ranged from 38 to 6133, with a mean of 721.6, and median of 335. Non-significant study sample sizes ranged from 60 to 994, with a mean of 328.4, and median of 208. Only three studies reported an effect size, with effect sizes ranging from 0.49 to 0.74, with a median of 0.57, considered a moderate effect.
+Thwarted belongingness was found to predict suicide ideation, suicide risk, and suicidality across the mental health clinic, primary care, school, community, and detainee populations. One study conducted on a sample of 129 undergraduates found that thwarted belongingness contributed to 6% of the variance in suicide ideation (Davidson, Wingate, Rasmussen, & Slish, 2009). The effect of thwarted belongingness on suicide ideation was also reflected in studies using proxy measures, such as distress in interpersonal relations (Wilson, Kowal, Henderson, McWilliams, & Peloquin, 2013), detachment/estrangement (Davis, Witte, & Weathers, 2014), family belongingness (Ploskonka & Servaty-Seib, 2015), social support (Christensen et al., 2013), social relations (Joiner et al., 2009(1)), and interpersonal conflict and belongingness (You, Van Orden, & Conner, 2011). Some of the studies used proxy measures because they undertook secondary analysis of an existing dataset, and thus had to examine the IPTS interpersonal risk factors as post-hoc constructs. Others did so to compare different facets of thwarted belongingness. For instance, Ploskonka & Servaty-Seib (2015) explored the relationship between three domains of belongingness (family, peer, and academic institution) and suicide ideation in a sample of 249 undergraduates. They found that the only domain that significantly contributed to suicide ideation was family belongingness, suggesting that it may be one of the most important sources of belongingness.
+In regards to the non-significant tests, many studies that included measurements of both perceived burdensomeness and thwarted belongingness found that only perceived burdensomeness was a significant predictor of suicide ideation within hospital, mental health clinic, and school settings. In one undergraduate sample, the effect of thwarted belongingness on suicide ideation became non-significant after adjusting for depressive symptoms (Hill & Pettit, 2013). Additionally, in an online sample, thwarted belongingness was only significant after accounting for mediation by hopelessness (Kim & Yang, 2015).
+3.1.3.	IPTS main effect: Perceived burdensomeness and suicidal ideation
+Sixty-nine tests were conducted on the effect of perceived burdensomeness on suicide ideation. Of these, 57 (82.6%) were significant, and 12 (17.3%) were not significant. Significant study sample sizes ranged from 47 to 6133, with a mean of 419.6, and median of 245. Non-significant study sample sizes ranged from 38 to 815, with a mean of 286.8, and median of 205. Only six studies reported an effect size, with effect sizes ranging from 0.61 to 12.60, with a median of 1.42, considered a large effect.
+Perceived burdensomeness was found to predict suicide ideation and suicide risk across the hospital, mental health clinic, primary care, school, community, and online populations. Some of the studies indicated that perceived burdensomeness contributed substantial additional variance (36% and 41%) to suicide ideation, above and beyond the contribution of depressive symptoms and hopelessness (Davidson et al., 2009; Van Orden, Lynam, Hollar, & Joiner, 2006). However, these studies were limited by their cross-sectional design and use of primarily Caucasian samples. The effect of perceived burdensomeness on suicide ideation was also reflected in studies using proxy measures, such as whether people's lives would be positively impacted by one's death (Kanzler, Bryan, McGeary, & Morrow, 2012). For instance, in a sample of 103 patients experiencing chronic pain recruited from a mental health outpatient clinic Kanzler et al. (2012) found perceived burdensomeness to be the sole predictor of suicidal ideation, even after controlling for age, gender, depressive symptoms, and pain severity. However, this study was limited by its use of a non-validated single-item assessment for perceived burdensomeness and low base rate of suicidal ideation.
+Most of the studies that did not find a significant effect for perceived burdensomeness on suicide ideation also found no significant effects for other IPTS variables and covariates. For example, perceived burdensomeness alongside the three-way interaction of thwarted belongingness, perceived burdensomeness and hopelessness (Cukrowicz, Jahn, Graham, Poindexter, & Williams, 2013), and the three-way interaction of direct combat exposure, depression, PTSD, and hopelessness (Bryan,
+Ray-Sannerud, Morrow, & Etienne, 2013) did not significantly predict suicide ideation in the mental health clinic and primary care settings. These studies were limited by their cross-sectional design and lack of power to detect moderate effect sizes.
+3.1.4.	Acquired capability and suicide ideation
+There were 21 tests of the relationship between acquired capability and suicide ideation, with 12 found to be (57.1%) significant, and 9 (42.8%) non-significant. Significant study sample sizes ranged from 38 to 1208, with a mean of 324.4, and median of 168. Non-significant study sample sizes ranged from 55 to 1167, with a mean of 374.5, and median of 327.5. No effect size data was available. Acquired capability was found to predict suicide ideation, suicide risk, suicide potential, suicidal symptoms, and suicidality across the mental health clinic, school, and community populations (including military and detainee samples). It has been found to explain a significant portion of variance in suicidal ideation beyond the contribution of prior suicide attempt, stress, depression, and hopelessness in a military sample (Shelef, Levi-Belz, & Fruchter, 2014), and in one study using an undergraduate sample, contributed to 4% of the variance in suicide ideation (Davidson et al., 2009). In one of the few studies conducted on acquired capability conducted outside of the United States, Shelef et al. (2014) found that in a sample of 168 soldiers recruited from the Israel Defence Forces, suicide attempters were found to have significantly higher levels of dissociation and acquired capability compared to psychologically treated and healthy control groups, where depression and acquired capability were found to explain a significant portion of variance in suicide ideation.
+3.2.	Suicide attempt
+3.2.1.	IPTS full model: Three-way interaction of thwarted belongingness, perceived burdensomeness, and acquired capability on suicide attempt
+Seven tests of the interaction between thwarted belongingness, perceived burdensomeness, and acquired capability on suicide attempt were found, 3 (42.8%) of which were significant, and 4 (57.1%) nonsignificant. Significant study sample sizes ranged from 313 to 6133, with a mean of2312.6, and median of492. Non-significant study sample sizes ranged from 181 to 376, with a mean of 278.5. Only one study reported an effect size, that of 1.01, considered a large effect.
+In a cross-sectional study of 313 patients recruited from outpatient and inpatient facilities affiliated with a major U.S. Army medical centre (one of the first studies to assess the full model) the three-way interaction of thwarted belongingness, perceived burdensomeness, and lifetime number of suicide attempts (proxy for acquired capability) was found to predict recent suicide attempt and current suicide status controlling for the covariates of depression, hopelessness, and borderline personality disorder symptoms (Joiner et al., 2009(2)). It was noted that the strength of this effect was similar to other traditionally strong predictors such as family history of suicide. However, like many of the other studies, this study was limited by its cross sectional design and use of proxy measures to assess the IPTS constructs. For instance, lifetime number of suicide attempts was used as a proxy for acquired capability, neglecting other experiences of physically painful or fearinducing experiences which also contribute to the development of acquired capability.
+In another cross-sectional study conducted on 492 patients seeking treatment at a mental health clinic, Anestis & Joiner (2011) found that the three-way interaction predicted participant's lifetime number of suicide attempts, controlling for depression and participant sex. In one of the largest studies on the full model, the interaction between suicide ideation and acquired capability, but not the main effect of acquired capability, was found to predict suicide attempt in a community sample of 1167 adults (Christensen et al., 2014).
+A non-significant effect for the three-way interaction was observed in in-patient settings. For instance, Monteith, Menefee, Pettit, Leopoulos, & Vincent (2013) found that only the two-way interactions of perceived
+burdensomeness and acquired capability, and thwarted belongingness and acquired capability predicted suicide attempt cross-sectionally. Here, the only variable that was found to distinguish participants who reported no suicide attempts in the past from those who reported one suicide attempt was recent suicidal ideation.
+3.2.2.	IPTS main effect: Acquired capability and suicide attempt
+Nine tests were conducted on the effect of acquired capability on suicide attempt. Of these, 5 (55.5%) were significant, and 4 (44.4%) were non-significant. Significant study sample sizes ranged from 44 to 376, with a mean of 177.7, and median of 145.5. Non-significant study sample sizes ranged from 52 to 6133, with a mean of 1659.2, and median of 226. Only three studies reported an effect size, with effect sizes ranging from 0.51 to 1.09, with a median of 0.76, considered a moderate to large effect.
+Acquired capability was tested across the hospital, mental health clinic, community, and detainee populations. In one of the three longitudinal studies included in the review, baseline history of suicide attempt (a proxy for acquired capability) was found to predict suicide attempt at 12 months after hospitalisation in an in-patient, primarily Caucasian hospital sample (Czyz, Berona, & King, 2015). Another study conducted in the UK by Ireland & York (2012) found that in a sample of 191 detainees, engagement in a range of self-damaging behaviors (proxy for acquired capability) significantly predicted self-injurious behavior (proxy for suicide attempt) cross-sectionally.
+Of the non-significant studies, acquired capability was found to not be significantly associated with past suicide attempt, nor differentiate individuals in the suicidal behavior group from individuals in the non-suicidal behavior groups. One cross-sectional study conducted in a community sample, found that the main effect of acquired capability was only a significant predictor among the middle-aged (44-48) age group (Christensen et al., 2013).
+3.2.3.	Thwarted belongingness and suicide attempt
+Eleven tests were conducted on the effect of thwarted belongingness on suicide attempt. Of these, 4 (36.3%) were significant, and 7 (63.7%) non-significant. Significant study sample sizes ranged from 131 to 1167, with a mean of 704. Non-significant study sample sizes ranged from 181 to 6133, with a mean of 1185, and median of 376. Only three studies reported an effect size, with effect sizes ranging from 0.51 to 0.89, with a median of 0.54, considered a moderate effect.
+Thwarted belongingness was found to predict suicide attempt in studies set in hospital, mental health clinic, school, and community populations. In one cross-sectional study of 131 patients in treatment for opiate dependence, Conner, Britton, Sworts, & Joiner (2007) found that in a model including the effects of drug use severity, aggression, depression, hopelessness, thwarted belongingness, and perceived burdensomeness, only scores on belonging were associated with lower probability of having a history of attempted suicide. The effect of thwarted belongingness on suicide attempt was also reflected in studies using proxy measures such as belongingness (reverse proxy) (You et al., 2011) in a sample of 814 patients in a substance use treatment program.
+3.2.4.	Perceived burdensomeness and suicide attempt
+There were 13 tests of the relationship between perceived burdensomeness and suicide attempt, 3 (23%) significant, and 10 (76.9%) non-significant. Significant study sample sizes ranged from 215 to 1167, with an average of 554.2, and median of 417.5. Nonsignificant study sample sizes ranged from 52 to 6133, with an average of 1110.1, and median of 313. Only two studies reported an effect size, with effect sizes ranging from 0.52 to 1.70, with a median of 1.11, considered a large effect. The significant studies were conducted in mental health clinic and community populations. For instance, in a crosssectional study of 215 mental health out-patients, Hawkins et al. (2014) found that perceived burdensomeness was significantly
+associated with past suicide attempt, adjusting for depression, although effect sizes were small. In another cross-sectional study, perceived burdensomeness significantly predicted suicide plans/attempts, alongside thwarted belongingness and acquired capability, adjusting for gender, age, and the two-way interaction between thwarted belongingness and perceived burdensomeness in a sample of 1167 community-based participants (Christensen et al., 2014).
+3.3.	Alternative relationships
+3.3.1.	Mediation & moderation effects
+When undertaking the systematic review, the authors came across many studies that tested the effect of thwarted belongingness, perceived burdensomeness, and acquired capability as mediators across the hospital, primary care, mental health clinic, school, and community settings. The following factors were found to significantly mediate the relationship between constructs of the IPTS and suicidal ideation or behaviors:
+•	Thwarted belongingness: attachment security, agreeableness, parental displacement
+•	Perceived burdensomeness: anger, depression, post traumatic disorder symptoms, childhood emotional abuse, sexual orientation victimisation, sexual identity, body mass index, negative cognitive style, maladaptive perfectionism, basic need satisfaction
+•	Both thwarted belongingness and perceived burdensomeness: neuroticism, extraversion, forgiveness of self and others, family discrepancy, discrimination
+•	Acquired capability: over-exercise
+3.3.2.	Other two-way interactions
+Other two-way interactions among the IPTS risk factors were found to be significant in the literature. These were conducted across the hospital, mental health clinic, school, and community settings and included the interactions between thwarted belongingness and acquired capability in predicting suicidality, current risk for suicide, and suicide attempt; perceived burdensomeness with individuals' reproductive potential, health, and romantic relationship satisfaction in predicting suicide ideation; thwarted belongingness and optimism, and perceived burdensomeness and optimism in predicting suicide ideation; and acquired capability with agitation, and over-arousal on suicidality and suicidal symptoms.
+3.3.3.	Other three and four-way interactions
+Other significant three and four-way interactions among the IPTS risk factors were reported in the literature. These were conducted across the mental health clinic, school, and community settings and included: the three-way interaction of thwarted belongingness, perceived burdensomeness, and acquired capability on suicide ideation; the three-way interaction of age, combat exposure, and belongingness on suicide ideation; and the four-way interaction of thwarted belongingness, perceived burdensomeness, acquired capability and negative urgency on suicide attempt.
+4.	Discussion
+4.1.	Overview of the support for the Interpersonal Psychological Theory of Suicide's main predictions
+The current review aimed to systematically examine current evidence testing the effects of thwarted belongingness, perceived burdensomeness, and acquired capability on suicide ideation and attempt. Contrary to our expectations, the studies provided mixed support across the theory's main predictions. The main effect of perceived burdensomeness on suicide ideation was the most tested and supported relationship, with over three-quarters (82.6%) of the studies found to be significant across hospital, mental health clinic, primary care, school,
+community, and online populations. It was found to contribute a considerably larger amount of variance (36% to 41%) in suicide ideation compared to the contribution of thwarted belongingness, and in some cases overrode thwarted belongingness as the only significant effect. The main effect of thwarted belongingness on suicide ideation, on the other hand, though found to be significant across a range of settings, was tested less frequently than perceived burdensomeness, and was less supported, with over half (60%) the tests being non-significant due to the stronger effects of perceived burdensomeness and other covariates. In cases where it was found to be significant, thwarted belongingness seemed to contribute a smaller amount of variance in suicide ideation (6%) compared to perceived burdensomeness, and had a moderate median effect size, compared to the large median effect size reported for perceived burdensomeness. Contrary to the IPTS prediction that thwarted belongingness and perceived burdensomeness would be specific to suicide desire, approximately a third of the tests of thwarted belongingness on suicide attempt, and a quarter of perceived burdensomeness on attempt were significant, with a moderate median effect size for the former, and a large median effect size for the latter.
+In comparison to the main effects of perceived burdensomeness and thwarted belongingness, the main effect of acquired capability on suicide attempt was tested considerably less, with results providing only partial support. Just over half of the studies found a significant effect for acquired capability on suicide attempt across hospital, mental health clinic, and community populations, with a moderate to large median effect size. Additionally, contrary to the theory's predictions of acquired capability being specific to suicide attempt, half of the tests on acquired capability and suicide ideation were significant. However, it is important to note that this percentage may have been influenced by the reclassification of composite outcomes under suicide ideation.
+Studies testing the IPTS predictions regarding the interaction effects were scant in comparison to those testing the main effects of thwarted belongingness and perceived burdensomeness, and showed mixed results. Two thirds (66.6%) of the tests on the interaction between thwarted belongingness and perceived burdensomeness in predicting suicide ideation were found to be significant, with a moderate mean effect size. The specificity of their interaction contributing to suicide ideation only was supported by the literature. Moreover, only three (42.8%) out of the seven tests on the interaction between thwarted belongingness, perceived burdensomeness, and acquired capability on suicide attempt were significant, with over half of the tests on the full model found to be non-significant across the hospital, mental health clinic, and community populations. However, given that these nonsignificant effects were found in studies with samples sizes ranging from 181 to 376, these findings may be the product of too many low-powered studies to detect an effect for the full IPTS model, as a large effect size was found in one of the significant studies. Nevertheless, studies that did identify significant interaction effects tended to have similar sample sizes compared to those that did not find an effect.
+Overall, these results suggest that, at this point in time, the IPTS may not be as clearly defined nor supported as initially thought. Some of the conflicting findings across thwarted belongingness, acquired capability, and the two-way, and three-way interactions provoke a number of questions, including: (a) whether the interpersonal risk factors have different relationships on suicide ideation and attempt than stipulated by the theory (i.e., alternative interactions), (b) whether the measures commonly used across the studies adequately capture the constructs, (c) whether the theory is only accurate in predicting suicidal outcomes for a subset of suicidal individuals, and (d) whether there are other crucial variables that may help to better predict suicide ideation and attempt, which are not accounted for in the theory. In relation to (a), it may be that perceived burdensomeness is a more robust interpersonal risk factor for suicide ideation, in comparison to thwarted belongingness, which seems to also have associations with suicide attempt. However, in relation to (b), it may be the case that the measures used to assess
+thwarted belongingness, particularly the thwarted belongingness subscale on the Interpersonal Needs Questionnaire (INQ; Van Orden, Cukrowicz, Witte, & Joiner, 2012), do not fully capture the construct. This is an issue that has been raised by other researchers who have observed thwarted belongingness to have non-significant effects on suicide ideation when measured directly, as opposed to when measured using a proxy (Bryan, Clemans, & Hernandez, 2012). As research may privilege testing the relationship of perceived burdensomeness over thwarted belongingness, due to the conflicting findings of the latter, future research could look at validating broader proxy measures for thwarted belongingness, and examining what components may be missing from existing measures in order to balance out the evidence base.
+In relation to (c), whether the theory predicts suicidal outcomes for a subset of individuals, recent work using latent class analysis indicates that there are subclasses of individuals experiencing suicide ideation or attempt who display different symptom patterns and risk trajectories over time (Logan, Hall, & Karch, 2011). As suicidality is a heterogeneous outcome, it may be the case that the theory has more explanatory power for certain subsets of individuals. For example, in the case of acquired capability, studies that found a non-significant effect for the role of acquired capability on suicide attempt tended to have larger sample sizes (i.e., had greater statistical power) than those which found a significant effect. This suggests that other factors, such as sample characteristics and study setting may play a role in detecting a relationship. Future research testing the IPTS risk factors across different sub-sets of individuals would help to further specify the generalizability and explanatory strength of the IPTS predictions.
+In relation to (d), whether there are other crucial variables of interest not accounted for in the theory, studies have begun to examine the integration of the IPTS with other models of depression and suicide-related behavior, such as Hopelessness Theory (HT; Abramson, Metalsky, & Alloy, 1989) and the weakest link theory of suicidal ideation (Kleiman, Law & Anestis, 2014; Kleiman, Riskind, et al., 2014). Research is also being conducted on counterpart theories, such as the Integrated Motivational-Volitional Model of Suicidal Behavior (IMV; O'Connor, 2011), which builds upon the IPTS through the incorporation of thwarted belongingness, perceived burdensomeness, and acquired capability as moderators with other constructs, such as defeat and humiliation appraisals and entrapment; the work of which is essential to furthering theoretical endeavors within the field.
+In relation to clinical implications, these remain unclear due to the disparity in the number of studies focusing on the different IPTS constructs, and in particular, the lack of studies testing the critical interaction effects. Though work has been undertaken to outline how the IPTS can be used as a framework for identifying pernicious risk factors and tailoring assessments and interventions to address these factors (Stellrecht et al., 2006), further research elucidating the strength of the critical interaction predictions is needed to aid in the development of interventions that are able to specifically target the IPTS constructs to reduce suicidal ideation and suicide attempt. On a preliminary note, the results of the systematic review suggest that intervention-based efforts focused on identifying and decreasing levels of perceived burdensomeness in patients may be a more potent pathway for minimising risk of suicide-related behavior compared to that of thwarted belongingness. There is also evidence suggesting that interventions based on reducing levels of the three interpersonal risk factors may act to reduce different aspects of suicide-related behavior than initially stated by the IPTS, the pathways of which could be influenced by additional presenting risk or protective factors. Here, given the focus of the theory on identifying interpersonal risk factors, patients may feel more comfortable talking about feelings of belonging and burden with a clinician, as opposed to discussing suicidal behaviors. Focusing clinical discussions on risk factors, rather than suicidal behaviors, may help to increase engagement with clinical services and circumvent the potential stigma of discussing suicide (Calear, Batterham, & Christensen,
+2014; Gulliver, Griffiths, & Christensen, 2010). This interpersonal focus may also promote clinician empathy by highlighting the clinician's role as an important source of social support in the suicide risk factor framework, and could provide flow-on effects in improving the therapeutic alliance and patient outcomes (Baldwin, Wampold, & Imel, 2007; Lambert & Barley, 2001).
+4.2.	Strengths and limitations
+4.2.1.	Study strengths and limitations
+A major strength of the studies included in the current review was that they examined the IPTS across a large range of settings, and were not limited to testing the theory's main predictions. Many explored other interactions between the IPTS interpersonal risk factors and related constructs, contributing to our understanding of how distal risk factors influence suicide-related behavior through the IPTS proximal risk factors. However, many studies were limited by their cross-sectional design (63 out of 66), largely relying on retrospective reporting of suicidal ideation or behaviors), use of undergraduate samples with a low level of suicide ideation and attempt that were primarily Caucasian and female, use of self-report measures, evaluation of suicide ideation only (where suicide attempt was often underpowered), small sample sizes, and in some cases, small effect sizes for significant findings. Additionally, though the present review provides coverage of four additional years of publications on the IPTS, the same limitations regarding the lack of studies investigating the interrelation of the theory's constructs remain from previous systematic reviews. More high powered studies testing these critical interactions are needed to more comprehensively evaluate support for the theory.
+4.2.2.	Systematic review strengths and limitations
+To our knowledge, this is the first systematic review on the IPTS that examines the English-language literature on validation studies covering the full theory across multiple populations. By specifically analyzing the results of studies that adjusted for the presence of other IPTS variables and/or mental health-related measures, the review was able to robustly examine the strength of the theory's predictions. Additionally, the inclusion of studies using proxy measures of the IPTS variables
+highlighted alternative measurement pathways that may aid in better operationalisation of the IPTS constructs.
+Although comprehensive, a limitation of the present review was that it did not include articles that used non-standard terminology, nor articles published in languages other than English. The reclassification of suicide composite measures as suicide ideation, though helping to clarify the IPTS risk factor relationships with either suicide ideation or attempt, may also have inadvertently obscured more complex discussion of concurrent suicide-related behaviors. Here, it is important to note that the suicide composite measures that were reclassified as suicide ideation may not have been directly comparable, and should thus be interpreted with caution. Additionally, due to the lack of available data reported by the reviewed studies, the review relied primarily on summarising the results of significance tests, as opposed to effect sizes, limiting estimation of the magnitude of the relationships across studies. Moreover, when effect sizes were reported, Odds Ratios were converted to Cohen's d for comparability between continuous and dichotomous outcomes, which relied on the assumptions about the underlying distributions. Lastly, due to the comprehensiveness of the review, resulting in heterogeneity of studies, and the lack of reporting of effect size data, we were unable to conduct a meta-analysis.
+5.	Conclusions
+The review indicates that the relationship between perceived burdensomeness and thwarted belongingness on suicide ideation, and their interaction with acquired capability on suicide attempt appears to be less straightforward than originally stated in the IPTS. There is a need for more high powered studies examining the two-way and three-way interactions of the theory's constructs, use of longitudinal designs, and further tests of alternative interaction and mediation effects identified by some studies, highlighting potential for re-thinking the relationships as predicted by the IPTS. Future research focused on expanding the availability of valid measurement approaches for the interpersonal risk factors, and further elaborating upon their mixed relationships with suicide ideation and attempt across multiple populations is important to advance both theoretical and clinical progress in the field.
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+harmful content were also identified. Despite the number of apps available, and their varied purposes, there is a clear need to develop useful, pragmatic, and multifaceted mobile resources for this population. Clinicians should be wary in recommending apps, especially as potentially harmful content can be presented as helpful. Currently safety plan apps are the most comprehensive and evidence-informed, for example, “Safety Net” and “Mood-Tools—Depression Aid”.
+Introduction
+Rationale
+Suicide is a leading cause of death globally, particularly amongst young people [1]. Although immediate help during a crisis is critical, those who may be experiencing suicidal ideation or crisis experience barriers to help-seeking, such as not perceiving a need for professional help, lack of time, preference for informal help, access to and cost of services, and fear of stigma and disclosure [2]. With the increasing ubiquity of mobile phones, health applications (apps) have the potential to improve access and availability of evidence-based support to this group, as apps are low-cost, convenient, and discreet. Apps may be especially suited to deliver suicide prevention interventions with their ability to deliver support and intervention in situ and at the time of crisis. As suicide ideation and suicide risk change rapidly, access to high quality mobile resources may save lives.
+Consumers are rapidly embracing apps, proactively seeking apps to manage their personal health. Recent data suggest 85% of young people in the USA own a smartphone, three quarters of whom have used their device to access health information [3]. In a survey in the psychiatric out-patient setting, 69% of respondents and 80% of those aged 45 years or younger indicated a desire to use a mobile app to track their mental health [4].
+This consumer enthusiasm for apps to manage mental health has spurred the development of numerous apps for suicide prevention. Many of these offer digitised versions of tools and strategies common in mental health. However, to our knowledge the content of these apps has not been investigated. There is also an absence of efficacy data for apps related to suicide prevention, although the publication of designs [5], proof-of-concept results [6], and protocols for evaluation studies [7] are indicative of the future research direction. Assessment of content is vital, as the Android [8] and iOS [9] app stores do not have guidelines specifically related to the restriction of pro-suicidal content, or app content quality. Therefore, we currently do not know whether apps provide potential harmful content which promotes suicide or encourages suicidal behaviour [10], nor whether their content is consistent with clinical and population based policy.
+In previous work, Donker et al. found that mental health apps evaluated in randomised controlled trials [11] were not publicly available, while those with no research evidence were. Reviews of apps for other mental and physical disorders support this, reporting low adherence to clinical best practice, or the provision of unreliable, unsuitable tools [12-14]. In the present review, in view of the absence of published efficacy data for existing apps, we use the corpus of extant research trial evidence to address whether the features of publicly available health apps for suicide prevention are consistent with the research evidence. Suicide prevention strategies identified within the apps were ranked according to the strength of the evidence, as indicated by inclusion in the World Health Organization report by Scott and Guo [15], inclusion in other published systematic reviews [16-18], or inclusion in the Suicide Prevention Resource Centre Best Practices Registry [19].
+The findings from this review will inform clinicians and consumers of the content quality of suicide prevention apps currently available in the marketplace. An examination of the evidence base of app components will assist clinicians in recommending particular apps as part of adjunctive care and those promoting apps through the web to prioritise those most consistent with current evidence. Ultimately, this review will assist consumers to find apps consistent with best practice, and developers to consider the evidence-base of content during app design.
+Objectives
+Using descriptive methodology, the primary aim of this study was to compare evidence-based strategies undertaken for suicide prevention with the content of publicly available apps providing tools for suicide prevention.
+Methods
+Eligibility criteria
+Huckvale et al. highlighted the difference between informational and tool-based apps in a review of apps for asthma [14]. Tool-based apps are “active” or “interactive” as defined by De Jaegere et al. [20], specifically requiring active involvement from the user, or allowing users to interact with one another. Meanwhile, “passive” apps are those that solely present content, which could be in a variety of formats such as text or video, but require no user input or interaction beyond navigating through the content. In this review, only “active” or “interactive” apps were included due to the previously identified challenges in identifying the provenance of information contained within suicide prevention apps [21].
+In the current review, free and paid-for apps containing content related to suicide were included if they could be downloaded via the official Android and iOS stores. Apps were excluded if they: contained no “active” or “interactive” suicide prevention content; referred to suicide non-literally, for example in branding, or music titles; referred specifically to self-harm with non-suicidal intent; were related exclusively to depression, bipolar disorder, or other mental health conditions, unless suicidality was explicitly mentioned; or were not in English, or included character sets which did not display correctly.
+Information sources
+Apps were identified by searching the Australian Google Play store (Android) via its web interface, and the Australian iTunes store (iOS) using its search application programming interface (API). Results were limited by the search engines to a maximum of 250 (Android) or 200 (iOS) apps, and all of these search results were screened.
+Search
+A set of core search terms related to suicide was created: suicid*; parasuicid*; kill me/myself/ yourself; take my/your [own] life; self[-]harm*; DSH. To ensure consistency in the stemming of search terms across the two app stores, these keywords were manually expanded to create a comprehensive set of terms (see S2 Text). The terms related to deliberate self-harm (DSH) allowed the initial identification of apps where self-harming behaviours are with, without, or with unclear suicidal intention. Search terms on the Google Play store were surrounded by quotation marks to ensure the exact phrases were matched-this functionality was performed automatically by the iTunes search API. The unique identifier, title, description, and price of each app were retrieved from the app store, and apps which appeared in the results for multiple search terms were de-duplicated.
+App selection
+During the screening stage, two reviewers independently assessed the title and description of each app against the inclusion and exclusion criteria. The reason for each exclusion was recorded. Results of the screening were compared, and discrepancies were resolved by discussion until consensus was achieved. All apps that were identified as being eligible for inclusion were downloaded and installed on a Samsung Galaxy S4 mini (Android version 4.2) or iPhone 5s (iOS version 7.1) for full content review.
+Data collection process
+Following download, each app was opened and assessed independently by the two reviewers to confirm eligibility. The content and features of the apps were then independently reviewed for both harmful and suicide prevention content. The reviewers used a custom coding scheme (see Data Items section), and coded the interventional components directly into a database created for the review. Discrepancies were resolved by discussion until consensus was achieved.
+Data items
+Suicide prevention strategies are broad, ranging from population based activities (such as restricting access to means) to specific treatment interventions (such as dialectical behaviour therapy, DBT). Moreover, suicide is commonly experienced in a range of mental and physical health conditions. Apps may also contain potentially harmful content, and may be targeted at different user groups with different purposes (for example consumers, or clinicians). To accommodate these multiple facets, we developed four broad categories on which data were extracted for each app, as described below.
+App characteristics. The download cost for each app, whether free or paid-for, was recorded. Apps which contained any suicide prevention content were broadly categorised based on their primary function. The following distinct foci were determined: primarily or solely suicide prevention; depression (for example, an app which mentions suicide in the context of depression); deliberate self-harm; physical health or other mental health (for example, an app may include suicide as one of many health topics); or setting-based psychological or general support (for example, a university information app which mentions suicide prevention as part of its welfare programme). This categorisation allowed us to compare apps with a specific focus on suicide prevention and those in which suicide prevention is embedded within a wider context.
+Harmful content. Harmful information was coded using a synthesis of schemes used by Biddle et al. [10], Tam et al. [22], and Westerlund et al. [23] in their reviews of suicidal content on the internet. The harmful categories were: describing or facilitating access to lethal means; providing encouragement to people to end their life; portraying suicide in a fashionable or appealing manner; or an open category for other harmful content.
+App quality. In line with previous eHealth and suicide prevention reviews [10,20, 21,24] we rated broader app quality indicators. These quality-related features included: the type of developer or provider of the app (Q1); whether the provider name or contact details was explicitly stated within the app (Q2); whether references for the source of app content was included (Q3); whether a privacy policy was included within the app or app store description (Q4); whether the app could be protected with an account login, password or personal identification number (Q5); and whether bugs or reliability issues were apparent through use of the app (although the reviewers did not seek to exhaustively test the app for reliability; Q6).
+Suicide prevention tools. The spectrum of suicide prevention strategies is wide, spanning public health interventions associated with prevention in the general population, those targeted
+at higher risk groups, and mental health interventions for treatment and maintenance. In this review, strategies were coded based on the spectrum initially presented by Mrazek and Haggerty [25], and as reported by Scott and Guo in their report for the World Health Organization [15]. For convenience, we divided the strategies into the following five categories: public health, screening, accessing support, mental health/treatment strategies, and follow-up strategies.
+Public health strategies. Suicide prevention strategies include public health techniques targeting: information about legislation and policies restricting access to lethal means (S1); guidelines for media reporting of suicides (S2); and material about organisational, regional, or national suicide prevention strategies and policies (S3). These public health strategies were expected to be largely information-based and unlikely to be delivered through an interactive mobile app, however they were retained in the coding scheme for completeness.
+Screening strategies. This category consisted of strategies to improve screening and detection of suicidal risk, with apps targeted at physicians (S4); those in gatekeeper roles (S5); or for individuals to self-screen (S6).
+Accessing support strategies. Content to encourage or facilitate getting access to help included: requesting help and support from peers or family (S7); accessing help via a gatekeeper (S8); accessing non-crisis support services (S9); and access to crisis support and helplines (S10). For those apps which provided crisis support details, an additional data item was recorded to assess whether the crisis contacts were always visible within the app (for example, through a “get help now” button; S10a), as suggested by De Jaegere et al. [20].
+Mental health/treatment strategies. Mental health strategies focussed on preventing suicide either before or after an attempt. These strategies included: psychotherapy (S11); pharmacotherapy (S12); non-drug physical therapies (S13); the use of safety plans (S14); and postvention support for those bereaved by suicide (S15). As suicide safety plans contain multiple components and address a number of suicide prevention strategies, we performed a separate sub-analysis of the content of these apps. As with the public health category, we did not expect all of these strategies to be deliverable through an app (for example, drug or electroconvulsive therapy), however they were retained for completeness, and to record possible inclusion, for example, as part of a treatment diary.
+Follow-up strategies. Additional longer-term strategies focussed specifically on follow-up support after a suicide attempt. These strategies included: ongoing outreach and contact (S16); adherence management (S17); and peer support for those who have made a suicide attempt (S18).
+Evidence quality. After coding the apps’ suicide prevention strategies into the five categories described above, the quality of evidence for each strategy was rated from the extant literature for reducing suicide. As noted previously, we developed a coding scheme based upon the WHO report by Scott and Guo [15], and prevention strategies were coded as having strong evidence (E1) if they were consistent with findings in this report. Supplementary evidence was gathered from reviews by Mann et al. [16], Leitner et al. [17], and Shekelle et al. [18], and strategies with some degree of evidence from these reviews were coded as E2. Finally, if there was a lack of evidence within these reviews, a final check was made with the Suicide Prevention Resource Centre Best Practices Registry [19] to check if the strategy was at least consistent with expert ratings of best practice (E3). Otherwise, we coded a strategy as containing no relevant evidence (E4).
+Summary measures
+The number and percentage of apps satisfying each of the coding elements are reported. For each suicide prevention strategy contained within the apps, the coded evidence quality is also
+Fig 1. PRISMA flowchart showing the app search, screening, and review.
+doi:10.1371/journal.pone.0152285.g001
+reported. The number of strategies and apps containing recognised evidence is reported, along with the number of suicide prevention strategies per app.
+Results
+App selection
+The PRISMA flowchart for the review is shown in Fig 1. From the original 1271 search results, the descriptions of 856 unique apps were screened, and 123 apps were downloaded for review. Seventy-four apps were excluded following download, including 13 that did not contain any suicide prevention content. Eight of these 13 apps were games with the aim of killing or inflicting harm upon the character, including Russian Roulette. One of the excluded apps suggested risky behaviour, including deliberate self-harm or taking drugs, as an alternative to a suicide attempt. These suggestions contained disclaimers relating to the risk, possible legal consequences, and the lack of concordance with professional medical advice.
+Fifty-two of the reviewed apps were excluded as they did not provide interactive features to support suicide prevention. As expected, these informational apps described a wider range of suicide prevention strategies than could be incorporated into a tool-based app. In addition to components identified in the evidence review (described in the following sections), information was provided about: media reporting guidelines (one app; S2), national suicide prevention guidelines (one app; S3), gatekeeper screening for suicide (seven apps; S5), gatekeeper access to support (eight apps; S8), pharmacotherapy (four apps; S12), and non-drug physical therapies (one app; S13). None of these excluded apps contained information on adherence management (S17), or peer-support for those who attempt suicide (S18). The remaining 49 apps (Android: 20, iOS: 29) were included in the evidence review (S1 Dataset).
+App characteristics
+All 20 of the reviewed Android apps were free, while seven of the iOS apps required payment to download (four at AU$1.29, one at AU$2.49, one at AU$3.79, and one at AU$18.99).
+Approximately half of the apps downloaded and reviewed were suicide-specific (n = 24, 49.0%). Of those apps with a wider context, five apps had a focus on depression (10.2%), four on deliberate self-harm (8.2%, none with a specific focus on self-harm with suicidal intent), six on general health information (12.2%), and 10 on general support (20.4%).
+Harmful content. In addition to the potentially harmful content described for the excluded apps, two additional apps contained a list of means of instant death, although this was presented as suggestions for removing access to means. The risks of presenting lethal means have been discussed in previous work looking at the presentation of suicide on the internet [10,22].
+App quality. The majority of apps were developed (Q1) by academic/healthcare institutions, or commercial organisations (20 apps, 40.8%, each). Five apps (10.2%) were privately developed by individuals, and the type of provider was not clear for four apps (8.2%). Despite this range of developers, only 34 (69.4%; 13 suicide-specific apps, 54.2%) included contact details of the provider within the app (Q2).
+Although the apps were interactive, delivering a resource to users, just six (12.2%; one suicide-specific app, 4.2%) referenced the source of the content (Q3). While many apps prompted users to enter personal data, less than a half (19 apps, 38.8%; seven suicide-specific apps, 29.2%) included a privacy policy (Q4). Fewer still offered the option to protect the app with an account login, password, or personal identification number (eight apps, 16.3%; three suicidespecific apps, 12.5%; Q5). Nineteen apps (38.8%; eight suicide-specific apps, 33.3%) demonstrated obvious bugs or reliability issues during the content review (Q6).
+Suicide prevention tools. Table 1 shows the suicide prevention tools that were found in the 49 reviewed apps. The 24 apps which pertained primarily to suicide prevention are shown separately. Accessing peer support and safety plans were the most common features in the apps with a suicide prevention focus, as well as those with a broader focus. Follow-up strategies were least commonly identified within both groups of apps.
+Public health strategies. The only public health strategy identified within the apps related to means access restriction (S1), although this was at the level of an individual, rather than a population. Seven apps (six suicide-specific) allowed the user to identify lethal means that should be removed from their environment in the context of a safety plan (S14). No apps contained interactive features relating to media reporting guidelines (S2), or suicide prevention policies (S3).
+Screening strategies. Sixteen apps provided interactive screening tools for depression or suicidality: three for mental health professionals (S4), and 13 for individuals to self-screen (S6). No apps provided gatekeeper screening tools (S5).
+Two of the self-screening apps were suicide-specific, whereas none of the professional screening apps were primarily focussed on suicide. Nevertheless, one of the professional screening apps contained customised instruments to assist mental health professionals in screening for both suicidality and depression. The second app contained the Hamilton Rating Scale for Depression [26], and the third app included an extended version of the PHQ-9 [27] with additional questions related to suicide, paranoia, hallucinations, and mania.
+Of the 13 self-screening apps, two (both suicide-specific) contained a custom screening tool for detecting suicidality, including items on suicidal thoughts, social withdrawal, denying responsibilities, and other warning signs. The remaining self-screening apps focussed on depression: four presented an extended DSM scale, four used a modified or expanded PHQ-9 scale [27], and one which was designed specifically for post-natal depression, reproduced the Edinburgh post-natal depression survey [28]. The remaining two apps provided custom screening tools, one app specifically for depression based on a list of symptoms, and the other app provided multiple custom tools to screen for anxiety, depression, substance use, and suicide.
+Eight of the self-screening apps directed users to seek support from health or mental health professionals, or provided crisis support information when users screened high on depression or suicidality measures. Two apps additionally suggested users might be suitable for psychotherapy or antidepressant treatment, but did not directly suggest seeking help. Three further apps did not direct users to help-seeking options, however two were designed to be used as a checklist prior to an appointment with a health professional, and the final app used the screening results to populate a list of suggested tasks, including seeking help, in another section of the app.
+Accessing support strategies. Apps enabling access to support directed users to either peer support networks, non-crisis support, or crisis support services. None of the reviewed apps provided interactive access to gatekeeper services (S8). Of the apps providing access to help, 27 (16 suicide-specific) apps allowed users to access support from their peers, friends, or family (S7). Approximately half of the apps providing this function did so as part of a safety plan (n = 14; 13 suicide-specific). Eight of the non-safety-plan apps (three suicide-specific) allowed the user to nominate people as supporters and facilitated easy contact during a crisis. The remaining five apps (all non-suicide-specific) additionally allowed users to interact with one another within the app-users could share and discuss common experiences, and support others. This interaction and support took many forms with users interacting through photo sharing in one app, and by video sharing in another. One app also included a personal peer-to-peer support function where users could request support, or nominate times throughout the week when they were available to provide support. Of the five apps which offered peer interaction, four offered some degree of content moderation. Two of these apps specifically indicated that discussion of dangerous, unsafe, or violent acts would be removed; one other app included a function for alerting the service provider about worrying posts; and in one other app all content was centrally approved before being made publicly available.
+Twelve apps (10 suicide-specific) also provided access to non-crisis support services (S9), with 11 apps (10 suicide-specific) doing so within a safety plan. The remaining app was developed specifically for a clinical psychology practice and provided active access to the practice via a direct text message.
+A further 13 apps (10 suicide-specific) provided access to crisis support services (S10), seven of which (four suicide-specific) were independent of safety plans. Of these, five apps (four suicide-specific) contained interactive crisis support or helpline components. These features included: finding the nearest crisis centre based up location/GPS data (four apps, which were localised versions of the same app); and the ability for the user to enter their own crisis support contact (one app). Two apps also offered features for users to interact with other people. Both apps allowed users to initiate contact with an organisation-affiliated support service, either by text message or online chat capabilities. None of the 13 apps which provided access to crisis support services ensured that this access was visible at all times within the app (S10a).
+Mental health strategies. Safety planning (S14) was a prevalent mental health strategy contained with the reviewed apps, and is reported separately in a following section. Two apps also provided some degree of interactive psychotherapeutic content (S11), and another two provided postvention support for those bereaved by suicide (S15). No apps provided interactive features related to pharmacotherapy (S12) or physical therapies (S13).
+Both of the apps which delivered psychotherapy were based on cognitive therapy-one in the context of depression, and the other for deliberate self-harm. Both apps used thought challenging techniques: the depression app provided a tool that assisted users in identifying and challenging negative thoughts, while the DSH app provided a space to think about negative thoughts and to reframe them positively. The psychological content of both apps was selfguided, with no personalised input from a mental health professional. However, the DSH-ori-entated app did offer advice based on user-entered responses to motivations behind the current urge to harm, and suggested strategies or activities to distract users until the urge subsided.
+The two postvention apps provided an interactive plan for those bereaved by suicide. This was similar to a safety plan, in which the user completed sections of their plan after watching short videos that discussed different aspects of suicide bereavement. Elements of the plan included information, thoughts, and feelings associated with the event, and coping strategies and long term goals. The apps also highlighted that those bereaved by suicide may be at increased risk for suicide themselves, and encouraged seeking support if suicide ideation was present.
+Follow-up strategies. Finally, one app contained content specifically targeted at supporting someone who survived a suicide attempt. In addition to a safety plan, this app provided an appointment reminder function, which was coded as ongoing contact/outreach (S16). No apps addressed adherence management (S17) or peer support (S18) following a suicide attempt.
+Safety plans. As discussed above, many suicide prevention tools were incorporated into apps as part of a safety plan (S14). Safety plans were one of the most prevalent app features, with 14 apps (13 suicide-specific) enabling users to create a plan.
+Half of these safety plan apps (seven apps, six suicide-specific apps) allowed users to identify lethal means that they should remove from their environment in a crisis (S1). All safety plan apps allowed users to identify peer supporters who could be contacted in a crisis situation (S7). Additionally, 10 of the apps connected to the user’s contact/address book, enabling users to contact peers from within the app (all 10 apps), and facilitating the input of contacts by importing their details from the address book (five apps). In addition to peer support, 11 safety plan apps (ten suicide-specific) included details of non-crisis support services (S9) including psychiatrists, psychologists, mental health organisations and service providers, and general practitioners. All of these apps allowed users to enter their own contacts, and one app additionally assisted users in finding the nearest mental health resource based on location data obtained
+from the phone handset. Crisis support information (S10) was available within six of the apps (all suicide-specific) and was similar to non-crisis support, allowing users to input relevant crisis support line information. However, one app additionally prompted the user to call a national crisis support centre if pre-nominated warning signs were selected.
+Safety plans also contained components not otherwise coded in the description above. Just over half of the safety plan apps (eight apps, seven suicide specific) included a section for users to identify their individual warning signs for a crisis, with two apps (both suicide-specific), also allowing users to actively identify personal triggers. All 14 apps had a section for users to record coping strategies to ameliorate these factors, either allowing the user to enter their own strategies (12 apps), or allowing the user to listen to music or meditation tracks as a means of relaxation (two apps).
+In addition to specific means access restriction, an additional six apps featured sections for making the user’s environment generally safer and more comfortable, for example by not being alone. Four apps allowed users to nominate distracting places to go to in a crisis, such as social environments. Finally, seven apps encouraged users to record details associated with medium and longer term life planning, or reasons to live.
+Synthesis of results
+Ten distinct suicide prevention strategies were identified within the reviewed apps, one of which was associated with good evidence at level E1 (see Table 1). Five strategies were associated with secondary evidence (E2), three with concordance with best practice guidelines (E3), and one for which no relevant evidence could be identified (E4).
+Within the 49 apps, 94 individual interactive components were identified. Thirteen components (13.8%) were coded with E1 evidence, 46 (48.9%) were coded as E2, 34 (36.2%) were coded as E3, and one component (1.1%) had no relevant evidence (E4). Sixty individual components were identified in the 24 suicide-specific apps: 10 (16.7%) at E1,28 (46.7%) at E2, 21 (35.0%) at E3, and one (1.7%) at E4.
+Aggregating these results to the app-level, accounting for multiple components within each app and the evidence-based components within safety plans, 13 apps (26.5%; 10 suicide-specific, 41.7%) contained at least one element with some degree of evidence from the WHO report (E1). A further 28 apps (12 suicide-specific) contained at least one component with evidence from the literature reviews (E2), and the remaining eight interactive apps (two suicide-specific) contained at least one element which follows best-practice guidelines (E3). None of the reviewed apps were completely absent of components consistent with evidence or best practice.
+Excluding the safety plan apps, a mean of 1.1 (range: 1-2) identified suicide prevention strategies were found in each app. Safety plan apps, which inherently contain multiple components, contained a mean of 3.9 (range: 2-6) components.
+Within the 13 apps that contained the one identified high quality strategy coded as E1, the most comprehensive app was a safety plan app [29]. Overall, the most comprehensive app was also a safety plan app [30]. Both these apps were available for Android only. Outwith the safety plan apps, no apps which contained crisis contacts (E1) contained any other coded suicide prevention strategies.
+Discussion
+Summary of evidence
+This review has examined app store descriptions for 856 unique apps, the in-app content of 123 apps, and evidence for interactive suicide prevention strategies within 49 apps. Overall, providing access to crisis support services was the only strategy included within apps with E1 level evidence, with approximately a quarter of apps providing this feature. A further half of
+the apps were consistent with strategies identified in previous evidence reviews, and all apps contained elements consistent with at least best practice guidelines. A small number of apps with potentially harmful content were also identified during the review process.
+Twenty-four apps focussing specifically on suicide prevention were identified, all of which included features broadly concordant with the evidence base. This degree of concordance is higher than observed in reviews of other physical and mental health apps, and possibly reflects a higher degree of involvement from professional institutions in app development. For example, Nicholas et al. found that only 4% of apps for bipolar disorder were developed by institutions [12], and similarly Shen et al. reported that universities and institutions accounted for only 4.2% of developers of depression-related apps [13]. In contrast, institutions accounted for approximately half of developers of the reviewed suicide prevention apps. This potentially accounts for the difference in the proportion of apps that are evidence-informed between the current study and other mental health areas. This may also explain the reasonable provision of duty of care embedded within the relevant suicide prevention strategies. Most apps which offered self-screening tools alerted users towards help seeking options if risk of suicidality was detected, although the suggestion was not always direct or immediate. Apps which allowed users to interact with one another also contained content moderation, which is important considering the potential for sharing potentially harmful content.
+Despite the involvement of academic and healthcare institutions in their development, relatively few suicide prevention apps contained broader markers of app quality, such as referencing of source material. Indeed, a review by Aguirre et al. [21], specifically of suicide prevention apps, sought to review the evidence base of the content, however found it not possible due to the lack of information within apps indicating the provenance of the content. Apps also suffered from a lack of privacy policies, locking and protecting of apps, and reliability. These deficiencies may influence consumer and professional confidence in these apps.
+The components contained within the reviewed apps covered a broad range of suicide prevention content, with the strongest emphasis on safety planning and getting help in a crisis. However, the vast majority of apps only featured one interactive component. Given that the WHO report indicates good evidence for multifaceted suicide prevention strategies, the lack of comprehensive app-based support via the inclusion of numerous tool-based components represents a missed opportunity. Therefore, there is considerable scope for increasing the comprehensiveness of apps for suicide prevention. This could include targeted crisis support for individuals, including immediate access to support services through the app, and an active safety plan (despite the lack of clear evidence for this, it remains best practice and a prudent inclusion). Secondarily, non-crisis tools could include identifying suicide risk factors and triggers, and the delivery of psychological interventions.
+In addition to increasing the number of components offered, there is also a need for greater coverage of specific suicide prevention strategies that were missing or under-represented in the reviewed apps. While it may not be feasible to deliver large, public health strategies, pharmacotherapy, or physical therapy through an app, there is room for development of apps to deliver psychotherapy specifically for suicide prevention, improved physician-led screening for suicidality and wider risk factors, and for assertive follow-up following a suicide attempt. Although these strategies lacked the highest grade of evidence, there was some evidence in the literature.
+It is perhaps reassuring that there are a number of suicide prevention apps already publicly available to support individuals who may be in crisis, and that the interactive components generally follow best practice guidelines and strategies for which there is at least some degree of evidence. The identification of these good-quality apps, however, remains a challenge. Just under 90% of the apps identified in the app stores contained no suicide prevention strategies, and some contained potentially harmful content. With no regulation in the app marketplace, it
+currently falls on clinicians and consumers to delineate app quality. Therefore app developers have a challenge in not only creating suicide prevention apps with evidence informed content, but in dissemination strategies so that the app is identified and used by the target audience.
+Limitations
+There are a number of possible limitations with the current review. App stores allow publishers to restrict distribution to particular territories, and therefore not all apps may be available globally. As app store searches are localised to one particular country, it is possible that some suicide prevention apps were not found in the search of the Australian stores. However, an ad-hoc search of the term “suicide” on the American, Australian, British, Canadian, French, and German iOS app stores found 100% concordance, and no apps that were not available in each territory. This provides confidence in this review reflecting the global app market.
+Unlike searches of literature databases, app store search results provide a static snapshot of a dynamic marketplace. Apps can be updated at any time, removed entirely from the app stores, or disappear from the search results due to decreasing popularity. As an illustration, an ad-hoc search of the Australian iOS store at the time of writing found seven of the 149 apps originally identified through the “suicide” search term were no longer available. This illustrates a methodological challenge inherent in such reviews, as the results can only provide a snapshot into the current offering of available apps. This can also be a challenge for clinicians recommending an app, as there is no guarantee that an app will continue to be available.
+Mapping of app components to the evidence base has been, to some degree, hampered by the extent to which both apps and suicide prevention programmes adopt a multifaceted approach. Identifying which features or individual components are effective is therefore a challenge. This is further exacerbated by a relative paucity of good quality evidence for specific suicide prevention interventions that would be appropriate for inclusion into an app. As reported by Leitner et al.: “the research literature has adopted a ‘scattergun’ approach... The evidence base for any single form of intervention is therefore very limited” [17]. As there is a lack of a gold standard for effective suicide prevention interventions, we adopted the WHO report as that standard. Implementation of quality suicide prevention strategies, whether in app form or indeed wider policy implementation, could benefit from standard guidelines.
+Conclusions
+Despite a lack of evidence in the literature, there are a growing number of apps publicly available for suicide prevention. Many of these provide no interactive features, representing a lost opportunity to engage users in suicide prevention programmes. There are also a small number of apps which, to varying degrees, present potentially harmful content-of greatest concern is the encouragement to engage in risky behaviours such as drugs and deliberate self-harm to manage a crisis. Of those that do provide interactive prevention content, there was limited concordance with high quality evidence-based practice. However, all apps contained at least one component that was broadly consistent with either known evidence or best practice guidelines. While this represents a promising first step in harnessing apps to compliment suicide prevention awareness and strategies, there is a need for suicide-prevention apps to move beyond best practice into the delivery of genuine evidence-based practice with apps supported by empirical data on their effectiveness at reducing suicidal behaviours.
+Supporting Information
+S1 Dataset. Review Data.
+(CSV)
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+IT IS NOW WIDELY ACKNOWLedged that schizophrenia contributes substantially to the global burden of disease.1,2 It is also well known that schizophrenia is associated with elevated suicide rates.3 Less widely appreciated is the fact that people with schizophrenia are at increased risk for premature death associated with comorbid somatic conditions.4 Apart from adverse effects related to medication, schizophrenia can trigger a cascade of socioeconomic and lifestyle factors that, in turn, can translate into adverse physical health outcomes. These comor-bid physical conditions contribute to increased mortality risks among people with schizophrenia.
+The association between severe mental illness and increased mortality rates has long been recognized.5 With respect to the
+group of disorders now labeled schizophrenia, increased mortality rates have been the object of scrutiny since the early 20th century.6-8 The quality of research on this topic has improved greatly in recent decades, with access to larger, better-characterized samples and the availability of high-quality mortality data for the general population. Access to these data allows the calculation of the standardized mortality ratio (SMR), which compares mortality in people with schizophrenia vs the general population. The SMRs are calculated by dividing the observed mortality rates in a given population (eg, the number of deaths in a group of individuals with schizophrenia) by the expected mortality rates in that same group as predicted by age- and sex-specific mortality rates for a standard population. Thus, an SMR of 2.0 would indicate that people
+with schizophrenia are twice as likely to die compared with the general population. The SMRs can be calculated for overall mortality (all-cause) or for more specific, widely used categories (eg, cancer, cardiovascular disease, endocrine disorders, or suicide).
+In recent years, several scholarly reviews4,9-11 have noted higher mortality in schizophrenia compared with the general population. A meta-analysis,4 based on 18 studies published between 1969 and 1996, reported an all-cause SMR for people with schizophrenia of 1.51. Another meta-analysis,11 based on 20 studies published between 1973 and 1995, reported a similar SMR for people with schizophrenia (1.57). Although these 2 systematic reviews agreed on the size of the pooled SMR associated with schizophrenia, there were discrepancies in the sex difference of overall mortality ratios. Brown4 found a small but significant male excess in the overall mortality ratio, whereas other studies12,13 reported either no sex dif-ference11 or higher mortality ratios in females compared with males.
+In collating data from different sites, systematic reviewers need to appreciate the structure of the underlying data. In light of the differing population age structure and disease profile among sites,1,14 we would expect substantial variation in mortality ratios among sites. For example, one would predict that SMRs for people with schizophrenia would differ between developed and developing nations, where the profiles of disease and the access to services vary markedly.
+Because of the increased focus on mental health care seen in many countries during the last few decades, one might predict that SMRs associated with disorders such as schizophrenia should be decreasing over time.15,16 However, several authors have suggested that SMRs in schizophrenia have been increasing during recent decades. For example, Osby et al17 found a linear increasing trend of mortality during 5-year periods from 1976 to 1995 among people with schizophrenia. The meta-analysis by Brown4 also reported significantly higher mortality in the 1980s compared with the 1970s. Deinstitutionalization may have influenced recent secular changes in mortality rates in schizophrenia. Although deinstitutionalization started in the 1950s, findings on its relationship to mortality have been inconsistent.10,11,18
+The aims of this study were to undertake a systematic review of mortality in schizophrenia and to examine a limited number of planned sensitivity analyses. In keeping with our previous systematic review of the in-cidence19 and prevalence20 of schizophrenia and considering that variability is to be expected in systematic reviews of SMRs,4,21 we sought to preserve the expected variation in the data rather than to focus only on pooled values derived from meta-analysis. Thus, for the main analyses, we present distributions of mortality estimates with measures of central tendency (eg, median or means) and quantiles (10% and 90% quantiles). On the basis of all-cause SMR, we predicted that the SMRs of males and females would not differ significantly. We also predicted that SMRs from the developed world would differ from those from the developing world (nondirec-tional hypothesis). We wished to explore the impact of study quality on SMRs. With the assumption that higher-
+quality studies would be more likely to identify deaths in schizophrenia, we predicted that SMRs derived from such studies would be higher compared with those from lower-quality studies. On the basis of previous systematic reviews and commentaries, we predicted that SMRs would increase over time.
+METHODS
+DATA SOURCES
+Most mortality studies are based on record linkage. People with schizophrenia are identified via psychiatric case registers and then subsequently linked to registers of cause of death. Some studies13,22 report mortality ratios based on hospital inpatient cohorts. Other studies23,24 have used community-based follow-up data for people with schizophrenia who are first identified through community surveys and then followed up for extended periods.
+IDENTIFICATION OF STUDIES
+Guidelines outlined by the Meta-analysis of Observational Studies in Epidemiology25 were followed to identify and collate mortality studies. The broad search string of (schizo* or psych*) and (mortality or outcome orfollow-up) was used in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify all research studies that investigated mortality in schizophrenia. Potentially relevant articles (in all languages) were accessed to review the full text. Citations from significant articles and review articles were scrutinized to locate additional relevant articles, book chapters, and conference papers. The Web of Science Cited Reference Search system was also used to locate relevant articles. Finally, letters or e-mails were sent to the senior authors of articles that met the inclusion criteria. These authors were provided with an interim list of included studies and asked to nominate missing studies.
+INCLUSION AND EXCLUSION RULES
+Studies were included if they met all the following criteria: (1) published and/or available between January 1, 1980, andJanu-ary 31, 2006, (2) reported deaths in people with schizophrenia as diagnosed by any criteria, (3) studied a population 15 years and older, (4) reported primary data on all-cause mortality and/or cause-specific mortality, and (5) reported SMRs and/or data on observed and expected deaths sufficient to calculate SMRs. Studies were excluded if they (1) involved people with a diagnosis other than schizophrenia (ie, studies that reported on broader categories of psychosis were excluded), (2) reported duplicate data, (3) reported SMRs solely attributable to suicide (this was the focus of a recent systematic review and meta-analysis3), and (4) reported mortality in subgroups of the population (eg, homeless people,26 twins,27 and those involved in clinical trials).
+DATA ABSTRACTION
+Once a study was included, data were extracted and entered into a 3-level, normalized database that included study-level variables (eg, authors, year of publication, and site), middlelevel variables (eg, age group, recruitment duration, casefinding method, and diagnostic criteria), and estimate-level variables (eg, general and specific-cause SMRs for all persons, males, or females). Two or more of the authors checked all data used in the analysis. When disagreements arose, these were re
+solved by consensus. If required, we contacted the original authors for clarification of issues. The full data set is available from the authors (www.qcmhr.uq.edu.au/epi).
+To assess the impact of overall quality of the distribution of SMRs, we devised a quality score. On the basis of operationalized criteria, this score rewarded studies that (1) used superior research design features (eg, more thorough case ascertainment, published diagnostic criteria, methods to confirm diagnosis, and longer periods of follow-up) and (2) provided comprehensive reporting of the study results (eg, provision of numerator, denominator, SMRs, details of subject attrition, and description of the completeness of the data source). Full details of the quality score used in this review are available from the authors (www.qcmhr.uq.edu.au/epi).
+In systematic reviews, it is important to avoid double counting of the index variable (deaths) by the same or different studies. Thus, a key feature of this review is the application of sequential filters to identify discrete mortality estimates. We applied a similar sorting algorithm to that used in our previous reviews of schizophrenia.19-20 Briefly, the mortality estimates were sorted into different causes of death. Study-level and middlelevel filters were applied to isolate data from multiple studies that overlapped in both time and place. The third filter was used to select 1 representative mortality estimate for inclusion in the cumulative distribution using the “most informative” rule. For example, if 1 study presented multiple overlapping ratios, the ratios based on the largest sample were preferred (ie, the widest age range was preferred over narrower age strata).
+The highest-order (and most reliable) category of death, allcause mortality, can be further subdivided according to rules such as those codified by the International Classification of Diseases, Ninth Revision (ICD-9) .28 Almost all included studies in this review were coded with the ICD-9. Although death can result from the combination of many different health problems, in circumstances in which several codes may be suitable, emphasis is given to the underlying cause of death. More specific causes of death can be allocated to categories according to organ systems (eg, cardiovascular or gastrointestinal) or nature of disease (eg, cancers are coded together). Apart from codes for these specific domains, studies occasionally report SMRs for middle-level categories such as all-unnatural (ICD-9 codes E800-E999) (which includes codes for suicide, accident, and homicide) and all-natural (ICD-9 codes 001-799; the remainder from all-cause when all-unnatural cause is excluded).
+The SMRs were extracted from the publications or calculated by dividing the sum of observed deaths by the sum of expected deaths (when sufficient data were available to calculate these). The distributions of SMRs were assessed in cumulative plots, with every SMR contributing to the distribution. The distribution of the data was assessed in rank order for SMRs (lowest to highest ranks) with the cumulative percentage of SMRs shown on the vertical axis. Key features of these distributions are presented (eg, median, mean, geometric mean, standard deviation, and quantiles at 10%, 25%, 50%, 75%, and 90%).
+For all-cause death, we were often able to extract data on case fatality rate (CFR). The CFR is calculated by dividing the number of deaths in people with schizophrenia during a certain period by the number of people with that disorder at the beginning of the period. An annualized CFR was derived to allow comparisons among studies of different durations.14
+In keeping with definitions from our previous systematic reviews of schizophrenia,20-29 we divided studies according to the per capita gross national product of the study site (based on 2004 data)30 and used a standard World Bank definition of country status31: (1) least developed countries,mean income of less than US $2995; (2) emerging economy countries,mean income between US $2995 and $9266; and (3) developed coun-tries,mean income of greater than US $9266.
+To assess secular trends, we used meta-regression to examine the relationship between the midpoint of the follow-up period and all-cause SMR for persons. Study quality scores were divided into tertiles, and the distribution of all-cause SMR for persons were compared according to these 3 levels.
+We performed statistical analyses for the test of significance between distributions of different SMRs. These analyses take into account (1) the need to control for within-study variation (estimates drawn from the same study tend to be more alike than SMRs drawn from different studies) and (2) the use of a log transformation to analyze distributions that are often positively skewed. Analyses were performed with SAS statistical software, version 9.2 (SAS Institute Inc, Cary, North Carolina).
+We also undertook a secondary analysis based on conventional meta-analytic techniques. Because SMRs are known to vary widely among sites because of population and disease frequency differences, we adopted a random-effects model to estimate a pooled SMR for all-cause mortality for persons.21 When necessary, 95% confidence intervals (CIs) were generated according to the formula detailed by Rothman and Greenland.21 Heterogeneity among the studies was tested using the Cochran heterogeneity statistic.32 Apart from the specific analyses related to sex differences, we restricted the analyses to persons to limit the number of planned comparisons. The funding source played no part in the design, analyses, writing, or submission of this study.
+RESULTS
+The electronic search identified 1726 articles, whereas manual reference checking identified an additional 26 references. We received responses from 16 authors, who provided an additional 11 references. Four articles from languages other than English were included after translation. Eleven studies33-43 were excluded because they completely overlapped with other included studies. Further details of the results of the search strategy and key features of the included studies are available from the authors (www.qcmhr.uq.edu.au/epi).
+The systematic review identified 37 studies9,12,13,18,22-24,44-73 that provided data on 561 SMRs for different causes of deaths drawn from 25 different countries: Australia (n = 2),59,68 Brazil (n=1),61 Bulgaria (n=1),53 Canada (n = 3),50,51,65 China (n = 1),53 Columbia (n = 1),53 Czech Republic (n=1),53 Denmark (n=2),63,64 Finland (n=3),18,22,23 France (n=2),46,48 Germany (n = 1),57 Hong Kong (n = 1),53 India (n = 2),12,53 Indonesia (n = 1),58 Ireland (n = 2)53,62 Israel (n= 1),73 Italy (n = 2),60,67 Japan (n=3),53,69,71 Norway (n=1),52 Russia (n=1),53 Sweden (n = 2),9,66 Taiwan (n = 1),49 the Netherlands (n = 1),13 the United Kingdom (n = 5),44,47,53,54,56 and the United States (n = 6).2445,53,55,70,72 The SMRs were based on an estimated total of 22 296 discrete deaths. Thirty-seven studies9,12,13,18,22-24,44-73 provided SMRs for all-cause mortality for either all persons, males, or females.
+Figure 1 shows the distribution for all-cause SMRs for all persons, males, and females. The median all-cause SMR for all persons (based on 38 SMRs) was 2.58, with 10% and 90% quantiles ranging from 1.18 to 5.76 (Table 1). In other words, people with schizophrenia had 2.5 times the risk of dying compared with the general population, and the central 80% of all SMRs varied over a 4-fold range. The median annualized all-cause CFR for all persons was
+95.4 per 10 000 population, with 10% and 90% quantiles ranging from 57.2 to 301.7 (5-fold range).
+The median all-cause SMR for males (3.02) was slightly higher than females (2.37); however, these 2 distributions were not statistically significantly different (F 1,18=0.0003; P =.99). For all persons, the median SMR for natural causes of death was 2.41, and the 10% and 90% quantiles ranged from 0.99 to 4.10 (Table 1). Elevated median SMRs were found in all of the specific causes of death apart from cerebrovascular diseases.
+Seven studies18,47,49,51,56,65,66 published data for the summary category of unnatural causes of death for all persons, males, or females. Table 1 gives the distributions of SMRs for unnatural causes of death. People with schizophrenia had 12 times the risk of dying of suicide compared with the general population (median SMR, 12.86).
+Twenty-two studies* were identified that contributed 28 SMRs for developed countries, 3 studies53,58,61 contributed 6 SMRs for emerging economy countries, and 1 study53 contributed 4 SMRs for least developed countries. When divided according to this criterion, the allcause SMR distributions were not significantly different (F2,34=0.30; P = .74); the median all-cause SMRs for least developed, emerging economy, and developed countries were 2.02, 2.19, and 2.79, respectively (Table 2).
+When the all-cause SMRs for all persons were divided into study quality score tertiles, no significant differences were found between SMR distributions (F2,24=0.61; P =.55). On the basis of follow-up periods, we identified 8 studies24,45,51,54,55,63,71,72 with SMRs from the 1970s, 10 studies47,53,57-60,65-67,73 with SMRs from the 1980s, and 7 studies22,46,48,53,61,62,68 with SMRs from the 1990s. Concerning secular change, meta-regression confirmed a significant positive association between the follow-up period midpoint year and all-cause SMR (slope coefficient, 0.06; 95% CI, 0.01-0.11; z = 2.21; P = .03). The median SMRs for the 1970s, 1980s, and 1990s were 1.84, 2.98, and 3.20, respectively. Concerning CFRs, the median rates per 10 000 population (all-cause mortality) were 162.2,
+95.4, and 108.3 for the 1970s, 1980s, and 1990s, respectively. The CFRs for the 3 decades were not statistically significantly different (F2,23 = 0.38; P =.38).
+The 38 studies that report all-cause SMRs for all persons are shown in a traditional forest plot with a pooled estimate based on a random-effects model in Figure 2. Using traditional meta-analytic techniques, we found that the pooled random-effects all-cause SMR (based on 37 SMRs with finite standard errors) for all persons was 2.50 (95% CI, 2.18-2.83). The Cochran Q test found a marginally acceptable level of heterogeneity ( Q36=50.72; P =.06). We undertook several post hoc analyses to explore potential sources of variation (eg, published vs unpublished diagnostic criteria, cohorts based on first-episode patients vs all patients, cohorts based on inpatient and/or outpatient samples, sites clustered according to World Health Organization regions, and SMRs attributable to suicide sorted by decade). However, none of the post hoc comparisons resulted in significantly different SMR distributions (data not shown).
+COMMENT
+People with schizophrenia have a substantially increased risk of death compared with the general population. Overall, people with schizophrenia have 2.5 times the risk of dying. This review was able to extract data from 37 studies that were conducted in 25 countries. As predicted, the distribution of all-cause SMRs showed prominent variability.
+Confirming the hypothesis that the relative mortality risk associated with schizophrenia is increasing, we found that SMRs have increased in a linear fashion during the 3 decades examined in this study. This finding is consistent with earlier studies.4,17 Considering that (1) CFRs for schizophrenia did not significantly differ among the decades and (2) age-standardized mortality rates are generally decreasing in most nations,74-76 these findings suggest that people with schizophrenia have not fully benefited from the improvements in health outcomes available to the general population. The SMRs are ratio measures and thus reflect differential mortality. If mortality rates in the general population decrease over time at a faster rate than those for people with schizophrenia, then SMRs for people with schizophrenia will increase over time. The evidence from the current study suggests that this differential mortality gap has widened over time.
+Mental health services have advanced in many parts of the world during the past few decades. Apart from a different mix of community-based care, the introduction of the second-generation antipsychotic medications in the early 1990s was initially found to be associated with better quality of life and reduced risk of relapse.77-79 More recent trials have questioned the clinical superiority of second-generation antipsychotic medication,80,81 and concern is now widespread about the adverse effects associated with these medications.82 In particular, compared with typical antipsychotics, several of the second-generation antipsychotics are more likely to cause weight gain and metabolic syndrome.83 Because the metabolic syndrome is associated with a 2-
+to 3-fold increase in cardiovascular mortality and a 2-fold increase in all-cause mortality,84 these adverse effects would be expected to contribute to even higher SMRs in the next few decades.85,86 Unfortunately, we are unable to explore the role of atypical medications as a contributing factor for the increasing SMRs associated with schizophrenia (eg, deaths related to clozapine-induced agranulocytosis or deaths related to atypical antipsychotic-induced weight gain). Adverse health outcomes associated with weight gain and/or metabolic syndrome (eg, myocardial infarction, cerebrovascular accidents, or cancer) may take decades to fully emerge. Thus, it seems likely that studies undertaken in the 1990s (ie, the most recent studies included in this review) would capture only a small fraction of the eventual burden of mortality associated with the adverse effect profile of the second-generation antipsychotic medications. In light of the rising secular trends in SMRs already identified by this review, the prospect of further increases in mortality risks for schizophrenia is alarming.
+In keeping with the findings of Harris and Barra-clough11 and Simpson,10 we found no significant sex difference in all-cause SMRs. Thus, although many well-documented sex differences exist in the epidemiological features of schizophrenia,19,87,88 the increased risk of mor
+tality associated with schizophrenia affects men and women equally.
+Of the specific-cause SMRs, suicide was associated with the highest estimate: 12 times greater than expected from the general population. In keeping with previous reviews, the SMRs associated with many different types of natural causes of death were elevated in people with schizophrenia. Curiously, the category neoplastic disorder had one of the lowest median SMRs (1.37). Although the median was still greater than 1, several record linkage studies89 have suggested that cancers may be significantly less prevalent in people with schizophrenia. The current review examines only mortality, and studies that examine morbidity would be better able to explore this issue.90
+We found no significant difference in SMRs among sites when sorted by economic status. However, this metaanalysis identified just 3 studies53,58,61 that provided discrete SMRs from the least developed and emerging economy countries; thus, caution should be exercised in the interpretation of this finding. Furthermore, a single derived variable was used to define economic status, which was applied at the ecological level.
+What factors have contributed to the differential mortality risk associated with schizophrenia? Many demo-
+graphic, clinical, political, and cultural factors mediate pathways and barriers to health care in general (eg, availability of services, stigma, and disease profiles).91 With respect to schizophrenia, the onset of the illness can result in a cascade of unhealthy lifestyle factors that elevate the risk of various somatic diseases and consequently increase the risk of death. People with schizophrenia are thought to be less inclined to seek health care, to consume less medical care, to engage in high-risk behaviors, and to be less compliant with their treat-ments.82,90,92 However, in addition to factors that operate on the pathway to care, schizophrenia and its
+associated comorbid somatic conditions may be downstream expressions of common genetic or environmental factors.92,93 For example, it is feasible that polymorphisms in genes may increase the susceptibility to both schizophrenia and diabetes94 or that de novo germline mutations across many generations could result in an increased risk of schizophrenia95 and a wide range of adverse health outcomes. Prenatal nutritional disruptions may equally affect brain development and general metabolic functioning.96,97 Although the current review cannot address these issues directly, the worsening SMRs associated with schizophrenia noted in recent decades
+suggest that this already disadvantaged group is not benefiting from the improved health of the community in an equitable fashion. A systematic approach to monitoring and treating the physical health needs of people with schizophrenia is clearly warranted.98
+Several important caveats to this review should be noted. Publication bias is always an issue in systematic reviews. We endeavored to address this by obtaining data from all available sources, including those from electronic databases, citations and authors, and publications in languages other than English. Factors such as the reliability of psychiatric diagnoses and admission practices (between sites and across time) could contribute to the variability identified in this systematic review. The reliability of the categorization of cause of death is also a cause for concern. With respect to specific-cause mortality, changes in the coding rules for the ICD-9 and be-tween-site variability in the application of these rules also need to be taken into account.99,100 However, these issues do not affect all-cause SMRs (which were used for the main analyses in this review). The current study found a higher all-cause SMR (median SMR, 2.58; pooled metaanalysis SMR, 2.50) compared with the 2 previous reviews, which reported all-cause SMRs of 1.514 and 1.57.* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 The 2 previous systematic reviews were based on studies published before 199511 and 19964 compared with the current systematic review, which included 18 additional studies published after 1995.
+In conclusion, compared with the general population, people with schizophrenia have a 2- to 3-fold increased risk of dying. Suicide contributes to the increased mortality associated with schizophrenia; however, people with schizophrenia have increased mortality risks attributable to a wide range of somatic conditions. The increased mortality risk affects both sexes equally. Substantial variation occurs in all-cause SMRs among sites. In recent decades, the differential mortality gap associated with schizophrenia has been increasing. It is sobering to reflect on this paradox of schizophrenia treatment. As we become better at detecting and treating the core symptoms of schizophrenia, patients have worsening SMRs. Given the potential for an even greater disease burden as a result of the introduction of second-generation antipsychotic medications, research aimed at optimizing the physical health of people with schizophrenia needs to be undertaken with a sense of urgency.
+Submitted for Publication: November 4, 2006; final revision received January 16, 2007; accepted March 12, 2007.
+Correspondence: John McGrath, MD, PhD, FRANZCP, Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol Q4076, Australia (john _mcgrath@qcmhr.uq.edu.au).
+Author Contributions: Mr Saha has full access to all of the data in the study and takes responsibility for the integrity of the data.
+Financial Disclosure: None reported.
+Funding/Support: The Stanley Medical Research Institute supported this project.
+Additional Information: The following additional ma
+terial is available at www.qcmhr.uq.edu.au/epi: Figure S1: Flow Diagram (Selection Strategy) of Included Studies in the Mortality of Schizophrenia; Table S2: Quality Reporting Scale; Table S3: Summary Table of All-Cause Mortality and Standardized Mortality Ratio for Schizophrenia (1980-2006); Table S4: Standardized Mortality Ratios (SMRs) for Schizophrenia by Different Causes of Death for Males and Females; Table S5: Standardized Mortality Ratios for 3 Quality Score Tertiles of All-Cause Death; Table S6: Standardized Mortality Ratios for Schizophrenia of All-Cause Mortality for Various Post Hoc Analyses (for All Persons); and Microsoft Excel spreadsheet of the primary data for this systematic review, plus associated labels and formats.
+Additional Contributions: Dozens of researchers from around the world assisted in locating the data for this systematic review, and the staff of the Queensland Centre for Mental Health Research assisted in extracting the data and preparing the original manuscript.
\ No newline at end of file
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+The crude suicide rate for individuals aged 18-30 years has increased, and in 2015 the rate was 14.87 suicides per 100,000 people.1 Although the suicide rate among sexual minority young adults is unknown, suicide ideation and attempt occur more frequently among lesbian, gay, bisexual, and questioning (LGBQ or sexual minority) individuals than heterosexual people.2-7 Specifically, gay men, bisexual men, and lesbian women have a greater risk for suicide attempts than heterosexual adults.8 In general, religiosity is regarded as protective against suicidal thoughts and behaviors; yet, religion can be either a source of support or stress for LGBQ individuals.4,9-12 Consequently, it is
+From the 1Department of Psychiatry, University of Rochester Medical Center, Rochester, New York; 2Injury Control Research Center, West Virginia University, Morgantown, West Virginia; 3Center for Health Equity Research and Promotion, VA Pittsburgh Medical Center, Pittsburgh, Pennsylvania; 4Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; 5School of Social Work, University of Texas at Austin, Austin, Texas; 6Population Research Center, Austin, Texas; 7Counseling and Mental Health Center, University of Texas at Austin, Austin, Texas; and 8Department of Educational Psychology, College of Education, Austin, Texas
+Address correspondence to: John R. Blosnich, PhD, MPH, Injury Control Research Center, West Virginia University, 3606 Collins Ferry Road, Research Ridge, Suite 201, Morgantown WV 26508. E-mail: jblosni1@hsc.wvu.edu.
+0749-3797/$36.00
+https://doi.org/10.1016/j.amepre.2018.01.019
+unclear whether religiosity is protective against suicide ideation and attempt among LGBQ individuals.
+The mechanisms through which religiosity diminishes suicide risk are unclear.13-16 Particularly, moral objections (e.g., that suicide is an unforgiveable sin) may protect against suicidal behaviors,15 and religion may serve as a proxy for connections to community or social support.17 Thus, scholars have started differentiating among religious importance, seeking spiritual guidance, and religious attendance to determine whether these factors may serve as mechanisms of suicide prevention. Among the few longitudinal studies examining religion and suicidal behaviors, adults who attended religious worship at least once a month had lower odds of attempting suicide over the next 10 years compared with those who did not attend, and individuals who sought spiritual comfort had lower odds of suicide ideation for 10 years compared with people who were not spiritual.18 Similarly, there are inverse relationships between suicide ideation and religious attendance, religious well-being, and spiritual well-being among college students.16
+Religious groups’ perceptions vary about LGBQ individuals. High levels of individual religiousness are often associated with negative attitudes towards LGBQ peo-ple,19 and the link between internalized homonegativity and religiously based stigma is well documented, especially among non-affirming religious environments.9,10,12 Despite the fraught relations between religion and sexual orientation, many LGBQ individuals are religious, view religion as important, or have sought religious support after attempting suicide.9-11,20-22 Thus, the association between religion and suicidal behavior among LGBQ individuals have been mixed.
+Religiosity among LGBQ individuals and their parents have direct relationships to suicide attempts.12 For example, a study of LGB individuals in Austria with a religious affiliation had lower odds of attempting suicide than LGB adults who were not affiliated, and those who felt a greater sense of belongingness to their religious organization were less likely to endorse suicide ideation.9 Within a religiously diverse sample, the prevalence of passive (e.g., wish life would end) and active (e.g., considered suicide attempt) suicide ideation was greater among atheist/agnostic, Christian, non-religious, and other religiously affiliated LGB students than heterosexual students.4 Relatedly, LGB individuals who left their religion to resolve the conflict between their sexual orientation and religious affiliation had greater odds of attempting suicide than those with unresolved conflict.11
+LGBQ individuals may experience alienation and distress from religion or attempt to negotiate their intersecting religious and sexual identities.23,24 Consequently, the association between religiosity and suicidal
+behaviors is complicated for LGBQ individuals. Religion may not confer protection against suicidal behaviors or may be positively associated with suicidal thoughts and behaviors. Because few data sets contain information about sexual orientation, religiosity, and suicide ideation and attempt, there is a paucity of studies examining the association between religiosity and suicidal behavior among LGBQ individuals. The present hypothesis is that religiosity is negatively associated with suicide ideation and attempt among heterosexual individuals, but positively associated with suicide ideation and attempt among LGBQ individuals. Further, LGBQ status is associated with greater odds of suicide ideation and attempt among individuals endorsing greater religiosity.
+METHODS
+Study Sample
+Data are from the National Research Consortium of Counseling Center in Higher Education at the University of Texas at Austin. The Consortium conducts national studies on mental health among college students. In 2011, the Survey of Distress, Suicidality, Student Coping was conducted among probability-based samples from 74 higher education institutions and aggregated into a national data set made available to researchers. This survey was self-administered through a web-based questionnaire, the combined response rate between the undergraduate and graduate students was 26.3% and 26,292 students completed the survey. Because this study focused on young adulthood, the sample was restricted to individuals aged 18-30 years (n=21,247). Approximately 2.1% (n=550) were excluded for missing data about age, along with 4,495 individuals (17.1%) who were aged >30 years. Additional information about the methodology have been published.25 This study was approved by the University of Texas at Austin’s IRB.
+Measures
+The main outcome measures were suicide ideation in the past year, suicide attempt in the past year, and lifetime suicide attempt. Respondents were asked: Have you ever seriously considered attempting suicide at some point in your life? Individuals who answered yes were presented questions about suicidal behaviors. Those who answered no did not receive follow-up inquiries and were recoded as no on further suicide ideation and attempt questions. People who indicated lifetime suicide ideation were asked: During the past 12 months, have you seriously considered attempting suicide? Affirmative responses were defined as recent suicide ideation.
+People who indicated lifetime suicide ideation were asked: How many times in your life have you attempted suicide? Response options ranged from zero to five or more. All non-zero responses were defined as lifetime suicide attempt. Those who indicated a nonzero response were asked: How many of those attempts occurred in the past 12 months? Response options ranged from zero to five or more. All non-zero responses were defined as recent suicide attempt.
+Religiosity was operationalized as: How important are your religious or spiritual beliefs to your personal identity? Individuals responded on a Likert-type scale ranging from 1 (not at all important) to 5 (very important). Although the survey included a
+question about religious affiliation (e.g., Buddhist, Jewish), this variable was not included because: (1) it was not mutually exclusive, making it impossible to discern a dominant religion among those who endorsed multiple affiliations; and (2) despite overarching doctrine, many individuals seek alternative or affirming places of worship within an otherwise unwelcoming doctrine (e.g., a Baptist church that officiates same-sex marriages).26 The survey did not include measures of religious activities (e.g., frequency of worship).
+For sexual identity, respondents were asked: How would you describe your sexual orientation? Response options included: bisexual, gay or lesbian, heterosexual, questioning, and other. Among the 286 (1.3%) who indicated other, 268 supplied open responses. Although some of the other respondents could be included in the main sexual orientation groups (e.g., 59 respondents indicated straight), the majority of the responses (e.g., asexual, pansexual, queer) did not align with the existing categories. Thus, one respondent was recoded as lesbian/gay, 124 were recoded as heterosexual, and 143 were excluded from analyses. Because young people who are unsure of their sexual identity often report selfdirected violence, the questioning category was maintained.2
+Multivariable models were adjusted for sociodemographic characteristics. Gender identity was coded as female, male, or transgender and age was included as a continuous variable. Race and ethnicity was recoded into mutually exclusive groups of white, black, Asian, Hispanic, and other; for multivariable models, race/ ethnicity was dichotomized into white and racial/ethnic minority. International student status (yes/no) and partnership status were included. Respondents were asked: What is your current relationship status? (Select all that apply). The response options were: single and not currently dating, casually dating, in a steady dating relationship, partnered or married, separated or divorced, and widowed. Because respondents could indicate multiple categories, the variable was dichotomized into individuals who only endorsed single and not currently dating versus all other responses as a conservative definition of partnership status.
+Statistical Analysis
+Chi-square tests of independence were used to examine differences by sexual orientation in sociodemographic characteristics, religious importance, and prevalence of suicide ideation and attempt. Two sets of multivariable models were conducted to explore the relations of religious importance and sexual orientation with suicidal behavior. In the first set, recent suicide ideation was regressed on religious importance (as a continuous variable), stratified by sexual identity and adjusted for sociodemographic variables; this modeling was repeated for recent and lifetime suicide attempt. In the second set, recent suicide ideation was then regressed on sexual orientation, stratified by religious importance and adjusted for sociodemographic variables, and this analysis was repeated for recent and lifetime suicide attempt. Because of small cell sizes across the five Likert categories of importance of religion, this variable was recoded into a 3-category variable, 1-2 were merged (not important), 3 (moderately important), and 4-5 were combined (very important). Because of differences in self-directed violence among men and women, models were also stratified by gender identity.1,28 All estimates are reported as AORs with corresponding 95% CIs. Listwise deletion of all included dependent and independent variables was used for all analyses. All analyses were conducted using Stata/SE, version 12.
+RESULTS
+Among the analytic sample, 2.3% (n=485) individuals identified as lesbian/gay, 3.3% (n=696) identified as bisexual, and 1.1% (n=233) identified as questioning. All sociodemographics differed between sexual orientation groups (Table 1). Compared with heterosexuals, significantly greater proportions of sexual minorities reported that religion was not important. Notably, questioning individuals had the highest prevalence of recent suicide ideation (16.4%) and bisexual students had the highest prevalence of lifetime attempts (20.3%).
+In multivariable analyses stratified by sexual orientation, religious importance was not significantly associated with suicide ideation and attempt among bisexual individuals, but was significantly protective among heterosexual individuals (Table 2). Among lesbian/gay and questioning individuals, religious importance was associated with increased odds of recent suicide ideation, which seemed driven primarily by women. For example, among lesbian/gay individuals, increasing religious importance was associated with 38% increased odds of recent suicide ideation and for lesbian/gay women, specifically, was associated with 52% increased odds of recent suicide ideation. Additionally, for questioning individuals, increasing religious importance was also associated with increased odds of recent suicide attempt (AOR=2.78, 95% CI=1.14, 6.78). For lifetime suicide attempt, there was a negative association of religious importance among heterosexual women (AOR=0.90, 95% CI=0.85, 0.95), but weak positive associations for lesbian women (AOR=1.34, 95% CI=0.97, 1.85) and questioning men (AOR=1.53, 95% CI=0.98, 2.37).
+In multivariable analyses stratified by religious importance, there were mixed findings (Table 3). For example, lesbian/gay sexual orientation was not associated with greater odds of recent suicide ideation among individuals who reported religion was unimportant and moderately important; however, it was significantly associated with recent suicide ideation among individuals who reported religion as very important (Table 3). Conversely, bisexual and questioning sexual orientations were significantly associated with recent suicide ideation across all strata of religious importance; however, the patterns seemed to indicate the strongest effects were among the group for whom religion was very important.
+Because of the rarity of recent suicide attempt, some estimates in Table 3 could not be generated for all sexual orientations across all religious importance strata; those that were estimable were unstable and should be
+interpreted with caution. Among individuals who reported religion was unimportant, lesbian/gay sexual orientation was not associated with recent suicide
+attempt, but it was significant among the group for whom religion was very important. Bisexual sexual orientation was significantly associated with recent
+suicide attempt across all religious importance strata, but again the pattern of results suggested the strongest effects among the group for whom religion was very important.
+Lastly, LGBQ groups overall had greater odds of lifetime suicide attempt than heterosexual individuals (Table 3). In gender-stratified analyses, compared with heterosexual people, all sexual minority groups
+had greater odds of lifetime attempt, aside from gay men who viewed religion as very important, lesbian women who viewed religion as moderately important, and questioning men who viewed religion as unimportant.
+Data from Table 3 were also summarized in post-hoc analyses that estimated the adjusted prevalence of recent
+suicide ideation and lifetime suicide attempt in Appendix Figures 1 and 2 (available online). Results from recent suicide attempt could not be graphed because of suppression of some estimates across sexual orientation.
+Post-hoc analyses were also conducted to include a 3-item scale of social connectedness (i.e., how understood by others do you feel, how cared for by others do you feel, and how much do you feel that you can count on others). Each item had a 5-point Likert-type response that ranged from 1 (lower values) to 5 (greater values); reliability was acceptable (a=0.78). Overall, the adjustment of social connectedness did not change the pattern of findings for LGBQ respondents (Appendix Tables 1 and 2, available online); however, it did seem to account for many of the protective associations between religiosity and suicide ideation and attempt among heterosexuals (Appendix Table 1, available online).
+DISCUSSION
+The results partially supported the hypothesis that LGBQ groups do not experience the benefits of religiosity’s protective association against suicide ideation and attempt. Conversely, greater religious importance was significantly protective against both suicide ideation and attempt among heterosexuals in this sample. Moreover, these findings corroborate that gender differences in the association between religiosity and suicidal behaviors are minimal,16 suggesting that other factors, such as connectedness, may play a stronger role. For example, the change in results after adjusting for social connectedness suggests how religiosity confers protection against suicide ideation and attempt among heterosexuals; the lack of change among LGBQ individuals suggests other religious factors (e.g., antigay messaging and internalized homophobia) may be involved. In fact, among individuals with the strongest religiosity, LGBQ people seemed to have the greatest odds of suicide ideation and attempt; however, there was considerable heterogeneity among them.
+The positive associations among LGBQ groups are not surprising, given the relations between religion and LGBQ individuals, which are complicated at best and toxic at worst. For example, it is common knowledge that two of the world’s most common religions, Christianity and Islam, largely condemn homosexuality as a sin. However, significant positive associations were not consistent among all sexual minority groups. One potential explanation for this may be that different individual approaches are used to negotiate the intersection of sexual and religious identities. For example, some sexual minority individuals may withdraw from religion or seek affirming communities, whereas others
+may immerse themselves in religion.24,29 Thus, the heterogeneity in the results may speak to the potential nuanced ways that sexual minority communities navigate religious milieus.
+Moreover, religious-based conflict over sexual identity is often associated with conversion therapy (i.e., trying to change/suppress one’s sexual orientation),30 a practice that is denounced by the American Psychological Asso-ciation,31 among other professional organizations. This historic persecution of non-heterosexuality as well as more modern interpretation of scripture may have driven some religious institutions toward broadening their dogmatic practice to actively affirm and welcome LGBQ individuals.32 Yet, further research is needed about whether religions that are LGBQ-affirming may confer protective effects against suicidal behaviors among LGBQ individuals.
+More importantly, the present results have direct implications for mental health services, suicide prevention, and help-seeking efforts. Specifically, efforts that are built around faith-based organizations (FBOs) may not be appropriate for LGBQ individuals in distress, especially when religion may be a contributing element of distress for LGBQ individuals.33-37 This conundrum seems to have been overlooked in the suicide prevention literature, perhaps because of the paucity of quantitative studies, such as the present investigation. For example, the 2012 National Strategy for Suicide Prevention suggests FBOs be a major partner in suicide prevention and that, by promoting connectedness, FBOs may aid in suicide prevention.28 But to whom does this connectedness extend when ample literature suggests LGBQ people experience ostracism from their faith communities?24,38,39 Further, it is unclear whether enhanced training in suicide prevention for clergy and FBOs would serve LGBQ individuals if they perceived religious institutions as unwelcoming, thus undercutting help-seeking behaviors. Consequently, these findings, paired with the endorsement of FBOs as partners in suicide prevention, warrants research in several areas. For example, do LGBQ individuals actively avoid FBOs for mental health-related services? To what extent do FBOs serve LGBQ individuals, and do outcomes of service provision differ between heterosexual and LGBQ clients?
+Limitations
+There are a number of advantages to this study. Specifically, this large and diverse sample allowed investigating the differences among LGBQ individuals as well as rigorous adjustment for covariates (e.g., social connectedness). Despite the strengths of this research, there are several limitations. The data did not include
+questions about religious practice (e.g., religious attendance) or whether the associated religion espoused stigmatizing beliefs about sexual minorities; therefore, it was not possible to explore more nuanced relationships between religiosity and self-directed violence among LGBQ individuals. Although there is a religious affiliation variable, it was not included because it cannot account for the significant variation between denominations (e.g., Catholics, Protestants). With a sample from higher education institutions, these findings may not generalize to the broader population of LGBQ individuals. Although religious beliefs typically are instilled early in life by parents, because this is a cross-sectional analysis, it is not possible to ascertain any causal inferences between religiosity and suicidal behavior or if this relationship evolved over time. Although the response rate is similar for other large studies of young adults,40-42 the response rate was relatively low, which limits generalizability. The estimates for some outcomes, primarily recent suicide attempt, were unstable because of small sample size of the LGBQ groups. Finally, the measure of sexual identity did not allow for nuanced categorization (e.g., mostly heterosexual).
+CONCLUSIONS
+This study begins to address an important gap in the literature by exploring the association between religiosity, suicidal behaviors, and sexual orientation. Previous literature suggested that religiosity may protect against suicidal behaviors, yet those protective benefits were not observed among LGBQ individuals in this sample. In fact, the results suggested that, among people who regarded religion as very important, sexual minority status was more strongly associated with suicide ideation and attempt than the associations observed among people who regarded religion as unimportant. Suicide prevention efforts that partner with religious-based services should be aware of potential conflicts between religion and LGBQ individuals. Faith-based partners in public health suicide prevention and intervention services should be willing and equipped to assist all people who seek their services, regardless of sexual orientation. Moreover, this study opens a more general question about how and if faith-based public health partnerships benefit LGBQ populations.
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+1.	Background
+Restricting access to lethal means is the suicide prevention intervention with the best evidence for effectiveness (Zalsman et al., 2016). Means restriction has most public health impact in relation to common, high-lethality suicide methods. After hanging, poisoning is the second commonest method of suicide in England, Scotland and Wales;
+accounting for 18% of male and 36% of female suicides in 2016 (Office for National Statistics, 2016a). Restricting means of overdose entails impeding access to the medication load available to at-risk persons to a level that, even if taken in one dose, will not pose serious harm (Hawton et al., 2013). Usually this involves adjusting the frequency (and therefore volume) of medication prescribed or available over-the-counter. The value of this approach is exemplified in the
+significant reduction in fatal paracetamol overdoses associated with UK legislation restricting pack size of over-the-counter analgesics (Barber and Miller, 2014). Where methods are not easily substituted by others, means restriction does not necessarily prompt means substitution (Sarchiapone et al., 2011). Indeed, the UK withdrawal of co-proxamol was associated with a significant reduction in deaths involving co-proxamol poisoning but no corresponding increase in deaths involving analgesics (Hawton et al., 2009). Physical disorders such as cancer (Henson et al., 2019; Ahmedani et al., 2017), osteoporotic fracture (Chang et al., 2018; Webb et al., 2012), back pain (Ahmedani et al., 2017), diabetes (Ahmedani et al., 2017; Webb et al., 2014), and heart disease (Ahmedani et al., 2017; Wu et al., 2018) are associated with an increased risk of suicide, and may provide affected individuals access to potentially lethal doses of prescribed medication Gorton et al., 2016). In a Swedish sample, 9% of patients diagnosed with diabetes who died from fatal poisoning had taken overdoses of diabetic drugs (Webb et al., 2014). For people with pain conditions, particularly chronic pain (Petrosky et al., 2018), opioids are a key target for means restriction, especially as the association of non-cancer pain and suicide risk is independent of psychiatric illness (Ilgen et al., 2013). In 2016 opioids accounted for 54% of all fatal drug poisonings (suicides and accidental overdoses) in England & Wales (ONS, 2016b). The most common opioid responsible was heroin and/or morphine (ONS, 2016b), although available data do not indicate what proportion involved ‘street’ opioids or those prescribed for chronic pain. With approximately 6000 people dying by suicide in the UK annually (ONS, 2016c), there is great interest among both clinicians and policymakers in the potential to restrict the volume of potentially lethal medication available to patients with physical illnesses. However, an improved understanding is needed regarding the role of access to these medications in pathways to suicide.
+Our research question was whether a greater proportion of psychiatric patients also diagnosed with physical illnesses who die by suicide poison themselves compared to individuals without physical comorbidities, and whether they are more likely to self-poison using medication prescribed to treat their physical health problems. We thereby aimed to explore the potential for means restriction interventions in a sub-group of psychiatric patients with co-morbid physical illnesses. Using national suicide case series data on psychiatric patients who died by suicide in England & Wales during 2004-2015, we aimed to describe the sociodemographic and clinical characteristics of psychiatric patients with a diagnosis of a co-morbid physical illness. We tested the hypotheses that:
+•	a greater proportion of deceased patients diagnosed with co-morbid physical illness fatally poisoned themselves than such patients without co-morbidity
+•	a greater proportion of deceased patients diagnosed with physical illness who fatally poisoned themselves overdosed on medication used for physical health problems versus such patients who died by intentional self-poisoning without co-morbidity
+•	among deceased patients with co-morbid physical illness who fatally poisoned themselves with medications prescribed to treat these conditions, a higher proportion had been prescribed the medication taken in overdose versus those without physical health disorders
+•	among deceased patients diagnosed with cancer, diabetes, and pain conditions the proportion who fatally self-poisoned using physical health medications was greater than among such patients diagnosed with other physical illnesses. These conditions have been linked with elevated suicide risk (Henson et al., 2019; Ahmedani et al., 2017; Webb et al., 2012), whilst also providing access to medications that are highly toxic in overdose (Gorton et al., 2016).
+2.	Methods
+2.1.	Study dataset
+Questionnaire data were collected as part of the National Confidential Inquiry into Suicide and Safety in Mental Health (Appleby et al., 1999). This database provides a national case series of patients under the care of mental health services who have died by suicide across the UK (i.e. England, Scotland, Wales and Northern Ireland). A detailed description of the National Confidential Inquiry's methodology is available elsewhere (Windfuhr et al., 2008). In brief, firstly, data on all deaths in England & Wales receiving a verdict of suicide or unnatural death of undetermined intent (‘open’ verdict) at coroner's inquest were received from the Office for National Statistics (ONS). Suicide research conducted in the UK conventionally includes open verdicts to avoid underestimating the number of suicide deaths (Linsley et al., 2001). Second, administrative contacts at NHS Trusts or Health Boards in the deceased person's district of residence identified whether contact had been made with secondary mental health services in the 12 months prior to death. Third, for those individuals with psychiatric contact, detailed data were collected via a questionnaire sent to the clinicians who had been responsible for that psychiatric patient's care. The questionnaire captured information on suicide method, demographic details, clinical characteristics, including any major physical illness at the time of death, aspects of care and treatment received.
+2.2.	Ethical approvals
+The National Confidential Inquiry has research ethics approval from North West - GM South REC (reference: ERP/96/136) and Section 251 Approval under the NHS Act 2006 (reference: PIAG 4-08(d)/2003), allowing collection of patient identifiable data for medical research.
+2.3.	Measures
+We defined physical health conditions on the basis of responses to the questionnaire item: “Did the patient have a major physical illness at the time of death? (include conditions even if well controlled by treatment)”. Free text responses to a further specifier permitted categorisation of conditions into those corresponding to International Classification of Diseases (ICD-10, 1992) categories (diseases of the musculoskeletal system, circulatory system, nervous system, digestive system, and endocrine disease). We used clinician-derived search terms to identify conditions with heterogeneous descriptors. For our sub-analyses we defined a specific diabetes category and overlapping categories for pain conditions and cancer.
+We categorised the substances used in self-poisoning on the basis of fixed-choice responses to the questionnaire item: “If self-poisoning, specify substance (if more than one substance, select most likely cause of death)”, to develop a categorical measure of whether or not these drugs are prescribed to treat physical illnesses. This was coded by a psychiatrist (AP), including free text responses to the “Other drug (please specify)” category. Categories within the physical illness treatment group were: opioids (morphine, codeine and methadone), paracetamol/ opioid compounds, other analgesics, insulin, cardiac medications, and other specified drugs for physical conditions (Box 1).
+We categorised the source of substances used in self-poisoning cases using fixed-choice responses to the relevant questionnaire item (prescribed for the patient; prescribed for someone else; not prescribed). For data collected from 2012, where opioids were reported in self-poisoning cases, further detail was available on whether these were prescribed for the patient for treatment of pain or for the treatment of drug misuse, prescribed for someone else, obtained illicitly, or obtained over-the-counter. We analysed drugs used for physical health conditions in cases of self-poisoning, irrespective of whether they had been prescribed for
+98%. We excluded 6% (1014 cases) with missing data for presence/ absence of physical co-morbidities, leaving a final dataset for analysis of 14,648 patients. Of these, 3525 (24%) had a recorded diagnosis of one or more co-morbid physical illness, most commonly diseases of the musculoskeletal (884, 25%); circulatory (822, 23%); endocrine (646, 18%); nervous (608, 17%); and digestive systems (580, 16%). Overall, 66% had a condition from a single major category of physical illness, 25% from two major categories, and 9% from three or more. Overlying these diagnostic categories, 16% (546 patients) had a pain condition and 9% had a cancer diagnosis.
+3.2.	Patient characteristics of those with a co-morbid physical illness
+The median age of psychiatric patients who died by suicide and had a co-morbid physical illness was 53 years (interquartile range (IQR) 43-64); significantly older than those without a physical health condition (median age 44, IQR 33-54; p < .001). Patients with a physical illness were more likely to be female, white, widowed, and to live alone than other patients (Table 1). They were less likely to be unemployed, unmarried or homeless. Whilst the proportions with a history of selfharm did not differ (around 68% in both groups), those with a physical health condition less often had a history of violence (19% v. 22%; p < .001) or of alcohol (42% v. 46%; p < .001) or drug misuse (27% v. 35%; p < .001). Patients with a physical illness were more likely than those without to have a primary psychiatric diagnosis of affective disorder, and less likely to have schizophrenia (including other delusional
+the patient or for someone else, or obtained illicitly.
+2.4. Statistical analysis
+Chi-square tests (with a 2-sided p-value threshold of < 0.05) were used to compare proportional distributions of sociodemographic and clinical characteristics between psychiatric patients with versus without diagnosed physical illness. We fitted logistic regression models to estimate the strength of these associations, with and without adjustment for age, gender, ethnicity, and presence of a primary drug dependence/ misuse disorder (which may itself be associated with chronic pain conditions). Odds ratios (ORs) and their 95% confidence intervals (CIs) were presented. Pairwise deletion was applied to address missing data; ie. if an item of information was unknown, the case was removed from the analyses of that variable. All analyses were conducted using Stata version 15.0 (StataCorp, 2017).
+2.5. Sensitivity analyses
+We conducted sensitivity analyses to assess robustness of findings when using a more stringent definition of medications that may have been prescribed to treat physical health conditions. This excluded drugs that can be used to treat psychiatric conditions (e.g. gabapentin and pregabalin for anxiety) or to address the side effects of psychotropics (e.g. metformin for antipsychotic-induced weight gain). We also repeated our analysis for data from 2012-2015 excluding opioids not prescribed for pain, medications prescribed for someone else, and nonprescribed medications (including over-the-counter paracetamol/ opioid compounds). In a post hoc sensitivity analysis we tested whether our findings were accounted for by the older age of those with co-morbid physical illness, and their greater prevalence of affective disorder.
+3. Results
+3.1. Descriptive statistics and prevalence of physical illnesses
+Between 1st January 2004 and 31st December 2015 inclusive, the National Confidential Inquiry was notified of 57,863 suicides in England & Wales (43,539 cases with a suicide verdict; 14,324 with an open verdict). Of these, 15,934 (28%) people had been in contact with secondary mental health services in the 12 months before they died. Questionnaires were returned on 15,662 patients, a response rate of
+disorders) or personality disorder (Table 1). They were less likely to have been a psychiatric in-patient at the time of death, to have recently (<3 months) been discharged from psychiatric in-patient care, or to have been under the care of a crisis resolution/home treatment team. They had more often attended their last contact with mental health services and were more likely to have been adherent with medication treatment compared with patients with mental illness alone. Nearly half (47%) had been in contact with services in the week before death, which was significantly fewer than for patients without a physical condition (51%; p < .001), with 68% exhibiting psychiatric symptoms at this appointment, proportionally more than other patients (63%; p < .001). However, these differences were unlikely to be clinically significant.
+3.3.	Method of suicide and substances used in self-poisoning
+A significantly greater proportion of psychiatric patients who had been diagnosed with a physical illness died by self-poisoning compared to those without physical co-morbidity (37% v. 20%, p < .001; AOR 2.47, 95% CI 2.26-2.70; Tables 2 & 3). The proportions who died by hanging/strangulation (33% v. 47%; p < .001), jumping/multiple injuries (12% v. 16%; p < .001), and gas inhalation (1% v. 3%; p < .001) (Table 2) were significantly lower in the physical co-morbidity group,
+although some of these differences were unlikely to be clinically significant.
+It was possible to classify the specific drugs used in cases of selfpoisoning in 3283 (86%) of cases; in 445 patients (12%) the data were missing and in 77 (2%) the substances were described as “multiple toxicity”. More patients with a physical illness were described as using multiple drugs in the overdose compared to those without a physical illness (37, 3% v. 37, 2%; p = .02), although this difference was unlikely to be clinically significant. Opioids were the most common type of drug used in all cases of self-poisoning, but particularly for those with a physical illness, nearly a third (30%) of whom died by opioid overdose compared with those with mental illness alone (22%; p < .001) (Table 2). Patients with physical illness were also more likely to use paracetamol/opioid compounds (11% v. 7%; p < .001) and insulin (4% v. 1%; p < .001) and less likely to use SSRIs/SNRIs (7% v. 11%; p < .001) or antipsychotics (8% v. 13%; p < .001) in self-poisoning.
+Overall, half (586, 50%) of psychiatric patients with a co-morbid physical illness who died by self-poisoning had used medications for a physical health disorder (i.e. opioids, paracetamol/opioid compounds, other analgesics, insulin, cardiac medications, and other specified drugs for physical conditions). This compared to a third (680, 34%) of those without a physical illness (p < .001) (AOR 2.10, 95% CI 1.80-2.46; Table 3). The majority (436; 64%) of this latter group had used opioids in overdose.
+3.4.	Sub-group analyses
+3.4.1.	Method of obtaining medication
+Details of how the substances were obtained were available for 2097 (55%) of the 3805 patients who died by self-poisoning, before excluding cases without data on physical illness. For the 1306 with a physical illness who died by overdose with any medication, data were available on how they obtained the drugs in 727 (56%), of whom 523 (72%) were prescribed those drugs, 20 (3%) used drugs prescribed for someone else, and 184 (25%) used unprescribed drugs.
+Focussing specifically on non-psychotropics, of the 586 patients with a comorbid physical illness who overdosed using a medication for a physical disorder, 246 (74% when excluding unknowns) had been prescribed this medication (Table 2). This compared to 102 (27%) of those without a documented physical illness who overdosed using prescribed non-psychoptropics (p < .001) (AOR 7.14, 95% CI 4.98-10.24; Table 3). The main substances used in the 102 cases without documented physical illness were opioids (52%), paracetamol/ opioid compounds (24%), other substances, e.g. propranolol (15%), and other analgesics (6%). A minority (14%) of this group had a diagnosis of drug dependence/misuse, and 44% had a history of drug misuse; these patients may have been prescribed opioids for drug misuse. Others may have been prescribed medication for a health condition not viewed by the clinician completing the questionnaire as a major physical illness.
+A quarter of patients with comorbid physical illness who overdosed using a physical health medication had not been prescribed it. A clinically significant minority had overdosed on prescription-only medications not prescribed for them. Insulin had been prescribed to 32 (86%) of the 37 patients with diabetes who self-poisoned using insulin. Of the 12 patients diagnosed with cardiovascular conditions who selfpoisoned using cardiac medications, these were prescribed for 8 (67%). However, it was more common for patients without a documented co-morbid physical health problem to have used medications for a physical disorder prescribed for someone else (13% v. 5%; p < .001) or obtained elsewhere (60% v. 21%; p < .001), presumably over-the-counter or illicitly.
+3.5.	Sub-analyses: patients with cancer, diabetes, and pain conditions
+When repeating the analysis for patients diagnosed with cancer
+compared to those with other physical illnesses, there was no association of death by self-poisoning with medication used for treating physical disorders (49% v. 50%; p = .973) (Table 3). Substances used most commonly in overdose in patients with cancer were: opiates (29%), paracetamol/opiate compounds (16%), and paracetamol (12%).
+Similarly, there was no association of death by self-poisoning with substances for physical disorders for patients with diabetes (54% v. 49%; p = .203) compared to those with other physical illnesses. Substances used most commonly in overdose among patients with diabetes were: insulin (21%), opiates (18%), and tricyclic antidepressants (11%).
+However, patients with a pain condition (the largest sub-group) were significantly more likely to overdose with drugs for non-psy-chiatric conditions compared to other patients with a physical condition (63% v. 46%; p < .001; AOR 2.12, 95% CI 1.56-2.88). The majority (67%) of substances used in overdose in patients with a pain condition were pain medications (opioids 46%; paracetamol/opiate compounds 12%; paracetamol 6%; any other pain meds 3%), whilst 9% used tricyclic antidepressants.
+3.6.	Sensitivity analyses
+The above associations remained unchanged in sensitivity analyses using a more stringent definition of drugs that could have been prescribed for treating physical health problems (Supplementary file). A post hoc sensitivity analysis to test whether our findings partly reflected the older age of those with co-morbid physical illness and their greater prevalence of affective illness, we found no association between older age or affective disorder and self-poisoning.
+4.	Discussion
+4.1.	Main findings
+We found that almost a quarter of psychiatric patients who died by suicide over the period 2004 to 2015 had a co-morbid physical health condition, and that over a third of this group died by self-poisoning. Our findings of an association between physical health problems and fatal overdose among psychiatric patients suggest that access to means is a key explanation. We found striking differences in the suicide methods used by psychiatric patients with and without physical health
+problems. Hanging (followed by overdose) was the most common method used by those with no physical co-morbidities; matching the national picture for psychiatric patients (NCISH, 2017), and the general population (ONS, 2016c). However, self-poisoning (followed by hanging) was the leading method used by patients with physical health problems, suggesting that overdose is the most accessible approach for this patient group if contemplating suicide. Restricting access to this method is more feasible than for hanging.
+The substances used in overdose by patients with a co-morbid physical health condition were more likely to be medications prescribed to treat physical health problems, and less likely to be psychotropics, even though these patients probably had access to both. Nearly half of those with a co-morbid physical health condition who died by selfpoisoning did so using a medication for such a condition. Of specific sub-groups, patients with pain conditions, for whom chronic pain is itself a risk factor for suicide (Racine, 2018) were most likely to overdose with drugs for physical disorders. This was likely due to a high proportion of this group using toxic pain medications in overdose. The tendency of patients with physical co-morbidities to overdose using non-psychotropics rather than psychotropics may relate to perceived lethality of non-psychotropics, potentially greater lethality of non-psychotropics, or to prescribers being more primed to consider overdose potential when issuing and monitoring potentially cardiotoxic psychotropic drugs (Hawton et al., 2010) than medications used for physical health problems. Whilst acknowledging the poor predictive value of suicide risk classification scales (Steeg et al., 2018), our findings suggest that needs-based assessments of psychiatric patients with physical health problems should focus on addressing modifiable risk factors such as reviewing the need for more toxic medications, particularly opioids (Ilgen et al., 2016), considering safer transdermal routes for opioid administration (Coplan et al., 2017), and addressing inadequately-treated pain (Yarborough et al., 2016). Guidelines on safe prescribing aim not to compromise on optimal pain management, but to reduce the potential for opioid addiction, diversion and fatalities (Volkow et al., 2019).
+4.2.	Findings in the context of other studies
+No other studies have sought to investigate this research question among psychiatric patients. More widely, a systematic review of studies investigating the association between non-psychotropic medications
+and attempted suicide found cardiovascular medications not to be associated with any increased risk, but concluded that associations with other medications remained inconclusive (Gorton et al., 2016). Separately, two studies of US veterans with non-cancer pain found an association between dose of opioids and risk of suicide (Ilgen et al., 2016), presumably with dose a marker of pain severity, but no clear excess risk of overdose in these patients over other methods (Ilgen et al., 2013).
+4.3.	Strengths and limitations
+We examined a national, comprehensive case series of all suicides amongst patients with recent contact with psychiatric services over a 12 year period. Consultants completing the questionnaire were unaware of the study's hypotheses, so it was unlikely that clinicians’ recall bias for overdose using physical health medications might explain our findings. Our categorisation of physical illnesses was systems-based but also acknowledged the overlapping categories of cancer and pain conditions. We adjusted our models for variables identified as potential con-founders a priori, such as drug dependence/misuse. Alternative explanations for associations identified are the under-identification of drug dependence/misuse, and the assumption that opioids used in overdose were obtained for a physical health problem rather than for abuse or intentional overdose. We had access to data on how medications were obtained for only 55% of the case series, but findings were similar in a sensitivity analysis confined to those who died from 2012-2015.
+The study's main limitation is that its use of survey data captured only those co-morbid physical health problems and overdose medications of which the responding consultant was aware. Under reporting of physical health problems is likely to have occurred where the patient was only briefly under their care (particularly in liaison settings), where clinical notes were unclear regarding physical health conditions or medications, or where the clinician did not judge the condition to be a ‘major physical illness’. This may have excluded conditions like acne that contribute significant clinical distress and for which medications prescribed to treat it have been linked with suicide risk (Sundstrom et al., 2010). Under reporting of specific physical health medications used in overdose is likely to have occurred where the completing clinician's response denoted multiple unspecified drugs. Over half of all general population drug poisoning deaths involve more than one drug and/or alcohol and the substance primarily responsible for the death is not identifiable (ONS, 2016b). We could also not be certain that medications used in overdose had been specifically issued to treat that patient's physical health problem, as opposed to being obtained specifically to attempt suicide. We did not have data specifying whether onset of physical illness had preceded psychiatric illness or vice versa, and it was possible in some cases that patients had been diagnosed with a physical health problem some time before their psychiatric illness commenced. This preceding physical illness may have also influenced some patients in their choice of self-poisoning agent.
+Detailed data on how opioids and paracetamol/opioid compounds were obtained were only available from 2012 onwards, but we addressed this in our sensitivity analysis. This extra analysis also ruled out the older age of those with co-morbid physical illness, and their greater prevalence of affective illness, as an explanation for our findings. Finally, by examining a national case series design, without living controls, we could estimate proportional contrasts between the groups but not incidence, or absolute/relative risks.
+4.4.	Clinical and policy implications
+These findings provide evidence to suggest that access to means of lethal overdose may contribute to suicide risk in psychiatric patients with physical co-morbidities, particularly those with chronic pain. Such patients would be more likely than other psychiatric patients to have supplies of prescribed non-psychotropics at home, particularly patients
+in chronic pain. Such availability creates the potential for suicide attempts with high lethality, particularly during a flare-up of a physical condition. All clinicians involved in the care of these patients should ensure careful prescribing for this patient group, with clear risk management. This could include regular reviews to check that indications remain, referral to pain clinics to consider transdermal opioid administration, and raised frequency of issuing pain medication prescriptions, although the latter may compromise patient convenience and therapeutic alliance. Assertive pain management is critical because inadequately-treated pain is itself a risk factor for suicide (Yarborough et al., 2016). Future research should seek to evaluate the effect of improved pain management pathways and prescribing guidelines on risk of overdose among psychiatric patients.
+Restricting access to non-prescribed medications has been partly addressed at the population level (Hawton et al., 2013, 2009) with a restriction on analgesic pack size, but there is also a role for community pharmacists in responding to customers trying to purchase over-the-counter analgesics above recommended limits (MHRA, 2014). A non-confrontational approach that responds to distress, and shows awareness of local service provision is more likely to be acceptable to patients. Our findings also suggest that access to medications prescribed for household members should be considered for psychiatric patients with or without physical illness. Carers have a role in safeguarding their own medications, as well as those of a psychiatric patient at risk.
+Finally, our findings show that opioids are a substance commonly used in lethal overdose among psychiatric patients, whether they have physical health problems (30%) or not (22%). Access to naloxone for carers and professionals, accompanied by training, is a high-risk intervention worth considering among some psychiatric patients (Ashrafioun et al., 2016). Qualitative work is needed with carers regarding their attitudes towards such a safeguarding role.
+5.	Conclusions
+Overdose, rather than hanging, is the leading method of suicide in the 24% of psychiatric patients who die by suicide and have co-morbid physical health problems; accounting for over a third of cases. In such patients, particularly for those in chronic pain, the medications used in overdose are more likely to be those for a physical health disorder; primarily opioids. Psychiatric patients with physical health co-mor-bidities therefore require careful needs-based risk assessment, with clinicians reducing access to the means of overdose where possible. Optimal care includes addressing inadequately-treated pain, reviewing the need for more toxic medications, considering transdermal routes, and involving carers in safeguarding household medications.
+A. Pitman, et al.
+Journal of Affective Disorders 257 (2019) 173-179
+Wales, the Scottish Government Health and Social Care Directorate, the Northern Ireland Department of Health, the States of Guernsey and the States of Jersey. AP is supported by the University College London Hospitals & National Institute for Health Research (UCLH NIHR) Biomedical Research Centre (BRC). None of these funders had any role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
+Data availability
+The National Confidential Inquiry case series database is not publically available, but requests to conduct analyses in collaboration with the Centre for Mental Health and Safety team are granted, subject to internal peer review.
+Limitations of the study
+Use of survey data may have resulted in under-reporting of physical health problems and/or overdose medications.
+Supplementary materials
+Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.06.027.
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+Ilgen, M.A., Kleinberg, F., Ignacio, R.V., Bohnert, A.S., Valenstein, M., McCarthy, J.F., Blow, F.C., Katz, I.R., 2013. Noncancer pain conditions and risk of suicide. JAMA Psychiatry 70 (7), 692-697. https://doi:10.1001/jamapsychiatry.2013.908.
+Ilgen, M.A., Bohnert, A.S., Ganoczy, D., Bair, M.J., McCarthy, J.F., Blow, F.C., 2016. Opioid dose and risk of suicide. Pain 157 (5), 1079-1084. https://doi:10.1097/j.pain. 0000000000000484.
+Linsley, K.R., Schapira, K., Kelly, T.P., 2001. Open verdict v. suicide - importance to research. Br. J. Psych. 178, 465-468. https://doi.org/10.1192/bjp.178.5.465.
+MHRA, 2014. The Blue Guide; Advertising and Promotion of Medicines in the UK. https://www.gov.uk/government/publications/blue-guide-advertising-and-promoting-medicines.
+National Confidential Inquiry into Suicide and Homicide by People with Mental Illness, 2017. Annual Report: England, Northern Ireland, Scotland and Wales. University of Manchester October 2017.
+Office for National Statistics, 2016. Statistical Bulletin. Suicides in the UK: 2016 registrations. https://www.ons.gov.uk/peoplepopulationandcommunity/ birthsdeathsandmarriages/deaths/bulletins/suicidesintheunitedkingdom/ 2016registrations#suicide-methods.
+Office for National Statistics, 2016. Statistical bulletin: Deaths related to Drug Poisoning in England and Wales: 2016 Registrations. https://www.ons.gov.uk/ peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/ deathsrelatedtodrugpoisoninginenglandandwales/2016registrations.
+Office for National Statistics, 2016. Statistical Bulletin. Suicides in the UK: 2016 registrations. https://www.ons.gov.uk/peoplepopulationandcommunity/ birthsdeathsandmarriages/deaths/bulletins/suicidesintheunitedkingdom/ 2016registrations.
+Petrosky, E., Harpaz, R., Fowler, K.A., Bohm, M.K., Helmick, C.G., Yuan, K., Betz, C.J., 2018. Chronic pain among suicide decedents, 2003 to 2014: findings from the national violent death reporting system. Ann. Intern. Med. 169 (7), 448-455. https:// doi:10.7326/M18-0830.
+Racine, M., 2018. Chronic pain and suicide risk: a comprehensive review. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 87, 269-280. https://doi.org/10.1016/j.pnpbp. 2017.08.020.
+Sarchiapone, M., Mandelli, L., Iosue, M., Andrisano, C., Roy, A., 2011. Controlling access to suicide means. Int. J. Environ. Res. Public Health 8 (12), 4550-4562. https:// doi:10.3390/ijerph8124550.
+StataCorp, 2017. Stata Statistical Software: Release 15.
+Steeg, S., Quinlivan, L., Nowland, R., Carroll, R., Casey, D., Clements, C., Cooper, J., Davies, L., Knipe, D., Ness, J., O'Connor, R.C., Hawton, K., Gunnell, D., Kapur, N., 2018. Accuracy of risk scales for predicting repeat self-harm and suicide: a multicentre, population-level cohort study using routine clinical data. BMC Psychiatry 18, 113. https://doi.org/10.1186/s12888-018-1693-z.
+Sundstrom, A., Alfredsson, L., Sjolin-Forsberg, G., Gerdén, B., Bergman, U., Jokinen, J., 2010. Association of suicide attempts with acne and treatment with isotretinoin: retrospective Swedish cohort study. BMJ 341, c5812. https://doi.org/10.1136/bmj. c5812.
+Volkow, N.D., Jones, E.B., Einstein, E.B., Wargo, E.M., 2019. Prevention and treatment of opioid misuse and addiction: a review. JAMA Psychiatry 76 (2), 208-216. https:// jamanetwork.com/journals/jamapsychiatry/article-abstract/2716982.
+Webb, R.T., Kontopantelis, E., Doran, T., Qin, P., Creed, F., Kapur, N., 2012. Suicide risk in primary care patients with major physical diseases: a case-control study. Arch. Gen. Psychiatry 69 (3), 256-264. https://doi:10.1001/archgenpsychiatry.2011.1561.
+Webb, R.T., Lichtenstein, P., Kapur, N., Ludvigsson, J., Runeson, B., 2014. Unnatural deaths in a national cohort of people diagnosed with diabetes. Diabetes Care 37 (8), 2276-2283. https://doi:10.2337/dc14-0005.
+Windfuhr, K., While, D., Hunt, I.M., Turnbull, P., Lowe, R., Burns, J., Swinson, N., Shaw, J., Appleby, L., Kapur, N., 2008. Suicide in juveniles and adolescents in the United Kingdom. J. Child Psychol. Psychiatry 49 (11), 1155-1165. https://doi.org/10.1111/ j.1469-7610.2008.01938.x.
+Wu, V.C-C., Chang, S-H., Kuo, C-F., Liu, Chen, S-W., Yeh, Y-H., Luo, S-F., See, L-C., 2018. Suicide death rates in patients with cardiovascular diseases - A 15-year nationwide cohort study in Taiwan. J. Aff. Disord. 238, 187-193. https://doi:10.1016/j.jad. 2018.05.046.
+Yarborough, B.J., Stumbo, S.P., Janoff, S.L., Yarborough, M.T., McCarty, D., Chilcoat, H.D., Coplan, P.M., Green, C.A., 2016. Understanding opioid overdose characteristics involving prescription and illicit opioids: a mixed methods analysis. Drug Alcohol Depend 167, 49-56. https://doi:10.1016/j.drugalcdep.2016.07.024.
+Zalsman, G., Hawton, K., Wasserman, D., van Heeringen, K., Arensman, E., Sarchiapone, M., Carli, V., Hoschl, C., Barzilay, R., Balazs, J., Purebl, G., Kahn, J.P., Saiz, P.A., Lipsicas, C.B., Bobes, J., Cozman, D., Hegerl, U., Zohar, J., 2016. Suicide prevention strategies revisited: 10-year systematic review. Lancet Psychiat. 3 (7), 646-659. https://doi.org/10.1016/S2215-0366(16)30030-X.
+179
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+More than 75% of people living with mental health problems in low- and middle-income countries (LAMI countries) do not receive treatment for mental health problems.1 In an effort to close this vast treatment gap new models of treatment provision have been developed that propose a collaborative approach to care delivery, also known as task-sharing. This entails that the bulk of direct mental health service delivery is conducted by front-line health workers, rather than restricted to the domain of mental health professionals. This approach is also advocated by the World Health Organization’s (WHO’s) Mental Health Global Action Programme (mhGAP), an initiative aimed to equip primary healthcare workers to provide mental healthcare.2 Expanding the workforce and putting mental health services in place is crucial to close the treatment gap. However, this alone is inadequate without people with mental health problems actually making use of the services. Both availability and uptake of services are required to close the treatment gap. Thus, a major barrier for the scaling up of mental healthcare is the lack of awareness and demand for care.3 Low demand can be explained in part by non-detection or under-detection of mental health problems.4 There are a number of barriers to detection methods that are effective in high-income countries including: first, low literacy rates preclude use of self-administered self-report tools such as the Patient Health Questionnaire;5 second, high levels of stigma and existing belief systems regarding mental health problems discourage endorsement of psychiatric labels;6 and third, self-report instruments of serious mental illness, such as schizophrenia, have failed to detect cases in some LAMI countries.7,8 Therefore, development of innovative methods for identification of people with mental illness are needed to address limited literacy, cultural stigma, and applicability across a range of common and serious mental disorders.
+In response, community case-finding has been proposed to increase access to care in LAMI countries.9 Patel & Thornicroft10 propose a two-staged case-finding procedure with probable cases being identified through community case-finding followed by a
+diagnostic interview by a trained health worker. In practice, active case-finding has played an important part in increasing demand for, and accessibility of, mental health services in Nigeria and India.3, Case detection by lay-workers may, especially in low-income settings, hold some population-level advantages, including greater population coverage.12 Although proactive case finding has been used before, the current approach is, to the best of our knowledge, novel in a LAMI country setting, i.e. consisting of a structured approach using pictorial vignettes and an extensive development process emphasising compatibility with the sociocultural context. Also, to date, there has been limited evaluation of proactive case-finding strategies in LAMI country settings.
+The current study will evaluate the accuracy of a newly developed procedure for proactive case finding by community informants in Nepal, with the aim of increasing help-seeking for mental healthcare. Proactive case-finding has been introduced as a strategy to increase help-seeking, as it aims to bridge the gap between people in need of mental healthcare with available services. It provides an alternative to systematic community screening that is associated with high financial and resource burden. The developed procedure is based on the premise that well-placed informants, who are intimately connected to people in the surrounding community, have good insight into its members’ well-being. The hypothesis is that such a process of identification is best achieved through emblematic recognition, i.e. broadly matching of people they encounter in daily routine onto vignettes of mental health problems that have been made context-specific. This is the community version of a prototypematching approach for clinicians, which has demonstrated comparable validity to more complex diagnostic algorithms based on dichotomous decisions on individual symptoms.13,14 This is an approach that proposes to examine a diagnostic prototype (short narrative description) taken as a whole and to gauge the extent to which a patient’s symptom presentation matches the prototype.15 The developed procedure for proactive case-finding, which entails training of selected community members in the use of a structured
+tool (Community Informant Detection Tool, CIDT), is applied within a larger programme (Programme for Improving Mental Health Care, PRIME). PRIME aims to improve the coverage of treatment for priority mental disorders by implementing and evaluating a comprehensive mental healthcare package, integrated into primary healthcare in five LAMI countries (Nepal, India, South Africa, Ethiopia and Uganda).16 The care package includes the provision of psychosocial and pharmacological interventions by non-specialised primary health workers (following the WHO mhGAP Intervention Guide)2 and community counsellors. The objective of the study is to evaluate how accurate the CIDT procedure is in identifying people with priority mental disorders.
+Method
+Setting
+The research was conducted in Chitwan, a district in southern Nepal. Nepal is a low-income country, one of the poorest countries in Asia and is categorised by the World Bank as a fragile state.1 The total population of the country is approximately 26.5 million (Central Bureau of Statistics, 2011, www.dataforall.org/dashboard/ nepalcensus) with majority (86%) of the total population living in the rural areas. The country is passing through a transition following a 10-year intra-state conflict, between government forces and Maoists insurgents, which raged between 1996 and 2006 and claimed more than 13 000 lives. Previous studies have demonstrated the impact of political violence on psychosocial well-being and mental health in Nepal.18-21 The conflict has further shattered an already weak healthcare system. The war formally ended in November 2006 with a comprehensive peace agreement between an alliance of political parties and the Maoists. The present situation is characterised by instability and political deadlock. It is against the backdrop of recent violence and ongoing poverty that PRIME was implemented in Nepal. In Nepal’s healthcare system, there are sub-health posts that provide essential healthcare services and monitor community level healthcare activities; health posts that offer the same services with additional birthing centres, as well as the responsibility of monitoring the sub-health posts activities, and primary healthcare centres (PHCC) a higher level healthcare institution that serves as the first referral point for each electoral area. Currently, no mental health services are systematically available in primary healthcare.22
+Procedure
+The CIDT procedure, as introduced above, was constructed following a process that encompassed several steps. This process can be summarised as follows. First, vignettes were developed for some priority disorders (depression, alcohol use disorder, psychoses, epilepsy and conduct disorder) by taking vignettes in WHO’s mhGAP Intervention Guide2 as a starting point. Subsequently, an inventory of local non-stigmatising idioms related to the vignette was made based on prior ethno-psychological research in Nepal.6 Selection of most relevant idioms was done through prioritisation by an expert panel of Nepali mental health professionals. The CIDT therefore does not use the diagnostic labels but Nepali descriptions that have found to be commonly acceptable and understandable for each of the vignettes. To facilitate the process of prompt recognition of people that potentially match the vignette, pictures were developed (see online Fig. DS1 for an example CIDT sheet). Pilot testing of the procedure was instrumental in identifying the most relevant groups of people to serve as community informants. The pilot testing, as well as formative research done for the overall PRIME initiative,23 demonstrated high levels of perceived acceptability for the proposed procedure among key stakeholders.
+The CIDT procedure is used by community informants briefly trained in the essentials of public mental healthcare, the use of the procedure and the related ethical considerations. In their routine daily activities and tasks, the community informants aim to gauge the extent to which people in their direct vicinity match paragraph-long vignettes (aided with pictures) using a simple 5-point scale. If the community informant believes that a person in the community has significant features of the description (i.e. the person fits well with, or exemplifies, the description), then the informant answers two additional questions: one on whether the identified individual is perceived to have impaired daily functioning and a second question on whether the person would want support in dealing with these problems. In case of significant matching and a positive response to at least one of the additional questions, the community informant will encourage the person (possibly through their family) to seek help in the health facility where mental health services are being offered as part of PRIME, and where caseness can be confirmed by a trained health professional. No stigmatising diagnostic labels or psychiatric terminology is used, and encouragement for help-seeking is targeted to specific observable behaviours and/or signs of distress.
+To evaluate the accuracy of the CIDT we assessed whether the people identified by the community informants were correctly detected according to a structured diagnostic assessment performed by a clinician. This was done by comparing CIDT results with results of a structured clinical assessment following the Composite International Diagnostic Interview (CIDI).24 CIDT results comprised of either ‘probable caseness’, which were respondents that met the criteria outlined above, or ‘probable non-caseness’ which are respondents that do not meet those criteria. The probable negative cases were identified by asking community informants to select people they felt confident of not matching the pictorial vignette. The group is included only for the purpose of this study (true negatives and false negatives are needed for standard analyses on psychometric properties). In keeping with the proposed real-life application of the tool, respondents were not screened systematically; rather it presents a selected sample of people proactively identified by the community informants as probable positives or negatives. The goal of the CIDT procedure is not for community informants to make a specific diagnosis, but rather to identify someone with any mental distress that would benefit from treatment. It is therefore intended as a proxy indicator for people with mental disorders. As a result, the comparisons at the heart of the study are not based on identification of specific disorders but take them as a composite concept (i.e. a possible case of any disorder).
+The community informants that were part of the study included female community health volunteers (n = 8) and members of the local mother groups (n = 4), distributed over 6 wards. Members of mother groups do not require formal education, whereas female community health volunteers should minimally be literate and between 25 and 45 years of age. After receiving the 1-day training, they were asked to start using the CIDT forms. The training was provided by a health assistant, with years of experience in mental healthcare, who has been coordinating the training and implementation of the mental health services within the PRIME programme. The training consisted of minimal didactic teaching, and emphasised group discussion and practicing through role-plays. The community informants receive a small monthly allowance for their work (approximately US$2/month, paid as a flat rate as opposed to payment per identified case). All completed forms were handed over to a research assistant who subsequently arranged for the clinical assessment to happen with the identified person. When the forms were incomplete or unclearly completed the research
+assistants immediately contacted the community informant to clarify. Community members who were identified by the informants as potential cases were then approached by research assistants to request their permission to participate in the study. Participants were only included in the study, and clinical interviews were only conducted, after obtaining informed consent with signatures provided by literate participants and acknowledgement markings made by illiterate participants. Nepali psychosocial counsellors, with over 6 months of training and more than 5 years of experience in counselling in community settings, conducted the CIDI-structured clinical interviews and were masked to CIDT results at the time of the interview. Due to the shortage of mental health specialists in Nepal, psychosocial counsellors are the cadre of service providers with the most extensive skills-based clinical training other than psychiatrists who have limited availability to participate in research interviews given the overwhelming volume of clinical need. Psychosocial counsellors using structured clinical interviews have performed effectively in prior validation studies in Nepal.25
+The study was done in the area where pilot testing of the PRIME mental healthcare package was ongoing, which made referral to treatment possible. The treatment package included both psychotherapeutic and pharmacological interventions, and was offered in the nearby health facility. The PRIME programme and the CIDT procedure are endorsed by, and implemented in partnership with, the Ministry of Health and district level health authorities. Ethical approval was obtained from the Nepal Health Research Council and the Human Research Ethics Committee of the Faculty of Health Sciences, University of Cape Town (REC Ref: 412/2011). Data were collected in the period January to March 2013.
+Instruments
+In addition to the CIDT, we used different sections from the CIDI, notably the screens for psychosis, depression, alcohol use disorder, conduct disorder and oppositional defiant disorder. In addition, we used a 9-item screening questionnaire to detect epileptic seizures.26 The Nepali-language CIDI has been validated in Nepal, (area under the curve any disorder = 0.85, area under the curve depression = 0.97).27 The psychosocial counsellors received a week of training in the Nepali CIDI including 6 h of observed administration and review of scoring. The training was conducted by an Australian psychologist with experience in conducting structured clinical interviews. Additional training and detailed review of videotaped interviews was provided by an expatriate psychologist (M.J.D.J) and an expatriate psychiatrist (B.A.K.) who are both fluent in Nepali.
+Analyses
+Using descriptive statistics the results from the CIDT and the clinical assessments were compared, and plotted as true or false positives and true or false negatives. Next, positive predictive value (PPV) and negative predictive value (NPV), positive and negative likelihood ratios were calculated. Analyses were done for the entire sample, as well as for children and adults separately. Primary analyses were done with caseness defined as identification or diagnosis of any disorder (see above). In addition, exploratory analyses were conducted for the disorders separately. Analyses were conducted using the Statistical Package for Social Sciences (SPSS version 19.0).28
+Results
+The total sample consisted of 195 people. In total 210 people were identified by the community informants, of whom 5 people
+refused to participate and 10 people were unable to complete the interviews. See Table 1 for sociodemographic details of the sample for adults and children separately. In the combined sample, the average age was 32.21 years (s.d. = 15.47) and comprised of 59.5% female participants. Interrater reliability between the two counsellors based on independently conducted clinical interviews repeated with the same patient series was found to be good (intraclass correlation = 0.92; 95% CI 0.89-0.94). All of the study participants were identified by the community informants using the CIDT procedure, either as a probable positive (n = 110) or as a probable negative case (n = 85). After clinical assessments, 70 of the CIDT positive cases and 6 of the CIDT negative cases were found to meet criteria for a clinical diagnosis for one or more of the target disorders (i.e. true positives = 70, true negatives = 79). See Table 2 for a breakdown of the diagnoses (with the total diagnoses (101) exceeding the 76 individuals due to comorbidity).
+Table 2 further summarises the results of the analyses, including PPV (for the entire sample: 0.64), NPV (0.93), positive likelihood ratio (2.71) and negative likelihood ratio (0.12). The results for children and adults differ substantially only for the PPV (0.50 among children), due to the high number of false positives among the child sub-sample. When comparing the results between both types of community informants, we see differences on all indicators, with a far smaller proportion of false positives among the mother group participants. Finally, for explorative purposes we have included the analyses for the separate diagnostic clusters, indicating how well the separate disorder-specific vignettes of the CIDT detect those disorders.
+We also analysed these results when varying the CIDT decision algorithm. Results changed only marginally when allocating positives based on matching symptoms only, or based on symptoms plus both additional criteria of functioning impairment and need for support - rather than matching of symptoms plus at least one of the two additional criteria (which is the original algorithm that was used in this study).
+Mental healthcare in LAMI countries is characterised by low resources,29,30 which goes hand in hand with low demand for,
+and utilisation of, services. To fully capitalise on efforts to increase trained human resources and evidence-based treatments, an increase in demand and utilisation is imperative. Relying solely on self/family-referrals may risk missing out on a large group of clients that are simply not identified, yet need treatment. Use of self-report written screeners is not widely feasible given low literacy rates. A context-sensitive procedure of proactive casefindings, built on the notion of using non-stigmatising idioms and matching people on vignettes (aided with visual clues and two questions), has been piloted in rural Nepal to identify need for treatment and ultimately increase the demand for and utilisation of mental healthcare.
+Community informants can assign caseness in the persons rated with pictorial vignettes with accuracy for the majority of persons rated. Given that real-life use of the CIDT only results in reporting positives, as opposed to a usual universal or community screening procedure that will result in both negatives and positives, the key indicator of accuracy of the CIDT are the PPV (i.e. the proportion of positive test results that are true positives) and the positive likelihood ratio (i.e. how much to increase the probability of disease if the test is positive), which were 0.64 and 2.71 respectively for all respondents and all disorders in this study.
+With approximately two-thirds of the expected positives having a confirmed disorder, the community informants do well overall. By comparison many common standardised symptoms-based screening instruments have a similar (or lower) PPV31 or positive likelihood ratio32 - noted of course that these are generally disorder specific, and some also have higher values. This is quite surprising, given that the CIDT informants in our study do not have any formal training or even formal education in many instances. Although a promising strategy to identify cases that might have otherwise gone unnoticed, it does present with a risk for over-identification and additional burden for the health facilities,33 but this burden is not necessarily higher than would be encountered with a standardised screening instrument. Overburdening providers is a serious concern with many health facilities already strained. At the same time, it is plausible that the ‘false positives’ do indeed require treatment but are just subthreshold or have problems that were not assessed with the five CIDI disorders covered in the clinical interviews.
+These promising results may be explained by the focus on a vignette-based structure, which like the prototype matching approach for clinicians, is a form that is more congruent with human (and clinical) cognitive processes than checking whether each of a series of symptoms is absent or present.13 The use of this approach in a community setting is quite new, and, to the best of
+our knowledge, a first in a low-income setting. In addition, incorporating locally salient manifestations of mental health problems through idioms of distress and drawings, may have contributed to good accuracy. This was also demonstrated in the cross-cultural construct validation of a brief screener for psychosocial distress for children in conflict affected settings.34
+To implement this proactive case finding strategy, it is important to know who the most adequate community informants might be. Formative research had already indicated that in Nepal female community health volunteers and mother group leaders would be most appropriate. The results further demonstrate that mother groups outperform female community health volunteers as the former have significantly lower false positives. From a perspective of potentially burdening the system, involving the mother’s groups would lead to lower burden. This is an interesting finding given that female community health volunteers are currently an extension of the healthcare system in Nepal, and are excessively used for different task-shifting roles. Based on this finding it would actually be better to not add another task on this group of community health workers, but rather chose of group of people that is less taxed and may have better knowledge of the well-being of the community members.
+For determining the overall accuracy of the CIDT, it is not necessary to see how well the procedure picks up on individual disorders. Yet, it is interesting to see that the depression module was most accurate among the five included disorders (in terms of PPV). This goes counter to the notion that depression and mood disorders generally are the more challenging to identify by lay people,9, 5 and also contrary to the formative study into the feasibility of this approach. This is a promising result for a problem that is present so ubiquitously, but with near absence of treatment at present in rural Nepal. This would mean that the aim to increase demand for mental healthcare using the CIDT will not be limited to the more ‘visible’ disorders, such as schizophrenia or alcohol use disorder. It should be noted that the numbers for these subgroup analyses are very small, so this trend will need to be confirmed with a larger sample. An issue that should also be further considered when implementing this procedure is that it is less accurate in identifying children’s mental health problems. This fits with common patterns of under-detection of children’s problems.36 It is possible that this difficulty is especially pertinent for the conduct problems, and possibly less so for problems that are more clearly demarcated as pathological (i.e. severe mental retardation) by community members. Further development is needed to fine-tune the procedure towards children’s mental health problems.
+There are important potential downsides to this sort of case detection.12 Community informants could potentially abuse their new role, however informal it is, and force people to seek treatment who may not be willing to do so. Individuals who are identified may experience stigma and discrimination, particularly if community informants are not bound by a clear code of confidentiality. The power dynamics of using this procedure is something that was addressed in the training and has been monitored throughout the pilot phase. It has not yet led to any such incidents, but this does not mean it will not, especially when the strategy is scaled up making intensive monitoring much harder. Also, possible downsides of a vignette-based approach are that its users may selectively recall certain features of the prototype, and that it allows for a lack of standardisation between users.37 It is important to emphasise that the proactive case finding strategy is not meant as a form of systematic community screening. If it were, population prevalence rates would need to be taken into consideration resulting in lower PPVs. Selection of respondents for this study was therefore pragmatic, reflecting the CIDT’s intended real-life use. Although asking community informants to identify negatives they ‘felt confident’ about is congruent with the actual process of excluding cases when engaged in proactive case-finding; this identification may have had an effect on the NPV. A strength of the approach is the extensive development process that relied on available ethnographic study for selecting idioms of the vignettes and drawings, incorporating local stakeholder perspectives and finetuned a training package that balanced utilitarian and ethical concerns. This is important to emphasise, as potential replication of this approach without such preparation might impact the accuracy and introduce ethical or clinical risks.
+Future research is needed to assess the actual effectiveness of the proactive case-finding procedure. Where the present study evaluated the accuracy, the next step is to evaluate whether the use of the CIDT also results in an increase in demand for, and uptake of, mental healthcare. Furthermore, more work is needed to make the procedure more sensitive to capture children’s mental health problems in the future.
+Implications
+The procedure shows potential to identify the right people in need of treatment and the study suggests that it provides for a good surveillance procedure. About 64% of the people that the community informants identified as probable cases using the CIDT were actually positive cases based on clinical interviews and 93% of people that community informants were confident probable non-cases, were indeed found negative. It appears that the procedure does not need to exclusively rely on already overburdened community health volunteers. Given the selected use of proactive case finding, the procedure is not a substitute for systematic community screening. Actually, the CIDT may present a pragmatic alternative approach preferable to community screening.
+The CIDT can be an important demand-side strategy to increase help-seeking for settings that are integrating mental health into primary healthcare. It can be used in conjunction with the training and implementation of WHO’s mhGAP guidelines, and can be scaled up relatively easily to a national level. From a policy point of view this is important, given the commitment that so many countries have made towards this goal.38 The use of the proactive case-finding may lead to significantly increased coverage of mental healthcare in a target area where mental health services are put in place, provided that the community informants are selected to represent a small geographical area (village or part of a village) where they are intimately connected and known. As
+stated above, the last part is at present still an assumption. Currently, research is planned to evaluate the effectiveness in facilitating referrals and reinforcing treatment-seeking behaviour.
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+Though experts agree that suicide is characterized by a strong cultural element (Institute of Medicine of the National Academies, 2002; Maris, Berman, & Silverman, 2000), few studies have examined culturally relevant phenomena in delineating suicide risk for diverse ethnic groups in the United States. Some studies have found evidence for increased suicide risk and depression in accul-turated Central American and Mexican adult immigrants (Hovey, 2000a, 2000b) and youth (Hovey, 1998). Other studies have examined religiosity as a culturally relevant factor in buffering suicide vulnerability in African Americans (Stack, 1998; Walker & Bishop, 2005). However, only one study to our knowledge (see Kaslow et al., 2004) has examined African American ethnic identity in predicting suicidal behavior. One other study (Joiner & Walker, 2002) considered acculturative stress as a factor in suicidal ideation in African Americans. Neither study examined the moderating capacities of either acculturative stress or ethnic identity in understanding the relation of depression to suicidal thoughts in African Americans. Though there has been an increase in the African American suicide literature (e.g., Castle, Duberstein, Meldrum, Conner, & Conwell, 2004; Harris & Molock, 2000; Ialongo, Kaslow McCreary, & Pearson, 2002; Marion & Range, 2003; Palmer, 2001; Roy, 2003; Willis, Coombs, & Drentea, 2003) much more definitive work in this area is needed.
+African American suicide remains poorly understood. Risk factors that have been identified for suicide deaths in European
+American youth and adults do not hold up for African Americans (as an example, see Garlow, Purselle, & Heninger, 2007). Abe, Mertz, Powell, and Hanzlick (2004) compared medical examiner reports for 784 White and 348 Black suicide deaths and found that Blacks were younger, less likely to have a history of depression, and less likely to have financial problems, suicide gestures, chronic disease, and substance abuse relative to Whites who died by suicide. Given a narrow understanding (i.e., exclusion of culturally relevant variables) of causal factors in suicide risk assessment, fatal suicide attempts for African Americans are inherently less predictable than those of European Americans. As an example, alcohol or cocaine use, highly cited factors in suicide attempts (in primarily European American populations) were detected for less than 18% of African American youth suicide deaths compared with more than 40% of European American youth suicide deaths (see Garlow, et al., 2007). Though studies cite protective features of African American culture that mitigate suicide risk (see Early & Akers, 1993; Gibbs, 1997), investigations of culturally relevant phenomena have been limited primarily to studies of religiosity, spirituality, or social factors (see Compton, Thompson, & Kaslow, 2005; Kaslow et al., 2004; Marion & Range, 2003). Broader investigations to contextual factors such as acculturation and acculturation stress, which have been identified for some underrepresented groups in the U.S., may provide a better understanding of African American suicide risk. We will explore the cross-cultural relationships of acculturative stress and ethnic identity to suicide ideation in a sample of African Americans and European American college students.
+Among college students, suicide is a leading cause of death (Center for Disease Control and Prevention [CDC], 1997). Risk factors are said to include depression (Lester, 1999) and also hopelessness (Heisel, Flett, & Hewett, 2003). However, studies
+very rarely explore suicidality and cultural milieu beyond those of American Indian (Middlebrook, LeMaster, Beals, Novins, & Manson, 2001), Asian American (Range et al., 1999), Latino (Hovey & King, 1997), and African American (Walker & Bishop, 2005) youth and young adults in the U.S. Interestingly, European American, and African American college students did not differ significantly on religiosity associated with suicidal ideation (Walker & Bishop, 2005). To our knowledge, broad cultural studies of belief systems, behavioral acceptability, and sociocultural experiences have not been widely explored for European American college students. Nevertheless, such investigations contribute to a more comprehensive understanding of suicide risk.
+Suicide risk assessment for African Americans remains a complex task as emerging data reveals that African Americans’ pattern of suicide risk diverges significantly from previously identified patterns for delineating risk. Garlow, Purselle, and Heninger (2005) reported marked ethnic group differences in suicide mortality such that the mean age for Black suicide death in Fulton County, Georgia was 32 years compared with 44 years for White suicide deaths. This shift in age-risk may have implications for distinctive risk factors. Other studies suggest geographical (Willis et al., 2003) and familial differences (Roy, 2003) in suicide death among African Americans such that African Americans are more likely than Whites to have died in urban areas and less likely to have a family history of suicide death. Unexplained ethnic group differences in suicide behavior and mortality merit broader, cultural, and ethnic levels of analyses.
+African American college students are said to disclose suicidality less readily than their White counterparts (Morrison & Downey, 2000) even when suicide acts are imminent (Molock, Kimbrough, & Lacy, 1994). African American youth in transition to university settings may be faced with unique contextual experiences (e.g., increased perceived discrimination) that are predictive of suicide risk levels. Though suicidality was not explored in available studies of discrimination experiences, discrimination was implicated in 35% of stressful life experiences for Black college students. Swim (2003) found that students reported weekly experiences of racism on average. These environmental antagonists and other contextual experiences are rarely explored in suicide research.
+Acculturation
+Acculturation is a complex, psychosocial phenomenon that involves individual and group-level changes in cultural patterns for ethnic minorities as a consequence of contact with the ethnic majority (see Chun, Organista, & Marin, 2003). Acculturative stress is the stress that is associated with cultural adaptation, which may occur at the risk of certain psychological consequences. Acculturative stress has been linked to symptoms of suicide and depression in Latino populations (Hovey, 1998, 2000a, 2000b), depression in African, Asian, and Latin American international college students (Constantine, Okazaki, & Utsey, 2004) and bulimic symptoms in African American and Hispanic college students (Perez, Voelz, Pettit, & Joiner, 2002).
+Though studies have explored the significance of acculturative stress for African Americans both conceptually (Anderson, 1991) and empirically (Joiner & Walker, 2002), exploratory investigations of the psychological and emotional impact of acculturation and acculturative stress rarely include African Americans. Pope-
+Davis, Liu, Ledesma-Jones, and Nevitt (2000) linked acculturative stress to racial identity defined as “a measure of the importance that members of an ethnic group place on their cultural heritage” (p. 197). They remarked that conceptual ambiguities have hindered the development of studies that investigate racial and ethnic identity because theories typically fail to explain the process by which identification (with one’s cultural group) occurs. Nevertheless, Pope-Davis and colleagues asserted that, when studied together, acculturation and ethnic identity may create a more complete picture of African American psychosocial development.
+The Group for the Advancement of Psychiatry (GAP, 1989) and others (Davis, 1980; Gibbs, 1984, 1997; Gibbs & Hines, 1989; Walker, Utsey, Bolden, & Williams, 2005) posited that cultural changes may be related to African American suicide deaths. These changes have potentially occurred via acculturation that likely brings about an erosion of religious, spiritual, and social protective factors as well as cultural beliefs (e.g., suicide as unacceptable). Many studies in African American suicide have focused on the religiosity-spirituality spectrum as a protective factor in African American suicide deaths, citing religious well-being and spirituality as cultural buffers (Marion & Range, 2003), coping resources (Kaslow et al., 2002), or deterrents (Early & Akers, 1993). Other studies have emphasized the importance of social support as a protective factor in suicidal ideation (Compton, Thompson, & Kaslow, 2005; Nisbet, 1996). Though religiosity, spirituality, and social support have revealed important buffering conditions, the effects of other sociocultural variables have remained gravely understudied.
+Ethnic Identity
+The U.S. Public Health Service (2001) report cited ethnic identity and acculturation along with other factors in understanding the severity of mental health challenges for ethnically diverse groups. According to Phinney (1992), ethnic identity is a reliable construct for understanding adherence to values and beliefs that are reflected by a cultural group. In college student populations, identity resolution may be particularly salient as students separate from families of origin and venture independently into a new stage of life. Though both European American and African American youth experience group esteem, ethnic identification has been observed more saliently for African American adolescents (French, Seidman, Allen, & Aber, 2006).
+Studies have found that ethnic identity buffers potentially negative mental health outcomes. Ethnic identification has been linked to positive self-esteem in Black college students (Phelps, Taylor, & Gerard, 2001) and is suggested for incorporation in drug prevention programs for young African American adults (Brook, Balka, Brook, Win, & Gursen, 1998). Ethnic identity or other sociocultural factors may, at least in part, account for differences in depressed students who may or may not be suicidal.
+To our knowledge, cross-cultural investigations in suicide and identity are nonexistent though challenges with identity resolution are potentially suicidogenic across cultural groups. With one exception, investigations of African American adult suicide have largely ignored the potential relationship of identity and suicide risk. Kaslow et al. (2004) found that African American suicide attempters scored lower on the Multigroup Ethnic Identity Measure (MEIM; Phinney, 1992) than nonattempters. Thus, adult
+suicide attempters reported lower rates of belongingness and group orientation. Studies that include predominantly European American samples frequently cite sexual identity crises as precipitants to suicidal behavior (see Kulkin, Chauvin, & Percle, 2000, for review).
+Current Study
+The purpose of the present paper was to explore ethnic group differences in the relationship between suicide and depression, one of the most common risk factors for suicide ideation and attempts (Goldsmith, Pellmar, Kleinman, & Bunney, 2002). Importantly, we evaluated the depression-suicide relationship in the context of third variables, ethnic identity, and acculturative stress. Given that factors in African American suicide have differed unexpectedly from those of European Americans, we speculated that accultura-tive stress and ethnic identity, important sociocultural variables might distinguish certain subgroups of individuals who are at risk. Though Perez et al. reported evidence of acculturative stress in a sample of European American college students, we posited that acculturative stress might affect African American and European American depression and suicide differently. Additionally, ethnic identity is a cross-cultural construct in which a comparative study is advantageous in parceling out potentially unique factors across ethnic groups. The cross-cultural emphasis proposes a precise, model of the depression-suicide relationship that is expected to better predict suicide ideation for African Americans than European Americans and expand existing models of suicide, a complex phenomenon. Such precision broadens the spectrum of variables that are considered in suicide assessment and scientific inquiry (see Triandis & Brislin, 1984).
+The explicit hypotheses for the current study were: (a) depressive symptomatology is positively correlated with suicidal ideation for both African Americans and European Americans; (b) accul-turative stress moderates the relationship between depressive symptomatology and suicidal ideation for African Americans but not European Americans such that the relationship between selfreported depressive symptomatology on suicide ideation is increased for acculturatively stressed individuals and; (c) ethnic identity moderates the relationship between depression and suicidal ideation for African Americans but not European Americans such that the relation for suicide and depression is strengthened in the absence of a strong ethnic identity.
+Method
+Participants
+The participants were 459 university students who participated in this study to partially fulfill a requirement for an introductory psychology class or to gain some other academic credit. Mean age for the total sample was 20.88 years (SD = 3.08 years). The ethnic composition of the sample was 64.5% African American (n = 296) and 35.5% European American (n = 163). Female participants represented the majority of European American (60%; n = 168) and African American (70%; n = 114) participants. There were 248 (248; n = 54%) students enrolled in a predominantly White public university in the southeastern U.S. There were 163 (163; n = 36%) students were enrolled in a historically Black public
+university in the southeastern U.S. The institution-type was not reported for 10% (n = 48) of students.
+Measures
+Societal, Attitudinal, Familial, and Environmental (SAFE) Acculturative Stress Scale. Levels of acculturative stress were measured by a modified, short version of the original 60-item SAFE scale used in previous studies (Fuertes & Westbrook, 1996; Mena, Padilla, & Maldonado, 1987). The short version of the SAFE scale measured acculturative stress in social, attitudinal, familial, and environmental contexts, along with perceived discrimination toward immigrant populations (Mena, Padilla, & Maldonado, 1987). Example items include, “In looking for a job, I sometimes feel my ethnicity is a limitation,” and “It is hard to express to my friends how I really feel.” According to Mena and colleagues, scores on the SAFE scale correlated negatively with both “ethnic loyalty” (r = —.35, p < .001) and “loyalty to parents” (r = —.25, p < .001). Participants were required to rate each SAFE item that applied to them on a Likert Scale, ranging from 1-not stressful to 5-extremely stressful. In this study, items that were “not applicable” were skipped and scored “0.” Consequently, the individual total scores were prorated to reflect possible skipped items. The possible scores for the SAFE ranged from 0 to 120. Joiner and Walker (2002) previously detailed evidence for convergent and discriminant validity for African Americans. The SAFE has also been shown to be reliable for Asian Americans and international students (a = .89; Mena et al., 1987), a heterogeneous group of Hispanic Americans (a = .89; Fuertes & Westbrook, 1996), and Black college students (a = .87, Joiner & Walker, 2002; a = .87 (Perez, Voelz, Pettit & Joiner, 2002). Similar alpha was obtained in this sample (a = .89; n = 459).
+Multigroup Ethnic Identity Measure (MEIM). The MEIM (Phinney, 1992) is a measure of ethnic identification based on the elements of ethnic identity that are said to be common across ethnic groups (Phinney, 1992). Some evidence indicates that the MEIM is a useful global measure of ethnic identity (see Roberts et al., 1999). Phinney (1998) asserted that ethnic identity can be considered a component of acculturation that focuses on the individual’s attachment and relation to his or her own (sub)group of the larger society. Anderson (1991) further explained that racial pride equips Black people to cope with acculturative “threats.” The MEIM consists of 14 items that assess three aspects of ethnic identification (i.e., positive ethnic attitudes and sense of belonging; ethnic identity achievement; and ethnic behaviors/practices). In this study, participants were required to rate each item on a Likert Scale, ranging from 1-strongly disagree to 4-strongly agree. The items were summed for a total score; higher scores represented more positive ethnic group identity. The MEIM has been shown to be valid and reliable for Asian American, Black, Mexican American, and White students (see Phinney, 1992 for a review; see also Sellers et al., 1998). The MEIM was also found to be reliable in the current sample (a = .87; n = 449).
+Beck Suicide Scale (BSS). Suicidal ideation was measured by the BSS (Beck & Steer, 1993), a 21-item self-report inventory. Each item consists of groups of statements that represent increasing levels of severity on a scale ranging from 0 to 2. As an example, one “0” statement is “I have no wish to die.” The “2” statement in that group is “I have a moderate to strong wish to die”.
+Items 1 through 19 contributed to a possible total score that ranged from 0 to 38. Items 20 and 21 referred to past suicide attempts and were optional. The BSSs reliability and validity have been well supported (see Beck & Steer, 1993; see also Beck, Steer, & Ranieri, 1988). In the current study, a = .91; n = 431.
+Beck Depression Inventory (BDI). Levels of depressive symptoms were assessed by the BDI, a 21-item self-report inventory. Each item was rated on a scale ranging from 0 to 3. Thus, possible inventory scores ranged from 0 to 63 in which higher scores represented increased severity. Although the BDI is not indicative of the full clinical syndrome of depression, it is a reliable and well-validated measure of depressive symptomatology (see Beck, Steer, & Garbin, 1988 for a review; see also Kendall, Hollon, Beck, Hammen, & Ingram, 1987). In the current study, a = .84; n = 432.
+Procedure
+The present study was granted full institutional review board approval. Participants were solicited from undergraduate and graduate courses in two southeastern university psychology departments. Each participant was informed that she or he would be administered a questionnaire packet that included questions about their behavior, views, and feelings with regard to depression, cultural identity, and suicidal thoughts. Each participant was also given a consent form that stated that consent for participation in the study was assumed upon completion of the anonymous questionnaire packet. The primary investigator, a licensed clinical psychologist and suicidologist, was immediately available in the event that any study participant was at risk for imminent danger. Students were informed that participation in the study could cease at any time and referral to the university counseling center or psychology clinic for free services would be available if needed. None of the participants discontinued participation, requested a referral for psychological services, or demonstrated imminent risk for danger. Approximately 25 minutes were required to complete the questionnaires.
+Results
+Means, standard deviations, and intercorrelations for all measures are presented for African American and European American college students in Table 1. All values were within expected limits. As Table 1 shows, self-reported depressive symptoms were similarly correlated with suicidal ideation for both African American (r = .54, p < .01) and European American (r = .54, p < .01) college students such that the more depressive symptomatology that was reported, the more suicidal thoughts reported. As expected, both acculturative stress and ethnic identity were associated with suicidal thoughts for African American college students such that higher acculturative stress (r = .29, p < .01) and lower ethnic identity (r = —.23, p < .01) correlated with increased suicidal thoughts. Acculturative stress was also associated with suicidal thoughts in European American college students (r = .19, p < .05). This is consistent with Perez, Voelz, Pettit, and Joiner’s (2002) findings and may reflect culture-related stress as a function of being immersed in a novel setting (i.e., college setting).
+Hierarchical Multiple Regression
+Hierarchical multiple regression was used to identify the presence and nature of moderating effects (Aiken & West, 1991; Cohen & Cohen, 1983). As recommended, scale scores were centered to reduce multicollinearity between the main effect and interaction terms (Cohen & Cohen, 1983). Further, West, Aiken, and Krull (1996) noted that centering continuous variables ensures the interpretation of effects would occur at a meaningful value (i.e., the mean, which has a value of 0 with centered variables).
+Acculturative Stress as a Moderator for Depressive Symptoms and Suicide Ideation in African Americans
+To test a main hypothesis that acculturative stress moderates the relationship between depressive symptoms and suicide ideation for African Americans, a hierarchical regression equation was constructed with BSS scores as the dependent variable and BDI scores as the predictor variable. SAFE scores were added in the second step. In the third step, the interaction of BDI and SAFE scores was entered into the regression equation as a predictor. A significant overall effect was found for the regression equation (r = .61; F(3, 295) = 57.50, p < .001). Thus, together, depressive symptoms, acculturative stress, and the depressive symptoms x acculturative stress interaction accounted for 37.2% of the variance in predicting suicide ideation. The main effects for depressive symptoms and acculturative stress were significant in predicting BSS scores (see Table 2). The depressive symptoms x acculturative stress interaction was also significant (partial correlation = .30, t(294) = 5.45, p < .001).
+Holmbeck (1997) suggested evaluating high and low scores of the moderator variable to interpret the interaction. Accordingly, we examined the relation between BDI scores and BSS scores among two subgroups of participants: those who reported low and those who reported high levels of acculturative stress (i.e., those who scored one standard deviation above the SAFE mean, and those
+who scored one standard deviation below the SAFE mean). The regression equation predicted BSS scores for those high in acculturative stress (r = .62, p < .001), but not for those low in acculturative stress (r = .05, p = .75). This pattern of results indicates that the nature of the relationship between depression and suicide differed for individuals who reported high levels of accul-turative stress and those who reported lower levels of acculturative stress.
+Ethnic Identification as a Moderator for Depressive Symptoms and Suicide Ideation in African Americans
+To test a main hypothesis that ethnic identification moderates the relationship between depressive symptoms and suicide for African Americans, a hierarchical regression equation was constructed with BSS scores as the dependent variable and BDI scores as the predictor variable entered first into the regression equation. In the next step, MEIM scores were added. In step three, the interaction of BDI scores and MEIM scores was entered into the regression equation as a predictor. A significant overall effect was found for the regression equation (r = .62; F(3, 295) = 61.62, p < .001). Thus, together, depressive symptoms, ethnic identity, and the depressive symptoms x ethnic identity interaction accounted for 38.4% of the variance in predicting suicide ideation. The main effects for depressive symptoms and ethnic identity were significant in predicting BSS scores (see Table 2). The depressive symptoms x ethnic identity interaction was also significant ( partial correlation = —.32, t(294) = —5.86, p < .001), thereby demonstrating a moderating effect for ethnic identity.
+To interpret the interaction, we evaluated the relation between BDI scores and BSS scores among those who reported low and those who reported high levels of ethnic identity (i.e., those who scored one standard deviation below the MEIM mean, and those who scored one standard deviation above the MEIM mean). The
+regression equation predicted BSS scores for those high in ethnic identity (r = .55, p < .05), but more so for those low in ethnic identity (r = .75, p < .001). This pattern of results indicates that the strength of the depression-suicide relationship was greater for African American students who reported low levels of ethnic identity.
+Acculturative Stress as a Moderator for Depressive Symptoms and Suicide Ideation in European Americans
+To test a main hypothesis that acculturative stress moderates the relationship between depressive symptoms and suicide for European Americans, a hierarchical regression equation was constructed with BSS scores as the dependent variable and BDI scores entered as the predictor in Step 1 of the regression equation. In Step 2, SAFE scores were entered in the equation. In the third step, the interaction of BDI and SAFE scores was entered into the regression equation as a predictor. A significant overall effect was found for the regression equation (r = .55; F(3, 162) = 22.78, p < .001). Thus, together, depressive symptoms, acculturative stress, and the depressive symptoms x acculturative stress interaction accounted for 30.3% of the variance in predicting suicide ideation in European Americans. The main effect for depressive symptoms but not acculturative stress was significant in predicting BSS scores (see Table 2). The depressive symptoms x acculturative stress interaction was also not significant (partial correlation = .10, t(161) = 1.23, p = .221).
+Ethnic Identification as a Moderator for Depressive Symptoms and Suicide Ideation in European Americans
+To test a main hypothesis that ethnic identity moderates the relationship between depressive symptoms and suicide for European Americans, a hierarchical regression equation was constructed with BSS scores as the dependent variable and BDI scores entered as the predictor in the regression equation. MEIM scores were entered in Step 2. In Step 3, the interaction of MEIM and BDI scores was entered into the regression equation as a predictor. A significant overall effect was found for the regression equation (r = .55; F(3, 162) = 23.21, p < .001). Thus, together, depressive symptoms, ethnic identity, and the depressive symptoms x ethnic identity interaction accounted for 30.3% of the variance in predicting suicide ideation. The main effect for depressive symptoms but not ethnic identity was significant in predicting BSS scores (see Table 2). The depressive symptoms x acculturative stress interaction was also not significant (partial correlation = —.12, t(161) = -1.49, p = .138).
+Discussion
+The overall aim of the current paper was to investigate the relationship of acculturative stress and ethnic identity to selfreported depressive symptoms and suicidal ideation in a cross-cultural sample. As expected, we found that depressive symptomatology was correlated with suicidal ideation in both African American and European American college students. Our finding that the strength of the depression-suicide ideation correlation was similar for European American and African American college students is noteworthy as some studies have indicated that African
+Americans who die by suicide are less likely than European Americans to demonstrate symptoms of depression. It may be that African American college students are as likely to consider suicide when depressed, but this does not confer increased risk for a fatal suicide attempt. Because studies report high rates of suicide attempts for both African American males (Centers for Disease Control, 2004) and females (Nisbet, 1996) that mimic and/or exceed those of European Americans, additional studies of moderating and mediating effects of cultural phenomena in suicide fatalities are warranted.
+We found that acculturative stress was related to suicidal ideation in both African American and European American students. However, ethnic identity was only associated with suicide ideation in African Americans. Further, the depression-suicide relationship strengthened for a subgroup of African Americans. That is, we found that acculturative stress moderated the effect of depression on suicidal ideation for African Americans such that suicidal ideation was increased for African American college students who were depressed and also acculturatively stressed. Depression was not moderated for European American college students or for African American students who were not acculturatively stressed. This finding sheds light on subgroups of depressed African Americans who may consider suicide. That is, the experience of accul-turative stress, not low or high levels of acculturation per se, kindles suicide ideation. Though acculturation level was not included as a variable in this study, other studies have measured psychological effects of acculturation level with mixed results (see Neff & Hoppe, 1993; Rogler, Cortes, & Malgady, 1991, for review). Contradictory conclusions have emerged such that acculturation is said to be positively adaptive for some while others argue that adopting the mainstream culture is psychologically toxic. In a study of acculturation level and suicide attempts and ideation, Walker, Utsey, Bolden, and Williams (2005) found that selfreported suicidal thoughts and attempts decreased as a function of a higher acculturation status. Since this finding was contrary to prediction, Walker and colleagues speculated that “unacculturated persons [may] specifically experience more acculturative stress as a pressure to assimilate to mainstream society” (p. 213). Future studies should likely explore the relationships of both acculturation level and acculturative stress along with ethnic identity in predicting suicidal ideation.
+We hypothesized that ethnic identity would moderate the relationship between depression and suicide ideation such that the relation for suicide ideation and depression is strengthened in the absence of positive ethnic identity. Similar to the pattern of findings for acculturative stress, African American (but not European American) college students who were less attached to their ethnic group reported a stronger relationship of depression to suicidal ideation than those who endorsed a stronger attachment to their group. This is consistent with Kaslow et al.’s finding that African Americans who reported lower ethnic group identification were more likely to have attempted suicide than other Africans Americans who were seeking medical care (i.e., not in psychiatric crisis).
+To our knowledge, this is the first study to investigate the moderating effects of acculturative stress and ethnic identification in relation to depression and suicide ideation. We found convincing evidence that certain subgroups of African American college students who report symptoms of depression are more likely to consider suicide given poor group identity or high levels of accul-
+turative stress. European American college students, while stressed by the process of adjusting to a new environment, were not similarly at risk. Though the proposed model is not exhaustive toward discriminating cross-cultural determinants of suicide ideation, the findings offer important insight to how third variables might be informative in minimizing assessment errors (e.g., false positives).
+Overall, the current study highlights the relevance of cultural factors in the provision of mental health services, and therefore has implications for the evaluation, intervention, and treatment of African American college students in particular. As an example, the interactive risk of depressive symptomatology and accultura-tive stress (or ethnic identification) should be included in suicide risk assessment protocol. Though negative life events and stressful circumstances are known to trigger suicidal ideation and crises, stressors associated with the acculturative process amplify risk for African American college students. Future studies may also consider the compound effects of discrimination, perceived racism and other race-related stressors in addition to culturally relevant factors.
+Theoretical advances that embrace complex psychological, sociocultural, and biological models of suicide risk generate meaningful approaches to understanding and preventing suicide. In the current study, suicidal ideation increased in the presence of poor group identity and acculturative stress for African Americans. This conclusion factors into the multidimensional nature of suicide risk and the need for research that is more comprehensive, evaluation, and treatment.
+Some cautions and limitations should be noted. The first limitation of the current study is related to the selection of participants. The students’ suicide history was not known, and the overall variability in BSS scores was low. Though significant effects were observed despite the low variability in suicidal ideation, future studies may focus on clinical samples where suicide history is established. These studies might also represent more diverse age groups and levels of education. Given different rates of suicide across age groups (Garlow, Purselle, & Heninger, 2005), data that demonstrate suicide risk should be disaggregated such that risks for college age African Americans are not compared with those of elder African Americans. Older African Americans may respond differently to acculturative stress. Group identity may be even more resolved, given time and enduring effects of segregation. The range of education and perhaps, the range of socioeconomic status (SES) in the current study were restricted. Though the college sample used in the present study was consistent with those used in past suicide research (which presupposed that “advantaged” individuals, higher in SES and education, demonstrate higher levels of suicidality; see Selkin, 1983), the study’s generalizability beyond college samples is limited.
+The use of the single-informant, self-report, cross-sectional methodology added to the study’s limitations. Questionnaire items may have elicited minimization or exaggeration of psychological symptoms and cultural variables. Future studies would benefit from an outside, independent observation of the participants’ emotional and psychological status. In addition, the cross-sectional nature of the study only provided a snap-shot in time, and therefore, the data were not sufficient for causal assumptions. Future studies incorporating longitudinal analyses could potentially provide evidence that increases or decreases in acculturative stress,
+ethnic identification, and depressive symptomatology affect changes in level of suicidal ideation.
+Overall, this study makes a timely contribution to the suicide literature. As the U.S. population continues to increase in cultural diversity, a more “inclusive” understanding of suicide risk is needed. The outcome of this study provided empirical evidence for the negative impact of acculturative phenomena and low ethnic identification differentially for African American and European American college students. Moreover, the data indicated that vulnerability toward suicidal ideation was associated with acculturation-related distress and insufficient group identity. Both quantitative and qualitative investigations should fully explore culturally relevant phenomena in suicide risk. Because African Americans’ patterns of suicide defy conventional models of suicide risk, investigations of culturally relevant factors are fundamental to studies of African American suicidal behavior.
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+Background
+There is a growing interest in addressing spirituality and religion in health care, with evidence emerging that personal spiritual and religious practices, and support of these by practitioners, can influence mental health in a positive way. Spirituality is understood, in this context, as a search for connectedness and meaning, transcendence and belonging. Being religious is conceptualised as the outward practice
+of spiritual beliefs situated in a particular organised religion (Moreira-Almeida et al. 2016). For youth who identify as lesbian, gay, bisexual, transgender plus [plus other minority sexual groups] (LGBT+), there are distinct challenges to spiritual and religious expression (Liboro 2015). Negative thoughts or experiences arising from LGBT+ youths’ personal religious beliefs (Hamblin and Gross 2013), attitudes from others or cultural experiences of historical religious beliefs, can affect the youth’s self-perception in a negative way leading to mental health issues (Liboro 2015). Conversely, there is the potential for religious or spiritual beliefs to provide both personal and community support in a positive manner (Tenner 2015). Given a young LGBT+ person’s vulnerability at a significant time of their life, developing an understanding of the implications of spirituality and religion for this group is essential. The success of health-care agencies towards this population can therefore depend on their capacity to subscribe to a spiritual approach, routinely assessing spiritual needs or having a religious understanding (Kralovec et al. 2014).
+There is evolving evidence indicating that the expression of spirituality and religion can have a positive impact upon people’s lives, with some studies supporting the assertion that having a faith can lead to better mental health outcomes (Ream and Savin-Williams 2005; Rodriguez 2009). In a review of the literature addressing the relationship between the incidence of mental disorders and religion or spirituality in the general population, a significant number of studies (72.1%) reported positive outcomes between spiritual or religious involvement and mental illness including depression, substance use and suicide; far less (4.7%) showed negative results (Bonelli and Koenig 2013). However, the potential positive influence of religion and spirituality on mental health has been the subject of debate. King (2014) concluded that those who described themselves as spiritual (but not religious) appeared to be more vulnerable to psychological issues, suggesting that ‘those with a spiritual view of life appeared to be vulnerable to mental and substance misuse disorders’ (King 2014:109).
+There has been a major about turn in attitudes towards people with same-sex attractions and a greater acknowledgement of the religious and spiritual lives of people who identify as LGBT+ (Gibbs and Goldbach 2015). Still, LGBT+ youth can face significant challenges in establishing a sense of identity in a predominately heterosexual and transphobic world (Matthews and Salazar 2012). Many LGBT+ youth have to face many challenges alone without the support of family and peers and often in hostile and unsupportive environments (Levy and Edmiston 2014; McCann et al. 2019).
+Support from religious organisations may be helpful in challenging and stressful times. However, non-affirming societal beliefs around same-sex intimacy or gender identity can exacerbate minority stress and internalised homophobia (Meyer 2003; Rostosky and Riggle 2017). The minority stress model demonstrates the potential psychosocial stressors related to being LGBT+ and the damaging effects on health and well-being. Negative societal responses can lead to feelings of guilt, shame, demoralisation, low self-esteem and social exclusion (Meyer 2003; Lease et al. 2005; Rosario et al. 2006). This phenomenon has been associated with a significant increase in depression, anxiety and suicidal thoughts and behaviours. There are also strong links with substance use and eating disorders (Barnes and Meyer 2012).
+Despite the identified mental health challenges, affirming religious beliefs have been shown to be a protective factor in counteracting harmful stressors among sexual minority youth (Wilkinson and Pearson 2009; Barnes and Meyer 2012; Foster et al. 2011). Coming out, a significant time for LGBT+ youth, may lead to rejection from their spiritual or religious community. The resultant existential conflict can lead to increased anxiety and depression (Gibbs and Goldbach 2015). Young people may become more distant from their family of origin through moving away from their spiritual faith thus limiting access to emotional support during times of need (Barnes and Meyer 2012). By re-examining spirituality, youth may develop coping strategies and resilience through a renewed sense of faith and finding affirmative people and spiritual communities that are open, supportive and can validate expressions of sexuality and gender identity and encourage good mental health (Koenig 2009). However, the picture remains incomplete; hence, the current systematic review focuses on the subjective experiences of LGBT+ youth regarding spirituality and religiosity that may guide and inform future mental health practice and service developments.
+Methods
+The aim of this review was to synthesise current evidence regarding the experiences and perceptions LGBT+ youth regarding the expression of their spirituality and their mental health needs. Therefore, the questions of this review are:
+1.	What are the experiences and mental health needs of youth who identify as LGBT+ regarding religion or spirituality?
+2.	What are the implications for mental health services in relation to the religious/ spirituality needs of youth who identify as LGBT+ ?
+Search and Selection Strategy
+A subject librarian assisted with the literature search strategy. The databases used in the search were CINHAL, MEDLINE, PsychINFO and Sociological Abstracts. The search terms used were: spiritual* OR relig* OR sacred OR transcendent AND GBLT OR gay OR lesbian OR bisex* OR trans* OR intersex OR queer AND mental health OR psychosocial OR well-being OR self-esteem OR homonegativity AND youth OR adolecen*. The inclusive dates were 31 May 2008 to 1 June 2018 to best capture contemporary mental health practices and individual experiences in the changing sociopolitical climate for youth who identify as LGBT+. The search strategy utilised in one of the electronic databases is contained in Table 1.
+The search yielded 314 hits in total. Following the removal of duplicates and a check for relevance, 44 papers remained. Full texts of papers were screened leaving 10 papers suitable for the review. To be included in this review studies had to be empirical peer-reviewed research in English and focus on mental health and spirituality or religious experiences of youth up to the age of 25 years who identified
+as LGBT+. Studies not meeting the criteria were excluded. Reasons for exclusion included wrong population, wrong subject or failed to address the research questions (Fig. 1).
+Quality Assessment
+A quality assessment tool was used to review the papers (Critical Appraisal Skills Programme 2018). Specific questions were applied to each of the relevant studies (Table 2). Each question was scored zero, one or two out of a score of 20. A score of zero was given if the paper had no information, one if there was a moderate sum, and a score of two if the question was fully addressed (Rushbrooke et al. 2014). A score of 17 and above, demonstrating a high-quality study, was achieved by three of the studies (Gattis et al. 2014; Page et al. 2013; Quinn et al. 2016). A total of five studies scored between 14 and 16, indicating deficits in the clarity of aims, data collection methods, research relationships considered and ethical considerations (Eick et al. 2016; Gold and Stewart 2011; Jeffries et al. 2014; Kubicek et al. 2009; Lauri-cella et al. 2017). The remaining two studies scored below 14, due to limited information that impacted on the overall quality and were related to the aims, ethics, and clarity and detail of findings (Hatzenbuehler et al. 2012; Nielson 2017). All of the studies were included in the review as they met the study inclusion criteria.
+Characteristics of the Selected Studies
+The ten studies that addressed the review questions are presented in Table 3. The majority of studies (n = 9) were conducted in the USA, with the remaining study carried out
+in Israel. The studies had sample sizes ranging from 1 to 1413 participants. The age of youth participants ranged from 12 to 25 years (n=7). Five of the studies used quantitative methods, two studies used qualitative methods, and two were mixed methods studies.
+Data Extraction and Analysis
+The process of data analysis and synthesis involved the extrapolation of themes that addressed the aims of the research. These were coded from the results of the included studies, organised according to concepts and verified and agreed by the research team (Caldwell et al. 2011).
+Findings
+The aim of this systematic review was to consider empirical studies regarding the spirituality and religious experiences of LGBT+ youth regarding the expression of their sexuality and their mental health needs. Following data analysis, three main themes emerged. These were (1) attitudes and beliefs; (2) individual sexuality experiences; and (3) spirituality as coping and support.
+Attitudes and Beliefs [Discrimination, Gender Differences and Shame]
+Adolescence is a crucial time in the formation and development of a person’s sexual and religious/spiritual identity. It is often a period of experimentation and of testing one’s own beliefs and ideas and engaging in critical reflection on life’s possibilities and future directions. The situations where these experiences may be carried out can present challenges, particularly in perceived heterosexist environments. Some of the studies included in the review identified schools as potentially discriminatory and stressful environments where homophobia, biphobia and transphobia often exist (Eick et al. 2016; Gattis et al. 2014). Victimisation experiences, including bullying, shaming and violence, can lead to poor academic performance, motivation and attendance. The challenges faced by LGBT+ youth can also lead to higher rates of anxiety, depression, suicidality, substance use and prostitution than in the heterosexual population (Gattis et al. 2014).
+In one study, addressing prejudice and stereotyping towards homosexual students in Israeli schools using contact interventions (Allport 1954), there were improvements in student emotional, cognitive and behaviourial attitudes (Eick et al. 2016). This study by Eick was a mixed student population of straight and LGB youth and the improvements concerned this whole sample. Studies that examined the relationship between religion, mental health and internalised homophobia in LGBT+ youth found that belonging to a religious denomination that was gay affirming can act as a protective factor for discrimination and depression (Page et al. 2013). Conversely, where homonegativity prevails, in the form of discrimination, stigma and persecution, there can be a disintegration/dissonance between religiosity and sexuality. Tensions can often exist creating feelings of incompatibility, imbalance and doubt. Individuals may feel alienated, isolated and marginalised through the discrimination displayed by some religious organisations (Gattis et al. 2014; Page et al. 2013; Quinn et al. 2016). Further conflict can exist between religion, spirituality and sexual identity (e.g. ‘reparative therapy’). As a result, LGBT+ youths can be wary of ‘organised’ or established religious groups (Gattis et al. 2014). In the Black Church, where the dominant position was non-LGBT+ affirming, LGBT+ people tended to be ‘closeted’ and sexually secretive to cope with the challenges of homonegativity. However, due to social, political and family centrality, Black LGBT+ members often remained active in the church. Religion and spirituality remained prominent in young Black men’s lives despite heteronormativity. Some study respondents thought that challenging the negative views of clergy towards LGBT+ congregation
+members was futile. Although some commentators agree that stigma reduction strategies can reduce internalised homophobia, increase self-esteem and reduce depression and isolation in LGBT+ youth, many felt let down and had to eventually leave faith communities (Gattis et al. 2014; Quinn et al. 2016).
+Individual Spirituality Experiences [Conflict, Oppression, Identity Formation]
+A conflict was found to exist between sexual and spiritual identity and religious teachings about LGBT+ concerns. This tension appeared to lead to the LGBT+ community becoming increasingly marginalised from many faith-based communities. Approximately 90% of more than a dozen nationally representative survey respondents described present-day Christianity as anti-homosexual (Barnes and Meyer 2012). Perhaps as a reaction to this, or as a means of coping, many youth who identify as LGBT+ have dissociated from non-affirming religious institutions. The conflict between religion and sexuality is strongly associated with internalised homonegativity and poor mental health (Lauricella et al. 2017; Page et al. 2013). Early on in a person’s sexual development, LGBT+ youth are often not able to clarify their sexual orientation, may have little or no contact with the LGBT+ community and often get involved in religious activities as a way of suppressing their own desires (Lauricella et al. 2017). Later in their development, some of these individuals may still hold on to feelings of shame concerning their sexual identity and try to eliminate their urges through prayer and other means. Some people, however, may go on to find a more accepting spiritual community or find other ways of reconciling their sexuality with their childhood religion (Lauricella et al. 2017).
+Oppression is a social construct that creates the closet in which LGBT+ people reside either partially or fully (Rhodes 1994). It is recognised as the place between self-identifying as gay and disclosing one’s sexual orientation to others. In a webbased survey of 47 respondents, Gold and Stewart (2011) explored how LGB undergraduate students negotiated and defined their spiritual identities during this coming out process. When attempting to navigate their burgeoning sexual identity with that of their spiritual identity, students spoke of experiences of irreconciliation, progressive development, arrested development, completed development and reconciliation. The authors considered that it was through these processes that the individuals were able to begin to negotiate and in turn construct their own new internal identities (Gold and Stewart 2011).
+Spirituality as Coping and Support
+Spirituality has been described as acceptance and ‘loving kindness’. It can involve personal relationships with a powerful essence, a strong connection to nature and a respect for all forms of life. It has to do with love, understanding and compassion. There may be a closeness to a higher being or ‘god’ (Gold and Stewart 2011). Although there is the beginning of a sea change in the psychology of religion, with an increased acknowledgement of the religious and spiritual lives of people who identify as LGBT+, there still persists a need for more evidence-based research
+1 Springer
+for young people coming to terms with their sexuality and exploring their religious beliefs (Page et al. 2013; Ream and Rodriguez 2014). The anti-homosexual stance previously held by organised religious groups, however, may be changing, since a 2011 survey found that 58% of respondents believed that society should accept homosexuality (Pew Research Center 2011).
+Hatzenbuehler et al. (2012), exploring religion and health risk behaviours, identified that religious climate among youth who identify as LGB was a predictor in excessive alcohol use and risky sexual behaviour. The study demonstrated that LGB youths living in countries with more supportive religious climates showed fewer health risk behaviours, meaning religion can also be protective factor for LGB youths. The authors highlighted the need to develop prevention intervention programmes for LGB youth living in high-risk environments, in particular youth living in unsupportive religious climates. Similarly, Jeffries et al. (2014) advocated the need to consider factors involving religion and spirituality in young HIV-infected men as a way to help tailor appropriate interventions for this population and help enhance faith-based practice developments.
+In another study investigating individual resilience experiences, Kubicek et al. (2009) explored the role of religion and spirituality in the lives of a sample of young gay men and looked specifically at how homophobic messages taken from religious contexts were internalised by this group. This unique mixed methods study looked at how these young men attempted to resolve the conflict between these messages and their sexual identity and discovered how they had made a number of important conscious choices about their lives, including religious and spiritual beliefs in an effort to solidify their identity. The study describes their experiences and processes in identifying the positive and nurturing aspects of religion such as feeling a sense of acceptance and support from a higher power. The group at times had to reframe or simply reject the negative messages they had heard whilst growing up which had the effect of incorporating a stronger sense of spirituality into their lives. It is important to note that for the participants of this study, they relied on the belief that sexual orientation as an innate and unchangeable aspect of their selves.
+Discussion
+The development of a LGBT+ sexual identity is a complex and often difficult process. This review has demonstrated both the positive and negative experiences of LGBT+ youth in relation to faith-based or spiritual upbringing. Important issues have been raised and will now be discussed further through the implications for practice, education and future research.
+Implications for Practice
+The World Psychiatric Association proposes that full consideration should be given to spirituality in holistic assessments, that is, the biopsychosocial, cultural and spiritual elements (Moreira-Almeida et al. 2016). Despite this, there is no evidence of
+formal training about spiritual elements in the education and training of mental health practitioners (Castaldelli-Maia and Bhugra 2014; Schuck and Liddle 2001), or any formalised, recognised way of going about this. The underlying principles should be person-centred approaches to care, supports and treatment including respect, sensitivity and curiosity for spirituality experiences. Practitioners should be able to demonstrate awareness, respect and sensitivity to peoples’ spiritual experiences and beliefs. Furthermore, clinicians should be aware of the potential benefits and the harm of religious, spiritual and secular world views. The importance of maintaining a strong sexual and gender identity for LGBT+ youth and the development of resilience is indicated in the current review (Page et al. 2013). Parents, teachers and mental health practitioners can focus on relevant stressors that are evident in the lives of LGBT+ youth and provide the necessary supports and psychosocial interventions that may promote greater resilience and coping strategies for LGBT+ youth. Also, there needs to be more collaborative work with faith leaders to support LGBT+ youth and their families (Moreira-Almeida et al. 2016; Page et al. Rodriguez 2009). Given that spirituality and religion can be sensitive issues, there needs to be training and education to underpin any such practice (Lease et al. 2005).
+Implications for Education
+Schools are important places to address discrimination, prejudice and victimisation. The review has revealed that through positive and supportive environments, where negative attitudes and beliefs were challenged, knowledge, beliefs and attitudes improved (Eick et al. 2016). Non-LGBT+ affirming religions have been associated with greater internalised homonegativity (Barnes and Meyer 2012), emotional distress (Wilkinson and Pearson 2009) and poorer self-esteem (Ream and Savin-Williams 2005). There is also a strong association between religion, mental health and minority stress (Newcomb and Mustanski 2010). There is a need for more evidencebased research into young people coming to terms with their sexuality and exploring their religious beliefs (Ream and Rodriguez 2014), and greater exploration of potential for tolerance and acceptance among religious communities (Park et al. 2016). There needs to be a recognition and development of multicultural competencies. Educational and training initiatives should contain sexuality and spirituality components for holistic practitioners (McCann and Brown 2018). Raising awareness and increasing knowledge of the ways that social identities can influence students who are searching for meaning and purpose in their lives is important. Reflection and increased dialogue around acceptance and tolerance should be supported and encouraged (Park et al. 2016). There should be appropriate spaces in campus for exercising spiritual activities such as meditation, prayer and reflection.
+Implications for Future Research
+The review has identified several areas where more research is needed to better support youth who identify as LGBT+. Whilst empirical research has identified significant links between spirituality, religion and health, there needs to be more
+1 Springer
+LGBT+-specific research establishing needs and evaluating potential interventions (Hill and Pargament 2003; Castaldelli-Maia and Bhugra 2014; Yip 2008), and also the potential pitfalls related to youths’ experiences of religion and spirituality and how this might have a negative influence. Some of the emerging issues that require further investigation are the prevalence of depression and substance use among LGBT+, and the relationship of spirituality and religion to the manifestation of these issues. Spiritual understandings and experiences also need to be taken into account during diagnosis, so that accurate account of spiritual and religious views or effects from these are clearly articulated and understood, rather than being categorised as a constituent element of mental disorder. Spiritual distress, for example, which manifests in a feeling of a lack of meaning in life, could be mistaken for depression. Spiritual distress has been classified as a nursing diagnosis in NANDA International (NANDA-I) since 1978. It is defined as a ‘state of suffering related to the impaired ability to experience meaning in life through connectedness with self, others, world or a Superior Being ’ (Herdman and Kamitsuru 2014: 372).
+More research needs to be carried out around suggested interventions and treatments that may have a spiritual element such as self-help groups; religious communities; talking therapies; mindfulness; and tai chi, for example. Overall, more needs to know about the potential of addressing and supporting LGBT+ youths’ spiritual needs and its effect on their overall outcome (recovery and staying well if they are diagnosed with mental health issues) and also prevention of mental health issues. If there is potential for spiritual interventions to improve quality of life and well-being, then more needs to be done to explore this possibility in this cohort. What has become increasingly apparent from this systematic review is the distinct lack of empirical research that specifically addresses the psychosocial experiences of LGBT+ youth with regard to spirituality and religiosity. There are opportunities to conduct multi-centre, international and longitudinal research studies that utilise a range of methodologies and designs that will contribute significantly to the evidence base and increased understanding of the relevant issues and concerns for LGBT+ youth.
+Strengths and Limitations
+There is an increasing interest in the spirituality and religious experiences of youth who identify as LGBT+ and its importance in relation to mental health and psychosocial well-being. This systematic review has revealed valuable sources of information that may guide practitioners, service providers, educators and researchers. There are limitations in the studies included in this review primarily due to the relatively small sample sizes, the robustness of some of the study designs and the absence of intervention and evaluation studies. The authors have attempted to exercise rigour in their selection of studies and have utilised relevant frameworks and methodological strategies throughout to address these issues.
+Conclusion
+Practitioners need to be aware of and sensitive to individual religious and spirituality issues. Negative experiences of religious institutions may affect self-perceptions and a willingness to engage in healthy behaviours. Religious and spiritual activities may help with negative coping behaviours such as drug use, risky sex and prostitution. Spiritual coping may promote better mental health and increase self-esteem. It may support healthy living and help motivate people to make positive changes in their lives. Support for marginalised groups should be a pivotal point for all churches and religious institutions that are open, non-judgemental and accepting of all, and given the potential (positive or negative) influence of spirituality on the LGBT+ youth, particularly in relation to their mental health (Kralovec et al. 2014), it is important that health researchers lead the way in promoting this support and providing a distinct evidence base to support it.
+Funding No funding was received for this project.
+Compliance with Ethical Standards
+Conflict of interest The authors declare that they have no conflict of interest.
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+Introduction
+Suicide is a leading cause of death worldwide. Current estimates indicate that an individual will die by suicide somewhere in the world every 40 s (World Health Organization (WHO), 2014). This public health concern is perhaps even more alarming and puzzling when it comes to suicide death among youth -estimated to be the second leading cause of death among young people 10-24 years old (Centers for Disease Control and Prevention (CDC), 2017b; Patton et al., 2009; WHO, 2014). The purpose of the current review is to provide a recent estimate of worldwide suicide mortality rates in adolescents and to examine cross-national trends in these rates. Extending prior research, this study explores suicide mortality data in detail, including patterns in suicide methods, how access to lethal means relates to suicide rates, and how suicide rates vary cross-nationally based on indices of economic quality and inequality.
+Although the specific causes of suicide among young people are complex and remain somewhat elusive (Bridge, Goldstein, & Brent, 2006; Cha et al.,
+2018; Hawton, Saunders, & O’Connor, 2012; Tur-ecki & Brent, 2016), it is clear that suicide is a major public health concern among adolescents. Suicidal thoughts and behaviors are relatively rare during childhood but increase significantly during the transition to adolescence (Dervic, Brent, & Oquendo, 2008; Hepp, Stulz, Unger-Koppel, & Ajdacic-Gross, 2012; Nock, Borges, Bromet, Cha, et al., 2008; Nock et al., 2013). In addition to the increased prevalence during adolescence, there is also significant escalation from suicidal thoughts to suicidal behaviors during this developmental period. Most youth who transition from suicidal thoughts to suicidal behaviors will do so within 1-2 years after the onset of suicide ideation (Glenn et al., 2017; Nock et al., 2013). Moreover, available country-level estimates suggest that the suicide rate among adolescents has increased in recent years (OECD, 2017b). For example, in the United States of America (USA), the age-adjusted suicide rate increased by 24% from 1999 to 2014; the increase in rates for females was greatest among those aged 10-14 years, while males aged 10-14 years experienced the second largest percent increase among males during this time (Curtin, Warner, & Hedegaard, 2016). Among 15- to 19-
+year-olds, suicide rates increased for both sexes from 2007 to 2015; among females, the rate in 2015 was higher than any time in the prior 40 years (Curtin, Hedegaard, Minino, Warner, & Simon, 2017). Taken together, adolescence is a key developmental period for effective suicide intervention and prevention (Gould, Greenberg, Velting, & Shaffer, 2003; NAASP, 2014; WHO, 2014; Wyman, 2014).
+A number of prior studies have estimated crossnational trends in suicide mortality rates among youth. Most of this previous research has used the World Health Organization’s (WHO) Mortality Database (WHO, 2018b), which provides one of the best sources of information about worldwide mortality rates. Using this database, Wasserman, Cheng, and Jiang (2005) estimated a worldwide suicide rate for 15- to 19-year-olds of 7.4/100,000 people based on suicide mortality data collected in 1995 from 90 countries. The highest suicide rates in youth have been observed in New Zealand (Bridge et al., 2006; Chaet al., 2018; Kõlves & De Leo, 2016; McLoughlin, Gould, & Malone, 2015; Roh, Jung, & Hong, 2018), Finland (Bridge et al., 2006; Cha et al., 2018; McLoughlin et al., 2015; Roh et al., 2018), Ireland (Bridge et al., 2006; McLoughlin et al., 2015), Guyana (Koõlves & De Leo, 2016), Sri Lanka (Wasserman et al., 2005), and a range of former Soviet Union states (Bridge et al., 2006; Cha et al., 2018; Koõlves & De Leo, 2014, 2016; McLoughlin et al., 2015; Roh et al., 2018; Wasserman et al., 2005). In addition, suicide rates are found to be higher in older versus younger youth (Bridge et al., 2006; Cha et al., 2018; Roh et al., 2018). Finally, adolescent suicide deaths are much more common (2-4x higher) in males than females (Bridge et al., 2006; Cha et al., 2018; Kõlves & De Leo, 2014, 2016; McLoughlin et al., 2015; Roh et al., 2018; Varnik et al., 2009; Wasserman et al., 2005), consistent with sex differences in suicide rates observed among adults (Bachmann, 2018; Canetto & Sakinofksy, 1998; Chang, Yip, & Chen, 2019; Nock, Borges, Bromet, Cha, et al., 2008; Schrijvers, Bollen, & Sabbe, 2012; World Health Organization (WHO), 2016b). The major exceptions to this sex difference in youth have been observed in China (Bridge et al., 2006; McLoughlin et al., 2015; Wasserman et al., 2005), India (McLoughlin et al., 2015), Cuba(Wasser-man et al., 2005), Ecuador (Wasserman et al., 2005), El Salvador (Wasserman et al., 2005), and Sri Lanka (Wasserman et al., 2005), all of which have reported higher suicide rates among females than males in at least one study.
+The current review provides an updated estimate of worldwide suicide mortality rates among youth, aged 10-19 years, and examines cross-national trends in suicide rates. Like most prior studies examining worldwide suicide rates, this review uses the WHO Mortality Database. The present review builds on prior studies in three important ways. First, this review examines the time period from early to late adolescence (10- to 19-year-olds) and compares rates
+among younger (10- to 14-year-old) and older (15- to 19-year-old) adolescents. The current focus on this age range is critical, as most prior studies have examined either narrow age ranges (e.g., 1519 years) that leave out key periods of adolescence, or wider age ranges extending into early adulthood (e.g., 5-29 years). Prior research reveals marked differences in the incidence of, and risk factors for, suicide-related outcomes across adolescent and adult developmental periods (Koõlves & De Leo, 2015; Lewinsohn, Rohde, Seeley, & Baldwin, 2001; Nkansah-Amankra, 2013), highlighting the need to more precisely examine the period of adolescence. Second, this review provides a more recent estimate of suicide rates by focusing on data since 2010. The majority of prior reviews have examined suicide rates over the past 15-20 years. Given the significant changes in suicide death rates over time (Curtin et al., 2016; OECD, 2017b), an updated review is needed. Third, this review considers a number of cross-national trends in suicide mortality rates. In addition to examining cross-national suicide rates as a function of age and sex, this review examines crossnational trends in specific suicide methods (Hepp et al., 2012; Kõlves & De Leo, 2017; Varnik et al., 2008, 2009), how rates vary based on access to lethal means (e.g., firearms and railways), and how rates vary as a function of economic quality and inequality (Bachmann, 2018; Shah, 2012).
+Method
+Search strategy for suicide mortality data
+In line with prior studies (Bachmann, 2018; McLoughlin et al., 2015; Nock, Borges, Bromet, Cha, et al., 2008), we used two main strategies to obtain cross-national data on suicide deaths in 10- to 19-year-olds: (a) We accessed publicly available data sources of either cross-national or country-specific mortality data, and (b) we conducted a systematic review of empirical studies reporting national or cross-national suicide death data.1 The resulting source for suicide mortality data was the WHO’s Mortality Database (WHO, 2018b). The WHO Mortality Database (last updated May 2018) collects mortality and vitality statistics directly from nations’ civil registration systems across the world and presents standardized mortality data by age, sex, year, and cause of death (coded according to the International Classification of Diseases, 10th revision [ICD-10; WHO, 2016a]). Available national data are categorized by data quality. Although the WHO recognizes 194 member states as of 2016, the completeness of data coverage varies by country, and several countries do not submit mortality statistics to the WHO. Developed countries more consistently report annual and complete data than developing nations, which often submit partial data covering subnational regions.
+We restricted our use of the WHO database in three ways. First, we only included countries that had data available since 2010 in order to provide the best estimate of recent suicide rates. Data for the most recent year available for each country were included in this review. Second, since the present review focused on examining suicide death data in detail, we only included data for countries that were evaluated as ‘high’ quality (e.g., identifying ICD codes for the vast majority of suicide deaths), and excluded countries with ‘medium’ and ‘low’ quality data. Death registration data quality
+classifications are based on three indices: (a) whether mortality data is submitted by ICD code, (b) whether mortality data has been submitted for multiple years, and (c) average usability of data submitted since 2007 (WHO, 2018a). Usability scores account for the proportion of reported deaths that are assigned to a poorly defined ICD death code. As of 2016, a nation’s data are considered ‘high’ quality if that nation has supplied at least 5 years of data since 2007 that have achieved an average usability score of 80% or higher. A classification of ‘medium’ quality denotes that a nation’s mortality data have an average usability score between 60% and 80%. ‘Low’ and ‘very low’ quality indicate usability scores below 60% and 40%, respectively.
+Based on these criteria, 45 countries were included in this review2 : Africa (n = 1), Asia (n = 6), Europe (n = 28), North America (n = 6), Oceania (n = 2), and South America (n = 2; see Table 2). For each country, we extracted suicide mortality data for 10-to to 19-year-olds using the WHO Cause of Death Query Online (CoDQL) tool. When available, we also extracted the following suicide mortality data: (a) age group: 10- to 14-year-olds and 15- to 19-year-olds, (b) sex: male and female, and (c) suicide method. WHO data provide method of suicide death based on the International Classification of Diseases, 10th revision (ICD-10; WHO, 2016a) codes X60-X84 indicating selfinflicted death3 (see Data Analysis section).
+Population data
+Population estimates were extracted from the United Nations (UN) Population Division’s World Population Prospects 2017 database (UNPD, 2017), which provides population data for all countries included in this review. The World Population Prospects database compiles national census data and data from specialized population surveys to provide population estimates by country, age, and sex (UNPD, 2017). Given that several countries do not report population estimates directly to the WHO, the WHO Mortality Database only provided total population data for approximately half of the countries included in this review. To calculate age-standardized death rates for nations that do not regularly report population data together with vital registration data, the WHO collaborates with the UN Population Division to collect global health statistics and population totals (WHO, 2018a). When possible, WHO population estimates and UN estimates were compared by year and age and were found to be nearly identical.
+Access to lethal means
+Data on access to lethal means were obtained from publicly available datasets. For train- and firearm-related suicide death, access to means was operationalized as density of means (i.e., railways and firearms), either per geographic area or persons.
+Railway density data (km of lines per 1,000 km2) per country were obtained from the International Union of Railways (International Union of Railways (UIC), 2016). Countries were categorized into one of seven density ranges (lowest density = 0-5 km of lines per 1,000 km2; highest density > 75 km of lines per 1,000 km2). Although railway density information was not obtained for the same year as the mortality data, these estimates have increased only marginally (3.6%) over the past decade (UIC, 2016) and thus remained relatively stable during the period of data collection for this study.
+Firearm accessibility was measured as the estimated number of civilian firearms per 100 persons, with data for each country obtained from the Small Arms Survey (Small Arms Survey, 2018). It is important to note that although the proportion of suicides deaths via firearm is sometimes used as aproxy for gun ownership (Alvazzi del Frate & Pavesi, 2014), the Small Arms Survey did not use suicide by firearm in
+calculating rates of civilian firearm ownership per country (Karp, 2018). Therefore, the firearm access and suicide death by firearm variables are independent, allowing their association to be examined. However, unlike railway estimates, firearm density has increased significantly over the past decade (estimated increase of 32% from 2006 to 2017 due to enhanced research methods and increased civilian holdings; Karp, 2018) and therefore was not a stable estimate over the data collection period for this study.
+Urban population data (percentage of a country’s total population living in urban areas in 2018) were obtained online from the United Nations Population Division (UNPD, 2018). These data were used as a proxy for access to tall structures, or heights, for jumping.
+Economic quality and inequality
+Included countries were classified by economic level according to the World Bank Income Groups (2019). These groupings are determined by the gross national income (GNI) per capita, reflecting the average income of a country’s citizens. Groups are defined as high-income (>$12,506 in US Dollars), uppermiddle-income ($3,896-$12,055), lower-middle-income ($996-$3,985), and low-income (<$995) (World Bank Group, 2019). Previous studies have used the World Bank Income Groups to examine how a country’s economic quality relates to mental health outcomes (Ayuso-Mateos, Nuevo, Verdes, Naidoo, & Chatterji, 2010; Bromet et al., 2011; Nock, Borges, Bromet, Alonso, etal., 2008; Stein et al., 2010). The World Bank Income Group ratings were obtained for the same year as the most recent available WHO mortality data for each country.
+Economic inequality was measured with the Gini index, or Gini coefficient, which measures income distribution in a country and is the most commonly used measure of economic inequality. The Gini index is assessed on a scale of 0 to 1, with 0 representing the least possible amount of inequality and 1 representing the greatest possible inequality in a country (Subramanian & Kawachi, 2004). Prior research has used Gini coefficients to compare how economic inequality relates to a range of psychiatric disorders (Burns, Tomita, & Kapadia, 2014; Cifuentes et al., 2008; Johnson, Wibbels, & Wilkinson, 2015; Yu, 2018). For the current study, Gini coefficients were obtained from the World Bank (2019) for the most recent available year. Although this may not align with the same year as the WHO mortality data for a given nation, Gini coefficients have been relatively stable over time (Li, Squire, & Zou, 1998).
+Data analysis: estimates of suicide mortality
+Pooled estimates. To estimate the pooled suicide mortality rates across all available countries (n = 45), we used the ‘metafor’ R package (Viechtbauer, 2010) to conduct a series of random-effects meta-analyses. Because suicide is a relatively rare event, there were several instances, especially pertaining to subgroups (e.g., females 10-14 years old), for which there were no suicide deaths. To account for the existence of these cases, we used the Freeman-Tukey transformation (Freeman & Tukey, 1950), which allows for proportions that equal 0. We calculated pooled estimates for suicide death by all methods, cross -tabulated by age group (10- to 19-year-olds, 10- to 14-year-olds, 15- to 19-year-olds) and sex (males and females combined, males only, females only). Each analysis produced an estimate of prevalence, which we standardized to prevalence per 100,000 people, as well as a 95% confidence interval for the estimate. The meta-analysis also produced two metrics of heterogeneity: the I2 statistic, which quantifies the percent of variability across cases that is not due to chance, and a Q statistic, which, when significant, reflects a
+high level of heterogeneity between cases (Higgins & Thompson, 2002).
+Estimates by country. For country-level data, we calculated the mortality rate for each country, standardized to suicide deaths per 100,000 people. Given that we were interested in country-to-country differences, we did not use meta-analysis. In line with recommendations for reporting mortality rates (United States Department of Health, 2018) and consistent with prior reviews (Kolves & De Leo, 2017), we excluded from analyses any cell with fewer than 10 events (i.e., suicide deaths).4 Therefore, of the 45 countries with data available for suicide by any method, analyses included anywhere from 10 to 37 countries (M = 21.78 countries, SD = 11.30). When examining estimates by country and by method, there were ultimately fewer countries included due to the possibility that there were 0 suicides by any given method. We calculated statistics cross-tabulated by age group (10- to 19-year-olds, 10- to 14-year-olds, 15- to 19-year-olds) and sex (males and females combined, males only, females only). We also calculated the ratio of suicide mortality rates by males and females.
+Suicide methods. We created higher-level groupings of suicide methods based on ICD-10 codes (WHO, 2016a), leading to a total of nine groups of methods: (a) self-poisoning, including drugs, medications, solvents, gases, and pesticides (codes X60-X69); (b) hanging/suffocation (code X70); (c) drowning (code X71); (d) firearms (codes X72-X74); (e) explosion, fire, steam, or hot objects (codes X75-X77); (f) sharp or blunt objects (codes X78-X79); (g) jumping from a height or jumping/lying in front of a moving object (codes X80-X81), which were combined because counts were too small for each code to be examined separately (referred to collectively as ‘jumping/lying’ from this point forward); (h) motor vehicle (code X82); and (i) other/unspecified methods (codes X83X84). Using meta-regression, we calculated deaths per 100,000 people for males and females together, as well as males and females separately.
+Data analysis: moderators of suicide mortality
+Economic quality. To explore whether suicide rates differed by income group across all countries, we conducted a moderated meta-analysis (i.e., meta-regression with dummy variables) based on the recommendations provided by Viecht-bauer (2010). As with the other meta-analyses performed, because meta-analysis is robust to very infrequent event counts, we included in the analysis any country with available data, even if they did not have more than 10 suicide deaths. Of the 45 countries with income group data, 34 were high, nine were upper-middle, and two were lower-middle. Given that we did not want to have two countries drive the moderated metaanalysis, we combined the lower- and upper-middle-income countries into one ‘middle-income’ group. We were also interested in whether the ratio of male:female suicides differed by income group. To explore this, we conducted a t-test using the male:female suicide death ratio as the outcome and income group as the predictor, in all adolescents (10- to 19-year-olds, among countries with >10 suicides) and 15- to 19-year-olds (also among countries with >10 suicides). We did not examine this relationship for the 10- to 14-year-old group because there were too few countries with more than 10 suicides (n = 8) to make a meaningful inference about the data.
+Economic inequality. To explore whether suicide rates differed by economic inequality, we conducted a set of analyses similar to those for economic quality, but used the Gini coefficient instead of the economic quality group. Because the Gini coefficient, and therefore economic inequality, is a
+continuous variable, these analyses differed from those for economic quality in two ways: (a) The moderated meta-regression did not use dummy codes and (b) we conducted a correlation between Gini coefficients and male:female ratios instead of t-tests.
+Access to lethal means. We examined how suicide methods varied as a function of lethal means access -specifically firearms (number of firearms per 100 people), railways (rail density per 1,000 km2), and access to tall structures (% of individuals residing in urban areas). For each of the three moderators, we calculated a series of moderated meta-regressions for each method (i.e., separate models for each method) across all ages and sexes, rather than separately by age groups and sex in order to avoid potential type I errors as a result of multiple comparisons. In these cases, a significant Q statistic indicated the presence of a moderation effect.
+Results
+Suicide mortality
+Pooled estimates. Table 1 displays the pooled suicide rates for adolescents by age group and sex. The pooled suicide rate across all sexes, 10- to 19-year-olds, was 3.77 per 100,000 (95% CI = 3.15-3.45, I2 = 96.87%, Q = 1,587.92, p < .001). There was considerable heterogeneity across analyses, reflecting the variability in cross-national suicide rates and
+pointing to a need for caution in interpreting these findings.
+Estimates by country. Table 2 displays the country-level descriptives by age group and sex. The rates for all ages (10- to 19-year-olds) inclusive of males and females ranged from 1.31/100,000 people (Israel) to 9.72/100,000 people (Estonia). The rates for 10- to 14-year-olds, both sexes (20/35 countries had <10 cases and were excluded), ranged from 0.28/100,000 people (United Kingdom) to 4.71/ 100,000 people (Kyrgyzstan). The rates for 15- to 19-year-olds, both sexes (0 countries had <10 cases), ranged from 2.30/100,000 people (Israel) to 17.6/ 100,000 people (New Zealand).
+Pertaining to the ratio of males to females who died by suicide, males were more likely than females to die by suicide in all countries except Uzbekistan (where the male:female ratio was 0.95). In all other
+countries across both age groups, the male:female suicide ratio ranged from 1.14 (Sweden) to 2.73 (Italy). A similar pattern was found across most countries when examining 15- to 19-year-olds only; the male:female ratio ranged from 1.21 (South Korea) to 3.13 (Italy), with the exception of Uzbekistan (ratio: 0.87). We do not report ratios for 10- to 14-year-olds because data were only available for both sexes for 7 out of 35 countries. However, for almost all countries where data were available for both sexes of 10- to 14-year-olds, rates were higher among males than females (with the exception of Canada).
+Suicide methods. Figure 1 presents stacked bar charts showing the distribution of suicide methods across countries, for both males and females combined and separately. Several findings were consistent across countries. Hanging/suffocation was the
+most common method of suicide across all countries and for both sexes. With few exceptions (e.g., Estonia, New Zealand, Uzbekistan, Kyrgyzstan, Mexico, and Ireland), jumping/lying was the second most common method of suicide across both sexes. When examining sex differences in suicide methods across all countries with >10 cases, a significant difference was found only for self-poisoning (Q = 21.25, p < .001) such that males were more likely to selfpoison than females. The average self-poisoning mortality rate per 100,000 people was 0.42/ 100,000 for males and 0.21/100,000 for females. (Of note, this study was underpowered to examine self-poisoning by specific substance, such as drugs vs. other substances.) For all other methods, sex differences were nonsignificant (Q = <.0001-2.15 p = .142-.995).
+Moderators of suicide mortality rates
+Economic quality. Results of the moderated metaanalysis showed that pooled estimates did not differ by World Bank Income Group across any of the demographic groups (i.e., overall and by age or sex; Q = 0.02-3.25, p = .071-.895). There was no difference in the male:female ratio for the 10- to 19-year-olds (t = 0.29, p = .786) or the 15- to 19-year-olds (t = 0.75, p = .495).
+Economic inequality. Results of the moderated meta-analysis showed that pooled estimates did not differ by Gini coefficient across any of the demographic groups (i.e., overall and by age or sex; Q = 0.14-1.03, p = .310-.710). There was also no correlation between the male:female suicide ratio for 10- to 19-year-olds and Gini coefficient (r = .29, p = .226). However, there was a significant correlation between the male:female suicide ratio for 15- to 19-year-olds and Gini coefficient such that in countries with more inequality (higher Gini), there was a larger ratio of male:female suicides (r = .55, p = .023).
+Associations between access to lethal means and suicide mortality rates
+Access to firearms. When examining number of firearms per 100 people as a moderator of suicide mortality rates, we found that greater firearm access moderated the rate of suicide due to firearms (Q = 32.40, p < .001) but was unrelated to all other methods of suicide (p = .272-.979).
+Access to railways. When examining access to railways as a predictor of suicide mortality rates, we found significant omnibus tests for hanging/ suffocation (Q = 16.88, p = .009) and jumping/lying (Q = 16.51, p = .011). Omnibus tests for other suicide methods were nonsignificant (Q = 1.98-6.95, p = .326-.921). When examining pairwise post hoc
+comparisons for hanging/suffocation, there were, in general, differences between the lowest and highest densities, with higher rates of suicide due to hang-ing/suffocation in the areas with lower railway density. Specifically, rail density of 0-5 km per 1,000 km2 of lines differed significantly from all higher densities (all p < .001), > 100 km per 1,000 km2 of lines differed significantly from all lower densities (p = <.001-.002), and 5-10 km per 1,000 km2 and 50-75 km per 1,000 km2 differed significantly (p = .037). When examining pairwise post hoc comparisons for jumping/lying, there were significant differences among all pairs (Q = 6.91122.67, all p < .001), where areas of lower rail density had lower rates of suicide due to jumping/ lying than areas of higher rail density.
+Urban population. When examining whether percent of the population in an urban area moderated the suicide mortality rate, we found no moderation for any of the suicide methods (Q = 0.003-2.72, p = .099-.951).
+Discussion
+Our findings replicate and extend prior research in six important ways. First, our review provides an estimated suicide rate for the 10- to 19-year-old period (using WHO Mortality Data from 2010 to 2016) of 3.77/100,000 people. There are two important considerations when interpreting this rate: (a) Considerable heterogeneity was found in suicide mortality rates cross-nationally (which we discuss in the next section), and (b) our analyses included all available high-quality WHO mortality data, but only represent a subset of primarily Western countries worldwide (an issue we discuss in the Limitations section). However, this overall suicide rate among 10- to 19-year-olds is consistent with a prior study (Roh et al., 2018) that found a suicide rate of 3.94/ 100,000 people among 10- to 19-year-olds from 29 Organisation for Economic Co-operation and Development (OECD) countries during the period from 1995 to 2012. In addition, our 15- to 19-year-old rate of 6.04/100,000 people is similar, although lower, than the rate of 7.4/100,000 people found by Wasserman et al. (2005) among older (15-19 years old) adolescents from 90 WHO countries in 1995. Therefore, although these studies included data from different countries over different time periods, rates were relatively consistent for this population.
+Second, at the country level, this review replicates higher suicide rates for adolescents from New Zealand, as well as Estonia and Uzbekistan (both former Soviet Union States) (Bridge et al., 2006; Cha et al., 2018; Kõlves & De Leo, 2016; McLoughlin et al., 2015; Roh et al., 2018; Wasserman et al., 2005). Given that high rates in these regions have been documented for decades, a variety of explanations have been provided, although many have not
+been examined empirically or compared cross-na-tionally.
+High rates of suicide mortality among youth in New Zealand have been recognized as a major public health concern for decades (Associate Minister of Health, 2006). Disproportionately high suicide death rates have been found among youth from indigenous Maori populations, especially young Maori males. This disparity may be partially explained by the socially and economically disadvantaged status of Maori populations in New Zealand, evidenced by the disproportionate number of Maori youth receiving welfare services (Beautrais & Fergusson, 2006).
+Additionally, the elevated suicide rate among Maori youth may reflect the unique effects of colonization experienced by indigenous youth, including cultural alienation and identity confusion (Beautrais & Fer-gusson, 2006). Moreover, New Zealand consistently reports high rates of child abuse and neglect and bullying in school. A longitudinal study of 55,000 New Zealand children (under the age of 18) found that 23.5% had a report about their welfare made to Child Protective Services (CPS) by the age of 17 (Rouland & Vaithianathan, 2018). Adolescents involved with CPS and other social welfare systems were found to be at elevated risk of suicide death
+(Beautrais, 2001). In a 2015 cross-national study of OECD countries, New Zealand reported the second highest adolescent bullying rate of the 51 countries examined, with over 25% of adolescents experiencing some form of bullying multiple times a month, and 18.1% of adolescents met the criteria for ‘frequent bullying’ - more than twice the rate of the 50 other countries surveyed (OECD, 2017a). Government initiatives in New Zealand have aimed to address this elevated suicide risk and improve mental health care (Associate Minister of Health, 2006). However, prevention efforts, including means restrictions, are challenging, as most suicide deaths occur by hanging in private dwellings (Taylor, 2010). Of note, hanging is used more commonly among Maori than nonindigenous populations (Taylor, 2010). A recent review indicated that, although research is growing, few intervention and prevention programs in New Zealand have been evaluated (Coppersmith, Nada-Raja, & Beautrais, 2018).
+Elevated suicide rates were also reported among youth in Estonia and Uzbekistan, consistent with prior studies finding higher rates among youth living in former Soviet Union states (Bridge et al., 2006; Cha et al., 2018; Kõlves & De Leo, 2014, 2016; McLoughlin et al., 2015; Roh et al., 2018; Wasserman et al., 2005). However, unlike New Zealand, little research has explored specific risk factors that contribute to high rates in these regions. Under the Soviet Union, suicide was a classified subject and suicide statistics were kept secret or discarded, delaying and discouraging the emergence of suicide as an acknowledged public health concern in this region (Wasserman & Varnik, 1998). The collapse of the Soviet Union in 1991 led to numerous social, political, and economic difficulties as former Soviet states rebuilt as independent nations. Estonia, in particular, experienced a sharp increase in suicide deaths among its Russian immigrant minority following independence from the Soviet Union as Russian immigrants lost their previously privileged status (Varnik, Kõlves, & Wasserman, 2005). Suicide rates in this region may also be related to the transition from a strict Soviet campaign against alcohol to more lax policies regulating alcohol (Koõlves & De Leo, 2016). Increased accessibility of alcohol led to a higher incidence of alcohol-related suicide deaths among adults (Koõlves, Milner, & Varnik, 2013; Varnik, Kõlves, Vali, Tooding, & Wasserman, 2007), and studies conducted in some former Soviet countries found that changes in national alcohol consumption were linked with fluctuations in mortality rates (Koõlves & De Leo, 2016; Koõlves et al., 2013). More research is needed, however, to clarify how alcohol consumption among adolescents in these countries may contribute to higher rates of suicide among youth.
+A third finding from this review replicates a well-established trend that suicide rates are higher among older adolescents (Koõlves & De Leo, 2017;
+Roh et al., 2018) and young adults (Bridge et al., 2006; Cha et al., 2018) compared with younger adolescents. The age finding is also consistent with research describing the trajectories of suicidal thoughts and behaviors during adolescence, specifically that the onset of suicide ideation typically occurs during early adolescence (around ages 1113) and, for a subgroup of youth, transitions to suicidal behavior during later adolescence (around age 15 or 16; Glenn et al., 2017; Nock et al., 2013). This previous research may suggest the existence of a developmental process (or set of processes) by which adolescents become more capable of engaging in suicidal behavior as they transition from early to later adolescence. Although the nature of these developmental processes remains unclear, there are several key differences between younger and older adolescents that may be relevant to changes in suicide risk (Dervic et al., 2008). First, the rise in suicidal thoughts and behaviors across adolescence coincides with increases in rates of other forms of psychopathology that confer risk for suicide, such as nonsuicidal self-injury, depression, substance use disorders, and certain anxiety disorders (Costello, Copeland, & Angold, 2011; Glenn et al., 2017; Nock et al., 2009). Second, compared to younger adolescents, older adolescents engage in more risk-taking behaviors (Braams, van Duijvenvoorde, Peper, & Crone, 2015), another known risk factor for suicidal thoughts and behaviors (Ammerman, Steinberg, & McCloskey, 2018). Third, older adolescents have more fully developed cognitive facilities than younger adolescents, which may intensify the complexity and severity of maladaptive thinking. For instance, increases in metacognition and abstract reasoning may enhance the ability to ruminate (Papageorgiou & Wells, 2003), and increases in future thinking abilities may facilitate hopelessness (Kosnes, Whelan, O’Donavan, & McHugh, 2013). Thus, types of negative cognition commonly associated with suicidal thoughts and behaviors (Cha, Wilson, Tezanos, DiVasto, & Tolchin, 2019) may become more advanced during older adolescence. Moreover, normative developmental changes in adolescent social networks that make peers more influential may also contribute to increased potential for imitation of risky behaviors, including modeling of suicidal behavior (Pickering et al., 2018). Finally, individuals with histories of multiple suicide attempts are at especially high risk of later suicide death (Kochanski et al., 2018). Beginning at age 17, most suicide attempts are repeat attempts (Goldston et al., 2015); thus, older adolescents may be at increased risk of suicide death due to increased experience engaging in suicidal behavior. Taken together, these observations suggest that higher rates of suicide during older adolescence may be due, at least in part, to other developmental changes during this period.
+Fourth, higher suicide rates were reported among males (4.83/100,000 individuals) compared with females (1.95/100,000 individuals) in this age range. This sex effect is consistent with many prior studies in youth (Bridge et al., 2006; Cha et al., 2018; Kõlves & De Leo, 2014, 2016; McLoughlin et al., 2015; Miranda-Mendizabal et al., 2019; Roh et al., 2018; Varnik et al., 2009; Wasserman et al., 2005) and is also found among adults (Bachmann, 2018; Canetto & Sakinofksy, 1998; Chang et al., 2019; Nock, Borges, Bromet, Cha, et al., 2008; Schrijvers et al., 2012). Higher suicide death rates among males have been attributed to a range of factors, including greater use of lethal means (e.g., hanging and use of firearms; Callanan & Davis, 2012) and higher incidence of risk factors related to suicide death, such as substance use and aggressive and risk-taking behaviors (Bozzay, Liu, & Kleiman, 2014). Although the sex effect (with higher rates among males) was observed in most countries, the main exception to this trend was Uzbekistan (ratio male:female 0.95), where suicide rates between sexes were relatively comparable. In addition, 10 other countries (across North America, Europe, and Asia) had a male:female ratio of <2, which is surprising given prior findings that the suicide rate among males is at least 2-4 times higher than among females (Bridge et al., 2006; Cha et al., 2018; Kõlves & De Leo, 2016; McLoughlin et al., 2015; Roh et al., 2018; Varnik et al., 2009; Wasserman et al., 2005). Taken together with prior studies, these findings suggest that although overall suicide rates are higher among males than females, this sex difference is not uniform cross-nationally nor stable over time. Variation in suicide deaths by sex underscores an important role for cultural factors in suicidal behavior among youth.
+Fifth, this review extends prior work by examining cross-national differences in suicide methods among adolescents. Hanging/suffocation was the most common method of suicide death worldwide among 10- to 19-year-olds, followed by jumping/ lying in front of a moving object or jumping from a height, consistent with prior studies in youth (Hepp et al., 2012; Kõlves & De Leo, 2017; Varnik et al., 2008, 2009). Although hanging/suffocation is also a common method of suicide death among adults, previous research has found that jumping from a height and railway suicide deaths are much more common in youth than adults, and intoxication is less common among youth than adults (Hepp et al., 2012).
+Although certain methods were more common overall, there were also differences between sexes. Males were more likely to die by self-poisoning than females, contrary to prior findings of higher rates of self-poisoning in females compared with males (Hepp et al., 2012; Koõlves & De Leo, 2017). However, a number of studies have found that selfpoisoning by drugs is higher for females, while
+self-poisoning by other substances is higher for males (Rajapakse, Griffiths, Christensen, & Cotton, 2014; Varnik et al., 2008, 2009). Although the current study was unable to examine differences in self-poisoning by specific source, this remains an important direction for future research. Interestingly, there were no sex differences in suicide by firearm, as has been found in previous research (Hepp et al., 2012; Kõlves & De Leo, 2017; Varnik et al., 2009). However, given that suicide death counts were low, the nonsignificant findings should be interpreted with caution.
+Notably, we found that use of particular suicide methods varied based on cross-national differences in access to these methods. Specifically, increased access to firearms within a country was strongly related to suicide death by firearm in that country, but not to suicide death by other methods. However, as discussed in the Methods section, firearm access was not stable over the period of data collection for this study - a notable limitation and opportunity for future research. Nevertheless, these findings highlight the importance of means restriction of firearms in countries with greater firearm access. Improved firearm legislation in New Zealand (Beautrais, Fergusson, & Horwood, 2006) and firearm storage in the United States (Brent et al., 1991; Grossman et al., 2005) have been related to reduced suicide rates. In addition, greater access to railways was associated with jumping/lying in front of a moving object (e.g., train) or jumping from a height. Some successful prevention strategies for railway deaths include use of sliding doors to limit access to rail track and creating deep channels between rails (Krysinska & De Leo, 2008; Pirkis et al., 2015). Moreover, a recent meta-analysis of prevention strategies for ‘suicide hotspots’ (where most deaths were due to jumping from a height) found evidence for reduced suicide rates with means restriction by building fences or rails to limit access (Pirkis et al., 2015).
+Although we were able to measure access to certain suicide methods (i.e., firearms and railways), we were unable to estimate access to methods used for hanging/suffocation - the most commonly reported method among youth. Just as it is difficult to measure access, it is also difficult to restrict access to means used for hanging/suffocation - an issue that has made suicide prevention by hanging/suffo-cation in youth extremely challenging (Sarchiapone, Mandelli, Iosue, Andrisano, & Roy, 2011). This is particularly alarming in light of recent findings from the United States, indicating that suicide deaths by hanging/suffocation are on the rise among youth (Bridge et al., 2015; Sullivan, Annest, Simon, Feijun, & Dahlberg, 2015).
+Sixth, the current review found that suicide rates overall were not significantly associated with the included indices of economic quality and inequality. Although ours is not the only study to find that
+economic indices did not significantly relate to suicide rates (Bremberg, 2017; Vijayakumar, Nagaraj, Pirkis, & Whiteford, 2005), this finding is somewhat surprising in light of converging evidence, suggesting that economic factors have a significant impact on suicidal behavior (Bachmann, 2018). For instance, economic crises, and high unemployment rates in particular (Nordt, Warnke, Seifritz, & Kawohl, 2015), have been broadly linked to suicide deaths (WHO, 2014). In addition, lower socioeconomic indices are associated with suicide attempts across the world (Andres, Collings, & Qin, 2009; Burrows & Laflamme, 2010; Fang, 2018; Ki, Sohn, An, & Lim, 2017). However, less research has examined how economic indices relate to suicide deaths cross-nationally. It is important to note that in the current study, the majority of included countries (93%) were from high- or upper-middleincome groups. Therefore, the null findings may be due to restricted worldwide coverage - an issue we discuss in the Limitations section. Moreover, the included measure of national economic inequality may fail to capture important heterogeneity of inequality between cities within a country (Glaeser, Resseger, & Tobio, 2009).
+Although economic indices were not related to suicide rates in the overall sample, there was a moderate to strong correlation between economic inequality (Gini coefficient) and the male:female suicide death rate ratio among 15- to 19-year-olds; that is, greater income inequality was associated with a higher suicide rate for males compared with females within a country. This finding is consistent with some research, suggesting that economic hardships may be related to poorer mental health outcomes among males compared with females. For instance, in a large cross-national study, Gini coefficients were significantly related to depressive symptoms in males but not females (Yu, 2018). In addition, in the United States, lower-income school contexts have been related to increased suicide ideation and attempts among males but not females (Fang, 2018). Moreover, a study in Denmark found that lower income and unemployment were related to suicide deaths for all adults, but effects were greater among males (Andres et al., 2009). Greater risk among males in poorer economic circumstances may be related to their role as primary income earners for their families in many countries (Mann & Metts, 2017). However, further research is needed to understand the mechanism of risk, particularly among youth, and to suggest potential targets for prevention.
+Limitations and future directions
+Although this review significantly extends knowledge of cross-national suicide trends in youth, limitations of this research warrant discussion. First, this review was limited in its worldwide
+coverage of only 45 (mostly high- and middleincome) countries out of the 194 WHO member countries. There are surprisingly little cross-national data on suicide mortality rates that are publicly available beyond the WHO Mortality Database. Of the countries included in the WHO database, data that are determined to be of ‘high’ quality are predominantly from countries in Europe, North America, Asia, and two high-income countries in Oceania (New Zealand and Australia); coverage of South America and Africa is limited. As a result, the current findings may not accurately estimate suicide rates in youth worldwide, particularly among these underrepresented regions.
+The dearth of good-quality suicide mortality data worldwide may be due to significant underreporting (e.g., stigma) and misclassification of suicidal behaviors (e.g., lack of knowledgeable medical professionals), particularly in countries where suicidal behavior is illegal (Bachmann, 2018; De Leo, 2015; WHO, 2014). Additionally, many countries - including India, China, and the majority of nations in Africa - have not yet developed national death registration systems. As of 2010, <30% of the global population resided in countries with established death registration systems, resulting in lower quality mortality data for these regions (Bhalla, Harrison, Shahraz, & Fingerhut, 2010). The WHO estimates that suicides in countries without goodquality data account for approximately 71% of global suicide deaths annually. Good vitality registration data are disproportionately available for wealthier countries, with high-quality coverage for 95% of suicides in high-income countries but only 8% of all estimated suicide deaths in low- or middle-income countries (WHO, 2014). Greater cross-national coverage is greatly needed to more accurately estimate worldwide suicide mortality rates. Notably, in 2014, the World Bank and the WHO published a global investment plan to increase the number and quality of national civil registration and vital statistics systems (CRVS) in low- and middle-income countries (World Bank & World Health Organization (WHO), 2014). The initiative aims to both strengthen existing national CRVS systems and to catalyze the implementation of new systems by developing model CRVS legislation and expanding training for physicians and other medical staff responsible for registering vital statistics (World Bank & World Health Organization (WHO), 2014)).
+Second, due to low counts, we were limited in our ability to examine all suicide methods. For instance, we had to combine jumping from a height and jumping/lying in front of a moving object. Although both are violent methods, prevention efforts, such as restricting access to means, may be distinct. In addition, we were unable to examine differences in the specific substances used for self
+poisoning. As already noted, there may be important sex differences in overdosing via drugs (higher in females) versus other substances (higher in males) (Rajapakse et al., 2014; Varnik et al., 2008, 2009).
+Third, we examined cross-national trends based on binary female and male sex rather than gender or gender identity. At present, there is a substantial dearth of information regarding suicide death rates among nonbinary, transgender, and gender-expansive youth. Only recently have large-scale, nationally representative studies, such as the USA’s Youth Risk Behavior Surveillance System (CDC, 2019) and the National Violent Death Reporting System (NVDRS; CDC, 2016), begun to consider including gender-expansive demographic characteristics in their measures (CDC, 2017a). Recent reporting data suggest important sex-based differences in suicide rates and forms of suicidal behavior. For example, a report of data from the NVDRS between 2013 and 2015 examining suicide decedents, 12- to 29-year-olds, in 18 states within the United States (Ream, 2019), found that 13% of transgender males and 8% of transgender females' suicide deaths were due to firearms as compared to 55% and 34% of cisgender, heterosexual males and females, respectively (Ream, 2019). However, these data were limited in two key ways: They were not nationally representative, and transgender identity was coded based on information included in reports from law enforcement and medical examiners (Ream, 2019). The latter is a significant limitation given that gender identity is often not listed on death certificates or coroners’ reports (Haas & Lane, 2015; Haas et al., 2010; Ream, 2019). However, data from individual studies conducted in the United States suggest rates of suicide ideation (i.e., thoughts of killing oneself) and suicide attempts (i.e., selfdirected injury with at least some intent to die) may be as high as 31% and 17%, respectively, among transgender youth, compared with 11% and 6% in matched cisgender peers (Reisner et al., 2015). There is little information available for transgender youth in Asian, African, and South American countries (Adams, Hitomi, & Moody, 2017; McNeil, Ellis, & Eccles, 2017). Future crossnational research would benefit from incorporating comprehensive demographic questions surrounding gender and gender identity to estimate the suicide death rate among nonbinary, transgender, and gender-expansive youth.
+Fourth, although this review examined several important cross-national trends, many potential moderators of interest could not be examined (see Cha et al., 2018; for a review of other important sociodemographic factors). For instance, we were not
+able to examine racial and ethnic differences within countries given the limited demographic information provided in the WHO Mortality Database. Examining suicide rates only at the aggregated country level may mask important differences based on race and ethnicity within countries. Illustrative of major differences are high suicide rates among indigenous, or native, youth in countries such as New Zealand (Beautrais & Fergusson, 2006), Australia (Cantor & Neulinger, 2000), Brazil (Coloma, Hoffman, & Crosby, 2006), and the United States (Leavitt et al., 2018). Moreover, high suicide rates have been reported among racial minority youth (e.g., Black children, 5-11 years old, in the United States, Bridge et al., 2015). Differences in youth suicide risk also have been reported as a function of immigrant generation status (Pena et al., 2008). These examples highlight the importance of examining crossnational and intranational trends based on racial, ethnic, and generational status factors in future research.
+Finally, this review provides primarily descriptive and correlational information about worldwide suicide rates in adolescents. Although useful for understanding current cross-national trends, inferences should not be made about causation. Future research is needed to understand how factors such as access to lethal means and economic inequality may directly influence suicide rates.
+In summary, the current review provides an updated estimate of worldwide suicide rates in adolescents, 10-19 years old, using the WHO Mortality Database from 2010 to 2016. Replicating prior research, suicide deaths were overall more common among male and older (15-19 years old) adolescents, hanging/suffocation was the most common method, and highest rates were found in Estonia, New Zealand, and Uzbekistan. This review contributes new information through findings that access to firearms and railways were related to suicide deaths by firearms and jumping/lying in front of a moving object or jumping from a height, respectively. Similar to prior reviews, this study was limited in its worldwide coverage of suicide rates and trends. Important future research directions include expanding the worldwide coverage to more low- and middle-income countries, examining suicide trends among nonbinary gender groups and by race/ethnicity within countries, and clarifying factors that account for cross-national differences in suicide rates.
+Acknowledgements
+The research was partially supported by a grant from the National Institute of Mental Health (L30 MH101616;
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+1.	INTRODUCTION
+The schizophrenia spectrum disorders (henceforth referred to as schizophrenia) are neurode-velopmental illnesses with a lifetime prevalence near 1%; they can cause extensive functional impairment and have for too long carried low expectations for recovery (Lieberman et al. 2013). Only 10-15% of people with schizophrenia are employed, and many remain on disability (Harvey et al. 2012). In 2013, excess total costs of schizophrenia in the United States were estimated at $155.7 billion, including significant direct health care costs but mostly indirect costs related to losses to the labor market (Cloutier et al. 2013). In 2009 the National Institute of Mental Health (NIMH) funded a set of research studies called Recovery After an Initial Schizophrenia Episode (RAISE) in order to build on national and international studies to change this gloomy state of affairs (Heinssen et al. 2017). The RAISE studies contributed to the creation of a new way to organize treatment, called coordinated specialty care (CSC), which has the promise of improving the course of schizophrenia (Dixon et al. 2015, Kane et al. 2016). The creation and dissemination of CSC programs across the United States and the contribution of the RAISE projects can be understood as the intersection of trends in both science and policy that converged to create the foundation for changes in care and care delivery (Dixon 2017a,b). This article discusses the key dimensions of these dramatic changes anchored in the RAISE projects.
+This review is divided into four sections. Section 2 considers the pre-RAISE era, with a focus on the scientific and policy context of the project in the United States: What led to RAISE? Section 3 focuses on the findings of the RAISE studies, including both scientific and policy/service delivery dimensions. We emphasize the RAISE early treatment program (RAISE-ETP) project, which is the large randomized trial of a CSC model (Kane et al. 2016). Section 4 discusses key unanswered questions and challenges in the aftermath of the RAISE studies. Section 5 concludes.
+2.	UNDERSTANDING THE SCIENTIFIC AND POLICY CONTEXT FOR RAISE AND OTHER FIRST-EPISODE PSYCHOSIS SERVICES IN THE UNITED STATES
+2.1.	Policy and Service System Issues
+Early intervention services for psychotic disorders have been implemented in Australia and Northern Europe for over two decades, survived experimental tests for efficacy in Denmark and the United Kingdom, and have since 2000 been part of a national implementation plan in England (Dep. Health 2000, Srihari et al. 2012). The prospect of rapidly providing care after the onset of psychosis is consistent with approaches to other medical disorders, and it presented itself as a “best bet” for many national health care systems that invested in this opportunity even as research and testing of specific models were still underway (McGorry 2012). Why was the United States so late in developing a national strategy for early psychosis?
+The rise of the community mental health movement in the middle of the twentieth century reflected the belief that early intervention would reduce chronic disability for many mental illnesses (Grob & Goldman 2006). This had been a central promise of the moral treatment era in the midnineteenth century, embodied in the rise of asylums, and also a central promise of the mental hygiene movement of the early-twentieth-century progressive era, embodied in the development of psychopathic hospitals and youth guidance centers. Unfortunately, the interventions at each turn of these reform cycles failed to deliver on their promises. By the mid-1970s the community mental health centers were criticized for their failure to sufficiently prevent the severe disability associated with chronic mental illnesses (Gen. Account. Off. 1977, Tessler & Goldman 1982).
+Thus, the disappointment following the initial optimism led to a series of policies that turned away from early intervention and neglected the potential for such treatments and services. It did not help that the clinical and neuroscience evidence at the time did not support moving further. A weak technology does not advance service delivery.
+The mid-1970s critique of the NIMH community mental health center program for its failure to focus on chronic mental illness led the NIMH to support the development of community support programs and system reforms, redirecting public sector attention to improving services for individuals already disabled by mental illness. These individuals, such as people with mostly chronic schizophrenia, became the target population for public mental health systems now in the throes of a community support reform cycle. Individuals with less disabling or early-stage mental illnesses were not targeted, or even eligible, for services (Grob 1994, Grob & Goldman 2006, Tessler & Goldman 1982).
+The lack of public sector priority for individuals in the early stages of psychosis was exacerbated by the fact that the system increasingly relied on Medicaid for funding (Frank et al. 2003). Access to Medicaid for young adults at the peak age of onset for psychosis was dependent on eligibility for the Supplemental Security Income (SSI) disability program. To be eligible one had to be disabled already. Single individuals in the early stages of psychosis typically did not qualify for SSI, and thus they were also ineligible for Medicaid unless they had dependent children and were impoverished. In some states, access to public sector services was difficult for individuals who were not on Medicaid (Goldman et al. 2013).
+Individuals in the private sector were also disadvantaged by insurance rules. They were removed from parental health insurance unless they paid very high COBRA premiums or were full-time students. They lost insurance if they left the workplace due to their illness. The classification of their mental illness as a preexisting condition allowed commercial payers to place them into a new high-risk insurance pool and thereby inflate their premiums or exclude them from coverage
+altogether. In any case, they fell out of the private sector and, as we learned above, also had trouble qualifying for public sector services (Goldman et al. 2013).
+It was not until the Affordable Care Act (ACA) was passed in 2009 that some of those exclusionary rules were weakened, allowing more individuals with a first episode of psychosis to retain insurance coverage (Goldman 2010). Patients gained access to parental and other private sector insurance through new underwriting rules for health insurance exchanges, and they could qualify for Medicaid in states that accepted federal support to extend this entitlement to low-income individuals. There were other reforms in the ACA that provided better services for individuals experiencing the early stages of a psychotic illness. Furthermore, supplements to the federal block grant specifically earmarked for early intervention made more services available (Goldman & Karakus 2014).
+2.2.	The Development and Identification of Evidence-Based Practices for Schizophrenia in the United States: The PORT Initiative
+An important antecedent to the RAISE studies and the dissemination of CSC programs was the identification of evidence-based practices in general. The components of CSC tested in RAISE were based almost entirely on evidence-based interventions for established schizophrenia, applied to the early stages of psychosis. The Agency for Health Care Policy and Research (now called the Agency for Healthcare Research and Quality) began to fund so-called Patient Outcomes Research Teams (PORTs) in the late 1980s and early 1990s in recognition of the fact that many (if not most) treatment decisions in medicine were made without any systematic input from scientific data about efficacy, effectiveness, and cost. The earliest PORTs focused on management of back pain, acute myocardial infarction, and cataracts (Goldberg & Cummings 1994). The first PORT that addressed a mental illness was awarded to investigators at the University of Maryland and Johns Hopkins, and it focused on schizophrenia. The PORT studies attempted to systematically review evidence from relevant clinical studies to make treatment recommendations to clinicians for specific patient populations.
+Subsequently, three sets of PORT recommendations for schizophrenia were published, all of which largely identified recommended treatments based on empirical support rather than expert opinion (Buchanan et al. 2010, Dixon et al. 2010, Kreyenbuhl et al. 2010, Lehman & Steinwachs 1998a, Lehman et al. 2003). In the first set of PORT recommendations (Lehman & Steinwachs 1998a), 18 of the 30 recommendations focused on the use of antipsychotic medications for acute and maintenance treatment. The recommendations identified appropriate dosage ranges and also highlighted the utility of clozapine. Relevant to early psychosis treatment, one of the original recommendations specified that patients experiencing a first acute episode should be treated with dosages in the lower end of the overall recommended range for people with more long-standing conditions.
+Regarding psychologic and psychosocial treatments, the team recommended vocational rehabilitation (for individuals having characteristics associated with good employment outcomes), family support, individual and group therapies consisting of education and cognitive and behavioral skills training, and assertive community treatment (ACT). The first Schizophrenia PORT was also funded to assess the extent to which routine care conformed to evidence-based treatment recommendations; Lehman & Steinwachs (1998b) found that overall conformance to the recommendations was modest (generally below 50%) and higher for pharmacological than for psychosocial treatments. The findings of the initial Schizophrenia PORT underscored the gap between science and practice.
+By the time the second PORT recommendations for schizophrenia (Lehman et al. 2003) were issued, the scientific literature had been able to address the impact of second-generation
+antipsychotic agents, and the specificity and availability of psychosocial interventions had improved. This updated report continued to recommend implementation of ACT and family interventions lasting at least nine months and including illness education, crisis intervention, emotional support, and training in how to cope with illness symptoms and related problems. The update also elaborated on group and individual therapy to include cognitive behavioral therapy (CBT) as the therapy of choice for residual psychotic symptoms. Social skills training was newly recommended. The team also identified supported employment as the service of choice in place of the broader concept of vocational rehabilitation for anyone interested in obtaining employment. It is notable that the second PORT continued to clarify the empirical foundation for future CSC programs.
+The last set of PORT recommendations, published in 2010, continued to highlight the need to use lower doses of antipsychotic medications and to avoid the use of clozapine and olanzapine as a first-line treatment in early psychosis (Buchanan et al. 2010, Kreyenbuhl et al. 2010). The emerging evidence for psychosocial treatments provided more precise information on relevant populations and expected outcomes (Dixon et al. 2010). In addition, these recommendations supported alcohol and substance use services and weight management, given the high co-occurrence of these comorbidities with schizophrenia and the availability of effective treatments. The key elements of treatment for alcohol or drug use disorders for persons with schizophrenia include motivational enhancement and behavioral strategies that focus on engagement in treatment, coping skills training, relapse prevention training, and its delivery in a service model that is integrated with mental health care. Regarding weight loss, a psychosocial intervention that is at least 3 months long that includes psychoeducation focused on nutritional counseling, caloric expenditure, and portion control; behavioral self-management including motivational enhancement; goal setting; regular weigh-ins; self-monitoring of daily food and activity levels; and dietary and physical activity modifications was recommended. This 2010 review did not find sufficient evidence for an overall recommendation of a specific single or multicomponent treatment for early psychosis. However, as discussed below, several preliminary studies found results favoring family interventions, CBT, and supported employment, all in antipsychotic-treated populations. Furthermore, the review reported on evidence from international randomized controlled trials (RCTs) supporting multielement interventions for early psychosis that provide comprehensive packages of psychosocial and medication supports.
+The most recent Schizophrenia PORT identified five published papers on CBT for early psychosis, of which three included actual CBT trials. Three of the five papers came from a UK longitudinal study named the Study of Cognitive Reality Alignment Therapy in Early Schizophrenia (SoCRATES) (Lewis et al. 2002, Tarrier & Wykes 2004, Tarrier et al. 2006). In the study, the SoCRATES intervention group received a stage-based, manualized CBT intervention, whereas the control groups received supportive counseling or treatment as usual. The SoCRATES intervention group showed greater improvements on delusions and auditory hallucinations indexes compared to treatment as usual (TAU) and supportive counseling groups, but SoCRATES was only better than TAU on the Positive and Negative Syndrome Scale (PANSS) positive symptom subscale. Moreover, whereas individuals in the CBT and supportive counseling groups appeared to get better faster than those in TAU, medical records indicated that the three groups did not differ significantly on rehospitalization or relapse rates. The fourth CBTpaper reported on a quasi-experimental study conducted in Australia and found no differences between the CBT intervention group and standard care (Jackson et al. 2005). However, the lack of an adequate control sample, a weak CBT intervention, and enriched standard care (offered in the pioneer EPPIC program) limited inferences. The final CBT study compared active cognitive therapy for early psychosis to befriending as part of an early intervention program in the United States. Again, this study found no significant differences between the CBT and comparison groups (Jackson et al. 2008).
+The Schizophrenia PORT identified four published papers on family interventions, of which two were controlled studies. Of the controlled studies, one study that took place in China found greater symptom improvement and lower rates of hospitalization among those who received the family intervention (Zhang et al. 1994). The second controlled study, which took place in the Netherlands, found no differences between individuals receiving the family intervention and individuals not receiving it (Linszen et al. 1996). In the follow-up study, five years later, individuals who had received the family intervention had spent less time living in institutional settings (Lenior et al. 2001, 2002).
+With regard to supported employment, only one study had been identified at the time of the 2010 Schizophrenia PORT. This study evaluated an occupational intervention for early psychosis (Killackey et al. 2008). Specifically, the EPPIC program in Australia randomly assigned patients either to receive individual placement of support (IPS) along with EPPIC’s services for early psychosis or to receive EPPIC services alone. Both groups were followed for six months, and the results indicate that those individuals who received IPS had better employment outcomes.
+In addition to the monotherapies mentioned above, PORT took into account three RCTs across eight publications of comprehensive, multielement, psychosocial treatment programs in Europe. Each of these programs used ACT or an equivalent approach as the treatment structure and enhanced it by including various evidence-based treatments, including CBT, skills training, and psychoeducation. Five of the studies came from the OPUS project in Denmark (Bertelsen et al. 2008; Jeppesen et al. 2005; Kassow et al. 2002; Petersen et al. 2005a,b), two came from the Lambeth Early Onset (LEO) project in the United Kingdom (Craig et al. 2004, Garety et al. 2006), and one came from a small RCT in Norway (Grawe et al. 2006). Notably, these studies were pragmatic in design: They tested ecologically relevant interventions in real-world samples and measured a range of salient outcomes across clinical (relapse, remission, rehospitalization), functional (social, education, and employment) and economic (cost) domains. Notably, these second-generation studies (Srihari et al. 2012) went beyond establishing efficacy for single-component interventions (e.g., CBT) to test comprehensive models of care that responded to the diverse needs of patients and families presenting for care. Positive outcomes compared to usual care were reported over follow-up periods of up to two years across these domains. Notably, as discussed below, these improvements were not sustained when individuals were assessed three years after being discharged from these specialized services to usual care (Bertelsen et al. 2008, Gafoor et al. 2010).
+In summary, research conducted in other countries had made great strides in demonstrating the effectiveness of comprehensive early intervention services (Srihari et al. 2012). These countries did not have in place policies, such as those in the United States, which impeded the development and widespread dissemination of these services. Notable public sector pioneers in the United States included Oregon’s EASA program (established in 2001; http://www.easacommunity.org/), Massachusetts’s PREP (2003; Caplan et al. 2013), North Carolina’s OASIS (2005; Uzenoff et al. 2012), San Francisco’s PREP (2006; http://felton.org/ social-services/early-psychosisschizophrenia-prep/), and Connecticut’s Specialized Treatment Early in Psychosis (STEP) program, which launched the first US pragmatic RCT of teambased care for early psychosis (2006; Srihari et al. 2009). All these programs were serving early psychosis patients, beginning to move beyond delivering standard psychopharmacology trials and toward delivering different types of comprehensive treatment models. Thus, by the first decade of the millennium, there was an emerging foundation for an evidence-based approach for first-episode psychosis. The United States had clearly lagged behind many other countries in developing the clinical and policy context necessary to launch such programs. The time was ripe for the RAISE studies to tackle this situation and build on the evidence base that international colleagues had developed.
+3.	THE CREATION AND RESULTS OF RAISE PROJECTS
+The NIMH RAISE initiative aimed to develop and test an intervention that would engage individuals with early psychosis, improve recovery trajectories, and prevent or limit long-term disability, while reducing the costs associated with psychotic disorders. RAISE supported the development, testing, refinement, and implementation of CSC in real-world, community-based behavioral health centers in the United States. The focus was on the feasibility, effectiveness, and acceptability of the program in real-world settings. Clinicians who were already members of the behavioral health workforce, rather than specially trained research staff, delivered the program after modest amounts of training, within already existing treatment centers, and using preexisting billing/reimbursement structures to pay for the services whenever possible. The RAISE initiative also intended to foster the rapid expansion of CSC services in the community once the studies ended (Azrin et al. 2015). The initiative funded two studies: the RAISE Early Treatment Program (RAISE-ETP) and the RAISE Implementation and Evaluation Study (RAISE-IES). The initial contracts were funded with economic stimulus dollars made available as a response to the Great Recession of 2008.
+3.1.	Key Findings from RAISE-ETP
+The RAISE-ETP was built around the CSC intervention labeled NAVIGATE (Mueser et al. 2015). The four manual-based key interventions were psychopharmacology, for which a computerized prescriber decision support system called COMPASS was developed (Robinson et al. 2018); individual resilience therapy; family therapy/psychoeducation; and supportive employment/education (see http://raiseetp.org for manuals). A cluster-randomized design was employed and involved 34 nonacademic, community mental health centers in 21 states across the United States. Seventeen clinics were randomized to deliver NAVIGATE and 17 clinics were randomized to provide usual care. The staff at the NAVIGATE-assigned clinics were then trained in all four modalities utilizing a variety of tools and techniques. Research diagnostic interviews and major outcome assessments were conducted by blinded, remote, centralized raters using live twoway video. The primary outcome measure was the Heinrichs-Carpenter Quality of Life Scale. A total of 404 first-episode psychosis patients with a mean age of 23 were enrolled (Kane et al. 2015).
+At the two-year follow-up, the NAVIGATE-treated patients did significantly better on the Quality of Life Scale, the Positive and Negative Syndrome Scale, the Calgary Depression Scale for Schizophrenia, the length of time staying in treatment, and the degree of improvement in work/school engagement. There was no significant difference in the rate of hospitalization between the two groups, though rates overall were relatively low (Kane et al. 2016). The median duration of untreated psychosis (DUP) in this sample was 74 weeks (Addington et al. 2015). When the influence of DUP on quality of life outcomes was examined, it proved to have a highly significant moderating effect, with individuals having a DUP shorter than 74 weeks deriving significantly more benefit from the CSC than those with longer DUP (Addington et al. 2015, Kane et al. 2016). These findings further underscore the potential value of reducing DUP.
+RAISE-ETP utilized a computerized prescriber decision support system, which also helped to facilitate evidence-based care (Robinson et al. 2018). Over the two years, the 223 NAVIGATE participants compared to the 181 clinician-choice participants had more medication visits, were more likely to be prescribed an antipsychotic (and also an antipsychotic conforming to NAVIGATE prescribing principles), and were less likely to be prescribed an antidepressant. (As noted previously, at the same time they also had significantly lower scores on the Calgary Depression Scale for Schizophrenia.) NAVIGATE participants experienced fewer side effects and also gained less weight; other vital signs and cardiometabolic laboratory findings did not differ between treatments.
+www.annualreviews.org • The RAISE Initiative 243
+Adherence estimator scores (McHorney 2009) decreased (fewer beliefs associated with nonadherence) with NAVIGATE but not clinician-choice care.
+The recruitment of 404 individuals receiving treatment at community mental health centers across the United States after the onset of a first episode of psychosis also provided a window into the medication histories and medical status of these individuals at the time of referral (Robinson et al. 2015b). A total of 159 patients (39.4% of the sample) were identified as potentially benefiting from changes in their psychotropic prescriptions. Of these, 8.8% received prescriptions for recommended antipsychotics at higher-than-recommended dosages; 32.1% for olanzapine (often at high dosages); 23.3% for more than one antipsychotic; 36.5% for an antipsychotic and also an antidepressant without a clear indication; 10.1% for psychotropic medications without an antipsychotic; and 1.2% for stimulants.
+With regard to medical status (Correll et al. 2014), in 394 of404 patients with cardiometabolic data [mean (SD) age = 23.6 (5.0) years; mean (SD) lifetime antipsychotic treatment = 47.3 (46.1) days], 48.3% were obese or overweight, 50.8% smoked, 56.5% had dyslipidemia, 39.9% had prehypertension, 10.0% had hypertension, and 13.2% had metabolic syndrome. Prediabetes (glucose based = 4.0%; hemoglobin Ajc based = 15.4%) and diabetes (glucose based = 3.0%; hemoglobin A1c based = 2.9%) were less frequent. Total psychiatric illness duration correlated significantly with higher body mass index, fat mass, fat percentage, and waist circumference (all P < 0.01) but not elevated metabolic parameters [except triglycerides to HDL-C ratio (P = 0.04)]. Conversely, antipsychotic treatment duration correlated significantly with higher non-HDL-C, triglycerides, and triglycerides to HDL-C ratio and with lower HDL-C and systolic blood pressure (all P < 0.01). Olanzapine was significantly associated with higher triglycerides, insulin, and insulin resistance, whereas quetiapine fumarate was associated with significantly higher triglycerides to HDL-C ratio (all P < 0.02).
+In patients with first-episode schizophrenia syndrome, cardiometabolic risk factors and abnormalities are present early in the illness and are likely related to the underlying illness, unhealthy lifestyle, and antipsychotic medications, which interact with each other. Given that these risk factors become even more pronounced in chronic psychosis populations, CSC providers are presented with an opportunity to engage in prevention of cardiovascular morbidity and mortality (Srihari et al. 2013). Specific approaches include smoking prevention and cessation, counseling and lifestyle modification to prevent or limit weight gain, preferred use of lower-risk antipsychotics (Tek et al. 2015), routine monitoring, and referral to and coordination of access to appropriate medical care.
+In terms of cost effectiveness, the Net Health Benefits Approach was used to evaluate the probability that the value of NAVIGATE benefits would exceed the program’s costs relative to community care from the perspective of the health care system (Rosenheck et al. 2016). The NAVIGATE group improved significantly more on the Quality of Life Scale (QLS) and had higher outpatient mental health and antipsychotic medication costs. Effectiveness was measured as a one standard deviation change on the Quality of Life Scale (QLS-SD). The incremental costeffectiveness ratio was $12,081/QLS-SD, with a 0.94 probability that NAVIGATE was more cost effective than community care at $40,000/QLS-SD. When converted to monetized quality-adjusted life years (QALY), NAVIGATE benefits exceeded costs, especially at future generic drug prices. Notably, low-DUP and high-DUP patients had a somewhat different pattern of cost effectiveness. Among low-DUP patients, the total costs of NAVIGATE averaged $1,368 per patient per six months less than community care (14.8%; P = 0.72); among high-DUP patients, NAVIGATE showed increased costs of $3,839 (64%; P = 0.05) per patient per six months. The incremental cost-effectiveness ratio (ICER) was calculated as the difference in average annualized total costs divided by the difference in effectiveness (improvement in the QLS from baseline).
+Bootstrap analyses produced an ICER of $1,035/QLS-SD among low-DUP patients, compared to an ICER of $41,307/QLS-SD among high-DUP patients, with wide 95% confidence intervals (CIs).
+RAISE-ETP investigators performed a number of secondary analyses that shed light on some of the core processes and relationships among symptoms in early psychosis. NAVIGATE-treated patients experienced increased perceived autonomy support, which was related to improved quality of life (Browne et al. 2017). NAVIGATE treatment was also associated with a greater increase in participation at work or in school; this difference appeared to be mediated by the use of supported employment and education services. No group differences were observed in earnings or public support payments (Rosenheck et al. 2017a). Interestingly, obtaining benefits was predicted by more severe psychotic symptoms and greater dysfunction and was followed by increased total income, but it was also associated with fewer days of employment and reduced motivation (e.g., sense of purpose, greater anhedonia) (Rosenheck et al. 2017b). At the same time, during the first year of NAVIGATE treatment, tests of the bidirectional associations between motivation and social and occupational functioning suggest that motivation contributes to better occupational functioning but not better social functioning. Higher social functioning, on the other hand, predicted increased motivation. This suggests that improving occupational functioning in this population may benefit from targeting patient motivation directly (e.g., through motivational interviewing) or indirectly (e.g., by improving relationships and support networks) (Fulford et al. 2017).
+Overall, the RAISE-ETP project demonstrated that CSC could be delivered at a range of community mental health centers, and that such care was associated with significantly better outcomes in a number of different domains. Health economic analysis also indicated that overall the intervention was cost effective (Rosenheck et al. 2016). These results provided further encouragement to national efforts to make CSC more broadly accessible to patients (and their families) experiencing a first episode of schizophrenia.
+3.2.	Key Findings and Products of RAISE-IES
+The RAISE-IES study was initiated as an RCT comparing the RAISE connection model—what we would now call a CSC—to case management plus usual care. However, NIMH redirected the project in 2010 to other tasks, as described below. First, the program was implemented in two sites, recruiting a total of 65 individuals and following them for up to two years. Participants had reduced symptoms and improved social and occupational functioning over time (Dixon et al. 2015). Processing speed was identified as a significant moderator of improvement in occupational global assessment of functioning; treatment fidelity, engagement, and family involvement were found to be mediators of improvement in occupational and social functioning; and processing speed was identified as a significant moderator of improvement in occupational functioning (Marino et al. 2015). A closer examination of work and school participation revealed that individuals who engaged in vocational activity typically did so within months: 28 participants (43%) engaged in work or school at baseline, rising to 44 participants (68%) reporting vocational activity at some time in the first 6 months and 51 (78%) reporting activity in the first 12 months; only two additional participants began vocational activity after their first year of participation. Almost all participants (N = 59) met with the supported employment and education specialist at least three times (Humensky et al. 2017).
+RAISE-IES also conducted two qualitative sub-studies focusing on engagement of clients and family members (Lucksted et al. 2015, 2017). Four factors were associated with engagement of clients, including tailored care, engagement of family members, attributes of the program, and personal factors. A main factor contributing to engagement was the program’s ability to focus
+on the patients’ goals and to demonstrate that the team cared about helping individuals achieve these goals. Participants found nonclinical services such as those focused on employment and education to be a key facilitator of engagement. Other important components included shared decision making, individualized care, flexibility, and warm and respectful communication from staff (Lucksted et al. 2015). The authors concluded by recommending that teams provide recovery-oriented, flexible services that show compassion and warmth while focusing on patients’ life goals (Lucksted et al. 2015).
+The study of engagement among family members underscored that critical family member experiences of engagement included outreach, communication and support from teams, flexibility within the program model, and individualized treatment. Family members also shared their own challenges to engagement, which included personal responsibilities, lack of time and resources, and balancing the autonomy of their loved one with providing care and support (Lucksted et al. 2017). The authors concluded by recommending that teams provide families with individualized support while also helping them manage the stress related to their members’ experiences (Lucksted et al. 2017).
+The RAISE-IES project developed resources and tools to help administrators and individuals start their own CSC programs, including treatment manuals and program guides (see https://www.nimh.nih.gov/health/topics/schizophrenia/raise/coordinated-specialty-care-for-first-episode-psychosis-manual-i-outreach-and-recruitment.shtml and https://www. nimh.nih.gov/health/topics/schizophrenia/raise/csc-for-fep-manual-ii-implementation-manual_147093.pdf). RAISE-IES devised practical strategies to monitor treatment fidelity (Essock et al. 2015b), created an online interactive tool to estimate costs and resources for early psychosis care across a population (Humensky et al. 2013), and outlined approaches to financing the CSC program (Frank et al. 2015). RAISE-IES also showed it was possible to sustain a long-term program by collaborating with state mental health authorities to fund CSC services (Essock et al. 2015a). As a result, the New York Office of Mental Health (OMH) implemented the OnTrackNY initiative, a statewide first-episode psychosis treatment program which builds on the successful RAISE initiatives in New York State (Bello et al. 2017). This study demonstrated the feasibility of starting and maintaining a CSC program within the US health care system.
+As the RAISE studies were being completed and reports published, the US Congress recognized the value of CSC programs by adding 5% to the community mental health block grant program. This amounted to an additional $25 million for states and federal territories to share. Notably, the legislation required that the monies be used to develop and support evidence-based programs for individuals experiencing early psychosis. The 5% set-aside for CSC programs continued in 2015, and the allocation was doubled in 2016, providing an additional $50 million for states to share to develop CSC programs (Dixon 2017a). In 2008, only a few states had such programs. By 2016, 36 states had begun implementing one or more CSC programs. By 2018, that number will grow to 48 states (R. Heinssen, personal communication).
+4.	THE US LANDSCAPE POST-RAISE: WHAT NEXT?
+The RAISE studies are best contextualized within a two-decade-long international literature that began with observational studies of increasingly mature service interventions and resulted in a growing consensus on the principles that should inform the care of early schizophrenia (Edwards & McGorry 2002). This set the stage for a progression of experimental studies, which were necessary for translating knowledge from research into public health benefit. This project was advanced by a series of pragmatic randomized trials that retained the experimental benefit of minimizing selection bias (via randomization) while also allowing for more realistic samples, interventions,
+and patient-oriented outcome measures (Hotopf et al. 1999). The pioneering OPUS and LEO trials both tested ACT-style services with the ability to provide community outreach as well as high-intensity and well-resourced care (clinician:patient ratios of 1:10 to 1:12), and established the efficacy of comprehensive specialty care services for early psychosis (Srihari et al. 2012). The STEP RCT extended these results with a model of care designed for the constraints of a US public mental health center, with office-based care, limited outreach and clinician:patient ratios of 1:50. This trial demonstrated the effectiveness of CSC in a real-world US setting (Srihari et al. 2015), a finding that was further elaborated by the RAISE-ETP (Kane et al. 2016). Subsequent reports from RAISE have supported cost effectiveness in the United States (Rosenhecket al. 2016), again adding to the similar conclusions of the international literature on societal economic benefit (Alison et al. 2012) and strengthening consensus on the need for policy commitments to support further implementation and refinement of models of care (Fleischhacker et al. 2014, Lieberman et al. 2013). EASA in Oregon and OnTrackNY in New York State provide two examples of the increasing number of states that are attempting to disseminate CSC statewide.
+In this context, the status quo of current care systems in the United States is indefensible. Individuals with new-onset psychosis and their families face unnecessary suffering: delays to care are inordinately long (Addington et al. 2015, Compton et al. 2011), and best-practice services are not routinely available (Dixon 2017a). The stakes are high, with mounting morbidity, premature mortality (Pompili et al. 2011, Schoenbaum et al. 2017), and economic costs (Alison et al. 2012) that only partially measure the true human costs of delayed and inadequate care. This is an important and optimistic moment in US healthcare policy for vulnerable early schizophrenia patients. The community mental health block grant set-asides of 2014 and 2016 seeded the growth of CSC programs. The inclusion of this funding in the recently passed 21st Century Cures Act (http:// docs.house.gov/billsthisweek/20161128/CPRT-114-HPRT-RU00-SAHR34.pdf) has established this modest but important financial incentive within US health care policy, and it offers a backbone upon which the US implementation gap can be closed. Several influential national agencies, including the NIMH, the Robert Wood Johnson Foundation, the National Association of State Mental Health Program Directors (NASMHPD), the Centers for Medicare and Medicaid (CMS), the National Alliance on Mental Illness (NAMI), and Mental Health America (MHA), have supported wider dissemination of specialized models of care for early psychosis. The United States thus appears poised to catch up with implementations of early intervention services in other developed economies.
+Several challenges and questions delimit the potential impact of CSCs on the disease course and overall health of individuals diagnosed with schizophrenia; they will be the focus of this final section. These include both failures to implement what we know and significant knowledge gaps that require research. One way to structure these challenges is to consider the gaps and what patients need before, during, and after CSC is delivered.
+4.1.	Before Coordinated Specialty Care
+The time from onset of diagnosable illness to effective treatment is measured in months to years across mental illnesses in the United States (Kessler et al. 2005); psychotic disorders are no exception, with an average DUP ofover ayear (Addington et al. 2015). DUP has been robustly associated with poor outcomes across health care systems (Marshall et al. 2005, Perkins et al. 2005). These unacceptable delays to care occur during periods of highest risk for self-harm and aggression (Nielssen & Large 2010, Pompili et al. 2011), but they more commonly cause avoidable suffering for the affected youth and their families as they traverse chaotic and disorganized pathways to care. Therefore, maximizing the benefits of CSC requires optimized efforts to identify, refer, and
+promote engagement with CSC treatment as soon as possible after onset. [Notably, ongoing and interleaved research efforts have focused on identifying those at risk and testing approaches to prevent the onset of psychosis (Fusar-Poli et al. 2012, 2014), but consideration of that important task is beyond the scope of this review.] Early psychosis populations in any area can be divided into two groups for outreach purposes: those who are yet to seek help and those who have already come into contact with the health care system but are yet to receive CSC or best-practice care. Each group requires separate attention.
+Multiple attempts across the world to reduce DUP provide a wealth of lessons and some notable successful examples (Lloyd-Evans et al. 2011). The seminal TIPS program demonstrated that multipronged efforts that address lack of awareness (via a public information campaign) and at the same time provide clear direction on how to access responsive services can halve DUP in a large geographic sector (Friis et al. 2005). Several ongoing early detection efforts in the United States funded by a recent NIMH Request For Applications, including a quasi-experimental replication of TIPS (Srihari et al. 2014), will deliver more rigorous information on how to effect early detection and referral. Another project will address identification delays by using standard targeted provider education plus novel technology-enhanced screening and at the same time address engagement delays by using a mobile community-based, telepsychiatry-enhanced engagement team (Carter 2016). Other research studies will test methods to increase community literacy in the Latino population (Lopez 2017) and develop Internet-based strategies to reach young people through social media (Kane 2015). New York City has taken a public health approach and now requires all individuals hospitalized with first-episode psychosis to be identified and reported (https://www1. nyc.gov/site/doh/providers/reporting-and-services/notifiable-diseases-and-conditions-reporting-central.page); the city also offers a critical time intervention model staffed by a peer and a professional, called NYCStart, aimed at enhancing optimal follow-up care (https://www1. nyc.gov/site/doh/health/health-topics/crisis-emergency-services-nyc-start.page). Simon et al. (2017) have developed an algorithm to identify individuals experiencing the first presentation of psychosis using chart reviews and claims. Other strategies will need to reach into jails and prisons as well as schools and other community structures to identify and engage youth who are experiencing the onset of psychosis (Ford 2015).
+4.2.	During Coordinated Specialty Care
+This category subsumes all of the questions we have about how to implement what we know and how to expand our knowledge of what works and for whom. Although CSC has been defined as including specific care components—including medication and primary care coordination, family support and education, case management, psychotherapy, and supported employment and education—there is to date no standard CSC program and no well-validated measure of fidelity, though Addington et al. (2016) have begun this process. Although the RAISE-ETP study and the block grant’s facilitation of the national rollout of CSC have created a vast array of experiences across the chaotic US health care system, systematic knowledge regarding how to deliver CSC in different settings to different populations is lacking. Training of the workforce to deliver CSC and the development of strategies to sustainably finance it are two foundational challenges yet to be met (Dixon 2017a).
+Several approaches are available to help organize the task of spreading evidence-based care models (Aarons et al. 2011). One approach from the Institute of Medicine offers a compelling way to address the challenges of delivering care in the US system, with its myriad regulatory demands, inefficient medical record systems, and limited reimbursement for psychosocial services. Learning health networks have been proposed as a means to engender a collaborative model
+wherein “science, informatics, incentives, and culture are aligned for continuous improvement and innovation ... and new knowledge is captured as an integral by-product of the care experience” (Inst. Med. 2013, p. ix). These, or related approaches, help support CSC implementations, allow knowledge sharing, and maintain quality. This approach to creating a learning community may be employed in the government evaluation of the use of the SAMHSA block grant supplement to support implementation of CSC. Participating sites are convening to evaluate technical assistance, with the added benefit of creating contacts among the various CSC sites across the United States.
+The challenge of refining and improving CSC is no less daunting than the challenge of delivering the current best practices. The knowledge gaps are vast. To name a few, problems with cognition (Revell et al. 2015), substance use (Seddon et al. 2016), and suicidality (Coentre et al. 2017) require further attention. Cognitive remediation is not considered a required component of CSC at this point, but models are being tested and there is some evidence of effectiveness. A recent systematic review of RCTs investigating cognitive remediation after a first episode of psychosis found that one of seven neurocognitive domains showed a significant positive effect (verbal learning and memory), and five others showed borderline significant benefits. There was a significant effect on functioning (0.18; CI = 0.01, 0.36; p < 0.05) and symptoms (0.19; CI = 0.02, 0.36; p < 0.05). The effect of cognitive remediation on functioning and symptoms was larger in trials with adjunctive psychiatric rehabilitation and small group interventions (Revell et al. 2015).
+Although some studies have demonstrated reductions in hospitalization with CSC, hospitalization and psychotic relapse persist, stimulating efforts to improve the utilization of clozapine and long-acting injectable medications. Clinical trials have demonstrated the effectiveness of comparatively low doses of antipsychotic medication in the early stages of schizophrenia, with the majority of patients achieving substantial improvement in psychotic signs and symptoms (Robinson et al. 2015a). At the same time, first-episode patients are potentially more vulnerable to side effects, even with lower doses. In many cases, they are highly ambivalent about taking medication in the first place, so that tolerability and early identification and management of adverse effects become a high priority. The fact that no specific antipsychotic medication has shown to be superior in reducing positive symptoms in first-episode patients underscores the importance of selecting treatments based on tolerability. However, clozapine has shown to be effective when patients have failed two or more adequate trials of other medications, even during the first episode of treatment (Agid et al. 2011).
+The recommendations for longer-term maintenance pharmacologic treatment have come a long way since the earliest controlled trials (Kane et al. 1982) indicating that patients who had recently recovered from a first episode of schizophrenia would benefit from continued antipsychotic medication to reduce the risk of subsequent psychotic relapse. Additional studies confirmed the efficacy of antipsychotic medications in reducing the risk of relapse following a first episode of psychosis (Robinson et al. 2005). However, not allpatients will experience an exacerbation of symptoms following medication discontinuation, though the majority will (Robinson et al. 2005). At the same time, we remain hard pressed to identify the subgroup who might not require such treatment, at least during the early phase of illness. Alvarez-Jimenez et al. (2016) recently reviewed studies of treatment discontinuation in first-episode psychosis, including affective psychosis. They suggest that individuals who do not have a diagnosis of schizophrenia, achieve clinical remission for at least three months, and attain early functional recovery with strong support may be possible candidates for discontinuation of antipsychotic medication accompanied by effective psychosocial interventions. Further, there is a clear need to learn more about the adverse cardiometabolic effects of antipsychotic medications, even as they remain essential tools to manage psychotic symptoms and associated aggression and they can reduce suicidality (in the case of clozapine). Studies of CSC have also demonstrated that there is still a group of nonresponders whose care demands further research.
+Overall, it is important not to permit awareness of the benefits of CSC to prevent consideration of the well-known heterogeneity in early psychosis samples in terms of prognosis (without treatment) and of responsiveness to available treatments and to efforts at early detection. A one-size-fits-all approach based on average effects from even rigorously conducted clinical studies risks over- and undertreating different subgroups and delaying identification of those who are refractory to current best practice. Careful ascertainment of sociodemographic and clinical characteristics can be leveraged in predictive models to allow us to determine what works for whom. Also, emerging knowledge of distinct etiologies and pathophysiologies currently categorized within the schizophrenia spectrum may yield more personalized treatments. Moreover, we need better-validated measures of functional outcome or community adaptation to help define the value of CSC for affected youth and their families, but also society at large. Whereas composite measures such as QALY allow comparisons across medical conditions, these may not be adequately sensitive to meaningful changes in the state of individuals with psychotic illnesses (McCrone 2011). A panel of measures that assess distress, impairment, and disability will likely be necessary to evaluate the societal value of early intervention services and to calibrate the level of policy support for wider dissemination of such services.
+An additional question that inevitably arises when considering the implementation of CSC is to whom it should be offered. As discussed above, the RAISE-ETP study showed much greater benefits for individuals with DUP of less than 74 weeks (Kane et al. 2016). Here, DUP was defined as the time between onset of psychosis and exposure to antipsychotics in a sample in which participants did not have more than six months of total exposure to antipsychotic medication (Addington et al. 2015). DUP varies widely across the many early psychosis studies, which differ in inclusion criteria as well as definitions and assessment strategies for DUP, making cross-study comparisons difficult (Golay et al. 2016). Some CSC programs offer services only to individuals within a specified time of illness onset (e.g., two years) regardless of previous treatment, which will by definition cap the DUP of the individuals served (Bello et al. 2017). There is no evidence to support a DUP after which the CSC model has minimal value over usual care. Three months is a commonly accepted DUP target (Cotter et al. 2017) but the fact that CSC was compiled from treatment known to be effective in chronic schizophrenia suggests such team based, specialty care models may benefit patients later in the illness course.
+Another common question facing policy makers is whether to offer CSC to individuals with affective psychotic disorders such as major depression and bipolar disorder. Arguments against this decision are that the CSC research has largely focused on schizophrenia-type disorders, the benefits of CSC in other psychotic illnesses are less well tested, and the impact of DUP is less clearly delineated. At the same time, other scholars argue that it is very difficult to differentiate these illnesses in youth, and there is not likely any specificity to the benefits of this comprehensive teambased model for all young people with psychosis. A policy framework that focuses on providing evidence-based treatment to youth with behavioral health care disorders at their earliest phases rather than focusing on specific disorders may be the most coherent population-based approach.
+4.3.	After Coordinated Specialty Care
+The issue of patients’ needs after CSC includes a consideration of how long CSC should last and what young people experiencing psychosis and CSC care need in an ongoing way. Followup studies of several CSC RCTs to date, including OPUS and LEO, suggest that the benefits observed at the time of program completion are not sustained 5 and 10 years later (Bertelsen et al. 2008, Gafoor et al. 2010, Secher et al. 2015, Sigrunarson et al. 2013). Interestingly, the TIPS study that focused on early detection did observe greater rates of recovery in the early-detection versus
+usual-detection group after 10 years (Hegelstad et al. 2012). Would extending the duration of CSC programs mitigate the erosion of benefits? The Prevention and Early Intervention Program for Psychoses in Ontario provided extended continuity of lower-intensity care for three additional years after the two-year standard CSC program (Norman et al. 2011). Scholars examining the program found that the improvements observed at two-year follow-up were maintained at five years, with ongoing improvement in global functioning. Chang et al. (2015, 2017) performed an RCT in Hong Kong that compared individuals who had a one-year extension of the two-year CSC program called EASY to individuals who got stepped-down care. Individuals with extended EASY had improved outcomes in numerous domains immediately after the one-year extension (Chang et al. 2015), but there were no group differences one year later (Chang et al. 2017). Another RCT compared individuals who had received two years of OPUS followed by usual treatment with individuals who had received five years of OPUS. Group differences were limited to increased likelihood of remaining in contact with specialized mental health services, higher client satisfaction, and stronger working alliance (Albert et al. 2017). Overall, the treatment extension and follow-up studies do not reveal uniform findings. There are signs indicating that ongoing treatment produces persistent benefits, whereas evidence of the persistence of such benefits after CSC is lacking. There are many possible explanations for these findings, including sampling and attrition issues, variability in the quality of treatments compared, limited implementation of CSC treatment, and variability in DUP, to name a few. More research is clearly needed on the overall optimal length of CSC and what should come next.
+The overall failure to produce sustained benefits in the aftermath of CSC treatment presents the field with an enormous challenge. Alvarez-Jimenez et al. (2013) have developed an online approach called HORYZONS that uses expert moderation and “super-users” (peer moderators) to provide follow-up care to young people as they are completing their course of CSC treatment at the Orygen program. This is being tested in a randomized trial. CSC programs are focusing on developing approaches to step down and follow up, including ongoing vocational and educational supports, family education, and alumni groups (personal communication, T. Sale, EASA). The heterogeneity of responses to CSC demands tailored solutions. Within the US health care system, the separation of CSC programs from the overall delivery system likely hinders the seamless integration of CSC into optimal longitudinal care. The transformation and integration of CSC programs into learning health networks may produce self-correction of CSC practices as systems learn what works and what does not, and it may perhaps contribute to an overall improvement of usual care.
+5.	CONCLUSION
+In the end, evidence is mounting for the positive impact of CSC on a range of outcomes for individuals in the early stages of psychosis. The studies hint at the benefits of early intervention to reduce DUP and to improve outcomes. To some extent, implementing CSC within a system of mental health services increases its capacity to provide evidence-based care for individuals at any stage of a psychotic illness. Earlier treatment means earlier benefits in terms of immediate outcomes but may not improve longer-term outcomes and prevent disability. The ultimate promise of prevention of long-term disability, which has motivated so many of the cycles of mental health service reform in the past, remains elusive. CSC programs have established their value in improving early outcomes; they should be available as standard care for new-onset psychosis and can provide a humane and rigorous platform upon which to build further studies, develop new treatments, and refine the delivery of services. Doing what we know works can thus support ongoing research to answer lingering questions and to avoid paralysis in the face of important uncertainties.
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+INTRODUCTION
+Depression is one of the most frequent causes of disability and lost workdays worldwide, and women are more likely than men to suffer from this common mental disorder (Hasin et al. 2018, Kessler 2003, Marcus et al. 2012). In the United States, women are twice as likely to suffer from depression than men, and approximately 25% of women will be diagnosed with depression in their lifetime (Hasin et al. 2018, Kessler et al. 2003). Depression profoundly affects social functioning and results in lower rates of labor force participation, reduced work hours, and lower earnings (Bland et al. 1988, Jayakody & Stauffer 2000, Lerner & Henke 2008). Such reduction in one’s capacity to be part of the labor force results in the economic deprivation and financial hardship that begin to define poverty, which also includes social, political, and cultural factors (UNESCO 2019), such as age, class, and race/ethnicity.
+In the United States, women are more likely than men to live in poverty and are more likely than men to have food insecurity, inadequate nutritional intake, unstable housing, partner conflict, and other difficulties that affect mental health (Chant 2006, Edin & Kissane 2010). Managing sporadic income and making difficult decisions on purchasing and basic needs also increase cognitive load (often referred to as the amount of attention and working memory required for a task) (Mani et al. 2013). As these concerns preoccupy daily life, cognitive resources available to guide choice, behavior, and emotional regulation are reduced. Such stressors also increase allostatic load or “wear and tear on the body,” as characterized by McEwen & Stellar (1993, p. 2094; see also McEwen 2003), and the neurobiology of regions such as the hippocampus, amygdala, and prefrontal cortex undergoes structural remodeling, altering behavioral and physiological responses and making it even more difficult to function (McEwen et al. 2015, Nasca et al. 2017).
+Another main factor associated with poverty and depression in women is parenting status (England & Sim 2009). Although the intersectionality of depression, parenting, and poverty in women has been acknowledged in the literature and reported across diverse geographical regions, societies, populations, and social contexts, there is limited literature that explores the links among mental health, parenting, and economic stability for women. Moreover, data are limited on how these life-altering factors relate to a broader intervention and policy agenda.
+This review summarizes the mental health and economic literature regarding how maternal depression intersects with intergenerational poverty. We provide a conceptual model asserting that treatment of depression and integration of its treatment into social services systems with employment opportunities can improve work productivity and enhance the capacity to care for one’s family. Finally, this review discusses challenges and recommendations regarding interventions and policies to treat maternal depression in large-scale social services systems.
+The review utilizes three theories from social epidemiology that highlight the relationship between depression and economic status: social causation, social selection, and interactionist. According to the social causation theory, environmental and societal conditions lead to increased risk of depression. By contrast, the social selection hypothesis suggests that individual differences in the likelihood of depression influence the likelihood of employment and impact potential for earnings, making it more likely that a person with depression will be poor. The interactionist hypothesis combines the two theories of social causation and social selection and posits that individual differences influence economic outcomes, which in turn have impacts on depressive symptoms (Conger & Donnellan 2007, Wadsworth & Achenbach 2005).
+We recognize that the causes of depression are multifactorial and include a combination of psychosocial, environmental, genetic, cognitive, and neurobiological factors and that an understanding of the complex and multidimensional nature of poverty and depression is necessary. We also acknowledge the need for public health, population-based approaches that address the fact that on average, women are twice as likely to be diagnosed with major depression compared with men over all ages and nations (Hyde & Mezulis 2020). Addressing genetic and neurocognitive and developmental factors at a population level remains difficult. As such, this review focuses on the research addressing relationships between maternal depression and economic status while considering the social causation, social selection, and interactionist theoretical frames. Included in this review are examples of programs intended to improve economic status and research on the effects of treating depression in low-income pregnant and parenting women, with resultant economic benefits (potential evidence for the social selection theory). The interactionist hypothesis and the policy implications of the reported relationship between depression and economic status specific to low-income mothers also are reviewed and discussed with a call for additional research that can help to establish the optimal sequencing and combination of depression treatment and poverty alleviation interventions and policies.
+Current evidence indicates that all major racial and ethnic groups have reductions in employment associated with poor mental health (Demirhan & Demirhan 2019). Additionally, most of the research cited in this review is based on interventions in and policies of the United States. Although a few studies from other countries are included, addressing global policies is beyond the scope of this article. Compared with other countries, the United States has distinct policies, including employment and economic policies (Cambron et al. 2015). For example, even though parents and caregivers are working longer hours, America’s child poverty rate is twice that of most wealthy countries (Hardy et al. 2018). For these reasons, the conclusions in this review are generalizable within the context of US policy affecting low-income pregnant and parenting women.
+POVERTY AND WOMEN IN THE UNITED STATES
+Women may be particularly influenced by the causes and effects of poverty, and women’s experience of poverty differs from that of men (Aydiner-Avsar & Piovani 2019, Chant 2006). Chant (2006) described three factors that contribute to women’s poverty relative to men. First, women have fewer possibilities to translate work into income because of (a) their extensive responsibility for reproductive, caregiving, and domestic roles, including cleaning, cooking, and child care; (b) the conceptualization of their productive activities as “helping” men; and (c) their concentration within sectors that are either an extension of their reproductive roles (and thus lower paid) and/or within the informal economy (Edin 2000, Edin & Kissane 2010). Second, even when women earn wages, family structures and social norms often interfere and influence women’s decision-making capacity and decisions on how income is used. When women do make economic decisions, they are less likely to make decisions that improve their personal well-being (Edin 2000, Edin & Kissane 2010, Oliker 1995). Third, economic resources that enter the household via women are more likely to be spent on household and children’s needs. In addition to these gender differences, there is evidence that the presence of major depression is more strongly associated with job loss in women than in men (Andreeva et al. 2015, Martínez et al. 2020).
+POVERTY AND PARENTING
+Longitudinal studies with large samples support the conclusion that alterations in the quality of caregiving are one pathway by which poverty adversely impacts child development. Support from friends and family can improve the parent-child relationship in the context of poverty (Elder et al. 1985, London et al. 2004, Lundberg & Pollak 2007, Moore et al. 2017, Perry et al. 2019, Zaslow et al. 2005). A large literature demonstrates that parenting quality in stressful circumstances, such as those of scarcity and ill health, influences children’s biology and behavior. The detrimental effect of poverty in childhood on health and well-being has been widely documented (Aber et al. 1997, Caughy et al. 2003, Wood 2003), and researchers have argued that economic disadvantage increases the chances that children will fail to thrive (Shaefer et al. 2018). However, recent research demonstrates that although poorer households have poorer health, the impact of income is relatively small compared with the impact of the mother’s own health and parenting quality, which plays a much larger role in determining child outcomes (Ciciolla et al. 2017, Luthar & Ciciolla 2015, Perry et al. 2019, Tirumalaraju et al. 2020, Washbrook et al. 2014).
+MECHANISMS LINKING DEPRESSION AND ECONOMIC MOBILITY IN LOW-INCOME PREGNANT AND PARENTING WOMEN
+At the population level, depression has been associated with work absenteeism, impaired work performance, and increased health care costs for employers (Fournier et al. 2015, Mojtabai et al. 2015, Moussavi et al. 2007). Among mothers, depression is the mental health problem most likely to be associated with poverty. Lower-income mothers are more likely to be depressed than higher-income mothers (28% versus 17%, respectively) (Golin et al. 2017), and depressive symptoms are four times more common among lower-income women who are parents than among middleincome mothers (Green et al. 2016).
+Particular to women who are pregnant or parenting, depression and depressive symptoms have also been shown to have a negative impact on the transition from welfare to work (Bailey & Danziger 2013, Danziger et al. 2001) and subsequent lack of employment (Mojtabai et al. 2015, Whooley et al. 2002). In a longitudinal study of 2,235 nationally representative mothers, those who reported a poverty-level income were more likely to have high depressive symptoms than
+the women who were never below the poverty level (Pascoe et al. 2006), and in several samples of parents who applied for social services to aid the poor, close to half of the parents had clinically significant depressive symptoms (Fuller & Kagan 2000, Gupta & Huston 2009, Pavetti et al. 1996, Quint 1994).
+Policymakers are focused on enhancing women’s economic status through increasing employment in the paid workforce as lack of employment has also been found to contribute to an increased risk of major depressive disorder (Dooley et al. 1994, Kessler et al. 1989, Pieters & Klasen 2020). Traditionally, the focus of increasing women’s employment has been on social factors that may impact employment (e.g., child care) and other structural barriers like transportation and flexible scheduling. Factors that impact an individual woman’s ability to participate in the workforce, such as level of education and training, are considered by many policy makers, yet policies and many studies on women’s economic advancement make little mention of psychological difficulties (Cambron et al. 2015). Depression is an important barrier to economic advancement and to willingness to enter the labor force (Mossakowski 2009), and the existence, duration, and age of onset of depressive symptoms may prevent some pregnant and parenting women from leaving welfare for work in a timely manner (López-López et al. 2020). Despite the lack of focus on the mental health problems of women receiving social services for the poor, recent research indicates that women receiving welfare assistance may experience higher levels of depressive symptoms and general psychiatric distress than the general population and that this distress can affect economic self-sufficiency. Psychological factors thus play a critical role in the success of economic and social policy efforts and are often overlooked in the economic opportunity landscape for pregnant and parenting women (Coley et al. 2007, Danziger et al. 2001, Dooley & Prause 2002, Gibson et al. 2018).
+Poverty and depression are likely to be bidirectional in terms of causation and are hypothesized to operate in a cycle that perpetuates poor economic and psychiatric outcomes (Lund et al. 2010). The onset of mental illness may increase the risk of poverty (social selection or drift), and conversely, the experience of poverty may increase the risk of depression (social causation). However, it could be that the cycle of poverty and depression is linked to a third set of factors related to the intersection of poverty, gender, and mental illness, such as exposure to violence, access to treatment and health care, and chronic medical conditions (Ridley et al. 2019). Another hypothesis is that poverty leads to stress and negative affect (social causation) and that, in turn, stress and negative affect increase risk aversion, which could make it more difficult to take the steps needed to escape poverty (Haushofer & Fehr 2014).
+Research on the epidemiology of depression finds a consistent and robust relationship between depression and socioeconomic status as measured by income, education, and employment status (Gariépy et al. 2016). Researchers have argued that this relationship is the result of lower-socioeconomic-status individuals experiencing a greater number of stressful life events and having fewer financial resources to buffer the impact of the stressors (Lorant et al. 2003). In one of the most comprehensive reviews on the topic, Lund et al. (2010) surveyed 115 studies and found that although the direction and strength of the poverty-mental health relationship vary across studies, taken in its entirety, the evidence suggests that some aspects of poverty (e.g., lower education, food insecurity, financial stress, lower socioeconomic status) are consistently related to depression, and the association between depression and other measures of poverty, such as income and employment, is less clear. Specific to depression, those with more assets may be less likely to experience depressive symptoms, as assets provide financial resources that can be used to buffer the impact of stressful life events. Assets also may have a positive effect on depressive symptoms because they reduce economic pressure on individuals and offer more opportunities (Enns et al. 2016, 2019; Rohe et al. 2017).
+www.annualreviews.org • Mental Health and Wealth 185
+Specific to low-income pregnant and parenting women, it is possible that having a job reduces the probability of having depressive symptoms, while the lack of employment results in an increased likelihood of depressive symptoms (Richard & Lee 2019). An alternative interpretation suggests that depressive symptoms may prevent low-income pregnant and parenting women from undertaking the tasks necessary to find employment or that parenting women with depressive symptoms may lack the agency and sense of efficacy needed to take on new challenges. Even after someone has obtained employment, depressive symptoms can play an important role in selfsufficiency outcomes. Some pregnant and parenting women may succeed in obtaining employment but have difficulty keeping their jobs or performing them effectively because of depressive symptoms that interfere with daily functioning (Jayakody & Stauffer 2000) and work-child care balance. Depressive symptoms and disorders affect a woman’s productivity and social functioning: The degree of impairment is statistically comparable to the impairment associated with chronic medical conditions (Aydiner-Avsar & Piovani 2019, Demirhan & Demirhan 2019, Raver 2003). Depression can also play a role in the success of education and job training programs because those suffering from depression are more vulnerable to interpersonal problems and irritability and may experience diminished social functioning (Schless et al. 1974, Seedat et al. 2009, Weissman et al. 1971). In the most severe forms, depression can make job search and work participation impossible. Furthermore, the experience of poverty among low-income pregnant and parenting women means that their children face the related dimensions of disadvantage and the environmental stressors associated with living in poverty.
+Social Causation Hypothesis
+The associations between economic circumstances and depressive symptoms in mothers are well documented, but important questions remain regarding fundamental causal processes. We focus on three hypotheses to frame how depressive symptoms are associated with economic outcomes in women and resultant interventions and policy approaches (Gupta & Huston 2009, Marcus et al. 2012): (a) low economic status causes depression (social causation) (Aydiner-Avsar & Piovani 2019); (b) depression causes low economic status (social selection) (Blane et al. 1993); or (c) there is an ongoing bidirectional bridging relationship between economic circumstances and depression, with each affecting the other (Bruce et al. 1991, Conger & Donnellan 2007, Dohrenwend & Dohrenwend 1969, Schofield et al. 2011).
+According to the social causation model, environmental, sociopolitical, and job loss and income declines precipitate depression. From this perspective, taken at its extreme, if all women were exposed to the same social environments from birth, they would achieve a similar level of economic success. If this hypothesis held true, low-income mothers who faced significant adversity, discrimination, and other stressful circumstances would be likely to develop depressive symptoms. Although the cross-sectional nature of many studies prevents us from disentangling the causal direction of the high rates of depressive symptoms experienced by lower-income women compared with the general population (Morris 2008, Ribeiro et al. 2017, Silva et al. 2016), data on the links between early stressful life experiences and job loss and income declines lend support to the social causation framework. Natural experiments have demonstrated that loss of employment or income reduces mental health (Pierce & Schott 2020) and that large income increases improve mental health (Apouey & Clark 2015, Lindqvist et al. 2020, Wolfe et al. 2012). One randomized experiment in Oregon found that receiving health insurance reduced rates of depression by about a quarter among low-income individuals (Finkelstein et al. 2012). Longitudinal data with controls for individual characteristics and repeated measures allow for an examination of the relationship of depressive symptoms and economic outcomes across time.
+Support for the social causation hypothesis is evident in one of the few longitudinal studies that simultaneously tracked family income, parenting style, and child outcomes using US cohort data as analyzed by Dearing et al. (2004). These authors found that reductions in income were significantly associated with maternal depression in the first 3 years of children’s lives. Furthermore, they observed that it was the stress of poverty that caused depression (rather than the other way around) and that depression was likely to result in harsher and/or more inconsistent parenting.
+Additionally, nationally representative samples found that earning a low income or being unemployed when in a low-income bracket appears to increase risk for depressive symptoms (Dooley et al. 1994). In a sample of low-income women from the Project on Devolution and Urban Change, those who were not employed had a higher risk of depressive symptoms, regardless of whether they received welfare assistance, compared with those who were employed (Polit et al. 2001). In the population-based US National Household Survey on Drug Abuse, low-income mothers had significantly higher rates of poor mental health compared with higher-income mothers, and the percentage of women with major depression in the not-working group was higher than in the working-at-all group (Jayakody & Stauffer 2000).
+Social Selection Hypothesis
+The social selection hypothesis posits that characteristics of individuals, including genetic composition and cognitive and behavioral attributes, predispose some individuals to poor mental health that leads them to reduced earnings and employment over the life course (Mojtabai et al. 2015, Whooley et al. 2002). According to the social selection hypothesis, a mother with depressive symptoms would be unable to obtain stable employment because of her psychological distress, including the motivation or ability to seek a new job, and her resultant lack of earnings would prevent her from escaping poverty (Dooley et al. 2000, Mossakowski 2009).
+Specific to depressive symptoms, the social selection hypothesis posits that clinically significant levels of depressive symptoms may lead to lower earnings and/or increase the likelihood of unemployment. Unemployment and lower earnings could then result in an increase in use of social services programs that provide aid to the poor (Lerner & Henke 2008). For example, Noonan et al. (2016, p. 201) found that the presence of maternal depressive symptoms during the first year of a child’s life “increases the likelihood that children and households experience food insecurity” from 50% to 80% by the time the child is 2 years old. Additionally, Noonan et al. (2016) found that elevated levels of maternal depressive symptoms increased the likelihood of enrollment in social services programs that aided the poor, including the Supplemental Nutrition Assistance Program (SNAP), Medicaid, and Temporary Assistance for Needy Families (TANF). In a representative population-based survey, the National Longitudinal Survey of Youth, mothers rated as at risk of depression on the Center for Epidemiologic Studies Depression Scale (CES-D) (a depressive symptom screening tool) were significantly more likely to enroll in cash assistance for the poor at a 2-year follow-up point compared with mothers with lower CES-D scores at baseline (Dooley & Prause 2002).
+It is also possible that high levels of depressive symptoms in mothers might conversely lead to reduction in the receipt of welfare benefits as depressive symptoms may interfere with a mother’s ability to adhere to requirements, such as employment and training requirements, of social services programs that aid lower-income families. Support of this theory was found in a cross-sectional study where mothers with positive depression screens were more likely to have been sanctioned for not meeting the participation requirements of a welfare program in the past 12 months compared with mothers with depressive symptoms (Casey et al. 2004, Lindhorst & Mancoske 2006).
+www.annualreviews.org • Mental Health and Wealth 187
+Studies of young mothers with children enrolled in the federally funded Head Start program have found that mothers with higher depressive symptoms at baseline reported lower future earnings compared with mothers with lower depressive symptoms at baseline (Raver 2003). In another study of mothers with depressive symptoms participating in 20 federally funded welfare-to-work experimental programs, welfare-to-work programs increased earnings less for the most depressed mothers than for the moderately depressed and the least depressed over a 3-year period (Bloom & Michalopoulos 2001).
+Although not specific to mothers, it is worth noting that recent data from the Avon Longitudinal Study of Parents and Children (ALSPAC) identified the chronicity, recency, and level of depressive symptoms in early childhood and adolescence as predictors of poor educational attainment and low income in early adulthood (López-López et al. 2020). Similarly, in another longitudinal sample of low-income employed mothers, those with high levels of depressive symptoms at the onset of the study had increased odds of unemployment during the subsequent 5 years compared with those not at risk for depression (Whooley et al. 2002).
+Regarding TANF programs that maintain the strictest restrictions on work participation and sanctions for violations of TANF policy, a recent study found that low-income single mothers in receipt of TANF in states with the most stringent work requirements were much more likely to have worse mental health than their counterparts living in states with flexible work requirement and sanction policies (Davis 2019). A 2007 study that focused on low-income mothers with depressive symptoms when they first received welfare found that depressed mothers were less likely to report that they engaged in job search activities compared with those who did not have depressive symptoms at baseline (Zabkiewicz & Schmidt 2007).
+Possible pathways by which mental distress leads to reduced income include an inability to procure skills, training, and social benefits due to diminished energy and higher levels of discouragement (Krueger & Mueller 2011), poor physical health resulting from increased psychological distress (Scott et al. 2016), and a change in family structure or environment (housing instability) due to depression and, subsequently, a reduction in household resources (Cambron et al. 2015). In a study by Whooley et al. (2002), people at risk of depression at baseline were almost twice as likely to have low income (<$25,000 in 1995-1996) 5 years later compared with those without risk of depression. Unfortunately, these results were not disaggregated by gender.
+This research demonstrates the association of depression and low employment. Although depression may overlap with other personal traits and social factors, it is reasonable to expect that effective treatment of depressive symptoms could help women seek and maintain employment and increase earnings (Ridley et al. 2019).
+Interactionist Hypothesis
+The interactionist model conceptualizes reciprocal influences of mental health, wealth, and wellbeing by incorporating both social causation and social selection (Conger & Donnellan 2007). In this context, the term interactionist means bidirectional. Figure 1 provides an illustration of our proposed model to guide research characterizing the links among health (particularly mental health), wealth, and social and economic well-being for pregnant and parenting women. In our model, both (a) characteristics of depressed mothers and (b) social opportunities and threats affect each other in an interactive way.
+Conger & Donnellan (2007) used the term interactionist to describe reciprocal or bidirectional processes, although they did not necessarily examine interactions or statistical techniques of moderation. The authors noted that both individual differences and conditions of the social and economic environment affect economic well-being. They proposed that individual cognitive
+and personality characteristics affect the likelihood of attaining high or low socioeconomic status as an adult. In turn, adults’ socioeconomic status most likely contributes to depressive and other psychiatric symptoms. For the purposes of this review, the model can be used to understand the consequences of an integrated, aligned intervention that improves women’s depressive symptoms and economic status, increases social and economic mobility (e.g., education, employment, social capital), and improves outcomes for their children.
+A bidirectional negative relationship between major depressive disorder and employment has been found in several studies (Andreeva et al. 2015, Dooley et al. 2000, Olesen et al. 2013). In a longitudinal analysis of low-income women participating in an employment-based antipoverty program from 1994 through 1998, Gupta (2006) examined depressive symptoms and earnings for women at two points in time across 3 years. Women who worked more hours and had higher incomes reported a larger decline in depressive symptoms from time 1 to time 2 compared with women who worked fewer hours and had lower incomes (Gupta 2006). In support of the social causation theory, there was a trend in Gupta’s study suggesting that women with lower welfare receipt and higher earnings had lowered depressive symptoms from time 1 to time 2. The social selection hypothesis was also supported because women with lower levels of depressive symptoms at time 1 were more likely than those with higher levels of depressive symptoms to have reductions in welfare receipt and increased incomes over the subsequent 3 years (Gupta 2006).
+Although not focused solely on depression, several studies have examined the impact of interventions for trauma and interpersonal violence in a TANF context. A study by Mascaro et al. (2007) detailed the complicated interaction between mental health and employment through an examination of depressive symptoms in women who had reported interpersonal violence and suicidality and were involved in a trauma intervention. At 6 months and 1 year after completion of the intervention, women who had gained employment were less likely to be depressed on a depression symptom screener compared with women who had remained unemployed or lost their employment. When the authors controlled for baseline employment status, this initial finding was attenuated: Women with high depressive symptoms at baseline were more likely to lose employment and less likely to gain employment over the course of the yearlong study compared with those women with low levels of depressive symptoms at baseline. The importance of examining subgroups of women has been noted in additional studies of women with high levels of trauma symptoms. Exposure to early trauma and adversity was associated with long-term unemployment
+in a sample of low-income women, with the mechanisms that helped explain these associations being depressive symptoms (Cambron et al. 2015). Research on the social stigma associated with receipt of welfare also has highlighted the interactions that occur and can be statistically assessed in the relationships between receipt of welfare, depressive symptoms, and employment. A theoretical body of work on social status and psychological distress has identified a perception or “signal” of low social rank associated with low income as the primary mechanism to increase depression among low-income populations. Recently, Pak (2020) used data from the 2008-2014 Health and Retirement Study to examine depressive symptoms associated with food stamp participation and noted that the stress and stigma of receiving benefits were mechanisms identified in increasing the risk of major depressive disorder for men and not women.
+Overall, the longitudinal research presented supports the interactionist hypothesis, which suggests a cascading effect of economic circumstances affecting depressive status, which in turn affects future economic mobility. Research on the relationship between mental health and economic outcomes could be amplified to test the interactionist hypothesis in understanding how depressive symptoms and economics interact with one another over time.
+INTERVENTIONS BASED ON THE SOCIAL CAUSATION HYPOTHESIS
+In this section, we examine evidence testing the social causation hypothesis to uncover how earnings, history of welfare receipt, employment, and income affect or predict depression and depressive symptoms in pregnant and parenting women.
+Temporary Assistance for Needy Families
+The Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) of 1996 created a shift from a welfare system based mainly on the provision of cash assistance without time limits to one requiring employment and imposing other participation criteria as well as time limits on cash assistance. One major goal of this legislation was to move single parents into the workforce. The changes in federal welfare laws in the United States resulted in a dramatic decrease in the welfare rolls and an increase in single mothers entering the workforce (Danziger et al. 2001, Mueser & Troske 2003, Slack et al. 2007). The shift in welfare laws also catalyzed an examination of structural barriers to employment faced by low-income single mothers. PRWORA was based on the key assumption that most welfare recipients could gain employment, but some researchers noted that depressive symptoms and other psychiatric problems would pose significant barriers to gaining and maintaining employment (Danziger et al. 2001, Hall et al. 2017, Jagannathan et al. 2010, Moore et al. 2017).
+Although welfare receipt provides cash assistance, there is little evidence that the intervention improves mental health. Many studies suggest that women who are current or former recipients of cash assistance have higher levels of depressive symptoms compared with their counterparts who have never received cash assistance (Dooley & Prause 2002). Two larger studies have documented that the effects on depressive symptoms for women when leaving welfare for work appear to be mixed. In the Three-City Study (Coley et al. 2007), which followed close to 2,000 low-income single mothers in Boston, Chicago, and San Antonio across two waves (1999 and 2001), mothers who became employed or remained employed across both waves showed reduced depressive symptoms compared with mothers who left work or never became employed.
+In the second study, the Minnesota Family Investment Program (MFIP), different components of welfare and their effects on family outcomes were examined by researchers prior to the 1996 federal welfare reform legislation. In a cohort of 879 mothers, Gennetian & Miller (2002) found
+that MFIP increased employment rates, decreased poverty, and decreased maternal depressive symptoms. Through an experimental design, the authors found that incremental increases in income for mothers reduced depressive symptoms when compared with a control group that did not receive these increases.
+Employment
+The most rigorously designed experimental studies of policies to increase employment in low-income parenting women have demonstrated little impact on reducing depressive symptoms. Zaslow et al. (2001) reviewed 18 sites and a total of seven experiments across sites prior to the changes made in federal welfare regulations in 1996. Morris (2008) analyzed the same 18 sites several years later and noted that any impact on maternal depressive symptoms depended on both the age of the children in the family and the type of program tested. Women with school-age children demonstrated reduced depressive symptoms depending upon the program, whereas for women with preschool-age children, the programs increased depressive symptoms, but these effects depended partly on the policy tested. Of three programs that offered financial incentives for work (but did not mandate employment), two reduced depressive symptoms and a third had no impact according to follow-up measurements. Among parents of preschool children, programs that emphasized rapid employment were the most likely to increase maternal depressive symptoms (Morris 2008). The latter finding could be partially explained by the fact that the stressors associated with increased employment, such as the need for transportation and child care and maternal concern for the well-being of children, outweigh any beneficial effects of increased earnings on maternal mental health (Chase-Lansdale et al. 2003, Edin & Kissane 2010).
+Increasing Earnings and Wages
+Increased wages from employment appear to lessen depressive symptoms in women. For example, the work trajectories of women participating in an employment-based antipoverty program were categorized across 2 years (Yoshikawa et al. 2006). While controlling for family demographics and work experience, the study showed that those with full-time employment and wages that increased in value reported lower levels of depressive symptoms compared with women in either the parttime low-wage employment or the rapid-cycle (in and out of jobs) groups. Specifically, CES-D scores were on average five points lower in the full-time employed group with wage growth compared with the two other groups of women. Working more hours was also associated with lowered depressive symptoms. Raver (2003) found that mothers’ increased work hours over a period of several months predicted lowered depressive symptoms.
+Strong associations between income and mental health are reported in cross-sectional and longitudinal analyses, but the evidence concerning causal direction is less consistent (Gugushvili et al. 2019, Platt et al. 2016, Zimmerman & Bell 2006). The varied findings are likely due to different samples, measurement of income and depressive symptoms, and consideration of gender.
+Individual Development Accounts
+Individual development accounts (IDAs) have been posited to help the poor (and, specifically, poor women) develop assets, which, in turn, would have a number of economic, social, and psychological benefits for families. IDA programs provide participants with incentives to save for the purchase of specific assets, such as a home, an education, or the development or expansion of a business. If the participants’ savings are used to purchase a program-approved asset, those savings are matched
+with program funds. IDA programs typically require program participants to take both general financial literacy training and asset-specific financial education courses, such as home ownership education or small business management. Federal funding was allocated to support IDA programs with the enactment of the Assets for Independence Act (AFIA) in 1998. The Assets for Independence Program (Mani et al. 2013) is now the largest funding source of IDAs in the United States, with sponsored IDA programs in 49 states and the District of Columbia. Yet few studies have directly examined the impacts of IDAs on depression. One of the largest studies used longitudinal data collected as part of the American Dream Demonstration experiment, in which applicants to a large IDA program were randomly assigned to either an IDA program or a control group (Rohe et al. 2017). Assignment to the IDA program was not associated with reduced depressive symptoms; rather, the value of assets and perceived financial stress were inversely associated with depressive symptoms at 10-year follow-up. Results by gender are difficult to disentangle because the experiment did not specifically focus on women.
+Another type of IDA, which has typically been more targeted at pregnant and parenting women, is a child development account (CDA)—a type of asset-building account created for children at birth. In Oklahoma, primary caregivers of children born during 2007 were randomly offered a CDA (n = 1,358) or no CDA (control group; n = 1,346). Baseline and follow-up surveys measured the participants’ depressive symptoms with a shortened version of the CES-D and found that CES-D scores for the CDA group were significantly lower than for the control group when controlling for baseline CES-D score (Huang et al. 2014). Although often framed as an economic intervention for children, CDAs may improve mothers’ psychological well-being, an effect that may be partially mediated through changes in children’s social-emotional development.
+Earned Income Tax Credit
+The Earned Income Tax Credit (EITC) is a refundable tax credit that has lifted millions of families out of poverty (Simon et al. 2018). The credit provides a subsidy as a percentage of income and thus effectively increases the wages of the working poor. A broad base of research suggests that the EITC improves health outcomes and that its most robust results are seen among single mothers and children (Gangopadhyaya et al. 2019). The specific mechanism for these improvements is a reduction in maternal stress (Simon et al. 2018); other examined pathways include improvements in health insurance coverage and employment for mothers (Gangopadhyaya et al. 2019). Specifically, one study examining the impact of the EITC among lower-income mothers found increases in happiness and feelings of self-worth as a function of EITC receipt (Boyd-Swan et al. 2016). In this study, a reduction in self-reported symptoms of depression was found in married mothers but not in single mothers. Given the overall positive effects on health from the EITC, there have been recent calls to expand its use specific to low-income pregnant and parenting women. Such efforts have called for lower-income pregnant women to become automatically eligible for the EITC (Simon et al. 2018). Extant evidence suggests that expanding the EITC with a focus on mothers may be more likely to improve health than expansions focused on fathers or single men, but this conclusion may reflect a need for more research to uncover whether male health remains unchanged following receipt of EITC (Evans & Garthwaite 2014).
+It also is worth noting that in Canada, there is a Canada Child Benefit (CCB) paid to parents of children aged 0-17 years. Unlike in the United States, benefits do not depend on earned income specifically, so families with no income still qualify for the benefit, and there is a National Child Benefit (NCB) that is province-specific in implementation. Milligan & Stabile (2011) used data on child benefits across province, time, and family type to study outcomes spanning test scores and maternal and child mental and physical health. Their findings suggested that child benefit
+programs in Canada had significant positive effects on both children’s test scores and maternal mental health.
+The research on tax benefits and child benefits indicates broad benefits to maternal mental health and child outcomes in most developed countries. Child benefit programs, as well as social assistance programs that target groups such as single mothers with young children, expand the budgets of qualifying families. Economists note two potential mechanisms through which this increase of the family budget may improve outcomes for low-income mothers and their children. First, families may use the income to purchase more goods and services, including those goods that are valuable in maintaining child well-being and enhancing child development, such as food, clothing, educational resources, andbooks (Mayer 1997, Yeung et al. 2002). Second, indirect effects can occur, such as reduced stress and improved marital and family relations and support, increasing opportunities for employment, which may in turn benefit women and children (Currie et al. 2010, Dooley & Prause 2002, Mascaro et al. 2007).
+Conditional Cash Transfers
+Conditional cash transfer programs aim to reduce poverty by making welfare programs conditional upon a receiver’s actions. Money is transferred only to persons who meet certain criteria. In 2007, the Center for Economic Opportunity of the New York City Mayor’s Office initiated the first conditional cash transfer program in the United States, Opportunity NYC-Family Rewards (hereafter, Family Rewards), which provided assistance to 2,377 New York City families. The program was explicitly modeled after Mexico’s Oportunidades (Aber 2009). The New York City program was privately funded and operated for 3 years (2007-2010) to provide cash rewards in the areas of children’s education, preventive health care, and employment (Riccio et al. 2010). There were two main mechanisms through which it was hypothesized that Family Rewards could improve the health of low-income families. First, through health-related incentives, the program might encourage participating families to increase their use of preventive care services. Second, the increase in family income brought about by the cash transfer could increase the ability of families to invest in healthy lifestyles and reduce financial stress. The main study of Family Rewards compared the outcomes of the Family Rewards participants with those of a control group of 2,372 families. Ninety-four percent of Family Rewards participants were low-income mothers. The experiment led to improvements in health insurance coverage and in mothers’ perceptions of their own health and hope for the future, mainly through improvements in reported financial well-being. Specifically, improved financial well-being explained 32% of the gap in “hope” scores between the intervention and control groups at 42 months, while preventive care use explained 21% of the difference (Courtin et al. 2018).
+Outside of the United States, there is a larger evidence base developing on the indirect effect of cash transfers on poverty alleviation and mental health in women. Overall, this literature has demonstrated that positive “economic shocks” delivered to individuals through cash transfer programs (Haushofer & Shapiro 2016) or through economic transfers and poverty alleviation programs (Banerjee et al. 2015) yield mental health benefits in terms of reduced depressive and anxiety symptoms. In South Africa, Green et al. (2016) andFernald et al. (2008) found no effects of entrepreneurship assistance on depressive symptoms in women. In Fernald and colleagues’ (2008) study, a subgroup analysis suggested that credit access decreased depressive symptoms only among men. Green et al. (2016) hypothesized that the gains women derived from increased economic security were offset by stressors associated with planning, launching, and maintaining a new business.
+Haushofer & Shapiro (2016) randomized Kenyan villages and households to receive unconditional cash transfers (programs that aim to reduce poverty by providing cash without any
+conditions upon the receivers’ actions) of $400 or $1,500 compared with a group that received no money; results showed that the cash transfers had a positive impact on self-reported distress and depressive symptoms among adults. Recipients of the largest transfers also exhibited reduced levels of the stress hormone cortisol. Similarly, Ozer et al. (2011) compared Mexican women who participated in Oportunidades, a government-sponsored conditional cash transfer program, with a matched sample of women not exposed to the program and found that women in the treatment group had lower self-reported depressive symptom scores. The authors also presented evidence that this quasi-experimental effect was mediated by reductions in perceived stress and increases in perceived control.
+Summary of Findings on Interventions Based on Social Causation Hypothesis
+In summary, there is some support for the social causation hypothesis (Gibson et al. 2018, Moore et al. 2017). Longitudinal studies that have used repeated measures designs have found that increases in income from earnings and wages or other mechanisms have led to reductions in depressive symptoms. Yet, results from studies incorporating additional methods are mixed, especially because employment and increased hours and employment-related demands can also increase role strain for pregnant and postpartum women.
+Cash welfare receipt appears to have neither positive nor negative effects on mental well-being for mothers. Other features of employment and social services policy appear to be important. For example, welfare policies that require quick job entry for mothers with young children increase depressive symptoms, perhaps because of child care and other barriers and low-quality jobs. However, at the causal level, the evidence supporting the power of economic factors to change depressive symptoms is still sparse.
+Earnings, employment, and tax and cash transfer policies need further examination to understand their effects on maternal mental health and which subgroups of mothers they are most likely to affect. In other words, what are the mechanisms (mediators and/or moderators) by which economic factors benefit maternal mental health? Some research examining the social causation hypothesis has been conducted globally. In this work, the impact of economic interventions on mental health symptoms and subsequent improvements in economic and social mobility has been examined, and cash transfer programs have been found to reduce depressive symptoms in mothers compared with controls (Samuels & Stavropoulou 2016).
+INTERVENTIONS BASED ON THE SOCIAL SELECTION HYPOTHESIS
+Tests of the social selection hypothesis can be found in studies that examine the impacts of the provision of depression treatment to low-income pregnant and parenting women and examine employment outcomes. While fewer studies exist that have examined economic outcomes of depression treatment for women, a few studies (Booshehri et al. 2018, Brenninkmeijer et al. 2019, Lagerveld et al. 2012) have indicated economic improvement after mental health treatment and provided support for the social selection hypothesis. Yet most of the studies have found that the treatment of depression alone does not substantially improve labor force participation (Bee et al. 2010, Brenninkmeijer et al. 2019, Hollinghurst et al. 2010, Nieuwenhuijsen et al. 2014).
+In one longitudinal study (Simon et al. 2001), persons with major depression were randomly assigned to receive one of three different antidepressants, and improvements in depressive symptoms were significantly related to increased employment after 1 year of treatment. Specifically, those in remission and without depressive symptoms had a higher probability of paid employment
+and missed 10 fewer days of work compared with those with persistent depressive symptoms that met criteria for major depressive disorder. Unfortunately, the data are not available by gender.
+Outside of the United States, Bass et al. (2016) conducted a randomized controlled trial of a group-based economic intervention in the South Kivu province of eastern Democratic Republic of Congo. Bass et al. (2016) investigated the impact ofvillage savings and loans associations on economic, social, and psychological outcomes among female sexual violence survivors (all mothers) with elevated mental health symptoms and impaired functioning. While female sexual violence survivors with mental health symptoms were successfully integrated into a community-based economic program, the immediate program impact was seen only for increased food consumption and reduced experience of stigma. Impacts on depression severity were not realized. Bass and colleagues (2016) suggested that targeted mental health interventions may be needed to improve psychological well-being among women.
+POLICY IMPLICATIONS
+How we conceptualize the association between poverty and depression in pregnant and parenting women has important implications for policy. In reviewing the evidence, we have focused on studies in the United States. Most public policies for low-income mothers have included job and skill training or work requirements. Sometimes policies will also include economic incentives and work supports such as child care and transportation, but few have focused centrally on treatment for depression as a way of helping women to escape poverty.
+One policy response to the social selection hypothesis is to increase access to and coverage of depression treatment (Green et al. 2016) or paid family leave (Ybarra & Noyes 2019), and the available evidence, although still quite limited, suggests that such interventions can improve economic outcomes (Lund et al. 2010). From this perspective, high levels of depressive symptoms among low-income pregnant and parenting women can be framed as a large barrier to overall economic and social mobility for families (Cambron et al. 2015,Jayakody & Stauffer 2000, Miranda & Patel 2005, Radey et al. 2020, Thornicroft & Patel 2014). The addition of employment-directed interventions to clinical interventions for depression has been shown to improve occupational outcomes and depressive symptoms but has not been widely investigated among low-income women (Lagerveld et al. 2012, Nieuwenhuijsen et al. 2014).
+Studies testing treatment for depression offer some evidence that mental health assistance and treatment have positive effects on employment success and earnings, but these studies are not specific to the population of low-income pregnant and parenting women. The findings may apply, but we need additional research to determine their generalizability. For the populations sampled, it appears that reductions in depressive symptoms allow for a greater likelihood of obtaining and sustaining employment, yet for reasons we have outlined we may expect different intervention effects for women.
+While a strong argument can be made for increasing access to and coverage of mental health treatment, the severe shortage in human resources (Patel et al. 2018) makes universal access difficult. For this reason, some have argued (Brownell et al. 2016, Forget 2013, Shaefer et al. 2018, Van Parijs 2004) that it would be beneficial to introduce broad-based poverty alleviation programs such as universal income policies as these could have a positive impact on the mental health of low-income pregnant and parenting women. In other words, these universal, broad-based poverty alleviation programs represent a pathway for indirect effects on maternal mental health. This strategy is based on the social causation hypothesis that poverty leads to mental ill health and thus suggests that investments in poverty alleviation programs can be framed as indirect methods of improving maternal mental health outcomes (Courtin et al. 2020, Topitzes et al. 2019).
+FUTURE POLICY DIRECTIONS
+Overall, these findings support the need for policies that integrate welfare and employment with mental health services for low-income pregnant and parenting women and thus suggest a bidirectional and interactive relationship between income and depressive symptoms for mothers. Yet the two systems of (a) welfare and employment policy and (b) mental health services and health care policy typically operate within different agencies and departments with very little overlap in programs and regulations. The evidence reported here supports the idea that depression can adversely affect employment and income and that improvement in depression positively affects economic opportunities. Specifically, this research suggests that one pathway to employment and higher incomes for low-income pregnant and parenting women is better mental health. Therefore, widely available assistance for mental health, especially for low-income pregnant and parenting women, could provide major contributions to programs designed to increase earnings and incomes.
+Policies designed to increase employment should acknowledge the individual characteristics and barriers faced by low-income pregnant and parenting women. Policies that increase total income for employed mothers are more likely to improve well-being than those that involve simply an exchange of welfare for work (Morris 2008). The concerns reported by working mothers about child well-being that, in turn, lead to increased depressive symptoms speak loudly to the need for public policies that create work supports (adequate child care in particular) and supports to manage the stress associated with balancing multiple demands from new roles, thus enabling mothers to balance work and parenting. Policies to address this issue include paid family leave, child care subsidies, earnings supplements, health insurance, universal access to mental health visits and support groups, and workplaces with sufficient flexibility to allow mothers to deal with family concerns and needs.
+Simply gaining employment is not a remedy that will alleviate economic or mental health burden. In fact, policies that emphasize immediate job entry for mothers with few skills lead not only to increased depressive symptoms but also to unstable employment (Morris 2008).
+MENTAL HEALTH OUTREACH FOR MOTHERS
+Limited evidence is available regarding interventions that address the complex burdens of depression and employment among low-income pregnant and parenting women (Moore et al. 2017). Yet, one such intervention, the Mental health Outreach for MotherS (MOMS) Partnership, has successfully reduced depressive symptoms among overburdened, underresourced pregnant and parenting women. Launched in New Haven, Connecticut, and now being replicated in five other states, the MOMS Partnership offers 8 weeks of cognitive behavioral therapy (CBT) for treatment of depressive symptoms. MOMS uses a model that engages mothers from the community and trains them to codeliver CBT-based interventions alongside clinicians. Importantly, this MOMS model is fully embedded in the TANF system in two states, thus demonstrating the feasibility of the innovative use of government TANF funds to broadly and simultaneously support maternal mental health and economic mobility.
+A recent pilot project in the Washington, DC, TANF system deployed the MOMS 8-week CBT program among two cohorts (n = 36) of pregnant and parenting female TANF participants. TANF staff, consisting of a social worker and a community mental health ambassador (a mother from the local community), were trained to deliver the CBT intervention to TANF participants. Participants completed baseline, midpoint, and endpoint measures to assess depressive symptoms, parenting stress, basic needs, employment, and acceptability. Fidelity of the intervention was tracked via audio recordings of sessions. Results examined from baseline to 8 weeks postintervention demonstrated the acceptability and feasibility of the MOMS approach. TANF participants
+reported being highly satisfied. Depressive symptoms and parenting stress were significantly reduced from the beginning to the end of the intervention, and mothers reported being more able to meet their family’s basic needs from the beginning to the end of the intervention. Additionally, employment (20 hours or more) increased significantly, by 30%, from the beginning to the end of the intervention. Moreover, TANF staff delivered the intervention with high fidelity (Smith et al. 2021).
+A major implication of the MOMS pilot project findings for policy is the value of integrating welfare and employment opportunities with mental health services for pregnant and parenting women. Furthermore, the findings suggest that in addition to the effects of the MOMS CBT group-based treatment and the use of a community mental health ambassador, group-based economic activities, such as those employed by MOMS, may also provide a means of affecting economic outcomes and depressive symptoms for low-income parenting women. In one study (Pronyk et al. 2008), participants reported that having an environment for social connection made them feel supported and connected; such benefits may in turn lead to improvements in maternal mental health. Traditional intervention research for people with common mental disorders living in poverty has focused on alleviating the burden of symptoms through psychotherapy and/or psychosocial programming (Lund et al. 2010). Research now needs to examine more fully the integration of economic and mental health interventions for low-income pregnant and parenting women.
+CONCLUSION
+Since our specific focus in this review is on the mental health of low-income pregnant and parenting women, most of our attention is on the links among poverty, depression, and social and economic mobility for women. It is important to see interventions in these specific areas as part of an ambitious set of policies to reduce poverty itself and to improve outcomes for low-income mothers.
+Our results make at least two important contributions to the literature on poverty, mental health, and parenting specific to women. First, we have summarized the gendered nature of the mental health and wealth relationship and the large body of work showing that poverty and mental health are in fact related in pregnant and parenting women (Lund et al. 2010). A gendered understanding of poverty and depression is crucial for exploring the differing impacts and resultant policy interventions. Women face the dual burden of widespread poverty and heightened risk for depression.
+Second, particular to low-income women who are pregnant and parenting, depressive symptoms have been shown to have negative impacts on the successful transition from welfare to work. Applicants to programs that aid the poor, such as the TANF program, experience depressive symptoms at much higher rates than do members of the general public. Yet the traditional focus in studies of barriers to employment among women has been on structural barriers, including access to child care and transportation, and assessment of individual barriers has often been limited to demographic factors, such as lack of schooling and work experience or physical health limitations. However, depression appears to be an important barrier to economic mobility, and the existence of depressive symptoms may prevent women from leaving welfare for work and remaining employed.
+The mechanisms that might explain this link between poverty and depressive symptoms, however, remain uncertain for low-income pregnant and parenting women (Burns 2015). Additional evidence is greatly needed to guide policy makers. For example, it is likely that raising income levels will affect depressive symptoms in women differently than in men. Women who have the personal resources to access networks of support will be disproportionately helped by raising their income, whereas women who lack access to social capital and supports are likely to be less affected
+by changes in their income and will require additional support to change employment trajectories, parenting practices, and, in turn, child outcomes.
+Research on the relationship between poverty, stress, and parenting must take into account the reciprocal relationships and interdependence between parents and children who are facing adversity together and the particular role of gender in intergenerational poverty and mental health (Gugushvili et al. 2019). Future research needs to focus not only on the overall effects on women but also on the interactive effects of poverty alleviation policies and programs on women and children together.
+Poverty is only one of a number of factors that affect parenting, so it should not be assumed that changes in income (especially minimal changes, such as those that typically result from government initiatives) will necessarily reduce low-income pregnant and parenting women’s depressive symptoms sufficiently to change parenting style (Belsky & Vondra 1989). For example, Fram’s (2003) study of mothers receiving welfare payments in the United States found that social support acted as a buffer against the effect of mothers’ stress and disciplinary practices characteristic of parenting style. Fram (2003) found that when a combination of factors supporting resiliency— more education, more earnings, and better neighborhoods—came together, parenting practices and child outcomes tended to be better. Despite the strong body of research linking poverty to poor child outcomes, there is equally good evidence to show that mothers living in poverty possess strong coping skills in the face of adversity.
+Because psychological factors play a critical role in the success of economic policy efforts, efforts to address depression in parenting women are a potentially important function of welfare and social services receipt that have often been overlooked. In this article, we have reported on findings from a pilot study to embed high-quality depression treatment for mothers into the Washington, DC, TANF system. The MOMS findings suggest that one pathway to employment and higher incomes for low-income pregnant and parenting women is better mental health. Therefore, widely available assessments and interventions for depression and other psychological distress through the TANF system could prove to be a scalable method to help mothers increase earnings and employment and reduce the cycle of intergenerational poverty.
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+INTRODUCTION
+An irony of human nature is that while our survival depends on group living, the mere existence of group categories creates prejudice—a preference for one’s own group or animus toward another and its members—which leads to discrimination, conflict, and the undermining of society itself (Dovidio & Gaertner 2010). How do humans learn to favor some groups over others? Why does merely knowing a person’s ethnicity or nationality affect how we see them, the emotions we feel toward them, and the way we treat them? Answers to such questions are crucial to our understanding of human social behavior. Although the origins of human prejudices are extraordinarily complex—a multilevel mix of history, geopolitics, social structures, intergroup relations, and social identities—our understanding of how prejudice operates in an individual’s mind and behavior has been advanced considerably by the contributions of social neuroscience (Amodio 2014, Kubota et al. 2012).
+Social neuroscience is a field of research that probes the connection between the brain and social behavior. It typically does so from two complementary angles. One angle seeks to understand neural functions as they relate to various social processes, with a focus on the operations of specific neural structures, neurotransmitters, or genes. The other seeks to understand psychological processes by applying knowledge about neural function and the tools of cognitive neuroscience. Research on the psychology of prejudice has benefited most from this second approach; by incorporating models and methods of neuroscience, social neuroscientists have
+made important new discoveries about how humans perceive groups, form and express prejudice, and regulate their intergroup behaviors.
+In this article, we present what has been learned so far from the social neuroscience of prejudice. In the following sections, we describe research on how people perceive groups and categorize their members, how prejudice is learned and represented in the mind, how it relates to judgment, perception, emotion, and behavior, and how its effects may be regulated. Rather than provide an exhaustive list of findings, we take a step back and ask, What has the neuroscience approach revealed, so far, about the psychology of prejudice? In each section, we discuss key social neuroscience findings, consider interpretational challenges and connections with the behavioral literature, and highlight how they advance psychological theories of prejudice.
+SOCIAL CATEGORIZATION: THE ANTECEDENT OF PREJUDICE
+Social interactions are often thought to begin with the perception of a person’s face; yet even this initial perception can be influenced by targets’ social categories and the categorization goals of the perceiver. By investigating the processes involved in social categorization with neural assessments, social neuroscience has produced new evidence for top-down effects of group membership on visual processing while detailing the mechanisms through which social categories influence perception. Here, we describe findings from social neuroscience on how we categorize individuals based on visual cues and how categorization may arise even in the absence of visual cues to group membership.
+The Time Course of Social Categorization
+An essential precursor to prejudice is social categorization (Allport 1954). Although existing behavioral studies suggest that social categorization occurs quickly (Macrae & Bodenhausen 2000), social neuroscience research has helped illuminate the precise time course of social categorization processes (Ito & Bartholow 2009). In particular, research using event-related potentials (ERPs)— patterns of electroencephalographic (EEG) activity linked to a stimulus (e.g., a face) or action, measured with millisecond resolution—has revealed that social categorization involves multiple distinct processes that unfold over the course of just a few hundred milliseconds (Figure 1) (Amodio et al. 2014).
+In an early ERP study of intergroup categorization, Ito & Urland (2003) recorded White participants’ EEG while they viewed pictures of White and Black male and female faces. Although the participants’ task was to classify faces by either their gender or their race, ERPs revealed that regardless of the task, neural activity at approximately 120 ms indicated stronger early neural responses to Black than White faces (see also Kubota & Ito 2007). This initial effect was indicated by the N100 (or N1) ERP component, which reflects early orienting and attention processing in the occipitoparietal and occipitotemporal regions (Clark et al. 1995), perhaps in response to the coarse visual cue of skin tone.
+A similar pattern is observed with the P200 (or P2) component, which reflects goal-directed attention and perceptual matching, and peaks at approximately 180-200 ms over central and frontal scalp sites. The P200 has been shown to differentiate both race and gender, and research suggests that this effect may depend on a perceiver’s implicit and explicit goals (Amodio 2010). Among White participant perceivers, the P200 is typically larger in response to Black than White faces (Ito & Urland 2003); however, research with both Black and White participants has observed larger P200 responses to outgroup faces regardless of race in some studies (Dickter & Bartholow
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+Figure 1
+Event-related potential (ERP) waveforms in response to Black and White faces, viewed by White American participants. The number zero on the x-axis indicates stimulus onset time. The positive (P) and negative (N) deflections in the waveform represent typical ERP components, named here according to their polarity and the approximate poststimulus time (in milliseconds) of their peaks. Negative voltages are plotted above zero on the y-axis, following electrophysiological convention, although ERP waveforms are sometimes plotted with negative voltages displayed below zero. Figure adapted with permission from Amodio et al. (2014).
+2007, Willadsen-Jensen & Ito 2008) but larger P200 responses specifically to Black faces in others (Volpert-Esmond & Bartholow 2019). This pattern suggests that the P200 is responsive to cues that are the most motivationally relevant to a participant in a given situation (e.g., situations in which the presence of Black faces is salient, as opposed to participants’ group membership).
+The race effect on the P200 has been observed even when participants are instructed to attend to a target person’s gender and not their race (Ito & Urland 2003; but see Volpert-Esmond & Bartholow 2019), to a nonsocial feature of a face image (Ito & Tomelleri 2017), or to individuating information (Kubota & Ito 2017), indicating that the P200 is often sensitive to race and a participant’s own goals, despite explicit task instructions. In a study assessing frontal EEG in addition to ERPs in a race priming task, greater left frontal cortical activity—associated with approach motivation and goal activation—predicted larger P200 responses to Black relative to White faces, consistent with the interpretation of the P200 as reflecting goal-directed social categorization (Amodio 2010). Furthermore, the magnitude of this race-P200 effect has been linked to behavioral expressions of implicit prejudice (Amodio & Swencionis 2018) and racial bias in a first-person shooter game (Correll et al. 2006).
+Depending on the task, these activations may be followed by the N200 (or N2; approximately 260 ms), such that White American participants typically exhibit larger N200 responses to White than Black faces (Dickter & Bartholow 2010, Ito & Tomelleri 2017). Although the psychological significance of this effect is not well understood, the N200 has been associated in other work with response selection and conflict processes because it originates in dorsal anterior cingulate
+cortex (dACC) (Folstein & van Petten 2008). The typical finding of larger N200 response to ingroup targets in race categorization tasks may reflect response conflict associated with making an ingroup classification (given the initial tendency to orient to outgroup faces).
+Finally, in some tasks (e.g., the classic oddball task), a P300 (or P3, also a late positive potential; approximately 450-600 ms) is observed. The P300 has been associated with response evaluation, expectancy violation, and endogenous attention (Bartholowet al. 2001, Ito & Bartholow2009) and, in the brain, a distributed set of noradrenergic activations (Nieuwenhuis et al. 2005). However, the P300 is strongly affected by task difficulty, and its late timing—often following the delivery of a categorization decision in behavior—suggests it may reflect an evaluation of one’s response and the updating of task expectations rather than a component of the social categorization process itself.
+Together, ERP studies have begun to characterize the rapid sequence of social categorization processes, beginning as early as 100 ms following face onset and involving stages of category detection, goal-directed attention, classification response selection, and response evaluation (Figure 2).
+Further support for the early detection and categorization of race is suggested by race effects in primary visual cortex (V1), observed in functional magnetic resonance imaging (fMRI) studies. Using multivoxel pattern analysis (MVPA), an analytic technique that uses patterns of brain activity to differentiate between mental states or representations, these studies found that patterns of activity in this region could decode the race of a face (Brosch et al. 2013, Gilbert et al. 2012). In another study, MVPA revealed that an individual’s arbitrary group membership, independent of race, was also able to be decoded in V1 (Ratner et al. 2013). These fMRI results corroborate the early categorization effects seen in ERPs by showing race and arbitrary group detection in V1—the anatomical starting point of the cortical visual stream.
+In some cases, a person may be perceived according to multiple social categories (e.g., race and gender). In this context, fMRI research has begun to reveal the complex and dynamic interplay of top-down and bottom-up processes involved in social perception (Freeman & Johnson 2016). For example, this research has shown that overlap in a perceiver’s mental representation of two social categories (e.g., race and gender) correlates with the degree to which neural patterns linked to each category are activated in the fusiform cortex when viewing a face (Stolier & Freeman 2016). These data suggest that as a face is being encoded, preexisting cognitive representations of social categories in the anterior temporal lobe (ATL) and orbital frontal cortex converge with visual
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+inputs in the fusiform cortex through a rapid iterative process to shape the perception of social category membership. When a single-category decision is required, ambiguity in these representations is resolved with input from the dACC (Stolier & Freeman 2017), which is broadly involved in the detection of conflict and allocation of control (Shenhav et al. 2013). Other research has linked individual differences in neural patterns associated with racial categorization to prejudice (e.g., biased altruism intentions) (Zhou et al. 2020). Together, these findings begin to elucidate the neural and psychological processes involved in the initial perception and social categorization of a person’s face.
+Categorization in the Absence of Visual Cues to Group Membership
+In everyday life, social categorization is highly context dependent (Turner et al. 1994), with particular category distinctions emerging over the course of a perceiver’s experience as their goals and situations change. How do people distinguish ingroup from outgroup members in dynamic environments with other agents and their respective, intersecting group memberships? By some accounts, categorizing people by specific social categories is a byproduct of adaptations that evolved for detecting more general coalitions (Sidanius & Pratto 2012, Pietraszewski et al. 2014). Such accounts suggest that humans need a flexible, common neural code for learning about and representing ingroup and outgroup targets, invariant to the particular social category or features along which group boundaries are drawn (for a review, see Cikara & Van Bavel 2014). On what brain regions would a common neural code rely? And, more importantly, what would be the primary structure of the code (e.g., ingroup versus everyone else; threatening outgroup versus everyone else; distinct codes for ingroup, neutral outgroups, and threatening outgroups)?
+To adjudicate among these competing categorization structures, one fMRI study used MVPA to test whether participants’ neural responses associated with thinking about political partisans (Democrats versus Republicans) could be used to successfully decode whether they were thinking about teammates as opposed to competitors created in the lab (Rattlers versus Eagles) (Cikara et al. 2017). Only two regions were associated with representing the higher-order concepts of us versus them across both political and lab-based groups: the dACC/middle cingulate cortex and the anterior insula (AI). The dACC (referenced above) and AI have been posited as hubs in a salience network that focuses attention on the most relevant internal and external stimuli (both social and nonsocial) in service of selecting the most sensible behavioral response (e.g., freeze, fight, flight) (Menon & Uddin 2010). This pattern of neural representation associated with the ingroup is consistent with the hypothesis that salience—specifically functional significance or evaluation (e.g., Will this person help me or not?)—is the primary dimension distinguishing representations of us and them (Fiske 2018; see, however, Koch et al. 2016). Furthermore, this analysis revealed the structure of this neural code: Classification accuracy across categories was driven predominantly by the correct categorization of ingroup targets, consistent with theories indicating ingroup identity and preference are more central than outgroup processing in group perception and cognition (Balliet et al. 2014, Brewer 1999).
+But how do people resolve the challenge of categorization in the absence of labels or visual cues to group membership? One possibility is that they simply substitute judgments of similarity to one’s self on relevant features (e.g., How did you vote in the last election?). In line with this proposition, neuroimaging studies report that a ventral region of medial prefrontal cortex (vmPFC)—which has been associated with thinking about one’s own as well as similar others’ traits, mental states, and characteristics (Denny et al. 2012, Jenkins & Mitchell 2011)—is also more engaged when people categorize ingroup relative to outgroup members (Molenberghs & Morrison 2012, Morrison et al. 2012).
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+However, in addition to relying on similarity as an input, people’s inferences about social group dynamics may be further improved by integrating information both about how agents relate to oneself as well as how they relate to one another (e.g., How do I get along with Susan? With Doug? How do they get along?). This approach allows perceivers to infer social group structure (i.e., clusters over individuals) (Gershman & Cikara 2020).
+In a series of behavioral experiments framed as learning about strangers’ political issue positions, the degree to which participants were willing to align with one of two agents was affected by the presence of a third agent, who formed a cluster that either did or did not include the participant. Specifically, participants favored Agent B over A when C’s placement created a cluster that put the participant in the same group as Agent B, despite the fact that Agents A and B were equally similar to the participant (Lau et al. 2018) (see Figure 3). In a companion fMRI study (Lau et al. 2020), trial-by-trial estimates of similarity between participants and each individual agent recruited vmPFC and pregenual ACC, in line with previous work. By contrast, latent social group structure-based estimates recruited right AI (which overlapped with a region identified by a nonsocial structure learning task) (Tomov et al. 2018), suggesting that right AI supports domain-general structure representation. Most interesting, however, was that neural signals of social group structure further explained ally-choice behavior, whereas interagentsimilarity signals did not. This suggests that people base their identification of their ingroup more on the structure of the group as a whole than on their own similarity with individual group members.
+Summary: Social Categorization
+Social neuroscience research has significantly advanced our understanding of the social categorization process by delineating its timing and subprocesses in ERP studies and, in recent fMRI research, by addressing the neural and psychological processes through which categorization unfolds in more complex, intersectional social environments. In line with theorizing of intergroup relations on the basis of functional relations (Fiske 2018, Koch et al. 2016), these results suggest that generalized group concepts rely on domain-general circuitry associated with latent structure learning and the encoding of stimuli’s functional significance.
+HOW IS PREJUDICE LEARNED, REPRESENTED, AND ACTIVATED?
+One of the most intriguing findings in intergroup psychology is that prejudiced responses are activated automatically upon encountering a group-based cue—an effect that connects the perception of a group member to the activation of the perceiver’s prejudice (Devine 1989, Fazio et al. 1995). Although this effect has been widely replicated, many questions remain. For example, how are these automatic associations learned? How are they represented in the mind? And how do they affect behavior? Social neuroscience research has shed considerable new light on these issues by integrating theory and methods from neuroscience, particularly as they relate to learning and memory, to address questions about prejudice.
+Although the traditional view in social cognition assumes that intergroup associations are formed and represented in a single semantic network, we now know that human learning and memory involves multiple interacting neurocognitive systems (Poldrack & Foerde 2008, Squire & Zola 1996). A consideration of multiple memory systems is important because it suggests that multiple kinds of information are encoded, beyond semantic knowledge, and that these different kinds of information are expressed in particular channels of behavior. These systems include memory processes addressed in traditional prejudice research, such as semantic (or conceptual) knowledge and associations, as well as others that have only recently been applied to human social cognition and prejudice, such as Pavlovian and instrumental learning (Amodio 2019, Amodio & Ratner 2011a). A sample of these learning and memory systems is shown in Figure 4, along with their respective neural substrates and putative channels of expression. In this section, we describe advances in our understanding of how intergroup bias is learned and represented in the mind,
+based on contemporary neuroscience models of learning and memory, and discuss their implications for how biases may be activated and expressed in behavior.
+An Affective Basis of Implicit Prejudice? The Role of Pavlovian Aversive Conditioning
+An enduring, yet complicated, idea in the social neuroscience of prejudice is that the amygdala underlies implicit prejudice. This idea is complicated because evidence for the amygdala’s role in prejudice is mixed, yet the notion that Pavlovian aversive conditioning—learning to fear a neutral stimulus—could contribute to bias formation remains plausible. The amygdala is a small structure located bilaterally within the temporal poles. Given its critical role in Pavlovian aversive conditioning, it was initially regarded as the neural center of learned fear in both animals and humans (LeDoux & Hofmann 2018). Although this fear center interpretation has since been revised and elaborated (e.g., Holland & Gallagher 1999, LeDoux2012), the idea that the amygdala, and its role in Pavlovian aversive conditioning, could underlie implicit bias remains intuitive and intriguing to prejudice researchers.
+Consider the amygdala’s neural circuitry: Signal of a learned threat can travel from its initial sensation, in the retina or cochlea, to the amygdala via a single synapse, such that the amygdala can initiate a defensive response within approximately 100 ms (LeDoux & Hofmann 2018). Perceptual information enters the amygdala via the lateral nucleus and, if associated with a learned threat, activates the central nucleus, which in turn initiates freezing and heightened vigilance (e.g., potentiated startle) in preparation for fight or flight. This rapid response occurs while more elaborative processing continues in other neural regions—a pattern resembling dual-process accounts of prejudice in which an automatic response proceeds before a more deliberative response can take over (e.g., Devine 1989). These characteristics have several implications for theories of prejudice.
+First, research on the amygdala and aversive conditioning suggests a distinct affective basis for acquiring prejudice, as well as a plausible mechanism to explain the rapid, nonconscious, and unintentional negative responses to racial outgroup members that characterize automatic prejudice. Like most other animals, humans acquire fear-conditioned responses to stimuli (Delgado et al. 2006), including human faces (Ohman & Dimberg 1978), and thus, in theory, this mechanism could also support learned aversions to groups. Some research has attempted to demonstrate a Pavlovian basis of prejudice using prepared fear or reversal learning paradigms (Dunsmoor et al. 2016, Olsson et al. 2005), but these results have been inconclusive regarding a prepared fear to Black faces (among White participants) or have not clearly replicated (Mallan et al. 2009; Molapour et al. 2015; Navarrete et al. 2009, 2012). To our knowledge, research has not yet directly tested the hypothesis that social prejudice can be formed through Pavlovian aversive conditioning.
+Second, an aversive conditioning model of prejudice is useful because it predicts a particular pattern of behavior in human intergroup interactions—that of freezing, anxiety, vigilance, and avoidance. Similar behaviors have been observed in social psychological studies of intergroup interactions; for example, anti-Black prejudice in White participants has been associated with adopting greater physical distance from Black partners (Amodio & Devine 2006, McConnell & Leibold 2001), heightened vigilance (Richeson & Trawalter 2008), nonverbal signs of anxiety (Dovidio et al. 2002, Fazio et al. 1995), and physiological arousal (Amodio 2009, Trawalter et al. 2012). It further explains why intergroup anxiety amplifies implicit prejudice but not implicit stereotyping (Amodio & Hamilton 2012). Hence, an aversive conditioning mechanism of bias, while novel to psychological theories of prejudice, helps to explain a broader range of prejudiced behaviors.
+Third, and more broadly, social neuroscience research positing an aversive conditioning component of prejudice sparked a paradigm shift in social cognitive models of prejudice. Whereas prior theories assumed that prejudice emerges from a single cognitive network of semantic concepts (i.e., stereotype knowledge), conditioned fear involves threat associations, formed through highly arousing aversive experiences, and is expressed primarily in behavior and autonomic arousal. Hence, this research revealed a second mechanism for social learning and prejudice and, by linking the study of prejudice to broader models of learning and memory, pointed to additional mechanisms of social learning and prejudice that had yet to be studied (Amodio & Ratner 2011a, March et al. 2018).
+It is notable, however, that despite the existence of Pavlovian aversive conditioning in humans and its likely role in nonverbal and affective expressions of prejudice, neuroimaging evidence for the amygdala’s role in prejudice has been mixed at best (Chekroud et al. 2014). Indeed, most fMRI studies of race perception have not observed a difference in amygdala response to viewing racial outgroup compared with ingroup members (e.g., Beer et al. 2008; Gilbert et al. 2012; Golby et al. 2001; Knutson et al. 2007; Mattan et al. 2018; Phelps et al. 2000; Richeson et al. 2003; Ronquillo et al. 2007; Stanley et al. 2012; Telzer et al. 2013; Van Bavel et al. 2008,2011). Of those that did, race effects were observed under specific conditions: for example, when Black and White faces were presented very briefly (Cunningham et al. 2004), when participants made superficial rather than individuating judgments (Wheeler & Fiske 2005), or when the target face’s gaze was direct but not averted (Richeson et al. 2008). Other research found that the amygdala effect—greater to Black than White faces—was stronger among African American participants than White participants (Lieberman et al. 2005). Notwithstanding limitations common to early fMRI studies (e.g., small samples, less stringent corrections for multiple comparisons), these instances of positive findings, in which amygdala effects were observed under some conditions but not others, suggest a more complex account of the amygdala’s role in prejudice.
+Research using the startle eyeblink method to assess the amygdala response to racial outgroups has added to our understanding of its role in prejudice. These studies suggest that the amygdala primarily guides attention to race, based on its motivational relevance, especially in situations of threat or anxiety. This perspective stems from the method’s amenability to larger sample sizes and more varied experimental designs, compared with fMRI, as well as its historical roots in research on attention and motivation (Filion et al. 1998). For example, an early study of White participants found greater startle response to Black faces than to both White and Asian faces (Amodio et al. 2003). Although this finding was initially interpreted as revealing an amygdala substrate for prejudice, further analysis suggested that this effect was primarily associated with participants’ anxiety about appearing prejudiced to others (i.e., their external motivation to respond without prejudice), even among people with egalitarian attitudes. Subsequent startle eyeblink and fMRI studies similarly found that amygdala responds not to race per se but to situational factors and task strategies (Brown et al. 2006, Mattan et al. 2018, Van Bavel et al. 2008, Vanman et al. 2013, Wheeler & Fiske 2005). That is, these findings suggest that the amygdala response to racial outgroup members often reflects attention driven by social goals and concerns, such as anxiety about appearing prejudiced and attention to task-specific response cues, rather than the direct threat of an outgroup member (Amodio 2014, Chekroud et al. 2014). Moreover, high implicit prejudice has been linked to greater social concerns about appearing prejudiced (Devine et al. 2002), and this link may explain why higher implicit prejudice has been associated with increased amygdala responses to race in some work (e.g., Phelps et al. 2000).
+In summary, Pavlovian aversive conditioning likely contributes to a specific aspect of prejudice—one that operates automatically, is associated with negative affect, and is expressed in nonverbal behaviors such as freezing and social distancing. However, despite early excitement
+about the possibility that the amygdala underlies implicit prejudice, this idea has not been supported by the fMRI literature. Instead, amygdala activations in intergroup contexts appear to reflect attention to relevant group cues, as determined by one’s social motivations and goals, or one’s anxiety about appearing prejudiced. Nevertheless, the role of the amygdala in prejudice formation remains plausible and ripe for study, as a Pavlovian learning process provides the best account of some forms of intergroup behavior.
+Stereotypes and Conceptual Evaluations: The Role of Semantic Memory
+Stereotypes represent the conceptual attributes linked to a particular group, as defined within a particular culture or society. Stereotyping involves the encoding and storage of group-based concepts, the selection and activation of these concepts into working memory, and their application in judgments and behaviors (Fiske 1998). As such, stereotyping involves cortical structures that support more general forms of semantic memory, object memory, retrieval, and conceptual activation, such as the temporal lobes and inferior frontal gyrus (IFG) (Martin 2007), as well as regions involved in impression formation, such as the medial prefrontal cortex (mPFC) (Amodio & Frith 2006). Social knowledge—about people and groups—has been specifically linked to ATL, including the temporal pole (Olson et al. 2013, Zahn et al. 2007). Hence, stereotypes and conceptual evaluations—to the extent they represent a social form of semantic processing—should also be associated with activity in these regions.
+In an fMRI study of racial stereotypes, Gilbert et al. (2012) used MVPA to dissociate neural activity representing judgments of Black and White individuals on the basis of either stereotype-associated traits (athleticism) or evaluations (potential friendship). Race-based differences in stereotype trait judgments were represented in the mPFC, similar to observations of gender stereotype judgments (Contreras et al. 2012, Quadflieg et al. 2009), whereas evaluative judgments were represented in orbitofrontal cortex (Gilbert et al. 2012). To probe stored representations of stereotypes and evaluations, the authors looked for regions in which multivoxel patterns could reliably predict participants’ scores on independent implicit association test (IAT) measures of racial stereotyping and evaluation, respectively. They found one region that accurately represented both implicit stereotyping and implicit evaluation: the ATL. That is, when subjects made trait judgements, stereotyping IAT scores were associated with one pattern of ATL activity; when they made evaluative judgements, evaluative IAT scores were associated with a different pattern within the same region. These findings support a semantic memory basis for implicit bias rooted in conceptual associations, including both stereotypes and evaluations.
+Consistent with an ATL substrate of stereotype representation, Spiers et al. (2017) observed that the formation of racial stereotypes, acquired as participants read descriptions of outgroup members’ negative behaviors, was tracked uniquely by activity in the temporal poles. In other research, disruption of ATL activity via transcranial magnetic stimulation attenuated the behavioral expression of implicit gender stereotype associations (Gallate et al. 2011). Furthermore, ERP studies have linked stereotype processing to the N400 ERP component (e.g., White et al. 2009), a neural signal originating from the temporal lobe that is associated with language and semantic memory processes and occurs about 400 ms following word presentation (Kutas & Federmeier 2011). When judging a novel group member, group stereotypes represented in the ATL may influence one’s impression via signals to the mPFC (Amodio 2014), consistent with anatomical connections between these regions (Olson et al. 2013). Hence, while the neural basis of stereotyping remains understudied, existing research consistently identifies the ATL as supporting the representation of social stereotypes and, through connectivity with the mPFC, the application of stereotypes in impression formation.
+Prejudice Formation Through Social Interaction: The Role of Instrumental Learning
+Ironically, most psychological research on impression formation concerns indirect experiences of others—in lab studies, we learn about others by reading descriptions, observing behaviors, or applying stereotypes. Yet much of real-life social behavior involves direct interaction, and thus a current major goal of social cognition research is to understand how we form impressions of people and their groups through social exchange. Recent social neuroscience findings suggest this form of direct interaction-based social cognition may be rooted in instrumental learning—a mode of feedback-based reward reinforcement associated with activity of the striatum (Hackel et al. 2015). The striatum, which comprises the caudate, nucleus accumbens, and putamen, supports the learning and representation of reward value and, through its connectivity with the PFC and motor cortex, guides choice and goal-directed action (O’Doherty et al. 2017).
+Although social psychologists have long hypothesized a role for instrumental learning in attitudes and social behavior (e.g., Breckler 1984), this idea has only recently been tested using contemporary reinforcement learning paradigms and computational modeling (Behrens et al. 2009, Hackel & Amodio 2018). Behavioral studies confirm that people incrementally update their attitudes about both persons (Hackel et al. 2019) and groups (Kurdi et al. 2019) in a manner predicted by reinforcement models. Convergent fMRI research has linked this process to the striatum (Hackel et al. 2015). Human learners can similarly form and update trait-like inferences in response to feedback (Hackel et al. 2015, 2020)—a process supported by the striatum in combination with regions often implicated in social cognition (e.g., right temporoparietal junction, precuneus, intraparietal lobule). These findings suggest that instrumental learning may support both an action-based form of social attitude and the formation of conceptual trait impressions.
+In the context of prejudice, instrumental learning represents the formation of reward associations through repeated action and feedback—for example, through the process of approaching an ingroup or outgroup member and encoding their response. Instrumental associations should be more directly linked to action, given their learning mode and underlying neural circuitry, relative to semantic or Pavlovian associations, and thus instrumental forms of prejudice may be most strongly expressed in behavior (Amodio & Ratner 2011a). Unlike semantic learning, which pertains to specific conceptual associations, instrumental learning represents probabilistic reward associations and does not require awareness for its learning or expression (Knowlton et al. 1996). For this reason, a model of instrumental prejudice may help us understand aspects of implicit prejudice—particularly those expressed via action, as opposed to those observed in word associations. Finally, instrumental associations are malleable, fluctuating according to the reward history of a social target, in contrast to Pavlovian associations, which are difficult to alter (LeDoux & Hofmann 2018). Thus, manipulations known to change instrumental reward associations may inform new interventions for how to reduce this aspect of prejudice. Predictions such as these, based on the emerging literature on instrumental learning in social cognition, are currently guiding a new wave of research on the social neuroscience on prejudice.
+Habits: A Basis for Automatic Prejudice?
+Automatic prejudices are often likened to habits; they appear to emerge from repeated negative experiences with outgroup members, unfold without intention, and resist change (Devine 1989). While this is an intuitive analogy, is there evidence that prejudice can operate like a habit?
+Habits typically emerge from instrumental learning—responses that begin as goal-directed, reward-driven actions and that, over time and with repetition, become routinized as automatic responses that persist irrespective of reward (Robbins & Costa 2017, Wood & Rünger 2016).
+Whereas goal-directed instrumental learning is primarily associated with the ventral striatum (VS), habit-driven responses have been linked to the dorsal striatum (Foerde 2018).
+Although social neuroscience has yet to investigate the role of habit in prejudice, behavioral research suggests that a habit-like process, such as model-free learning, can underlie social attitudes toward both persons and groups (Hackel et al. 2019, Kurdi et al. 2019). These findings suggest that habits may indeed play a role in prejudice. However, unlike the habit analogy for automatic stereotyping, a habit component of prejudice would most likely be expressed in action and choice, given its roots in instrumental learning. While further research is needed, a consideration of habits as a mechanism for prejudice promises to inform our understanding of how implicit bias is expressed and potentially reduced.
+Summary: The Social Neuroscience of Prejudice Formation and Representation
+A major contribution of social neuroscience research on prejudice has been to link different aspects of prejudice—stereotypes, affective bias, and discriminatory actions—to neurocognitive models of learning and memory. It reveals that intergroup bias, and implicit bias in particular, is not one phenomenon but a set of different processes that may be formed, represented in the mind, expressed in behavior, and potentially changed via distinct mechanisms.
+EFFECTS OF PREJUDICE ON PERCEPTION, EMOTION, AND DECISION MAKING
+Once categorization has occurred and prejudice is activated, the effects modulate other psychological processes—what we see, how we feel, and how we form judgments—all of which can influence behavior. In this section, we review discoveries from social neuroscience on the effects of prejudice on face perception, intergroup emotion, and decision making.
+Face Perception
+Since the so-called new look proposal that motivation influences object perception, prejudice researchers have considered the possibility that prejudice shapes how we see ingroup and outgroup members (Kawakami et al. 2017). Social neuroscience has advanced this line of inquiry by introducing methods from vision neuroscience to complement behavioral methods that, on their own, cannot easily discern changes in perception from changes in a person’s judgment of their perception. In doing so, this approach has produced new and more rigorous evidence for the effects of prejudice on early face processing while elucidating the mechanisms through which top-down social factors influence visual perception. In contrast to the categorization research we discuss above, what follows is a review of work that seeks to understand more specifically how prejudice-biased perception gives rise to discriminatory phenomena (e.g., race-based misidentification in lineups).
+Humans are expert face perceivers, and the capacity to identify a human face, encode its features, track its orientation, and recognize its identity is supported by an extensive network of neural regions that include the fusiform cortex, occipital cortex, and temporal lobe (Duchaine & Yovel 2015). An initial stage of face perception is the configural encoding of a stimulus as a face—that is, determining that the arrangement of an object’s features matches the canonical configuration of a human face: two eyes above a nose, above a mouth. Simultaneously, the brain encodes specific facial features, although configural processing is typically prioritized. Configural face processing is associated primarily with the fusiform gyrus, whereas featural processing occurs in temporo-occipital cortex (Duchaine & Yovel 2015).
+www.annualreviews.org • The Social Neuroscience of Prejudice
+In an early fMRI study of the own-race bias effect, whereby ingroup faces are recognized better than outgroup faces, Golby et al. (2001) observed greater activity in the fusiform gyrus when White participants’ viewed ingroup than outgroup faces, and this neural pattern predicted better memory for ingroup faces. This finding revealed greater configural encoding of ingroup than outgroup faces—a difference in the early perceptual encoding of an image as a human face. More recent work that examined the effect of race on a phenomenon called repetition suppression suggests that prioritized ingroup processing in the fusiform contributes to the outgroup homogeneity effect, which, similar to the own-race bias effect, refers to people’s tendency to view outgroup members as less distinguishable than ingroup members (Hughes et al. 2019, Reggev et al. 2020).
+Most studies examining race effects on face perception have used an ERP approach, with a focus on the face-selective N170 component—a neural signal associated with the initial configural encoding of a face, which is generated in the fusiform and temporo-occipital cortices and occurs at just ~170 ms after face onset. Early findings of race effects on the N170 appeared mixed— some found larger responses to racial ingroups (Ito & Urland 2005, Feng et al. 2011), others to racial outgroups (Walker et al. 2008), and many others found no differences (e.g., Caldara et al. 2003, He et al. 2009, Wiese et al. 2009). However, more recent research has clarified that group effects on face encoding depend on a perceiver’s task goals and social motivations (Ofan et al. 2011, Senholzi & Ito 2013). When race is relevant to one’s goal, configural processing of goalrelevant group members is enhanced; when race is not relevant, faces of both groups are processed similarly. For example, when a Black face represents a threat cue [e.g., because a participants’ group dominance motives were activated or because the participant was worried about appearing prejudiced to others (Ofan et al. 2014, Schmid & Amodio 2017)], theN170 maybe larger to Black than White faces. By contrast, when a White participant is motivated to discount or stereotype outgroup members, their N170 response may be smaller to Black than White faces (Schmid & Amodio 2017).
+Several factors have now been shown to influence the effect of race on configural face encoding, such as categorization goals (Ito & Urland 2005), social power (Schmid & Amodio 2017), economic scarcity (Krosch & Amodio 2019), implicit prejudice (Ofan et al. 2011), intergroup anxiety (Ofan et al. 2014), perceiver race (Vizioli et al. 2010), group identity (Scheepers et al. 2013), and intergroup contact (Walker et al. 2008). Such effects have been found among people of many different nationalities, including Canadian, Chilean, Chinese, Israeli, Japanese, Korean, and Swiss, and their relevant ethnic outgroups (e.g., Caldara et al. 2003, Ibánez et al. 2010). Increased con-figural processing, as indicated by the N170 or fMRI measures of fusiform activity, has also been observed for novel (Van Bavel et al. 2011) and minimal (Ratner & Amodio 2013) ingroup members, university ingroup members (Cassidy et al. 2014), and sex-typical faces relative to sex-atypical faces (Freeman et al. 2010). In some studies, the N170 to racial outgroups was also delayed (Ofan et al. 2011, Stahl et al. 2008, Wiese et al. 2009, Zheng & Segalowitz 2014)—a pattern consistent with a shift to feature-based processing as a result of impaired configural processing (Rossion et al. 2000). Collectively, this research demonstrates an effect of intergroup bias on the earliest stages of face processing that, under certain conditions, may impede a perceivers’ ability to process outgroup faces the same way as ingroup faces—an effect that has been dubbed perceptual dehumanization (Fincher & Tetlock 2016, Kawakami et al. 2017) and linked to outgroup homogeneity effects (Hughes et al. 2019).
+Most importantly, these race effects on configural encoding may function to justify and promote discriminatory behavior (Krosch & Amodio 2019). In complementary ERP and fMRI studies, White participants determined how much money each of a set of White and Black individuals deserved. Participants exhibited a selective delay in the N170 (using EEG) and reduction in fusiform activity (using fMRI) to Black, compared with White, faces that emerged only under
+conditions of perceived economic scarcity (Figure 5). Moreover, in both studies, the magnitude of this encoding deficit was associated with the degree of anti-Black disparity in participants’ money allocations. These findings are consistent with the idea that intergroup prejudice (e.g., induced by scarcity) can lead perceivers to view outgroup members in a way that facilitates harmful behavior (Fincher & Tetlock 2016, Rai et al. 2017, Zhou et al. 2020).
+Together, these studies reveal that prejudice and intergroup dynamics can indeed shape the earliest stages of face processing and that they do so flexibly and in a goal-consistent manner. Moreover, by identifying specific factors that affect early social perception (e.g., prejudice, power, scarcity), this work suggests contexts in which the effects of prejudice on perception may be modulated and thus potentially reduced.
+Emotion
+A central goal of prejudice research is to inform our understanding of discrimination and intergroup behavior. Although prejudice is typically measured in terms of an attitude—that is, on a single dimension of valence, ranging from negative to positive—attitudes are often not fine-grained enough to predict specific behaviors; for example, when do negative attitudes predict neglect, as opposed to fear or attack (Fiske 2018)? To understand the specific behaviors associated with prejudice, a more nuanced analysis of discrete intergroup emotions is needed (Mackie & Smith 2018, Neuberg & Schaller 2016).
+A distinctive feature of intergroup emotions is that they may conflict with the emotional responses people feel in interpersonal contexts. In other words, in intergroup contexts, people’s emotional responses may shift to reflect the priorities and interests of the group instead of the individual (Mackie & Smith 2018). Nowhere is this pattern better characterized than in the domain of how we feel in response to ingroup versus outgroup members’ suffering. The social neuroscience of intergroup empathy has illuminated that there are distinct pathways that contribute to ingroup help and outgroup neglect versus outgroup harm (Vollberg & Cikara 2018).
+Empathy is a multifaceted construct, comprising both cognitive and affective components that reflect our reactions to others’ experiences and feelings. Understanding a target’s experience in the absence of any concomitant affect has been associated with a distributed set of brain regions including mPFC, temporoparietal junction, temporal pole, and precuneus—regions involved in
+the representation of trait impressions, perspective taking, person knowledge, and self-awareness, respectively (Amodio & Frith 2006, Olson et al. 2013, Saxe 2012). Experiencing an emotion in reaction to someone else’s emotion, by contrast, is typically associated with engagement of dACC and AI (Lamm et al. 2019, Zaki & Ochsner 2012). Because the AI and dACC are associated with both the firsthand experience of pain and empathy for others, early theories posited that the affective components of empathy were the product of simulating others’ pain (Hein & Singer 2008). However, both regions are involved in a variety of functions, including the detection of cognitive conflict, tracking of value, and salience (see also the section titled Social Categorization: The Antecedent of Prejudice). Therefore, more recent formulations posit that dACC and AI consistently correlate with empathy due to their general function of encoding salient cues and value (Decety 2011).
+While there remains ambiguity surrounding the precise functions of these regions in the experience of empathy, there is relatively greater consensus surrounding the phenomenon of intergroup empathy bias. Dozens of physiological, fMRI, and EEG studies indicate that people are less likely to empathize with others when they are socially distant, such as when they belong to different racial or national groups (Cikara et al. 2011b, Cikara & Van Bavel 2014, Han 2018). For example, participants in an fMRI study exhibited greater dACC engagement when watching members of their racial ingroup (Caucasian or Chinese) relative to the outgroup being pricked by a needle (Xu et al. 2009). This dACC and AI bias pattern has replicated across cultures, including Chinese (Sheng et al. 2014), Australian (Contreras-Huerta et al. 2013), and European (Azevedo et al. 2013) participants, and across group contexts, including sports fans (Cikara et al. 2011a, Hein et al. 2010).
+Notably, however, findings from at least two studies diverged from this pattern. In the first case, participants who viewed images of same-race and other-race targets suffering in the aftermath of Hurricane Katrina exhibited similar degrees of dACC and AI activation across both conditions (Mathur et al. 2010). Similarly, Arabs and Israelis exhibited equivalent dACC and AI responses to stories of ingroup and outgroup pain (Bruneau et al. 2012). These patterns may also be moderated by the majority or minority status or power of the groups under inquiry [e.g., Black versus White participants in South Africa viewing Black and White targets’ suffering (Fourie et al. 2017)]. Future work is tasked with determining whether these discrepancies are due to differences in samples, stimulus sets, or statistical power.
+Similar patterns have been documented via reduced motor resonance—activation of an observer’s motor system, attuned to the perceived movement of another—with outgroup relative to ingroup targets (Avenanti et al. 2010, Fini et al. 2013, Gutsell & Inzlicht 2010). For example, watching ingroup members as opposed to outgroup members receive an injection resulted in increased event-related desynchronization of beta rhythms in sensorimotor cortex, which the authors interpreted as greater resonance with ingroup pain (RieÊansky et al. 2015; see also Levy et al. 2016).
+However, lapses in empathy alone cannot explain overt intergroup conflict. After all, the absence of empathy is merely apathy, which is generally not a strong predictor of aggressive behavior. Thus, a growing body of work has focused on understanding the conditions under which people experience the exact opposite of empathy—specifically, pleasure in response to others’ misfortunes (Schadenfreude). People are least likely to experience empathy and most likely to experience Schadenfreude in intergroup contexts when they see outgroups as both competitive with their own interests and high status: Not only are their goals at odds with ours, but they also pose a legitimate threat (Cikara 2015, Harris et al. 2008). In an fMRI study testing the link between Schadenfreude and harm (Cikara et al. 2011a), Red Sox and Yankees fans reported how much they felt pleasure, anger, and pain after watching baseball plays in which their team and their rival
+scored or failed. Not surprisingly, participants reported feeling pleasure when players on their own team succeeded and a rival team failed, even against the Orioles (a relatively less competitive team in the same league). Pleasurable baseball plays, including rivals failing to score against the Orioles (the pure Schadenfreude condition), activated responses in the VS, a region associated with learning from rewarding events. Weeks later, those participants who exhibited greater VS activation in response to watching their rivals fail also reported an increased likelihood of aggressing against rival team fans (relative to Orioles fans). Note also that no such correlation emerged with dACC or AI [mirroring the absence of a relationship between reduced empathy and aggression (Vachon et al. 2014)]. In a related fMRI study, soccer fans exhibited VS activity when watching a rival team’s fan—someone who is merely affiliated with the rival team—receive a painful electric shock. Increased VS in this context was correlated with a decreased willingness to help the rival fan (Hein et al. 2010).
+The unique association of outgroup harm with activity in the VS is notable because there are several regions in the brain associated with the registration of pleasure (including AI, vmPFC, and medial orbitofrontal cortex). VS, however, is associated with reward prediction errors for the purposes of planning future behavior. According to one model, the capacity for intergroup aggression may have developed, in part, by appropriating basic reinforcement-learning processes and associated neural circuitry—including VS—to overcome harm aversion (Cikara 2015). As such, the repeated experience of Schadenfreude in response to outgroup suffering may be the slippery slope that slowly transforms unthinkable actions into acceptable ones.
+As we have emphasized throughout this article, these emotional responses are malleable and context dependent. If the nature of one’s relationship with an outgroup member changes, their degree of empathy follows. For example, participants expressed greater empathy toward an outgroup member who volunteered to receive electric shocks in order to spare the participant, in comparison to an ingroup member who did the same. Specifically, greater responses in AI associated with receiving help from an outgroup member predicted significantly greater AI activation in response to seeing other outgroup members in pain (relative to a baseline, before they received help) (Hein et al. 2016).
+Social neuroscience research has also expanded our understanding of guilt, which, in response to one’s intergroup transgression, is a powerful elicitor of self-regulation and prosocial behavior (Allport 1954). This research has linked guilt to a two-stage regulatory response: The initial experience of guilt is associated with increased dACC activity and reduced left PFC activity—a pattern associated with self-directed attention and behavioral inhibition, presumably to process one’s misdeed and plan for reparation (Amodio et al. 2007, Fourie et al. 2014). This response then transforms into a state of readiness when an opportunity for reparation emerges, at which point one’s initial feelings of guilt are associated with increased left PFC activity and the engagement of prejudice-reducing behaviors (Amodio et al. 2007). Several other emotions central to intergroup prejudice and behavior, such as disgust, hope, anger, and pity, to name just a few, are ripe for further investigation.
+Decision Making
+Intergroup attitudes and emotions interact with other processes (e.g., valuation, stereotypes, social goals) to inform our social choices: whom to learn from, how to allocate our resources, how much to punish, and what norms to follow in social settings. A rapidly growing area of research in intergroup decision making has begun to leverage knowledge acquired in the cognitive neuroscience of nonsocial learning and decision making (for a review, see Ruff & Fehr 2014) to better understand how group contexts moderate these processes.
+Conformity. We have already reviewed evidence that people exhibit greater sensorimotor resonance with ingroup relative to outgroup members experiencing pain, but it is crucial to understand whether other behaviors that rely on matching a target’s experience are sensitive to target group membership. For example, even chimps yawn more after watching video clips of familiar relative to unfamiliar conspecifics yawning (Campbell & De Waal 2011). To the extent that imitation is a rudimentary form of learning, such results suggest that people learn more from ingroup than outgroup members. Recent findings comport with this prediction. In one study, participants rated a series of images on their valence, from negative to positive (Lin et al. 2018). Then, during an fMRI scan, American participants observed ratings of those same images ostensibly from other American and Chinese participants. Participants not only shifted their evaluations to conform more with ingroup relative to outgroup members’ ratings, but this conformity behavior also correlated with increased mPFC, left amygdala, left VS, bilateral AI, and bilateral ventrolateral PFC responses—regions associated with positive valuation and value integration. Based on these results, the authors argued that rather than reflecting mere signaling strategy, conformity with the ingroup [or distinguishing oneself from the outgroup (Huang et al. 2019)] carries intrinsic value.
+Moral judgments and punishment. Not all victims and perpetrators are equivalent; our judgments of wrongdoing are often modulated by targets’ group memberships. Although there is a wealth of literature examining the neural substrates of moral decision making, this work has only recently integrated considerations of group membership. For example, participants in an fMRI study reported being more upset when the victim of physical harm was a fellow university student (relative to a student from a rival university), but only when the perpetrator of harm was an outgroup member (i.e., a student from the rival university) (Molenberghs et al. 2014). Only one region was associated with this moral response—left orbitofrontal cortex—which the authors speculated may support increased moral sensitivity by upregulating AI and amygdala responses to this special class of scenarios.
+And what of lesser transgressions? Violators of social norms are often (although not always; Mendoza et al. 2014) punished more severely if they are outgroup relative to ingroup members. Using transcranial magnetic stimulation, one study found it was possible to eliminate this group bias among soccer fans by disrupting activity in right (but not left) temporoparietal junction, a region associated with mentalizing. More specifically, they found that disrupting right temporoparietal junction reduced retaliation intentions, suggesting a link between mentalizing and punishment motives (Baumgartner et al. 2013).
+Resource allocation. Finally, harkening back to some of the early work on intergroup relations in social psychology, which examined effects of group membership on resource distribution (Tajfel & Turner 1979), recent social neuroscience studies have begun to examine the neural systems that generate biased resource allocations between ingroup and outgroup members. In Krosch & Amodio’s (2019) fMRI study, described above, the degree of anti-Black disparities in White participants’ monetary allocations was associated with activity in a fusiform-striatum pathway; that is, smaller resource allocations to Black recipients were predicted by reduced activity in the fusiform face area while viewing those recipients, coupled with reduced activity in the striatum. The authors speculated, based on this pattern, that scarcity may induce a form of perceptual dehumanization of racial outgroup members, which then signals their devaluation during allocation decisions.
+In the context of Europe’s refugee crisis, one study tested the relative effects of peer-driven norms of altruism and oxytocin administration on resource allocations to refugees (Marsh et al. 2017). Their results were moderated by participants’ xenophobia: Low xenophobia participants were more inclined to help refugees than to help natives, and oxytocin to these participants
+increased donations for both groups. High xenophobia participants, by contrast, gave more to refugees than natives only when oxytocin was combined with the activation of altruism norms. However, we would be remiss if we did not note the large related literature examining the role of oxytocin in ethnocentrism (De Dreu et al. 2011), ingroup defense (De Dreu et al. 2010), and even outgroup attack (Zhang et al. 2019), indicating oxytocin’s nuanced and complex influence on intergroup processes. Findings such as these begin to describe the neural processes associated with intergroup resource allocation decisions and, by doing so, shed new light on the psychological processes involved.
+Summary: Intergroup Perception, Emotion, and Decision Making
+Social neuroscience research has refined our understanding of how prejudice influences the visual processing of faces, intergroup emotion, and decision-making processes, particularly as each type of response pertains to behavior. These findings set the stage for important work to come on how these processes drive the impact of prejudice on critical everyday outcomes such as hiring, housing, voting, medical recommendations and care, and conflict resolution.
+SELF-REGULATION OF PREJUDICE
+Despite the ease with which prejudice forms and springs to mind, many people consciously object to prejudice and strive to respond in an egalitarian manner (Devine 1989). This conflict— between biased impulses and egalitarian intentions—has long been recognized in social psychology (Allport 1954), and interventions to enhance control are an effective short-term strategy for reducing prejudice (Burns et al. 2017). However, while behavioral research has identified many factors that promote control, it has not addressed some crucial questions about the prejudice control process. For example, how is control initiated? Does control involve more than one process? On which psychological and behavioral processes does control operate? And why are some people better at controlling prejudice than others? Our ability to develop effective interventions to reduce prejudice depends on answers to questions such as these.
+Social neuroscience studies have shown that prejudice control involves multiple processes and that a consideration of these processes provides a more comprehensive account of intergroup behavior (Figure 6). Early neuroscience research on the regulation of prejudice adapted a cognitive neuroscience model of control, whereby control comprises (a) a monitoring process, supported by dACC, which detects the activation of bias, and (b) a regulatory process, supported by lateral PFC, which implements an intended response (Botvinick et al. 2001). When the monitoring process registers conflict, it signals the regulatory system to initiate control. According to this model, prejudice control is initiated when a conflict is detected between an activated bias (e.g., a stereotype-driven response) and an intended alternative response (Amodio et al. 2004, Richeson et al. 2003). Moreover, this conflict monitoring process has been shown to operate without awareness, suggesting that prejudice control may be initiated rapidly, without conscious deliberation (Nieuwenhuis et al. 2001).
+The conflict-detection component of prejudice control was tested in a study that assessed dACC activity in participants performing a task that required them to inhibit the automatic expression of racial stereotypes on some trials but not others (Amodio et al. 2004). Here, dACC was indexed by the error-related negativity component of the ERP. Error-related negativity amplitudes were greater on trials requiring stereotype inhibition, and the magnitude of this neural signal predicted participants’ success at controlling stereotype application in their behavior. Moreover, by demonstrating stereotype-related dACC activity on trials leading to both successful and unsuccessful control, this experiment dissociated the process of bias detection from the process
+of implementing a controlled response. Finally, by using an ERP index of dACC activity, which assesses changes in neural activity on the order of milliseconds, this work revealed that a neural signal to initiate control occurs rapidly (within about 300 ms of target onset) and thus likely without conscious deliberation.
+This finding has been replicated and extended in several studies of prejudice control, using a variety of tasks and multiple ERP indices of dACC activity (Amodio & Swencionis 2018; Amodio et al. 2006, 2008; Bartholow et al. 2006; Correll et al. 2006). For instance, to address a prior finding that some people with egalitarian beliefs struggle to control automatic stereotypes more than others, one study showed that this individual difference in control could be explained by individuals’ sensitivity to stereotype-based conflict, as indicated by dACC activity (Amodio et al. 2008). Other research has shown that personal and normative impetuses to control prejudice may rely on different mechanisms of conflict detection—a dACC process for detecting internal cues for control and an mPFC (and rostral ACC) process for monitoring external (e.g., social) cues—to explain why control based on external cues is often less effective than control based on internal cues (Amodio et al. 2006). Hence, by distinguishing the conflict detection process as separate from the implementation of control, these studies provided novel accounts for enduring questions about prejudice control.
+Similar effects have been observed using fMRI. In a study examining the neural correlates of the racial prejudice IAT—a task that requires controlled processing to complete bias-incompatible trials—dACC activity was associated with the ability to detect the correct, unbiased response amid biased automatic tendencies (Beer et al. 2008; see also Knutson et al. 2007). The role of dACC in the detection of potential bias was also shown in an fMRI study by Norton et al. (2013), in
+which participants were asked to assign a stereotypic trait to one of a pair of target individuals. When targets in a pair differed in their race (one Black and one White), thereby creating the potential for stereotyping, participants slowed their response—a phenomenon the authors dubbed racial paralysis—and this reaction was associated with heightened dACC activity. dACC activity has even been observed during the passive viewing of racial outgroup faces, suggesting that the mere appearance of racial cues may engage a readiness for control (e.g., Cunningham et al. 2004, Richeson et al. 2003). Together, these studies demonstrate the involvement of the dACC in the detection of bias and the initiation of prejudice control, advancing our understanding of how control fails or succeeds.
+Social neuroscience research has also shed new light on how control is implemented; that is, on what is being controlled during prejudice control. In several fMRI studies with White American participants, participants exhibited greater right IFG activity in response to presentations of Black faces compared with White faces (e.g., Beer et al. 2008, Cunningham et al. 2004, Lieberman et al. 2005, Mitchell et al. 2009, Richeson et al. 2003). Given research indicating that right IFG supports response inhibition (Aron et al. 2014), these findings suggest that exposure to Black faces elicited a form of behavioral inhibition. A similar pattern of right IFG activity was observed when participants were asked to evaluate members of widely stigmatized groups—a question that presumably requires the inhibition of a biased response (Krendl et al. 2009). Together, these findings suggest IFG supports an inhibitory form of prejudice control.
+Whereas right IFG is associated with the inhibition of action, activity in the left lateral PFC has been associated with the production of goal-directed action. In the context of prejudice, this region has been linked to the successful implementation of an intended response over an automatic stereotype. In an EEG study designed to assess this process as it unfolded in real time (Amodio 2010), brain activity was recorded in subjects as they completed a stereotype priming task that, on some trials, required participants to replace an automatic stereotype response with a correct, unbiased response. Greater left dorsolateral prefrontal cortex (dlPFC) activity was associated with more success in overriding an automatic stereotype with an unbiased response. Furthermore, an analysis of ERPs during this process revealed that the effect of left dlPFC activity on stereotype control was mediated by rapid attentional orienting to racial outgroup cues, as indexed by the P2 component of the ERP. This pattern suggested that dlPFC activity tuned perceptual attention to relevant stimuli, in the manner of proactive control (e.g., Amodio & Swencionis 2018), to promote the control of action. In another EEG study, noted above, greater left dlPFC activity was associated with participants’ choice to engage in prejudice-reducing activities following a manipulation that made them feel guilty about their personal biases (Amodio et al. 2007).
+These PFC findings suggest that, depending on the task, prejudice control may operate by inhibiting an unwanted behavioral response or by promoting goal-directed action to override an unwanted bias, or both, consistent with cognitive neuroscience models of PFC function (Miller & Cohen 2001). These expressions of control clarify and advance prior models of prejudice control that focused on correction, suppression, and inhibition (Amodio & Devine 2010) or which assumed that control processes operated on internal mental representations rather than behavior. This model of control also updates an early view of prejudice control, whereby control was thought to operate via lateral PFC downregulation of the amygdala. Although this idea was suggested by some correlational findings, it is inconsistent with primate anatomical studies, which found sparse, if any, direct connections between these regions (Amodio & Ratner 2011b, Ghashghaei et al. 2007).
+The new model of prejudice control suggested by social neuroscience has important implications for interventions to reduce prejudice. This model suggests that a prejudiced response may occur for multiple reasons, each associated with a different underlying process (Amodio 2014). For example, a person may fail to detect the conflict between their biased response tendency and
+either their egalitarian goals or normative antiprejudice social cues—a process that depends on the dACC or mPFC (or rACC), respectively. Alternatively, they may have trouble inhibiting a biased response, despite having detected it—a process linked to right IFG. Or they may have trouble identifying and implementing a desired egalitarian response—a process supported by left dlPFC. As such, this model suggests that an intervention could target one or more of these specific processes. Moreover, different individuals may fail for different reasons and thus require different interventions. A consideration of these control processes and their relevance to subgroups of individuals promises a more refined and effective approach to prejudice reduction.
+Summary: Self-Regulation of Prejudice
+Considered together, social neuroscience research on prejudice control has significantly expanded psychological theory by identifying and distinguishing multiple mechanisms of control (Figure 6). These include the detection of bias and initiation of control in dACC—a process that can operate rapidly and in the absence of deliberation and which can explain individual differences in prejudice control failures. This work has also elucidated mechanisms of control implementation, distinguishing between response inhibition, associated with right IFG, and the selection and application of intentional behavior, in left dlPFC. Together, these findings have advanced our understanding of the psychology of prejudice control and suggest new opportunities for prejudice reduction interventions.
+NEXT QUESTIONS AND NEW CHALLENGES
+When we consider the real-world effects of prejudice in society, it becomes obvious that social neuroscience research on prejudice still has much to do. To date, research from this field has focused on the psychological building blocks of prejudice—for example, processes of social categorization, prejudice formation, intergroup emotion and perception and, more recently, the neu-rocomputational basis of these processes. However, as this field continues to develop, it must make connections to the real-life forms of prejudice that persist in society, from expressions of bias in real dyadic cross-group social interactions and the spread of prejudice across members of a community to institutional discrimination, systematic forms of oppression such as voter suppression, and even ethnic conflict and genocide. These goals will require new methods, greater ecological validity, and increased collaboration with scientists and scholars from other disciplines.
+Ambulatory (i.e., wearable) neuroimaging technologies now make it possible to record participants’ neural and physiological activity during direct social interaction, potentially increasing ecological validity and permitting real dyadic analysis. For instance, methods such as ambulatory EEG and functional near-infrared spectroscopy, in which participants wear a sensor cap but can otherwise move naturally, offer the possibility of examining neural activity during more naturalistic intergroup interaction. Furthermore, the enhanced study of dyadic interactions will elucidate the effects of an actor’s prejudice on a target’s response to being stigmatized (e.g., Welborn et al. 2020). As these technologies develop, they will increasingly inform questions about the neural basis of real-world prejudice.
+Questions about how information spreads across a social group and influences its members’ behaviors have recently been examined using network analysis (Weaverdyck & Parkinson 2018). Such methods examine similarities in patterns of brain activity across members of a group and compare them with patterns of judgments toward other group members. Similar methods can address the spread of prejudice and stereotypes within a community, potentially informing the connection between individual-level neural activations and group-level processes (Parkinson & Du 2020).
+Finally, researchers have begun to examine the neural processes involved in real-world intergroup conflict. One fMRI study examined neural activity of White and Black South Africans viewing testimony from the Truth and Reconciliation Commission on their experiences under apartheid—an extremely emotional event that elicited outwardly egalitarian behaviors among White participants despite their pro-ingroup patterns of neural activity (Fourie et al. 2017). Other research has begun to examine the neural roots of dehumanization as it relates to real-world national and ethnic conflict (Bruneau et al. 2018). The broader goal of this work is to identify ways to apply knowledge of the neuroscience of prejudice to interventions to reduce intergroup animus and conflict. Hence, in our view, the most critical questions and challenges facing this field in the next decade concern its ability to connect basic neurocognitive process to a broader array of intergroup contexts, factors, and outcomes.
+CONCLUSIONS
+The social neuroscience of prejudice is a rich and thriving area of research that addresses questions about the psychology of prejudice with the tools of cognitive neuroscience and psychophysiology. Here, we have highlighted major theoretical advances produced by this literature to date: from how we perceive groups to how prejudice is learned and represented in the mind, how it influences our perceptions, emotions, and decisions, and how it can be regulated. By applying theories and tools of neuroscience to this complex social phenomenon, this work has produced a more refined understanding of the psychological processes involved in prejudice, along with new insights and theoretical connections that might not have emerged from traditional behavioral approaches. Nevertheless, this area of research is still relatively new; as the fields of cognitive neuroscience and intergroup psychology continue to evolve and advance, so, too, will the social neuroscience of prejudice. In this field marked by innovation, we look forward to the new discoveries that will further our understanding of prejudice and its role in social behavior.
+Annu. Rev. Psychol. 2021.72:439-469. Downloaded from www.annualreviews.org
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+www.annualreviews.org • The Social Neuroscience of Prejudice	469
+Í3
+Annual Review of
+Psychology
+Volume 72, 2021
+Contents
+Annu. Rev. Psychol. 2021.72:439-469. Downloaded from www.annualreviews.org
+Access provided by Universidade Federal de Minas Gerais on 12/25/22. For personal use only.
+Active Forgetting: Adaptation of Memory by Prefrontal Control Michael C. Anderson and Justin C. Hulbert...................................1
+“Reports of My Death Were Greatly Exaggerated”: Behavior Genetics
+in the Postgenomic Era K. Paige Harden ...........................................................37
+The Psychology of Reaching: Action Selection, Movement
+Implementation, and Sensorimotor Learning Hyosub E. Kim, Guy Avraham, and Richard B. Ivry............................61
+Transcranial Magnetic Stimulation and the Understanding of Behavior David Pitcher, Beth Parkin, and Vincent Walsh .............................97
+Memory and Sleep: How Sleep Cognition Can Change the Waking Mind for the Better
+Ken A. Paller, Jessica D. Creery, and Eitan Schechtman ...................123
+The Cultural Foundation of Human Memory Qi Wang ..................................................................151
+Trade-Offs in Choice Franklin Shaddy, Ayelet Fishbach, and Itamar Simonson ....................181
+The Origins and Psychology of Human Cooperation Joseph Henrich and Michael Muthukrishna ..................................207
+Language as a Social Cue Katherine D. Kinzler......................................................241
+Intergenerational Economic Mobility for Low-Income Parents and Their
+Children: A Dual Developmental Science Framework
+Terri J. Sabol, Teresa Eckrich Sommer, P Lindsay Chase-Lansdale, and Jeanne Brooks-Gunn ................................................265
+Moral Judgments Bertram F. Malle	293
+Integrating Models of Self-Regulation
+Michael Inzlicht, Kaitlyn M. Werner, Julia L. Briskin, and Brent W. Roberts	319
+vi
+Annu. Rev. Psychol. 2021.72:439-469. Downloaded from www.annualreviews.org
+Access provided by Universidade Federal de Minas Gerais on 12/25/22. For personal use only.
+The Psychology of Moral Conviction
+Linda J. Skitka, Brittany E. Hanson, G. Scott Morgan, and Daniel C. Wisneski.347
+Social Influence and Group Identity Russell Spears.............................................................367
+Socioeconomic Status and Intimate Relationships Benjamin R. Karney.........................................................391
+Experimental Games and Social Decision Making Eric van Dijk and Carsten K.W De Dreu......................................415
+The Social Neuroscience of Prejudice David M. Amodio and Mina Cikara............................................439
+Psychology of Transnational Terrorism and Extreme Political Conflict Scott Atran................................................................471
+Prejudice and Discrimination Toward Immigrants Victoria M. Esses..........................................................503
+Prejudice Reduction: Progress and Challenges
+Elizabeth Levy Paluck, Roni Porat, Chelsey S. Clark, and Donald P Green ...533
+The Science of Meaning in Life Laura A. King and Joshua A. Hicks..........................................561
+Psychological Underpinnings of Brands
+Richard P Bagozzi, Simona Romani, Silvia Grappi, and Lia Zarantonello......585
+Practicing Retrieval Facilitates Learning Kathleen B. McDermott	609
+Life Change, Social Identity, and Health
+Catherine Haslam, S. Alexander Haslam, Jolanda Jetten, Tegan Cruwys, and Niklas K. Steffens...................................................635
+Stress and Health: A Review of Psychobiological Processes Daryl B. O'Connor, Julian F Thayer, andKavita Vedhara......................663
+Understanding Human Cognitive Uniqueness
+Kevin Laland and Amanda Seed	689
+Psychology as a Historical Science Michael Muthukrishna, Joseph Henrich, and Edward Slingerland...............717
+Indexes
+Cumulative Index of Contributing Authors, Volumes 62-72 ..................... 751
+Cumulative Index of Article Titles, Volumes 62-72 ............................756
+Contents vii
\ No newline at end of file
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+Background
+Epidemiology
+The World Health Organisation (WHO) estimates that about a million people die by suicide every year, representing a “global” mortality rate of 16 per 100,000 or one death every 40 s making it the tenth leading cause of death worldwide [1]. Suicide rates in many developing countries have been steadily rising, and the overall worldwide suicide rate has increased during the last 50 years [2].
+Suicide and non-fatal suicidal behavior are significant public health issues worldwide requiring effective preventive interventions. However, there is still a need to identify what prevention strategies should be prioritized to achieve the biggest impact on a reduction of suicide attempts and suicide deaths [3].
+Suicidality is a problem caused by multiple factors, making it difficult to treat by individual medical, psychological, educational, social or political methods.
+Thus national suicide prevention programs (NSPP) were initiated in the 1990s aiming to take a holistic approach to combat suicide. There are 28 countries known to have national strategies for suicide prevention. Prevention programs are designed to identify vulnerable groups, improve the assessment and care of people with suicidal behavior, and improve surveillance and research. They also aim to raise awareness by improving public education. NSPPs attempt counter the stigma toward people exhibiting suicidal behavior and those who suffer from mental disorders. Institutions such as the World Health Organization (WHO) and the International Association for Suicide Prevention (IASP) have developed common guidelines and the following recommendations to set up suicide prevention strategies including NSPP [2, 4, 5]:
+1.	Preventive measures should address suicide and suicide attempts. The loss of human resources, socioeconomic burden, and costs for the healthcare of these individuals are considerable.
+2.	The support and rehabilitation of persons at risk can prevent some suicides. A holistic approach is necessary.
+3.	National governments are responsible for developing strategies to provide financial and technical support that involve society as a whole.
+4.	Measurable objectives and systematic studies must be forthcoming.
+National suicide prevention programs in Norway, Sweden, Finland and Australia
+Norway published the first national suicide prevention strategy in 1995, one that has been revised and updated several times. Aspects of the second and third prevention strategies are the focus of their program. Approaches to enhance the mindfulness of politicians, governmental departments and the general population were taken for this purpose. Medical and social welfare programs were optimized, and aftercare improved. An external board was assigned to evaluate individual projects and the entire program [5, 6]. Their findings were summarized in a publication by Soras in 2000 [7]. A follow-up project to the national plan “Measures against suicide 2000-2002” was evaluated and published by Mehlum and Reinholdt in 2001 [8].
+The aim of Sweden’s national suicide prevention program established in 1995 was a consistent drop in the number of suicides and suicide attempts, to reduce the factors encouraging suicidal behavior in children and youths, as well as the early detection of suicidal tendencies in endangered groups. They aspired to increase the level of awareness in the general population in the first, second and third prevention strategies [9].
+From 1986 to 1991, Finland enacted a research program on suicide and developed preventive strategies. One major goal was to engage high-risk groups. A suicide prevention program was implemented in 1992 as the first governmental program with activities involving all levels of prevention strategies [10]. Moreover, an external board was assigned to evaluate and improve this program [11].
+A national youth-suicide prevention program existed from 1995 to 1999 in Australia. This initiative then evolved into a national suicide prevention program involving first, second, and third prevention strategies. It was one of this program’s aims to observe a lower suicide rate and reduction in suicidal thinking and behavior. The program also intended a better psychological strain and mental health [12]. Following implementation of the original National Youth Suicide Prevention Strategy (NYSPS) in 1995, they did in fact observe a substantial decline in suicide in young men. A study by Page et al. [13] reported a minor discernible impact on suicide rates in those areas that had participated in local
+targeted suicide prevention activities in the period following the NYSPS.
+One of our main problems is how to evaluate suicide prevention programs. As Kerkhof and Clark [14] stated in their editorial, there are obvious limitations in studying effectiveness, e.g., there are no experimental designs that might be applied. So far, little research has been done investigating the effect of national suicide prevention programs, whereas there have been studies about local interventions programs to prevent suicide and suicide attempts; e.g. [15].
+The aim of the present study was to analyze the effectiveness of national suicide prevention programs taking a statistical approach involving the segmented regression analysis of interrupted time series data. We posed the following questions:
+1.	Does the implementation of a national suicide prevention program lead to a significant reduction in suicide rates?
+2.	Are there gender-related differences?
+3.	Are there age-related differences?
+Methods
+One of the major difficulties is verifying the success of NSPP, which we considered as a decrease in suicide attempt or suicide death rates within the population of a country. It is extremely hard to tell which parameters are actually responsible for success among the specific suicide-decreasing effects, spontaneous changes in long-term development, social changes and data inaccessibility due to privacy protection laws, as well as obtaining comparable data. All these factors make it difficult to prove the exact cause of a measured change, making it important therefore to clearly define the structure and approach of the analysis. For the purpose of a conscientious decision-making process three experienced psychiatrists/suicidologists (WF, HT, UL), as well as a statistician (CS), reviewed the existing literature as well as different statistical approaches. Within several face to face meetings, the described approach was defined, and the criteria for the selection of these programs were agreed.
+Criteria for selection of verum and control countries
+The verum countries
+Our main criterion for selecting the verum countries was the existence of a comprehensive national suicide prevention program for at least five years.
+Comprehensive statistical analysis should be available. Nations that have an NSPP are Australia, New Zealand, Finland, Norway, and Sweden. As the New Zealanders implemented their program in 1998 only for young people, we had to exclude them from our statistical
+analyses. The Netherlands, Great Britain, the USA, France, and Estonia have also implemented programs; they failed, however, to meet our inclusion criteria. We ultimately selected Finland, Norway, Sweden, and Australia for the verum group. All these countries have published their programs’ results comprehensively [6-13].
+Control countries
+We compared the verum countries with control countries selected according to the criteria below. They should not have an NSPP and should not differ in the following aspects (at the time of the study):
+1.	Culture and religion: e.g., suicide rates in Mediterranean countries are lower than in Northern European countries. Other examples are countries with a mostly Muslim population, which may be caused by the strong taboo about this problem in the society.
+2.	Historical and political factors: studies have shown that substantial historical developments such as political changes have influenced the dynamics of suicide statistics. Thus Eastern European countries, as well as Germany, could not be included.
+3.	Socio-economic structure: we decided that the control countries should have a western democracy with distinctive market-based economies similar to the verum countries.
+4.	Population size: Statistics from countries with small populations were not included because lower numbers of suicides, minimal increases or decreases can skew the statistics.
+5.	Quality of published statistics: data had to be well founded, reliable and accessible, and should have been collected during the same time period.
+Countries whose statistics were erratically collected were excluded (i.e., African states, China).
+Finally, Canada, Austria, Switzerland and Denmark were selected as control countries.
+Criteria for selecting the time period
+The verum countries implemented their programs in the 1990s. Long periods of observation were planned due to annual variability, which exerts strong effects, especially in countries with a smaller population. The second reason is the possibility that prevention programs gradually lead to success.
+For this study, we had to assess the suicide rates prior to the NSPP to detect any differences. For the verum countries, time 0 (T0) was defined as the year the NSPP was implemented. Countries differed in this respect, thus T0 for Finland is 1992, and 1994 for Norway. T0 is the year 1995 for the control countries. We decided on
+six years (T0 to T + 5) after the implementation of the NSPP to qualify as the period of analysis. All countries had to be statistically represented at the beginning of the analysis. For that reason, T-22 was established retrospectively as the starting point.
+Data were collected from World Health Organization statistics on suicides and demographics separated by age and sex [16].
+Statistics
+To estimate the impact of the NSPP on suicide rates in verum and control countries we applied a segmented regression analysis of interrupted time series data [17-21]. This method estimates changes in levels and trends controlling for baseline levels and trends, which is one of its major strengths. The observation period is divided into pre- and post-intervention segments for which separate intercepts and slopes are estimated. A linear relationship between time and outcome is assumed, and a least squares regression line is fitted to each segment of the independent variable. To take into account the autocorrelation among observations, we estimated the effect of the intervention using the ARIMA model (autoregressive integrated moving average) and tested for autocorrelation of the error terms via the Ljung-Box-test.
+The time series regression equation for our analysis is:
+Yt = P0 + P1 * time + P2 * phase + P3 * time_after_NSPP + et
+Yt is the outcome variable, in our model this is the number of suicides per 100.000 in year t, “time” is the number of years at time t from the start of the observation period starting with 1 at time point “t-22”; “phase” is an indicator variable, which is 0 for the time points before and 1 for the time points after the NSPP introduction, “time after NSPP” is how many years after NSPP introduction which is set to 0 for the years before the NSPP introduction and taking on the values of 1 to 5 for the years after NSPP introduction and et represents random variability at time t not explained by the model.
+The coefficient p0 estimates the baseline level of suicides per 100.000, p1 estimates the baseline trend before NSPP introduction, which is the change in the mean number of suicides per 100.000 occurring each year before the implementation. The coefficient p2 estimates the change in level in the mean yearly number of suicides per 100.000 immediately after the NSPP implementation and P3 estimates the change in trend in the mean number of suicides per 100.000 after its implementation.
+All analyses were conducted separately for men and women and split into four age groups each (< 24 years,
+25-44 years, 45-64 years and > 65 years). Furthermore, we analyzed the difference in suicide rates between verum- and control countries, again separately for men and women and the different age groups to estimate how the change in suicide rate in the verum countries differed from the change in the control countries. For all analyses, we used SPSS for Windows version 23. A significance level of <0.05 was considered significant.
+Results
+Analysis of verum countries - males
+Table 1 and Fig. 1 show the parameter estimates from the linear segmented regression model for all males in the verum countries. Right before the beginning of the observation period, an average of 23 per 100.000 males in the verum countries committed suicide per year. Before implementation of the NSPP, there was a significant year-to-year change in the mean number of suicides (p < 0.001). The immediate change in the number of suicides directly after the intervention was not significant (p = 0.536). However, the level change becomes significant two years after the intervention and remained significant for the following three years. The year-to-year trend in the mean number of suicides per 100.000 after the intervention changed significantly (p = 0.006).
+Observing the different age groups, we detected significant level changes in the group of males under age 24 after 5 years (p = 0.049) of NSPP Within the 25-to-44-year-olds, we noted significant level changes after 1 (p = 0.041), 2 (p = 0.001), 3 (p < 0.001), 4 (p < 0.001) and 5 (p < 0.001) years after the NSPP implementation. The trend change that occurred after the NSPP was implemented also reached significance (p = 0.014) in this age group.
+The group of 45-64-year-old males revealed significant level changes after 2 (p = 0.010), 3 (p = 0.001), 4 (p = 0.001) and 5 (p = 0.001) years of NSPP.
+Males older than 65 years showed significant changes in suicide rates after 3 (p = 0.011), 4 (p = 0.005) and 5 (p = 0.007) years of NSPP.
+We observed a significant baseline trend in all age groups except that of the males older than 65. These trends were positive for the males younger than 24 years and the group of 25-44-year-olds, which evolved into a negative trend after the NSPP implementation in both groups. However, this trend change only attained significance for the group of the 25-to-45-year-olds (p = 0.014). The trend for the group of the 45-64-year-old males was already slightly negative before the NSPP and was strengthened by it, an improvement that did not reach significance (p = 0.155).
+Analysis of verum countries - females
+Right before the beginning of the observation period, an average 8 of 100.000 women committed suicide per year in the verum countries. The baseline trend before implementation of the NSPP indicates that the suicide rates remained constant over the years up to the year of NSPP introduction. However, two years after its implementation our analysis showed a statistically significant level change in the suicide rate (p = 0.018). The same applies for years 3, 4 and 5 after implementation (Table 2, Fig. 2). The year-to-year trend in the mean number of suicides per 100.000 after implementation did not change significantly (p = 0.333).
+Assessing the different age groups, we identified significant level changes in the group of females aged between 45 and 64 years after 3 (p <0.021), 4 (p <0.011) and 5 (p < 0.012) years after the NSPP implementation.
+Females older than 65 years showed significant level changes immediately after NSPP implementation (p = 0.002) and after 1 (p < 0.001), 2 (p < 0.001), 3 (p < 0.001), 4 (p < 0.001) and 5 (p < 0.001) years of NSPP These two female groups’ baseline trends were significant. The trend was already negative before the NSPP in the group of 45-to-64-year-old females, while the trend for the females older than 65 was constant. The trend became negative in both groups, but the changes did not reach significance.
+Analysis of control countries - males
+The average number of suicides per 100.000 for all males in the control countries right at the beginning of the observation period was 28 per year. As we expected, there were no level changes or a significant trend after the period of NSPP implementation in the verum countries. However, although the trend change was not significant, it became clearly negative (Table 3, Fig. 3).
+The analysis of males according to age group showed no significant trend or level changes in either males <24 years and those aged 25-to-44 years. We noted a significant (p = 0.010) baseline trend of - 0.56 within the 45-to-64 age group, which changed by -1.1 after the time of NSPP implementation in the verum countries. However, this change was not significant (p = 0.303). Interestingly, males older than 65 years showed significant level changes after 2 (p = 0.040),
+3 (p = 0.005), 4 (p = 0.002) and 5 (p = 0.002) years. We also detected a significant trend change of - 2.6 (p = 0.045).
+Analysis of control countries - females
+The analysis of all females in the control countries revealed no significant level or trend changes (Table 4, Fig. 4). The average number of suicides per 100.000 at the beginning of the observation was 13 per year.
+The analysis of females according to age group showed no changes in trend or level for females < 24 years nor in females in the group of 25-to-44 year-olds. We observed a significant (p = 0.001) baseline trend of - 0.4 in the 45-to-64-year-old group. Similar to males, females older than 65 years showed significant level changes in suicide rates after 2 (p = 0.020), 3 (p = 0.012), 4 (p = 0.015) and 5 (p = 0.025) years.
+Comparison between verum and control countries
+To compare verum and control countries we calculated the difference in the two suicide rates for every year (i.e., the rate of the verum countries minus the rate of the control countries), thus we could analyze both rates in one ARIMA model. Taking the difference collapses the two time series into one and estimates a difference-in-differences effect enables us to make a statement about how the change in the verum countries differed from that in the control countries.
+One would expect a statistically significant negative level change a few years after implementation of the NSPP (e.g., the suicide rate in the verum countries would be expected to drop while that in the control countries would remain constant). However, we detected no significant level or trend change regarding the overall rates for all demographic groups including males or
+females and made the same observation when analyzing the males and females divided into different age groups.
+However, the difference in suicide rates right at the beginning of the observation period was significant for all male and female groups except the males <24, males aged 25-to-44 years, and females <24 (e.g., all males: difference of - 5.6, p = 0.018; all females: difference of - 5.2, p < 0.001). As mentioned above, segmented regression analysis controls for baseline level and trend.
+Discussion
+Overall, this study demonstrates that National Suicide Prevention Programs are effective, but this effect seems to correlate with age and sex.
+Segmented regression analyses of interrupted time series data have shown a statistical significant decline in suicide rates in the verum countries in males, with the
+strongest effects in groups aged 25-to-44 years and 45-to-64 years. We noted a significant effect in females aged 45-to-64 and > 65 years, although this effect was not as strong as it had been in males. We did not detect this effect in the control countries (except in those > 65 years of age). After analyzing the differences in suicide rates between verum and control countries, no significant level changes or trend changes appeared.
+Several working groups have investigated various suicide prevention strategies or programs.
+Major efforts have focused on the accessibility of suicide means. There is strong evidence that restricting the availability of methods (e.g., firearms) can reduce suicides [22-24]. Men are more likely to use guns as suicide method. That might partly explain the significant effects observed in males age 25-64 years. Another example is the detoxification of the English gas in the 60s
+which lead to clearly reduced suicide rates [25]. Similar results could be found in Saxony (Germany, “coal gas story”). A 74% reduction in suicide rates were shown due to the detoxification of the city gas [26].
+The establishment of suicide prevention centers like the “Samaritans”, “Befrienders International” or “Lifeline” caused a perceptible but nevertheless minor preventive effect [27, 28].
+Further approaches like “Tele-Help” or “Tele-Check” were associated with lower suicide numbers [29].
+Others have examined the influence of medication on suicidal behavior. Lithium, a mood stabilizer, is well established as a drug that reduces suicides [30].
+Advanced training for general practitioners was implemented in the 1980s by the Swedish government. Since general physicians became better able to detect depression than beforehand, suicide rates dropped considerably [31].
+The interpretation of statistical data and the causal combination with events or the course of suicide statistics give rise to a complex challenge. Multifarious, unforeseeable factors can play an important role in the appearance of suicidal behavior. Thus the genuine situation in different nations can only be compared under certain limitations.
+There are relatively few studies investigating the effectiveness of suicide prevention programs, and those reveal inconsistent outcomes [32-34]. Countries such as Finland and Scotland have reported a significant reduction in suicide rates [35], whereas others (e.g., Norway, Sweden or Australia) reported limited effects in certain subgroups.
+Our study results endorse the overall effectiveness of National Suicide Prevention Programs. A major reduction in suicide rates, especially in males over 25 years, is presumably related to all arrangements regarding preventing strategies of these programs rather than to one single strategy. There are a couple of hypotheses as to why we found no statistical differences when comparing verum and control countries:
+About 800.000 suicides occurred worldwide representing an annual age-standardized suicide rate of 11.4 per 100,000 population. We know that suicide rates are higher in males (15.0/100000) than in females (8.0/100000). It is acknowledged that three times as many men died by suicide as women; another possible explanation that this study could only reveal differences within the group of men.
+Suicide rates are highest in both males and females aged over 70 years. But several countries have different
+statistical patterns in their age related suicide rates. As the WHO report stated in some countries there is a peak in suicide rates in young adults that subsides in middle age and in other regions suicide rates increase steadily with age [2]. One could argue that our findings in age group 25-64 are partly related to such different patterns.
+Prevention programs aiming to help special age groups may play an important role. Within this study’s framework, we were not in a position to analyze other factors associated with changing suicide rates, such as access to and availability of health care providers. Furthermore, the observation period after NSPP implementation was quite short (five years). Certain strategies might well need longer to reveal their effectiveness.
+Despite the effort to decrease suicide rates via different approaches also the economic effects are remarkable. Vasiliadis et al. recently showed that suicide prevention programs such as the European Nuremberg Alliance against Depression (NAD) are cost-effective and may result in significant potential cost-savings due to averted suicide deaths and fewer life years lost [36].
+It is extremely challenging to investigate changes in implemented prevention strategies such as suicide rates within different countries. Matsubayashi and Ueda (2011) investigated the effect of national suicide prevention programs on suicide rates in 21 OECD nations [37]. Overall, they found that suicide rates decreased after the government initiated a nationwide suicide prevention program, as we did in this study; more so in men than in women. Remarkably, they detected the strongest
+effects in youth (< 24 years old) and the elderly (> 65 years old). They also noted a limited effect on the working-age population. They discuss those differences as a result of specific goals within the prevention programs, such as reducing the access to firearms. One could argue that a comparison of 21 countries may be too ambiguous, as major cultural, religious, socio-economic and political differences can play an important role. That is why we carefully selected countries that were fairly similar in those specific areas - a clear strength of this study.
+A very recent narrative analysis conducted by Zalsmann et al. [38] investigated the effectiveness of different suicide prevention strategies. Due to the heterogeneity of populations and methodology, formal meta-analyses could not be applied. They investigated different suicide prevention methods including school-based awareness program that reduced suicide attempts. They concluded that no one strategy is clearly superior to the others. Our results also support the idea that different approaches appear effective in different groups according to age and gender, for example. That might be another reason for the results found in this study.
+Several limitations of this study provide guidance for future research:
+•	Extensive programs have not been running long enough.
+•	The present study covered just four control and four verum countries, meaning that our results cannot be extrapolated to other countries.
+•	The length of our observational period after NSPP implementation is relatively short - later influences could not be excluded.
+•	Our study approach did not enable us to investigate whether specific components of an NSPP exert different influences on suicide rates.
+•	Our data did not provide information on whether other activities not implemented in a national strategy such as general welfare programs may also influence suicide rates. According to the WHO report [2], current data show a decrease in suicide rates in different countries even in those without an NSPP, which makes our findings not generalizable.
+Despite the encouraging drop in suicide rates, it is very important that future evaluations of suicide prevention programs include the number of suicide prevention interventions implemented successfully as well as the number of hospitalized suicide attempts. The systematic collection of specific data (including suicides and suicide attempts) is key. There are many countries that collect no such data at all or only very minimal data.
+Conclusion
+To the best of our knowledge, this is the first study investigating the effectiveness of national suicide prevention programs applying segmented regression analysis of interrupted time series.
+Our study implies that the implementation of a national strategy is an effective tool to reduce suicide rates. Special attention should be drawn to different approaches regarding age groups as well concerning females. Future research should investigate longer time periods and different aspects of prevention programs and what other factors may influence suicide rates.
+As stated in the WHO’s framework “Public Health Action for the Prevention of Suicide” [2], it is “imperative that governments - through their health, social and other relevant sectors - invest human and financial resources in suicide prevention.”
+Lewitzka et al. BMC Psychiatry (2019) 19:158
+Page 10 of 10
+Publisher's Note
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+Received: 1 March 2018 Accepted: 14 May 2019
+Published online: 23 May 2019
+References
+1.	Turecki G, Brent DA. Suicide and suicidal behaviour. Lancet. 2016;387:1227-39.
+2.	WHO. Preventing suicide: A global imperative http://www.who.int/mental_ health/suicide-prevention/world_report_2014/en/. Accessed 20 May 2019.
+3.	Krysinska K, Batterham PJ, Tye M, Shand F, Calear AL, Cockayne N, Christensen H. Best strategies for reducing the suicide rate in Australia. Aust N Z J Psychiatry. 2016;50:115-8.
+4.	WHO 2012 http://apps.who.int/iris/bitstream/10665/75166Z1/ 9789241503570_eng.pdf. Accessed 20 May 2019.
+5.	IASP 2015 https://www.iasp.info/suicide_guidelines.php. Accessed 20 May 2019.
+6.	Norwegian Board of Health (1995). The National Plan for suicide prevention 1994-1998. Oslo https://www.med.uio.no/klinmed/english/research/centres/ nssf/articles/prevention/The_national_plan_for_suicide_prevention_1994-1998.pdf. Accessed 20 May 2019.
+7.	Sorâs I. Handlingsplan mot selvmord - hva viser evalueringen? Suicidologi. 2000;5:12-3.
+8.	Mehlum L, Reinholdt NP. Handlingsplan mot selvmord: Gode erfaringer skal fores videre. Suicidologi. 2001;6:16-8.
+9.	The Swedish National Council for Suicide Prevention. Support in suicidal crises: the Swedish National Program to develop suicide prevention. Crisis. 1997;18:65-72.
+10.	Jenkins R, Singh B. National Suicide Prevention Strategies. Psychiatr Fenn. 1999;30:9-30.
+11.	Beskow J, Kerkhof A, Kokkola A, Uutela A. Suicide prevention in Finland 1986-1996. External evaluation by an international peer group. Psychiatr Fenn. 1999;30:31-46.
+12.	Commonwealth of Australia. L.I.F.E. Living is for everyone. A framework for prevention of suicide and self-harm in Australia, areas for action.
+Department of Health and Aged Care 2000, Canberra. Available via https:// www.lifeinmindaustralia.com.au/about-us/the-life-framework. Accessed 20 May 2019.
+13.	Page A, Tylor R, Gunnell D, Carter G, Morrell S, Martin G. Effectiveness of Australian youth suicide prevention initiatives. Br J Psychiatry. 2011;199:423-9.
+14.	Kerhof JF, Clark DC. How to evaluate national suicide programs? Crisis. 1998;19:2-3.
+15.	Ono Y, Sakai A, Otsuka K, et al. Effectiveness of a multimodal community intervention program to prevent suicide and suicide attempts. A quasi-experimental study. PLoS One. 2013;(8):e74902.
+16.	http://www.who.int/whosis/en/, access at the time of data analysis: July 2005.
+17.	Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27:299-309.
+18.	Ma ZQ, Kuller LH, Fisher MA, Ostroff SM. Use of interrupted time-series method to evaluate the impact of cigarette excise tax increases in Pennsylvania, 2000-2009. Prev Chronic Dis. 2013;10:e169.
+19.	Gebski V, Ellingson K, Edwards J, Jernigan J, Kleinbaum D. Modelling interrupted time series to evaluate prevention and control of infection in healthcare. Epidemiol Infect. 2012;140:2131-41.
+20.	Penfold RB, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr. 2013;13:S38-44.
+21.	Taljaard M, McKenzie JE, Ramsay CR, Grimshaw JM. The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care. Implement Sci. 2014;19(9):77.
+22.	Bronisch T. Der Suizid: Ursachen Warnsignale Prevention. C.H.Beck;1995, München.
+23.	Anestis MD, Anestis JC. Suicide rates and state Laws regulation access and exposure to handguns. Am J Public Health. 2015;105:2049-58.
+24.	Reisch T, Steffen T, Habenstein A, Tschacher W. Change in suicide rates in Switzerland before and after firearm restriction resulting from the 2003 “Army XXI” reform. Am J Psychiatry. 2013;170:977-84.
+25.	Kreitman N. The coal gas story. United Kingdom suicide rates, 1960-71.
+Br J Prev Soc Med. 1976;30:86-93.
+26.	Felber W. Die Entgiftung des Stadtgases und die Suizidrate in Sachsen. Symposium S-138: Die Reduktion von Suizidraten durch Beeinflussung der Suizidmethoden. Berlin: DGPPN-Kongress. p. 21-24.11.2007.
+27.	Leenaars A, Lester D. Impact of suicide prevention centers on suicide in Canada. Crisis. 1995;16:39.
+28.	Lester D. Evaluating the effectiveness of the Samaritans in England and Wales. Int J Health Sci. 1994;5:73-4.
+29.	DeLeo D, Carollo G, Dello Buono M. Lower suicide rates associated with a tele-help/tele-check servie for the elderly at home. Am J Psychiatry. 1995; 152:632-4.
+30.	Lewitzka U, Severus E, Bauer R, Ritter P, Müller-Oerlinghausen B, Bauer M. The suicide prevention effect of lithium: more than 20 years of evidence- a narrative review. Int J Bipolar Disord. 2015;3:32.
+31.	Rutz W, von Knorring L, WalinderJ. Long-term effects of an educational program for general practitioners given by the Swedish committee for the prevention and treatment of depression. Acta Psychiatr Scand. 1992;85:83-8.
+32.	De Leo D, Evans RW, editors. International suicide rates and prevention strategies. Gottingen: Hogrefe&Huber;2004.
+33.	Wassermann D. Evaluating suicide prevention: various approaches needed. World Psychiatry. 2004;3:153-4.
+34.	Wassermann GA, McReynolds LS, Musabegovic H, Whited AL, Keating JM, Huo Y. Evaluating project connect: improving juvenile probationer's mental health and substance use service access. Adm Police Ment Health. 2009;36:393-406.
+35.	Kerkhof AJ. The Finnish national suicide prevention program evaluated. Crisis. 1999;20:50,63.
+36.	Vasiliadis HM, Lesage A, Latimer E, Sequin M. Implementing suicide prevention programs: costs and potential life years saved in Canada. J Ment Health Policy Econ. 2015;18:147-55.
+37.	Matsubayashi T, Ueda M. The effect of national suicide prevention progams on suicide rates in 21 OECD nations. Soc Sci Med. 2011;73:1395-400.
+38.	Zalsman G, Hawton K, Wasserman D, et al. Suicide prevention strategies revisted: 10-year systematic review. Lancet Psychiatry. 2016;3:646-59.
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+Introduction
+Socioeconomic status, which captures social circumstances across the life course, is a powerful predictor of ill health. Studies have found increased morbidity and disability in individuals who are socioeconomically disadvantaged1-3 and the disease burden in this group is increasing with population ageing.4-6 However, to our knowledge, a comprehensive overview of the associations between socioeconomically patterned mental and physical health conditions is lacking.
+Previous investigations have explored the relationship between socioeconomic status and multimorbidity.3,7-14 These findings showed that having two or more diseases and developing multimorbidity was more common in people with low socioeconomic status. A limitation of these studies is the relatively restricted range of
+morbidities investigated (communicable diseases are typically not included) and a failure to capture the temporal sequence between specific diseases. Considering temporality in disease onset could yield new insights into the cascades of health conditions that characterise morbidity in people with socioeconomic disadvantage.
+To address these limitations, we examined the development of mental and physical health conditions among individuals with low and high socioeconomic status to determine temporal sequence and inter-relationships in the emergence of socioeconomically patterned conditions. We used a range of disease endpoints, adopting a data-driven approach, as we were not aware of any previous evidence-based test of hypotheses on disease cascades that characterised morbidity in people from different socioeconomic backgrounds.
+Research in context
+Evidence before this study
+Low socioeconomic status, which captures multiple aspects of disadvantage, is a known risk factor for several diseases.
+We searched PubMed for research on low socioeconomic status and morbidity, without language or date restrictions, up to Feb 10, 2019, and identified thousands of studies using the search terms “socioeconomic” in combination with “cancer”, “infection”, “cardiovascular”, “coronary heart disease”, “stroke”, and “psychiatric disorders”. Studies on socioeconomic status in relation to other diseases, such as “diabetes”, “endocrine disorder”, “respiratory disease”, “skin disease”, “neurodegenerative disease”, “dementia”, and “digestive disease” were also very common. Few studies examined “multimorbidity” and we found no research on temporal sequences in mental and physical diseases across all bodily systems according to socioeconomic status.
+Added value of this study
+To facilitate a more comprehensive evaluation of morbidity associated with socioeconomic disadvantage, we combined individual-level data from two large cohort studies and examined low socioeconomic status as a risk factor for a range
+of hospital-treated diseases. We determined temporal sequences in the emergence of diseases that were socioeconomically patterned. We repeated analyses in a third independent cohort. Across three indicators, low socioeconomic status was robustly associated with 18 (32-1%) of 56 specific diseases or health conditions, including 16 strongly interconnected conditions (hazard ratio >5 for each disease to be followed by another disease). This disease cascade started with psychiatric disorders, substance abuse, and self-harm and was followed later by diseases of the pancreas, liver, kidney, vascular and respiratory system, lung cancer, and dementia. Diabetes was associated with the cascade, but not with early psychiatric and behavioural disorders.
+Implications of all the available evidence
+Low socioeconomic status is a risk factor for a range of disorders, including mental and behavioural problems, which seems to set in motion a lifelong cascade of physical diseases. These findings suggest that policy and health-care practice addressing psychological health issues in social context and early in the life course might be an effective strategy for reducing socioeconomic inequalities in health.
+Methods
+Study design and population
+In this multi-cohort study, we used data from two Finnish prospective cohort studies: the Health and Social Support (HeSSup) study15 and the Finnish Public Sector (FPS) study.16 We tested the generalisability of our findings with an independent UK cohort study—the Whitehall II study.17 Ethical approval for these three studies was obtained from local committees on the ethics of human research. The derivation of the analytical sample used in each of these studies is shown in figure 1 and the appendix (p 2).
+In the HeSSup study, 21 486 of the men and women who responded to the survey between June 7, 1998, and May 23, 2000, or Jan 7, and Aug 12, 2003, had no missing data on residential area deprivation, and were successfully linked electronically to national hospitalisation and mortality registers until Dec 31, 2012.15 The FPS sample comprised 87 760 men and women who responded to at least one of four surveys done between March 1, 2000, and June 30, 2002, March 1, 2004, and June 30, 2005, March 1, 2008, and Nov 30, 2009, and Dec 1, 2011, and Nov 30, 2013, and had data on residential area deprivation.16 Study participants were linked to electronic health records until Dec 31, 2016.
+For our replication analyses, we used data from the Whitehall II study, which comprises 9838 government workers who participated in clinical examinations between Sept 10, 1985, and March 29, 1988, had no missing data on occupational position and covariates, and were linked electronically to national hospitalisation and mortality registers from Jan 1, 1997, when these records achieved a high level of national coverage, to March 31, 2017.18
+Assessment of socioeconomic status at baseline
+To explore the consistency of our results, we used three different indicators of socioeconomic status in our analyses. In the Finnish studies, we derived a score for residential area deprivation, similar to that developed by Townsend and colleagues,19 and a measure of educational attainment. The area deprivation score was obtained from Statistics Finland and is based on the proportion of adults with low education, the unemployment rate, and the proportion of people living in rented housing in each 250 m by 250 m grid area.20 Higher scores on the continuous index denote greater deprivation. We categorised these data as follows: low socioeconomic status (an area deprivation score higher than national mean), intermediate socioeconomic status (deprivation score from national mean to 0-5 SD below), and high socioeconomic status (the remaining data).
+Educational attainment, obtained from Statistics Finland via record linkage (for the FPS study) or from a survey (for the HeSSup study), was based on the following two categories: high (tertiary qualification, college or university) and low (all other qualifications, including none).
+In our replication analysis, we indexed socioeconomic status by a third indicator, the British civil service occupational grade.17 Broadly equivalent to the Registrar General’s indicators of occupational social class,21 this index of socioeconomic circumstances is related to salary, occupational prestige, level of responsibility at work, and future pension, and has three groups as follows: high (administrative occupations), intermediate (professional and executive occupations), and low (clerical and support occupations).
+Assessment of lifestyle risk factors at baseline
+Using predefined operationalisations, we chose the following baseline risk factors to determine the extent to which the associations between socioeconomic status and diseases were attributable to standard lifestyle factors: current smoking (yes vs no), risky alcohol use (consumption >210 g per week vs other), physical inactivity (yes vs no), and obesity (body-mass index >30 kg/m2 vs other).
+Follow-up for diseases, health conditions, and mortality Participants from the HeSSup study15 and FPS study16 were linked by their unique identification number to national registries of hospital discharge information (recorded by the National Institute for Health and Welfare) and mortality (recorded by Statistics Finland). These electronic health records include cause and date of hospitalisation or mortality and their coverage (all hospital types, including private hospitals, and records cover emergencies) reflects the comprehensive nature of Finland’s public health-care system. Additional information on site-specific cancers, diabetes, cardiovascular diseases (including hypertension), psychotic disorders, dementia, Parkinson’s disease, multiple sclerosis, epilepsy, asthma, chronic obstructive bronchitis, inflammatory bowel disease, liver disease, rheumatoid arthritis, gout, and renal failure was available via record linkage to the National Cancer Registry and the Drug Reimbursement Register of the Social Insurance Institution of Finland.
+Whitehall II study members were linked to the UK National Health Service (NHS) Hospital Episode Statistics (HES) database for hospital admissions and the NHS Central Registry for mortality. In studies of chronic diseases, the sensitivity and specificity of the HES database have been high.18,22
+In all cohort studies, the diagnosis for incident disease was coded according to the WHO International Classification of Diseases Tenth Revision (ICD-10). We focused on fifteen ICD-10 disease chapters that concern infectious and parasitic diseases (A00-B99), neoplasms (C00-D48), diseases of the blood (D50-D89), endocrine, nutritional, and metabolic diseases (E00-E90), mental and behavioural disorders (F00-F99), diseases of the nervous system (G00-G99), the eye (H00-H59), the ear (H60-H95), the circulatory system (I00-I99), the respiratory system (J00-J99), the digestive system (K00-K93), the skin (L00-L99), the musculoskeletal system (M00-M99), and the genitourinary system (N00-N99), injuries and poisoning (S00-T98), and external causes (V01-Y98).
+Statistical analysis
+Linked records captured 1204 ICD codes, including 56 major diseases or health conditions used in this analysis (for a complete list see appendix pp 2-13). Our primary analysis included two steps as follows: examination of associations between socioeconomic status (the exposure) and first new onset of health conditions after baseline (outcome) and mapping of
+Figure 1: Selection of participants for primary and replication analyses FPS=Finnish Public Sector. HeSSup=Health and Social Support.
+temporal sequences of interconnected health conditions in analyses stratified by socioeconomic status.
+First, having assessed the proportional hazards assumption (appendix pp 18-21), we examined associations between socioeconomic status and each of the 56 diseases in separate models using Cox proportional hazards regression. Follow-up continued until disease onset, death, or end of follow-up, whichever occurred first. Hazard ratios (HRs) computed for low socioeconomic status with high socioeconomic status as a reference were adjusted for the following potential confounding factors: age, sex, lifestyle factors (current smoking, heavy alcohol consumption, physical inactivity, and obesity), and cohort. In our analysis of socioeconomic status and new onset hospital-treated obesity, we did not control for baseline obesity. To identify diseases that were more common in participants with low socioeconomic status, related to socioeconomic differences that were likely to be meaningful for public health and unlikely to result from multiple testing,23,24 only socioeconomic status-disease endpoint associations that
+yielded predefined HRs equal to or greater than 1-223,24 and were statistically significant across the two different socioeconomic indicators (area deprivation and low education) were regarded as being sufficiently robust.
+Second, we determined potential temporal sequences at recorded diagnosis of diseases that were robustly associated with socioeconomic status by testing prospective associations between all socioeconomically patterned disease pairs separately in individuals with low socioeconomic status and high socioeconomic status. We used Cox proportional hazards regression and determined temporal order in testing the associations between disease pairs based on the mean age at diagnosis; a disease with an earlier onset was treated as the predictor and a disease with a later onset as the outcome. Followup started at recorded diagnosis for the first disease and continued until the date of diagnosis for the next disease, death, or end of follow-up, whichever occurred first. We adjusted HRs and 95% CIs for age, sex, and study.
+We constructed disease cascades from identified sequential interconnected disease pairs, starting from a single disease and continuing as far as interconnected disease pairs were available.25 We considered diseases and disease pairs to be interconnected if the HR for the association between them exceeded an arbitrary threshold of 5 in the socioeconomic status group, irrespective of the indicator used to define the group. In sensitivity analyses, we used alternative HRs of greater than 2-5 and greater than 10 as criteria for interconnectedness.
+For participants with high socioeconomic status, we analysed interconnected disease cascades, focusing on health conditions with HRs for indicators of socioeconomic status less than 1-0 (ie, diseases that were more common in high socioeconomic status groups than in low socioeconomic status groups). This more relaxed threshold was used because very few health conditions were more common in high socioeconomic status groups compared with low socioeconomic status groups.
+To examine the generalisability of the findings from the Finnish cohorts in the primary analysis across geographical regions and health-care settings, we tested robust associations between socioeconomic status and health conditions in a replication analysis using the Whitehall II cohort.
+All analyses were done using SAS version 9.4 and statistical code is provided in the appendix (pp 13-17).
+Role of the funding source
+The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. MK, JV, JP, and MJS had full access to all the data in the study. All authors had final responsibility for the decision to submit for publication.
+Results
+178 375 participants from the two cohorts of the primary analysis were eligible for inclusion (113 578 from the FPS
+study and 64797 from the HeSSup study). 109 246 (61-2%) participants responded to the baseline questionnaire, were successfully linked to registers of socioeconomic status and health, and were included in the analytic sample (figure 1). 83 066 (76-0%) of 109 246 participants were women. The mean participant age was 44-3 years (SD 11-0) and the range was 17-77 years. According to area-based deprivation, 36 216 (33-2%) participants were in the low socioeconomic status group. 52 990 (48-5%) participants were in the low education group (appendix p 23).
+During 1 110 831 person-years at risk, we recorded 245 573 hospitalisations in the 109 246 participants (figure 1). Compared with high socioeconomic status, low socioeconomic status was associated with an increased risk of 18 (32-1%) of the 56 health conditions for both indicators of socioeconomic status (HR >1-2; figure 2). By descending magnitude of association (ie, mean HR for the two indicators of socioeconomic status) these were self-harm, poisoning, psychotic disorders, arteriosclerosis, chronic obstructive bronchitis, lung cancer, dementia, obesity, disorders of substance abuse, pancreatitis, heart failure, anaemia, mood disorders, renal failure, diabetes, cerebral infarction, ischaemic heart disease, and disease of the liver. For disorders of substance abuse and ischaemic heart disease, the association was stronger in the first 3 years of follow-up than from year 3 onwards (appendix p 19). Minimally adjusted associations are also reported in the appendix (p 25).
+Three further health conditions were associated with area deprivation, but not low education, eight health conditions with low education, but not area deprivation, and 23 with neither area deprivation nor low education (figure 2). Four health conditions were more common in groups with high socioeconomic status (melanoma, spontaneous abortion, hypertension in pregnancy, and breast cancer). Of these four conditions, only breast cancer was associated with both indicators of high socioeconomic status (ie, low area deprivation and high education).
+Figure 3 shows interconnections between socioeconomically patterned health conditions and the mean age at diagnosis in participants with low socioeconomic status. The association between many disease pairs was stronger during the first 3 years of follow-up than from year 4 onwards (appendix p 22). 16 (88-9%) of 18 socioeconomically patterned health conditions were strongly interconnected as defined by HR greater than 5 for associations between disease pairs in the low socioeconomic status group, including both those with area deprivation and low education. Among participants
+with high socioeconomic status, we observed no strong interconnections between the four common diseases for this group (appendix p 26).
+Using mean age at recorded diagnosis, we were able to formulate a cascade of diseases in socioeconomically disadvantaged participants (figure 4). This cascade started
+with mental and behavioural disorders (psychiatric disorders, self-harm, and substance abuse) and was followed by pancreatitis, liver disease, anaemias, renal and heart failure, ischaemic heart disease, cerebral infarction, heart failure, arteriosclerosis, chronic obstructive bronchitis, lung cancer, or dementia. Diabetes was strongly connected
+with this cascade via association with renal failure, whereas hospital-treated obesity was not associated with any of the diseases in the cascade. In groups with low socioeconomic status defined using only one indicator, we observed additional connections between health conditions, particularly among participants with low education (appendix pp 27-28).
+When repeating our analysis of the 18 socioeconomically patterned health conditions among participants with low socioeconomic status using alternative thresholds (HRs 2-5 and 10) for connectedness (appendix p 29), the cascade of diseases starting from mental and behavioural disorders and including subsequent physical diseases remained apparent.
+In a subsidiary analysis of bidirectional associations, several physical diseases were associated with subsequent mental ill health—eg, heart failure (HR 3-18, 95% CI 1-31-7-71), cerebral infarction (3-68, 1-81-7-45), chronic obstructive bronchitis (3-63, 1-50-8-82), pancreatitis (7-65, 3-94-14-85), and renal failure (5-38, 2-23-13-02) predicted later mood disorders.
+The eligible population for the replication analysis was 14 121 men and women from the UK Whitehall II study.17 10 308 (73-0%) responded to the baseline survey and 9838 (69-7%) had no missing data on socioeconomic status or covariates and were successfully linked to electronic health records (figure 1; appendix p 24). In 186 572 person-years at risk (mean follow-up 19-0 years), we recorded 60 946 hospitalisations. All 18 associations between socioeconomic status and disease endpoints in the primary analysis were replicated (HRs >1-3; figure 5; minimally-adjusted HRs are presented in the appendix p 30). We found imprecision in the estimates for poisoning, selfharm, lung cancer, and arteriosclerosis as evidenced by the wide confidence intervals that included unity.
+Discussion
+In this study, we examined a range of mental and physical diseases and health conditions and found that low socioeconomic status was associated with 18 (32-1%) of the 56 diseases studied, independent of lifestyle factors and obesity and the indicator of socioeconomic status
+used (area deprivation, education, or occupational position). 16 (88-9%) of these 18 socioeconomically patterned diseases were interconnected, directly or indirectly, with mental health problems and substance abuse, including conditions such as pancreatitis, liver, renal, cardiovascular, and cerebrovascular diseases, chronic obstructive bronchitis, lung cancer, and dementia. With a less stringent threshold for interconnectedness, the cascade from mental disorders to physical illness was replicated and comprised all 18 diseases. When a higher threshold for interconnectedness was set—a minimum HR of 10 between diseases—mental health problems and substance use remained strongly connected with diseases of the liver, the cardiovascular and cerebrovascular system, and dementias, the latter emphasising the importance of socioeconomic patterns in diseases related to the CNS.
+Our findings are supported by several strands of evidence. The observed link between mental health and substance abuse, and between mental health and physical diseases, has been confirmed by meta-analyses measuring the impact of socioeconomically patterned adverse childhood experiences on mental disorders and chronic physical diseases in adulthood.26 The morbidity trajectories identified in our study included several of these chronic physical diseases, such as liver, respiratory, and cardiovascular diseases. Studies have shown that mental disorders increase the risk of physical diseases, both communicable and non-communicable, via a higher tendency to commit risky behaviours, reduced self-care, and complications in help-seeking.27,28 Additionally,
+psychotropic medications have adverse effects on many aspects of physical health that accumulate over time.27,28
+The link between mental and physical disorders could be strengthened by the bidirectional nature of this association. Although mental disorders are a risk factor for various physical diseases, the opposite direction of causality has also been observed. For example, socioeconomically patterned chronic conditions, such as cerebral infarction, heart failure, and chronic obstructive bronchitis, can increase the risk of mental disorders.28 We confirmed these associations and found associations between pancreatitis and renal failure and subsequent mood disorders.
+The range of health conditions in our study expands upon previous research on socioeconomic status and multimorbidity. A study of older Taiwanese people found low education to be associated with increasing trajectories of cardiovascular and chronic non-specific lung diseases12 and a UK study reported a link between low occupational grade and an increased risk of developing cardiometabolic multimorbidity (ie, two or three of diabetes, myocardial infarction, and stroke).3 A Canadian study identified an association between lower income and greater overall multimorbidity,13 a German study of primary care patients identified an association between low education and a higher number of diagnoses, particularly cardiometabolic diseases,14 and a study of Australian women found a relationship between low education and difficulties in managing income and increased self-reported multimorbidity in repeated questionnaire surveys.11
+We adjusted the association between socioeconomic status and health conditions for lifestyle behavioural factors, such as self-reported heavy alcohol consumption, smoking, physical inactivity, and obesity. This approach is conservative, as these factors are both confounders and potentially part ofthe causal pathway from socioeconomic disadvantage to disease. Prospective life-course research supports socioeconomic disadvantage as an origin of unhealthy lifestyle behaviours and subsequent morbidity. In a cohort study of Finnish children and adolescents, for example, differences in risk factors between socioeconomic groups at the beginning of follow-up were small, but large differences emerged in the third decade of life.20 In addition to risk behaviours, such as unhealthy diet, physical inactivity, and smoking, low socioeconomic status was associated with a poorer glycaemic profile in early adulthood and, like our findings, an excess prevalence of obesity, diabetes, fatty liver, and cardiovascular disease in middle age.20
+Our findings have important implications for research and public health policy. The pattern of mental health problems and substance abuse preceding socioeconomically patterned physical diseases is not reflected in global strategies to prevent diseases. The WHO Sustainable Development Goals and the Global Action Plan for the Prevention and Control ofNon-Communicable Diseases, for example, have their main focus on physical health;6 the 2013-2020 WHO Global Plan for the Prevention and Control of Non-Communicable Diseases29 and the Global Burden of Disease Collaboration do not include socioeconomic disadvantage as a modifiable risk factor.30 Moreover, treatment of psychiatric disorders, physical disease, and substance abuse is often split between health-care and social services.27,28 This approach is unlikely to be optimal for tackling problems with shared health determinants, including socioeconomic inequalities in morbidity. The 2019 Lancet Commission drew attention to the need to improve protection of physical health in people with psychiatric disorders;27 our findings suggest this is particularly important for people living in socioeconomic disadvantage.
+This study has several limitations. The response to baseline assessment varied between 61% in the primary analysis and 70% in the replication analysis. Sample attrition might lead to an overestimation or underestimation of the true associations between socioeconomic status and health. We measured morbidity mainly using electronic health records, which covered hospital-treated diseases. For some conditions, such as asthma, diabetes, and hypertension, additional nonhospitalised cases were identified via linkage to records of eligibility for special reimbursement for medication. However, we will have inevitably omitted undiagnosed conditions and less severe cases that are largely dealt with in primary care (eg, obesity). Therefore, the observed interconnectedness between diseases reflected the temporal order of treatments rather than causal
+associations between health conditions. The age distribution of participants at study induction meant that we did not have data on children or very old people. The generalisability of our findings beyond Finland and the UK and to other health-care systems is also uncertain and requires testing.
+However, by applying a data-driven approach to a wide set of diseases and health conditions, we refocus the field of socioeconomic inequality research from traditional analysis of specific diseases to the study of interconnected diseases. A large sample size, longitudinal design, minimal sample attrition after baseline because of follow-up via electronic health records, and the validation of our results across different indicators of socioeconomic status and health-care settings are further strengths.
+In conclusion, by mapping morbidity from electronic health records we showed that low socioeconomic status is a risk factor for a spectrum of interconnected diseases and health conditions. Our analyses of interconnected diseases highlight the importance of mental health problems and substance abuse in the cascade of socioeconomically patterned physical illnesses. These findings suggest that policy and health-care practice addressing mental health issues in social context and early in the life course might be effective strategies for reducing health inequalities.
+Articles
+(RG/16/11/32334), the Academy of Finland (311492), Helsinki Institute 16 of Life Science, and the Finnish Work Environment Fund (190424).
+References
+1	Dalstra JA, Kunst AE, Borrell C, et al. Socioeconomic differences in the prevalence of common chronic diseases: an overview of eight European countries. Int J Epidemiol 2005; 34: 316-26.
+2	Stringhini S, Carmeli C, Jokela M, et al. Socioeconomic status, non-communicable disease risk factors, and walking speed in older adults: multi-cohort population based study. BMJ 2018; 360: k1046.
+3	Singh-Manoux A, Fayosse A, Sabia S, et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: a cohort study. PLoS Med 2018; 15: e1002571.
+4	Mackenbach JP, Stirbu I, Roskam AJ, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med 2008; 358: 2468-81.
+5	WHO. Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Final Report of the Commission on Social Determinants of Health. Geneva: World Health Organization, 2008.
+6	UN. Transforming our world: the 2030 agenda for sustainable development. New York, NY: United Nations, 2015.
+7	Frolich A, Ghith N, Schiotz M, Jacobsen R, Stockmarr A. Multimorbidity, healthcare utilization and socioeconomic status: a register-based study in Denmark. PLoS One 2019; 14: e0214183.
+8	van den Akker M, Buntinx F, Metsemakers JF, Roos S, Knottnerus JA. Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol 1998; 51: 367-75.
+9	Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.
+Lancet 2012; 380: 37-43.
+10	Park B, Ock M, Lee HA, et al. Multimorbidity and health-related quality of life in Koreans aged 50 or older using KNHANES 2013-2014. Health Qual Life Outcomes 2018; 16: 186.
+11	Jackson CA, Dobson A, Tooth L, Mishra GD. Body mass index and socioeconomic position are associated with 9-year trajectories of multimorbidity: a population-based study. Prev Med 2015; 81: 92-98.
+12	Hsu HC. Trajectories of multimorbidity and impacts on successful aging. Exp Gerontol 2015; 66: 32-38.
+13	Canizares M, Hogg-Johnson S, Gignac MAM, Glazier RH, Badley EM. Increasing trajectories of multimorbidity over time: birth cohort differences and the role of changes in obesity and income. J Gerontol B Psychol Sci Soc Sci 2018; 73: 1303-14.
+14	Schafer I, Hansen H, Schon G, et al. The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study.
+BMC Health Serv Res 2012; 12: 89.
+15	Korkeila K, Suominen S, Ahvenainen J, et al. Non-response and related factors in a nation-wide health survey. Eur J Epidemiol 2001; 17: 991-99.
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+Am J Public Health 2007; 97: 874 79.
+Marmot MG, Smith GD, Stansfeld S, et al. Health inequalities among British civil servants: the Whitehall II study. Lancet 1991; 337: 1387-93.
+Kivimaki M, Batty GD, Singh-Manoux A, Britton A, Brunner EJ, Shipley MJ. Validity of cardiovascular disease event ascertainment using linkage to UK hospital records. Epidemiology 2017; 28: 735-39.
+Townsend P, Beattie A, Phillimore P. Health and deprivation: inequality and the North. London: Croom Helm, 1988.
+Kivimaki M, Vahtera J, Tabák AG, et al. Neighbourhood socioeconomic disadvantage, risk factors, and diabetes from childhood to middle age in the Young Finns Study: a cohort study. Lancet Public Health 2018; 3: e365-73.
+Elovainio M, Ferrie JE, Singh-Manoux A, et al. Socioeconomic differences in cardiometabolic factors: social causation or health-related selection? Evidence from the Whitehall II Cohort Study, 1991-2004. Am J Epidemiol 2011; 174: 779-89.
+Sommerlad A, Perera G, Singh-Manoux A, Lewis G, Stewart R, Livingston G. Accuracy of general hospital dementia diagnoses in England: sensitivity, specificity, and predictors of diagnostic accuracy 2008-2016. Alzheimers Dement 2018; 14: 933-43.
+Olivier J, May WL, Bell ML. Relative effect sizes for measures of risk. Commun Stat 2017; 46: 6774-81.
+Siontis GC, Ioannidis JP. Risk factors and interventions with statistically significant tiny effects. Int J Epidemiol 2011; 40: 1292-307 Jensen AB, Moseley PL, Oprea TI, et al. Temporal disease trajectories condensed from population-wide registry data covering 6-2 million patients. Nat Commun 2014; 5: 4022.
+Hughes K, Bellis MA, Hardcastle KA, et al. The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet Public Health 2017; 2: e356-66.
+Firth J, Siddiqi N, Koyanagi A, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry 2019; 6: 675-712.
+Prince M, Patel V, Saxena S, et al. No health without mental health. Lancet 2007; 370: 859-77.
+WHO. Global action plan for the prevention and control of noncommunicable diseases 2013-2020. Geneva: World Health
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+GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1923-94.
+e149
+www.thelancet.com/public-health Vol 5 March 2020
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+Introduction
+News reporting of suicide has increased substantially in recent decades.1-4 A number of studies have shown that media reports of suicide are associated with increased numbers of suicides.5-10 Media related imitation of suicide has been dubbed the Werther effect, based on a reported spike in suicides in young men in Germany and across Europe after the publication of Goethe’s The sorrows of young Werther in 1774, depicting the circumstances leading to the suicide of the male protagonist
+Werther.11 More than 150 studies have investigated the effects of suicide related to media reports.10 Most have used before and after comparisons or time series designs, testing whether media reporting was associated with subsequent changes in suicides at an aggregate level across a region of exposure. The Werther effect is discussed mostly in relation to non-fictional news stories,8 particularly stories about deaths of celebrities by suicide,6 and stories with a dramatic or romanticised depiction of suicide, or featuring an explicit and detailed description of a suicide method.12-14
+In acknowledgment of the Werther effect, mental health and suicide prevention organisations worldwide, including the World Health Organization, have developed guidelines for responsible reporting of suicide by the media with a specific focus on news and information media.15 16 These guidelines are now a standard component of many national and regional suicide prevention strategies.16 Typically included in the guidelines are specific suggestions about ways to minimise harm (eg, by avoiding glorification of suicide, discussions of specific suicide methods, and repeated reporting about the same suicide). The guidelines also recommend including information on the role of treatable mental illness, where and how to seek help for suicidal thoughts, and a message of hope that suicide is preventable. But specific information on individual deaths by suicide continues to be published; the suicide of the actor Robin Williams is an example of the guidelines not being fully followed.17 18
+Controversies around suicide and the media remain, despite a global focus on avoiding the Werther effect and compelling associations in the literature. Research shows that not all media coverage of suicide is associated with subsequent increases in suicides, resulting in a debate lasting decades on the impact of media reporting of suicide on subsequent suicides.9 10 13 14 19 In several countries that have implemented media guidelines, journalists and media professionals have pushed back, arguing that the body of evidence is not compelling enough to warrant changes to the way suicide is reported.10 20
+Meta-analyses can better quantify the combined evidence of a Werther effect across published studies, but these studies are scarce. One meta-analysis of 10 studies examined media reporting on deaths of celebrities by suicide and found an average increase of 2.6 suicides per million people (95% confidence interval 0.9 to 4.3) in the month after the reports of death.6 In the largest meta-analysis so far, Stack9 combined findings from 55 studies examining non-fictional reports of suicide as a predictor of suicide, and found that only 36% identified an apparent Werther effect. This meta-analysis did not, however, define clear inclusion and exclusion criteria; consider the quality of the studies; account for potential duplication of results; and, crucially, involve the abstraction of quantitative data on suicides (as is normally the case). The outcome of the meta-analysis was a binary variable of increase versus no increase in suicides.
+Media coverage of celebrity deaths by suicide is a small proportion of all suicide reporting8-10 13 14 and the guidelines make recommendations about all forms of reporting of suicide.16 Meta-analyses on the effects of general reporting of suicide (that is, any reporting related to suicide) are lacking. General reporting of suicide might involve deaths of celebrities or other individuals, or might include more general discussions on the topic of suicide. These studies typically use broad search terms to identify media reports (eg, suicide or various suicide methods).
+The aim of this systematic review and meta-analysis was to examine and quantify the findings from the literature on the
+Werther effect. We aimed to evaluate the effects of three types of media reporting on suicide on the subsequent incidence of suicide. The primary objective was to summarise the evidence on the association of media reporting of deaths of celebrities by suicide on total suicides over a short period of time (up to two months). The secondary objectives were to summarise the association of media reporting of information about the specific methods used by the celebrities on suicides by the same method, and the association of general reporting of suicide on the total number of suicides. We hypothesised that reporting of the deaths of celebrities by suicide would be associated with an increased incidence of suicide in the general population, and that increases by the same method would be strongest. We did not have a clear hypothesis for general reporting of suicide because of the variety of content, some of which might be harmful and some protective.13 14 For our meta-analysis, we use the term “intervention” to refer to media reporting of suicide. The study was conducted according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines.
+Methods
+Search strategy
+We defined news and information media as all non-fictional accounts of suicide on TV, in print, in online news, or in educational non-fiction media (eg, non-fiction books or films). Studies on the effects of searching for suicide related information online (eg, Google searches) were not eligible because these studies do not distinguish between positive (eg, for help services) and negative (eg, pro-suicide websites) searching.21 We searched PubMed/Medline, Embase, PsycInfo, Scopus, Web of Science, and Google Scholar for relevant studies from their inception to September 2019. These databases show modest to strong overlap in coverage.22 Google Scholar was used specifically to identify grey literature.23 We used the search terms suicide (suicid*) AND imitation (Werther; Papageno; copycat; imitat*; contagio*; suggesti*); AND media (media; newspaper*; print; press; radio*; televis*; film*; book*; documentar*; internet; cyber*; web*).
+The titles and abstracts of the retrieved articles were screened for relevance, and the full text versions of studies that might meet the inclusion criteria were reviewed. The reference lists of the full text articles were also screened for relevant studies, and a cited reference search was conducted for all relevant primary articles with Google Scholar. English and non-English language articles were included. Non-English articles often had English abstracts, and we used Google Translate and consulted with fluent language speakers to assess the inclusion criteria and extract the data.
+Study selection
+Studies were eligible for inclusion if they used a before-and-after design, compared single or multiple times before-and-after media reports related to suicide, or an interrupted times series design; if they used death by suicide as the outcome variable; and if they reported non-fictional media stories (that is, stories in news and information media).
+Exclusion criteria
+We excluded studies that did not have original data. We also excluded studies that examined associations in subgroups of the population because the findings might not be representative of the total population. For our analysis of media reporting on the method of suicide, we excluded studies reporting on an
+emerging new suicide method if the incidence of the respective suicide method at baseline (that is, before onset of media reporting) was low (<5%). These studies also typically measured possible effects over a longer than usual period of time. We excluded studies that provided only associations for a follow-up period of more than two months, because this is beyond the typical time frame for studying imitation effects, and might be based on mechanisms that are different from imitation.24 Also excluded were studies with data before the second world war; those with media interventions that were not about suicide; those which applied non-eligible designs; those that were at critical risk of bias; or those that duplicated data from another study.
+If studies had duplicated data (data on the same celebrities in the same setting reported in more than one study), we included one study. We selected this using a hierarchical approach based on: (1) the lowest risk of bias; (2) covering the longest period of time or the largest number of celebrities; and (3) the most recent. The 31 studies selected were included in the qualitative and quantitative synthesis (supplementary appendix).
+Data extraction
+We extracted these data from the studies: study location; study period and length; length of the observation period after media reporting; unit of analysis at which outcome data were measured (eg, daily or weekly); how the media intervention was measured (eg, binary variable representing the presence or absence of reporting or a continuous variable representing the number of news stories); whether the study reported on deaths of celebrities by suicide or general reports of suicide; number of interventions (eg, number of media reports over time); type of media (print media v other forms of media, such as television, online, or mixed media); any outcome reported related to the specific suicide method used in a reported suicide (exclusively or in addition to total suicides); whether the analysis was adjusted or unadjusted for confounders (in addition to any adjustments for seasonal or long term time trends); which confounders were measured and adjusted for; type of estimate extracted (rate ratio or expected and observed suicides); study design (single arm before and after comparison, multiple arm before and after comparison, interrupted time series)25-28; analysis technique; method to control for time trends; and source of the outcome data. In a single arm before-and-after comparison, suicides were observed in one group before and after the intervention. In a multiple arm before-and-after comparison, suicides were observed in multiple groups because there were multiple sites for one intervention or one site but multiple interventions occurring at different times.25
+Additional information was obtained for studies of deaths of celebrities by suicide: number of celebrities; type of celebrity (eg, entertainer); and level of recognition of the celebrity (local, international). For level of recognition, we used information from the study and online sources (eg, Wikipedia). Local celebrities were famous in one country or region (eg, a local politician) and international celebrities were known in a western or global context or were described in the original publication as international. A mixed code was used for celebrities with different levels of recognition. For studies looking at increases in the incidence of suicide by the same method as reported in the media, we recorded the suicide method.
+We obtained rate ratios and standard errors from each study by one of the following methods:
+•	Extracting directly a rate ratio and either a standard error, 95% confidence interval, t value, or other estimate to calculate a standard error
+•	Using the number of expected and observed suicides to calculate rate ratios and standard errors
+•	Extracting the observed number of suicides in the before and after intervention periods (along with the corresponding times) and calculated rate ratios and standard errors
+•	Obtaining a coefficient and standard error from a linear regression model that was converted to a rate ratio with the study’s population at the mid-point
+•	The authors of the original study providing us with rate ratios and standard errors.
+For each study, we recorded how the estimate was derived (obtained directly from the study, combined estimates using meta-analysis, or reanalysis of the data by the authors).
+We aimed for one quantitative outcome, but two studies (table S1) reported multiple quantitative estimates because the results were presented separately for different news sources. Hence we combined these into one estimate using random effects meta-analysis (see below).
+The search strategy was performed by two of the authors (TN and MB). Decisions on excluding studies after full text review were made by TN and separately by MJS. Discrepancies were discussed and resolved. Quantitative data were abstracted by MJS and discussed with TN. Metadata of studies were obtained by TN and MB initially, and separately by MJS. Discrepancies were discussed and resolved among the team.
+Risk of bias
+Risk of bias was assessed for each study based on the Robins-I tool.29 This tool was originally designed for non-randomised cohort studies, and does not directly apply to our study designs. The general concept, however, is applicable to interrupted time series designs,30 and the authors of Robins-I have published on issues that will be looked at in a future version for studies of interrupted time series.31 We developed a specific adaption for this study with six domains of bias: bias as a result of confounding issues; bias in classification of interventions; bias because of preparatory phases; bias because of missing data; bias in measurement of the outcome; and bias in selection of reported results.
+Studies were considered at low risk of bias if all domains were coded as low risk; at moderate risk if at least one domain was coded moderate but none as serious; at serious risk if at least one domain was assessed as serious but none as critical; and at critical risk if any domain was coded as critical. Like an earlier study that applied the Robins-I tool to natural experiments,30 we found that the first domain, risk of bias as a result of confounding, generally determined the overall risk of bias. This domain comprised coding for subdomains if the number of pre-intervention times was sufficient to allow characterisation of the series; appropriate analysis techniques were used to account for time trends and time patterns; seasonality was accounted for; and possible confounders were measured and controlled for. Risk of bias because of selective reporting was also relevant for some studies. We assessed if the outcome measurement and analyses were clearly defined and consistent in the methods and results sections of the studies, and if there was some risk of selective reporting from multiple analysis methods, multiple follow-up times, or multiple subgroups. The full quality assessment plan is in the supplementary appendix.
+As recommended in the Robins-I tool, studies with up to moderate risk were included in the primary and secondary analyses, and studies at serious risk were included in sensitivity analyses only. Studies at critical risk of bias were excluded.
+Assessments of the risk of bias were based only on the data we abstracted. If the authors provided a reanalysis of their data, for example, only the reanalysis was assessed for risk of bias, not the original study. Similarly, if a study reported total suicides as a side outcome, only the components relevant to the abstracted data (total suicides) were assessed. Our quality ratings, therefore, do not always apply to the original studies. Risk of bias was assessed independently by TN and MJS, and discrepancies were discussed and resolved.
+Quantitative data synthesis
+We described the studies using descriptive statistics. For our primary analysis, we estimated the pooled rate ratio for the effect of media reporting on deaths of celebrities by suicide on total suicides. We also conducted two secondary analyses. In the first (secondary analysis A), we estimated the pooled rate ratio for reporting about the method used in a suicide by a celebrity on suicide by the same method. In the second (secondary analysis B), we estimated the pooled rate ratio for general reporting of suicide on total suicides. The primary and two secondary analyses were restricted to studies at moderate risk of bias. In sensitivity analyses, we repeated these analyses adding studies at serious risk of bias.
+All pooled rate ratios were estimated with a random effects model, with standard errors calculated by the Knapp-Hartung method.32 Heterogeneity of effect sizes was assessed with the I2 statistic and Cochran’s Q test. For I2, values around 25% indicated low heterogeneity, around 50% moderate heterogeneity, and around 75% high heterogeneity.33 Publication bias was assessed visually by contour enhanced funnel plots34 and quantitatively with Egger’s regression test for asymmetry.35
+Sources of heterogeneity
+Meta-regression was used to identify the factors that might contribute to heterogeneity. We conducted univariate meta-regressions for each variable and combined significant variables (P<0.05) into a multivariate model. The meta-regressions were estimated with a random effects model with standard errors calculated by the Knapp-Hartung method. We combined the coefficients algebraically (that is, a linear combination of coefficients presented on the exponential scale) so that we could show rate ratios and 95% confidence intervals in each category of a variable. For the studies in the primary analysis, we examined the period published (up to 2005, 2006-10, 2011-15, 2016 or later), follow-up time (< 14 days, >15 days) location (Asia, Europe, North America-Australia), design (multiple arm before-and-after comparison, interrupted time series analysis), length (per 1000 days), period of analysis (day, week, month), adjustment for confounders (no, yes), celebrity recognition (local, international, mixed), celebrity type (entertainer, other), and number of celebrities (1, >2). We used similar variables for the studies in secondary analysis A, along with a variable on the method of suicide reported (hanging v other methods) but combined several categories where only one study was available for analysis. We did not conduct a meta-regression for studies in secondary analysis B because heterogeneity was low. All analyses were conducted in Stata 16.0. This study was registered with PROSPERO (https://www. crd.york.ac.uk/PROSPERO/, registration No CRD42019086559, 18 January 2019).
+Patient and public involvement
+There were no funds or time allocated for patient and public involvement so we were unable to involve patients. We have invited patients to help us develop our dissemination strategy.
+Results
+Study characteristics
+We retrieved 8823 references and 1496 remained after removal of duplicates (fig 1). After screening the titles and abstracts, the full texts of 143 studies were assessed and 112 were excluded: 26 because suicide was not an outcome; 30 because of strong data duplication with other studies; 17 because the intervention (media story) was not about suicide; 19 because of reports on emerging suicide methods; five because suicide was analysed only in a population subgroup; and five had data from before the end of the second world war. Also excluded were: two case studies; two studies measuring the outcome for longer than the maximum follow-up; one study about a fictional intervention; and two studies with annual outcome data. After quality assessment, three studies were excluded because of a critical risk of bias. The remaining 31 studies were included in our review: 23 were from database searches, three from Google Scholar, and five from cross reference searches.
+Study characteristics are summarised in table 1 and table 2 (and table S1). The 31 studies were published between 1974 and 2019 and examined the period 1947 to 2016. Nineteen studies examined the total number of suicides as the outcome and two examined increases in suicides by the same method reported in the media; 10 studies reported both. Twenty two studies examined media reporting of deaths of celebrities by suicide and nine studies evaluated general reporting of suicide. Studies were from Asia (Taiwan, Hong Kong, South Korea, and Japan), Europe (Austria, Germany, Hungary, the Netherlands, Slovenia, France, and Israel), North America (United States and Canada), and Australia. Most studies (n=20) used an interrupted time series design, 10 a multiple arm before-and-after design, and one a single arm before-and-after design. Seven studies had follow-up of 1-7 days, eight had 8-14 days, 12 had 15-30 days, and four had 31-60 days. The median follow-up time was 21 days (range 1-60 days).
+Quality assessment
+We classified 24 studies as being at moderate risk of bias because of confounding issues and seven at serious risk of bias. We judged 22 studies as being at low risk of bias because of classification of interventions, six at moderate risk, and three at serious risk. All 31 studies were at low risk of bias because of preparatory phases. Twenty eight studies were at low risk of bias because of missing data, two were at moderate risk, and for one the risk was unknown. Thirty studies were at low risk of bias because of measurement of the outcome, and one was at serious risk. Twenty nine studies were judged to be at moderate risk of bias because of selection of reported results, and two were at serious risk. Overall, 20 studies were assessed as moderate risk and 11 as serious risk of bias (table S2).
+Quantitative data synthesis
+Figure 2, figure 3, and figure 4 show the forest plots for the primary and secondary analyses. For the primary analysis (fig 2), on the impact of media reporting of deaths of celebrities by suicide on total suicides, 14 studies met the inclusion criteria. The pooled rate ratio was 1.13 (95% confidence interval 1.08 to 1.18, P<0.001) over a median follow-up of 28 days (range
+7-60 days). For the secondary analysis A (fig 3), on reporting of method of suicide of celebrities on suicides by the same method, 11 studies met the inclusion criteria. The pooled rate ratio was 1.30 (95% confidence interval 1.18 to 1.44, P<0.001) over a median follow-up of 28 days (range 14-60 days). For the secondary analysis B (fig 4), on the impact of general reporting of suicide on total suicides, five studies met the inclusion criteria and the pooled rate ratio was 1.002 (95% confidence interval 0.997 to 1.008, P=0.25) for a one article increase in the number of reports on suicide. The median follow-up was 1 day (range 1-8 days).
+Heterogeneity
+Estimates of heterogeneity were large and significant for the primary analysis (I2=83.5%, P<0.001) and the secondary analysis A (I2=72.1%, P<0.001) but not for the secondary analysis B (I2=0.02%, P=0.40). We therefore undertook meta-regressions for the first two sets of studies to identify possible sources of heterogeneity.
+In univariate analyses of the 14 studies in the primary analysis, differences in the pooled rate ratios between subgroups were observed for three variables (table 3): publication date (P=0.04, I2=29.1%), celebrity type (P=0.009, I2=47.9%), and number of celebrities under investigation (P=0.009, I2=47.0%). Weak evidence that reporting of deaths of celebrities by suicide was associated with suicides was found for studies published before 2005 (rate ratio 1.12, 95% confidence interval 0.99 to 1.26, two studies) but clear evidence of a positive association was found for the three other times (2006-10: 1.13, 1.04 to 1.23, three studies; 2011-15: 1.06, 1.02 to 1.10, four studies; >2016: 1.16, 1.11 to 1.22, five studies). Suicides by entertainers showed a positive association between reporting and suicide (rate ratio 1.17, 1.12 to 1.23, six studies) as did studies about other types of celebrities (1.08, 1.04 to 1.12, eight studies). Studies about one celebrity (1.17, 1.12 to 1.23, seven studies) and multiple celebrities (1.08, 1.04 to 1.12, seven studies) showed positive associations between reporting of deaths of celebrities by suicide and suicide.
+Because celebrity type and number of celebrities were collinear, we entered only publication date and number of celebrities in a multivariate meta-regression. We found no differences between subgroups in the pooled rate ratio for either variable (table S3) but the overall I2 for the model was lower, indicating low to moderate heterogeneity compared with the primary analysis (I2=34.6%). In the meta-regressions of the nine studies in the secondary analysis A, none of the factors was associated with the reporting of method of suicide on total suicides (table S4).
+Publication bias
+Figure 5 shows the contour enhanced funnel plots for the three analyses. For the primary analysis, the funnel plot was asymmetrical with more study specific rate ratios falling to the right of the pooled rate ratio line than the left. Few rate ratios were within the P value greater than 10% contours of statistical significance. Studies appeared to be missing from the region between the null value and the pooled rate ratio. Egger’s regression test for funnel plot asymmetry was significant (P=0.01). The funnel plots for the two secondary analyses were symmetrical around the pooled rate ratio, and Egger’s test was not significant for either analysis (P=0.23 for secondary analysis A and P=0.13 for secondary analysis B).
+Sensitivity analyses
+We undertook sensitivity analyses that included studies at serious risk of bias (fig S1). For media reporting of deaths of celebrities by suicide on total suicides, 20 studies met the inclusion criteria. The pooled rate ratio was 1.10 (95% confidence interval 1.06 to 1.14, P<0.001, I2=93.4%) over a median follow-up of 28 days (range 7-60 days). Investigation of heterogeneity failed to identify new factors that could account for differences between the studies. Heterogeneity remained large and persisted for all variables (table S5). Egger’s regression test for funnel plot asymmetry (fig S2) was close to significance (P=0.06). For reporting of the suicide method used by a celebrity on suicide by the same method (12 eligible studies), the pooled rate ratio was 1.32 (95% confidence interval 1.19 to 1.47, P<0.001, I2=74.9%) over a median follow-up of 28 days (range 14-60 days). We were unable to find sources of heterogeneity (table S6). Egger’s test was not significant (P=0.10). For general reporting of suicide (nine studies, median follow-up 7 days, range 1-30 days), the pooled rate ratio was 1.002 (95% confidence interval 0.999 to 1.005, P=0.11, I2=0.02%) for an increase of one article. Egger’s test was not significant (P=0.60). Because three studies in the primary analysis were about the same celebrity (Robin Williams), we performed a final sensitivity analysis where we excluded two of the studies, retaining the study with the lowest risk of bias.36 The pooled rate ratio was 1.10 (95% confidence interval 1.06 to 1.15, P<0.001, I2=61.8%) over a median follow-up of 28 days (range 7-60 days).
+Discussion
+Main findings
+To our knowledge, this systematic review and meta-analysis is the most comprehensive to date of the effects of media reporting of suicide on subsequent suicides. The evidence indicates an increase in total suicides in the period after the reporting of a death of a celebrity by suicide. When the suicide method used by the celebrity was reported, evidence of a corresponding increase in the number of suicides by the same method was found. This effect appeared to be larger than for increases in total suicides, although suicides by a specific method typically only account for a limited proportion of all suicides. General reporting of suicide did not appear to be associated with increases in total suicides but the evidence was based on a small number of studies, mainly from the same region of the world.
+At least three mechanisms might explain the increases in the number of suicides associated with reporting of suicide: identification with the deceased person, which might occur more frequently when the reported suicides are about individuals with high social standing37 38; increased media reporting of suicide leading to normalisation of suicide as an acceptable way to cope with difficulties7; and information on suicide methods, which might influence the choice of suicide method by a vulnerable individual.38 Our findings support several of these mechanisms. Firstly, reporting on deaths of celebrities by suicide appears to increase total suicides, suggesting that the phenomenon goes beyond the influence of knowing the suicide method used by the celebrity. Secondly, some evidence exists of stronger effects in studies focusing on suicide by entertainers, compared with other celebrities, consistent with their strong public identity, which has been previously described for entertainment celebrities in particular.39 Studies that focused on increases in suicide after one (rather than several) suicide by a celebrity often reported on entertainers, suggesting that these celebrities were well known and of interest to the public. Thirdly, the
+finding of a pronounced increase in suicide by the same method as that of a celebrity suggests that transfer of information about the method might be another relevant factor in the association. Media reporting on a suicide method increases the cognitive availability of this method,7 and individuals considering suicide might be more likely to subsequently select the method used by celebrities. The evidence suggests that suicide by hanging, for example, especially among men aged 45-64 years, increased after the suicide of Robin Williams by the same method.36
+Support for the effect of media coverage of suicide also comes from individual level studies that typically used outcomes such as suicidal thoughts rather than suicidal behaviour. Harmful effects on mood, self-esteem, and suicidal thoughts, especially in those who have previously contemplated suicide, have been identified.40-42 Individuals with suicidal thoughts, particularly new thoughts and a suicide plan, have an increased risk of suicidal behaviour.43 The increases in total suicides, and greater increases in suicides by the same method reported in the media, as identified in our meta-analysis, suggest that media stories on deaths of celebrities by suicide might do both: increase suicidal thoughts and contribute to planning suicide with a specific method. Suicidal thoughts are a common occurrence. A recent survey in the United States estimated that 9.4 million adults (4% of the population) had seriously considered taking their own life in the previous 12 months, and 2.7 million (1% of the population) made plans to do so,44 suggesting media reports of suicide have the potential to negatively influence many vulnerable people who might be swayed by news items.
+We found that the size of the association for suicides after the reporting of deaths of celebrities by suicide was smaller than in a previous meta-analysis that included fewer studies and did not assess the risk of bias comprehensively (based on 10 studies).6 Reasons for the smaller association might include the broader literature search, exclusion of duplicate data, and exclusion of studies at critical risk of bias. A rate ratio of 8-18% increase in suicide, however, highlights that media exposure relating to deaths of celebrities by suicide has a strong influence on the incidence of suicide in a population. In contrast, the global financial crisis of 2009 was associated with a 6% increase in suicide (although over a longer period of time).45 The estimated increase for reporting on deaths of celebrities by suicide might also underestimate the effects of media reporting on well known celebrities. Some of the studies included focused on individuals with questionable prominence, including mid-level or regional politicians and others not likely to be known by most of the population.11 Estimates were higher for well known celebrities, such as Robin Williams. Unlike in fixed effects meta-analyses of drug trials in defined populations, no true single effect exists for the association of media reporting on suicide with the number of subsequent suicides. Associations will probably vary depending on factors such as the prominence of the person in the media reports, the population’s connection with that person, and the extent to which the death is reported responsibly by the media in the region where the study is conducted. The World Health Organization has emphasised that media professionals should be cautious when reporting on suicides in general and on deaths of celebrities by suicide in particular.16
+For general reporting on suicide, taking into account all media reporting of suicide, no association with increases in the number of suicides was found. These studies usually evaluated the effect of the number of news articles on suicide on the next day or in the next week whereas studies of reporting on deaths of celebrities reported the presence or absence of a death of celebrity by suicide. The studies on general reporting of suicide
+also tended to use wide ranging search strategies to identify a broad variety of media reports related to suicide. This search strategy might have resulted in media reports associated with suicides but might have been distorted by inclusion of other reporting types that do not cause harm. Previous research suggests that not all reporting on suicide is associated with increases in the number of suicides.13 14 The risk appears to vary with reporting characteristics.13 14 Increases are particularly likely for a subset of media reporting that describes suicide methods13; depicts suicide as inevitable14; or publicises false public myths about suicide.13 Some media reports on suicidal thoughts feature stories of hope and healing, rather than suicide attempts or deaths, and might help to prevent suicides (the so-called Papageno effect).13 46 47 None of the studies in our meta-analysis considered the qualities of media reports based on media recommendations, and the variability in reporting qualities is likely large. Hence, a resulting underestimation of the effects for media stories that are inconsistent with media recommendations is possible. Future research should aim for a clear definition of the reporting to separate associations for different types of reports.
+Our meta-analysis generally took a conservative approach by limiting the analysis to studies at moderate risk of bias and focusing entirely on total suicides (rather than subgroups) as the outcome for the primary analysis. If, for example, a study reported on the effects of news media reporting on the incidence of suicide in teenagers, the data extracted for our meta-analysis were for the total population, even if the study put a focus on its specific findings for the subgroup of teenagers. This approach was to ensure that selective reporting of findings in subgroups did not bias our estimates.
+Like previous reviews,6 9 we found strong heterogeneity in risk estimates across studies on reporting of suicide by celebrities in particular. A large part of the heterogeneity was a result of the type of celebrity or number of celebrities analysed, suggesting that individuals best known to the public are those most likely to trigger more suicides. None of the characteristics explained the method specific increases in suicides. The remaining unexplained heterogeneity suggests that factors out of scope of our analysis might impact on the risk of increases in suicides after suicide reporting, including overall trends in the incidence of suicides in a country or over a period of time when media reporting occurs; socioeconomic conditions that might influence suicide reporting and imitation effects; and precise measurement of social identification with celebrities and other individuals who die by suicide.
+We observed a number of significant and positive effect sizes and an absence of non-significant effect sizes in some regions of the funnel plots for studies of reporting on deaths of celebrities by suicide. This lack of symmetry could indicate publication bias. Many factors can contribute to asymmetry in funnel plots, however, and precise interpretation is difficult when the underlying evidence is based on observational data.48 49 Unpublished studies could have shown no association. If true, this means that our meta-analysis will have overestimated the association between media reporting of suicide by celebrities and subsequent suicides. A sensitivity analysis including studies with serious risk of bias indicated a similar pattern to the overall findings, suggesting that effect estimates for lower risk studies were similar.
+Strengths and limitations
+The strengths of our meta-analysis included its wide ranging systematic search strategy; screening of more studies than in
+previous quantitative meta-analyses on this topic; thorough check for duplicate data; and the comprehensive quality assessment of the primary studies. Our approach was intentionally conservative, focusing on studies with a low to moderate risk of bias and only with estimates related to total suicides in the population. This study also looks at the research on the effects of general reporting of suicide on subsequent numbers of suicides, although only five studies were available for this analysis.
+Limitations included our inability to test causality because of the before-and-after and interrupted time series designs of the original studies, high levels of heterogeneity that could not be fully accounted for, and possible publication bias. Further, it was not possible to generate absolute risk estimates because the included studies mostly did not report the baseline risk of suicide in their respective settings. Despite the wide ranging search strategy, non-English language studies in the international literature might not have been indexed in the databases we searched. Our analysis covered only a proportion of suicide related media items. Studies on the effects of media items covering the spreading of novel suicide methods, such as charcoal burning in parts of Asia,50 were not included because of the low prevalence of these methods at baseline. Studies on fictional suicides were not included to avoid a further increase in heterogeneity between studies. Hence we cannot draw conclusions on these types of studies; our meta-analysis included studies with a narrower focus on interventions related to the reporting of suicide and suicidal behaviour. Finally, the only outcome considered in this meta-analysis was suicide. Although this outcome is of highest relevance to suicide prevention, media reports can impact on other domains as well, including help seeking behaviour and stigmatisation that have not been looked at in this meta-analysis.7
+Conclusions
+In this large and up-to-date systematic review and meta-analysis, we looked at the impact of suicide reports in news and information media on subsequent numbers of suicides. Our results support the continued use and promotion of guidelines on responsible media reporting of suicide, which are the best available interventions to address and prevent imitation effects in the population.15 16 Collaboration between suicide prevention experts and media professionals in implementing these guidelines is an essential part of any suicide prevention strategy. Caution should be exercised in reporting suicides by celebrities in particular. The media will continue to report on newsworthy suicides but have a social responsibility to mitigate the likelihood of the Werther effect.
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+Introduction
+Poor school attendance due to absence (authorised or unauthorised) from available sessions or exclusion (where a headteacher forbids a student to attend for a fixed number of sessions or permanently) leads to multiple immediate and long-term socioeconomic disadvantages. It is associated with a range of negative outcomes across the life course, including poor educational
+attainment, unemployment, and poverty.1-5 Several smallscale studies in the UK, USA, and Australia, with sample sizes ranging from less than 100 to 13 000, suggest that absence from school is more common in children with a mental disorder, specifically depression, anxiety, and disruptive behaviour disorders, through school refusal, truancy, or the condition itself.6-11 Studies from the UK12,13 and the USA14 report an association between
+Research in context
+Evidence before this study
+We searched PubMed for papers published in English between database inception and July, 20, 2021, using the search terms ((children) OR (adolescents)) AND ((school attendance) OR (school absence) OR (exclu*) OR (truan*) or (school disengagement) OR (School Refusal)) AND ((depression) or (anxiety) or (adhd) or (autism) or (learning difficulty) or (schizophrenia) or (bipolar) or (self-harm) or (eating disorder) or (drugs) or (alcohol) or (conduct disorder)). We found 13 small-scale cross-sectional surveys that used questionnaires to assess mental disorders and one national electronic cohort study linking education and secondary healthcare datasets. School absence and exclusion were found to be associated with neurodevelopmental disorders, depression, anxiety, disruptive behaviour, substance misuse, or self-harm, but current evidence is sparse and based on small numbers.
+Added value of this study
+Our population-based, electronic cohort study was larger than most previous studies, including more than 400 000 pupils, and linked routinely collected primary and secondary healthcare data to educational data. Previous studies have been based on secondary care data only and probably missed disorders,
+such as anxiety, that are more commonly managed in primary care. Our study encompasses a wide range of clinically diagnosed and recorded mental and neurodevelopmental disorders up to the age of 24 years, and so includes conditions, such as bipolar disorder and schizophrenia, that are less frequently studied in this context are are more often diagnosed in late adolescence and early adulthood. Furthermore, the large size of this study allows for the inclusion of people with less common diagnoses, such as eating disorders. We found strong associations across all disorders and self-harm with absenteeism and exclusion from school. Odds ratios for both outcomes increased with the number of comorbidities and deprivation.
+Implications of all the available evidence
+Poor attendance affects the educational attainment of children and future social and developmental outcomes. Children with mental or neurodevelopmental disorders or who self-harm are more likely to miss school through absenteeism and exclusion than their peers. Exclusion or persistent absence are potential indicators for current or future poor mental health that are routinely collected and could be used to target assessment and early intervention.
+neurodevelopmental disorders (ie, ADHD and autism spectrum disorder [ASD]) and self-harm with persistent absenteeism. Similarly, school exclusion appears to be strongly associated with ADHD, ASD, and mental disorders, particularly depression, in UK-based and international studies.13,15 In these mostly cross-sectional studies, diagnoses were assessed by use of questionnaires or interviews. However, children and young people (<24 years) with these disorders are more commonly from disadvantaged families and might be less likely to participate in research surveys.16-18 They also have higher levels of attrition at follow-up16-18 for reasons including impairments affecting the young person or their parent and impacting survey completion or a related absence when surveys are done in a school setting. Furthermore, birth cohort studies often include insufficient numbers of children with mental health conditions to support indepth analysis of rarer conditions.
+For the sail Databank see	In this study, we capitalised on electronic linkage
+https://saildatabank.com/ between routinely collected primary and secondary health-care data on clinical diagnoses and data on school attendance and exclusions at a population level. Our hypothesis was that school absences and exclusions are associated with a broad range of diagnosed and recorded neurodevelopmental and mental disorders and self-harm by 24 years of age within our cohort of pupils, even after adjusting for sex, age at the start of the academic year, and deprivation. Once established (and previous literature is scarce), this hypothesis would lead to further questions for more detailed study.
+Methods
+Study design and participants
+In this nationwide, retrospective, electronic cohort study, we drew our cohort from the 5 341 392 individuals in the Welsh Demographic Service Dataset to include individuals aged 7-16 years (16 years being the school leaving age in the UK) enrolled in state-funded schools in Wales in the academic years 2012/13-2015/16 (between Sept 1, 2012, and Aug 31, 2016) who had primary and secondary care linked data and no conflicting data in the education dataset that pointed to a many-to-one correspondence between the anonymised linkage field and the internal pupil identification number. Ethics approval was granted from the Secure Anonymised Information Linkage (SAIL) Information Governance Review Panel, an independent body consisting of a range of government, regulatory, and professional agencies, in line with ethical permissions already granted to the analysis of data in the SAIL Databank (approval number 0808).
+Procedures
+We linked data on an individual level via the Adolescent Mental Health Data Platform, an international data platform that supports mental health research in children and young people. For our study, the Adolescent Mental Health Data Platform used datasets from the SAIL Databank, a repository of routinely collected health and education datasets for the population of Wales.19,20 All data are treated in accordance with the Data Protection Act 2018. Individuals within the datasets are assigned a
+unique anonymised linkage field that replaces any identifiable information, such as names, and enables anonymised linkage across the different datasets.
+The datasets in the SAIL Databank that we used were: the Welsh Demographic Service Dataset (a demographics register of people registered with general practitioner [GP] practices in Wales) on Nov 1, 2018; the Office for National Statistics deaths register on March 28, 2019; the Welsh Index of Multiple Deprivation 2011 (an official measure of small area [defined as containing approximately 1500 individuals] deprivation in Wales, based on employment opportunities, income, education, health, community safety, geographical access to services, housing, and the physical environment; quintile 5 represents the most deprived areas) on Nov 1, 2018; the Welsh Longitudinal General Practice Database (on Aug 20, 2018) and the Patient Episode Database for Wales (on Jan 31, 2019), which contain attendance and clinical information for all GP interactions and hospital inpatient and day case activity in Wales, respectively; and the Welsh Government Education Dataset (appendix 2 p 6). The Welsh Government Education Dataset includes records for all children registered at mainstream state schools in Wales or educated in settings other than school. It contains information on attendance, exclusions, eligibility for free school meals, and receipt of a statement of special educational needs (SEN). Attendance records were available from the academic year 2007-08 to the academic year 2015-16. Each school reported, per pupil, the number of authorised and unauthorised absences for that year out of a total number of possible sessions per year. Exclusion records (categorised as permanent, fixed, or lunchtime) were available from the academic year 2012-13 to the academic year 2015-16. A child might have SEN status if they have a learning difficulty or disability (including neurodevelopmental or mental disorders) that requires special education provisions to be made for them.21
+We queried primary and secondary care datasets to extract recorded neurodevelopmental and mental disorders and self-harm using code lists from the ICD (version 10) for secondary care and read codes (version 2)22 in primary care. The codes were collated from published articles and code lists or were compiled in collaboration with clinicians (appendix 2 p 7). Neurodevelopmental disorders (ie, ASD and ADHD), learning difficulties, and conduct disorder were extracted for our cohort of pupils from their birth until they reached 24 years of age because these conditions often arise early in development and are diagnosed at a young age. Other mental disorders (including depression, anxiety, eating disorders, bipolar disorder, schizophrenia, alcohol misuse, and drugs misuse) or self-harm were extracted for our cohort of pupils between the ages of 10 years and 24 years. We categorised all F ICD-10 codes and E read codes not included in other category code lists, such as those for mania, into the other psychotic disorders category. Each pupil had a flag per each disorder categorised as a binary
+variable (recorded present or absent). The age at first diagnosis was extracted for each pupil and disorder. Where a pupil could not be linked to primary or secondary care datasets, this was flagged as linked or unlinked.
+We extracted SEN status for each pupil as a binary variable (present vs absent) to understand the extent to which it, in addition to a disorder, affected outcomes. We counted the number of morbidities per person to assess the effect of comorbidities (defined as two or more of the studied disorders recorded for the same individual, not necessarily concurrently).
+Outcomes
+We defined absenteeism as a binary variable, categorised as 1 when a pupil missed more than 10% of sessions in 1 year and categorised as 0 otherwise. The choice of 10% was based on a report from Estyn (the quality inspectorate of education in Wales), which described that, of pupils who were absent for more than 10% of sessions, fewer than 80% achieved the level expected of them by age 11 years in mathematics, science, and either English or Welsh as a first language and fewer than 40% achieved level 2 (equivalent to five GCSEs at grades A*-C) at 16 years of age.23,24 10% is the level used in England to define persistent absenteeism,25 although the level used in Wales is 20%.26 Exclusion (a record of any type of exclusion in a specific academic year) was also categorised as a binary variable (yes vs no).
+Statistical analysis
+Data were retrieved from the SAIL Databank by use of IBM DB2 9.7 SQL. Statistical analysis was done by use of R (version 3.3.3), accessed through RStudio (version 1.2). We analysed the association between the outcome variables (absenteeism and exclusion) and the existence of a record of each neurodevelopmental or mental disorder and self-harm, up to 24 years of age, using generalised estimating equations (R library geepack).27 Generalised estimating equations with exchangeable correlation structures using binomial distribution with the logit link function were used to calculate odds ratios (OR) for absenteeism and exclusion, adjusting for sex, age at the beginning of the academic year, and deprivation. We used a long data format with one row per pupil and year. The experimental unit (id) was the pupil, and the repeated measurements were ordered by academic year (wave). 95% CIs for proportions and percentages were estimated by the Wilson score method with continuity correction. We did not explore causality; therefore, the time dependence of measurements per person (up to four measurements at different academic years) was modelled with a correlation function as a source of variance, which was marginalised over so that the variance of the estimated covariates were calculated efficiently.28 We used an exchangeable correlation structure, in which any two measurements for the same pupil had the same correlation. We analysed each
+recorded disorder separately using a sub-cohort consisting of those presenting with these disorders together with pupils in our cohort with no record of any of these disorders (our controls). We tested multiple models, sequentially adding age (week of birth), sex (male vs female), and deprivation quintile (quintile 5 representing the most deprived) as covariates.29,30 We tested the goodness of fit for each of these models by calculating the Quasi-likelihood model information criteria.31 We stratified the population by condition and analysed the association between the outcome variables and sex, age, and deprivation separately. For the main analysis, we pooled pupils with ADHD and ASD (under neurodevelopmental disorder) and pooled pupils with depression, anxiety, eating disorders, schizophrenia, bipolar disorder, and other psychotic disorders (under any mental disorder).
+In the first sensitivity analysis, we ran the main analysis on both the subgroup that had linked healthcare data and the full dataset. Neurodevelopmental and mental disorders typically show comorbidity over the life course. We assessed the sensitivity of our analysis to comorbidities by conducting three extra sensitivity analyses. First, we compared the model results between those with one morbidity and those without any of the morbidities studied. Second, we compared the model results between those with more than one morbidity and those without any of the morbidities studied. Finally, separately, we ran the model with the number of comorbidities as a covariate.
+We conducted several other sensitivity analyses. For some children, certain types of mental disorders are an entry point for SEN status within the education system. We assessed sensitivity to SEN status by exploring the interaction between any of the disorders studied and SEN status. We also ran our models in the subpopulation of participants first diagnosed or with their first record
+before 17 years of age (ie, while of school age). Furthermore, we compared individual-level yearly rates of absences (number of sessions absent/total number of possible sessions per year) for those excluded versus for those not excluded and calculated the Pearson correlation coefficient between individual-level yearly rates of absences and individual-level yearly rates of exclusions.
+Role of the funding source
+The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
+Results
+437 412 individuals had education and demographic data and were aged 7-16 years during the 2013-16 academic years, of whom 213 816 (48-9%) were female and 223 596 (51-1%) were male (figure 1). Of these 437 412 individuals, 22 775 (5-2%) had no linked hospital or primary care records. We considered health data for these individuals as missing at random because missingness was not based on health or education status.
+In the group with health-care data, 212 848 (51-3%) of 414637 pupils were boys and 201789 (48-7%) were girls (appendix 2 p 1). In the group without health-care data, 10 748 (47-2%) of 22 775 pupils were boys and 12 027 (52-8%) were girls (appendix 2 p 1). Compared with those with health-care data, a higher proportion of individuals with missing health-care data resided in quintile 2 areas on the Welsh Index of Multiple Deprivation and a lower proportion resided in quintile 5 areas (appendix 2 p 1). We repeated the main analysis in the group with linked primary and secondary health-care data (n=414637) and in the larger group (n=437 412). Results were equivalent (appendix 2 pp 25-26) so we removed those without linked health-care data from all main and other sensitivity analyses (list-wise deletion). Each pupil contributed 1-4 years of data. The distribution of morbidity, sex, and deprivation by number of years of data contributed is shown in appendix 2 (pp 2-4). Demographics and morbidity were not correlated with the number of years of data contributed so we also viewed this as missing at random.
+Of the 414 637 pupils with primary and secondary care data comprising our study population, 201 789 (48-7%) were female and 212 848 (51-3%) were male. Their mean age on Sept 1, 2012, was 10-5 years (SD 3-8). Ethnicity data were not available. 57 930 (14-0%) pupils had at least one of the disorders studied (a neuro-developmental or mental disorder or a record of selfharm) by the age of 24 years, and 42 734 (10 -3%) while of school age. 356 707 (86-0%) of414 637 individuals had no record of self-harm or any of the disorders studied. The numbers of diagnosed individuals, age at first diagnosis, and SEN status before 17 years of age are detailed in appendix 2 (p 7).
+118 140 (28-5%) had recorded absenteeism during at least 1 school year, of whom 5901 (5-0%) had a neuro-developmental disorder, 17 724 (15-0%) had a mental disorder, and 5164 (4-4%) had a record of self-harm. 20 507 (17-4%) of the 118 140 with recorded absenteeism were diagnosed while of school age, ofwhom 5762 (28 -1%)
+had a neurodevelopmental disorder, 12 164 (59 -3%) had a mental disorder, and 4450 (21-7%) had a record of selfharm.
+The proportion of absentee pupils with no record of any of the disorders studied remained stable in primary school (7-11-year-olds) at around 12-5% and increased
+in secondary school (11-16-year-olds) to around 18% for 16-year-olds (figure 2). For the raw counts used to create figure 2, please see the appendix (pp 16-20). Across all ages, a higher proportion of pupils with a neuro-developmental disorder, mental disorder, or self-harm record were absent from school compared with pupils without a record (figure 2). In the last 2 years of primary school (10-11-year-olds), pupils with a subsequent diagnosis of schizophrenia or drugs misuse had the highest rate of absenteeism at around 30-33% (figure 2). In the last 2 years of secondary school (ages 15-16 years), pupils with a record of bipolar disorder, schizophrenia, alcohol misuse, drugs misuse, or self-harm had the highest rate of absenteeism at around 40-55% (figure 2).
+Goodness-of-fit tests (appendix 2 p 27) showed that including sex, age, and deprivation as covariates sequentially improved the fit, so we present both unadjusted and adjusted results (table 1). Having a record of a neurodevelopmental disorder (OR 2-1, 95% CI 2-0-2-2), mental disorder (2-9, 2-8-2-9), or self-harm (4-0, 3-8-4-1) was associated with absenteeism (table 1). Adjusted ORs (aORs) ranged from 2-0 (95% CI 1-9-2-0) for pupils with a neurodevelopmental disorder to 4-2 (3-4-5-3) for those with schizophrenia and 5-5 (4-2-7-2) for those with bipolar disorder (table 1).
+Of those with a record of neurodevelopmental disorders, learning difficulties, conduct disorder, depression, other psychotic disorders, or drugs or alcohol misuse, boys were less likely to be absent than were girls (appendix 2 p 9). For those with a record of anxiety, eating disorders, bipolar disorder, schizophrenia, or self-harm, sex was not significantly associated with absenteeism (appendix 2 p 9). For pupils with a record of neurodevelopmental disorders, conduct disorder, depression, anxiety, eating disorders, drugs or alcohol misuse, or self-harm, age was associated with absenteeism, with slight increases in ORs per year (appendix 2 p 9). The sample sizes for bipolar disorder and schizophrenia were too small to assess the association of deprivation quintile with absenteeism; however, the odds of being absent increased with increased deprivation (5th vs 1st quintile) for all other variables apart from other psychotic disorders, ranging from 1-5 (95% CI 1-3-1-9) for conduct disorder to 2-8 (2-2-3-6) for alcohol misuse (appendix 2 p 9).
+15 199 (3-7%) of 414637 pupils had been excluded from school at least once, 243 (0-1%) of whom were excluded permanently. 1979 (13 -0%) of 15 199 had a neurodevelopmental disorder, 3161 (20-8%) had a mental disorder, and 1518 (10 -0%) had a record of self-harm. 4568 (30 -1%) were diagnosed while of school age, of whom 1925 (42 -1%) had a neurodevelopmental disorder, 2048 (44-8%) had a mental disorder, and 1291 (28-3%) had a record of self-harm. Children aged 7-11 years with no record of the studied diagnoses or self-harm were very unlikely to be excluded (1174 [0-5%] of 233 191). They were more likely to be excluded if they had a record of ASD (211 [4-7%] of4464) or conduct disorder (193 [8 -0%] of 2415). Exclusions generally
+became more common among older children (figure 3). For those with no disorder or self-harm, the proportion of exclusions increased to 2-9% (2770 of95 977) among those aged 15 years, before decreasing to 2-2% (2064 of94 172) in the last year of secondary school (age 16 years). Notable increases in exclusion rates were seen among pupils aged 14 years with ADHD (374 [15-1%] of 2483), conduct disorder (207 [14-5%] of 1433), drugs misuse (205 [24-2%] of 848), alcohol misuse (150 [14-6%] of 1026), and selfharm (443 [10-7%] 4135), although exclusion rates tended to decrease in the final year of secondary school (figure 3). The proportion of pupils with severe mental illness who were excluded was also high, with 16 (17-4%) of 92 with bipolar disorder excluded at age 15 years and 16 (18-4%) of 87 with schizophrenia excluded at age 14 years (figure 3). For the raw counts used to create figure 3, please see the appendix (pp 21-24).
+Goodness-of-fit tests (appendix 2 p 27) again showed that including sex, age, and deprivation as covariates sequentially improved model fit. Having a neuro-developmental disorder, a mental disorder, or a record of self-harm were all associated with being excluded from school (table 2). After adjusting for sex, age, and deprivation, pupils with a record of drugs misuse had the highest odds of being excluded (table 2). To note, alcohol misuse, self-harm, schizophrenia, and bipolar disorder also had high aORs (table 2).
+Across disorders, apart from bipolar disorder, boys were significantly more likely to be excluded than were girls (appendix 2 p 13). Boys with a record oflearning difficulties, anxiety, eating disorders, schizophrenia, other psychotic disorders, or self-harm had an OR for being excluded between 2 and 3 (appendix 2 p 13). Being older was associated with a higher odds of exclusion for individuals with a record of most variables studied (OR range 1-09-1-19), except for bipolar disorder, schizophrenia, other psychotic disorders, drugs misuse, and alcohol misuse (appendix 2 p 13). The sample sizes for bipolar disorder and schizophrenia were too small to assess the association of deprivation quintile with exclusion; however, the odds of exclusion were higher in the most deprived areas than in the least deprived areas for all variables apart from other psychotic disorders, with the OR varying from 1-4 (95% CI 1-1-1-9) for those with conduct disorder to 3-3 (2-7-4-0) for those with anxiety (appendix 2 p 13).
+Pupils in our cohort had up to eight morbidities in total. 41 018 had one morbidity, 12 096 had two, 3495 had three, and 1321 had four or more (appendix 2 p 10). Absenteeism was more likely in pupils with comorbidities than in pupils with one morbidity, except in the case of bipolar disorder (table 1). Pupils with a single diagnosis of an eating disorder, schizophrenia, or other psychotic disorder were not at higher risk of being absent compared with their healthy peers (table 1). When the number of comorbidities was modelled as a covariate, the OR of being absent was between 1-2 and 1-4 for each additional
+comorbidity, except for bipolar disorder for which the OR was 1-0 (appendix 2 p 11). SEN status did not reduce the ORs for being absent in those with anxiety, eating disorders, schizophrenia, or alcohol misuse (appendix 2 p 12). For those with ADHD, ASD, learning difficulties, conduct disorder, depression, bipolar disorder, drugs misuse, other psychotic disorders, or a record of selfharm, having SEN status reduced the OR for absenteeism to 0^59-0^89 compared with not having SEN status (appendix 2 p 12). The results for pupils with a record before 17 years of age were similar to those of the main cohort, except for pupils with a record of alcohol or drugs misuse, bipolar disorder, or depression who had slightly higher odds of being absent, and for pupils with schizophrenia who had slightly lower odds (table 1).
+In the group of pupils with more than one morbidity, aORs for being excluded were consistently higher than for those with one morbidity (table 2). When the number of comorbidities was modelled as a covariate, the OR of being excluded was between 1-2 and 1-8 per each additional comorbidity (appendix 2 p 14). SEN status was associated with decreasing ORs for being excluded for those with neurodevelopmental disorders, conduct disorder, depression, bipolar disorder, other psychotic disorders, drugs misuse, and self-harm (appendix 2 p 15). aORs for being excluded differed between the subpopulation diagnosed while at school and the main
+cohort, depending on the variable (table 2). Of note, there was little difference for neurodevelopmental disorders, the aOR for exclusion was lower for people with schizophrenia when diagnosed while at school, and the aORs for exclusion were somewhat higher for individuals with drugs or alcohol misuse recorded while of school age (table 2).
+The individual-level yearly absence rate (number of sessions missed/total number of possible sessions per year) was higher in the group with a record of exclusion than in the group without a record of exclusion (appendix 2 p 5). The correlation between individuallevel yearly absence rates and individual-level yearly exclusion rates was r=047 in the full cohort and r=045 for those with recorded exclusions.
+Discussion
+Our study, which involved more than 400 000 pupils, highlights that children and young people diagnosed with a neurodevelopmental disorder or mental disorder, or who have a record of self-harm, before 24 years of age are much more likely to miss school than their peers, even after adjusting for age, sex, and deprivation. Our data and study size enabled us to include disorders typically not included in studies of school-aged children, such as rare disorders and disorders that typically present after individuals have left school (eg, schizophrenia), that
+might confer antecedent clinical vulnerabilities.32 School absenteeism and exclusion rates were higher after 11 years of age for all children but disproportionally more so in those with a record of a disorder or self-harm, even if it was recorded during school age. This finding could reflect a reduced direct influence of parents on older
+children’s attendance or the smaller size of primary schools compared with secondary schools. Generally, individuals with more than one recorded morbidity were more likely to be absent or excluded than were those with only one morbidity, which was exacerbated with each additional disorder. Within the diagnosed populations,
+girls with neurodevelopmental disorders, learning difficulties, conduct disorder, depression, other psychotic disorders, or drugs or alcohol misuse were more likely to be absent than were boys, and boys were more likely to be excluded than were girls across all studied disorders apart from bipolar disorder. This finding aligns with the view that boys externalise mental distress through their behaviour, which in turn impacts the school environment and results in their exclusion, whereas girls, and especially those with emotional disorders or delayed diagnosis of neurodevelopmental disorders, tend to be more anxious and withdraw from social contact.32 Age was found to be associated with both outcomes in relation to most disorders. We also found associations between both outcomes and deprivation within most disorders studied. Having SEN status reduced the likelihood of being absent or excluded, most notably for
+those with records of neurodevelopmental disorders or bipolar disorder, compared with those with a record but no SEN status, potentially highlighting the positive impact of recognition, diagnosis, and educational interventions.
+Our findings strengthen those found previously in much smaller population-based studies. In the ALSPAC study29 of a UK birth cohort, by 8 years of age, 19% of children with ADHD and 31% of those with conduct disorder were excluded from school compared with 1-9% and 2-8% of those without ADHD or conduct disorder, respectively. In another study ofa UK cohort (BCAMHS),30 psychiatric symptoms (assessed through validated questionnaires) were a significant predictor of exclusions.
+Our study is based on routinely collected data encompassing a wide range of clinically diagnosed and recorded disorders. It benefits from well documented,
+often validated, and curated lists of ICD-10 and read (version 2) codes to ascertain each of the disorders. Arguably, diagnoses made by clinicians for those in contact with services provide more complete case ascertainment than do surveys or cohort studies, which are susceptible to selection bias due to low recruitment and high attrition in populations with psychiatric disorders. However, a common feature of all database studies of routinely collected data is the underestimation of the number of disorders in the population as not all those affected consult their GP, or conditions might not be recognised or recorded.33 Additionally, there is no validated measure of the clinical problems recorded, which prevents any estimation of severity, and administrative data are vulnerable to random errors in data entry.
+This study’s novelty lies in its linkage of education, health (including primary care), and deprivation datasets for a whole population (Wales) at an individual pupil level over 4 school years for a wide range of disorders. Linking health and education data on this scale allows us to gain valuable insights on the education of children with neurodevelopmental disorders, mental health disorders, or self-harm. Because many older adolescents with common mental disorders are managed in primary care, it is important to include this data source. A whole population dataset enabled us to include pupils with rarer conditions such as schizophrenia and bipolar disorder. Linking diagnoses up to the age of 24 years allowed for assessment of conditions more frequently diagnosed after school leaving age (eg, schizophrenia), for which their antecedents or premorbid presentation, such as cognitive or social deficits, apathy, or selfmedication with drugs, might affect attendance and exclusion. We did not take physical comorbidities into account, although we note the strong association between poor mental and physical health,34 because some absences would have been due to physical morbidity and medication rather than the mental or neurodevelopmental disorder, which would have complicated the interpretation of our findings.
+Our estimates might underestimate the effect of mental health difficulties on exclusions and absenteeism. Younger children will have had less time for evidence of their diagnosis to be recorded, especially for those conditions that tend to appear later in adolescence, and some young people who are diagnosable will not present to services. There is some evidence30 to suggest that, for each diagnosed child, there could be a number that have multiple symptoms but do not meet the criteria for a diagnosis. These children might well have issues at school that could lead to poor attendance or exclusion. Some children, especially those with ADHD, ASD, or learning difficulties, might not have been included in our dataset because they are in schools for children with special educational or behavioural needs or are homeschooled. Different pupils contributed different numbers of years to the analysis. We are satisfied that there were
+no demographic or mental health-related differences between these pupils. 5 -2% of pupils with education data in the 2013-16 academic years did not have any linked health data and were removed from our analyses.
+There are various processes through which school attendance might be associated with neurodevelopmental disorders, mental disorders, and self-harm. These processes include disruptive behaviours resulting in exclusion, physical comorbidities or somatic symptoms (eg, stomach pain and headaches) leading to authorised absence, symptoms associated with anxiety and depression leading to school refusal, family problems, and peer problems (eg, bullying). If absence from school results in social isolation and poorer academic performance, it could exacerbate mental health and attendance issues if the cycle is not disrupted. Our study cannot infer causal relationships and further research should focus on the direction of the association, which could be bidirectional for individual disorders and outcomes. Clinical record data might not be ideal to use in these future studies because the documentation in clinical records will not represent an accurate measure of the time of first onset of the symptoms or disorder. However, even without an understanding of the direction or mechanisms of the association, the demonstration of an association using real life outcomes and data is important.
+Poor school attendance affects the educational attainment of children and future social, developmental, employment, and physical health outcomes. Many governments, including UK Governments, have recognised the importance of regular school attendance and have issued related guidelines, which include penalty notices for the carers or parents of persistently absent children and the use of incentives to encourage high attendance.35 Exclusions from schools in England and Wales are intended to be used in serious breaches of behaviour policies—for violence, sexual abuse, the supply of illegal drugs, or the use of weapons.36,37 Currently, rates of exclusion in England are rising, raising concerns about school-based policies to improve behaviour and support teachers. Similar initiatives are in place in the USA and elsewhere.38
+Linking routinely collected health and education data has the potential to improve services for children39 by identifying those in need, alongside gaps in provision. Our analysis clearly shows that children with neuro-developmental disorders, mental disorders, and selfharm spend less time at school. As such, exclusion or persistent absence is a potential indicator for current or future poor mental health that is routinely collected by schools and local education authorities and could be used to target assessment and early intervention.40 There is growing interest in school-based prevention and early intervention programmes that focus on improving the school climate for reducing adolescent mental health problems,41,42 which has relevance now as children return to school following closures and blended learning in
+response to the COVID-19 pandemic. Other interventions have included psychological interventions that focus primarily on anxiety and depression symptoms.43 Schoolbased mental health provision and integration with mental health services has been highlighted as a major strategic priority in the UK.44 This approach could benefit young people, as supported by our finding that having a SEN status decreases the odds of being absent or excluded, even if it does not remove the risk completely. Attendance and exclusion data, which are already collected by schools, could provide useful information about where to focus sparse resources. School-based mental health prevention strategies might also help to build resilience, enabling pupils to develop strategies for managing and improving their mental health and wellbeing, and to understand when and how to seek additional help.
+Future research could further explore whether improvements in school attendance over time serve to reduce the incidence of mental disorders and whether the timing of diagnosis is an important factor in the risk for absenteeism or exclusions. This can be done by looking at causal relationships between mental health and school outcomes using a longer follow-up period. Other avenues for research include evaluating the effect of physical comorbidities on school outcomes and the differential associations of pairs of disorders with school outcomes.
+To conclude, people up to 24 years of age who have mental or neurodevelopmental disorders or self-harm have poorer attendance at school than their peers who do not have disorders or self-harm. Exclusion or persistent absence is a potential indicator of current or future poor mental health that is routinely collected by schools and local education authorities and could be used to target assessment and early intervention.
+Articles
+16	Frojd SA, Kaltiala-Heino R, Marttunen MJ. Does problem behaviour 31 affect attrition from a cohort study on adolescent mental health?
+Eur J Public Health 2011; 21: 306-10.	32
+17	Wolke D, Waylen A, Samara M, et al. Selective drop-out in longitudinal studies and non-biased prediction of behaviour	33
+disorders. Br J Psychiatry 2009; 195: 249-56.
+18	Saiepour N, Ware R, Najman J, Baker P, Clavarino A, Williams G.
+Do participants with different patterns of loss to follow-up have	34
+different characteristics? A multi-wave longitudinal study.
+J Epidemiol 2016; 26: 45 49.
+19	Ford DV, Jones KH, Verplancke JP, et al. The SAIL Databank: building a national architecture for e-health research and	35
+evaluation. BMC Health Serv Res 2009; 9: 157.
+20	Lyons RA, Jones KH, John G, et al. The SAIL databank: linking multiple health and social care datasets. BMC Med Inform Decis Mak 36 2009; 9: 3.
+21	Sadler K, Vizard T, Ford T, et al. Mental health of children and young people in England, 2017. Nov 22, 2018.https://digital.nhs.uk/ data-and-information/publications/statistical/mental-health-of-children-and-young-people-in-england/2017/2017 (accessed	37
+Nov 12, 2021).
+22	NHS Digital. Read codes. 2018. https://digital.nhs.uk/services/ terminology-and-classifications/read-codes (accessed Nov 2, 2021).	38
+23	Estyn. Effective practice in improving attendance in primary
+schools—June 2015. June 12, 2015. https://www.estyn.gov.wales/ thematic-report/effective-practice-improving-attendance-primary-schools-june-2015 (accessed Nov 2, 2021).	39
+24	Estyn. Attendance in secondary schools—September 2014.
+Sept 1, 2014. https://www.estyn.gov.wales/thematic-report/ attendance-secondary-schools-september-2014 (accessed	40
+Nov 2, 2021).
+25	Department of Education. A guide to absence statistics.
+March, 2019. https://assets.publishing.service.gov.uk/government/ 41 uploads/system/uploads/attachment_data/file/787314/Guide_to_ absence_statistics_21032019.pdf (accessed Nov 2, 2021).
+26	Welsh Government. Absenteeism from secondary schools, 2018/19.
+Aug 29, 2019. https://gov.wales/sites/default/files/statistics-and- 42 research/2019-08/absenteeism-from-secondary-schools-september-2018-august-2019-318.pdf (accessed Nov 2, 2021).
+27	Hojsgaard S, Halekoh U, Yan J. The R package geepack for	43
+generalized estimating equations. J Stat Softw 2006; 15: 1-11.
+28	Liang KY, Zeger SL. Regression analysis for correlated data.
+Annu Rev Public Health 1993; 14: 43-68.
+29	Paget A, Parker C, Heron J, et al. Which children and young people 44 are excluded from school? Findings from a large British birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). Child Care Health Dev 2018; 44: 285-96.
+30	Ford T, Parker C, Salim J, Goodman R, Logan S, Henley W.
+The relationship between exclusion from school and mental health: a secondary analysis of the British Child and Adolescent Mental Health Surveys 2004 and 2007 Psychol Med 2018; 48: 629-41.
+Pan W. Akaike’s information criterion in generalized estimating equations. Biometrics 2001; 57: 120-25.
+Pine DS, Fox NA. Childhood antecedents and risk for adult mental disorders. Annu Rev Pyschol 2015; 66: 459-85.
+John A, Marchant AL, Fone DL, et al. Recent trends in primary-care antidepressant prescribing to children and young people: an e-cohort study. Psychol Med 2016; 46: 3315-27.
+van der Lee JH, Mokkink LB, Grootenhuis MA, Heymans HS, Offringa M. Definitions and measurement of chronic health conditions in childhood: a systematic review. JAMA 2007;
+297: 2741-51.
+Welsh Assembly Government. Strategies for schools to improve attendance and manage lateness. 2011. https://dera.ioe.ac. uk/2945/3/110308section3en.pdf (accessed Nov 2, 2021).
+Department for Education. Exclusion from maintained schools, academies and pupil referral units in England. September, 2017. https://assets.publishing.service.gov.uk/government/uploads/ system/uploads/attachment_data/file/641418/20170831_Exclusion_ Stat_guidance_Web_version.pdf (accessed Nov 2, 2021).
+Welsh Government. Exclusions from schools and pupil referral units (PRU). April 1, 2015. https://gov.wales/exclusion-schools-and-pupil-referral-units-pru (accessed Nov 2, 2021).
+US Department of Education. Key policy letters signed by the Education Secretary or Deputy Secretary. Oct 7, 2015. https://www2. ed.gov/policy/elsec/guid/secletter/151007.html (accessed Nov 2, 2021).
+Downs J, Gilbert R, Hayes RD, Hotopf M, Ford T. Linking health and education data to plan and evaluate services for children. Arch Dis Child 2017; 102: 599-602.
+Kearney CA, Graczyk P. A response to intervention model to promote school attendance and decrease school absenteeism. Child Youth Care Forum 2014; 43: 1-25.
+Shinde S, Weiss HA, Varghese B, et al. Promoting school climate and health outcomes with the SEHER multi-component secondary school intervention in Bihar, India: a cluster-randomised controlled trial. Lancet 2018; 392: 2465-77.
+Bonell C, Blakemore S-J, Fletcher A, Patton G. Role theory of schools and adolescent health. Lancet Child Adolesc Health 2019; 3: 742-48.
+Fleming T, Dixon R, Frampton C, Merry S. A pragmatic randomized controlled trial of computerized CBT (SPARX) for symptoms of depression among adolescents excluded from mainstream education. Behav Cogn Psychother 2012; 40: 529-41. Department of Health & Social Care, Department for Education. Transforming children and young people’s mental health provision: a green paper. December, 2017. https://assets.publishing.service.gov. uk/government/uploads/system/uploads/attachment_data/ file/664855/Transforming_children_and_young_people_s_mental_ health_provision.pdf (accessed Nov 2, 2021).
+34
+www.thelancet.com/psychiatry Vol 9 January 2022
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+WHAT IS ALREADY KNOWN ON THIS TOPIC
+The increase in suicides after media stories about suicides by celebrities is referred to as the Werther effect
+Much less is known about the protective effects of media stories of hope and recovery in the context of suicidal crises
+Some evidence from randomised controlled trials shows a beneficial effect of media narratives of hope and recovery on suicidal thoughts and help seeking intentions
+WHAT THIS STUDY ADDS
+During 34 days of wide scale public exposure to Logic’s song “1-800-273-8255,” Lifeline received 9915 excess calls (95% confidence interval 6594 to 13 236):
+6.9% over the expected number
+In the same period, 245 fewer suicides (95% confidence interval 36 to 453) occurred: 5.5% below the expected number
+A media event intended to tell a “suicide prevention story” was associated with both an increase in calls to the National Suicide Prevention Lifeline and a simultaneous reduction in suicides in the United States
+2018), Lifeline received an excess of 9915 calls (95% confidence interval 6594 to 13 236), an increase of 6.9% (95% confidence interval 4.6% to 9.2%, P<0.001) over the expected number. A corresponding model for suicides indicated a reduction over the same period of 245 suicides (95% confidence interval 36 to 453) or 5.5% (95% confidence interval 0.8% to 10.1%, P=0.02) below the expected number of suicides.
+CONCLUSIONS
+Logic’s song “1-800-273-8255” was associated with a large increase in calls to Lifeline. A reduction in suicides was observed in the periods with the most social media discourse about the song.
+Introduction
+Repetitive reporting on suicide deaths or potentially lethal actions has been shown to trigger further suicides, known as the Werther effect.1 A recent meta-analysis found that news reporting on celebrity suicides—often highly repetitive over the following weeks2—was associated with a 13% increase in suicides.1
+Some other suicide related narratives might have preventive effects—media stories of people who managed to cope with suicidal crises without dying by suicide have been associated with reductions in subsequent suicides.3 The possible protective effects of stories of hope and recovery from suicidal crises is referred to as the Papageno effect.3 In contrast with studies on the Werther effect, most studies on the Papageno effect have used experimental designs. These trials typically use suicidal thoughts rather than suicide death as the outcome. Consistent with the research evidence for the Papageno effect, some of these studies indicate that media narratives of hope and recovery from a suicidal crisis are associated with reduced suicidal thoughts, particularly in people with some risk factors for suicidal behaviour.4-6 A noted limitation of these studies is that findings about suicidal thoughts do not necessarily generalise to suicidal behaviours and, most importantly, suicides.
+Suicide prevention and education efforts must harness positive media to educate the general public and high risk groups about suicide prevention without doing harm to individuals at risk. But a major dilemma for research in this area has been that stories of hope and recovery receive much less media coverage than stories of suicide death.
+On 28 April 2017, the American hip hop artist Logic released his song “1-800-273-8255,” prominently featuring the number of the US National Suicide Prevention Lifeline (referred to as Lifeline). The narrative of the song is centred around someone calling the 1-800 number for Lifeline and then telling the counsellor that they don’t want to live anymore. The accompanying music video, which was released four months later and has since received more than 419 million views on YouTube, depicts a young black man struggling with discrimination and bullying from peers and adults for being gay. He prepares for his suicide, but ultimately takes his phone and calls Lifeline, which marks a turning point towards improvement and mastery of his crisis.7
+The release of the song in April 2017 marked the start of a series of media events promoting the story of hope and recovery featured in the song, along with the phone number of Lifeline. The song was performed at the MTV Video Music Awards in late August 2017 to 5.4 million viewers and ultimately marked a breakthrough for “1-800-273-8255.”8 The song, which was labelled a “suicide prevention anthem” by the media, entered the top 10 of the Billboard Hot 100 music charts in the US, remaining there for several weeks and ranking as high as number 3 in September 2017.9 10 The song’s release was also associated with a nearly 10% uptick in online Google searches for Lifeline in the 28 days after its release.11 By the end of 2020, the song had surpassed one billion streams on Spotify.
+Logic’s song likely represents the broadest and most sustained suicide prevention messaging directly connected to a story of hope and recovery in any location to date and is thus a serendipitous event for research. To assess whether the song was associated with help seeking or suicides, we conducted a time series analysis examining the associations between Logic’s song and daily calls to the Lifeline number as well as daily suicides in the US.
+Methods
+Public attention to Logic’s song
+Three known distinct events directed strong public attention to Logic’s song: the release of the song on 28 April 2017, Logic’s performance at the MTV Video Music Awards on 27 August 2017, and his performance at the Grammy Awards on 28 January 2018. All these events gave widespread public attention to the message of the song—that help from Lifeline is available and effective. To obtain estimates for the timespan of public attention related to each of the events as a proxy for assessment of the impact period, we retrieved all original tweets geolocated to the US that contained the search terms “Logic” and “1-800-273-8255” from Brandwatch (www.brandwatch.com). Our approach was similar to previous studies estimating exposure periods for suicide related media events.2 Brandwatch is a data reseller that stores the entire historical Twitter stream of more than 350 million tweets per day, giving us access to all public tweets, retweets, and replies across the total observation period. More than 90%
+of tweets can be successfully matched to a country of origin.
+This search allowed us to generate an exhaustive dataset with all mentions specifically related to Logic’s song, excluding tweets produced by accounts that Twitter considered malicious bots, from 1 March 2017 to 30 April 2018, covering the entire period before the release and during the song’s presence in the Billboard Hot 100.
+We visually inspected the daily time series of tweets to identify peaks in tweeting behaviour qualitatively. Consistent with our study preregistration, we were mainly interested in the three events that were most relevant to dissemination of Logic’s song (song release April 2017, MTV Video Music Awards August 2017, and Grammy Awards January 2018), but explored the time series of tweets for further peaks as well. Subsequently, we visually assessed the duration of any peak to capture the period until the day that attention wore off. In addition, we performed a post hoc change-point analysis to assess whether findings from visual inspection differed from quantitative assessment of change-points in the time series data (see supplementary text S2 for details on the methodological approach for identifying peaks and their duration).
+Lifeline calls and suicide data
+We obtained the total number of calls to Lifeline across the US directly from Lifeline. Call data were provided as daily aggregates for the period 1 January 2010 to 31 December 2018. National suicide data were obtained from the National Center for Health Statistics (part of the US Centers for Disease Control and Prevention). Suicide was defined using ICD-10 (international classification of diseases, 10th revision) underlying cause of death codes X60-X84, Y87.0, and U03. Data were provided as daily aggregates for the period 1 January 2010 to 31 December 2018.
+Statistical analysis
+Seasonal autoregressive integrated moving average models to estimate baseline trends in calls and suicides were fitted to the data up to 6 April 2017. This cut-off date was selected to allow for a three week preparatory period before the release of the song on 28 April, consistent with the observation that the first tweets about the song were posted as soon as three weeks before the release. The selection of models was aided by the SPSS Expert Modeler function, version 26 (IBM), choosing models with the lowest bayesian information criterion value, highest stationary R2 value (that is, variance attributable to the fitted time series model), and, when possible, a non-significant Ljung-Box Q statistic (indicating whether residuals could be assumed white noise, with stated degrees of freedom). The models derived from the baseline data were subsequently fitted to the full set of data for each series.
+Based on the time periods of strong social media attention on the song, we investigated the temporary
+association between each of the identified song related events and calls to Lifeline and suicides. We used dummy variables to model these associations as discrete pulses (that is, we modelled them as sudden changes from the baseline, starting and ending with the previously identified duration of the event of interest). These pulses were coded as binary variables, with a value of 0 before the onset of the event of interest, 1 during the event of interest (for 30 days, for example) and 0 thereafter. After fitting our models, we used model estimates to calculate the number of excess calls and suicides for each event (see supplementary text S1 for details of the statistical model. Supplementary table S7 provides an annotated syntax for the time series analyses ).
+As a further step, planned in our preregistration, we repeated the analyses of Lifeline calls and suicides using a single dummy variable to combine the effect of the three main media events that captured the most public attention.
+Possible confounding exogenous events
+Because of possible confounding by the release of 13 Reasons Why, a Netflix show that sparked strong criticism for violating media recommendations for safe portrayals of suicide,12-14 we included a dummy variable (coded 1 from the release date of 13 Reasons Why (31 March 2017 to 30 June 2017, and 0 otherwise).12 Notably, previous research found that the show was associated with a noticeable increase of 5.5% in suicides (95% confidence interval 5.5% to 21.1%) in the US among 10 to 19 year olds in the three months after its release.12 Our use of a three month period was consistent with social media data indicating that the show received the strongest attention in that period.12
+To identify any further events that might be associated with Lifeline calls and suicides, we used a list of Wikipedia entries of suicides by well known people between 7 April 2017 (immediately before the song’s release) and 31 December 2018 (end of observation period). We accessed and assessed tweet volumes for all the identified (American and international) celebrities to identify the suicides that received strong public attention so that we could adjust for the occurrence of these confounding events in the model (supplementary table S1). Variables were subsequently added for the suicides of Chris Cornell (18 May 2017), Kate Spade and Anthony Bourdain (5 and 8 June 2017, respectively),15 Chester Bennington (20 July 2017), and Avicii aka Tim Bergling (20 April 2018).2 Consistent with research on the association between celebrity suicides and subsequent suicide prevalence in the general population, suicides of lesser known celebrities (Chris Cornell, Tim Bergling) were coded as dummy variables, with value 0 before their deaths, 1 for the 30 days after their deaths, and 0 thereafter. For Chester Bennington, Anthony Bourdain, and Kate Spade, a 60 day period was used,15 because these suicides continued to receive considerable public attention in the second month after their deaths (supplementary table S1).
+Finally, World Suicide Prevention Day is held annually on 10 September to promote awareness of suicide prevention.16 In the US, World Suicide Prevention Day is part of the annual National Suicide Prevention Week. We included dummy variables for the seven day period of these events in 2017 (10-16 September) and 2018 (9-15 September).
+Sensitivity analyses
+We performed three (not preregistered, exploratory) sensitivity analyses. First, we used daily unique calls to Lifeline (as opposed to total calls) to assess whether patterns were similar after removing repeat callers. Second, we changed the pre-intervention period to end by the day before the song’s release (27 April 2017) to investigate whether this affected key findings. Third, we conducted an additional analysis combining all song related media events (including events that emerged only from visual inspection) into a single variable and assessed its associations with calls and suicides.
+Patient and public involvement
+No patients or members of the public were directly involved in this study because of time constraints in planning, owing to the long period between the song’s release and the setting up of this research. We did, however, speak to patients about the study and we asked a member of the public to read our manuscript after submission.
+Results
+Public attention as indicated by tweets
+Logic’s song generated 81 953 tweets by 55 471 unique users, posted between 1 March 2017 and 30 April 2018 (fig 1). Daily tweets reached three peaks corresponding to the three main events—the song’s release in April 2017, the MTV Video Music Awards in August 2017, and the Grammy Awards in January 2018. Two smaller peaks were identified; based on a qualitative assessment of a sample of specific tweets in those periods: one peak occurred around the time of the song’s video release (17 August 2017) and the second one alongside media reports of an increase in calls to Lifeline associated with the song (aired on CBS on 10 October 2017).17 All peaks emerged rapidly, reaching their maximum within one day of the event. The duration of all five peaks was estimated, using the first day of the increase as the start of the impact period and ending the day the peak had worn off.
+Figure 1 shows the estimated impact periods for each event (see supplementary figure S1 for a large version of this figure showing the time series of tweets, Lifeline calls, and suicides, and the identified impact periods).
+Attention to the song was strongest immediately after Logic’s performance at the 2017 MTV Video Music Awards, with an average of 1324 daily tweets over a 28 day period. More time limited peaks were seen after the song’s release (1151 tweets a day for three days) and after the 2018 Grammy Awards (1883 tweets a day for another three day period). Overall, 56.3% of tweets
+about Logic’s song between March 2017 and April 2018 were posted in the 34 day high impact period covering these three media events (table 1).
+Association with Lifeline calls
+Descriptive information about Lifeline users (gender, age, and demographic distribution of calls) is not
+routinely collected and thus was not available for the entire dataset; supplementary text S3 provides a breakdown of a not fully representative subsample).
+A statistically significant association was found for calls to Lifeline for the 34 day period covering the three main events, but not for the two minor events (the video release and news about the apparent effect of the
+song). The strongest increase was seen immediately after the MTV Video Music Awards. The increase was smallest for the period after the song’s release. This pattern was consistent with the observed public attention to these events, which was most pronounced for the MTV Video Music Awards (fig 1).
+The observed number of calls during the periods of song related media events exceeded the range of forecasted calls (based on baseline data) for the song’s release (5.3%, 95% confidence interval 0.53% to 10.0%, three day period), the performance at the MTV Video Music Awards (8.5%, 5.1% to 11.9%, 28 day period), and the performance at the Grammy Awards (6.5%, 1.7% to 11.2%, three day period) (table 2, fig 1). No significant associations were found for the two smaller spikes in public attention. A combined analysis across the three main media events indicated an excess of 9915 calls (95% confidence interval 6594 to 13236), corresponding to an increase of 6.9% (95% confidence interval 4.6% to 9.2%, P<0.001). Supplementary table S2 provides the parameter estimates for all model components.
+Association with suicides
+The observed number of suicides in the periods of Logic related media events were within the range of forecasted values, using the model fit to the baseline data. Estimates for the three major media events pointed to a decrease in suicides, but these estimates were not significantly different from the expected number of suicides (table 3, fig 1). Combining the data for all three major events into a single variable yielded an observed number of suicides that was below the range of the model forecasts. Models including a discrete pulse for these events indicated a significant reduction in suicides, amounting to a decrease of 245 suicides (95% confidence interval 36 to 453 suicides). This corresponded to a reduction of suicides of 5.5% (0.8% to 10.1%, P=0.02) in the 34 day period. Supplementary table S3 provides parameter estimates for all model components.
+A sensitivity analysis using daily unique Lifeline calls (rather than total daily calls) showed similar patterns to the original analysis (supplementary table S4). A further sensitivity analysis, using a pre-intervention cut-off date of 27 April 2017, showed no deviations
+from the original analysis (supplementary table S5). A third sensitivity analysis, combining all Logic related media events (including those not individually associated with daily Lifeline calls) into a single model, indicated that the association with Lifeline calls remained significant, whereas there was no significant association with suicides (supplementary table S6).
+Discussion
+This interrupted time series analysis found that Logic’s song “1-800-273-8255” was associated with a noticeable increase in calls to Lifeline (an additional 9915 calls or increase of 6.9%) during the 34 day period when public attention to the song was substantial. Over the same period, there was some evidence of a reduction in suicides, amounting to 245 fewer suicides (decrease of 5.5%).
+These findings are consistent with a possible Papageno effect3 and are important from a suicide prevention perspective. Media campaigns for suicide prevention have received a groundswell of support internationally, but evaluations are scarce and often limited in terms of scope.18 Our finding of a substantial increase in actual help seeking and a possible decrease in suicides during the period of high public attention to Logic’s song support the real world effectiveness of this intervention. Previous peaks in calls to Lifeline were almost always associated with harmful media events, such as celebrity suicides.19 These events were often associated with increases in suicides,1 20 indicating that both increases in calls to Lifeline and increases in suicides might reflect considerable distress in the community from these media events. The overall patterns found for suicides by celebrities included as covariates in this analysis were largely consistent with previous research. Most notably, the suicides of Kate Spade and Anthony Bourdain,15 which received the strongest and most sustained attention across all celebrity deaths in the post-intervention period, were clearly positively associated with Lifeline calls, and increases of suicides were close to the boundary of significance. In contrast, the patterns observed for Logic’s song, consistent with the song’s narrative, indicated an increase in help seeking behaviour accompanied by a slight reduction in suicides.
+The effectiveness of the song on calls to a helpline is a novel finding. The results show that it is possible to promote help seeking for suicidal crises in the absence of negative news, and indicate that suicides could potentially be reduced with prevention focused campaigning, such as Logic’s song. Although the reduction in suicides was small, this finding shows that the song did not result in harmful effects on suicide occurrence, which would have been indicated by an increase in suicides. This is important because some prevention messaging that aimed to reduce suicides was ultimately associated with increases in suicides.12 These narratives, however, typically focus on suicide deaths and attempts, not on hope and recovery from suicidal thoughts and feelings.
+The amount of exposure generated by prevention messages about hope and recovery seems to be crucial in efforts to yield positive effects on behaviours including help seeking and potentially suicides. In accordance with the most sustained and strongest public attention to Logic’s song, as indicated by tweet volume, the uptick seen for Lifeline calls was strongest after his performance of the song at the MTV Video Music Awards in 2017 followed by his performance at the Grammy Awards in 2018, and then the song’s release. This pattern underscores the importance of reaching large proportions of the target population to achieve behavioural effects. Compared with other prevention events, public attention for Logic’s song, as reflected in tweet volume, was more sustainable, but the event did not necessarily result in more tweets overall. Based on query terms used for World Suicide Prevention Day 202016 in the US, for example, the day resulted in a total of approximately 94 000 tweets from 58 000 users, including more than 30 000 on 10 September 2020 alone. Overall social media attention was comparable in total magnitude to Logic’s song (82 000 tweets from 55 000 users), but the attention was concentrated on a single day, whereas Logic’s song was highlighted repeatedly over several specific and diverse media events. This amount of attention was, however, still considerably smaller than for some harmful media events in the recent past—for example, in the three months after the release of 13 Reasons Why, a TV show that violated key recommendations for safe portrayals of suicide12-14 and was associated with an increase in teenage suicides,12 there were tweets from 870 056 individual users about the show.12 This is more
+than 15 times larger than for Logic’s song. Although Logic’s song sets an important, extraordinary example for widely disseminated and quite sustainable suicide prevention messaging, exposure is still considerably stronger for some conceivably harmful media events.
+Different pathways and mechanisms might be at play in any reduction in suicides associated with Logic’s song. The present analysis indicates that periods that were strongly associated with an uptick in calls showed a simultaneous decrease in suicides. We have not established whether calling behaviour affected people who did (or did not) die by suicide after the song’s release. People who are at risk of suicide are often socially withdrawn, and some might not consistently use helplines. The proportion of socially isolated, suicidal callers, however, has been found to be particularly high among frequent callers of crisis lines,21 indicating that the threshold for calling a telephone crisis centre is lower than for accessing help services that require on-site visits. The song, however, might have triggered other or additional routes of action beside calling Lifeline. Finding romantic love and positive communication with his family, for example, are major contributors to the improvement in suicidal thoughts and feelings of the protagonist seen in Logic’s music video, and some people might have felt inspired to reach out for help from other sources. In-depth qualitative research might help shed light on the question of whether and how precisely people with suicidal thoughts and feelings were influenced by the song.
+Strengths and limitations of this study
+A strength of this study is the length of the time series, with daily data from 2010 to 2017 available to model the expected pre-intervention suicide counts. The model controlled and adjusted for several exogenous variables (namely, concurrent side events, such as suicides of well known celebrities, and National Suicide Prevention Week). Trends, temporal fluctuations, and seasonality were adjusted for in the SARIMA models. A further strength of this study is the use of daily data, resulting in precise modelling in accordance with periods when social media attention to Logic’s song was strong, as indicated by the volume of tweets about it. Estimating exposure based on social media data is more objective than merely estimating exposure times in the absence of any supporting data. This refined
+approach is consistent with studies indicating that public attention to social media discourse in terms of tweets is short lived, with lifespans of a tweet normally not extending beyond a few hours.22
+The main limitation of the study is that it was based on ecological data, so it was not possible to ascertain whether the people calling Lifeline or not dying by suicide had been exposed to Logic’s song and related media events, or what their motivations might have been for calling or not dying from suicide. The observational nature of this research does not allow us to establish causality. Furthermore, only total daily call and suicide data were available, without any stratified data for various demographic factors, such as gender, age, or location of residence. Specifically for suicides, data stratified by age or gender would have resulted in small numbers on some days. This is a limitation because adolescents and young adults are over-represented among the viewership of the MTV Video Music Awards.23 Although this age discrepancy does not equally apply to the other events related to the song—the Grammy Awards 2018 was watched by 17% of Americans aged 50 or older24—or to the music preferences for rap or hip hop music in the US,25 a somewhat stronger exposure of young people seems plausible. Notably, the demographic profile (as well as primary presenting problems) of callers in September 2017, the month with strongest attention to the song, did not grossly deviate from other months in the observation period after the song’s release, thus indicating that possible age effects, if present at all, might have been limited (see supplementary appendix).
+Other limitations of our approach include the inability to assess longer term associations beyond the periods of strong public interest. As previously noted, 56.3% of tweets about Logic’s song between March 2017 and April 2018 were posted in the 34 day high impact period defined in this study, indicating that the bulk of attention was covered by the selected period. This is consistent with the generally short lived public attention to media events and related public discourse, which requires constant repetition to become sustainable.22 26 Furthermore, the different peaks identified were dissimilar not only in duration but also in the maximum number of tweets on a given day. The present impact periods have been modelled as discrete pulses, consistent with the assumption that visible, large changes in attention (rather than overall number of tweets) might most likely affect behavioural outcomes, such as help seeking and suicide. This is consistent with related evidence that other media data, such as those from Google Trends, generally struggle to predict general suicide trends,27 whereas sudden strong changes in Google search behaviours, as seen during major events such as the release of 13 Reasons Why or the covid-19 pandemic, do seem to be useful in estimating suicide trends.28 29 Studies are needed to assess how long the effects of suicide prevention messaging generally last and what absolute amount and duration of attention, as reflected in social media,
+is necessary to yield any observable effects. Social media data, such as tweets, are only a proxy of public attention, however, and might not always reliably reflect actual exposure. Finally, the determination of suicide deaths is a challenging task, and suicide deaths are sometimes under-reported.30 Even though a certain degree of misclassification is possible, rapid fluctuations in classification accuracy of national suicide data, which could impact the present analysis, seem unlikely.
+Conclusions
+This analysis suggests that Logic’s song “1-800-2738255” was associated with a noticeable increase in calls to Lifeline and a simultaneous small reduction in suicides during peak social media discourse about the song. The latter outcome is worth underscoring—the occurrence of a widely disseminated song and video was associated with more than 200 fewer suicides than expected. These findings emphasise the potential population health benefits of working creatively and innovatively with other sectors, such as the music and entertainment industries, to promote new impactful stories of help seeking that resonate with broad audiences, leave a visible footprint on social media, and are safe in terms of not featuring potentially lethal actions but rather coping and mastery of crises.13 Interventions that follow these principles could help create behavioural change to increase help seeking and prevent suicide.
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+Non-suicidal self-injury (NSSI), suicidal ideation and suicide attempt are major public health problems in adolescents worldwide,1,2 and they represent some of the strongest and most consistent predictors of future suicidal behaviour across both in-patient and general populations.1,3 To sustain improvements in management and prevention initiatives, research continues to strive to better comprehend the complex interplay between many of the recognised psychosocial risk factors. Thus far, a substantial body of research has demonstrated significant independent effects between adverse childhood experiences (ACEs) and social support on self-injurious behaviour (SIB) and suicidality.4,5 Yet current knowledge surrounding these relationships is predominantly derived from Western countries and from adult or clinical popula-tions,4,5 with only a few studies undertaken in community adolescent populations in China.2,6 There is also little research on the interaction effects between ACEs and social support on NSSI, suicidal ideation and suicide attempt in adolescents, despite ACEs and social support being highly correlated.7 Finally, despite evidence differentiating boys and girls in terms of the prevalence and effects of different ACEs,8 the perceptions and utilisation of social support9 and the presentation of NSSI, suicidal ideation and suicide attempt,1 few studies have been undertaken to examine gender differences in the interaction between ACEs and social support on NSSI, suicidal ideation and suicide attempt. This is particularly important in nonWestern populations where there is a dearth of research despite the cultural context in China, which continues to demonstrate inherent gender discrimination.10,11 Therefore, our study first sought to
+investigate the independent effects of ACEs and social support on NSSI, suicidal ideation and suicide attempt in Chinese community adolescents; second, sought to examine the interaction effects between ACEs and social support on NSSI, suicidal ideation and suicide attempt and third, sought to ascertain whether there are any apparent gender differences in either independent or interaction effects for these relationships.
+Method
+Study sample and procedures
+Three provinces, namely Anhui, Henan and Guizhou, were chosen as our study fields for data collection. These provinces are broadly representative of the average population within China in terms of economic development and demographic composition, and are also where our adolescent health research network is located, thus facilitating the data collection. In each province, one region (Bengbu in Anhui province, Zhengzhou in Henan province and Guiyang in Guizhou province) was randomly selected. In each region, eight general junior and senior schools (four from rural areas) were randomly chosen to recruit participants. As 4 schools were combined junior and senior schools, only 20 schools were selected for inclusion in the survey. In total, 15 278 students aged 10-20 years, from grades 7-12, were contacted for this health survey and asked to complete an anonymous questionnaire. Informed consent was sought from parents/guardians, and 1.5% of
+the recruited participants or their parents/guardians opted out of the study. The design and data collection procedures were approved by the Ethics Committee of Anhui Medical University (2012534). The survey was conducted from November 2013 to January 2014.
+Measurement of sociodemographic profile, psychological symptoms, ACEs and social support
+Sociodemographic profile and psychological symptoms
+Demographic data for each participant was recorded, including age, gender (boys or girls), urban/rural residency, parents’ education level (less than junior middle school, junior middle school, senior middle school, college or more) and self-perceived economic status of the family (poor, moderate or good). Psychological symptoms, including emotional, behavioural and social adaptation symptoms, were evaluated by the psychological domain of the Multidimensional Sub-health Questionnaire of Adolescents12 (Cronbach’s a = 0.920 in this study).
+ACEs
+ACEs were defined as having experienced childhood maltreatment and/or household dysfunction. Childhood maltreatment was evaluated by the Child Trauma Questionnaire (CTQ),13 a widely used 28-item measure that assesses 5 different forms of childhood trauma (physical abuse, sexual abuse, emotional abuse, physical neglect and emotional neglect). The CTQ was translated and validated in Chinese.14 The participants were asked about abusive childhood experiences before 16 years of age. Responses ranged from ‘never true’ to ‘rarely true’, ‘sometimes true’, ‘often true’ or ‘very often true’. Respondents were defined as exposed to a category if they responded ‘very often’, ‘often’ or ‘sometimes’ to any item in that category. A Cronbach’s a coefficient of 0.737 was obtained for the CTQ in the current study. Household dysfunction questions were derived from the Centers for Disease Control and Kaiser Permanente Adverse Childhood Experiences Study in the USA.8 Household dysfunction was assessed through endorsement of the following experiences: (a) the divorce/separation of parents, (b) a parent serving time in jail, (c) having witnessed domestic violence, (d) having lived with someone who was mentally ill or suicidal or (e) having lived with someone with an alcohol or drug problem. Respondents were classified as exposed to household dysfunction if they responded; yes; to any item. The Cronbach’s a coefficient for the household dysfunction questionnaire was 0.705 in the present study. Because of the high interrelatedness of various types of ACEs (all P <0.01), an ordinal number of ACEs categories score was created by summing the dichotomous ACEs items (range, 0 (unexposed) to 6 (exposed to physical abuse, sexual abuse, emotional abuse, physical neglect, emotional neglect and household dysfunction)) to investigate the graded association of ACEs and both SIB and suicidality.8 The total score was then converted into four categories of summed score (0, 1-2, 3-4 and 5-6), with zero experiences selected as the referent for analysis purposes.
+Social support
+Social support was assessed by the 17-item Adolescent Social Support Scale,15 which includes three dimensions: objective support, subjective support and support availability. Participants reported whether an item from the scale was in ‘inconformity’, ‘little inconformity’, ‘uncertainty’, ‘little conformity’ or ‘conformity’. The scale scores, with a possible range of 17-85 (low to high social support), had a good internal consistency in the present study, with a significant Cronbach’s a coefficient of 0.940. The total score was divided into three levels (high, P75-P100; moderate, P25-P75; and low, P0-P25) for analysis.
+Measurements of NSSI, suicidal ideation and suicide attempt
+NSSI
+All participants received a screening questionnaire for NSSI, asking ‘In the past 12 months, have you ever harmed yourself in a way that was deliberate, but not intended to take your life?’. A list of eight NSSI methods were specified: hit yourself, pulled your own hair, banged your head or fist against something, pinched or scratched yourself, bitten yourself, cut or pierced yourself and burned yourself. Participants were then asked, ‘Have you ever done something with the intention of hurting yourself other than what was presented?’.6 For those who confirmed that they had engaged in NSSI, the frequency of NSSI was investigated. NSSI was dichotomised (frequency of NSSI of three or more versus fewer than three as yes or no, respectively) for analysis. The internal consistency reliability of NSSI was 0.749 in the current study.
+Suicidal ideation and suicide attempt
+Suicidal ideation and suicide attempt refer to the ‘middle school questionnaire’ of the 2013 Youth Risk Behaviour Surveillance System in the USA.16 Suicidal ideation was defined as a ‘yes’ in response to the question ‘Have you ever thought about killing yourself in the past 12 months?’. Suicide attempt was defined as a ‘yes’ in response to the question ‘Have you ever tried to kill yourself in the past 12 months?’.
+Statistical analysis
+Of the 15 278 school adolescents recruited, 458 (3.0%) were excluded from the study because of absence from school on the day of the survey or unwillingness to respond to the questionnaire, or high levels of missing data or obviously fictitious or inconsistent responses. Thus, a total sample of 14 820 (97.0%) participants was analysed.
+Sociodemographic data, ACEs, social support, NSSI, suicidal ideation and suicide attempt were described in both the total population and for boys and girls separately. Gender differences were assessed with the x-test for categorical variables and one-way analysis of variance for continuous variables. Binomial logistic regression models were used to examine the associations of NSSI, suicidal ideation and suicide attempt with ACEs and social support individually, and then in combination. In the models, adjustment was made for age, gender, regional area, school, urban/rurality, mother’s education level, economic status of family and psychological symptoms. In examining the association of NSSI with ACEs and social support, we also used the thresholds of NSSI score >1 and >5 for sensitivity analysis.
+Gender differences in the associations were examined via two odds ratios (Ratio of two odds ratios, RORs). 7 All analyses were conducted with SPSS software, Windows version 16.0 (SPSS Inc., Chicago, IL).
+Results
+Characteristics of participants
+Of the 14 820 participants, the mean age was 15.4 years (s.d. 1.8), and 50.2% were girls. Scores showed that 45.7% of the sample had experienced childhood emotional abuse, 20.3% had experienced physical abuse, 13.3% had experienced sexual abuse, 64.2% had experienced physical neglect, 58.5% had experienced emotional neglect and 42.5% had experienced household dysfunction; in total, 89.4% had experienced one or more ACEs and 46.3% reported three or more types of ACEs. Compared with boys, girls had significantly more psychological symptoms, fewer ACEs and a higher level
+of social support (P <0.001). Boys had significantly increased exposure to physical and sexual abuse, physical and emotional neglect, household dysfunction and NSSI (P < 0.001). Girls had significantly greater exposure to emotional abuse, suicidal ideation and suicide attempt (P <0.001). The details of gender differences and sociodemographic factors can be seen in Table 1.
+Effect of ACEs and social support on NSSI, suicidal ideation and suicide attempt, and gender difference
+Table 2 shows the number and percentage of participants according to NSSI category, among different levels of ACEs and social support. There were significant trends toward increased NSSI with higher ACEs and lower social support. Multiple adjusted odds ratios for NSSI were significantly increased with higher ACEs and lower social support (model 2 in Table 2). Even when ACEs and social support were included in the model simultaneously, there were main effects of ACEs score and level of social support on NSSI (model 3 in Table 2). Using the thresholds of NSSI > 1 and NSSI > 5 for separate data analysis, we found that the associations of ACEs score and level of social support (Supplementary Table 1
+148
+https://doi.org/10.1192/bjp.2018.263 Published online by Cambridge University Press
+available at https://doi.org/10.1192/bjp.2018.263) were similar to those found with NSSI > 3 (Table 2).
+Supplementary Table 2 shows data on suicidal ideation and suicide attempt in relation to ACEs and social support. There were significant trends toward increased suicidal ideation and suicide attempt with higher ACEs and lower social support. Multiple adjusted odds ratios for suicidal ideation and suicide attempt were significantly increased with higher ACEs and lower social support, respectively. When ACEs and social support were put in the model simultaneously, the main effects of the ACEs and social support on suicidal ideation and suicide attempt remained, with the exception of social support on suicide attempt in boys.
+No gender differences were found in the independent effects of ACEs or social support on NSSI, with the exception of the lowest social support having a stronger effect in girls than in boys (Table 3). In the data analysis for NSSI > 1 and NSSI > 5, gender differences in ACEs score or social support on NSSI (Supplementary Tables 3 and 4) were similar to those found with NSSI > 3 (Table 3), whereas the NSSI > 5 data showed a borderline significance for the lowest social support having a stronger effect in girls than in boys. There were no gender differences in the effects of
+ACEs, Adverse childhood experiences; NSSI, non-suicidal self-injury.
+a.	Unadjusted model.
+b.	Adjusted for age, regiona l areas, schoo l , urban/rura l ity, mother’s education l eve l , economic status of fami l y, psychol ogica l symptoms, ACEs score and l eve l of socia l support.
+c.	Calculated by adjusted odds ratio.
+ACEs or social support on suicidal ideation (Supplementary Table 5), but the effects of high ACEs score and low or moderate social support on suicide attempt were significantly stronger in girls than in boys (Table 4).
+Further analysis was conducted to examine whether specific types of ACEs demonstrated gender differences in the effect on NSSI, suicidal ideation and suicide attempt (Supplementary Tables 6-8). Each type of ACEs was significantly associated with NSSI, suicidal ideation and suicide attempt in boys and girls,
+except for emotional and physical neglect with suicide attempt in boys (Supplementary Table 8). Emotional abuse increased the risk of NSSI, suicidal ideation and suicide attempt more than other types of ACEs among both genders. When exposed to emotional abuse, girls were more likely to engage in NSSI than boys (Supplementary Table 6). When exposed to physical abuse, girls were also more likely to report a suicide attempt than boys (Supplementary Table 8). Conversely, suicidal ideation was more common among boys than girls when exposed to emotional
+neglect (Supplementary Table 7). No further gender differences were found.
+Interaction effect between ACEs and social support on NSSI, suicidal ideation and suicide attempt, and gender difference
+Although ACEs and social support were highly correlated in the present study (Supplementary Table 9), there were no interaction effects between ACEs and social support on NSSI (total sample, P = 0.062; boys, P = 0.521; girls, P = 0.115), and suicidal ideation (total sample, P = 0.087; boys, P = 0.061; girls, P = 0.075). However, there was an interaction effect of ACEs and social support on suicide attempt in the total sample (P = 0.001) and in boys (P = 0.002), although it was not significant in girls (P = 0.334).
+Discussion
+A large-scale, school-based survey was conducted to examine the independent and interaction effects between ACEs and social support on NSSI, suicidal ideation and suicide attempt in adolescents. Our data reveals that ACEs and social support have main effects on both SIB and suicidality independently, as well as interaction effects between ACEs and social support on suicide attempt but not NSSI or suicidal ideation. Similar relationships are found across genders; however, girls were more likely to engage in NSSI and suicide attempt when social support was low, and an interaction effect between ACEs and social support on suicide attempt was found only in boys.
+The effects of ACEs on NSSI, suicidal ideation and suicide attempt
+Previous research has shown a relationship between ACEs, NSSI and suicidality.4 An investigation in 957 undergraduate students in Canada suggested that those experiencing more adverse familylife events and higher perceived relational trauma were more at risk of engaging in NSSI behaviours.18 A prospective study in youths aged 14-26 years also suggested that physical abuse, emotional abuse and emotional neglect were associated with subsequent risk of suicidal behaviour.19 Our study has extended this literature by demonstrating gender-specificity effects in the relationships, and by addressing the limitation of the lack of equivalent research within Chinese community populations. However, the relationships observed between all maltreatment types and NSSI, suicidal ideation and suicide attempt conflicts with other studies20 in clinically referred youth, which indicate that only indirect childhood maltreatment (i.e. witnessing domestic violence) is significantly associated with NSSI, whereas direct forms of maltreatment (physical or sexual abuse) are not. Similarly in adult samples, varied effects were found with different forms of childhood adversity, as childhood abuse was not significantly associated with NSSI and suicide attempt after adjusting for the correlation with low maternal or paternal care.21 Thus, various study samples, different definitions of ACEs and diverse control variables should be considered to interpret the results of the study.
+The tradition of son preference remains prevalent in China.10 This may contribute, at least in part, to the poorer mental health outcomes (including depression, anxiety, low self-esteem, sensitivity to negative life events and interpersonal pressure) previously observed in female Chinese school children.22 Yet, no study has attempted to identify gender differences in the relationships between ACEs, SIB and suicidality in Chinese adolescents. Although the risk of NSSI, suicidal ideation and suicide attempt increased in line with a greater number of ACEs categories
+generally, our study suggests that girls who experienced a higher number of ACEs (categories) seem to be more vulnerable to suicide attempt (but not NSSI or suicidal ideation) than boys, which is in line with prior studies.23,24 Moreover, girls were found to be more susceptible to NSSI and suicide attempt when they encountered particular forms of maltreatment, primarily emotional or physical abuse. This substantiates findings from a series of studies, including Isohookana et al,23 who examined psychiatric in-patients aged 12-17 years and found that a higher number of ACEs was associated with an increased risk of NSSI and suicide attempt in girls, but not in boys. Garcia et al24 also showed that significant correlations were found between childhood trauma scores and psychotic symptoms, depressive symptoms and poorer functionality, but only in women, whereas childhood trauma was associated with poorer social cognition in both males and females. Further studies are needed to ascertain whether there are gender differences in the relationship between ACEs, SIB and suicidality.
+The effects of social support on NSSI, suicidal ideation and suicide attempt
+Social support was found to have an independent effect on NSSI, suicidal ideation and suicide attempt, even after adjusting for sociodemographic risk factors, psychological symptoms and ACEs. Previous findings5,25 have also indicated that individuals with higher social support have a significantly lower odds of engaging in NSSI, suicidal ideation and suicide attempt, which is consistent with our study. One study in a clinical sample of adolescents suggested that perceptions of school support were independently and negatively associated with suicidal ideation, especially among adolescents who also reported perceptions of lower parent support.5 Furthermore, lower perceived parental support was independently associated with greater odds of history of suicide attempt. One study in community and in-patient mental health settings25 also showed that children and adolescents who had some form of social support had a 26% decrease in the odds of engaging in NSSI when compared with their counterparts who lacked social support. Collectively, this supports Ayub’s26 suggestion that social support may play a significant role in the prevention of suicidal thoughts and behaviours, and that psychologists should include family and friends in their approaches to treating suicidal youth. This may be especially important in China, where mental illness is frequently blamed on the family and the individual.11 Moreover, considering that Chinese students are often burdened with tremendous academic pressure,27 support within the school environment may be particularly beneficial in this context.
+The inverse association between social support and NSSI was found to be stronger in girls than in boys. This may contribute, in part, to the explanation of why the risk of suicidal behaviours is greater in girls despite having fewer ACEs and higher social support than boys. Traditionally within China, boys are more likely to be socialised to be independent than girls, which may help to account for the increased sensitivity to lower social support observed among females in our sample. Other studies examining developmental trajectories of suicidal ideation showed that support from family and friends differentiated suicidal ideation trajectories for both boys and girls.28 However, an ecological study in adults in 75 regions of 23 European countries revealed inverse relationships between social support and suicide rates for both genders, with some indication of a stronger relationship among men.29 Future studies may look to examine whether different types of social support (e.g. peer or parental support) are more influential for males or females.
+Interaction effects between ACEs and social support on NSSI, suicidal ideation and suicide attempt
+Our study extends existing knowledge by demonstrating the interaction effect between ACEs and social support on suicide attempt, particularly in boys, but failed to establish a similar relationship for either NSSI or suicidal ideation. The relationship between ACEs, social support and SIBs and suicidality is complex. Christoffersen et al suggested that social support is a partial mediator between traumatic life-events and NSSI in young adulthood. A study of women in the USA found that the link between intimate partner violence and suicidal behaviour was moderated by social support.30 Extending this to the adolescent population, it would be reasonable to suggest that social support may also moderate the relationship between ACEs and SIBs in this population. That said, our study failed to establish interaction effects between ACEs and social support on NSSI and suicidal ideation. This may be partly attributable to the confounding effect of psychological symptoms, because these have found to be significantly related to ACEs, social support and SIB.2, ,7 In this study, statistical interaction effects of ACEs and social support on suicidal ideation were found after removing psychological symptoms from the multivariable model (results not shown). One possible alternative explanation may be that interaction effects are only apparent among those who are most seriously affected (such as those who have actual suicide attempts), as this group may be most likely to encounter the highest level of ACEs and lowest social support. Further studies on the interaction effects between ACEs and social support on NSSI, suicidal ideation and suicide attempt will be needed to further elucidate this complex interaction.
+Strengths and weakness of the study
+This study is the first to examine gender differences and interaction effects between ACEs and social support on NSSI, suicidal ideation and suicide attempt within Chinese adolescents. The study sample is representative, covering urban and rural areas in China, and the response rate of the participants was high. The large sample from urban and rural areas provided enough statistical power to examine gender differences, with multivariate adjustment analysis. However, the study has limitations. First, the study was crosssectional, therefore it is difficult to establish a causal relationship. Nonetheless, our findings pertaining to the association between ACEs and social support with SIB and suicidality were similar to those in previous cohort studies.19,28 Second, because of the use of self-reported questionnaires for data collection purposes, it is possible that recall bias may exist. This may ultimately influence the strength of the observed relationships, and our results may represent a more conservative estimation than is truly present. Third, the focus was solely directed toward the number and type of ACEs in this study, but it may be important to understand an individual’s subjective experience of the events. Finally, the study focused on adolescents in traditional school environments, therefore the findings did not represent adolescents who were absent from school, which is important because studies have shown that ACEs, lower social support and suicidality are more prevalent in individuals with lower educational achievement and socioeconomic status.31 Caution should be exercised in the application of the findings to the whole population of adolescents in China.
+Implications
+The findings indicate that ACEs and poorer social support are independently associated with an increased risk of both SIB and suicidality in school adolescents. Moreover, an interaction effect was observed between ACEs and social support on suicide attempt,
+which, by implication, suggests that the combination of these factors may be particularly detrimental in increasing the likelihood of behavioural enactment. In light of this, intervention and prevention strategies focused on enhancing perceived social support as a fundamental feature, particularly among female adolescents with a history of ACEs, may go some way toward mitigating the negative trajectory of ACEs in this population.
+Educational settings are likely to represent an important conduit through which to improve the quality and accessibility of social support available to vulnerable adolescents. On a targeted level, awareness-raising initiatives should aim to integrate a training element specifically focused on the psychoeducation of teachers and other school staff. This should centre around both improving their recognition of the signs and symptoms of mental illness, particularly among individuals with a known history of ACEs, and on developing basic training in mental health literacy to facilitate appropriate emotional responses.32 On a universal school level, interpersonal skills training for pupils, provided in the context of an educational setting, may help to improve skills in social and peer interactions with a view to enhancing the quantity and quality of social support available to adolescents.33 Finally, as part of a broader systems approach, schools may also seek to improve perceptions of social support through increased participation in school and community life.33 This can be achieved through the strengthening of ties between schools and communities to facilitate greater cohesion and engagement in extracurricular activities, which will ultimately contribute to the construction of wider social networks for adolescents.
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+1	| INTRODUCTION
+The effect that traditional media (e.g. newspapers, television) can have on suicide contagion has been well researched, and several countries, such as Australia, Canada, and the UK, have suggested guidelines for media reporting on suicide in order to reduce the risk of suicide contagion. Since the end of the 20th century, however, several new forms of media have emerged, and have quickly been adopted as a primary form of social interaction, especially for adolescents and young adults. This article will briefly review the existing literature regarding the media's effect on suicide and suicide contagion. Special populations will be discussed, including those who appear to be more susceptible, and those who may contribute more heavily, to suicide contagion. Current recommended guidelines on media reporting of suicide will be examined. A review of the current literature will explore the effect of new forms of media on suicide and suicide contagion. Finally, updated recommendations will be made for mitigating the effects of both traditional and new media on suicide contagion.
+“Suicide contagion” can be defined as the process by which one suicide becomes a compelling model for successive suicides (Gould, 2001). Other similar terms used are “imitation,” “copy-cat,” or “cluster” suicide. However, there seems to be no clear consensus on the exact definitions of these terms, and even the underlying concept or theory of “suicide contagion” itself has yet to be defined consistently (Cheng, Li, Silenzio, & Caine, 2014). In this paper, “suicide contagion” will refer to the spread of suicidal thoughts, behaviors, and deaths, specifically as a result of exposure to suicide-related material in traditional or new media, although this is certainly not the only way one can be exposed to suicide. “Suicide cluster” will refer to a larger-than-expected number of suicides, or attempted suicides, occurring more closely in time and geographical proximity than would typically be expected. Clusters can be viewed as statistical deviations from the norm, or as groups of suicides that are somehow linked, as a kind of contagion (Robertson, Skegg, Poore, Williams, & Taylor, 2012).
+The term “the Werther effect” has also been used to describe the phenomenon of suicide contagion. A 1974 article for the American Sociological Review first demonstrated that media attention to a suicide could lead to a spike in the number of suicides in the geographical area of the reporting (Phillips, 1974). Phillips coined the term “the Werther effect” to describe suicides that occur as a result of exposure to the media reporting of another suicide. The term refers to the book The Sorrows of Young Werther by Goethe, published in 1774, in which the protagonist, Werther, falls in love with a woman who is beyond his reach. He consequently decides to end his own life by sitting at his desk and shooting himself, wearing boots, a blue coat, and a yellow vest (von Goethe, 2012). After the book was published, several suicides occurred across Europe with significant evidence that at least some were influenced by the novel: victims were found dressed in similar clothing, they used the method as described in the book, or the book was found at the scene of the death (Jack, 2014). This is commonly regarded as the earliest known occurrence of suicide contagion related to a form of media.
+3	| EVIDENCE REGARDING TRADITIONAL MEDIA
+There have been several reviews of studies looking at the effect of traditional media on suicide contagion. Two comprehensive reviews, both titled “Suicide and the media,” were published in 2001. Gould reviewed studies on suicide-related effects of nonfictional (e.g. newspaper and television news reports) and fictional media (e.g. television soap operas, fictional movies), all published between 1967 and 1992. She concluded that there was substantial evidence to support the idea that publication of nonfictional stories in the media (i.e. newspapers) is associated with an increase in the rate of subsequent suicides. She also found that the magnitude of this increase is proportional to the amount, duration, and prominence of the media coverage. Regarding fictional media, the studies were found to have more contradictory results; however, Gould concluded that there was “ample evidence of an imitative effect of these broadcasts” (Gould, 2001).
+Pirkis and Blood reviewed much of the same literature as Gould, and they also found a relationship between nonfictional media portrayal of suicide and subsequent increase in actual suicides. They noted that this association fulfilled many of the criteria for causality, such as temporality and specificity. The effect appeared to vary as a function of time, with a peak at three days and attenuation by two weeks. Greater numbers of media items and more prominent “high impact” stories were associated with a stronger effect. They also found that the effect was greatest when the initial suicide victim was similar to the observer in terms of age or sex, or when the decedent was revered in some way by the observer, as with celebrities. Finally, there was evidence that, when there were explicit descriptions of the method of suicide included in the media, there was an increase in actual suicidal behavior employing that method (Pirkis & Blood, 2001a). In terms of fictional media, the authors examined studies that considered the relationship between fictional portrayal of suicide (e.g. film, television, music, plays) and subsequent suicidal behavior. They found the evidence for this effect to be more equivocal and not sufficient to make a case for causality, while acknowledging the need for more research in this area (Pirkis & Blood, 2001b).
+4	I SPECIAL POPULATIONS
+Studies related to the effect of reporting on celebrity suicides have mixed results. While there does appear to be an association between reports on celebrity suicide and an increase in rate of suicides, there have been inconsistent findings with different celebrities. One early study of media and suicide contagion found that suicide rates increased by 40% in the month after Marilyn Monroe died by suicide in 1962, but there was no significant change in the yearly rate of suicide. The increase was largely attributed to a spike in male suicides, suggesting a “reaction to loss” element. The next year saw an 42% increase in female suicide, which raised the issue of identification with the person whose suicide is publicized. However, after Ernest Hemingway suicided in 1961, there was no evidence of increase in suicide rates. The author hypothesized that if identification with the model is limited to the act of suicide alone, then the drive to imitate the behavior may be reduced (Motto, 1967). One study found a significant rise in the national suicide rate after celebrity suicides were covered on the front page of the New York Times (Gould, 2001; Wasserman, 1984). Another study found that even stories reporting on noncelebrity suicides were associated with a significant increase in the national suicide rate, although the increase was not as large as that seen after celebrity suicides were published (Gould, 2001; Stack, 1990). No significant increase in suicide was found following the death of grunge rock star Kurt Cobain, which may have, at least in part, been a result of the significant efforts by his widow, Courtney Love, to present his suicide in a negative light and minimize glamorization of his death (Gould, 2001). Other studies have suggested that celebrity type, region of the study, and other specific observer/model variables may have a significant impact on the contagion effect (Niederkrotenthaler et al., 2012; Stack, 2002; Yang et al., 2013).
+The rate of suicide between ages 10 and 24 in the United States increased dramatically between 2000 and 2015. Among young adolescents aged 10 to 14, there was a 35% increase in suicide rates, teenagers aged 15 to 19 showed a 22% increase, and suicides among young adults aged 20 to 24 increased by 21% (CDC, 2016). Adolescent populations appear to be more susceptible to imitating suicidal behaviors after being exposed to suicide, either in the media or through knowing the original victim through school or personally (Swanson & Colman, 2013). Suicide clusters occur predominantly among people aged 15 to 24, and one adolescent suicide is known to be a risk factor for additional suicides. In addition, the evidence suggests that when clusters occur, the deaths represent statistical increases over the expected suicide rate; that is, they are not just suicides that would have eventually occurred anyway (Robertson et al., 2012), suggesting that exposure to suicide activity may lead vulnerable adolescents to attempt suicide when they otherwise might not have.
+5	| EXISTING GUIDELINES
+Many countries have developed guidelines promoting responsible reporting of suicide by the media. They tend to have similar content but differ in the way in which they have been developed and implemented (Pirkis, Blood, Beautrais, Burgess, & Skehan, 2006). Recommendations apply to online content including citizen-generated media coverage, social media sites, blogs, and online content from traditional media organizations’ websites (Reporting on Suicide, 2015) (Table 1).
+The effectiveness of these guidelines has not been rigorously evaluated given methodological obstacles and low base rate for completed suicide (Sudak & Sudak, 2005). There have been studies showing that suicide rates decreased after implementation of guidelines; however, correlation could not be proven (Pirkis et al., 2006).
+Several studies have attempted to assess adherence to suicide reporting guidelines across various countries and forms of media, including new media, with disappointing results (Abbott, Ramchand, Chamberlin, & Marcellino, 2017; Easson, Agarwal, Duda, & Bennett, 2014; John et al., 2016; Utterson, Daoud, & Dutta, 2017; Young, Subramanian, Miles, Hinnant, & Andsager, 2017). A recent study examining the fidelity of media reporting on Robin Williams’ suicide found that 55% of articles surpassed the 80% threshold for “high fidelity” and 85% of articles applied at least 70% of the guidelines. The most commonly overlooked recommendation was “tell others considering suicide how they can
+get help,” which was missing from over 70% of the articles (Creed & Whitley, 2017). Suggestions have been made to increase adherence to guidelines, including involving media organizations in the development of suicide reporting strategies to increase buy-in and increasing efficiency of disseminating recommendations to media organizations (Scherr, Arendt, & Schafer, 2016). While more research is needed, the development of new instruments to systematically evaluate news reports could advance our knowledge in the near future (John et al., 2014, 2016; Nutt, Kidd, & Matthews, 2015).
+Another notable void is the lack of accountability for those violating the existing guidelines or laws on media reporting of suicides. While there are many countries, such as Australia, Canada, the UK, and the USA, who have developed guidelines for suicide reporting, New Zealand stands alone as the country with criminal laws governing what can be said publicly about a suicide. These legal restrictions have been developed to prevent suicide contagion in New Zealand. Starting in 1988, it became a criminal offense to report details of suspected suicides without the coroner's ruling that it was safe to publish the details. In 2006, this law was tightened further with the Coroners Act, which restricts reporting or publicly discussing specific aspects of individual suicides. In recent years, the 2016 Coroners Amendment Act narrows reporting restrictions to the details most likely to lead to copycat behavior. Despite these well-laid-out legal restrictions, there has been no record of anyone ever having been prosecuted or fined for breaching them (New Zealand Ministry of Justice, 2006, 2016).
+6	I NEW MEDIA
+Suicide rates in the USA have been slowly but steadily increasing over the last several years. In 2006, the suicide rate was less than 11 per 100,000 individuals. That number has increased by over 20%, to 13.26 per 100,000 as of 2015 (American Foundation for Suicide Prevention, AFSP, 2016). While the cause of this increase is yet unknown and likely multifactorial, one undeniable fact is that, within this same time span, the Internet has provided the opportunity to disseminate a plethora of information across the globe in seconds. Given the convincing evidence for the existence of a contagion effect, it is prudent to examine the role that new media may be playing in this concerning trend.
+In this article, “new media” is defined as media that facilitates communication and requires an Internet connection, such as websites, social media, blogs, forums, video games, electronic messaging applications, online television and movie streaming services, and others, in addition to SMS text messaging available on cellular phones.
+7	| NEW MEDIA, SUICIDE, AND SUICIDE CONTAGION
+Research exploring the relationship between the Internet and suicide has steadily increased in the last two decades. The most common topics published between 1997 and 2015 include searching for suicide-related information online, online suicide interventions and their effectiveness, conversations about suicide and suicide methods on Internet forums, and online behaviors and how people use the Internet. Less than 4% of the publications during the same time period focused on suicide contagion or suicide pacts made online (Krysinska et al., 2017). Most of the research has attempted to learn more about the individuals who are searching for suicide content, and much less has been published about the Internet as a source of suicide contagion (Robertson et al., 2012).
+Literature regarding the Internet and any potential influence it may have on suicidal behavior or self-harm in adolescents and young adults shows mixed results. Studies on general Internet use gave strong evidence, according to a systematic literature review of publications from 2011 through January 2015 by Marchant and colleagues. This review found that high Internet use (more than 2-5 h per day) and Internet addiction correlated with more suicidal ideation and self-harm behaviors, but causality was unclear. Studies exploring particular online media, such as social media, forums, or blogs, were smaller and provided lower quality evidence. Distressed online posts appear to be related to suicidal ideation and behavior in young people, but there is little evidence to suggest that social media use itself increases risk (Marchant et al., 2017).
+Despite the strong link between exposure to suicidal content in traditional media and subsequent increases in suicide rates, the relationship has been more difficult to characterize when content is consumed online. Suicide-related videos and images often have a high number of views and comments, and one high quality study found that comments on videos may perpetuate self-harm behavior among teens and young adults (Marchant et al., 2017).
+Twitter posts in the UK were monitored after a British soap opera aired an episode centered on an assisted suicide; although mentions of the word “suicide” did increase after the show aired, there was no evidence of increased communication of suicidal intent. On the contrary, suicide-related communication on Twitter tended to be more protective (Scourfield et al., 2016).
+A recent Japanese study found evidence of suicide contagion after celebrity suicides that resulted in a strong reaction to the death on Twitter. Actual suicide rates increased when the celebrity death, often a young entertainer, generated a large number of tweets. Interestingly, no increase in suicide rate was noted when the death received little attention on Twitter, even if there was considerable coverage in traditional media (Ueda, Mori, Matsubayashi, & Sawada, 2017).
+Social media and text messaging have further complicated the already onerous task of identifying suicide clusters, which can impede timely intervention. Electronic communication technology has made the traditional definition of a cluster more fluid, with geography becoming less important as information is spread further and more rapidly, significantly expanding the size of an individual's circle of influence (Robertson et al., 2012).
+One more recent, and very concerning, trend is the so-called “Blue Whale” game that appears to exist on social media networks around the world. The game is made up of “mentors,” giving children and adolescents 50 tasks to complete and send photo proof of. These tasks range from watching horror films and listening to “psychedelic” music to self-mutilation, such as carving symbols or words into their arms or cutting their lips, to dangerous activities such as climbing on a roof. The game concludes with the 50th task, which is to carry out their own suicides. Hundreds of suicides are suspected of being linked to this game around the world, in countries such as Russia, France, Romania, and Brazil; however, not all of these are confirmed (Ferreira de Sousa, de Deus Quirino Filho, de Cassia, Bezerra dos Santos, & Rolim Neto, 2017).
+In the spring of 2016, the Internet-based entertainment company Netflix released a series co-produced by actress and pop singer Selena Gomez called 13 Reasons Why, based on the 2007 young adult book of the same name by Jay Asher. The series is based on the aftermath of the suicide of a 15-year-old girl who left 13 audio recordings on cassette tapes, each addressed to a person that she felt contributed in some way to her decision to commit suicide. The final episode includes a graphic three-minute long depiction of the suicide (Howard, 2017). Internet search-engine queries on suicide increased by 19% in the 19 days following the release of the series. There was evidence of increased suicide awareness, but the searches also indicated an increase in suicidal ideation. It is unclear whether or not any of the searches preceded actual attempts (Ayers, Althouse, Leas, Dredze, & Allem, 2017). One small study in an urban teaching hospital in New Jersey found that there was a statistically significant increase in psychiatric presentations to their emergency department in the days following the release of the series; however, there was no change in the number of presentations related to suicidal ideation or attempts, and no significant increase in the number of psychiatric admissions (Salo et al., 2017).
+Given the evidence covered so far, it is no surprise that the series attracted much attention from the media, concerned parents, schools, and the health community. There was much debate over the public health implications of the series. Critics claim that the show targets a teen audience, but presents mature topics in an “adult way.” They allege that the series romanticizes suicide, does not focus enough on mental illness, villainizes parents and school officials, depicts the suicide as a rational choice and a form of revenge, and makes the victim appear to be a role model. The graphic depiction of the suicidal act at the end of the series has caused great concern that it may lead to a contagion effect, and multiple prolonged scenes with physical and sexual violence that appear in other episodes may trigger vulnerable individuals (Jacobson, 2017). Some also feel that the problem is not that the series was created, but that many young people “binge watched” it without parental guidance or knowledge that it existed (Knopf, 2017). Proponents of the show feel that it raises awareness of the adolescent suicide problem and highlights many problems that have been linked to suicide risk: social media harassment; sexting; sexual, emotional, and physical assault; and alcohol and other drug abuse (Tasman, 2017).
+Of note is that the second season of the show has already begun filming. The National Association of School Psychologists (NASP) has issued recommendations on how to manage effects from the first, or second, season. They recommend that parents watch the series with their children, discuss the material, and actively listen to any concerns they may have without judgement. It is also important for parents to remember that discussing suicide does not increase the risk of suicide or “plant the idea,” but avoiding the subject when a person is vulnerable could lead to tragic consequences. Parents are encouraged to seek professional help from a school-employed or community-based mental health provider if they think their child, or someone they know, is at risk (Knopf, 2017). Another interesting suggestion is for mental health professionals to consider volunteering to speak at schools about adolescent suicide and other important concerns raised by the show (Tasman, 2017).
+9	| USING NEW MEDIA TO IDENTIFY AT - RISK INDIVIDUALS
+New media has provided access to enormous amounts of information and insight into people's lives and thoughts as never before. Recent research has focused on finding ways to use this information to identify vulnerable populations as early as possible, which would allow for proactive interventions, instead of intervening reactively as is most common currently.
+Much attention has been paid to search engines and finding trends in suicide-related search terms. Web searches for suicide-related terms tend to retrieve more protective than harmful websites; however, resources with harmful characteristics tend to be ranked higher in the results (Till & Niederkrotenthaler, 2014). In addition, a considerable proportion of the results expressed mixed or neutral attitudes toward suicide, and a small percentage were clearly
+pro-suicide (Thornton, Handley, Kay-Lambkin, & Baker, 2017). Suicide search trends have been found to correlate with actual suicide rates in Taiwan (Yang, Tsai, Huang, & Peng, 2011), and elaborate search-engine algorithms have been created to identify search terms and trends that more accurately detect high risk individuals who are searching for suicide-related information (Arendt & Scherr, 2017). However, more recent studies have questioned the use of this information, and have found that the validity of using search volumes to predict suicidal activity and suicide rates is actually low (Tran, 2017). For example, online searches for suicide-related terms in Italy are more likely to be related to personal interest or bereavement than suicidality (Solano et al., 2016).
+Social media posts can also be used to identify individuals who are at risk of suicide or self-harm. Twitter may provide an opportunity for real time monitoring of suicide risk factors on a large scale (Jashinsky et al., 2014). While having a Twitter account alone is not associated with suicidal behavior, tweets containing the phrases “want to die” and “want to commit suicide” are significantly related to suicidal ideation and behavior (Sueki, 2015). Other studies have suggested that simply monitoring posts for suicide-related terms is not sufficient. Sophisticated machine learning algorithms (Braithwaite, Giraud-Carrier, West, Barnes, & Hanson, 2016) and linguistic analyses can be used to identify tweets with higher levels of risk (O'Dea, Larsen, Batterham, Calear, & Christensen, 2017), and can even account for cultural variability in markers of suicide risk and emotional distress (Cheng, Li, Kwok, Zhu, & Yip, 2017). One study analyzed social media profiles of military personnel who died by suicide, and was able to identify temporal sequences in different types of posts that were unique to suicidal individuals (Bryan et al., 2017).
+Recently, Facebook introduced updated suicide prevention tools, in addition to the teams they already have in place, who monitor reported posts for suicidal content. They now use artificial intelligence technology to recognize patterns in a person's posts and make reporting these concerning posts easier for anyone who sees them. They also provide options for real-time assistance to people streaming on Facebook Live, which is a feature that allows users to live stream videos to their friends through the website or mobile application. Additionally, they now offer live chat support provided by crisis intervention organizations through their Messenger application (Callison-Burch, Guadagno, & Davis, 2017).
+10	| PREVENTION AND INTERVENTION OPPORTUNITIES
+Adolescents are more likely to communicate distress on social media to their peers, meaning that teens are likely exposed to high risk posts on a regular basis without recognizing it. Despite the many dangers and risks the Internet has created in terms of suicide risk, it also has potential to provide many new methods of prevention and intervention. In fact, the Internet may provide the opportunity to be a part of a community for many people who are otherwise isolated in everyday life, as evidenced by what appears to be the protective influence of low levels of Internet use versus no Internet use at all. In addition, young people are increasingly using social media to communicate hardships and emotional pain to their peers. Internet forums have been found to be supportive in terms of helping suicidal individuals find communities they could relate to, share their experiences with, and find mental health resources; however, there were also pro-suicide forums that encouraged and normalized self-harm and suicidal behaviors. Whether or not these exposures lead to suicidal behavior is still unclear and likely varies with individual circumstances (Marchant et al., 2017).
+One major advantage of the Internet is that is has provided access to communities that are traditionally difficult to engage, such as lesbian, gay, bisexual, transgender, and questioning/queer (LGBTQ) individuals or those who are severely isolated (Marchant et al., 2017; Robinson et al., 2017). Research has shown that school psychoeducational prevention programs that teach appropriate responses to suicidal posts may mitigate some of the negative influences on the Internet (Marchant et al., 2017). Young people can safely participate in developing suicide prevention messages for their peers and help disseminate them on social media, allowing the messages to have more meaning and influence than if they came from adults. Educating young people on howto talk about suicide safely online has multiple benefits, and there is no evidence of it causing distress (Robinson et al., 2017).
+Some researchers have suggested using new media as a way to provide therapy to young people in a medium they are comfortable with. A randomized controlled trial is underway to study the effectiveness of a safety plan via mobile app in reducing suicidal ideation and behavior versus the traditional safety plan on paper that is used by many institutions before discharging suicidal patients from the emergency room or inpatient psychiatric units (Andreasson et al., 2017). More research is needed on the effectiveness of Internet-based cognitive behavioral therapy (CBT) in reducing suicidal behaviors among school students, but early results indicate that young people were engaged and experienced reduced suicidal ideations and other mood symptoms (Hetrick et al., 2017). Educational websites targeting young people in crisis have demonstrated suicide preventive effects and have been effective in increasing suicide-related knowledge (Till, Tran, Voracek, & Niederkrotenthaler, 2017).
+11	| RECOMMENDATIONS
+Overall, the literature has shown that there are various ways in which new media may facilitate, encourage, or prevent suicidal behavior.
+Several recommendations have been made on how to mitigate the potential negative influences posed by the Internet. One view focuses on increasing regulation of the material that young people are exposed to, via either parental controls, government regulation, or social media platform policies regarding suicide-related content. Some have suggested penalizing media for glamorizing tragic events, or reporting pro-suicide website users to police (Klein, 2012; Marchant et al., 2017).
+Other strategies attempt to exploit the positive influences that new media can have on suicidal behavior and contagion, including crisis support, reduction of social isolation, delivery of therapy, and outreach. There are multiple websites that can provide resources and help for these vulnerable populations, including those run by organizations such as the AFSP (2018), Suicide Awareness Voices of Education (SAVE, 2018), and the Suicide Prevention and Resource Center (2018). In addition, the Trevor Project is an organization that is especially helpful for LGBTQ youth. They provide crisis interventions via phone, online chat, or text messaging that connect LGBTQ youth with counselors who are trained to recognize the special challenges and vulnerabilities faced by this population. They also provide suicide prevention training and other community resources for anyone affected by suicide, or who may be interested in helping this specific community (Trevor Project, 2017).
+In addition, online forums can provide safe, anonymous ways for people to connect with mental health professionals or support groups. Furthermore, covering suicide carefully, even briefly, can change public misperceptions and correct myths, which can encourage those who are vulnerable or at risk to seek help (Klein, 2012). Given the popularity of self-harm videos, some have suggested developing videos that focus on health and recovery (Marchant et al., 2017). Improved identification of clinically significant suicidality on social media sites can make it relatively effortless to send users a private message with information and links to resources such as hotlines, support groups, or websites with reliable psychoeducation (Braithwaite et al., 2016).
+Finally, it is important for mental health professionals to remember that they too can play a role in mitigating the harmful effects of traditional and new media on suicidal behaviors and suicide contagion, while also maximizing the positive influences. The American Psychological Association (APA) released guidelines for mental health professionals in 2003 to try to help mitigate the effect of suicide contagion. They include encouraging responsible reporting of suicide if interviewed by media, referring reporters to responsible reporting guidelines, giving fact-oriented quotes, stressing that treatable mental illness may underlie many suicides, and to write to newspapers and television news outlets about misguided reporting on suicides (Smith Bailey, 2003). In addition, mental health professionals should be familiar with appropriate websites and resources to recommend to clients and families who may benefit from preventive information (Perry, Werner-Seidler, Calear, & Christensen, 2016). The Centers for Disease Control and Prevention (CDC) has a useful website that publishes web-based and printable materials with up-to-date suicide prevention strategies, effective programs, and the latest research on preventive interventions (CDC, 2017).
+Mental health professionals should also become more comfortable with asking patients about their social media use as part of their routine assessments (Marchant et al., 2017). When working with patients who use the new media extensively and preferentially, the mental health professionals may even wanttoexplore ways of integrating itinto their practice. However, if a mental health professional opts to use new media to correspond with patients, it would be prudent to obtain advice from their malpractice carrier about ways to minimize risk. In addition, they should also, at a minimum, seek out and regularly review their professional association's ethical guidelines regarding the use of new media, and engage patients (or their parents, if the patient is a minor) in an informed consent discussion about the risks and benefits of using new media for clinical purposes before doing so, as well as periodically throughout its use.
+12	I CONCLUSION
+Despite much variability, research largely supports the idea that traditional and new media reporting on suicide can, and does, have a significant impact on suicide rates. Questions remain regarding the impact of specific components of “the Werther effect”; the complexity of the numerous factors related to characteristics of the initial suicide victim, quality and type of reporting in different forms of media, and traits of subsequent suicide victims, including their individual risk factors for suicide, make research in this area especially challenging. Nonetheless, over the last few decades, reporting recommendations have been developed to guide news organizations and other media entities in tailoring their publications in a manner that may reduce any potential harmful effects to vulnerable populations.
+The development of the Internet, and the successive astonishingly rapid growth and popularity of websites, blogs, social media, and other forms of instantaneous world-wide communication, has presented new challenges in the fight to prevent suicide. Parents of young children, adolescents, and young adults need to be aware of the potential risks involved with exposure to suicide-related content, and be ready and willing to monitor their children's media consumption, as well as discuss any concerns that may arise from it. Public health organizations, mental health professionals, media groups, and possibly even lawmakers, need to prioritize the monitoring of media and communication technology innovations, in addition to tracking and containing any dangerous new trends in suicidal behaviors. Mental health professionals and educators should be well versed in the latest literature on the topic of reputable and effective suicide prevention resources and strategies,1 as they have a responsibility to share accurate and quality information with at-risk individuals and their loved ones. Efforts are also needed to advocate for additional research on suicide, and for providing sufficient and relevant mental health resources to vulnerable populations.
+Finally, in addition to the public health implications associated with the issue of media's influence on suicidal behavior and contagion, there are also implications relevant for the forensic clinician. Forthose performing psychological autopsies in suicide cases, in this technological golden age, consideration of the potential contagion of specific ideas about suicide through new media should not be overlooked. Familiarity with various aspects of this issue may become pertinent in investigating specific suicide cases, especially in more susceptible populations such as adolescents.
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+(adjusted odds ratio [AOR]=3.04, 95% confidence interval [CI]=2.84— 3.26), a history of suicide attempts (AOR=2.77, CI=2.64-2.90), depressed mood (AOR=1.69, CI= 1.62-1.76), and nonalcoholic substance abuse or dependence (AOR= 1.13, CI= 1.07-1.19). Conclusions: For nearly a third of all suicide decedents, better mental health care might have prevented death. Efforts to reduce access to lethal doses of prescription medications seem warranted to prevent overdosing with commonly prescribed substances. (Psychiatric Services 65:387-390, 2014; doi: 10.1176/ appi.ps.201300124)
+In 2010, suicide accounted for approximately 38,000 deaths in the United States, corresponding to a suicide rate of 12.43 per 100,000 individuals (1). Although mental health treatment helps reduce suicidal behavior (2), each year an estimated 30% of suicide decedents will have received treatment within one month of their death (3). This fact suggests that providers may have opportunities to improve suicide prevention efforts. Compared with suicide decedents who did not receive mental health treatment, those who received treatment often had more severe symptoms (4). Research is currently scarce on the co-occurring health- and life-stress-related circumstances among suicide decedents who received treatment. Life events that are considered rele
+vant factors to suicidal behaviors (5) are routinely documented in the National Violent Death Reporting System (NVDRS) but have not yet been investigated in relation to mental health treatment before suicide. The objective of this explorative study was to assess associations between recent mental health treatment and circumstances of death among suicide decedents to better understand the unique qualities of individuals who had received mental health treatment and to help inform suicide prevention efforts.
+Methods
+We obtained 2005-2010 data from the NVDRS, which captures details on violent deaths among the deaths registered within each of 18 states (Alaska, Colorado, Georgia, Kentucky, Maryland, Massachusetts, Michigan, New Jersey, New Mexico, North Carolina, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Utah, Virginia, and Wisconsin). Data for Michigan and Ohio were available for only 2010. Data sources for NVDRS include death certificates, law enforcement reports, and coroner and medical examiner reports; these sources are used to more comprehensively describe each violent incident. Suicide deaths are identified according to the manner of death recorded in the various data sources. State abstractors follow a strict coding manual to ensure consistent reporting and reconcile any differences across the data sources (6).
+Adult suicide decedents who received mental health treatment
+within two months before death were compared with suicide decedents who were not known to have received mental health treatment shortly before death. Because help-seeking behavior among adolescents differs from behavior among adults, only decedents over age 18 were considered (7). Treatment was defined as seeing a psychiatrist, psychologist, general medical doctor, therapist, or other counselor for a mental health or substance misuse problem; receiving a prescription for a psychiatric medication; attending anger management classes; or residing in an inpatient or halfway house facility for mental health problems (6).
+To qualify as suicide by poisoning, a substance had to be ingested and deemed coresponsible for the death. Drugs on the scene that were not ingested were not counted (6). Suicides by poisoning were coded as poisoning involving commonly prescribed substances if one or more of the substances used in the act was technically a controlled substance that would require a prescription (6).
+Suicide decedents were compared with respect to sociodemographic characteristics, health- and stress-related characteristics, and the suicide method involved. Logistic regression was used to calculate odds ratios for receiving treatment before suicide for all above characteristics. We adjusted comparisons for age, sex, raceethnicity, and history of suicide attempt. History of suicide attempt was adjusted only for age, sex, and raceethnicity. Because of multiple testing, we set the level of statistical significance to #.001. We performed analyses using PASW Statistics 18. This study was determined to be exempt from human subjects review by the Institutional Review Board of the Centers for Disease Control and Prevention.
+Results
+Of the 57,877 suicides among persons >18 years of age recorded in NVDRS between 2005 and 2010, 16,471 (28.5%) had received treatment within two months of suicide. Of those who did not receive treatment in the two months before suicide (N=41,406), 3,198 (7.7%) had received mental health treatment in the past. Being male (adjusted odds ratio [AOR]=.47),
+race-ethnicity other than non-Hispanic white (AORs=.61-.73), and being ages 19-49 (AOR=.69-.91) or $70 (AOR=.63) were all associated with lower odds of receiving treatment (Table 1). Among life events registered in the NVDRS, intimate partner problems were the most prevalent type of problem before suicide and affected 15,168 (30.3%) of all 50,024 decedents with known circumstances (Table 1).
+Compared with persons who died from hanging, those who died by drug poisoning involving a substance that commonly requires prescription (AOR=3.04), by sharp instruments (AOR=1.30), or by falling or jumping (AOR=1.44) had higher odds of recent mental health treatment. Suicides by firearms were associated with lower odds of receiving treatment (AOR=.88) (Table 1). Among 3,758 persons who received treatment and died by poisoning involving commonly prescribed substances, 3,060 (81.4%) were tested for use of antidepressants at the time of death, with 2,278 of them (74.4%) testing positive.
+Among the decedents, having recent mental health treatment was positively associated with having depressed mood at time of death (AOR=1.69), a history of suicide attempt (AOR=2.77), and substance use problems other than alcoholism (AOR=1.13) (Table 1). Receiving treatment was inversely associated with having intimate partner conflicts (AOR=.75), perpetrating interpersonal violence (AOR=.64), fi-nancialproblems (AOR=.87), criminal legal problems (AOR=.60), other legal problems (AOR=.82), and homelessness (AOR=.66).
+Discussion
+Nearly a third of suicide decedents received help from some type of mental health care provider before taking his or her own life. Earlier studies have found a similar proportion of service utilization, which was even higher when general health care services— particularly, primary care provider visits—were also taken into account (3). The demographic distribution among suicide decedents known to have received mental health care in the two months prior to death generally reflected patterns of mental health seeking in the general population, in that
+smaller proportions of males, persons of minority race-ethnicity, individuals #30 years, and older adults ($70 years) were known to access mental health services before suicide. Genderspecific help-seeking behavior, stigma, and socioeconomic factors often play a large role in these treatment disparities (8). However, when controlling for age, race-ethnicity, sex, and history of suicide attempt, we still found that some health- and life-stress-related circumstances were more common among decedents who had sought treatment, which indicates an area for improvement in the delivery of mental health services.
+Depressed mood and substance misuse were associated with receiving mental health treatment. Although the effect size for substance misuse was relatively small, this association is consistent with research showing that patients were treated more often for depression when comorbidities were present (9). However, we also found that many suicide decedents who killed themselves by drug poisoning had received mental health treatment before their suicide, and commonly prescribed substances were often involved in these deaths. There is common agreement that drugs should be prescribed only in small package sizes to at-risk individuals to prevent suicide (10). In Britain, reduced pack size of analgesics have been shown to be effective in reducing suicides with paracetamol (acetaminophen in the U.S.) (11). More research is needed to investigate substances used and their responsibility for fatal outcomes among decedents, mechanisms involved, and best prevention practices. In contrast to suicide decedents who used poison, those who used firearms were less likely to have received treatment before death. Firearms are considered one of the most violent and lethal methods of suicide, and the use of violent methods has been described as reflecting a further step in the suicidal process. Individuals choosing firearms as their method may be less inclined to seek or accept treatment (12).
+The higher odds of receiving mental health treatment observed among persons with a history of suicide attempts underscore that mental health treatment can provide an opportunity to address the needs of some previous
+suicide attempters. More follow-up treatment, therapies tailored to specifically reduce self-directed violence (including cognitive or other therapies and strategies intended to improve
+coping skills to better handle risk factors associated with suicide [13]), and monitoring of prescription medications might reduce the risk of subsequent attempts.
+Connecting mental health providers to other services relevant to the circumstances frequently seen among decedents may also help prevent some suicides. Some life events, particularly
+intimate partner problems, were prevalent for more than 15,000 of all suicide decedents, reflecting sociological concerns with intimacy, including marriage and the association of divorce with high suicide rates (14). However, decedents experiencing partner problems had lower odds of receiving treatment before suicide. Clinicians and other public health professionals may be able to collaborate with successful programs and strategies that involve friends or family of the at-risk individual in order to reach out to individuals affected by family problems (15).
+This study had several limitations. NVDRS data do not indicate which type of mental health service was received. Different quantities and treatment types were subsumed as mental health treatment, and findings for specific treatments may be different. The data are not nationally representative but representative of only the 18 states participating in the NVDRS. The information provided on the circumstances of deaths was from proxies and was subject to recall bias. Further, we could not assess whether substances that commonly require prescription were actually prescribed, because drugs were assigned to the prescription drug category only on the basis of the substance name or the name of the metabolite identified. Some of these substances might have been acquired on the street. Even in cases in which the drug was actually prescribed, we cannot rule out that the prescription may have been prescribed for a person other than the decedent.
+390
+PSYCHIATRIC SERVICES ♦ ps.psychiatryonline.org ♦ March 2014 Vol. 65 No. 3
+Conclusions
+The findings suggest that the substances used in suicides by poisoning and efforts to reduce access to lethal doses of prescription medications warrant further research. Further, better collaboration between mental health service providers and providers of other services, including outreach to individuals with intimate partner problems, may help reduce suicide deaths.
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+Early career psychiatrists are crucial in the medical response to COVID-19. Although we are ready to provide help to those in need, we are made to count on insufficient access to WHO-standard personal protective equipment and training when trying to safely support others' mental health face-to-face. Furthermore, feelings of uneasiness or ill-preparedness arise when countries start redeploying mental health-care professionals to general medical care for patients with COVID-19 in overwhelmed health-care systems (table and appendix).
+Telepsychiatry (ie, providing mental health care remotely, using telecommunications such as telephone or video conferencing tools) in several settings is suddenly being introduced or massively expanded to serve patients with pre-existing disorders, health professionals on the frontline, and the general population, during a time of uncertainty, misinformation,
+and physical distancing.4 Still, telepsychiatry is scarce in several low-income and middle-income countries, posing challenges for health-care workers and patients where face-to-face care is not safe because of the risk of virus infection. We also perceive that attention given to the public's mental health during the outbreak came late, and overlooked vulnerable populations, such as refugees, people without secure housing, people living in overcrowded spaces, and patients with severe psychiatric disorders.
+Apart from disrupting usual mental health care, the COVID-19 pandemic could lead to further psychological trauma. The huge toll such trauma can take on medical professionals, which can include delusional episodes and suicidality, in countries as deeply struck by COVID-19 as Italy is of particular concern. Psychiatric sequelae could be reduced by the early involvement of mental health professionals in drawing up comprehensive public
+The coronavirus disease 2019 (COVID-19) outbreak has raised several concerns regarding its mental health effect on patients with psychiatric disorders and the health-care workforce.1,2 Worldwide, psychiatrists are navigating a fast, unpredictable tempest, in developing plans to respond to their own mental health needs and those of their country's population.
+We are a group of 16 early career psychiatrists connected by the Early Career Psychiatrists Section of the World Psychiatric Association,3 working across different WHO regions in countries (other than China) that have been severely affected by COVID-19. The pandemic led us towards a collective endeavour to share our country-specific experiences, plans, and concerns.
+health policies and in supporting the health-care workforce.
+Many early career psychiatrists are part of the millennial generation familiar with technology,5 and are channelling this strength to deliver far-reaching telepsychiatry, share online mental health-promotion resources, and connect with colleagues worldwide. Thanks to social media and the internet, international associations of early career psychiatrists are providing educational resources (eg, real-time news, journal clubs, and webinars), and group emotional support for peers. Colleagues in countries with a recent history of humanitarian and public health crises (eg, the epidemics of Zika virus disease in the Americas and Ebola virus disease in Africa), bring their experience of providing mental health care during and after such disasters, and those in countries with an earlier onset of the COVID-19 outbreak share the lessons already learned there. The spontaneity, resilience, and solidarity with which many colleagues have joined forces is inspiring.
+Early career psychiatrists are an essential resource in the mental health management of the COVID-19 pandemic and its aftermath. Mental health authorities are called to count upon early career psychiatrists, warranting the training and resources to enable us to safely and effectively work for our patients, colleagues, and communities. We express our gratitude to all early career psychiatrists taking risks to care for their patients, and we invite them to seek peer support and join forces both locally and across the world.
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+1 Introduction
+Cash transfers (CTs) - commonly understood as direct payments made to people in poverty - are among the most extensively studied and implemented interventions in low- and middle-income countries (LMICs) (Vivalt, 2015). Previous systematic reviews and meta-analyses of CTs found improvements on several outcomes. These outcomes include material poverty (Kabeer & Waddington, 2015), human capital (Baird et al., 2013b; Millan et al., 2019), social capital (Owusu-Addo et al., 2018), health (Lagarde et al., 2007; Behrman & Parker, 2010; Crea et al., 2015), intimate partner violence (Baranov et al., 2020; Buller et al., 2018), child labor (Kabeer & Waddington, 2015), the spread of HIV (Pettifor et al., 2013), spending on tobacco and alcohol (Evans & Poponova, 2014; Handa et al, 2018), and labor supply (Baird et al., 2018; Banerjee et al., 2017).
+Although these factors are relevant to wellbeing, measures of mental health (MH) and subjective wellbeing (SWB), which probe how individuals themselves assess the quality of their lives, are often thought to track wellbeing more accurately. Indeed, measures of SWB are increasingly considered to be essential components in applied policy analyses (Benjamin et al., 2020; Frijters et al., 2020). It therefore seems pertinent to evaluate the effectiveness of CTs with respect to these measures.
+Individual income and SWB are known to be positively associated (Powdthavee, 2010; Stevenson & Wolfers, 2013; Jebb et al., 2018), especially for those at low income levels (Clark, 2017; Deaton, 2008). A similar relationship is observed in the MH literature (Karimli et al., 2019; Tampubolon & Hanandita, 2014; Schilbach et al., 2016; Ridley et al., 2020). Moreover, mental health problems may engender and perpetuate poverty (Haushofer & Fehr, 2014). Unfortunately, the literature on the link between income and SWB and MH in LMICs has long lacked causal evidence, which the growing body of primary research on CTs may address.
+While CTs may improve the SWB and MH of recipients, these interventions could also have negative psychological consequences on non-recipients. Qualitative research suggests the presence of negative psychological spillovers (Fisher et al., 2017; MacAuslan & Riemenschneider, 2011), and some recent quantitative work echo this worry (Haushofer et al., 2019). For example, envy among non-recipients may be a concern (Ellis, 2012). Community disruptions and crime rates may also increase if CTs are mistargeting to formally ineligible recipients (Agbenyo et al., 2017; Fisher et al., 2017). However, there is also some evidence of positive spillovers. For example, CTs have been found to decrease the intergenerational transmission of depression (Eyal & Burns, 2019) and to lead to decreased suicide rates in the areas they are implemented (Alves et al, 2018).
+We know of no previous systematic reviews on this subject. A non-systematic meta-analysis by Ridley et al. (2020), which evaluates the impact of CTs on MH, is closest to our work.1 We build on their work in four directions. First, we conducted a full systematic review and search of the existing literature in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidance (Moher, Liberati, Tetzlaff, & Altman, 2010). Second, we consider SWB measures alongside MH measures2. Third, we consider quasi-experimental designs (in addition to randomised controlled trials (RCTs)). Fourth, we evaluate the quality of included studies, assess publication bias, and perform a moderator analyses across (1) outcome type (MH and SWB), (2) CT value, and (3) duration of the transfer.
+Methods
+2.1	Eligibility criteria
+For a study to be included it must satisfy four criteria: First, the study must investigate the effect of an unbundled cash transfer (defined below). Second, the study must include a measure of self-reported affective mental health or subjective wellbeing, but these need not be the primary focus of the study. Third, the study context must not be a high-income country.3 Fourth, the study design must be experimental or quasi-experimental4 and afford standardizing the mean difference between treatment and control groups.
+Regarding our first criterion, we distinguish between unconditional cash transfers (UCTs) and conditional cash transfers (CCTs). Conditional cash transfers formally require adherence to certain actions, such as school enrollment or vaccination. The strictness of conditions varies widely, and conditions are sometimes left unmonitored due to high administrative costs (Davis et al., 2016). UCTs have no requirements, although they are often targeted to a vulnerable subset of the population, commonly defined by a combination of regional statistics, means tests and selection by prominent members of the community. We consider noncontributory social pensions and enterprise grants to be UCTs. CTs are typically paid out in lump-sums or streams (monthly installments). Some stream or multi-installment CTs have graduation mechanisms where individuals stop receiving transfers once they meet certain conditions (Villa & Nino-Zarazua, 2019). All included CTs must be “unbundled”, i.e. implemented and tested independently of other services such as asset transfers, training, or therapy.
+Concerning our second criterion, we note that SWB measures tend to assess overall wellbeing (Diener, 2009; Diener et al., 2018), which sometimes include separate measures of positive and negative mental states (Busseri & Sadava, 2011). By contrast, affective MH questionnaires tend (1) to only measure the negative components of SWB, i.e., how badly someone is doing and, (2) to also capture information on an individual's behaviors and habits (in addition to their thoughts and feelings). In our analyses, we include measures of valenced mental states, but no measures of behavior or habits. See the “Measures” column of Table A3 in the appendix for a list of all included measures.
+2.2	Data
+We searched studies using academic search engines and databases. These included: EBSCO: MEDLINE, PsycINFO, PubMed, Business Source Complete, EconLit, Social Sciences Full Text (H.W. Wilson), APA PsycARTICLES, Psychology and Behavioral Sciences Collection, Academic OneFile, Academic Search Premier, CINAHL, Open Dissertations, Web of Science, Science Direct, JSTOR, ECON PAPERS, 3ie, IDEAS/REPEC, and Google scholar. These efforts were complemented by a forward and backward citation search of eligible studies, contacting authors, and through Google Scholar notifications. Our search string can be found in Appendix A.
+We stored all retrieved records in the reference management system Zotero. Double-blind screening of the titles and abstracts was done using the software Rayyan by JM and CK. Any disagreements were discussed until consensus was reached. Studies that passed the double-screening were reviewed in full text by JM.
+We extracted study details such as author name, CT program, number of participants, MH and SWB outcomes, and effect sizes. We also collected information on the size of the cash transfer, time between start of intervention and follow-up, and whether it was a CCT or UCT, paid out in a stream or lump sum, or directed towards adolescents, prime age adults or elders. All data were extracted by one author (JM) and the full extraction results were checked for accuracy by CK and ABM.
+2.3	Quality
+To assess the quality of included research, we evaluated the following domains: causal identification strategy, pre-registration, balance between treatment and control groups, attrition, sample size, contamination, treatment compliance, and whether intention-to-treat (as opposed to a complete case) analyses were performed.
+2.4	Statistical Methods
+We used the statistical programming language R for data analysis. Since most RCTs and quasi-experimental designs are based on mean differences,5 we standardized these using Cohen’s d. We used the independent t-statistic from a test of the mean difference to calculate Cohen’s d in nearly all cases. We use d = t'1/n! + 1/nc where n! - treatment sample size and nc - control sample size (Goulet-Pelletier & Cousineau, 2018). If the effect size of a study was expressed via odds ratios (n = 2), we converted from odds ratios to Cohen’s d using d = ln(01)V3/5.6
+If a study contained multiple outcome measures, we coded each as MH or SWB. To achieve a single effect size for each study-follow-up combination, we combined outcomes using the method of Borenstein et al., (2009), specifying a correlation of 0.7 for within construct aggregations, 0.5 for between constructs and 0.6 for both within and between aggregations. Specifying different correlations changes only the aggregate standard error, not the mean of effect sizes.
+We used random effects (RE) models for our meta-analysis, which assume that true effects of each included study are drawn from a distribution of true effects (Borenstein et al, 2010). Each study in our model was weighted by the inverse of the standard error of the study’s estimated effect size. Since there are sometimes multiple follow-ups in a study and multiple studies in a sample or program, we clustered standard errors at the level of the study and program. We assessed evidence of publication bias and p-hacking by using a funnel plot, the Egger regression test (Borenstein et al, 2011), and a “p-curve” (Simonsohn et al., 2014).
+We conducted meta-regressions to test if certain study characteristics moderated estimated effect sizes. We focused on three potential moderating variables: years since CT began, size of CT, and whether CTs had conditionality requirements.
+Concerning size of CT, we considered both the absolute and relative CT size. We operationalized absolute size as the average monthly value of a CT in purchasing power parity (PPP) adjusted US 2010 dollars, with lump sum CTs (comprising about 25% of our sample) divided by 24 months, which is the mean follow-up time.7 For relative size, we used monthly CT value as a proportion of previous
+household monthly income. This was either directly reported or easily derived in many studies (21 out of 37 studies). If a study did not report sample information on income, we used consumption (10 studies) or expenditure (3 studies) information as a proxy. To convert between individual income and household income (8 studies) we assumed that household income = individual income * ^household size (see Chanfreau & Burchardt, 2008). If there was insufficient information to impute average household income (4 studies), we used regional statistics. Finally, as a robustness test, we also computed yearly CT value as a proportion of annual gross domestic product per capita (GDPpc).
+3	Results
+3.1	Description of Studies and Quality
+We retrieved 1,870 records from implementing our search string. After removing duplicates, we were left with 1,147 records. After an initial round of double screening titles and abstracts by JM and CK, 143 met the eligibility requirements (see Figure 1 for a diagram of selection flow). After JM performed the final round of screening, there were 32 unique studies drawn from the initial search and five from Google Scholar alerts and citation searches. We thus included a total of 37 studies8 reporting on 100 outcomes. Table A3 in the appendix summarizes the key characteristics of the included studies. Of the outcomes, 46 measured depression or general psychological distress, 21 measured happiness or positive feelings, 18 measured life satisfaction and two measured anxiety. The remaining 13 were summary indices of MH, SWB, or both.
+Most of the studies were conducted in Africa (23), followed by Latin America (10) and Asia (4). The most commonly investigated CT type was UCT (26; 19 plain, 6 pensions and 1 enterprise grant) followed by CCTs (10) and one study that contained both a CT and UCT (Baird et al., 2013a). Country context
+was relatively evenly divided into low, low-middle, and upper-middle income countries (see Figure A2 in the appendix). Over half of the included studies included random assignment (22), while the rest were quasi-experimental (15).9 The average time from the start of the CT to follow-up was two years. The average monthly payment was $38 PPP. A quarter of the studies were implemented as predominantly lump sum (10). All other studies (27) were paid out on a monthly basis.
+In Table 1, we list the results of our quality assessments. While blinding of participants is impossible for CTs, blinding personnel and outcome assessment was mentioned (but not performed) in only one study (McIntosh & Zeitlin, 2020). Overall, few studies (9/37) referred to pre-registered protocols. The adherence to pre-specified statistical procedures and outcomes was generally unclear, thus making it
+impossible to assess whether outcomes were ‘cherry-picked’ post treatment. Moreover, about half of the included studies (17/37) did not assess treatment compliance. Therefore, aspects relating to implementation (e.g. intervention fidelity and adaptation) could not be assessed (Moore et al., 2015). Furthermore, contamination by the CT on control groups was rarely discussed or addressed. Only 13 out of 37 studies were geographically-clustered RCTs (cRCTs), which are more robust to possible contamination effects. Of the 15 quasi-experimental studies, one used a natural experiment (Powell-Jackson et al., 2016), two used instrumental variables (Ohrnberger et al., 2020a; Chen et al., 2019), and four used a regression discontinuity approach (based on a means test). The eight remaining studies used a propensity score matching approach. Of those using propensity score matching, six also employed a difference-in-difference estimator.
+Despite the aforementioned concerns, we assess the synthesized evidence to be fairly reliable. Importantly, most studies clearly explained their causal identification strategy, were well balanced, performed intention-to-treat analyses, and controlled for differential attrition when present. Sample sizes were generally large compared to common sample sizes in clinical or psychological studies (n<500; Billingham et al., 2013; Kuhberger et al., 2014; Sassenberg & Ditrich 2019).
+3.2	Baseline results
+For our baseline results, we aggregated effect sizes across studies using a random effects model. Throughout our analyses, we omitted measures of stress, optimism, and hope, and one outcome reported from Galama et al. (2017), which was a clear outlier.10 The average overall effect size, as indicated by a black diamond at the bottom of Figure 2, is 0.10 SDs in the composite of SWB & MH measures (95% CI: 0.08, 0.12; given by the width of the diamond). The overall effect size does not
+Cohen's d
+Note: Forest plot of the 37 included studies. Subjective wellbeing (SWB) and mental health (MH) outcomes in each study are aggregated with equal weight. Mo. after start is the average number of months since the cash transfer began. $PPP Monthly is the average monthly value of a CT in purchasing power parity adjusted US 2010 dollars. Lump sum cash transfers were converted to monthly value by dividing by 24 months, the mean follow-up time.
+change substantially when accounting for dependency between multiple follow-ups, and multiple studies in a program in a multilevel model (ES: 0.095, 95% CI: 0.071, 0.118, or if we combine all the outcomes, without first averaging at the study-follow-up level (ES: 0.091, 95% CI: 0.066, 0.116.
+Heterogeneity, as calculated by the D2index, is substantial; 63.7% of the total variation in outcomes is due to variation between studies.11 In other words, 63.7% of total variability can be explained by variability between studies instead of sampling error. To account for the impact of this substantial heterogeneity, we calculate a 95% predicted interval.12 The estimated 95% prediction interval, given by the dashed line bisecting the black diamond in Figure 2, suggests that 95% of similar future studies would be expected to fall between 0.001 and 0.201 SDs in our composite of MH and SWB.
+Figure 3 displays the risk of publication bias and “p-hacking” (researchers testing a high number of outcomes and cherry-picking the coefficients that fall below a threshold p-value). In Figure 3a, we show a funnel plot, with standard error plotted against effect size, and the mean effect shown as a black vertical line.13 If there are significantly more studies to the right than the left of the mean effect size, this would suggest that studies on the left may be missing, possibly indicating publication bias. This is known as asymmetry. Figure 3a shows little asymmetry, indicating that studies with more positive effects appear no more likely to be published. We use Egger’s regression test to check this quantitatively by regressing the standard error on the effect size. The test does not reject the null of funnel plot symmetry (p=0.549), supporting our reading of the plot.
+Figure 3b shows the percentage of results with different p-values. If “p-hacking” were an issue, we would expect that the distribution of p-values is left-skewed (an upward slope in the figure). The p-curve is downwardly sloped, which suggests no widespread p-hacking. However, it is possible that regression specifications with insignificant dependent variables were not reported at all. P-curves are unable to address such scenarios (Bishop & Thompson, 2016).
+3.3	Meta Regression and Moderator Analysis
+We focus on three types of variables that we expect to moderate the observed effects: (1) Whether a CT had conditionality requirements or not. (2) Value of CT (in absolute terms and relative to previous income). (3) Years since the transfer began, allowing us to assess whether effects dissipate over time. Throughout, we use multi-level models that account for multiple outcomes in a follow-up, multiple follow-ups in a study and multiple studies in a sample or program. Standard errors are clustered at the study and program level.14 In every specification presented, the dependent variables are the study’s estimated effect on MH or SWB. We standardized the effect sizes into Cohen’s d.
+In Figure 4, we present six plots that illustrate the bivariate moderating relationship of our variables of interest. Panel (a) shows the distribution and average effect size for UCTs and CCTS. Panels (b) through (f) show effect size on the y-axis and the time or size on the x-axis. Plots (b) through (f) are simple scatter plots meant to illustrate the raw correlation between two variables.
+In Table 2, we present our main results. All models include a measure of CT size and years since the CT began. Model 1 includes a dummy indicating whether the CT had conditionality requirements. Models 1, 2 and 3 estimate the effect of relative CT size. Models 4 and 5 estimate the effect of absolute CT size (using $PPP monthly value). Models 3 and 4 include an interaction term between payment mechanism and “years since CT began” to identify the effect of decay conditional on whether a CT was paid out in a lump sum or stream.
+In Model 1 we find that conditionality requirements reduce estimated effect sizes by almost 50%. In so far as UCTs are less costly to administer than CCTs, this suggests that UCTs are likely to be more efficient in promoting recipients’ wellbeing.
+In Model 2 we omit the indicator of whether CTs where CCTs or UCTs. Based on this specification, one can expect that doubling a recipient’s consumption (by receiving a CT 100% of previous consumption) to roughly lead to a 0.10 SD increase in MH/SWB at the average follow-up time. Results in Models 1 and 3 are similar. See panels (e) and (f) of Figure 4 for the correlational relationship between relative size of a CT and magnitude of effect.
+Models 4 and 5 shows our results for absolute CT value, yielding a significant and positive coefficient in both specifications. These results indicate that a CT with a monthly value of $100 PPP leads to an approximately 0.07 to 0.08 SD increase in SWB and MH outcomes. See Figure 4, panel (c) for the bivariate relationship. Increases in income are typically assumed to yield diminishing gains in wellbeing. To test if that is the case in our sample of studies, we log transformed our measures of relative and absolute CT size. We find a significant effect for log-relative value but no significant effect of logabsolute value (see Table A2 in the appendix).15
+Taken together, models 1, 2 and 4 provide evidence that the effect of CTs on wellbeing decays over time. Using the coefficient from Model 2, each year the effect is estimated to decline by 0.015 SDs. With that estimate, a CT which doubles household income would take almost two decades to decay.16 However, the effects of “years since CT began” could differ depending on whether the recipient was given the CT in a lump sum or still receives monthly transfers. Our bivariate plot (Figure 4, panel (b)) suggests a difference in decay between the two payment mechanisms. Lump CTs appear to decay over time while stream CTs (which are nearly all ongoing at the time of the last follow-up) show a flat trend. In Models 3 and 4 we formally test for differences in decay between lump and stream CTs. The interaction, “years since * CT is lump sum” gives the difference in decay between lump and stream CTs. Since stream CTs are ongoing, we expected lump CTs to exhibit a larger decay in effect size than streams. Surprisingly, this is not the case in models 3 and 4. These display a positive, albeit insignificant interaction term. Thus, although there is a significant overall decay in effect size (as indicated by Models 1, 2, and 5), we are unable to precisely estimate the effect over time for a specific payment type.
+Finally, we note that seven studies in our study include multiple follow-ups. As shown in Figure A1 in the appendix, six of these show a decline in effects size across follow-ups. A repeated t-test of whether mean effect size is different between first and second follow-up yields a p-value of 0.007, indicating that this decline is statistically significant.
+The relatively large and significant intercepts in Table 2 suggest that CTs could have an effect independent of the size of the cash transfer (i.e., an effect from being enrolled). An enrolment effect, however unintuitive, is not implausible. Being awarded an amount of cash might boost someone’s sense of good fortune, which could explain the intercept. Another explanation for the intercepts is that they are an artifact of a concave relationship between CT size and effect. A linear model will generally overestimate the intercept on data that contains a true concave relationship. However, the insignificance of the log-transformed absolute CT value is evidence against a clear concave relationship (see appendix Table A2, Model 2).
+In addition to these analyses, we also tested whether RCT design, type of measure, or the study context moderated the effect size (see Table A1 in the appendix). Whether a study uses a RCT design does not affect the magnitudes of the estimated effects of CTs. This suggests that studies which rely on natural experiments or other causal identification strategies are reasonably robust. However, we do find that, compared to pure MH measures, effects of CTs on measures of SWB are significantly larger. Moreover, the largest effect sizes occur for studies in which a compound index of both MH and SWB was used.17 Notably, CTs conducted in Latin America have a near zero estimated effect. This appears to be primarily driven by the fact that many CTs in Latin America have conditionality requirements. When including both a dummy for conditionality and for the CT being conducted in Latin America, we find that the coefficient on Latin America is roughly halved and significant at the 10% level only.
+As discussed in section 2, we ran alternative specifications of our size variables (see appendix Table A2). In particular, we checked if using CT value relative to GDP per capita changes our results. Although the coefficient is somewhat larger compared to results presented in Table 2 (with p<0.05), our conclusions remain unaffected.
+Finally, in appendix D we consider how our type of results could potentially be used in policy analyses to study cost-effectiveness. Specifically, we calculate how many “wellbeing-adjusted life years” (see De Neve et al. 2020, Frijters et al. 2020), a given type of cash-transfer could buy for a given transfer size. We find that 1000$ lump-sum payment may be expected to buy roughly 0.330 “wellbeing-adjusted life years”.
+3.4	Spillovers
+Four RCTs (two with multiple follow-ups) in our sample enabled assessment of spillover effects on non-recipients of CTs by including two control groups in a geographically-clustered RCT design: a spillover control made up of non-recipients living near recipients, and a “pure” control comprising non-recipients living spatially separate from the treatment locations.18
+This design allowed comparison of wellbeing across (a) non-recipients who are “treated” to a spillover effect by living near recipients to (b) recipients living further away (who form the “pure” control). To ascertain the average effect of spillovers we performed a meta-analysis of the observed effects, using a multilevel random effects model, inverse-weighted by study standard error, and errors clustered at the level of the sample. Our results are illustrated in Figure 5.
+The average effect of CTs on non-recipients’ MH and SWB (represented by the diamond), is close to zero and is not significant at the 95% level, suggesting no significant spillover effects on average.
+4	Discussion
+Our results represent a systematic synthesis and meta-analysis of all the available causal evidence of the impact of CTs on mental health and subjective wellbeing in low- and middle-income contexts. In sum, we find that CTs, on average, have a positive effect on MH and SWB indicators among recipients. More precisely, we find an average impact of about 0.10 SDs. Additionally, we observe that the effects
+RE Model	♦	-0.01 [-0.06, 0.03]
+I I I I I I
+-0.3 -0.1	0.1
+Note: A forest plot of the studies in our sample that include MH and SWB spillovers. A random effects multilevel model (with levels for study and sample) with robust standard errors (clustered at the level of the program) shows an effect of -0.01. The 95% confidence interval overlaps with zero. All of the CTs except Baird et al., (2013a) were implemented by GiveDirectly, an NGO.
+of CTs appear to only dissipate slowly over time. The estimated effects were substantially larger for unconditional CTs. Our results were consistent across a battery of robustness tests and the observed effects did not vary according to study design (RCT and quasi-experimental). Notably, our results indicate that CTs are less efficacious in Latin America, which may be explained by the prevalence of CCTs (as opposed to UCTs) in that region. We find no significant evidence of negative spillover effects on non-recipients. However, spillover effects were rarely reported upon (n=4). We therefore encourage more research on this aspect going forward.19
+4.1 Limitations
+Like most meta-analyses, using study averages for moderator variables means that we do not capture within-study variation, which limits the precision of our estimates. Some of our insignificant results may be due to low power. This could be remedied if we had access to the data at the level of the individual. Some of the studies we include have open access data policies (Haushofer et al., 2016; Paxson & Schady, 2010; Ohrnberger et al., 2020a). An individual level analysis may therefore be possible but was outside the scope of this paper. Another limitation arises from the paucity of longitudinal follow-ups. There was only one study in our sample that followed up more than five years after the cash transfer began (Blattman et al., 2020). This limits what we can say about the long run effects of CTs on SWB and MH. There is also only one study that discusses effects of CTs on the SWB and MH of individuals who share a household with recipients.20 Unfortunately, our evidence was limited to spillovers relating to non-recipients in the geographic proximity of recipients.
+An important feature of this meta-analysis is that it does not offer evidence on the mechanisms by which CTs improve SWB and MH. One possible mechanism worth investigating is whether the effect on
+SWB or MH stems from increased consumption relative to one’s peers or from previous levels of consumption. Indeed, there is a rich set of possible mediators and moderators, and we have only analyzed a small subset of them.
+Finally, we know of no other systematic review and meta-analysis which estimates the total effect of an intervention on SWB and MH. This limits our capacity to compare the cost-effectiveness of CTs to other poverty alleviation or health interventions.
+4.2 Implications and suggestions for future research
+Although there is some preliminary evidence that CTs are cost-effective interventions in LMICs compared to a USAID workforce readiness program (McIntosh & Zeitlin, 2020) and psychotherapy (Haushofer, Shapiro & Mudida, 2020), the work done to compare the cost-effectiveness of interventions in terms of SWB and MH is scarce, especially in LMICs. Our meta-analysis contributes to this literature by providing a comprehensive empirical foundation to compare the cost-effectiveness of cash transfers to interventions aimed at improving MH or SWB. Although limited, the practical implications of our meta-analysis are clear: direct cash transfers improve the wellbeing of poor recipients in LMICs.
+There are several research questions to be pursued in future work on subjective wellbeing and mental health. What are the long run (5+ years) effects of CTs? What are the effects on a recipient’s household and community? Relevant spillover data should be collected in RCTs or evaluated in quasiexperiments. The costs of CTs and other poverty alleviation interventions should be published. For instance, since a UCT requires less administration (as there are no conditions to monitor), it seems likely that UCTs are cheaper and, based on our results, more effective than CCTs. However, there appears to be no available evidence to answer this question. More broadly, we recommend a greater inclusion of SWB and MH data in intervention evidence collection efforts such as Aid Grade.21
+5 Conclusion
+Cash transfers have a small22 (d<0.2) but significant and lasting effect on wellbeing with only mild adaptation effects. Although modest in size, if SWB and MH measure wellbeing more directly than other indicators, these reported improvements are an indicator of genuine success. How important CTs are as a means of improving wellbeing depends on their cost-effectiveness relative to the alternatives. Even if effect sizes are small, CTs may nevertheless be among the most efficient ways of improving lives. There is no evidence that CTs have, on average, significant negative spillover effects within the community they are implemented in. However, the evidence on this is scarce, meriting further research on the topic.
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+1 | INTRODUCTION
+Suicide is the second leading cause of death among youth (Centers for Disease Control and Prevention, 2018). Suicide attempts are a significant predictor of suicide deaths, and nonfatal suicide attempts are 25-60 times
+more prevalent than fatal ones. Approximately half a million people were treated in emergency rooms following suicide attempts in 2015 (Centers for Disease Control and Prevention, 2018). Suicide has far-reaching impacts, affecting family members and friends of those who attempt suicide or die by suicide (Feigelman, Ceral, McIntosh, Brent, & Gutin, 2018).
+122^—Wl LEY--------------------------------------------------
+Recent research has highlighted the increased risk for suicidal behavior among those who have experienced certain adverse childhood experiences (ACEs; Choi, DiNitto, Marti, & Segal, 2017; Dube et al., 2001). Below, we review the literature on links between different types of ACEs and suicidality.
+1.1	| Child abuse and neglect and suicide
+Increased risk for suicidality among those who experience child abuse or neglect has been reported in meta-reviews (Devries et al., 2014; Evans, Hawton, & Rodham, 2005; Miller, Esposito-Smythers, Weismoore, & Renshaw, 2013) and has been replicated in numerous studies comprising different types of samples (Afifi, Boman, Fleisher, & Sareena, 2009; Sachs-Ericsson, Stanley, Sheffler, Selby, & Joiner, 2017). In a population sample, 78% of those who had attempted suicide had experienced childhood sexual abuse compared with 16% of those who had never attempted suicide. Approximately three quarters of those who had attempted suicide had experienced childhood physical abuse compared with 30% of those who had never attempted suicide. Further, those who had attempted suicide reported twice as many experiences of childhood emotional abuse than nonattempters (Briere, Madni, & Godbout, 2016). In a large retrospective cohort study using data from adult participants in a health maintenance organization, those who reported having experienced emotional, physical, or sexual abuse were three to five times more likely to have attempted suicide at some point in their lives (Dube et al., 2001).
+1.2	| Parent alcoholism and parent incarceration and suicide
+Data from a large representative sample of adult participants in the National Epidemiological Survey on Alcohol and Related Conditions indicated that those with a family history for paternal or maternal alcoholism were more likely to attempt suicide than those without a history of parental alcoholism (Thompson, Alonzo, Hu, & Hasin, 2017). The aforementioned study with adult health maintenance organization members also found that those who reported having a household member incarcerated were more than twice as likely to have attempted suicide than their counterparts (Dube et al., 2001).
+1.3	| Parental death and suicide
+In a Scandinavian population-based study, children whose parents had died when they were less than 18 years of age were twice as likely to have died by suicide during a 25-year follow-up compared with children matched on age and sex but who had not lost a parent in childhood (Guldin et al., 2015). Data from a Swedish national cohort showed that parental loss during childhood was associated with an increased likelihood of hospital admission following a suicide attempt in young adulthood (Rostila, Berg, Arat, Vinnerljung, & Hjern, 2016).
+1.4	| Family history of suicidality and suicide
+Having a family member attempt suicide or die by suicide is a significant risk factor for suicidal behavior (Guldin et al., 2015; Qin, Agerbo,
+Key messages
+•	Suicide remains a significant public health problem and is the second leading cause of death for adolescents and young adults.
+•	Adverse childhood experiences (ACEs) have been shown to be associated with an increased risk for suicidal ideation and suicide attempts.
+•	Our results, based on a U.S. nationally representative sample, indicated that physical, sexual, and emotional abuse, parental incarceration, and family history of suicidality increased the risk for suicidal ideation and suicide attempts in adulthood.
+•	An accumulation of ACEs was associated with increased odds of both suicide ideation and attempts.
+•	Intervention strategies need to prevent ACEs from occurring and, if they do occur, they should take into account the impact of cumulative ACEs on suicide risk.
+& Mortensen, 2002). Among children of parents with mood disorders, those whose parents had a history of a suicide attempts were five times more likely to attempt suicide than a comparison group whose parents had a mood disorder but no suicide attempt history (Brent et al., 2015). Another study found that males who died by suicide were significantly more likely to have experienced the suicide of another family member than were their male counterparts who were still living (Feigelman, Joiner, Rosen, & Silva, 2016).
+1.5	| Cumulative trauma
+Central to the study of ACEs is the notion that the higher the number of adverse experiences, the greater the likelihood of poor outcomes. The previously cited study of a large sample of health maintenance organization members in San Diego found that for each additional adverse event, the risk for making a nonfatal suicide attempt increased by 60% (Dube et al., 2001). In a prospective study of children in South Africa, an accumulation of nine types of ACEs predicted suicide attempts 1 year later, such that the risk for suicide attempts increased as the number of ACEs increased (Cluver, Orkin, Boyes, & Sherr, 2015). Among youth referred for delinquency to the Florida Department of Juvenile Justice, the more ACEs experienced, the greater the likelihood of having made a suicide attempt at some point in their lives (Perez, Jennings, Piquero, & Baglivio, 2016). Data from the National Comorbidity Survey indicated that even after controlling for psychological and demographic variables, the more ACEs experienced, the greater the risk for making a suicide attempt in adulthood (Afifi et al., 2008). Data from the National Epidemiological Survey on Alcohol and Related Conditions also indicated that a higher number of ACEs was associated with an increased risk for lifetime suicide attempts after controlling for demographic variables (Choi et al., 2017).
+THOMPSON et al.
+In sum, several studies have found that an accumulation of ACEs was associated with an increased risk for suicidal ideation and/or suicide attempts. Some of these studies have used national data (Afifi et al., 2008; Choi et al., 2017), many have measured a wide range of ACEs (Choi et al., 2017; Cluver et al., 2015; Dube et al., 2001; Merrick et al., 2017; Perez et al., 2016), some have assessed for suicidality in adulthood (Afifi et al., 2008; Dube et al., 2001) or prospectively (Cluver et al., 2015), and some have controlled for other suicidal behavior risk factors (Afifi et al., 2008; Choi et al., 2017; Perez et al., 2016). However, none has incorporated all of these methodological strengths into the same study. The purpose of the current study was to add to the body of research by testing the unique and cumulative associations of eight different ACEs with suicidal ideation and suicide attempts in adulthood using a nationally representative sample, after controlling for several established risk factors for suicide.
+------------------------------Wiley 1 123
+2.2	| Measures
+2.2.1 | Outcomes—Suicide attempts and ideation
+We created two dichotomous outcome variables that reflected if respondents had engaged in suicide ideation and if they had attempted suicide in adulthood. Because we were interested in ensuring that the ACEs temporally preceded suicidal ideation and suicide attempts, we used outcome measures assessed at Waves 3 and 4. Respondents were asked “During the past 12 months, did you ever seriously think about committing suicide?” and “During the past 12 months, how many times did you actually attempt suicide?” Respondents who reported having considered suicide at least once at either Wave 3 or 4 were classified as having ideated, and respondents who reported having attempted suicide one or more times at Wave 3 or 4 were classified as having attempted suicide.
+2	| METHODS
+2.1	| Sampling procedures and sample
+The sample was derived from the National Longitudinal Study on Adolescent Health (Add Health; Harris, 2009). A multistage-stratified cluster design was used to sample public and private high schools across the United States (Harris, 2013); 79% (n = 132 schools) of the recruited schools participated, and all students attending these schools were invited to complete in-home surveys. The Add Health study was approved by the University of North Carolina School of Public Health Institutional Review Board using guidelines based on the Code of Federal Regulations on the Protection of Human Subjects 45CFR46. Local IRB approval for the secondary analysis of the data also was obtained. Add Health participants provided written informed consent for participation in all aspects of the study. For less sensitive questions, interviewers read the questions and entered respondents' answers. For more sensitive questions, respondents listened to prerecorded questions through earphones and entered the answers directly via audio computer-assisted self-interviewing. In 1995, Wave 1 surveys were completed by 20,745 seventh to 12th graders. Among respondents eligible for follow-up, 14,738 (89%) completed a Wave 2 in-home survey 1 year later, 15,197 (77%) completed a Wave 3 in-home survey approximately 7 years after the Wave 1 survey, and 15,701 (80%) completed a Wave 4 in-home survey approximately 13 years after the Wave 1 survey. We restricted our sample to those who completed surveys at all four waves and had a valid sampling weight. The weight variable we used was recommended for longitudinal data with Wave 1 in-home survey participants who also completed the surveys at Waves 2, 3, and 4 (Chen & Chantala, 2014). This resulted in an analytic sample of 9,421 participants. Participants' mean age was 15.03 years (SE = 0.11) at Wave 1. The sample was evenly divided by gender (50% male and 50% female) and urbanicity (49% lived in rural or partly rural areas, and 51% lived in urban areas). Two thirds (66%) of the sample participants were non-Hispanic white, 12% were Hispanic, 15% were non-Hispanic black, and 7% were of another or mixed race. For more detailed information on survey design, see Harris (2013) and Chen and Chantala (2014).
+2.2.2 | Predictors—ACEs
+We assessed for eight types of ACEs. Although some ACEs were assessed at Wave 3 or 4, the questions used to assess for them were worded to capture respondents' experiences when they were in childhood or adolescence.
+Physical abuse was assessed at Wave 4 and reflected if respondents had been hit with a fist, kicked, or thrown on the floor, into a wall, or downstairs by a parent or adult caregiver more than once before they were 18 years of age (Brumley, Jaffee, & Brumley, 2017; Smith, Smith, Oberleitner, Gerkin, & McKee, 2018). Sexual abuse was assessed at Wave 4 by asking respondents if before the age of 18, a parent or other adult caregiver had touched them in a sexual way, forced them to touch him or her in a sexual way, or forced them to have sexual relations. Emotional abuse was assessed at Wave 4 based on how often a parent or other adult caregiver had said things to the respondents before their 18th birthday that hurt their feelings or made them feel like they were not wanted or loved. We used a cut point of six or more times to increase the measure's specificity in reflecting moderate-to-severe emotional abuse (Scheidell et al., 2017; Smith et al., 2018). Neglect was assessed at Wave 3 by asking respondents if a parent or other adult caregiver had not taken care of their basic needs, such as keeping them clean or providing food or clothing more than once before sixth grade (when a child is typically 11-12 years of age; Brumley et al., 2017). Parental death, assessed at Waves 1 and 2, measured if respondents had experienced the death of either parent in childhood. Parental incarceration was assessed at Wave 4 and measured if a respondent's biological mother or father had spent time in jail or prison before the youth was 18 years of age. Parental alcoholism was measured in the Wave 1 parent interview by asking if the child's biological mother and/or biological father currently had alcoholism. Family history of suicidal behavior assessed if a family member had tried to kill themselves during the past 12 months, assessed at Waves 1 and 2.
+2.2.3 | ACEs score
+A cumulative score was created by summing the eight types of adverse experiences.
+124-1—Wl LEY----------------------------------------------
+2.2.4 I Covariates
+Demographic covariates included gender, age in years, race, and urbanicity assessed at Wave 1. In order to determine the unique role of ACEs in predicting suicidal ideations and attempts, we also controlled for other known risk factors for suicidality that were available in the Add Health dataset. These included depressive symptoms (Brent, Baugher, Bridge, Chen, & Chiapptta, 1999; Goldsmith, Pellmar, Kleinman, & Bunney, 2002), problem alcohol use (Norstrom & Rossow, 2016; Parks, Johnson, McDaniel, & Gladden, 2014), drug use (Parks et al., 2014), delinquent behaviors (Thompson, Ho, & Kingree, 2007), and impulsivity (Liu, Trout, Hernandez, Cheek, & Gerlus, 2017). Depressive symptoms were assessed with a modified version of the Center for Epidemiologic Studies Depression Scale (Radloff, 1977) and were computed as the sum of 20 items answered on a 0-3 scale (a = 0.86; M = 6.90; SD = 0.12). Delinquency was assessed with the mean score of 15 items answered on a 0-3 scale (e.g., took something from a store without paying for it; a = 0.84; M = 0.28; SD = 0.01). Respondents were considered as having an alcohol problem if they reported that they had been drunk at least three to 12 times or had experienced negative consequences of alcohol use at least twice in each of three or more life domains (e.g., family and school), in the past year (15%). The mean of three items assessed on a 1-5 scale was used to measure impulsivity (e.g., “when making decisions, you generally use a systematic method for judging and comparing alternatives;” a = 0.70; M = 2.23; SD = 0.01). Other drug use was a dichotomous variable that reflected if a respondent had used marijuana, cocaine, or another illegal drug in their lifetimes (28%). These covariates have been used in previous research with Add Health data (Swahn & Donovan, 2004; Thompson et al., 2007; Thompson & Light, 2011).
+2.3	I Data analytic strategy
+Variance estimates were adjusted to account for the complex sample design. Analyses were conducted using SPSS 24. We used multivariate logistic regression to test associations between each of the eight ACEs with suicidal ideation and suicide attempts in adulthood, controlling for depression, delinquency, alcohol problems, drug use, impulsivity, gender, age, race, and urbanicity. We next tested the cumulative association of ACEs with risk for suicidal ideation and attempts while controlling for the same covariates. We first examined the percentage of respondents within each ACE category who had engaged in suicide ideation and attempted suicide. We next conducted multivariate logistic regression analyses controlling for the covariates and using an ordinal ACE variable, with no ACE experiences being the reference category. Because only 2.5% of the sample had experienced more than four ACEs, participants with four or more ACEs were combined into a category with those who had three ACEs, such that 0 = no ACEs, 1 = one ACE, 2 = two ACEs, and 3 = three or more ACEs. We then conducted another set of multivariate logistic regression analyses to examine the association of an ACE continuous variable, ranging from 0 to 8, with suicide ideation and attempts.
+THOMPSON et al.
+3	I RESULTS
+3.1	I Descriptive data
+Among the 9,421 survey participants in the analytic sample, the percentage that experienced a specific ACE ranged from a low of 4.7% for parental death to a high of 16.2% for emotional abuse Approximately half the sample (54%) did not report any ACE, 26% had experienced one, 12% had experienced two, and 8% had experienced three or more. In terms of suicidal behaviors, 12.5% reported having seriously considered suicide and 3.3% reported having attempted suicide (see Table 1).
+3.2	I Associations of ACEs with suicidal ideation in adulthood
+The odds of suicidal ideation in adulthood increased twofold to threefold among those who had experienced sexual abuse (p < 0.001), physical abuse (p < 0.001), or emotional abuse (p < 0.001) in childhood. The odds of having suicidal ideation in adulthood increased approximately 1.5 times among those who had a family history of suicidality (p < 0.05) or had a parent incarcerated in childhood (p < 0.001). Neglect, parental death, and parental alcoholism did not increase the odds of suicidal ideation in adulthood (see Table 2).
+In terms of cumulative ACEs, 8.1% of those with no ACEs reported ideation, 14.1% of those with one ACE reported ideation, 18.7% of those with two ACES reported ideation, and 26.2% of those with three or more ACEs reported ideation in adulthood. The odds of suicidal ideation in adulthood increased 1.69 times (95% CI [1.33, 2.15]) among those with one ACE, 2.31 times (95% CI [1.71, 3.13]) among those with two ACEs, and 3.13 times (95% CI [2.34, 4.20]) among those with three or more ACEs when compared with those who had not experienced any ACEs (see Table 3). Further, the continuous variable representing cumulative ACEs predicted suicide ideation is adulthood (AOR = 1.38; 95% CI [1.27, 1.49]).
+3.3	I Associations of ACEs with suicide attempts in adulthood
+The odds of making a suicide attempt in adulthood increased twofold to threefold among those who had experienced sexual abuse (p < 0.001), physical abuse (p < 0.001), or emotional abuse
+TABLE 1 Descriptive data for adverse childhood experiences and suicidal ideation and attempts (n = 9,421)
+Note. Both models controlled for depression, delinquency, alcohol problems, drug use, impulsivity, gender, age, race, and urbanicity. ACE: adverse childhood experience; AOR: adjusted odds ratio; CI: confidence interval.
+*95% CI does not include 1; p < 0.05.
+(p < 0.001) in childhood or had a family member attempt or complete suicide during their childhood (p < 0.01). The odds of a suicide attempt in adulthood increased 1.5 times among those who had a parent incarcerated in childhood (p < 0.05). As with suicidal ideation, neglect, parental death, and parental alcoholism did not increase the odds of suicide attempts in adulthood (see Table 2).
+In terms of cumulative ACEs, 2.0% of those with no ACEs attempted suicide in adulthood, 3.5% of those with one ACE attempted suicide, 4.2% of those with two ACES attempted suicide, and 7.9% of those with three or more ACEs reported a suicide attempt in adulthood. The odds of attempting suicide in adulthood increased 1.57 times (95% CI [1.01, 2.44]) among those with one ACE, 1.99 times (95% CI [1.22, 3.23]) among those with two ACEs, and 3.53 times (95% CI [2.20, 5.66]) among those with three or more ACEs when compared with those who had not experienced any ACEs (see Table 3). Further, the continuous variable representing cumulative ACEs predicted suicide ideation is adulthood (AOR = 1.38; 95% CI [1.22,1.55]).
+4	| DISCUSSION
+We found that for suicidal ideation, the most significant ACEs were sexual and emotional abuse, followed by physical abuse, parental incarceration, and family history of suicidality. For suicide attempts, the most significant ACE was sexual abuse, followed by emotional abuse, family history of suicidality, physical abuse, and parental
+incarceration. Neglect, parental death, and parental alcoholism did not increase the risk for suicidal ideations or attempts in adulthood. Our study also documented the increased suicidality risk conferred by an accumulation of ACEs. Taken as a whole, our results were consistent with previous research demonstrating the ACEs-suicidality link (Afifi et al., 2008; Choi et al., 2017; Cluver et al., 2015; Dube et al., 2001; Merrick et al., 2017; Perez et al., 2016).
+Despite this study's strengths, there were some limitations that should be noted. First, the reporting period for the outcome measures was limited to 1 year and thus likely did not capture all incidences of suicide ideation and attempts the respondents may have experienced in adulthood. Second, suicide attempts were only assessed if respondents acknowledged engaging in suicidal ideation. Thus, a respondent who attempted suicide without first considering suicide would be misclassified as a false negative. Third, we focused on suicide attempts rather than deaths. Nonetheless, focusing on suicide attempts is important because a nonfatal suicide attempt has been found to be the strongest predictor of death by suicide (Bostwick, Parbati, Geske, Alastair, & McKean, 2016), and risk factors for attempts and deaths are similar (Gould & Kramer, 2001). Fourth, although the reporting timeframe for the ACE variables was prior to adulthood, ACEs were assessed retrospectively. This may have resulted in an underestimate of the ACEs, especially if the respondent was very young when experiencing the ACE. Unfortunately, most ACE studies also have relied on retrospective accounts, with the one exception being the study with South African youth by Cluver et al. (2015) discussed earlier. Future research on ACEs should strive to collect data on ACEs prospectively throughout childhood. Fifth, data were based primarily on self-report only, which raises the concern of social desirability bias. However, relying on parent reports also would be problematic, as parents may underreport events in which they played a role or were implicated. It is possible that this was the case for the parent-reported parental alcoholism measure; if so, then this could explain why we did not find a significant association between parental alcoholism and suicidality in our study whereas another study that relied on selfreport of both suicidality and parental alcoholism did (Thompson et al., 2017). Future research should ideally use multiple informants when measuring ACEs.
+12^Wl LEY------------------------------------------
+Our findings highlight the importance of adopting strategies to prevent exposure to ACEs as part of a comprehensive approach to suicide prevention (Ports et al., 2017). Promising research indicates that building community capacity and enhancing social networks can reduce community-wide ACE prevalence (Hall, Porter, Longhi, Becker-Green, & Dreyfus, 2012). Another type of ACE prevention approach is parent education programs. In a meta-analysis of randomized controlled trials of parenting education programs, several programs showed significant reductions in child maltreatment. Further, these positive reductions were found across low-, middle-, and high-income countries and for primary (i.e., families randomly selected from community), secondary (i.e., families at risk for child maltreatment), and tertiary prevention programs (i.e., families in which child maltreatment had already happened; Chen & Chan, 2016). Home visitation programs and prenatal programs also have been found to reduce the likelihood of child maltreatment (Eckenrode et al., 2000). Further, suicide prevention programs that promote connectedness and healthy relationships may be expanded to address childhood adversities (Ports et al., 2017).
+Future research also should focus on identifying mediating mechanisms for the ACEs-suicidality association. It is important to note that even though a higher number of ACEs was associated with an increased risk for suicide ideation and attempts, most who experienced three or more ACEs did not have suicide ideation or make a suicide attempt in adulthood. Among those who experienced three or more ACEs, approximately 74% did not report suicide ideation, and 92% had not made a suicide attempt. This suggests the need for research to identify factors that promote resiliency among youth who experience many ACEs and protect them from an increased risk for suicidality. Although our study showed that ACEs were associated with an increased risk for suicide ideation and attempts, it did not test for variables that might explain this link. Fortunately, some research has already begun to examine mediating mechanisms. For example, in a cross-sectional, population-based survey of Canadian adults, depression, anxiety, substance use, and chronic pain helped to account for a portion of the association between different ACEs and lifetime suicide attempts (Fuller-Thomson, Baird, Dhrodia, & Brennenstuhl, 2016). Another study with youth referred to the Florida Department of Juvenile Justice found that aggression and impulsivity helped explain why an accumulation of ACEs was associated with an increased risk for suicide attempts (Perez et al., 2016). In the only prospective study on the link between ACEs and subsequent suicide ideation and attempts, poor mental health symptoms, but not drug and alcohol misuse, were found to mediate this association (Cluver et al., 2015). This finding suggests that among youth exposed to ACEs, effective mental health services could potentially reduce the likelihood of suicidality. Using data from the National Comorbidity Study, researchers found that the number of psychiatric disorders accounted for the association between verbal child abuse and suicide attempts but not for violent child abuse and suicide attempts (Sachs-Ericsson et al., 2017). These findings led the authors to speculate that different mediating mechanisms may account for the associations of different types of ACEs and suicide attempts. Future research should test potential mediating pathways that account for the association of an accumulation of ACEs and subsequent suicidality and also determine
+if these mediating pathways account for associations between individual ACEs and suicide risk. Studies that use prospective designs with more than two timepoints provide the strongest methodology for testing mediation, as mediation based on cross-sectional designs can produce biased estimates and hence faulty conclusions regarding mediating mechanisms (Cole & Maxwell, 2003). Longitudinal studies with two timepoints are better than cross-sectional data, but these “half-longitudinal designs” with mediators assessed at the same time as the predictor or the outcome can still result in biased estimates. Thus, research to investigate mediators of the association between ACEs and suicidal behaviors should rely on data collected from at least three timepoints and ensure constructs occur and are measured in the correct temporal order. That is, mediators should be assessed at a subsequent timepoint than ACEs, suicidal behaviors assessed at a subsequent timepoint than mediators, and prior levels of mediators and outcomes controlled. With this type of design, research can shed light on mechanisms by which ACEs lead to increased risk for suicidality.
+Moreover, future research should determine what ACEs are most important to include in a cumulative ACE measure. Consistent with some other studies, our research indicates that sexual, physical, and emotional abuse, having a family member who attempted suicide, and having an incarcerated parent are important ACEs to include in a cumulative ACE measure. Although some studies have included mental illness in household member as an ACE variable (Dube et al., 2001), no study, to our knowledge, has included family history for suicide as an ACE variable. It is possible that a family history of suicide may best be conceptualized as an inherited genetic risk rather than an ACE.
+In sum, our study replicated research showing that certain individual ACEs, as well as an accumulation of ACEs, significantly increased the risk for suicide ideation and attempts in adulthood. It added to the literature by testing the unique and cumulative associations of a wide range of adverse childhood events with suicide ideation and attempts occurring in adulthood using a nationally representative sample, after controlling for several established risk factors for suicide. These findings underscore the importance of preventing ACEs and implementing suicide prevention strategies among those with a high number of ACEs.
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+n> Check for updates
+Introduction
+Suicide rates among adolescents in the United States continue to rise (Centers for Disease Control and Prevention, 2019), despite a downturn in the incidence worldwide (World Health Organization, 2017). Moreover, 5.1% of male and 9.3% of female high school students in the United States report a suicide attempt (SA) in the past year (Kann et al., 2018).
+Risk factors for adolescent SAs span demographic, clinical, and social domains, meaning that the risk profiles for suicidal adolescents are multidimensional and heterogeneous. Female adolescents and
+adolescents who self-identify as LGBTQ are at increased risk (Kann et al., 2018; O’Brien, Putney, Hebert, Falk, & Aguinaldo, 2016). Previous history of SA and suicidal ideation (SI) (Nock et al., 2013), presence, persistence, and severity of SI (Czyz & King, 2015), and nonsuicidal self-injury (NSSI) (e.g. Asar-now et al., 2011) have all been reported to be predictors of suicide attempts. Similarly, psychiatric symptoms, such as depression and hopelessness, are consistent correlates and predictors of SA (King, Ewell Foster, & Rogalski, 2013), and symptoms of distress (e.g. anxiety and agitation) and impulse control (e.g. aggression, substance abuse) have emerged as the strongest predictors of attempts among adolescents who report ideation (Nock et al.,
+1056 Cheryl A. King et al.
+2013). Sleep disturbance has been reported as an imminent risk factor for SA and death by suicide (e.g. Koyawala, Stevens, McBee-Strayer, Cannon, & Bridge, 2015).
+Interpersonal factors such as low social connectedness also have been related to the likelihood of suicidal ideation and behavior (Czyz, Liu, & King, 2012; Gunn, Goldstein, & Gager, 2018). Bully victims and perpetrators have reported an increased incidence of SAs (Borowsky, Taliaferro, & McMorris, 2013), and physical and sexual abuse have been prospectively associated with SAs (Castellvi et al., 2017). Interpersonal conflicts and losses, and legal/ disciplinary problems are acute stressors associated with SAs and suicide (e.g. Gould, Fisher, Parides, Flory, & Shaffer, 1996).
+Given this heterogeneity of suicide risk factors, it is challenging for healthcare providers to assess level of risk and for intervention and prevention specialists to identify potent and potentially modifiable targets for risk reduction. Moreover, extant research has focused on single risk factors (Franklin et al., 2017), despite the growing recognition of the multidimensional nature of suicidal risk and current clinical practice, which attempts to integrate available information about multiple risk factors. Consequently, further research that takes into account multiple risk factors is sorely needed.
+The challenge of suicide risk assessment and identification of potent prevention targets is exacerbated for males and for adolescents who conceal or deny their suicidal thoughts. Adolescent females are more likely than males to report SI and behavior (Kann et al., 2018) and to obtain mental health services (Rhodes et al., 2012), yet the rate of suicide is much higher among adolescent males than females (Centers for Disease Control and Prevention, 2019). An improved understanding of the short-term risk factors for SAs among males may enable us to improve risk recognition and prevention. Similarly, although many of the most commonly used screening tools assess SI (e.g. Horowitz et al., 2012), recent SI is not a significant predictor of SAs for all subgroups of adolescents (e.g. King, Jiang, Czyz, & Kerr, 2014).
+Our objective was to examine predictors of SAs during the 3-months following adolescents’ ED visits in the Study One dataset of the Emergency Department Screening for Teens at Risk for Suicide (ED-STARS) Study. This large-scale study was implemented in collaboration with the Pediatric Emergency Care Applied Research Network (PECARN). Its primary aim was to develop the Computerized Adaptive Screen for Suicidal Youth (CASSY), a relatively brief suicide risk screen with the potential for widespread implementation in emergency departments (King et al., under review). Because our baseline assessment included a broad array of previously identified risk factors for SAs, this study also enabled us to examine predictors of SAs following ED visits using multivariable models.
+J Child Psychol Psychiatr 2019; 60(10): 1055-64
+We examined predictors in the total follow-up sample and in subsamples defined by sex and the presence of recent SI. We hypothesized that predictors of SAs would include indicators of SI and behavior (e.g. past week suicidal ideation, lifetime history of suicidal behavior) and, reflecting a different domain, one or more interpersonal risk factors (e.g. peer victimization, low social or school connectedness). We expect interpersonal factors to be important in light of longitudinal studies (e.g. Gunn et al., 2018) and theoretical formulations about the salience of interpersonal processes to suicidal risk (e.g. Durkheim, 1897; Joiner, 2005).
+Methods
+Participants
+Adolescents (ages 12-17) were recruited from 13 EDs in PECARN (June 2015-July 2016) and the Whiteriver Indian Health Service (IHS) Hospital, which serves the White Mountain Apache Tribe (November 2015-April 2017). Among 10,664 approached adolescents, 6,448 (60.5%) completed a suicide risk survey. A subset of patients (n = 2,897 (43.6%) enriched for suicide risk (Figure 1 and Appendix S1) was randomly assigned to a 3-month telephone follow-up; 2,104 participants completed this follow-up (72% retention). The sample included 1,327 females (63.1%) and 777 males (36.9%) with a mean age of 15.1 years (SD = 1.6). Additional demographic information is in Appendix S2.
+Procedure
+At PECARN sites, adolescents were recruited during screening shifts that were randomly selected for each site from time periods when research coordinators were on site (primarily afternoons and evenings due to higher volume of adolescent patients). At the IHS Hospital, recruitment was ED-linked with a daily admission review and IRB permission to contact at home for recruitment. Exclusion criteria were as follows: previous study enrollment, ward of State, non-English speaking adolescents (non-English speaking parents enrolled), medically unstable, and severe cognitive impairment.
+Adolescents completed a self-report survey assessing demographics and suicide risk factors in the ED (except for IHS site). Participants were included if adolescent and parent (n = 1,799, 85.5%),adolescentonly (n = 183,8.7%),orparentonly(n = 122, 5.8%) follow-upinterviewswereconducted. Follow-upinformant (parent oryouth vs. both) was unrelated to participants’ lifetime histories of suicidal ideation and behavior, and to the suicide attempt outcome. Participants with only youth or only parent follow-up interviews were, however, older than those with both interviews. (p < .001, Kruskal-Wallis test.). Written-informed parent/guardian consent and adolescent assent were obtained, in addition to IRB approval from all sites. Adolescentswho turned 18 prior to follow-up were reconsented.
+Measures
+This study incorporated adolescent data from the baseline selfreport survey (92 primary, 27 follow-up questions; details in Appendix S3). Due to ED space and time constraints, a concern for respondent burden, and a need to assess a wide range of risk factors to develop CASSY algorithms, brief, adapted versions of standardized scales were used for many risk factors, all of which had been previously associated with adolescent SAs.
+© 2019 Association for Child and Adolescent Mental Health
+An adapted Columbia-Suicide Severity Rating Scale (C-SSRS; Posner et al., 2008) was used to assess history of SAs at baseline and SAs between baseline and 3-month follow-up. SA was defined as a positive response to either of two questions: “In the past 3 months, have you made a suicide attempt?’ “In the past 3 months, have you tried to harm yourself because you were at least partly trying to end your life?’ Past week SI was assessed with question #3 from the Ask Suicide-Screening Questions (ASQ; Horowitz et al., 2012): “In the past week, have you been having thoughts about killing yourself?’ In defining subgroups of adolescents who did and did not report recent SI, we removed participants who selected “unknown’ or did not respond to the question.
+Additional suicide risk factors assessed at baseline included lifetime severity of SI and suicidal behavior, suicidal rumination, NSSI, depression, hopelessness, homicidal ideation, anxiety, agitation, sleep disturbance, adaptive functioning, alcohol and drug use, impulsivity, aggression, connectedness (family, school, social), peer victimization, physical and sexual abuse, negative life events, and identification as a sexual or gender minority.
+demographics and variables pertaining to suicidal thoughts, suicidal behaviors, and NSSI were added to the model in a stepwise fashion; the model with the lowest Akaike Information Criterion (AIC) was carried forward. Remaining candidates, including all other clinical risk factors examined (see Table 1), were considered using forward stepwise selection. In the final stage, variables were dropped using backward selection (p > .05), such that all variables were statistically significant in the final model.
+To account for the oversampling of higher risk groups for follow-up, a weight equal to the inverse of the sampling probability of each of the three risk groups was applied in analyses. For categorical variables, the reference level was “No’, “None’, or equivalent, when possible. White and non-Hispanic were used as reference populations. When model separation became an issue due to low counts, categories of predictor variables were combined. For each final model, we calculated the predictive performance of the model as the area under the curve (AUC), with a 95% confidence interval (CI). As a sensitivity analysis, we conducted a 10-fold cross-validation of the final model for the full sample. Statistical analyses were performed using SAS software version 9.4 (SAS Institute Inc, 2013).
+Statistical analysis
+Univariable associations between baseline demographic and clinical risk factors and SAs at 3-months were determined, and predictors with significant associations (p < .1) were candidates for inclusion in multivariable logistic regression models (Hosmer, Lemeshow, & Sturdivant, 2013). In stage one,
+© 2019 Association for Child and Adolescent Mental Health
+Results
+Retention
+Retention was greater for males than females (76.0% vs. 70.8%; p = .003) and varied by race (p < .001)
+and ethnicity (p < .001), with higher retention rates for Whites (75.1%) and multiracial youth (79.5%) than other races (range from 61.3-72.6%), and for non-Latinx than Latinx ethnicity (75.6% vs. 68.5%). Higher parental education was also associated with greater retention (p’s < .001). The retention rates for mothers and fathers, respectively, were as follows: high school or less (67.9%, 68.1%), some college/ technical (73.8%, 73.2%), college graduate (77.6%, 81.7%), unknown/not applicable (65.0%, 70.0%).
+Descriptive statistics: suicidal thoughts, suicide attempts, and NSSI
+At baseline, 1,090 adolescents (51.9%) reported a lifetime history of SI and 815 adolescents (39.4%) reported a lifetime history of suicidal behavior, including actual, aborted, and interrupted attempts. The mean number of lifetime SAs reported was 1.67 (SD = 6.91; Median = 0). Regarding number of pastyear NSSI incidents, 1378 adolescents (65.7%) reported none, 339 adolescents (16.2%) reported 12, 121 (5.8%) reported 3-4, and 261 (12.4%)
+© 2019 Association for Child and Adolescent Mental Health
+reported 5 or more (data missing, n = 5). A SA between ED visit and 3-month follow-up was reported for 104 adolescents (4.9%; 84 females, 6.3%; 20 males, 2.6%). There was one suicide death, which was included as a SA in analyses.
+Spearman’s correlations among risk factors are reported in Tables S1-S4. As examples of the strength of correlations, lifetime severity of SI was highly positively correlated with lifetime history of suicidal behavior (.70, p < .001) and moderately positively correlated with number of NSSI incidents during the past 12 months (.53, p < .001). Social and school connectedness were moderately positively correlated (.47, p < .001).
+Site differences were identified in suicide risk predictors and outcomes. This information is provided in Tables S5-S9.
+Predictors of suicide attempt during 3 months following ED visit
+Univariable associations with suicide attempts. -Sex, sexual, and gender minority status, and all of
+the examined psychosocial and clinical characteristics predicted SAs at 3-month follow-up (see Table 1).
+Multivariable regression models. The final multivariable model for the total sample included past week SI (yes/no), lifetime severity of SI, history of suicidal behavior, and school connectedness (AUC = 0.86, 95% CI: 0.82-0.89; Table 2). In the sensitivity analysis, the ORs, (CIs), and AUCs fitted from each of the 10 subsamples (each approximately 90% of full cohort) were similar, with a median AUC of 0.87 and IQR 0.84-0.90.
+To examine replicability of this model across sites, we examined a model including site and the interaction between site and the final model risk score (fitted logit values for each patient). The interaction was nonsignificant (p = .55), suggesting that the relationship between the predicted risk and SA outcome does not differ by site. Site was also unrelated to SA risk (p = .70) after taking into account risk factors.
+For adolescents without past week SI at baseline, the final model included lifetime SI severity and social connectedness (AUC = 0.84, 95% CI: 0.780.90; Table 3). For adolescents with recent SI at baseline, the final model included family public assistance, suicidal rumination (repetitive thoughts), and social connectedness (AUC = 0.69, 95% CI: 0.62-0.76; Table 3).
+For male adolescents, the final model included past week SI and lifetime SI severity (AUC = 0.89, 95% CI: 0.85-0.94; Table 4). For female adolescents, the model included past week SI, number of NSSI incidents during the past 12 months, and social connectedness (AUC = 0.84, 95% CI: 0.81-0.87).
+Discussion
+In this prospective study of adolescent ED patients, we identified baseline predictors of SAs across a 3month period of follow-up using multivariable models for the entire sample, and for subsamples defined
+by sex and the presence or absence of recent suicidal thoughts. These subgroups included two particularly vulnerable groups: adolescent males who receive fewer mental health services (Rhodes et al., 2012) and have a much higher rate of suicide than adolescent females (Centers for Disease Control and Prevention, 2019), and adolescents who do not report recent suicidal thoughts, which challenges risk recognition.
+Study results replicate the importance of previously identified suicide risk factors. Every clinical risk factor included in our baseline suicide risk survey was associated significantly with the likelihood of a SA between the baseline ED visit and 3month follow-up. Concordant with hypotheses, past week SI, lifetime severity of SI, lifetime history of suicidal behavior, and an interpersonal factor, school connectedness, emerged as the key predictors of attempts for the total sample. Moreover, emphasizing the importance of connectedness to our understanding of risk, either school or social connectedness emerged as a key predictor for three of the four subgroups of adolescents studied. Contrary to hypotheses, however, the model for males included only two factors: recent SI and lifetime severity of SI.
+Lifetime severity of SI was found to be a key predictor for the overall sample, and three of the four subgroups of adolescents examined. This finding is consistent with previous studies indicating that adolescents who develop a suicide plan are more likely to make an attempt than ideators without a plan (Nock et al., 2013), that intensity of SI predicts SAs (Peters, Mereish, Solomon, Spirito, & Yen, 2018), and that “worst ever’ SI is as strong a predictor of suicide risk as current ideation (Beck, Brown, Steer, Dahlsgaard, & Grisham, 1999). Similarly, the importance of lifetime history of suicidal behavior is consistent with studies showing that increased risk for subsequent self-harm and death by suicide persists for years after initially seeking health care for self-harm (Finkelstein et al., 2015).
+© 2019 Association for Child and Adolescent Mental Health
+School or social connectedness emerged as a key predictor for several subgroups of adolescents, which is consistent with a growing body of research (Gunn et al., 2018) indicating that higher levels of school connectedness were associated with less suicidal behavior in general school samples, high-risk adolescents, and sexual minority adolescents (Marraccini & Brier, 2017). Social connections may have long-term consequences for mortality as well as morbidity. A 14-year follow-up of adolescent hospitalized for SI and behavior found that those assigned to an intervention to mobilize social support from adults had reduced self-injury mortality (King et al., 2019). Therefore, social and school connectedness are likely to be an important target for risk assessment and preventive intervention.
+Adolescents who do not report recent SI, who comprised nearly one-third of the youth who made SAs in this study, can be challenging to identify in EDs and other settings where the focus is on current risk. In this subgroup, lifetime severity of SI and
+© 2019 Association for Child and Adolescent Mental Health
+social connectedness were the primary risk indicators. The accuracy of prediction in this ‘hidden’ subgroup provides particularly strong support for the need for suicide risk screening in the pediatric ED. Surprisingly, the accuracy of prediction for this subgroup (AUC = 0.84) was higher than the accuracy of prediction for the subgroup of adolescents who reported recent suicidal ideation (AUC = 0.69). This may be due to the inconsistency of adolescents’ reports of SI across study measures, which will be the focus of a future study.
+NSSI only emerged as a primary risk factor for females. It is unknown whether or not this relates to the different types of NSSI reported by females (Sornberger, Heath, Toste, & McLouth, 2012), social influences, and interpersonal challenges associated with engagement in NSSI (Victor & Klonsky, 2018), or females’ higher likelihood of experiencing suicidal thoughts and engaging in suicidal behavior (Kann et al., 2018). The more limited statistical power for adolescent males, due to fewer SA outcomes, may
+1062 Cheryl A. King et al.
+also be important as NSSI was a predictor of SAs among males in univariable analyses.
+The prediction model AUCs for the full sample, the sample of adolescents who did not report recent SI at baseline, and the subsamples of males and females each ranged between 0.84 and 0.89, which can be considered excellent classification accuracy (Hosmer et al., 2013), and contrasts with the disappointing performance of previous single risk factor approaches to suicide risk prediction (Franklin et al., 2017). Although the heterogeneity of suicide risk factors and the low base rates of SAs and suicide are challenges to risk stratification (Belsher et al., 2019), findings suggest that a multivariable prediction model can be useful for the short-term prediction of adolescent SAs. However, of equal or greater importance, these models identify potentially important targets for clinical risk evaluation and prevention. Screening tools for risk recognition can be developed using prediction algorithms developed from large data sources (Belsher et al., 2019). We used this strategy in developing the CASSY, which is being validated in a new sample.
+Results should be considered within the context of study limitations. We used brief and adapted scales to assess most suicide risk factors to reduce respondent burden and facilitate patient flow in EDs. Although each of the baseline clinical risk factors we assessed was found to be a significant univariable predictor of SAs, the use of brief scales may have reduced the reliability of measurement and our ability to fully capture each construct. Furthermore, this study was conducted primarily in pediatric EDs of academic health systems, which are not representative of the range of EDs in the United States. In addition, we had lower levels of retention for adolescents from racial and ethnic minority groups, females, and adolescents whose parents had less education. Although we considered weighting the sample for nonresponse, we chose to prioritize adjusting for the oversampling of higher risk groups because we had specific information pertinent to the oversampling and did not want to apply multiple weights to relatively small subgroups. Moreover, for the most part, these variables were not predictive of SA, and therefore our predictive models are most likely not biased due to nonresponse. Finally, despite the relatively large size of this study, the relatively low number of youth with SAs limited our statistical power for identifying multiple predictors, especially within critical subgroups such as males, for whom the number of attempts was smaller than for females. While in this study, our focus was on identifying key risk factors, in future reports we will describe how we also used study data to develop and validate an adaptive screening tool.
+In summary, in this short-term prospective study of predictors of SAs in a large and diverse sample of adolescents recruited from pediatrics EDs, we found that past week SI, lifetime severity of SI, lifetime
+history of suicidal behavior, and connectedness were critical risk and protective factors. We also documented variation in key risk factors across important subgroups, including adolescent males and adolescents who did not report recent SI. The risk and protective factors identified may be important to assess clinical risk evaluations and can serve as important targets for intervention and prevention strategies.
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+Introduction
+Collaboration with people with lived experience of mental illness (PWLE), also referred to as service users, is increasingly recognized as an integral strategy to improve mental health care.1-3 The commitment to collaboration has been endorsed by the World Health Organization (WHO),4 national governments in their mental health policies,5-8 and mental health professional organizations.9 However, there is a scarcity of evidence-based approaches with demonstrated safety for PWLE and effectiveness in improving care.10 This gap in evidence for collaboration with PWLE is especially pronounced in low- and middle-income countries (LMIC) where efforts are under way to rapidly expand access to mental health services in primary care.11
+Collaboration with PWLE can be particularly important to reduce stigma among primary care practitioners (PCPs), which is a barrier to effective integration of mental health care.12-14 Stigma among PCPs is one contributor to low rates of detection of mental illness,15-20 which is a common shortcoming in primary care mental health programs in LMICs.15,21,22 One avenue to reduce stigma is through social contact interventions between PWLE and stigmatizing groups, such as PCPs. In social contact interventions, stigmatized and stigmatizing groups interact through sharing personal stories, engaging in collaborative activities, and having structured and unstructured social interactions.23-25 Unfortunately, most research on social contact and mental illness stigma is limited to high-income countries, and few studies in any setting have demonstrated long-term attitudinal change (eg, only 1 study in LMICs included a 12-month follow-up12); nor have they routinely evaluated behavioral changes, such as the association between stigma reduction and improved clinical care.12,25-28 There has recently been a call for more methodological rigor in social contact intervention trails.25 Moreover, despite expanding research29 on the WHO Mental Health Gap Action ProgrammeIntervention Guide (mhGAP-IG)30—the global training initiative for primary care-based mental health services in low-resource settings—there is a lack of studies on structured involvement of PWLE and potential benefits of social contact during these trainings.
+Therefore, we conducted a pilot cluster randomized clinical trial (cRCT) of Reducing Stigma Among Healthcare Providers (RESHAPE) in Nepal. RESHAPE is a stigma-reduction intervention conducted in collaboration with PWLE to change attitudes of PCPs participating in mhGAP-IG training.31 This pilot cRCT was deemed necessary before proceedingto a full-scale trial to address the specific objectives of assuring that PWLE could safely participate, that PCPs would be willing to attend trainings with PWLE, and to establish parameters for recruitment, randomization, and retention. We also sought preliminary estimates of benefit among PCPs measured as reduction in stigma and improvements in clinical competency, operationalized as accuracy of mental illness diagnoses. A cluster design, with primary care facilities being the unit ofclustering, was used because of the shared mental health care responsibilities among PCPs working at the same facility.
+Methods
+Design
+The study protocol is available in Supplement 1 and has been published.32 This report follows the Consolidated Standards of Reporting Trials Extension (CONSORT Extension) reporting guideline for randomized studies,33 including extensions for pilot and feasibility trials34 and for cluster trials.35 This pilot cRCT was conducted in Chitwan, Nepal, using a 1:1 allocation ratioof primary care facilities (the unit of clustering). Nepal was selected because it exemplifies low-resource conditions in LMICs, and there was an existing research infrastructure evaluating mental health care integration into primary care through the Programme for Improving Mental Health Care (PRIME).36-38 No methodological changes were made after trial commencement.
+Ethical Review of the Study
+The study was granted ethical approval by the Nepal Health Research Council, Duke University institutional review board, and George Washington University institutional review board. All participants completed a signed consent form in Nepali. Before the start of PhotoVoice training, PWLE were evaluated by psychiatrists to appraise ability to safely participate in the program. The psychiatrist was available if PWLE had symptom relapse duringthe weeks of the PhotoVoice trainings. A psychosocial counselor was present to support PWLE for all PhotoVoice sessions and the PCP trainings. For the diagnostic accuracy component of the study, any patients found to have an incorrect diagnosis had their medical records corrected by the study psychiatrist, and they were started on the appropriate treatment for the corrected diagnosis.
+Participants and Setting
+All primary care facilities in Chitwan district in which mental health services had not yet been integrated were eligible for inclusion as clusters. All PCPs who had prescribing privileges at the primary care facilities were eligible. Primary care facilities typically have only 1 or 2 PCPs. Therefore, although each cluster included all eligible staff, there were few PCPs per facility. Approximately 1 year after PCPs were trained and supervised, patients whom they newly diagnosed with depression, psychosis, or alcohol use disorder were evaluated by a psychiatrist for accuracy of the PCP diagnosis. Caste and ethnicity data were recorded for all PCPs and patients participating in the study. Caste and ethnicity data were documented by research assistants based on participants’ last names, which indicated caste and ethnic background. In cases of last names that could be categorized into multiple groups, research assistants asked the participant to clarify the caste and ethnic identification. Caste and ethnicity were recorded for this study because the social categorizations have been associated with stigmatization and discrimination, mental illness risk factors, and differential treatment within the health system.39-45
+Interventions
+The RESHAPE intervention is designed based on social neuroscience, social psychology, and medical anthropology theories to create a what-matters-most approach to stigma reduction.46,47 Figure 1 depicts the components of RESHAPE. Full details on the content development and proof of concept testing have been published.31 The design of the RESHAPE intervention and implementation of this trial were conducted in collaboration with PWLE.
+The RESHAPE intervention engages PWLE to participate as cofacilitators in a 40-hour mhGAP-IG training30 adapted for Nepal.37 Within the mhGAP-IG training, PWLE present recovery testimonials through photographic narratives, using a technique known as PhotoVoice.48 PhotoVoice is a commonly used participatory methodology in global health.49 PWLE are eligible for the PhotoVoice skill-building if they have been treated at primary care settings for 1 of 4 priority disorders (depression, psychosis, alcohol use disorder, and epilepsy) and are now in a state of recovery, based on evaluations conducted by a Nepali psychiatrist. Selected PWLE were trained
+through 12 PhotoVoice sessions over 3 months in which they were taught to develop a photographic recovery narrative. PWLE constructed narratives that were approximately 7 minutes in duration and included 3 components: life before treatment, the experience of treatment, and life after treatment. On days 2 through 8 of the 9-day mhGAP-IG training, PLWE participated by providing their narratives followed by question-and-answer sessions, totaling approximately 45 minutes of direct facilitation per day. PWLE also participated in structured and unstructured social activities with PCPs (eg, ice-breakers, energizers, and meals).
+In addition, the RESHAPE model included presentations from aspirational figures. Aspirational figures were PCPs who had previously been trained to provide mental health services and who were recognized by supervisors as enthusiastic about treating patients with mental illness. These PCPs were referred to as aspirational figures because of the hope that PCP trainees will aspire to similar commitment to caring for patients with mental health concerns. Aspirational figures presented a myth-busting session and a recovery story from the perspective of a health care clinician. PWLE and aspirational figures participated over approximately 3 months of PCP trainings.
+The key themes addressed in RESHAPE were identified through the what-matters-most framework for understanding origins of stigma.31 The 3 stigma domains were survival threats, social threats, and professional threats. Survival threats included beliefs that people with mental illness are violent, including lay ethnopsychology understandings that the brain-mind (Nepali: dimaag) controls social behavior and inhibits violence, but a damaged brain-mind in mental illness leads to loss of behavioral control.20 Social threats referred to beliefs that interacting with people with mental illness could cause mental illness in health care clinicians and result in loss of social status,50 as captured in the saying “the doctor of mad patients is also mad” (Nepali: “paagal ko daktar pani paagal ho'').20 Professional threats included beliefs that providing health care for people with mental illness is
+ineffective, burdensome, and ultimately would jeopardize other patient care responsibilities.20,50,51 In addition, there was intersectional stigma resulting from the dual-burden of discrimination experienced by low-caste and ethnic minorities and women, who are disenfranchised in society and also considered more likely to experience mental illnesses and alcohol use disorders.39-42 The PhotoVoice narratives of PWLE and the discussion led by aspirational figures were structured to address these 3 stigma domains of survival, social, and professional threats.31
+The control condition-training as usual (TAU)—was the Nepali adaptation of mhGAP-IG without the structured participation of PWLE. mhGAP-IG included flow-charts on clinical decision-making with basic information on diagnosis and treatment.30 The mhGAP-IG was adapted for Nepal as part of PRIME.36 Through PRIME in Nepal, a 9-day PCP training was developed including approximately 40 hours of learning with 24 hours dedicated to mhGAP modules, 12 hours on psychosocial basics, and 4 hours on logistical implementation processes.37
+The RESHAPE version of the PCP training and the TAU control were time matched at 9 days of training such that some sections in the TAU group that would be covered by a psychiatrist were covered by PWLE in the RESHAPE approach. Group supervision was held separately by groups for approximately 4 to 6 hours in a 1-day session conducted once every 3 months following the training. The training and supervision were conducted by Nepali psychiatristsand psychosocial counselors.
+Outcomes
+The prespecified outcomes for determiningfeasibility and acceptability and progression to a full trial were identification of qualitative themes related to recovery; 75% fidelity of PWLE and aspirational figures to the items on the RESHAPE fidelity checklist; comparable PCP baseline characteristics for the groups; retention of 50% of service users trained in PhotoVoice; retention of 66% of PCPs at the end point; fewer than 15% missing items on outcome measures; and fewer than 10% adverse events. The current analysis focused on the quantitative outcomes. Qualitative outcomes have been previously presented.31,52,53 Fidelity on the RESHAPE fidelity checklist was recorded by a research assistant who observed all of the trainings and noted what activities were done for each section of the training related to RESHAPE components. For example, (1) did PhotoVoice narratives include the 3 components of pretreatment experience of mental illness, experience of treatment, and life after starting treatment?; (2) was there a question-and-answer session after the PhotoVoice presentation in which PWLE responded to PCP trainees?; and (3) did the myth-busting section by aspirational figures include all 8 myths? For PWLE, adverse events were measured at each PhotoVoice training session and after each PCP training by a psychosocial counselor asking about adverse experiences, including both specific concerns (eg, suicidality, symptom relapse) as well as giving PWLE an opportunity to raise any other concerns.52 Family members of PLWE were also given an opportunity to discuss any adverse events.53 Adverse events among PCPs were recorded at supervision sessions, and as well as ad hoc documentation of events raised by PCPs contacting the research team or clinical supervisors.
+Quantitative outcomes at the level of individual PCPs were included to evaluate within-group trends over time. The main assessment periods for PCPs in both groups were (1) baseline, which was thefirst day of the training; (2) midline, which was 4 months after training; and (3) end line, which was the primary end point occurring 16 months after training.
+The primary quantitative outcome measure was PCPs’ level of stigma as measured with the Social Distance Scale (SDS).54,55 The SDS consists of 12 questions about willingness to participate in different activities with PWLE (eg, how willing would you be to spend time with, work with, or have a meal with a person with mental illness). The SDS was previously used in Nepal56 and adapted from sections of the Stigma in Global Context-Mental Health Study.57,58 A number of secondary PCP outcomes were also included. The mhGAP Attitudes Assessment examines stigmatizing beliefs and stereotypes (eg, people with mental illness are violent, contagious, or to blame for their illness). The Implicit Association Test (IAT)59 is a computer-based implicit measure of stigma adapted for use with stimuli appropriate for Nepali health care clinicians.60 The mhGAP Knowledge Assessment is a
+26-item true-false and multiple-choice test.61 Clinical competency in common factors of mental health care was evaluated with the Enhancing Assessment of Common Therapeutic Factors (ENACT) tool.62 The ENACT tool, developed in Nepal,63 is used by raters observing standardized role-plays of PCP trainees. Actors presented 1 of 3 vignettes (depression, psychosis, or alcohol use disorder). At the end of the role-play, PCPs were asked to provide a provisional diagnosis.
+The evaluation of diagnostic accuracy of actual patients was done approximately 14 to 22 months after training to give PCPs at least 1 year of supervised practice to establish their diagnostic skills. After PCPs made a new diagnosis of mental illness for a primary care patient, the accuracy of diagnosis was determined by a Nepali psychiatrist administering the Nepali-validated version of the Composite International Diagnostic Interview (CIDI)64 to the patient. The psychiatrist was blinded to the PCP’s diagnosis of the patient. The diagnoses of interest were the priority disorders from the mhGAP-IG training modules, which included depression, psychosis, alcohol use disorder, and epilepsy. For the purposes of the current analyses, we excluded epilepsy because of its nature as neurological disorder, and we focused onaccuracy of depression, psychosis, and alcohol use disorder diagnoses. No changes were made to the assessment tools after the trial commenced.
+Sample Size
+Following recommendations for the design of pilot studies which discourage between group hypothesis testing with small samples,65-67 we did not use inference testing (ie, measuring between-group effect sizes) as the criteria for determining the number of clusters and participants. Instead, we used all eligible primary care facilities in Chitwan district. Based on results of this pilot study, we will be able to make inferences to inform estimation for the coefficient of intracluster correlation (k) for a fully powered trial using primary care facilities as the unit of randomization with a comparable population of PCPs and patients. No interim analyses or stopping guidelines were planned.
+Randomization and Masking
+Randomization of primary care facility clusters was performed by the study statistician using a random number generator in Stata statistical software version 14 (StataCorp),68 with no restrictions or stratification. PCPs were included in the study group to which their health facility was randomized. To address demand characteristics typical in social contact interventions,25 PCPs were informed that the study was an overall mental health training evaluation, rather than specific to stigma reduction. Research assistants, psychosocial counselors who performed ENACT, and psychiatrists who performed the CIDI were blinded to the study group. No a priori unblinding specifications were established. Potential sources of contamination across groups were the movement of PCPs from a facility in one group to a facility in another group (eg, moving from a primary care clinic in the RESHAPE group to the control group).
+Statistical Analysis
+The quantitative outcomes of interest for PCPs were summarized descriptively using appropriate summary statistics (mean and standard deviation for continuous outcomes and numbers and proportions for categorical outcomes). Using the baseline measurement for PCPs (ie, prior to beginning the training), preliminary estimates of within- and between-cluster variances and intracluster correlation coefficients were estimated using 1-way random effects analysis of variance. Within-group changes over time were estimated using separate linear mixed models for each group, with a random intercept for health facility. Regarding missing data, only participants with data available at follow-up time points were included in analyses and no imputation was conducted for missing participants. No between-group comparisons were estimated owing to the pilot nature of the study.65-67 Analyses were conducted using Stata statistical software version 16 (StataCorp) from February 2020 to February 2021.69
+JAMA Network Open | Global Health	Collaboration With People With Lived Experience of Mental Illness to Improve Primary Care Services
+Results
+Participants
+Thirty-four facilities were eligible for randomization (Figure 2). For the 17 facilities allocated to mhGAP-IG trainingas usual, 45 PCPs were eligible. For the 17 facilities allocated to RESHAPE, 43 PCPs were eligible. PCP demographics are shown in Table 1. Among the overall sample of 88 PCPs, 75 (85.2%) were men and 67 (76.1%) were upper caste Hindus; the mean (SD) age was 36.2 (8.8) years (range, 21-56 years). Nine of the PCPs (10.2%) were physicians, whereas the remaining 79 PCPs (89.8%) were health assistants or auxiliary health workers.
+This cluster randomized clinical trial study was conducted as planned from February 7, 2016, to August 10, 2018. Training of PCPs took place from February 7, 2016, through May 18, 2016. PCP assessments took place from February 7, 2016, through July 4, 2017. Patient enrollment occurred from July 16, 2017, through December 31, 2017.
+area, and 6 did not attend the end line evaluation session. In the RESHAPE group, 6 PCPs were reassigned, 3 did not attend the end line session, and 1 retired. Table 1 includes the baseline demographics of PCPs who participated in the end line vs those who were lost to follow-up. The participants lost to follow-up were more likely to be younger (aged <30 years), have a medical degree (MBBS), be stationed ata primary health care center, and have fewer than 5 years of experience in health care services. Only 4 physicians with MBBS (44.4%) enrolled were retained, compared with 54 auxiliary health workers (81.8%) and 17 health assistants (70.8%). Because many health facilities had only 1 or 2 PCPs at baseline, the PCP dropouts led to a loss of 3 clusters in the control group and 2 clusters in the RESHAPE group (ie, a loss of 14.7% of the clusters). See eTable 1 in Supplement 2 for information on missingness of data.
+We also tracked reassignment of PCPs across facilities within the study. The government reassigned 2 control PCPs to RESHAPE facilities between baseline and midline (both PCPs participated in the midline assessment and 1 participated in the end line assessment). One RESHAPE PCP was reassigned to a control TAU facility between midline and end line; this facility had 3 control PCPs in the study at the time. Taking all of these transfers into account, at end line there were 4 control PCPs working alongside RESHAPE-trained PCPs, which suggests that 12% (n = 4) ofall control PCPs assessed at end line potentially experienced contamination of attitudinal and/or behavioral changes.
+Regarding PWLE who cofacilitated the RESHAPE trainings, 15 PWLE were trained in PhotoVoice, of whom 11 (73.3%) went onto cofacilitate RESHAPE trainings for PCPs. Among the 4 PWLE who dropped out during the PhotoVoice training process, reasonsfor dropout were family refusal (n = 1), time constraints (n = 1), symptom relapse (n = 1), and concerns for additional stigma by speaking in public (n = 1).53 Of the 2 RESHAPE-based trainings conducted, 8 PWLE participated in each training (approximately 2 PWLE for each priority disorder: depression, psychosis, alcohol use disorder, and epilepsy). Both trainings were above the 75% fidelity benchmark for antistigma components conducted by the PWLE and aspirational figures.
+Outcomes
+For the primary stigma outcome, SDS, there was a mean change from baseline to end line of -10.6 points (95% CI, -14.5 to -6.74 points) for PCPs in the RESHAPE group compared with -2.8 points (95% CI, -8.29 to 2.70 points) in the control group, where decreases in scores corresponded to decreases in reported social distance, ie, lower stigma (Figure 3 and Table 2). For mhGAP Knowledge, mhGAP Attitudes, and ENACT competencies, there was within-group improvement for both RESHAPE and control. For IAT, neither the control nor RESHAPE showed within-group improvement.
+At 4 months after training, diagnostic accuracy in standardized role-plays was 78.4% (29 of 37) in the RESHAPE group and 57.5% (23 of 40) in the control group (Figure 3 and Table 2). At 16 months after training, accuracy was 78.1% (25 of 32) in RESHAPE and 66.7% (22 of 33) in the control group. Patients newly diagnosed during the period of 14 to 22 months after training were enrolled in the study for assessment of diagnostic accuracy (patient demographics are in eTable 2 in Supplement 2). For actual patient diagnoses confirmed with the psychiatrist-administered CIDI, 72.5% (29 of 40) of patients in the RESHAPE group were correctly diagnosed and 34.5% (10 of 29) in the control group. The incorrectly diagnosed patients were false positives (ie, the PCP diagnosed them with a particular disorder and the psychiatrist did not confirm the diagnosis). Notably, 13 of 14 patients diagnosed with psychosis by the control PCPs were false positives, and 8 of 14 patients diagnosed with psychosis by RESHAPE-trained PCPs were false positives. Among the disorders, the disorder with the largest absolute difference between groups in diagnostic accuracy was depression in both standardized role-plays and actual patient evaluations. No serious adverse events were reported for PWLE, PCPs, or patients in either group.
+Discussion
+This pilot cRCT of a stigma reduction intervention for PCPs was conducted in collaboration with PLWE. The goal was to determine the feasibility and acceptability of study procedures in preparation for a full trial. All a priori benchmarks for progression to a full trial were met, including retention rates ofparticipants, limited missingness ofdata, high intervention fidelity, and a lack ofseriousadverse events. These quantitative findings for feasibility and acceptability support our previously published qualitative findings for RESHAPE.31,52,53 The preliminary findings suggest that RESHAPE may have the potential to reduce stigma among PCPs without introducing substantial risk of harm to PWLE collaboratingintrainings. Regardinggeneralizabilitytothe broaderfield ofstigma interventions, the
+potential trend of greater stigma reduction in the RESHAPE group is consistent with other findings for social contact.14,26,27
+Exposure to structured recovery testimonials from PWLE may help to increase accuracy of diagnosis by PCPs. This is important because the study revealed high rates of incorrectly diagnosing patients with psychosis (ie, false positives). Misdiagnosis, especially of psychotic disorders, increases exposure to medications with adverse effects, and misdiagnosis is costly and stigmatizing for patients and families.70-72 Although some misdiagnoses may be mitigated by improving attitudes of PCPs, it also draws attention to the need for greater supervision of diagnostic practices after mhGAP-IG training.
+Strengths and Limitations
+This study had some strengths and limitations. The pilot design addressed a number of the limitations raised about social contact intervention research25: use of a control group, trial registration, reducing demand characteristics by including a range of outcomes beyond stigma, and evaluating behavior change in the form of clinical skills. Examining long-term outcomes, specifically a 16-month follow-up, was also considerably longer than the 6-month follow-up of most antistigma interventions.14,27 The pilot findings suggest that RESHAPE may be beneficial across outcomes, except IAT, a few months after training, but the longer-term benefit compared with standard training (ie, at 16 months) may be limited to fewer domains (social distance and diagnostic accuracy).
+The long duration of our pilot was important to estimate actual retention rates of participants and clusters. By having a long duration and a large number of clusters, we found that 14.7% of the clusters did not have enough PCPs working on site for participation in the end point. If we had fewer clusters or a shorter duration in the pilot, we may not have been able to establish a reliable cluster dropout rate. We also found that PCPs who were physicians, younger (aged <30 years), and had fewer years of experience in health care (<5 years) were the most likely to drop out. This is likely due to government programs that place young physicians in rural areas after they complete training for brief assignments of only 1 to 2 years. In addition, physicians assigned to rural areas have been criticized for absenteeism in which they are working at the government health facilities.73-75 More than half of the physicians enrolled in the study dropped out, compared with 82% of auxiliary health workers and 71% of health assistants who were actively engaged in health services to their communities. Our pilot also revealed PCPs being transferred across study group health facilities (eg, RESHAPE to control and vice versa). Regarding contamination, 12% of control PCPs were working alongside RESHAPE-trained PCPs at the end line assessment, which may have influenced their attitudes and clinical practices. In a full trial, alternative strategies are needed for defining and retaining clusters, as well as preventing contamination.
+A study limitation was that mental health specialists conducting the trainings could not be blinded to the participation of PWLE in the trainings. The presence of PWLE may have impacted the psychiatrist trainer’s behavior in some manner. Because of this, a full trial should record the psychiatrist trainer’s fidelity to mhGAP-IG components to see if this differs between groups. Based on the lessons learned regarding strengths and limitations of the pilot trial, a full cRCT of RESHAPE is now under way in Nepal.76
+Another limitation of this study was that it focused on in-service health training of certified PCPs, which is the equivalent of continuing medical education courses. However, strategies are also needed to reduce stigma and improve mental health diagnostic skills of during preservice training (eg, in medical schools and auxiliary health worker vocational training programs). Evidence-based preservice stigma reduction programs are also lacking in LMICs.28 To effectively reduce the mental health care treatment gap in Nepal, preservice and in-service stigma reduction and mental health training are especially important for auxiliary health workers and health assistants who provide the majority of care in primary care settings.
+JAMA Network Open | Global Health	Collaboration With People With Lived Experience of Mental Illness to Improve Primary Care Services
+Conclusions
+This pilot cRCT met its prespecified feasibility and acceptability measures, and a larger cRCT is ongoing. Ultimately, the potential to collaborate with PWLE to reduce stigma and improve diagnosis is encouragingfor enhancing the success of mhGAP-IG implementation and more broadly for successful integration of mental health services into primary care settings around the world.
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+Introduction
+People with severe mental illness (SMI), such as schizophrenia and bipolar affective disorder, have higher rates of morbidity and mortality (Lawrence et al., 2013; Liu et al., 2017). As a result, their life-expectancy is 10-20 years lower than that of the general population (Lawrence et al., 2013; Liu et al., 2017), even though incidence rates of many cancers, including colorectal and prostate cancer, are similar between people with and without SMI (Kisely et al., 2008). Given the similar incidence rates, differences in risk factor prevalence (smoking, alcohol consumption, obesity) are less likely to be the cause of higher cancer mortality in those with SMI. One explanation might be that people with
+Population Health Department, QIMR Berghofer Medical Research
+Institute, Herston, QLD, Australia
+2School of Public Health, The University of Queensland, Herston, QLD, Australia
+3School of Medicine, The University of Queensland, Brisbane, QLD, Australia
+4Metro South Addiction and Mental Health Service, Brisbane, Metro South
+Health, QLD, Australia
+5Department of Gastroenterology and Hepatology, Princess Alexandra
+Hospital, Brisbane, QLD, Australia
+6Departments of Psychiatry and Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada
+Corresponding author:
+Karen M Tuesley, QIMR Berghofer Medical Research Institute, 300
+Herston Road, Herston, QLD 4006, Australia.
+Email: Karen.Tuesley@qimrberghofer.edu.au
+SMI present with more advanced cancer at diagnosis or receive less cancer-directed treatment, which could be due to either diagnostic delays, poorer access to cancer services or lower participation in cancer screening programmes (Kisely et al., 2013).
+Prior studies have investigated particular types of cancer screening in people with SMI, but these were mostly conducted in specific populations and results may not be broadly generalisable (Howard et al., 2010). Furthermore, some studies used self-reported participation in screening, which may not be optimal in this population (Fujiwara et al., 2017; Howard et al., 2010; Mo et al., 2014; Siantz et al., 2017), while others had no comparison group (Howard et al., 2010; James et al., 2017). The aim of this study was to investigate the frequency of colorectal, prostate and cervical cancer screening among people with and without SMI, throughout Australia, using a large, nationally representative administrative data set of 10% of the Australian population.
+Methods
+We conducted a retrospective cohort study using de-identi-fied administrative data from a sample of 10% of all Australians registered for Medicare. Medicare is Australia’s universal health care scheme that provides access to government-subsidised medical services (via the Medicare Benefits Scheme [MBS]) and prescriptions (via the Pharmaceutical Benefits Scheme [PBS]) for all citizens and permanent residents. The 10% data set included both MBS and PBS information. Within the MBS data, each specific medical service subsidised by the Australian government is denoted by an item number. The PBS data included details of all medicines dispensed to patients at government-subsidised prices. In accordance with the 2014 National Health and Medical Research Council Statement on ethical conduct in human research, ethics approval was not required to use these de-identified data. We adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for reporting observational studies (Von Elm et al., 2007).
+Defining exposure
+We used PBS data to define people with SMI (schizophrenia or bipolar affective disorder). In Australia, the most commonly prescribed medications for these conditions are lithium and second-generation antipsychotic agents. Lithium is specific to bipolar affective disorder and rarely prescribed for other conditions, while second-generation antipsychotics require an indication-specific authority code for subsidy through the PBS. These are almost solely for treatment of either schizophrenia or bipolar affective disorder (Supplementary Table 1). During the study period, the only other PBS indication
+for second-generation antipsychotics for adults was behavioural disturbance secondary to either dementia or autism, and this was restricted to risperidone. Our study only included people aged 18-69 years (see in the following). As the onset of dementia before 70 years is uncommon (<4% prevalence; Anstey et al., 2010), and the prevalence of autism in adulthood is also very low (<1% in 2012; Australian Institute of Health and Welfare [AIHW], 2017), the few people receiving risperidone for behaviour disturbance in dementia or autism would have been minimal.
+We classified a person as having an SMI once they had two prescriptions for one of these medicines dispensed within a 12-month period (Supplementary Table 1). PBS data from the year prior to cohort entry were used to determine exposure status at cohort entry. Participants who had not met the criteria for SMI prior to study commencement (January 2004) were considered unexposed to SMI until they received a second prescription for an SMI medication.
+Prior to 2012, lithium prescriptions were only recorded in the PBS for people who held a means-tested concession card, as before that, pharmacies only recorded subsidised prescriptions. As lithium was relatively cheap, people without a concession card would have paid the full cost without subsidy. To minimise any bias as a result of this differential inclusion of people of lower socioeconomic status, we treated people who used lithium, and no other SMI medication, as unexposed to SMI. We conducted sensitivity analyses to (a) treat lithium-only users as exposed from 2012 onwards, (b) include lithium-only users as exposed for the whole study period and (c) exclude lithium-only users entirely. All authority prescriptions for second-generation antipsychotics were recorded for the entire study period for both concessional and non-concessional patients because of the higher costs of these medicines. First-generation antipsychotics are below co-payment, do not require an authority prescription recording indication for use, and make up only a small proportion of prescribed antipsychotics (Hollingworth et al., 2010). They were therefore not used for analysis.
+Data were available to 31 December 2014, which was the study end date. We created study cohorts to examine screening for three cancers according to age-specific screening recommendations. In each case, screening is billed to Medicare and therefore captured by MBS records. We did not examine breast cancer screening as the Australian Breast Screen programme is not funded through Medicare. Although there is no formal prostate cancer screening programme in Australia and population-based screening is generally not recommended in clinical practice guidelines, prostate-specific antigen (PSA) testing is both commonly requested by male patients and recommended by clinicians (Pickles et al., 2016). Thus, differences in PSA testing between people with, and without, SMI may serve as a marker of access to preventive care.
+The cervical cancer screening cohort included all women aged from 18 to 69 years, the eligible age group for screening under the National Cervical Screening Programme. During the study period, most pap smears were performed by a general practitioner (GP) and billed to Medicare (Lew et al., 2012). The colorectal cancer screening cohort included all men and women aged 50-69 years, while that for prostate cancer was restricted to men aged 50-69 years. The MBS data did not include screening performed through the Australian National Bowel Cancer Screening Programme (NBCSP) that was progressively rolled out over the period covered by our study. This national programme commenced in 2006, and initially, only people turning 55 and 65 years old each year were invited to participate and sent a kit for faecal occult blood testing (FOBT). In 2008, this programme was extended to people in the year they turned 50 years and in 2013 to people turning 60 years (Jenkins, 2016). From 2015 (after the end of follow-up in this study), the programme was extended to people aged from 50 years up to 74 years. Over the transition period from 2006, GPs were still encouraged to screen the many people who fell outside of eligibility for the NBCSP by requesting FOBT via the MBS (Foreman, 2009).
+Participants entered our study on 1 January 2004 or the date they reached the minimum age for entry into each cohort. Participants left the study either on 31 December 2014 or when they turned 70 years. Person years were adjusted to include only 12 months before the first MBS record and 12 months after the last MBS record, given we did not have death records or records of participants arriving or leaving Australia. We removed participants with no MBS or PBS records within 12 months of study entry and exit (Figure 1). We performed sensitivity analyses to include
+person years for both 2 and 3 years from an individual’s first and last MBS record.
+Variables
+FOBT was used to define colorectal cancer screening, pap smears for cervical cancer screening, and PSA testing for prostate cancer screening using MBS item codes (Supplementary Table 2). It was possible for an individual to have multiple MBS items relating to a potential cancer screening test, therefore only one test per calendar year per participant was recorded as an incidence of cancer screening so as to exclude tests repeated for follow-up of an abnormality. For colorectal cancer screening, we recorded the incidence of each of the three specific FOBT item codes, which differed according to whether one, two or three samples were collected during a 28-day period. As noted previously, colorectal cancer screening through the Australian NBCSP was not recorded in the MBS records; we therefore only included FOBT organised by a medical practitioner outside of this programme. We only included the PSA item codes relating to probable cancer screening and not for tests performed as follow-up for previously diagnosed prostate disease.
+Covariates included age at study entry, gender, state of residence and average annual number of GP visits. As only the year of birth was included in the de-identified data, we used the year’s midpoint (30 June) to estimate age at entry into the cohort. The MBS data contained five states of residence categories (New South Wales/Australian Capital Territory, Victoria/Tasmania, Queensland, South Australia/ Northern Territory and Western Australia), and we defined state of residence as the last category provided in the MBS records for each participant. Participants with missing state data were excluded from the cohort (Figure 1).
+We estimated the average number of GP visits per year across the study period using the total number of GP visits for all calendar years, divided by the total number of calendar years that the participant was included in the cohort. If the participant was prescribed an SMI-defining medication, average GP visits for the exposed time included the year they were defined as having an SMI. Average GP visits were also used as a categorical variable, split by less than five and five or more visits per year.
+Statistical methods
+We used Poisson regression to estimate incidence rate ratios (IRR) and 95% confidence intervals (CIs) for the association between SMI and rates of FOBT, pap smears and PSA testing. We performed multivariable analyses adjusting for age at entry, state of residence and gender (for colorectal cancer screening), with and without average GP visits in the models, as prior studies have shown that the number of GP visits may be a mediator for the association between SMI and cancer screening. As a sensitivity analysis, we split the cohorts into the two categorical groups for average GP visits to explore the association between SMI and cancer screening for people with similar GP contact. We also stratified the data by age categories (50-59 and 60-69 years for FOBT and PSA, and 18-29, 30-30, 40-49, 50-59 and 60-69 years for pap smear screening), to investigate whether associations between SMI and cancer screening varied by age group.
+Additional sensitivity analyses were performed for the colorectal cancer screening cohort, by splitting the data into two study periods (2004-2006 and 2007-2014) given the introduction of the NBCSP in 2006, as well as each of the three FOBT MBS codes (66764, 66767 and 66770).
+Results
+There were 760,058 people in the colorectal cohort, 918,140 in the cervical screening and 380,238 in the prostate cancer screening cohorts (Figure 1). Approximately 2% of each cohort had a diagnosis of SMI (Table 1). The maximum follow-up was 11 years, with a median of 7.5 years for the colorectal and prostate cancer screening cohorts and median of 11 years for cervical cancer screening. Table 1 shows the characteristics of the cohorts by SMI status. For all cohorts, the average number of GP visits was higher for people with SMI than those without SMI (Table 1). Age at entry and distribution by state were similar between the groups.
+The associations between SMI and cancer screening are shown in Table 2. Adjusting for age at entry, state and gender (FOBT only) did not materially change the results, and IRRs are adjusted for these factors unless otherwise stated. Having SMI was associated with lower rates of pap smears (IRR=0.83, 95% CI = [0.82, 0.84]) and PSA testing (IRR=0.83, 95% CI = [0.81, 0.85]) compared to people
+without SMI. When the average number of GP visits was included in the model, the IRRs declined further for pap smears (IRR = 0.74, 95% CI = [0.73, 0.75]) and PSA testing (IRR = 0.72, 95% CI = [0.70, 0.74]).
+Having SMI was associated with slightly higher rates of FOBT compared to not having SMI (IRR = 1.15, 95% CI = [1.10, 1.20]) although overall, FOBT rates were low for both people with and without SMI (2.6 and 2.2 per 100 person years, respectively). However, after adjusting for average number of GP visits, people with SMI had lower rates of FOBT (IRR = 0.90, 95% CI = [0.86, 0.94]). To investigate this further, we dichotomised the cohort into those with an average of less than five GP visits per year and those with an average of five or more GP visits per year. In those with an average of less than five GP visit per year, SMI was associated with lower rates of FOBT (IRR=0.83, 95% CI = [0.73, 0.94]). By contrast, in those who visited their GP an average of five or more times per year, SMI was associated with slightly higher rates of FOBT (IRR = 1.04, 95% CI = [1.00, 1.09]). Incidence rates for FOBT during 2007-2014 were close to double the rates in 2004-2006, but this did not substantially alter our unadjusted or adjusted IRRs (Supplementary Table 3).
+Our analyses stratified by age showed that having SMI was consistently associated with reduced rates of pap smear screening in the 30-39, 40-49, 50-59 and 60-69 year age groups but the association was weaker in women aged 1829 years (IRR=0.96, 95% CI = [0.93, 0.99]; Supplementary Table 5). When we adjusted for average GP visits, the association between having SMI and pap smear testing was similar across the different age groups (Supplementary Table 5) and was consistent with our main analysis. SMI was associated with slightly lower rates of PSA screening in men aged 60-69 years (IRR=0.78) than those aged 5059 years (IRR=0.84), although rates in both groups were still statistically significantly lower than in those from the general population (Supplementary Table 5). Adjusting for GP visits did not have an effect on the association between SMI and PSA testing in either age group. With respect to FOBT, rates were only statistically significantly higher in those with SMI among those aged 50-59 years (IRR = 1.20, 95% CI = [1.13, 1.28] compared with IRR = 1.06, 95% CI = [0.99, 1.13] for those aged 60-69 years, Supplementary Table 5). When we adjusted for GP visits, results for both age groups were consistent with our main analysis.
+People with SMI were more likely to have one or two sample FOBT tests (rather than three) compared to people without SMI. However, there was not a significant difference in screening rates for FOBT screens with three samples taken, which was also the most commonly performed procedure (Supplementary Table 4).
+Finally, other sensitivity analyses extending person years to 2 and 3 years from the first and last MBS date, to treat lithium-only users as exposed from 2012 onwards, to include lithium-only users as exposed for the whole study
+period, and to exclude lithium-only users entirely did not materially alter the results (see Supplementary Tables 6 and 7). Across the three cohorts, 65-75% of identified lithium users were also prescribed another SMI-defining medication.
+Discussion
+Main findings
+These results from a large, national longitudinal study showed that people with a SMI had significantly lower rates of pap smears and PSA testing. Overall, we did not see lower rates of FOBT, although rates were significantly lower among those with SMI who visited a GP on average less than five times per year.
+Context and implications
+Our results indicate that cervical and prostate cancer screening rates in people with SMI are lower than those from the general population, as has been suggested by some but not all of the smaller studies in less representative populations (Happell et al., 2012; Howard et al., 2010). The findings are also similar to those of a cohort study of pap smears in women with schizophrenia that was restricted to one Canadian province (Martens et al., 2009). Our results may partly explain why the cancer mortality to incidence ratio in these cancers is higher for people with SMI compared to the general population (Kisely et al., 2008) and that those with SMI are more likely to have metastases at presentation than those without SMI (Kisely et al., 2013). Even in the case of PSA testing where the value of screening is contested (Catalona, 2018), reduced uptake in people with SMI may serve as a marker of their access to preventive care in general. People with SMI may have a number of comorbidities or competing needs, and medical practitioners may therefore be less likely to consider preventive screening during consultations. There also may be cognitive and behavioural challenges with patients with SMI making it more difficult to gather medical histories and make treatment plans (Druss et al., 2002). In addition, clinicians may attribute emerging somatic symptoms to the underlying psychiatric disorder resulting in missed diagnoses, sometimes termed ‘diagnostic overshadowing’(Pelletier et al., 2015). It is also possible that people with SMI are treated differently by medical professionals with negative attitudes towards SMI patients leading to disparities in care (Walker et al., 2015).
+By contrast, FOBT rates were only lower in people with SMI compared to people without SMI among those who visited GPs less than five times per year. These findings are similar to a study restricted to US veterans that found that frequency of GP contact had an effect on colorectal cancer screening rates (Kodl et al., 2010). Poor access to health care may therefore have a greater impact on cancer prevention for people with SMI than the general population. The higher rates of FOBTs for people with SMI who visited doctors more
+frequently may also reflect the use of FOBT as a diagnostic tool, rather than screening test. GPs may opt for less invasive approaches to colorectal symptoms because of potential concerns that somatic symptoms reported by people with SMI might actually be a manifestation of their disease (psychosis), as well as the greater challenges of facilitating colonoscopy in those with SMI. We found some evidence of this in that those with SMI had higher rates (compared to those without SMI) of one or two FOBTs in an episode rather than the three recommended for screening. We did not have information on colonoscopy rates after FOBT but FOBT will not lead to a reduction in colorectal cancer mortality if not followed up with further diagnostic testing and treatment (Liss et al., 2016). This needs investigation in future studies.
+Strengths and limitations
+Our study had a number of limitations. We used medication to define SMI rather than medical records. While this may have created some misclassification of SMI status, it allowed us to investigate screening in a very large nationwide population. Importantly, the PBS item codes we used to define our exposed population are largely restricted to SMI. The one exception is risperidone, which is also indicated for behavioural disturbance in people with dementia or autism. However, these disorders are uncommon in the age groups included in our study (AIHW, 2017; Anstey et al., 2010). We were also unable to capture first-generation antipsychotics, so there is the potential that our control group included people with SMI taking first-generation antipsychotics only, thus making our estimates more conservative. However, clinical practice guidelines in Australia advise the use of second-generation antipsychotics in the first instance (Galletly et al., 2016), and the use of first-generation antipsychotics (for any indication) is decreasing, with a reduction from 39% to 23% of all antipsychotics prescribed in the period covered by the study (Hollingworth et al., 2010). Lithium use was also inconsistently recorded in the PBS before 2012, but our sensitivity analyses exploring this issue were not materially different from the main findings suggesting the effect was minimal. We were unable to adjust for sociodemographic measures other than age, gender and state of residence. Given that people with SMI are a generally disadvantaged group (Lawrence et al., 2013), other socioeconomic factors could have been mediators on the pathway between SMI and cancer screening. Finally, we did not have data on FOBT performed as part of the NBCSP. Nevertheless, we saw an increase in FOBTs performed after the commencement of NBCSP in 2006, and this may be due to an increase in testing for age groups not covered in the staggered roll out of the NBCSP, as well as additional guidance for GPs recommending FOBT screening during this time (Foreman, 2009). It is unknown whether FOBT rates through the NBCSP would be similar between those with and without SMI, although studies have
+found that other disadvantaged groups are less likely to participate in NBCSPs (He et al., 2017).
+This study has a number of strengths. It is the first nation-wide large-scale cohort study of cancer screening for people with SMI and had a follow-up of 11 years. The 2% prevalence of SMI is in keeping with rates reported in the World Health Organization (WHO) 10-country study (Jablensky et al., 1992). Use of linked administrative health records reduced the very real potential for selection bias that can occur when conducting research with people with SMI and allowed for accurate capture of screening tests performed. This study also provided cancer screening rates for a cohort with access to a universal health care system.
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+1.	Introduction
+Depressive symptoms are common mental disorders, with more than 264 million people suffered, leading to disability, suicide and death. Meanwhile, the prevalence of obesity is increasing and obesity is also becoming an emerging clinical and public health burden (Disease et al., 2018). Worse, obesity and depressive symptoms often co-occur at the individual level, and there has been shown a bidirectional relationship between obesity and depressive symptoms. Some data on the prevalence of comorbidity suggested that nearly 43% of adults with depressive symptoms have obesity (Pratt and Brody, 2014) and the highest prevalence of depressive symptoms was observed among obese adults (24%) (Carey et al., 2014). A meta-analysis of 19 studies showed that adults with depression had a 37% increased risk of being obese, and those who
+were obese had an 18% increased risk of depression (Mannan et al., 2016). Due to their co-occurrence, randomized clinical trials have gradually been conducted to explore integrated interventions and treatment for obesity and depressive symptoms (Linde et al., 2011; Ma et al., 2019; Pagoto et al., 2013).
+The coexistence of obesity and depressive symptoms was associated with increased health care use and costs (Nigatu et al., 2017), as well as many adverse health-related outcomes (Haregu et al., 2020; Licinio and Wong, 2003; Nigatu et al., 2016). These health outcomes are often worsened when obesity and depressive symptoms co-occur (Haregu et al., 2020; Licinio and Wong, 2003; Nigatu et al., 2016). Therefore, examining the interaction or combined effect of obesity and depressive symptoms may have important implications for alleviating disease burdens.
+Heart disease is a major concern of premature mortality and increased health care costs. The burden of heart disease, in number of disability-adjusted life years and deaths, continues to rise globally (Roth et al., 2020). Although obesity and depressive symptoms are known risk factors for heart disease (Yusuf et al., 2020), evidence on their possible synergistic effect on heart disease is scarce. A prior study suggested that the higher body mass index (BMI) was a stronger predictor of incident CVD in adults with depression than lower BMI (Polanka et al., 2018). Besides, one Germany cohort study showed that the combination of depressive symptoms and obesity had a significant higher risk of incident coronary heart disease (CHD) in men but not in women (Ladwig et al., 2006). The other cohort study conducted in Korea showed depression comorbid with overweight amplified the risk of heart diseases (Park et al., 2020). Both BMI and waist circumference (WC) are strongly and continuously associated with the risk of heart disease (Wormser et al., 2011). However, no investigation has examined central obesity, where the accumulation of abdominal fat has detrimental metabolic consequences and serves as an independent risk factor for cardiovascular events (Cornier et al., 2011). We aimed to explore the combined association of central obesity and depressive symptoms with the risk of heart disease in a national prospective cohort study of the Chinese population.
+2.	Methods
+2.1.	Study population
+Participants enrolled in our study were from the China Health and Retirement Longitudinal Study (CHARLS). The details of the CHARLS
+have been previously described (Zhao et al., 2014). In brief, CHARLS is a nationally representative longitudinal study by recruiting residents (>45 years) from 150 county-level units across 28 provinces in China. The baseline survey was conducted on 17,708 participants in 2011-2012 (wave 1), with a response rate of 80.5%, and involved self-administered questionnaires and physical examinations. Participants were followed up every two-three years until 2018 (wave2: 2013, wave3: 2015, and wave4: 2018). The follow-up rate is 76.6% in wave 4, 2018. (Zhao et al., 2020). People with a history of heart disease, stroke, or cancer at baseline were excluded. We further excluded people who had no data on age, sex, waist circumference, or depressive symptoms. In addition, people with an abnormal distribution of BMI < 14 or > 40 in the general population were further excluded. Finally, 10,722 Chinese men and women were eligible for this analysis (Fig. 1).
+Ethics approval for CHARLS was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All the participants have completed a written informed consent since the survey began.
+2.2.	Assessment of central obesity
+The measurement of WC, height, and weight were conducted by well-trained staff in the physical examinations. The WC was measured with a soft tape that circled the subject horizontally at the navel level. Participants breathed calmly at the standing pose, and when holding the breath at the end of the expiration, the staff took a reading. According to the International Diabetes Federation consensus statement, men with a WC of > 90 cm and women with a WC of > 80 cm were considered as having central obesity in the Chinese population (Alberti et al., 2006).
+2.3.	Assessment of depressive symptoms
+Depressive symptoms were identified using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). The CESD-10 contains 10 items and belongs to a 4-point rating scale: none or rarely, some days (1-2 days), occasionally (3-4 days), and most or all of the time (5-7 days). The total score ranges from 0 to 30, with higher scores indicating a higher level of depressive symptoms. Previous studies showed the internal consistency of the CESD-10 was good with the Cronbach alpha of 0.79 (Lian et al., 2021). Previous studies have confirmed that a cut-off value of 10 provides the optimal threshold to define clinically significant depressive symptoms (Cheng and Chan, 2005; Jing et al., 2020; Lian et al., 2021).
+2.4.	Ascertainment of heart disease
+Ascertainment of heart disease incidence was based on self-reported questionnaires. In the baseline survey, participants were asked by the question of “have you been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems by a doctor?” Those who answered “yes” were further asked when the condition was first diagnosed and which treatment was taken (Chinese traditional medicine, Western modern medicine, or other treatments). In the following surveys, the same questions were used to identify heart disease cases.
+2.5.	Assessment of covariates
+Data on sociodemographic characteristics, disease histories, lifestyle and behavior risk factors were based on interviewer-administered questionnaires, including age, sex, living area, marital status, education level, physical activity, smoking, drinking status, and history of hypertension, dyslipidemia, and diabetes that were accessed by selfreport of doctor diagnosis.
+2.6.	Statistical analysis
+Baseline characteristics of participants according to central obesity and depressive symptom status, presented as mean values (SDs) for continuous variables or percentages for category variables, were calculated using generalized linear regression (SAS GLM procedure) with adjustment for age at study entry. The differences of obtained values were tested using the same SAS procedure. The duration of follow-up for each participant was from the date of baseline survey (2011-2012) to the date of heart disease incidence (based on selfreported first diagnosed date) or the last survey (2018), whichever came first. Participants who did not develop heart disease during follow up and lost to follow-up were censored. All participants were divided into 4 groups: without central obesity and depressive symptoms, with central obesity alone, with depressive symptoms alone, or with both the two conditions. Person-years were calculated as the sum of follow-up duration of single participant for each group. Cox proportional hazard regression (SAS PHREG) was used to obtain the hazard ratios (HRs) with 95% confidence intervals (CIs) of heart disease in relation to central obesity and depressive symptom status, with the group having no central obesity or depressive symptoms being treated as the reference group. We adjusted age only in model 1. In model 2, we further adjusted for established cardiovascular risk factors to prevent distorting the real effect of central obesity and depressive symptoms on the incidence of heart disease risk, including sex, living area (urban or rural), level of highest education (primary school or lower, middle school, high school, college or higher), marital status (married, divorced, widow, or unmarried), history of hypertension (yes or no), history of dyslipidemia (yes or no), history of diabetes (yes or no), smoking status (never, former, or current), drinking status (never, former, less than once per month, once or more per month), and level of vigorous activity (0, 1-3,
+4-6, or 7/per week). The combined association of central obesity and depressive symptoms with incidence of heart disease was calculated using the relative excess risk due to interaction (RERI), with an additive model being employed: RERI = HRAB-HRA-HRB+1 (Andersson et al., 2005). We also conducted pre-defined stratified analyses according to sex and age. To test the robustness of the associations, we further performed sensitivity analyses by further adjusting for use of anti-depressant medication, by using a cut-off of 12 points in CESD-10 for assessment of depressive symptoms, by excluding individuals with a short length of follow-up (< 3 years) and by excluding individuals with a previous history of diabetes, hypertension and dyslipidemia, respectively. All analyses were carried out using SAS software version 9.4 and STATA version 16. P values < 0.05 were considered statistically significant.
+3.	Results
+Table 1 presents the baseline characteristics of 10,722 men and women according to the status of central obesity and depressive symptoms. Among all the participants, 5183 people (48.4%) had central obesity at baseline. Compared with people without central obesity, those with central obesity tended to be slightly younger and were more likely to be women, urban residents, and have a history of hypertension, dyslipidemia, and diabetes, but were less likely to smoke, drink, and do vigorous activity. The prevalence of depressive symptoms (25.9% vs 26.7%) did not substantially differ among people with and without central obesity. Among 10,722 participants, 2819 people (26.3%) had depressive symptoms. Compared with people without depressive symptoms, those with depressive symptoms were slightly older and were more likely to be men, rural residents, less educated, and have a lower BMI and waist circumference, but were less likely to smoke and drink.
+In the cohort, 3853 (35.9%) had central obesity alone, 1489 (13.9%) had depressive symptoms alone, and 1330 (12.4%) had both the two conditions. During 7 years of follow-up, we identified 1080 cases of heart diseases. Compared with people without central obesity and depressive symptoms, the age-adjusted HRs (95% CIs) of heart disease were 1.77 (1.52, 2.06) for those who had central obesity alone, 1.45 (1.19, 1.77) for those who had depressive symptoms alone, and 2.42 (2.02, 2.89) for those who had both central obesity and depressive symptoms (Table 2). After adjustment for covariates including demographic factors, disease histories, and lifestyles, the associations were attenuated but remained significant; the corresponding multivariable-adjusted HRs (95% CIs) were 1.39 (1.18, 1.64), 1.44 (1.18, 1.77), and 1.88 (1.55, 2.30), respectively (Table 2 and Fig. 2). However, the relative excess risk for heart disease due to interaction between central obesity and depressive symptoms was not significant (RERI = 0.05 [-0.33, 0.44]).
+We next performed stratified analysis by sex and age. The multivariable-adjusted HRs of heart disease for the group with both central obesity and depressive symptoms were somewhat greater in men than that in women (2.32 [1.60, 3.37] vs. 1.75 [1.35, 2.27]) and in middle-aged people than that in elderly people (2.11 [1.59, 2.80] vs. 1.66 [1.25, 2.20]). Tests for additive interaction between the two conditions yielded non-significant RERIs for either group.
+Sensitivity analyses were performed by further adjusting for use of anti-depressant medication and by using a cut-off of 12 points in CESD-10 for assessment of depressive symptoms. Other sensitivity analyses that excluded participants with a follow-up < 3 years, and people with a history of diabetes, hypertension and dyslipidemia, respectively, were also performed. Overall, the sensitivity analyses yielded very similar results (Supplementary Figure).
+4.	Discussion
+In this study, we examined the combined association of central obesity and depressive symptoms with the risk of heart disease in a large
+cohort of Chinese adults. We found that there was a heart disease risk gradient with the coexistence of central obesity and depressive symptoms, and the coexistence of the two conditions were associated with an 88% increased risk of heart disease than the absence of either condition. The combined association in men was more evident than that in women. However, no significant interaction on an additive scale between the two conditions was detected.
+Our study extended the evidence that the coexistence of obesity and depressive symptoms can potentially increase the risk of heart disease. In line with our results, a nationwide Korean cohort study reported the combination of overweight (BMI > 23 kg/m2) and depression had a higher risk (HR, 1.63; 99% CI, 1.29-2.07) of incident ischemic heart disease compared to those without either condition, which was much higher than depression (HR, 1.16; 99% CI, 0.81-1.67) or overweight (HR, 1.28; 99% CI, 1.19-1.36) alone (Park et al., 2020). A prior Germany cohort study demonstrated that only men with obesity (BMI > 30 kg/m2) and depressive symptoms reach the statistical significance for CHD incidence, where the HR was 2.32 (95%CI, 1.45-3.72) compared to those with normal BMI and no depressive symptoms, but not statistically significant in women (1.84, 95% CI 0.79-4.26) probably due to lack of power associated with low event rates (Ladwig et al., 2006). These results had important implications for the prevention and public health measures of CVD, where weight management, including general and abdominal obesity combined with depression treatment should be targeted to the high-risk groups. A recent randomized clinical trial tested a collaborative care intervention for coexisting obesity and depression by integrating behavioral weight loss treatment (dietary changes and physical activity), problem-solving therapy, and antidepressant medications for a duration of 12 months, resulting in improvements of both conditions (Ma et al., 2019). However, further studies are needed to verify whether the improvements will translate to important health outcomes, such as CVD over longer periods.
+Obesity and depressive symptoms may share common biological pathways for the development of CVD (Milaneschi et al., 2019), which might contribute to the observed risk gradient of heart disease. Previous studies linked central obesity with CVD via the inflammatory process, where adults with central obesity exhibit elevated proinflammatory cytokine levels that play important roles in endothelial dysfunction, hypertension, and atherosclerosis (Ellulu et al., 2017). The inflammatory markers, including leptin, tumor necrosis factor (TNF)-a, and interleukin-6 (IL-6) are mainly secreted from white adipose tissue in the abdomen (Shelton and Miller, 2011). Additionally, depressive symptoms are associated with decreased parasympathetic activity in the autonomic nervous system that could trigger inflammation response (Kop and Gottdiener, 2005). Thus, the inflammation response caused by obesity might be amplified or prolonged. Moreover, the high concentrations of proinflammatory cytokines caused by inflammation response
+may in turn enhance hypothalamic-pituitary-adrenal axis activity (Penninx et al., 2003), which is engaged in activating the release of glucocorticoids, with consequent increases in heart rate, blood pressure, and lipid metabolism abnormity (Mello et al., 2003), which are directly linked with the progression of CVD.
+On the other hand, behavioral pathways might be another potential mechanism. Obesity, especially the central or visceral type, is a predisposing factor for the development of several chronic diseases, such as hyperlipidemia, hypertension, and diabetes (Sowers, 2003). Meanwhile, depressive symptoms might interfere with adherence to self-care behaviors and treatments for these chronic diseases, including weight management (Berntson et al., 2015), poor adherence to recommended diet and physical activity changes (Berntson et al., 2015; Sumlin et al., 2014), and medication (Grenard et al., 2011), which may augment the progression of CVD among patients with obesity. Overall, the overlap of obesity and depressive symptoms are synergistic in terms of deterioration of cardiovascular function.
+To our knowledge, this is the first study to evaluate the combined association of obesity and depressive symptoms with incident heart disease among the Chinese population. The strengths of the present study included a large nationally representative sample of Chinese adults with up to a 7-year follow-up. Moreover, the assessment of obesity was firstly based on WC that reflected visceral fat accumulation, which could better predict obesity-related outcomes than subcutaneous fat measured by BMI (Janssen et al., 2004). However, several limitations to this study need to be acknowledged. First, the assessments of heart diseases and other medical history were defined by self-reported of doctor diagnosis, probably resulting in measurement error to our results. However, it has been proved to be with relatively good specificity and positive predictive values of self-reported illness in cohort studies (St Sauver et al., 2005; Yuan et al., 2015). Moreover, potential misclassification of heart disease could have occurred since the specific subtypes, such as CHD, heart failure, valvular heart disease, and other subtypes were not defined. Second, the duration or detailed kinds of medicine of depressive symptoms and obesity were not considered in these analyses, which might interfere with the risk of heart disease. Third, the details on depressive symptomology were lacking, though depression subtypes are differently associated with cardio-metabolic risk factors (Lasserre et al., 2017).
+5.	Conclusion
+In conclusion, our study provided evidence that the coexistence of central obesity and depressive symptoms were associated with a substantially increased risk of heart disease compared to those without these two conditions. This finding suggested the possibility that the integrated screening, monitoring, and treatment of obesity and depressive
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+Introduction
+Mental health is part of the Sustainable Development Goals, which set an agenda for improved treatment coverage by 2030 [1]. Treatment contact coverage is defined by the ratio of people who have contacted the service to the total target population in need of that service [2]. Increasing treatment coverage addresses the vast gap between availability of, and needs for, mental healthcare, especially in low- and middle-income countries (LMICs) [3,4]. The question is how to go about increasing coverage at a population level, especially in rural areas where there is little to no mental healthcare infrastructure. In keeping with the framework established by Tanahashi, which presents different levels of coverage related to the different stages of service provision [2], the fundamental issues underlying this question are (1) the allocation of resources in order to serve the maximum number of people, (2) the extent to which services are reaching the people they are intended for, and (3) the extent to which the services meet the people’s needs [2].
+The integration of mental healthcare in community and primary healthcare settings has been advocated as a strategy to reduce the treatment gap in LMICs. The call for decentralised mental healthcare integrated into general health service settings has been made since the early 1970s, and this strategy was implemented through the WHO Collaborative Study on Strategies for Extending Mental Health Care [5]. Although there was limited success in implementing this strategy in LMICs during the following decades, renewed efforts have been made more recently. The World Health Organization (WHO) has developed the Mental Health Gap Action Programme (mhGAP) intervention guide, providing evidence-based clinical guidance for health workers to detect and diagnose mental illness [6]. Furthermore, recent reviews demonstrate promising results for psychological treatments by non-specialists in LMICs [7,8]. Task-sharing strategies are currently being adapted and implemented in many LMICs [9]. Yet, to date, there are few evaluations of coverage of mental health programmes [10], and to our
+knowledge none that combines evaluation methods at the community, facility, and individual levels to assess the impact of district mental healthcare plans (MHCPs). The aim of this report is to evaluate contact coverage, detection, and treatment outcomes as a result of a complex multi-component district-level mental healthcare programme for adults in Nepal.
+Methods
+Setting
+The Programme for Improving Mental Health Care (PRIME) is a multi-country research programme that implements and evaluates district-level MHCPs in Ethiopia, India, Nepal, South Africa, and Uganda [11]. In Nepal, PRIME was implemented in Chitwan, a district in the south of the country with a total population of 579,984. During the evaluation phase the programme covered 10 primary healthcare facilities. Before the implementation of PRIME, mental health services were restricted to the district-level hospital. The Nepal health system consists of (1) district hospitals for specialised care, (2) primary healthcare centres for general medical care and first referral from health posts, and (3) the village-level health posts for basic health services. The major challenges in the existing health system ahead of implementing the MHCP were the lack of a formal government focal point for mental healthcare, the lack of basic psychotropic medicines in the essential medicines list, and the frequent transfer of primary health workers [12].
+Interventions
+The MHCP that was developed and implemented in Nepal, in partnership with the Ministry of Health, has been described in detail elsewhere [13]. In summary, the MHCP comprised interventions at the community, health facility, and health service organisation levels—see Table 1. The community-level packages included community sensitisation, proactive case detection [14], and adherence support through home-based care. In addition, community counsellors were trained to provide the Healthy Activity Programme [15] for depression and Counselling for Alcohol Problems [16] for AUD. The facility-level packages included training and supervision for health workers to detect, diagnose, and initiate treatment (i.e., emotional support, psycho-education and psychotropic medication) for individuals with a diagnosis of a priority disorder (i.e., depression, psychosis, AUD, and epilepsy) following the mhGAP intervention guide [6]. In most LMICs, epilepsy is considered a psychiatric condition and is treated by mental health specialists. Because of this, the WHO mhGAP includes epilepsy in its priority mental health conditions in the intervention guidelines. Based on our priority-setting activity in Nepal [ 17], we determined that epilepsy should also be considered to be a priority mental health condition that could be treated in primary care settings. Finally, health-service-organisation-level packages included ensuring reliable supply of psychotropic medication, referrals to specialised care, and mechanisms for monitoring, capacity building, and resource mobilisation. For all services, regular ongoing supervision was part of the MHCP. Different types of service providers were involved in implementing the interventions. At the health facility, medical officers (5 to 6 years of training), health assistants (3 years of training), and auxiliary health workers (15 months of training) were involved in assessment, diagnosis, and management of priority mental health conditions. The staff nurse and auxiliary nurse mid-wife (18 months to 3 years of training) were responsible for providing brief psychosocial support in the health facilities. At the community level, counsellors are a new cadre of psychosocial workers trained by non-governmental organisations, responsible for providing psychological treatment to those referred by primary health workers. Female community health volunteers were responsible for proactive case detection and home-based care.
+Adapted from [13].
+CAP, Counselling for Alcohol Problems; CIDT, Community Informant Detection Tool; FCHV, female community health volunteers; HAP, Healthy Activity Programme; mhGAP, Mental Health Gap Action Programme; n/a, not applicable.
+https://doi.org/10.1371/journal.pmed.1002748.t001
+Study designs
+This paper presents the primary results of a collection of study designs, in order to present findings for each component in the process of evaluating the above-mentioned district MHCP: a community study, routine service utilisation data, a facility study, and cohort studies —described below (see Fig 1 and Table 2). The study designs and analysis plans have been described in detail elsewhere [13,18-21]; summaries are presented below.
+We will structure the presentation of methods according to the 4 components of the service delivery pathway: (1) contact coverage of primary care mental health services, (2) detection of mental illness among participants presenting in primary care facilities, (3) initiation of minimally adequate treatment after diagnosis, and (4) the outcomes of patients receiving primarycare-based mental health treatment.
+Evaluating changes in contact coverage. We conducted a community study to determine whether adults affected by depression or alcohol use disorder (AUD) were more likely to contact a health worker for help coinciding with the PRIME implementation period. A detailed description of the aims, design, recruitment, and questionnaire are available [19]. Briefly, 2
+AUDIT, Alcohol Use Disorders Identification Test; HMIS, health information management system; MNS, mental, neurological, and substance abuse; n/a, not applicable; OPD, outpatient department; PANSS, Positive and Negative Syndrome Scale; PHQ-9, Patient Health Questionnaire-9 item; SIP-2R, Short Inventory of Problems-Revised; WHODAS, WHO Disability Assessment Schedule.
+https://doi.org/10.1371/journal.pmed.1002748.t002
+population-based cross-sectional surveys with independent samples were conducted, one before and one 30 months after implementation started. With 2,000 participants per round, the study had 80% power to detect a change in contact coverage from 5% to 25% among probable cases for each disorder, which we estimated would be 10% of the sample. Of the randomly selected adults (16 years and older, following Nepal legal classification) from randomly selected households in the implementation area, 99% provided informed written consent. The field workers orally administered a structured questionnaire that contained sections on demographic characteristics, food security, depression screening, depression symptoms in the past 12 months, and AUD screening. A probable case of depression had a PHQ-9 screening score of 10 or more or had depression-associated symptoms for at least 2 weeks in the past year [22]. A probable case of AUD had an AUDIT screening score of 9 or more [23]. Probable cases were asked whether they had contact with different health workers in the past 12 months, including non-specialist providers (e.g., medical officer, health assistant, auxiliary health worker) in government clinics. The timing of the data collection was as follows: baseline between May and July 2013 and endline between December 2016 and February 2017.
+In addition, we used 1-year routine service utilisation data to assess change in contact coverage for all 4 priority disorders (depression, AUD, epilepsy, and psychosis). Change in contact coverage was calculated as the number of cases diagnosed with mental illness in 10 health facilities for a period of 12 months before the start of the MHCP and for 12 months during the implementation of the MHCP (baseline: 1 January-31 December 2013; endline: 25 August 2014-24 August 2015). The reasons for using both methods for assessing changes in contact coverage are that (1) service utilisation data were available for all 4 disorders, whereas the community survey only focused on depression and AUD, and (2) we were aware of the risk of being underpowered in the community survey, due to limited financial resources to conduct the survey.
+Evaluation of changes in health workers’ detection of mental illness and initiation of adequate treatment. We conducted a facility study to determine whether adult attendees of primary healthcare facilities who were affected by depression or by AUD were more likely to be detected and adequately treated by clinicians during the PRIME implementation period [21]. Three cross-sectional surveys with independent sampling were conducted: before MHCP
+implementation and approximately 6 months and 24 months after initiating the MHCP. In the 10 health facilities, research staff recruited adults seeking outpatient services. All adult outpatients who were capable of providing informed written consent and who did not have an emergency medical problem were eligible for study recruitment. Among the eligible adult outpatients, 95% provided informed consent. In a private area adjacent to the waiting room, field workers verbally administered a structured questionnaire that contained sections on demographic characteristics and screening for depression and AUD. All participants who screened positive and a 10% random selection of screen-negative participants were given a consultation form for their clinician to complete and return to the participant. The form contained open-ended entries for diagnoses, treatments, advice, and referrals. A field worker made a copy of the form immediately after the consultation. A psychiatrist on the research team used the copy to determine whether each participant had been clinically diagnosed with depression or with AUD, and if so, whether there was evidence of minimally adequate treatment provision following mhGAP treatment guidelines. The timing of the data collection was as follows: baseline between September 2013 and February 2014, midline between August 2014 and August 2015, and endline between May and December 2016.
+Evaluation of changes in treatment outcomes. We conducted 4 cohort studies to assess whether patients diagnosed with depression, AUD, psychosis, or epilepsy benefitted from receiving treatment under the MHCP. Patients were followed up for 1 year, to assess change in symptom severity and functional impairment, using a before-and-after comparison without control groups. A detailed overview of the methods has been previously published [20]. Briefly, individuals were eligible for inclusion in the treatment cohorts if they were diagnosed with 1 of the 4 priority conditions by a primary health worker in the health facilities implementing the MHCP. In addition, participants needed to be adults, living in the study district Chitwan, and willing to provide informed consent. For participants with psychosis, a caregiver was also recruited into the study to participate in a caregiver component of the study interview. Sample size was calculated based on a 20% reduction in symptom severity at the 12-month follow-up, with a 90% power and 2-sided alpha of 0.05, as well as an attrition rate of 15% to 20%. To allow the analysis of equity of treatment effects, the sample size was set at 200 for the depression and AUD cohorts, and at 150 for the psychosis and epilepsy cohorts. Patients were screened with the PHQ-9 and AUDIT by PRIME field workers before their consultation with the medical officer. They were then again followed up after their consultation to assess whether a diagnosis was made. If diagnosed, they were recruited into the respective treatment cohort. In case of patients with multiple diagnoses, priority was given to the more severe disorder. Participants were allocated to the psychosis or epilepsy cohort, in case of comorbidity with depression or AUD. If an individual was diagnosed with both AUD and depression, the participant was recruited into the AUD cohort. Baseline assessments were initiated at the clinic on the day of recruitment, and completed in the participants’ home, on average 1 day after recruitment. The follow-up assessments were conducted in the participants’ homes 3 months (depression and AUD) or 6 months after recruitment (psychosis and epilepsy), and again 12 months after recruitment (all cohorts). Data were collected using Android devices linked to an online application (Mobenzi, https://www.mobenzi.com). Participants were considered lost to follow-up if data for the 12-month assessment could not be collected. The timing of the data collection was as follows: baseline between September 2014 and August 2015, midline between December 2014 and November 2015, and endline between August 2015 and July 2016.
+We mobilised the same field workers for all study components. Two months of extensive training was provided covering qualitative and quantitative research, interviewing skills, rapport building, informed consent, and inclusion/exclusion criteria. Additionally, 2 weeks of trainings were organised for each study component covering recruitment strategy and content
+of the questionnaire, including field practice. Field workers visited each sampled household or health facility, assessed eligibility criteria, performed the procedure for selecting participants, and obtained written consent among the selected participants for interviews. Interviews were conducted in a confidential place, and field workers used tablets for data collection.
+Study measures
+For marital status, participants were grouped into 2 categories based on whether they had ever been married or not. All participants who were either working in an occupation sector or studying were put into a single ‘employed’ category. Participants were classified as being food insecure if anyone in their household had gone hungry in the past month due to lack of resources.
+In the PHQ-9, participants reported the frequency with which they had experienced 9 symptoms over the past 2 weeks on a Likert scale ranging from 0 (‘not at all’) to 3 (‘nearly every day’), and scores from the 9 items were summed [24]. From a validation study in primary care settings in Nepal, a cutoff score of 10 or more had 94% sensitivity and 80% specificity, and internal consistency of a = 0.84 [22]. The 10-item AUDIT was developed by WHO and is used widely in LMICs [25]. With the sum of 10 items, a score of 8 or more is indicative of hazardous, harmful, or dependent drinking behaviours in the pastyear. Internal consistency of the AUDIT in Nepal has been shown to be a = 0.82 [23].
+The 12-item interviewer-administered WHODAS 2.0 has items relating to difficulties engaging in daily activities due to health problems in the past 30 days, and items are scored on a Likert scale from 1 (‘none’) to 5 (‘extreme/cannot do’) [26]. The WHODAS has been validated in a range of settings [26], and has been used in previous research in Nepal [27]. Internal consistency for the WHODAS based on baseline data is a = 0.84 (depression cohort) and a = 0.85 (AUD cohort). Item response theory-based weights were used for the total scoring, to allow comparisons across populations. The assessment of accuracy of diagnosis and minimally adequate treatment for depression and AUD was done by a mhGAP-trained psychiatrist following predetermined decision rules based on the mhGAP intervention guide, stipulating inclusion and exclusion criteria for diagnosis and treatment (see S1 Table for criteria). In case of doubt, another psychiatrist (BAK) was consulted, to come to a consensus decision.
+The 15-item SIP-2R is the short form of the Drinker Inventory of Consequences [28]. Each item is scored on a Likert scale from 0 (‘never’) to 3 (‘daily or almost daily’) with regard to the effects of drinking in the past 3 months. The 14-item PANSS is a symptom-based checklist for severity of psychosis symptoms [29]. The PANSS has not been validated in Nepal but has been culturally adapted for administration to patients and family members, with strong internal consistency for positive items (a = 0.82), negative items (a = 0.88), and combined (a = 0.89) in a rural sample in Nepal [30]. Internal consistency of the PANSS using the cohort baseline data was a = 0.84. The main outcome for patients with epilepsy was number of seizures in the past month. Participants in all studies were also assessed on a range of measures, including demographic and socio-economic, healthcare use, stigma, and discrimination measures (results not reported here).
+Statistical analyses
+We collected data on demographic and health-related characteristics for participants who were recruited into the baseline rounds of the community, facility, and cohort studies. We summarised data using medians and interquartile ranges for continuous measures and counts and proportions for categorical measures.
+For the community study participants with probable depression and with probable AUD, we reported the proportions who contacted any health worker or non-specialist health
+provider at each survey round. We used binomial regression to estimate the change in contact between rounds, and Cohen’s h for the effect size (ES). The regression estimates account for the complex survey design, i.e., strata and probability sampling weights. The analysis for participants with probable AUD was limited to men only, as previous analysis revealed that relatively few women had AUD [19]. These analyses were adjusted to account for the populationbased survey design.
+For calculating the change in contact coverage based on actual service utilisation data, we used the following equation:
+Number clinically diagnosed with a mental illness Contact coverage =  ------------—— ---------—n—... r .....
+Prevalence x Catchment population of health facilities
+The number of cases is based on all cases registered in health facility records over a period of 12 months. Baseline includes the total number of cases 12 months prior to PRIME, endline includes the total number of cases during 12 months when the PRIME cohort studies were implemented. The catchment population is the total adult population of the 10 Village Development Committees from the 2011 census (last available census data) (N = 63,189). Prevalence figures for depression and AUD are based on representative population-level prevalence rates from neighbouring India for (current) depression (2.7%), AUD (4.7%), and psychoses (0.4%) [31], and from a community study in Nepal for epilepsy (0.73%) [32].
+For the facility study, for the participants who screened positive for depression or for AUD, we reported the proportions who returned their clinical consultation forms. Among those who returned their forms, we reported the proportions who had been diagnosed with depression or with AUD, and among those with a diagnosis, the proportions who had evidence of minimally adequate treatment provision. We used binomial regression to estimate the change in diagnosis at each round in comparison to the baseline round, and Cohen’s h for the ES. As it was not possible to use binomial regression to estimate the change in treatment at each follow-up round due to 0 counts in the baseline round, we used Fisher’s exact test to compare the proportions against the baseline round, and used a 1-sample test of proportions to calculate the 95% confidence intervals at each follow-up round.
+For the cohort studies, differences in baseline demographic and clinical characteristics between participants with 12-month data and those lost to follow-up were assessed using nonparametric tests (Fisher’s exact test for categorical variables and Mann-Whitney U test for continuous variables). Because none of the continuous outcomes (WHODAS, PHQ-9, AUDIT, SIP-2R, and PANSS) were normally distributed, univariate negative binomial regression was used to assess change in total score on each outcome from baseline to midline and from baseline to endline in each cohort. Change in number of seizures in the past month in the epilepsy cohort was assessed using Poisson regression. Effect sizes (Cohen’s d) for paired sample analyses were calculated for each outcome. Equity of treatment effect by sex and caste was assessed using negative binomial regression, this time including sex or caste as an interaction term in the model. This was followed by a Wald chi-squared test.
+All community, facility, and cohort data were analysed using Stata (StataCorp, College Station, Texas, US) version 14.
+Ethics
+Ethical approval for the different study components was obtained from the Nepal Health Research Council; the Faculty of Health Sciences, University of Cape Town, South Africa; and the World Health Organization, Geneva, Switzerland.
+Results
+Change in contact coverage
+In the baseline community survey round, 1,983 participants were screened, of whom 60% were male and 46% were between 30 and 50 years of age (see Table 3). Over 1 in 10 (11%) were probable cases of depression. Of the probable cases of depression identified at baseline, 8.5% had contacted a health worker in the past 12 months, in comparison to 11.8% of probable cases at endline; this change of +3.3% (95% CI -5.1%, 11.7%) was not significant. Contact with a non-specialist provider showed a non-significant increase of 2.3% (95% CI -2.8%, 7.5%). For probable cases of AUD among men, non-significant changes were observed for contact with any health worker (+6.3%, 95% CI -3.3%, 15.9%) and for contact with a non-specialist provider (+3.0%, 95% CI -1.6%, 7.6%) (Table 4). Based on actual service utilisation data over 12 months, we observed significant increases in contact coverage for all disorders. As shown in Table 5, the increases ranged from 7.5% for AUD to 50.2% for psychoses.
+Detection of persons with mental illness
+In the baseline round, 1,252 participants were screened, of whom 65% were male and 46% were between 30 and 50 years of age (Table 3). There were 186 participants (15%) who screened positive for depression, of whom 179 returned their outpatient consultation forms. Using outpatient form data, 16/179 (8.9%) were judged to have received a diagnosis of depression. The proportion receiving a diagnosis increased from baseline by 15.7% (95% CI 7.3%, 24.0%), with an ES of 0.432, at the midline round and by 10.2% (95% CI 1.2%, 19.2%; ES 0.301) at the endline round. There were 92 participants (7.4%) who screened positive for AUD. Diagnosis increased from baseline by 58.9% (95% CI 42.0%, 75.7%; ES 1.562) at midline and by 11.0% (95% CI 0.7%, 21.3%; ES 0.500) at endline (Table 6).
+Initiation of adequate treatment
+Among facility survey participants who received a depression diagnosis at baseline, none received adequate treatment. At midline, among those diagnosed, 93.9% (95% CI 77.9%, 98.6%) received adequate treatment, as did 66.7% (95% CI 41.7%, 84.8%) at endline. Among those diagnosed with AUD, 95.1% (95% CI 88.6%, 98.0%) had adequate treatment at midline and 75.0% (95% CI 17.6%, 97.7%) at endline, up from 0% at baseline (see Table 6).
+Clinical and functional treatment outcomes
+A total of 2,139 patients were eligible and consented to take part in the cohort studies. Of these, 137 received a primary diagnosis of depression, 175 were diagnosed with AUD, and 42 were diagnosed with epilepsy—all were recruited into the respective cohort. A total of 95 caregivers of patients diagnosed with psychosis were also recruited into the psychosis cohort. Participants’ demographic characteristics are presented in Table 3. Attrition at the 12-month follow-up was 20.0%, 18.9%, 9.5%, and 9.5% for the depression, AUD, psychosis, and epilepsy cohorts, respectively. Participants lost to follow-up differed from active participants only in the epilepsy cohort: they were all single, and had greater baseline WHODAS and PHQ-9 scores (see S2 Table for reasons for loss to follow-up).
+Results of the negative binomial regressions and Poisson regression are presented in Table 7. Participants in the depression cohort showed significant improvement from baseline to endline, with a significant reduction in WHODAS (P = -15.89; 95% CI -23.03, -8.74; d = -0.41) and PHQ-9 scores (P = -7.22; 95% CI -9.54, -4.89; d = -0.58). In addition, 68.2% (95% CI 58.8%, 76.3%) of participants showed a 50% reduction in PHQ-9 (response) at endline. In
+the AUD cohort, change in score from baseline to endline was significant for the WHODAS (P = -8.57; 95% CI -12.64, -4.49; d = -0.35), AUDIT (p = -9.68; 95% CI -14.35, -5.00; d = -0.34), and SIP-2R (p = -9.13; 95% CI -12.73, -5.54; d = -0.42). Change in WHODAS score from baseline to endline among the psychosis cohort was also significant (p = -13.56; 95% CI -20.78, -6.34; d = -0.40), and so was change in PANSS score (P = -6.42; 95% CI -9.55, -3.28; d = -0.43). Change in WHODAS or symptom severity score was also significant at midline in the depression, AUD, and psychosis cohorts. However, change in WHODAS score or number of seizures in the epilepsy cohort was not significant, neither at midline nor endline. Moreover, among participants who scored above the validated PHQ-9 cutoff for depression at baseline, 86.7% (95% CI 77.8%, 92.3%) of the depression cohort scored below the cutoff at endline. For AUD, 31.9% (95% CI 24.7%, 40.1%) of participants scoring above the validated AUDIT cutoff at baseline scored below cutoff at endline.
+Equity analyses suggest that change in the primary outcomes for the depression cohort (PHQ-9), AUD cohort (AUDIT and SIP-2R), and psychosis cohort (PANSS) did not differ according to the sex or caste of the participants. In the epilepsy cohort, however, the decrease in number of seizures in the past month from baseline to endline was significantly greater among men compared to women (%2 = 10.4, p < 0.001). The decrease in number of seizures from baseline to endline was also greater among the ‘upper’ caste groups (Brahman/Chhetri) (X2 = 47.35, p < 0.001). This was due to 1 outlier (> 100 seizures reported by a participant of the Brahman caste). When outliers were excluded, change in number of seizures over time was no longer different by sex, but was significantly lower from baseline to midline among the ‘lower’ and ethnic minority castes (Janajati, Dalit, or other) (%2 = 61.4, p < 0.001).
+Discussion
+These combined outcomes demonstrate promising results of a district-level MHCP in a low-resource community and primary care setting. We see improvements in actual contact coverage, detection of mental illness by trained health workers, the initiation of minimally adequate treatment, and treatment outcomes. Together these results show the potential of a district MHCP to increase effective coverage for MNS disorders. However, there are also important areas that require further attention, such as preventing attrition in AUD detection rates over time, improving detection rates for depression, maintaining adequacy of treatment over time, and achieving better treatment outcomes for some disorders.
+This research programme is, to our knowledge, unique in that it aims to evaluate each of the steps in the process of integrating mental healthcare in community and primary healthcare platforms in a low-income setting. Through a combination of studies, it provides a population-level perspective on the impact of a district-wide MHCP, covering the extent to which (1) people are seeking care at health facilities, (2) disorders in people attending health facilities are being detected, (3) people being diagnosed are starting adequate treatment, and (4) people are benefiting from treatment.
+People seeking treatment (contact coverage)
+Based on a representative community study, we see modest, non-significant increases in contact coverage as a result of introducing the district-level MHCP; our measure included any treatment contact and contact with a primary health worker. The endline rate of 4%,
+however, does not come close to the targets that were set at the onset of the programme [33]. One possible explanation for this is that the community surveys, although representative, were underpowered to detect changes at the population level. True coverage change may be estimated more efficiently by combining routine clinic data with population prevalence estimates from national surveys [10]. For example, the change in contact coverage using actual service utilisation data is especially promising for psychosis, for which we achieved the target of 50% coverage. In interpreting the contact coverage rates using routine clinic data, it is important to keep in mind that these are based on contact with primary healthcare services. Contact with specialised services is excluded from the calculation and may explain why the baseline rates are low, especially for epilepsy and psychosis, compared to coverage rates in other studies and settings.
+When interpreting the different estimates of contact coverage, it is important to note that routine clinic data provide a more accurate measure of the numbers actually taking up services while the community survey is a more accurate measure of the proportion of the population at large seeking treatment. The former is limited in that it is difficult to ascertain the characteristics of people who need but do not seek care, and the latter is limited by underestimating numbers who actually take up care or by problems of requiring large samples in order to be adequately powered.
+The changed rates in treatment coverage reported in this study are similar to rates seen in high-income settings [3]. One of the elements that may have contributed to increased service utilisation, besides availability of services, is the proactive community case detection strategy that was part of the approach. Utilisation of the CIDT has been demonstrated to be a viable strategy to increase help-seeking for mental healthcare [34,35]. In future programmes the use of the CIDT should be combined with effective stigma-reduction interventions within the communities [36], in order to combine supply-side strategies with strategies that increase demand for mental healthcare.
+People with disorders being detected when attending facilities
+Once accessing health facilities with supervised mhGAP trained health workers, 3 out of 5 people with alcohol problems and 1 out of 4 with depression are detected when the knowledge and skills from training are still relatively fresh (6 months after training). Despite the detection rate remaining relatively stable for depression (1 out 5 patients at 2 years post-training), we see a big drop for AUD (1 out of 8 patients at 2 years), which is possibly due to high dropout rates over time, resulting in health workers losing faith in treating AUD. Although we see significant increase in detection of depression and AUD, many people with depression complaints still go undetected. This is not entirely unsurprising given the difficulties in diagnosing depression in primary care settings, also in high-income settings [37]. The global gaps in primary health workers’ detection of depression require further examination and potentially the development of new training and supervision strategies. One suggestion is to reframe the diagnosis of depression in primary care not as a binary approach, but as a staging approach to the identification and classification of mental disorder [38]. Finally, in our study the small change in depression is influenced by the high baseline detection rate for depression (nearly 9%), which is likely attributable to 1 health worker who had received mental health training in another location.
+With a cutoff score of 10 in primary care settings in Nepal, the PHQ-9 misclassifies approximately 6 participants as false positives for every 4 participants who are true positives [22]. This is comparable to false positive rates with the PHQ-9 in high-income settings. With this in mind, the identified detection rates at baseline, midline, and endline using the PHQ-9 likely underestimate the true detection rate given the high number of PHQ-9 false positives. Working with a PHQ-9 false detection rate of 60% and assuming that the primary health workers only identified true positives, then the upper limit for accurate detection of depression may have been 22% at baseline, 60% at midline, and 50% at endline. The actual detection rate likely falls somewhere below these rates and above the PHQ-9 results reported in our results section. Future studies should consider using structured diagnostic questions for confirmation of detection rates with primary health workers.
+People being diagnosed starting adequate treatment
+Nearly all (95%) people that health workers correctly detected with depression or AUD received minimally adequate treatment 6 months post-training. Twenty-four months after the training, we see a decline to 2/3 for depression and 3/4 for AUD. Importantly, these high rates support the feasibility of relatively short and focused training of health workers, which is at the heart of WHO’s mhGAP programme [39]. These results also re-emphasise the need for supervision to keep up good practice over time [40,41]. At the same time, it is worth noting that coding of treatment adequacy was based on ‘minimally adequate’ care. Unstable supply of psychotropic medicines during the early phase of the programme may explain some of the decline
+in adequate treatment. Further research is needed assessing the rate of optimal care (this study is currently ongoing, and will be published separately).
+People benefiting from treatment
+The combination of interventions provided through the MHCP—which includes psychotropic treatment, home-based care, and psychological treatments—has the expected beneficial effects for people with depression, AUD, and psychosis. At 12 months after treatment initiation, all cohorts see an 8%-16% reduction of functional impairment and 6%-10% symptom reduction (at midline, these values are 6%-15% and 9%-17%, respectively). For people with depression, 87% score below the cutoff of the validated symptoms checklist at 12 months post-treatment; for people with AUD this value is 32%. This study did not aim to evaluate the effectiveness of the provided treatments per se. Rather, it aimed to assess improved functioning and symptom reduction as indicators of feasibility of a community MHCP provided by non-specialists. Our findings support this feasibility, with the exception of the epilepsy cohort, which did not see significant improvement. That said, not having a control group remains an important limitation, especially given trends towards natural remission among people with depression and AUD [42]. The improvements among people with psychosis are similar to those in another recent study of mhGAP in a different rural region of Nepal [30]. Improvements among depressed patients are especially driven by the added value of psychological treatment by the community counsellors, whereas for patients with AUD, pharmacological treatment and psychoeducation by primary health workers appear to primarily explain the improvement [43]. The overall absence of treatment effects for epilepsy is surprising given established effectiveness of treatment as included in the mhGAP guidelines, as well as positive prior outcomes in Nepal [30]. There are a few possible explanations for this finding: (1) a relatively small sample size might have made for an underpowered study and (2) 40% of participants did not report any seizures in the month before baseline. The reasons for the lower change in number of seizures among the ‘lower’ castes and ethnic minority groups need to be studied further.
+The strength of this study is that it presents an evaluation of a real-world district-wide implementation of mental health services within community and primary healthcare platforms in a low-income country. The evaluation follows a theory of change that was developed at the outset of the programme [41], based on guidelines for the evaluation of complex interventions [44], and coordinated with studies in 4 other LMICs [18].
+There are several limitations to be noted. First, the use of screening tools (e.g., PHQ-9 and AUDIT) rather than structured diagnostic assessment risks misclassification of cases in the community and facility studies and an associated reduction of statistical power. Second, community- and facility-level impacts of PRIME for epilepsy and psychosis remain unknown, as these were not included in the study components. Third, while the assessment of adequacy of initiated treatment was done by a mhGAP trained psychiatrist using predefined criteria, we did not systematically assess the reliability of that assessment. Fourth, as noted above, the community surveys might have been underpowered. The sample size suggested by power analysis was reduced for budgetary reasons. We compensated for this by also evaluating changes in contact coverage using service utilisation data. Fifth, the study designs are observational and uncontrolled, which increases the risk for biases.
+This study has several implications for future implementation and research into scaling up mental healthcare in LMICs. First, for any population-level programme, it is essential to demonstrate changes in contact coverage. A programme like PRIME appears to improve contact coverage based on 12-month service utilisation data while failing to demonstrate such change using a representative community survey. Future studies evaluating contact coverage using
+representative samples might need to work with larger samples. Although over half of the people with psychosis appear to have been reached by the programme, there should be more focus on getting people into care, especially for depression, epilepsy, and AUD. Second, and related to the above, investments in making services available should be combined with efforts to increase demand for these services, for example by using proactive case-finding tools such as the CIDT [34]. A combined demand- and supply-side approach will optimise uptake and utilisation of care. Third, a brief mhGAP training to health workers appears adequate for drastically improving their capacity to detect cases of, and initiate minimally adequate treatment for, depression and AUD. At the same time, the attrition of detection rates for AUD over time calls for more focus on supervision and quality monitoring, and the depression detection rates still leave room for improvement, possibly by using different approaches to diagnosis. Fourth, while individuals with depression, AUD, and psychosis receiving mhGAP-based pharmacological and psychological treatment from non-specialist providers report clinical improvements, most of the changes have relatively small ESs, which calls for more focus on the quality of care in future implementation as a means of boosting clinical outcomes. Given the lack control groups, it is not possible to account for natural remission of symptoms as an explanation for these changes. Similarly, the lack of improvements within the overall epilepsy cohort requires further investigation. Taken together, these findings show encouraging improvements in effective coverage at the population level following the implementation of a local MHCP.
+Conclusion
+In efforts to respond to the enormous treatment gap for people with mental illness in LMICs, there is an urgent need for evidence regarding the feasibility of scaling up mental healthcare through community and primary healthcare platforms. PRIME is, to the best of our knowledge, the first programme to systematically evaluate the different assumptions about, and steps towards, making effective mental healthcare available at a population level. A primary indicator of success is effective coverage, defined as the proportion of people who need treatment who accessed services resulting in improvements in patient clinical and functional outcomes. Combining the results from the community, facility, and cohort studies, the programme appears to achieve effective coverage of 1 out of 34 participants with depression and 1 out of 23 participants with AUD—based on community and primary care services alone. Another important indicator is the extent of change that is the result of the implementation of the district MHCP. We demonstrated modest to large and targeted changes in contact coverage (ranging from 1 out of 13 participants with AUD to half of all patients with psychosis). Changes in health workers’ detection ranged from a small ES for change in health worker detection of depression at 24 months (d = 0.30) to a large ES for change in detection of AUD post-training (d = 1.6). We demonstrated that minimally adequate treatment was initiated at the lowest level for two-thirds of the cases with depression at endline, and up to 95% of the cases with AUD right after training. Finally, 3 months after patients initiated care, we observed small to moderate ESs for clinical outcomes (ranging from d = 0.25 for improved functioning among people with psychosis to d = 0.59 for reduction in symptoms for depression and AUD), changes that are maintained 12 months after starting treatment.
+These combined results make a strong case for the impact of a district MHCP in reducing the treatment gap and increasing effective coverage for priority mental disorders, while also pointing towards a set of strategies and new research questions that can contribute towards additional improvements for the future. Ultimately, populations in other low-income and fragile states with limited or non-existent mental health services desperately need models that build on the lessons learned in Nepal through PRIME’s public mental healthcare model.
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+1	| INTRODUCTION
+The life expectancy for patients with schizophrenia is more than 14-20 years shorter than for the general population,1,2 with 35% of excess deaths attributable to cardiovascular disease and diabetes.3 Patients with schizophrenia are at increased risk of developing cardio-metabolic disease, mediated by or coincident with obesity, for several reasons including a genetic predisposition for developing diabetes, reduced physical activity, poor diet and the use of antipsychotic medications.4,5
+Although the underlying mechanisms have not been fully elucidated, it is well-established that antipsychotic medications can lead to obesity, with clozapine and olanzapine having the greatest propensity for body weight gain.6 Among patients with schizophrenia, about half of those on clozapine and a third of those on olanzapine have metabolic syndrome.7
+Body weight gain is associated with poorer quality of life,8 reduced social engagement,9 and is the most distressing side effect reported to mental health helplines.10 Body weight gain also reinforces patients' negative views of themselves and may compromise adherence with treatment.10 Furthermore, being overweight or obese increases the risk of all-cause mortality with an association between body weight and higher mortality risk.11,12
+The current evidence for interventions addressing antipsychotic-associated obesity is limited. Physical activity interventions are compromised by low rates of uptake and acceptability,13 while many pharmacological treatments can result in unacceptable adverse events.14 For instance, sibutramine was withdrawn because of cardiovascular risks,15 while rimonabant was removed because of increased risk of depression, anxiety and suicide.16 Orlistat is associated with poor adherence because of steatorrhoea.17 Finally, there is only modest (and heterogeneous) body weight loss following the addition of met-formin18,19 or topiramate20 for obese and overweight patients on antipsychotics and/or those at risk for antipsychotic body weight gain.14
+As a result of these limitations, there has been increasing interest in glucagon-like peptide-1 receptor agonists (GLP-1RAs) to counteract the body weight gain associated with antipsychotic treatment in general,21 and clozapine and olanzapine treatment in particular.22,23 Glucagon-like peptide-1 (GLP-1) is an endogenous peptide, synthesized in the intestinal mucosa,24 which stimulates insulin secretion and decreases glucagon secretion in a glucose-dependent manner. It
+also delays gastric emptying and lowers food intake by promoting satiety.25
+GLP-1RAs have well-established glucose- and weight-lowering properties in patients with23 and without26 type 2 diabetes. GLP-1RA treatment is also associated with a lower risk of major adverse cardiovascular endpoints (composite endpoint including cardiovascular-related mortality, non-fatal myocardial infarction, and non-fatal stroke).27 In addition to daily injections, several GLP-1RAs are now available as weekly injections, which may improve adherence among patients with schizophrenia.
+To our knowledge, prior to conducting the comprehensive systematic review, at least three individual trials investigating the effect of GLP-1RAs (exenatide once-weekly or liraglutide once-daily) on antipsychotic-associated obesity28-30 had been published. A metaanalysis of participant-level data has the potential to identify whether the effects of GLP-1RAs vary for different antipsychotics and also to examine the influence of more clinically relevant participant-related factors than is possible in a meta-analysis of study-level data.
+In this study, we tested the hypotheses that
+•	GLP-1RAs would be superior to the control conditions for body weight loss, as well as all other anthropometric and cardio-metabolic outcomes;
+•	patients treated with clozapine or olanzapine would experience greater body weight loss with GLP-1RAs.
+2	| MATERIALS AND METHODS
+2.1	| Protocol and registration
+This study was registered with PROSPERO (CRD42017079791).31 We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations for the background, search strategy, methods, results, discussion and conclusions.32 Ethical approval was not required as all the included data had been previously published.
+2.2	| Search strategy
+The following databases were searched from inception to 24 October 2017: PubMed, PsycInfo, Embase, and the Cochrane Schizophrenia Group's Trials Register. Hand searches of references listed in included
+studies and other key publications were also conducted. Studies were limited to humans. Search terms included terms for antipsychotics and GLP-1RAs. There were no language limitations. PubMed search terms are provided in Table S1 (see the supporting information for this article).
+2.3	| Eligibility criteria and study selection
+We included all randomized controlled trials of patients on antipsychotic medications who were overweight or obese where a GLP-1RA was compared with placebo or usual care. All studies were independently screened at the title and abstract level by two authors (D. S. and M. H.). Studies that met the inclusion criteria on title and abstract review, or that could not be excluded on the basis of information provided in the abstract, were reviewed at full text level.
+2.4	| Data collection process
+Authors of the included studies provided access to de-identified individual participant data. Key authors of the included studies are also co-authors of this meta-analysis. These data were collated and analysed with validation by the corresponding authors of the included studies. Quality assessment was conducted by an author not involved in the included studies (M. H.).
+2.5	| Outcomes
+The primary outcome was the difference in endpoint body weight adjusted for baseline body weight and study between the GLP-1RA arm and control arm. We analysed the following secondary outcomes in the same way: metabolic syndrome components (waist circumference, blood pressure [BP], HDL, LDL, triglycerides [TGs] and fasting plasma glucose [FPG]), body mass index (BMI), HbA1c, homeostatic model assessment (HoMA), insulin, visceral fat and android-to-gynoid ratio (android adipose tissue surrounds the abdomen, chest, shoulder and nape of the neck, while gynoid adipose tissue surrounds the hips, breasts and thighs).
+Psychosis severity for individual patients was based on published inter-scale linkage thresholds for the PANSS, BPRS and GCI.33,34
+2.6	| Study quality
+We assessed study quality using criteria adapted from the Cochrane Collaboration guidelines32: (a) selection bias (random sequence generation and allocation concealment); (b) performance bias; (c) detection bias; (d) attrition bias; (e) reporting bias; and (f) other sources of potential bias including pharmaceutical company funding. Studies were deemed to be of low quality if they had three or more elements with a high risk of bias, while those of high quality had four or more elements with a low risk of bias.
+2.7	| Statistical analyses
+We conducted a one-step meta-analysis on individual participant data, where data from all included studies were modelled simultaneously, while adjusting for clustering of patients within included studies.35
+---------------------------------------WlLEY^95
+The primary and secondary outcomes were analysed as differences in endpoint values between intervention and control, and adjusted for baseline value and study as a random effect using ANCOVA with Bonferroni correction on SPSS version 24 for Mac OS. Where individual patient data were missing, we used the modified intention-to-treat model,29 where the last valid value after the baseline value was carried forward.
+We performed multiple linear regressions with endpoint variable as the dependent variable, including the baseline variable and, respectively, each of the following co-variables: demographics (age, sex), psychosis severity, metabolic variables (body weight, BMI, waist circumference, HbA1c, fasting blood glucose, HDL, LDL, TGs, systolic blood pressure [SBP], diastolic blood pressure [DSP], HoMA, insulin) and treatment variables (treatment arm, nausea, any adverse drug reaction and GLP-1RA agent). If any covariates were significant then they were all included in a multiple linear regression, using backward elimination. Adjusted R2 of the final model was calculated.
+We conducted sensitivity analyses to explore the impact of the specific antipsychotic used (patients on clozapine and/or olanzapine vs. patients on other antipsychotics) on treatment arm and endpoint metabolic variables, adjusted for the baseline variable, and conducted a meta-analysis for each metabolic variable using RevMan 5.3. We also carried out a sensitivity analysis by excluding patients with type 2 diabetes.
+Chi-square tests were conducted on the proportion of patients in the GLP-1RA and control groups who achieved >5% and >7% body weight loss. Number-needed-to-treat (NNT) was calculated for the proportion of patients with >5% or >7% body weight loss by dividing one by the risk difference.
+BMI was categorized as per WHO categories (overweight: 25-29.9, obese class I: 30-34.9, obese class II: 35-39.9, obese class III: 40 and above),36 and the proportion of GLP-1RA-treated patients and controls who shifted between categories from baseline to endpoint was analysed using a chi-square test.
+FPG was categorized as per ADA categories (normoglycaemic <5.6 mmoL/L, impaired FPG 5.6-6.9 mmoL/L, type 2 diabetes >6.9 mmoL/L). Chi-square tests between baseline and endpoint FPG categories were conducted for total participants, and those in the GLP-1RA and control arms.
+Adverse drug reactions (ADRs) were compared among treatment and control groups using chi-square tests with data available on nausea, diarrhoea, vomiting, other ADRs and any ADR, and a number-needed-to-harm was calculated for ADRs that were significantly different among GLP-1RAs and controls by dividing one by the risk difference. A regression analysis for body weight, adjusted for baseline body weight, study and nausea, was conducted to assess any potential impact of nausea as a mediating factor in body weight change.
+2.8	| Publication bias
+If the meta-analyses included 10 or more studies, we planned to test for publication bias using funnel plot asymmetry where low P values suggest publication bias.37
+296—*-Wl LEY-----------------------------------------------
+3	| RESULTS
+3.1	| Study selection
+Our search identified 56 unique articles. Of these, 43 were excluded at title and abstract level, leaving 13 articles for review at full text level. Three articles met the inclusion criteria28-30 with a total of 168 patients (GLP-1RA = 84, control = 84). Reasons for exclusion at full text level are provided in Supporting Information Figure S1 and Table S2 (see the supporting information for this article).
+3.2	| Study characteristics
+Studies were conducted in Denmark (n = 2) and in Australia (n = 1) (Table 1). Duration ranged from 12 to 24 weeks (mean 16.2 weeks, SD 4.0). Two studies used exenatide 2 mg subcutaneously (s.c.) once-weekly,28,30 and one study used liraglutide 1.8 mg s.c. once-daily,29 the standard maximum doses used for diabetes.38 All studies examined GLP-1RA for people on antipsychotic medications, with no notable changes in antipsychotic doses among participants. One study was restricted to participants receiving clozapine and olanzapine,29 and another to clozapine alone.30 The third study included a naturalistic patient sample treated with clozapine, olanzapine, aripiprazole, risperidone, paliperidone, quetiapine, ziprasidone, amisulpride and sertin-dole.28 After initial publication, an erratum on corrected metabolic blood markers was published for the third study, and these data were used in the current meta-analysis.39 Two studies were blinded and placebo-controlled,28,29 while the third was open label.30 All studies were of adults aged 18-65 years (mean 40.0 years, SD 11.1), 58.3% were male, and the mean BMI of participants was 35.4 kg/m2 (SD 5.7). All studies included patients with schizophrenia, while two also included schizoaffective disorder.28,30 One study also included patients with type 2 diabetes,30 while the other two specifically excluded type 2 diabetes.28,29 One study required patients to have impaired glucose tolerance.29 All studies provided data on body weight, BMI, FPG, HDL, TGs, SBP, DSP and HbA1c. Two studies provided data on insulin and HoMA,29,30 and two on android/gynoid ratio and visceral adiposity.28,29 Two studies (n = 97 and n = 28) showed significant effect on their primary outcome,29,30 while the third one (n = 43) was equivocal.28 Baseline characteristics of the combined dataset are provided in Table 2. All studies were rated to be of high quality (Supporting Information Table S3).
+3.3	| Primary outcome
+The mean adjusted difference in endpoint body weight among intervention and control groups was 3.71 kg lower for the intervention groups (2.44-4.99 kg, 95% CI) (Table 3). This was a statistically significant difference for treatment arm (p < 0.001), but not for study (p = 0.430).
+3.4	| Secondary outcomes
+Reductions in waist circumference, BMI, HbA1c, FPG, LDL and visceral fat were all significantly different between treatment and control
+(p values <0.001 to 0.03). Lower LDL and DSP were associated with study effect (Table 3).
+3.5	| Linear regression
+Treatment arm and the baseline variable were statistically significant in the multiple linear regressions of endpoint body weight, BMI and HbA1c (Supplementary Appendix A). Treatment arm, the baseline variable and the additional metabolic variable(s) provided in parentheses were statistically significant for endpoint waist circumference (baseline weight), endpoint FPG (baseline HbA1c), endpoint LDL (baseline TGs), endpoint TGs (baseline waist circumference) and endpoint visceral fat (baseline insulin).
+The variables (in parentheses) were significantly associated with the outcome; however, treatment type was not associated with changes in the following variables: endpoint HDL (baseline HDL), endpoint SBP (baseline SBP and DBP), DBP (baseline DBP and android/ gynoid ratio), HoMA (baseline insulin and visceral fat), insulin (baseline insulin and visceral fat) and android/gynoid ratio (baseline android/ gynoid ratio and TGs).
+Age, sex, psychosis severity, baseline SBP, nausea, any ADR and GLP-1RA agent were not significant in any of the linear regressions of endpoint variables. Adjusted R2 for the multiple linear regressions ranged from 0.284 for SBP to 0.958 for body weight (Supplementary Appendix A).
+3.6	| Sensitivity analyses
+For the sensitivity analysis by antipsychotic, patients on clozapine and/or olanzapine (n = 141) had a statistically significant reduction in body weight with GLP-1RAs (mean 4.70 kg, 3.13-6.27 kg, 95% CI), while those on other antipsychotics (n = 27) did not have statistically significant change in body weight (mean 1.5 kg, 1.47-4.47 kg, 95% CI). The difference between these two groups was statistically significant (p < 0.001). This pattern of statistically significant change in metabolic variables among patients on clozapine and/or olanzapine, but not other antipsychotics, was also seen for waist circumference, BMI, FPG and visceral fat. Both patients on clozapine and/or olanzapine, and those on other antipsychotics, had a statistically significant reduction in HbA1c (Figure 1, Supporting Information in Table S4). When patients only on clozapine were examined (n = 113), they had a 4.90 kg greater body weight loss (3.16-6.64 kg, 95% CI) with GLP-1RAs compared with controls. When patients only on olanzapine were examined (n = 25), they had a 4.70 kg greater body weight loss (1.15-8.25 kg, 95% CI) with GLP-1RAs compared with controls. The difference in comparative body weight loss between clozapine and olanzapine was not statistically significant (p = 0.845).
+When patients with type 2 diabetes were excluded, reduction in body weight with GLP-1RAs remained statistically significant (3.85 kg, 2.54-5.15 kg, 95% CI).
+3.7	| Percentage change in body weight
+A significantly greater proportion of patients on GLP-1RA treatment than controls had a body weight loss of >5% (36.9% vs. 10.7%,
+3.8	| Shift in BMI category
+Among patients on GLP-1RAs, 15 (17.9%) shifted down a BMI category, 69 (82.1%) remained in the same category, and none increased a category, while among controls seven (8.4%) shifted down a category, 72 (86.7%) remained in the same category, and four (2.4%) increased a category (x2 6.967, 2° of freedom, p = 0.031) (Supporting Information Table S5).
+3.9	| Shift in FPG category
+Among those with impaired FPG, 26 of 38 (68.4%) participants on GLP-1RAs had normal FPG at endpoint, while only 9 of 38 (23.7%) participants in the control arms had normal FPG at endpoint. Changes in FPG categories are provided in Supporting Information Table S6.
+3.10	| Adverse events
+Patients on GLP-1RAs reported significantly more nausea compared with controls (53.6% vs. 27.5%, p = 0.002), with a number needed to harm of 3.8 (2.4 to 9.7, 95% CI). Neither the presence of any ADR (76.8% vs. 62.9%, p = 0.073), diarrhoea, vomiting, nor other ADRs were significantly different between the two groups (Supporting Information Table S7). Nausea did not significantly impact the regression model for weight.
+3.11	| Publication bias
+We were unable to assess publication bias, as no analyses included 10 or more studies.
+4	| DISCUSSION
+This systematic review and patient-level meta-analysis suggests that GLP-1RAs can induce a clinically meaningful body weight loss in patients with schizophrenia on antipsychotic medications who are overweight or obese. Patients in the intervention arm lost 3.7 kg more body weight than controls. The NNT to achieve a body weight loss of at least 5% (considered clinically meaningful) was 3.8, while for loss of at least 7%, the NNT was 7.7. GLP-1RA treatment was also associated with greater reductions in BMI, FPG, HbA1c and visceral fat. Body weight loss was greatest for those on clozapine and/or olanzapine compared with other antipsychotics. Age, sex, psychosis severity, nausea, any ADR and GLP-1RA agent did not affect body weight or other metabolic variables.
+In terms of adverse events, nausea was more common in the GLP-1RA group, but was not associated with greater body weight loss, and thus unlikely to explain the findings. Antipsychotics, including clozapine and olanzapine, have antiemetic properties,40,41 which may have mitigated this adverse event. In addition, GLP-1RAs are not hepatically metabolized by cytochrome P450, and as such unlikely to interfere with elimination of antipsychotics.42
+Our findings are consistent with data in non-psychiatrically ill patients with23 and without type 2 diabetes.26 There is increasing acknowledgment of the role of GLP-1RAs as an efficacious pharmacological management strategy for the management of obesity, with suggestions that they are under-utilized in the general population.43
+Our finding of a reduction in visceral adiposity is also important, as this is an independent risk factor for cardiovascular disease,44 dia-betes45 and death.46 This is particularly relevant in schizophrenia, where antipsychotic use is both associated with increases in visceral fat and subsequent metabolic syndrome.47
+The improvements in FPG and HbA1c associated with GLP-1RA treatment are also of clinical importance, given the high rates of glucose intolerance (55%) and impaired FPG (21%) in patients on clozapine or other second-generation antipsychotics.48,49 Over one third of patients on clozapine develop type 2 diabetes.50 In turn, there is a higher mortality among those with serious mental illness and type 2 diabetes than those diagnosed with either type 2 diabetes alone, or serious mental illness alone.51,52 This finding highlights the advantages of body weight loss medications that are also approved antihypergly-caemic drugs with proven reductions in cardiovascular mortality in patients with high-risk type 2 diabetes.27
+Differences in endpoint body weight between GLP-1RA treatment and control interventions were greater for patients on clozapine and/or olanzapine, which is consistent with preclinical findings on olanzapine's and clozapine's disruption of the GLP-1RA pathway.22 This result is important, as clozapine remains the only antipsychotic indicated for patients with treatment-resistant schizophrenia, and has the best evidence for managing positive symptoms53 and reducing hospitalizations54 in this population. Body weight gain can be both a barrier to commencement of clozapine and a reason for its discontinuation. Our finding of a body weight loss of almost 5 kg more than in the control group among patients on clozapine was significantly greater than that reported for metformin in a recent meta-analysis of people on clozapine55 (-3.1, -4.9 to -1.4 kg, 95% CI) (p = 0.024). The potential superiority of GLP-1RAs over metformin is also supported by preclinical models. For instance, GLP-1RAs normalize
+glucose tolerance and decrease body weight in rats treated with clozapine, providing mechanistic justification of their therapeutic potential in this context.22 By contrast, metformin only partially attenuates glucose dysregulation in animal models of antipsychotic metabolic abnormalities.56 To date, there have been no head-to-head studies of the
+effects of GLP-1RAs versus metformin on body weight loss among overweight patients on antipsychotics.
+We were not able to include data on non-alcoholic fatty liver disease (NAFLD). NAFLD is a precursor for the development of incident type 2 diabetes and metabolic syndrome57 and has a mutual and bi-
+30^Wl LEY---------------------------------------------------
+directional relationship with these disease entities.58 There is recent evidence for the efficacy of liraglutide for non-alcoholic steatohepati-tis.59 Future studies of GLP-1RAs should include measures of NAFLD.
+A key strength of this study is the use of individual participant data. This approach allowed for consistent analytic techniques across studies, notably endpoint values adjusted for baseline values, as recommended by the European Medicines Agency.60 It also allowed sensitivity analyses by specific variables, notably antipsychotic, age, sex and study duration, and for correlations between change in BMI and baseline BMI.
+There are also limitations to this study. There were differences in study inclusion criteria. Although all studies used overweight or obesity as inclusion criteria, one study specifically recruited patients with prediabetes, while another also included patients with type 2 diabetes. Only one study included patients who were not on clozapine or olanzapine, and these patients may have differed in baseline characteristics. This limits both the power and the certainty of differences in the effect of GLP-1RAs on patients who were not on clozapine or olanzapine. One study was not blinded, increasing the risk of bias; however, sensitivity analysis by removal of this study did not markedly change the outcomes. It is unclear why DBP and HDL were significantly different because of study effect, but this result may have been related in part to the prediabetes entry criteria of the one study of liraglutide.29 None of the included studies could report whether the body weight gain was specifically attributable to antipsychotic use. The included studies did not use easily comparable psychotic symptomrating scales, making psychotic symptoms impractical to assess as an outcome. Study durations were too short to evaluate comparative risks of major adverse cardiovascular endpoints. We were only able to include 168 patients from three studies, which limits our ability to draw firm conclusions or infer clinical recommendations. We do not have data for older participants, and as such these results cannot be generalized to older adults on antipsychotic medications. Further studies are required in this population.
+In conclusion, our findings suggest a promising role for GLP-1RA treatment for body weight management in patients with schizophrenia treated with clozapine or olanzapine; however, there are insufficient data to comment on the role of GLP-1RAs for those on other antipsychotics. GLP-1RA agents are also well-tolerated, with nausea being the most common ADR. The availability of a once-weekly injectable formulation may also offer advantages when compared with traditional body weight loss or diabetic medications requiring daily administration. However, obviously the availability of an oral formulation would increase the ease of use. While several body weight loss agents have been withdrawn because of adverse cardiac effects, GLP-1RAs are associated with lowering of cardiovascular mortality.27 Our findings also suggest ancillary improvements in glucose homeostasis and visceral adiposity. While our data suggest that individuals taking clozapine or olanzapine may benefit most from GLP-1RAs with a less compelling argument for the use of GLP-1RAs for patients on other antipsychotics, this conclusion should be tempered by the fact that only one study included patients on antipsychotics other than clozapine and olanzapine. Further randomized clinical trials of GLP-1RAs in overweight or obese antipsychotic-treated patients with
+schizophrenia are required, particularly head-to-head trials comparing metformin and GLP-1RAs.
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+Introduction
+Recently, the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, Seattle (http://www.healthdata.org/) released the seventh edition of its Financing Global Health report[1]. A core objective of the report is to capture trends in development assistance for health (DAH) and government health expenditure with the aim to provide much-needed information to global health stakeholders about the levels and trends of global health financing.
+The Financing Global Health report splits funding across ten health focus areas, one of which is non-communicable diseases (NCD). Within the NCD health focus area, IHME further disaggregates donor funding into several more exact program areas, one of which is mental health. The Financing Global Health 2015 Report highlights that mental health receives little attention even though it is a major cause of disease burden-accounting for 6.5% of disability adjusted life years (DALYs) in low- and middle-income countries (LMICs) [2]. The lack of alignment between disease burden and funding has been discussed previously by this group [3].
+A valuable review of development assistance for mental health (DAMH) has been previously carried out by Gilbert and colleagues [4] who found that DAMH was less than 1% of total DAH. In addition to including a more extensive array of data sources, this paper will extend the work done by Gilbert and colleagues in two significant ways; first by contrasting DAMH against DAH for other disease categories (including HIV, TB, malaria and maternal and child health), and second by benchmarking allocated DAH against the core disease burden metric (disability-adjusted life year) as estimated by the Global Burden of Disease Studies (http://www.healthdata.org/gbd). This paper will explore DAH, and specifically DAMH, by health focus, geographical and income regions and over time. It will highlight the sources, channels and recipients of DAMH, and importantly, will report a standardised measure to clearly identify health financing gaps, development assistance (expressed in US dollars) per disability-adjusted life year-DAH per DALY.
+Methods
+DAH is the financial and in-kind contributions transferred from global health channels to low- and middle-income countries with the primary intent of maintaining or improving health. In order to track DAH, IHME collates information from audited financial records, project level data, and budget information from the primary global health channels. Tracked
+channels include bilateral aid agencies, such as the United States Agency for International Development and United Kingdom's Department for International Development; multilateral aid agencies, such as the World Bank and regional development banks; United Nations agencies, such as the World Health Organization and UNICEF; public-private partnerships, such as Gavi, the Vaccine Alliance and the Global Fund to Fight AIDS, Tuberculosis, and Malaria; and non-governmental organisations and private foundations. Resources disbursed through these organisations are tracked backward to assess the source of the funds and tracked forward to the recipient country. The diverse set of data are standardised and put into a single inflation adjusted currency (2015 US dollars), adjusted to reflect disbursements rather than simply commitments, and adjusted to remove double counting that occurs when organisations transfer resources between each other. Most important for this research, IHME also estimates the health focus areas and program areas targeted by each project [1, 5]. We extracted annual DAH estimates from 1990 through 2015.
+We tied these health financing estimates to health burden estimates for LMICs produced by the Global Burden of Disease 2015 Study (GBD 2015). GBD 2015 is a systematic and comprehensive framework that uses all available data to quantify mortality and morbidity in 188 countries from 1990 to the present. Mortality and morbidity are disaggregated into 301 medical conditions and causes of illness, including 19 mental and substance use disorders. Health loss due to mortality and morbidity are aggregated to quantify total health burden, measured using disability-adjust life years (DALYs). One disability-adjusted life year is one year of life lost due to premature mortality or several years of life lived with disability. DALY estimates, stratified by age and sex, are made for 1990 to 2015. Over 1,600 researchers from over 120 countries are involved in collecting data and analysing estimates for the GBD, which is coordinated by IHME [6±8].
+Results
+Health focus
+Total DAH in 2015 was estimated to be USD 36 billion. Of this, USD 110 million was estimated to be allocated to mental health. Fig 1 contrasts major health focus categories receiving development assistance over time. DAMH experienced a steady increase from USD 18 million in 1995 to USD 132 million in 2015, a 6-fold increase. Whilst this increase may appear substantial, it equates to only 0.4% of total DAH in 2015. NCDs, of which mental health is a subcategory, are allocated around 1% of total DAH. HIV receives the largest proportion of DAH and saw its allocation increase from USD 612 million to USD 11 billion over the 1995 to 2015 time period. HIV and maternal and child health each consume around 30% of the total DAH.
+Malaria experienced the greatest proportional gain in development assistance over this period increasing from USD 58 million to USD 2.3 billion.
+Sources and channels
+Over the 15 year period, 1990 to 2015, the United States government provided approximately USD 270 million of total DAMH; however, it was private philanthropy that was the most significant source (USD 435 million), accounting for one third of DAMH (Fig 2). NGOs and foundations channelled the overwhelming majority of DAMH (USD 780 million or approximately two thirds of total DAMH) over the 2000±2015 period. Most of the remaining DAMH was contributed by governments of high-income countries through bilateral aid agencies.
+When one examines the channels by which DAMH flows in detail, the World Health Organization distributed the second largest amount of DAMH (USD15 million) behind NGOs (USD54 million) in 2015 (see S2 Fig). Of the development banks, the African Development
+Africa Middle East (15%). East Asia and the Pacific received the smallest fraction of DAMH at 5%. In the same year, the distribution of DAMH across country income groupings was low (USD20 million), lower-middle (USD17 million) and upper-middle income (USD11 million) (S4 Fig). Proportionally the population distribution across these regions is low (42%), lower-middle (36%), and upper-middle (22%) (http://data.worldbank.org/news/new-country-classifications-2015).
+DAMH per DALY
+When DAMH is benchmarked against disease burden attributable to mental and substance use disorders from GBD 2013[2] by World Bank regions, a picture of inequitable distribution emerges (Fig 3). DAMH available per DALY of disease burden in 2013 ranged from USD 0.27 in East Asia and the Pacific to USD 1.18 in the Middle East and North Africa. Sub-Saharan Africa received USD 1.14 per DALY from mental and substance use disorder.
+Benchmarking development assistance against disease burden in LMICs allows for useful comparisons across disease categories. Fig 4 demonstrates there has been an increase in DAMH from USD 0.19 per DALY in 1995 to USD 0.85 per DALY in 2013, a 4-fold increase. Fig 5 demonstrates that HIV/AIDS has the largest ratio of funds to burden (USD 144 per DALY), around three times the amount of the second largest disease group recipient in 2013. Maternal and neonatal health, TB and malaria received between 32 and 48 USD of DAH per DALY in LMICs. Mental and substance use disorders and it
+Discussion
+Assessment of development assistance for health over a period of two decades reveals several observations. Most notably, it highlights significant increases in DAH across all major health groups. This has occurred over a period where health priorities have been tightly connected to
+Fig 4. DAMH per DALY across time, 1995-2013.
+doi:10.1371/journal.pone.0169384.g004
+the targets of the Millennium Development Goals. Consequently, the areas of HIV, TB and malaria in particular have seen substantial investment in terms of DAH. The closing of the MDG era has coincided with a revived investment and commitment to deriving global health estimates. The Global Burden of Disease Studies quantify health loss from hundreds of diseases, injuries, and risk factors, with the aim that information from these studies can be used to improve health systems and eliminate health disparities. It aims to align health systems with the needs of populations by assisting policymakers to identify the major health challenges facing their country. The joint use of estimates of disease burden and development assistance for health provides opportunity for a realignment of resource allocation.
+Apparent inequities extend well beyond the total proportion of development assistance allocated to mental health. Private philanthropy accounts for only a fraction of total DAH yet it is overwhelmingly the largest donor of DAMH—suggesting a lack of interest by governments to address mental health needs across the globe. In terms of recipients, there appears to be no clear association between need, in terms of absolute DALYs, and where DAMH is going.
+There are other important indictors which highlight the lack of resources in mental health. According to the World Health Organization Mental Health Atlas[9], there is only one psychiatrist per 200,000 people or more for about half of the world’s population. Around 80% of mental healthcare workers are based in inpatient and day care services. The capacity to build the workforce appears minimal with the same report estimating around 2% of physicians and nurses received at least 2 days of mental health training in the last 2 years. Furthermore, funding for research into mental illness is not on par with research funding allocated to physical conditions [10,11].
+South Asia received the largest portion of DAMH in 2013 in terms of absolute dollars. Of all World Bank regions, it has also experienced the largest percentage increase in DAMH since 1995. Whilst the direct drivers of these trends have not been documented it is interesting to note that South Asia, or more specifically India, has been the focus of a large and effective global mental health movement in recent years with significant progress being made in terms
+of research and policy. The allocation and distribution of developmental assistance is influenced by a complex interplay of geo-political factors, including political and strategic considerations [12]. The priorities of non-state development partners are regularly at odds with those of the national priorities [13]. Nonetheless, an understanding of the way both funding agencies and recipient governments or organisations view mental health will go partway in explaining the reasons behind the misalignment between disease burden and DAMH. Even in the face of economic arguments, as well as burden of disease evidence, governments in low and middle income countries have been slow to respond to the rising burden of mental and substance use disorders. The well-known study undertaken for the World Economic Forum estimated that the cumulative global impact of mental disorders in terms of lost economic output may amount to US$16 trillion over 20 years, equivalent to 25% of global GDP in 2010[14].
+However, the size of the burden for any group of disorders is insufficient, in its own right, to determine the magnitude of proportional investment within the health sector. Burden evidence needs to be combined with information on the cost-effectiveness of interventions to reduce the burden, especially in low and middle income countries. This information does exist for mental and substance use disorders. Work done for the Disease Control Priorities in Developing Countries third edition [15] and the WHO found that a scaled-up package of mental health interventions for key mental disorders in Sub-Saharan Africa and South Asia, would cost in the order of US$3±4 per head of population[15]. In addition to the availability of cost
+effective interventions, the return on investment in mental health is accumulating. The recent study by Chisholm and colleagues demonstrated that substantially scaling up effective treatment coverage for depression and anxiety disorders over the period 2016 to 2030 would conservatively lead to 43 million extra years of healthy life over the scale-up period. The economic value on these healthy life-years was estimated at USD 310 billion at net present value, with a benefit to cost ratio of 2-3±3-0 to 1 when economic benefits only were considered, and 3-3±5-7 to 1 when the value of health returns was also included[16].
+Even where the burden is high and cost-effective treatments exist, other factors influence governments and funders. It is beyond the scope of this paper to discuss these in detail but they include the importance of mental health as a public good, the societal impact of untreated mental illness (externalities), the need for regulation (including of service providers), protection from catastrophic costs and whether the private sector can provide mental health services. Using criteria such as these, an analysis for the World Bank found a strong case for government and public sector involvement in mental health treatment[17].
+Mental health has not, in most countries, become a priority commensurate with the extent of its burden and the potential to reduce the burden. Commenting specifically on the lack of action following the report for the World Economic Forum[14], Insel and colleagues argue that, mental illness is still perceived as an individual or family problem rather than a policy challenge with significant economic and political implications, and, in many low- and middleincome countries, treatment for mental illness is seen as an unaffordable luxury[18]. Tackling perceptions such as these will require a more sophisticated, multifaceted presentation of evidence to governments, funders and society than has been achieved to date. It is hoped that actions such as the inclusion of mental health in the Sustainable Development Goals[19] and commitments from major stakeholders in global health, such as those given at the April 2016 meeting, co-hosted by the World Bank and WHO, to make mental health a global health and development priority [20] will coalesce with mental health campaigns and movements within and across societies, to create the tipping point for mental health to at last become a global health priority.
+This paper demonstrates how it possible to track DAMH from global health channels to low- and middle-income countries. The primary limitation of this research relates to the underlying data used to generate estimates of DAMH. Budget, spending, and revenue data were collected for each major channel of development assistance. These data were disparate and vary greatly regarding the amount of project level data available and the information reported. In some cases, statistical models were used to estimate disbursement when only commitment data was available or to estimate disbursements for the most recent years, when reporting lags prevented project level reporting. In addition to this, and critical for this research, the disaggregation of development assistance for health across health focus areas, and identification of DAMH, is based primarily on keyword searches of project titles and project descriptions (S1 Table). These methods are not perfect as keywords searches relay on the comprehensiveness of the underlying project descriptions. While this means that these DAMH estimates should be considered approximations rather than precise estimates, these methods have been evaluated and vetted elsewhere [3,21,22], and the magnitudes and trends reported here conform to previous estimates. In addition to this, projects directed towards other sectors and health focus areas (e.g. poverty reduction, maternal and child health, and health system strengthening) which may indirectly finance the prevention or treatment of mental and substance use disorders are not included in this study, as there are not a comprehensive set of how much of government spending that is spent on mental health.
+Benchmarking development assistance for health per DALY provides only a single perspective on funding allocations. Allocating finite resources across sectors and health focus areas is
+complicated and requires a great deal of consideration beyond simply the underlying disease burden as discussed earlier. While decisions related to resource allocation should consider the cost-effectiveness of interventions, existing resources available, and a host of contextual and cultural issues [23], these factors do not preclude the DAH per DALY metric from being a valuable description of current resource allocations.
+The lack of alignment between disease burden and funding across disease categories raises the issue of whether the DAH, especially DAMH, is equitable and whether there is potential for large improvements in resource allocation. DAH, when assessed in a broader context, holds the potential to be a powerful indicator for progress in global health and global mental health.
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+Introduction
+Infancy and early childhood are sensitive and rapid periods of brain growth that coincide with the emergence of nearly all cognitive, behavioral, and social-emotional functions (Johnson, 2001). Throughout this period, the brain's eloquent networks are shaped and refined through processes that include myelination, dendritic arborisation and synaptogenesis, and synaptic pruning. These adaptive processes are modulated by neural activity and are responsive to environmental, genetic, hormonal, and other influences (Stiles and Jernigan, 2010). The development and pattern of myelination follows a well-described neuroanatomical arc (Brody et al., 1987), progressing in a posterior-to-anterior and centre-outwards spatiotemporal pattern that
+corresponding to maturing cognitive functions (McGee et al., 2005; Markham and Greenough, 2004). That is, there is a strong overlap in the emergence of a specific cognitive function and the myelination of brain regions and networks subserving that function (Fornari et al., 2007; Van der Knaap et al., 1991; Pujol et al., 2006). Beyond this temporal association, prior studies have further shown the importance of white matter and cortical myelination to cognitive development and brain plasticity (McGee et al., 2005; Pujol et al., 2004, 2006; Fields, 2008; Fornari et al., 2007), and altered myelination and white matter maturation in a variety of intellectual, behavioral, and psychiatric disorders (Bartzokis et al., 2003; Flynn et al., 2003; Davison and Dobbing, 1966; Wolff et al., 2012). We have further shown that early trajectories of myelination are associated with cognitive abilities and outcomes (O'Muircheartaigh et al.,
+2014; Deoni et al., 2014).
+The assembly and maintenance of the myelin sheath requires a carefully orchestrated delivery of nutrients, including lipids and fatty acids, proteins, minerals, and other micronutrients (Dobbing, 1964). Long-chain polyunsaturated fatty acids (LC-PUFAs), choline, iron, zinc, cholesterol, phospholipids, and sphingomyelin play essential roles in myelin elaboration, as key components of the myelin sheath and/or energy sources (Oshida et al., 2003; Saher et al., 2005; Hadley et al., 2016; Chang et al., 2009). Deficiencies in these nutrients throughout infancy can significantly alter myelin content, composition, and morphology, potentially disrupting normal brain function and impairing cognitive outcomes.
+Compositionally, human breastmilk provides many of the nutritional building blocks that support healthy physical growth, immune system development, and brain maturation (Kramer et al., 2008; Jacobi and Odle, 2012; Hoi and McKerracher, 2015; M'Rabet et al., 2008; Reynolds, 2001). This includes micro and macro-nutrients, short and long-chain PUFAs, phospholipids, neurotrophic factors, biofactors, and hormones that are important for myelination. While many of these nutrients are also provided by infant formula, their concentration often varies considerably from human milk, and does not mimic the changing nutritional composition of human milk across an individual feed (from foremilk to hindmilk), or from colostrum to mature milk (Ballard and Morrow, 2013). It is possible, therefore that given the importance of these nutritional components to brain development, nutritional differences between breast milk and infant formula, or between formula, may influence trajectories of brain myelination and, subsequently, affect cognitive development.
+With specific reference to brain myelination, breastmilk is an important source of long-chain PUFAs, including docosahexaenoic and arachidonic acid (DHA and ARA), the together comprise more than 20% of the brain's fatty acid content (Chang et al., 2009), and phospholipids such as phosphatidylcholine that make up 10% of the lipid weight of myelin. Approximately 40% of the lipid content of mature human milk is sphingomyelin (Blaas et al., 2011), a sphingolipid that plays a critical role in development of the myelin sheath (Oshida et al., 2003; Jana and Pahan, 2010). Breastmilk is also an important source of cholesterol, which is essential for myelin synthesis (Saher et al., 2005). Even in otherwise healthy children, prolonged deficits in these and other nutrients have been associated with developmental abnormalities and cognitive impairments. For example, prolonged essential fatty acid deficiency or low blood levels of ARA and DHA have been associated with learning disorders, ADHD, dyslexia, and autism spectrum disorder (Hadley et al., 2016).
+The nutritional composition differences between breast and infant formula milk may help to explain some of the observed difference in overall cognitive functioning and ability between breast and formula fed infants (Horwood and Fergusson, 1998). Even controlling for important confounds such as birth weight, pregnancy length, parent education level, and family socioeconomic status and demographics, the general consensus from prior studies is that children and adolescents breastfed as infants show improved performance on tests of cognitive functioning (Horwood and Fergusson, 1998; Anderson et al., 1999; Kramer et al., 2008; Mortensen et al., 2002; Huang et al., 2014). These results are also generally supported by brain imaging studies, which have shown increased white matter volume, total gray matter volume, and regional cortical thickness increases in association with breastfeeding duration and percentage of breastmilk in a infant's diet. These neuroimaging finds have further been associated with improved cognitive function as measured by IQ (Ou et al., 2015; Isaacs et al., 2010; Kafouri et al., 2013; Luby et al., 2016). Although these studies have been performed predominately in older children and adolescents, our group's prior work (Deoni et al., 2013a,b) extended these findings to infants, showing cross-sectional differences in early brain myelination between exclusively breastfed, exclusively formula-fed, and mixed-fed infants and toddlers. These differences were found to present prior to one year of age
+and extend throughout childhood, and were associated with duration of breastfeeding.
+An important limitation of past neuroimaging (MRI) studies, however, has been their cross-sectional nature with children pooled across large age-ranges, making it difficult to draw causative conclusions. In addition, formula-fed children are often treated as a single group without consideration of the potential differences in formula composition. These limitations generally stem from the retrospective nature of most studies, with nutritional composition information often not remembered or readily available. Though infant formula is tightly regulated (e.g., http:// www.fda.gov/ForConsumers/ConsumerUpdates/ucm048694.htm), there exists measurable differences in micronutrient, PUFA, and phospholipid content across different infant formulas.
+To investigate nutritional influences on longitudinal infant and child brain development in a naturalistic setting, we longitudinally characterised myelination in a large group (n = 150, 57 females) of healthy and neurotypically developing children from 3 months to 9 years of age. A total of 452 total MRI and neurocognitive datasets were acquired on these children. These children were drawn from a larger study of normal brain development and were selected since knowledge of infant feeding habits, duration of exclusive breastfeeding, and main infant formula composition was known. Brain myelination was quantified using a multicomponent relaxometry (MCR) technique termed mcDESPOT (Deoni et al., 2008), which decomposes the measured MRI signal into contributions from distinct sub-voxel anatomical water pools (MacKay et al., 1994). Through the acquisition of multiple T1 weighted and T1/T2 weighted spoiled and fully-balanced steady-state images with different flip angles, mcDESPOT applies a 3-pool tissue model (Deoni et al., 2013a, b) to quantify the T1 and T2 relaxation and volume fraction properties for water pools associated with intra- and extra-cellular water, water trapped within the lipid bilayers of the myelin sheath, and a non-exchange free water pool (i.e., cerebral spinal fluid). The volume fraction of the myelin-associated water, termed the myelin water fraction (MWF) is used as a surrogate measure of myelin volume, and has been verified via comparisons with histology (Wood et al., 2016), and used previously to investigate trajectories of early brain maturation (Deoni et al., 2012), myelin-function relationships throughout childhood (O'Muircheartaigh etal., 2013; O'Muircheartaigh et al., 2014), and myelin loss in adults with multiple sclerosis and other demyelinating disorders (Kolind et al., 2013, 2012, 2015). For children up to 5 years and 8 months of age, cognitive function and development was measured using the Mullen Scales of Early Learning (MSEL) (Mullen, 1995), a population-normed tool that provides standardised measures of fine and gross motor control, expressive and receptive language, and visual processing. In addition to domain specific scores, computed early learning composite (ELC) and verbal and non-verbal development quotients (VDQ and NVDQ) composite values reflect overall cognitive, verbal, and non-verbal functioning. Each of these age normalized composite values has a mean of 100 and standard deviation of 15.
+In addition to comparisons of brain and cognitive development trajectories associated with exclusive breast and formula-fed children, we further stratified the formula-fed children based on the main formula composition they received over the first 3 months of life and examined developmental differences between them. This analysis allowed us to more specifically investigate the role of nutritional composition on early brain growth. Finally, we extended this analysis to investigate the influence of individual nutrients on developmental myelin trajectories by examining the associations between specific formula nutrient levels and growth curve parameters.
+Overall, we find that compared to exclusive breastfeeding for 3 months, children who exclusively received formula milk have lower overall neurodevelopment, including both neuroimaging measures of myelination and measures of cognitive performance that persist into later childhood, even with groups matched for important socioeconomic and demographic factors. In addition, significant deviations in development are evident across children who received different formula compositions.
+Further, individual nutrient analysis suggests an important role for DHA, ARA, folic acid, sphingomyelin, iron, and phosphatidylcholine in brain development. These results further stress the importance of proper early nutrition for optimal brain development and, by consequence, cognitive outcomes in healthy children.
+Materials & methods
+Infant participants
+Infants included in this study were drawn from a large and ongoing longitudinal study of normal brain and behavioral development: the Brown university Assessment of Myelination and Behavior Across Maturation (BAMBAM) study (Deoni et al., 2012). BAMBAM currently includes more than 500 children recruited between birth and 5 years of age, and combines neuroimaging (MRI) measures, comprehensive observational and parent report measures of cognitive and behavioral development, on-going medical history information, biospecimen collection, and anthropometry. To obtain longitudinal measures of development, children are scanned and cognitive assessed at 6-month increments from time of recruitment until 2 years of age, and yearly thereafter. As a general study of neurotypical development, infants and young children with major risk factors for developmental, behavioral, or developmental disorders are excluded during enrolment. These risk factors included in utero exposure to alcohol, tobacco, or illicit recreational drugs; premature (<37 weeks gestation) or multiple birth; abnormalities on fetal ultrasound; complicated pregnancy (e.g., preeclampsia, gestational diabetes); 5 min APGAR scores < 8; NICU admission; history of neurological disorder or trauma (e.g., head injury, epilepsy); psychiatric or developmental disorders in first-degree relatives (including maternal depression requiring medication). Ongoing screenings, including the modified checklist for autism (MCAT) and child behavior checklist (CBCL) (Bilenberg, 1999; Chlebowski et al., 2013), and updated medical history information have been used to remove enrolled children with clinically concerning behaviors, diagnosed medical conditions, or head trauma following initial enrolment.
+A combination of retrospective and prospective infant nutrition data was acquired from parents using detailed medical histories and parent interview. This included type of infant formula used; percentage of breastfeeding; and length of exclusive breastfeeding. This information was updated at each study visit, which occurred approximately every 6 months for children under 2 years of age, and yearly for older children. Using this information, children were categorized as either exclusively infant formula-fed or exclusively (at least 90 days) breastfed. Children who were fed a combination of breastmilk and formula were excluded from this analysis. Infants within the exclusively formula-fed group were further sub-divided based on parental reports of the main formula composition they received in at least 80% of feedings throughout the child's first 3 months. All infant formulas consumed by children in this
+study were commercially available in the US.
+Using these criteria, 88 (34 female) exclusively formula-fed infants and young children were selected into group #1. This number included 21 (9 female) children who received formula #1; 28 (10 female) who received formula #2; and 39 (15 female) who received formula #3. A sample of 62 (23 female) exclusively breast-fed infants were also selected and matched to the overall formula-fed children with regards to mean age at scans (p = .24), pregnancy length (p = .39), birth weight (p = .52) and length (p = .09), male:female ratio (p = .85), parent marital status (p = .66), maternal and paternal education levels (p = .9 and p = .9, respectively), family size (p = 1), and the mean inter-scan interval (time between each set of repeat scans, p = .29). Group demographics are provided in Table 1. There were no significant differences in these demographic characteristics between the individual formula groups, mal-e:female ratio (p = .26), gestation (p = .17), birth weight (p = .08), birth length (p = .5), maternal or paternal education (p = .64), family size (p = .85), or marital status (p = .98). Two-tailed student t-tests were used to compare group mean age, pregnancy length/gestation duration, birth weight, birth length, and inter-scan interval. Chi-squared tests were used to compare group parental education level, marital status, and family size.
+A total of 231 scans were obtained on the breastfed children, and 221 on the formula-fed children (n = 42 for formula #1; n = 81 for formula #2; and n = 98 for formula #3). A pictorial display of the longitudinal imaging points and ages for each child is provided in Fig. 1. All child ages were corrected to a 40-week gestational age by subtracting the difference between 40 weeks and the child's actual gestation duration from the child's age.
+Imaging methods and analysis
+A multimodal imaging protocol was performed to assess brain morphology and myelination. mcDESPOT (multicomponent Driven Equilibrium Single Pulse Observation of T1 and T2) (Deoni et al., 2008) was used to quantify the myelin water fraction (MWF), a surrogate marker of myelin content or volume, throughout the brain. All infants were scanned during natural and non-sedated sleep using acoustically-reduced mcDESPOT imaging protocols described previously (Deoni et al., 2012) that comprise 8 T1-weighted spoiled gradient recalled echo (SPGR) images; 2 inversion (IR-) prepared SPGR images; and 16 T1/T2 weighted steady-state free precession (SSFP) images. Total imaging times ranged from 16 min for the youngest infants to 24 min for the older 4-year old and older children.
+All data were acquired on a Siemens 3T Tim Trio scanner equipped with a 12-channel head RF array. To minimize intra-scan motion, children were swaddled with a pediatric MedVac vacuum immobilization bag (CFI Medical Solutions, USA) and foam cushions. Scanner noise was reduced by lessening the peak gradient amplitudes and slew-rates, and using a noise-insulating scanner bore insert (Quiet Barrier HD Composite,
+UltraBarrier, USA). MiniMuff pediatric ear covers and electrodynamic headphones (MR Confon, Germany) were also used (Dean et al., 2014). Children were continuously monitored with a pediatric pulse-oximetry system and infrared camera. Data used for this analysis had no visible motion-artefacts present in their acquired data, however, 12 datasets (5 from the breastfed group and 7 across the formula-fed groups) were rejected for either incomplete data (2) or visible ghosting and ringing artefacts (10).
+Following data acquisition and inspection for image artefacts, conventional mcDESPOT preprocessing was performed consisting of image alignment (Jenkinson et al., 2002), non-brain signal removal (Smith, 2002), and correction for main and transmit magnetic field (B0 and B1) inhomogeneities (Deoni, 2011). A three-pool tissue signal model (the myelin-associated water; intra-extra axonal water; and a non-exchanging free-water pool) was then fit to the mcDESPOT data to derive voxel-wise MWF maps (Deoni et al., 2013a,b) using a stochastic region contraction approach (Deoni and Kolind, 2015).
+Each child's map was then non-linearly aligned to an existing study specific template using the Advanced Normalization Tools software package (Avants et al., 2011) using a previously described procedure (Deoni et al., 2012). Briefly, the high flip angle T1 weighted SPGR image from each child was non-linearly aligned to one of 14 age-specific templates (constructed at 3, 6, 9, 12, 15, 18, 21, 24, 30, 36, 42, 48, 64 and greater than 60 months), which have similar image size and tissue contrast. This transformation was then applied to the child's quantitative MWF image. An overall study template in approximate MNI space was also previously constructed from these age templates, with pre-computed transformations between it and each age template and this transformation was then applied to the MWF image.
+White matter masks, corresponding to 5 bilateral regions (frontal, temporal, occipital, parietal, and cerebellar WM) as well as the body, genu, and splenium of the corpus callosum were generated from the JHU white matter atlas (Oishi et al., 2011), registered to the study template, and superimposed onto each child's MWF map. Mean values for each region were calculated for each child and used for subsequent developmental analysis and trajectory modeling.
+Analysis of myelination trajectories
+To examine group-wise developmental differences between the breast and formula-fed infants, a non-linear mixed effects modeling approach was used to fit a modified Gompertz growth model (example shown in Fig. 2) (Dean et al., 2015) to the regional MWF data, with the form:
+Fig. 2. The modified Gompertz growth model used for all brain growth analysis labelled with relevant model parameters. Here, beta defines the onset of myelination; gamma is the initial rate of myelination; alpha is the MWF value at the shoulder point, or transition from rapid to slower myelination; and delta is the secondary slower rate of myelination.
+MWF(age) = aexp( — /} x ageexp — (/ x age + 3 x age})
+As shown previously, the modified Gompertz model provides the most robust and reliable fit to developmental MWF data compared to other models (Dean et al., 2015). Each of the 4 Gompertz curve parameters were compared between the breast and formula-fed groups using an unpaired t-test with significance defined as p < .001 (p < .05 corrected for the 32 regional and parameter comparisons).
+Examining this data further, we fit Gompertz growth models independently to children exclusively fed each of the three formula compositions (details provided below). Each model parameter was compared using an analysis of variance followed by a post-hoc Tukey test to determine which of the infant formula groups differed. Significance for these analysis was defined as p < .00052 (p < .05 corrected for the 96 comparisons performed).
+Cognitive assessments and analysis
+Alongside MR imaging, general cognitive ability and skills were evaluated in each child under 5 years and 8 months of age within 7 days of scanning using the Mullen Scales of Early Learning (Mullen, 1995). For older children, the Wechsler Intelligence Scale for Children, 5th Edition (WISC-V) was used. Due to the difference in cognitive assessment tool, we restricted our analysis here to only the MSEL data. The MSEL is a population-normed tool that provides domain-level assessment of fine and gross motor control, receptive and expressive language, and visual
+reception. In addition to age-normalized T-scores for each domain, the early learning composite (ELC) and verbal and non-verbal development quotients (VDQ and NVDQ, respectively) composite scores may be calculated that reflect overall cognitive ability, and verbal and non-verbal functioning. Longitudinal group differences (breast vs. all infant formula-fed, and between each formula brand) in ELC were examined using mixed effects modeling assuming a linear trend with age.
+Formula nutrient analysis and analysis
+To examine the potential relationship between specific nutrients to aspects of development, the nutritional composition of each infant formula composition was determined. Alpha-lactalbumin, Beta-lactoglob-ulin, ARA, DHA, Calcium, Phosphorus, Sodium, Potassium, Copper, Magnesium, Vitamin B12, and Folic Acid were measured in the analytical laboratories of Asure Quality, Auckland, New Zealand. The phospholipid profile (Phosphatidylcholine, Phophatidylinositol, Phosphatidylserine, Phosphatidylethanolamine, Sphingomyelin) of each product was determined at the analytical laboratories of Neotron, Italy using the method of Giuffrida et al. (2013). This method was validated for the quantification of Phosphatidylcholine, Phosphatidylethanolamine and Sphingomyelin. The method for sphyngolmyelin had a quantification limit of <200 mg/ kg.
+Nutrients that differed substantively (with a difference greater than 25% in concentration between the minimum and maximum value) between the 3 infant formulas were identified as: ARA, DHA, folic acid, phosphatidylcholine, and sphingomyelin (Table 2). Associations between these nutrient values and aspects of development were investigated by constructing a series of 4 general linear models (GLMs) that modeled each Gompertz model parameter as an outcome variable, and each nutrient value as a predictor variable. For the Gompertz parameters, all children were included in the same mixed-effects model.
+Results
+Fig. 3 contains the group-mean longitudinal MWF trajectories for the exclusively breast and all formula fed infants. In all investigated brain regions, we find differential patterns of development, with breastfed children qualitatively exhibiting a prolonged period of rapid development between 500 and 750 days of age, with an overall increase in myelin content by 2 years of age that persists throughout childhood. While the formula-fed group appears to show increased MWF before 1 year of age, they suffer a slower initial rate of MWF development between 1 and 2 years of age, and fail to reach the overall MWF magnitude of the breastfed group. Exploring these trajectory differences quantitatively (Table 3), in each brain region, there are statistically significant differences between all Gompertz growth model parameters in the frontal, temporal, and occipital white matter, and the body and genu of the corpus callosum. In the remaining regions (parietal and cerebellar white matter, splenium of the corpus callosum) there were significant differences between the a (asymptotic MWF value) and p (initial MWF onset) Gompertz terms; S (secondary rate of MWF development) was found to be significantly different in parietal white matter and splenium. In each of these regions, y (initial rate of MWF development) was not found to significantly differ between the groups.
+Examining differences between children who received different formula compositions, we find significant qualitative (Fig. 4) and quantitative (Table 4) differences in developmental patterns throughout the brain. In general, results from the ANOVA analyses revealed significant differences across the majority of brain regions examined and in almost all Gompertz model parameters. In particular, we note that children who received formula compositions with higher levels of DHA, ARA, choline, and sphingolipids (formulas #2 and #3) showed increased levels of myelin development. Of note, Formula #1, which showed to slowest myelin development, has the lowest concentration of DHA, ARA, and sphingomyelin, but has the highest concentration of iron and vitamin B12. Iron deficiency has previously been associated with cognitive impairments in older children (Lozoff and Georgieff, 2006).
+To determine if differences in cognitive maturation were also present in our sample of children, a linear mixed effects model was fit to the repeated Mullen composite scores (ELC, VDQ, and VNDQ). Results (Fig. 5 and Table 5) support our own prior results (Deoni et al., 2013a,b) as well as those of numerous cognitive studies comparing breast and formula-fed children (Horwood and Fergusson, 1998; Anderson et al., 1999; Kramer et al., 2008; Mortensen et al., 2002; Huang et al., 2014). Specifically, we find that while the mean trend for both groups fell within the normative range (85-115), there were statistically significant differences in the rate of cognitive change with age (slope) and a general increase in the mean (intercept) between the breast and formula-fed groups. Breastfed children exhibited an overall increase in ELC, VDQ, and NVDQ scores, and increased rates of development in ELC and VDQ, and an attenuated decrease in NVDQ with age. Thus, early differences in cognitive function were found to persist, and in the case of VDQ and NVDQ increase, into childhood.
+Repeating this same analysis for each infant formula, we find (Fig. 6 and Table 6) an overall correspondence between brain development profiles and trajectories of cognitive maturation. Specifically, children who received Formula #1, which shows the slowest myelination profile across the majority of brain regions, also have the most pronounced decline in cognitive function across early childhood. Formula #2, which had the closest myelination trend to breastfeeding also exhibit cognitive trends that are most consistent with breastfeeding. These results suggest not only the importance of early nutrition to brain and cognitive development, but also suggest a strong link between brain structure and cognitive performance.
+Investigating the influence of specific nutrient concentrations on the myelination model trajectory parameters (Table 7), we find that whilst each of ARA, DHA, folic acid, iron, choline, sphingomyelin, B12, and phosphatidylcholine contribute to myelination, sphingomyelin and phosphatidylcholine appear to have the most diffuse influence throughout the brain with the remaining nutrients more associated with development in focal brain areas.
+Discussion
+The impact of nutrition on human infant brain myelination has traditionally been indirectly investigated via studies of cognitive performance or using evoked potentials (Pivik et al., 2007), with few neuroimaging studies performed throughout infancy and early childhood (Deoni et al., 2013a,b; Luby et al., 2016). This study, therefore, adds to
+the existing literature examining the role of early life nutrition and feeding choice in infancy, presenting the first longitudinal neuroimaging results demonstrating differences in profiles of myelination and cognitive development between children exclusively breast or formula fed, and between children who received different infant formulas. These data suggested an important role for DHA, ARA, sphingomyelin and choline in early brain development, which was subsequently confirmed by examining the associations between these nutrients and brain growth model parameters.
+On a general level, our results indicate that exclusive breastfeeding for at least 3 months is associated with improved myelination diffusely throughout the brain by 2 years of age, including early and late maturing brain regions and networks associated with a broad array of cognitive and behavioral skills. Supporting this structure-function link, we also show improved overall cognitive ability and rates of cognitive development, including verbal and non-verbal functioning, in breast-fed children versus those who received only infant formula. Examining the longitudinal trends within our data we find that these structural and cognitive differences become evident by approximately 18 months of age (depending on brain region), and persist at least into early childhood (at least 5.5 years of age). Observed differences in myelination, an essential
+element of the brain's white matter structure (Fields, 2010; O'Brien and Sampson, 1965), may be predictive of previously observed white matter volume and integrity changes in older children and adolescents who were breastfed as infants (Isaacs et al., 2010). The importance of myelination to brain connectivity may also link our findings to prior reports of altered functional activation and connectivity in breastfed infants (Ou et al., 2015).
+In order to more specifically link observed breast vs. formula-fed differences to nutrition, as opposed to other potential socioeconomic or demographic aspects, we also contrasted the brain and cognitive developmental profiles in children who received different formula compositions (Figs. 4 and 6). Here we found significant and consistent differences in the profiles of myelination and cognitive maturation, with children who had the lowest myelin development overall having the worst cognitive scores and vice-versa. Of note, the formula compositions associated with the highest myelin levels and cognitive scores also had the highest concentration of long-chain PUFAs (DHA and ARA), choline, folic acid, sphingolipids (sphingomyelin) and phosphatides (phosphatidylcholine). This finding is in strong agreement with prior nutrition literature. Long-chain poly-unsaturated fatty acids (LC-PUFAs), in particular, ARA and DHA, help promote neuronal growth and white
+matter development (Innis, 2007). Preclinical studies of animals withheld AA and DHA via early weaning have reduced myelin basic protein expression, consistent with reduced myelin content (Kodama et al., 2008; Bruno and Tassinari, 2011). While folic acid deficiency is more often associated with neural tube defects (Bower, 1995), preclinical studies have also shown that postnatal deficiencies can negatively affect the fatty acid composition of myelin (Chida et al., 1972). Choline, a precursor to phosphatidylcholine as well as sphingomyelin, and choline-containing phosphoglycerides, comprise more than 10% of the lipid weight of myelin (Norton and Cammer, 1984). In in vitro studies of choline deficiency, significant reductions in phosphatidylcholine (—49%) and sphingomyelin (—34%) concentrations were found compared to cells grown in a choline-rich medium (Yen et al., 1999). These results are mirrored in in vivo human studies, demonstrating a 30% decrease in circulating phosphatidylcholine levels following a 3-week choline-deficient diet. Finally, both phosphatidylcholine and sphingomyelin are critical components of myelin, with dietary supplementation of sphingomyelin previously shown to improve myelination in a pre-clinical model (Oshida et al., 2003).
+In contrast, formula compositions high in iron, but lower in LC-PUFAs and sphingolipids, appear to be associated with slower and reduced overall myelination. Although the role of iron in myelin synthesis is not yet fully understood, both animal and human infant studies have revealed associations between iron deficiency and hypomyelination, reduced oligodendrocyte functioning, and decreased myelin basic protein concentrations. Iron may also play a specific role in myelin synthesis through oligodendrocyte energy metabolism, and fatty acid synthesis. Children with prolonged iron deficiency also suffer a variety of behavioral and cognitive impairments (Saloojee and Pettifor, 2001; Lozoff and Georgieff, 2006; Congdon et al., 2012). However, while iron deficiency has been well studied, little is known regarding potential outcomes associated with iron over supplementation. Breastmilk contains little iron
+content, and iron deficiency may arise after 4 months in exclusively breastfed infants (Kramer and Kakuma, 2012; Ballard and Morrow, 2013). However, healthy non-anaemic infants supplemented with iron exhibit reduced growth (Dewey et al., 2002) and increased fever and illness (Pasricha et al., 2013).
+There are important caveats to our examination of different infant formulas, including: 1. The retrospective nature of our investigation; and 2. The high variability of these nutrients. While formula and feeding information for this study was acquired prospectively, nutritional composition analysis was performed retrospectively using a single time assay. Thus, the specific nutritional formulations may have changed in the 6 years since the earliest imaging data was acquired. It is also important to note that it is not possible, using these observational data alone, to infer which particular nutrient (or combination) is most associated with preferential myelination trajectories. Such information is likely only be provided by pre-clinical models in which individual nutrients may be carefully varied and the effects followed.
+Given the temporal delay between infant feeding and the first appearance of differences in both myelination and cognition between the breast and formula-fed children, it is likely that prolonged breastfeeding durations, follow-on complementary feeding and other environmental influences (e.g., parental interaction) are important but unexamined contributors to our results. In past cross-sectional analysis examining associations between breastfeeding duration (and including complementary feeding past 6-months) and MWF, we showed prolonged breastfeeding was associated with increased myelination in the cerebellum, internal capsule, and parietal and temporal white matter (Deoni et al., 2013a,b). In addition to prolonged breast-feeding, numerous other environmental conditions have previously been associated with differences in cognitive development and outcomes in children, such as family socioeconomic status (SES) (Noble et al., 2015), parental education (Cromwell and Panksepp, 2011), parent-child interaction (Swain et al.,
+2007), physical activity (Best, 2010), and sleep quality (Peirano and Algarín, 2007). Although we aimed to mitigate these SES-related factors by matching children on the basis of maternal and paternal education levels, family size, and parental marital status, it is difficult to discount their contribution without accurate and detailed assessments of each.
+A variety of environmental and economic conditions have previously been associated with differences in cognitive development and outcomes in children, such as family socioeconomic status (SES) (Noble et al., 2015), parental education (Cromwell and Panksepp, 2011), parent-child interaction (Swain et al., 2007), physical activity (Best, 2010), and sleep quality (Peirano and Algarín, 2007). Although we aimed to mitigate these SES-related factors by matching children on the basis of maternal and paternal education levels, family size, and parental marital status, it is difficult to discount their contribution without accurate and detailed assessments of each. However, while the effect of these socioeconomic differences to cognitive outcomes in breastmilk and formula fed children is increasingly documented (Walfisch et al., 2013), the expected differences between formula-fed children may be less.
+An additional, and as yet unresolved, question related to neuro-cognitive outcomes associated with breastfeeding is whether they arise due to the specific nutritional, hormonal, and other constituents of breastmilk per se; if they are driven by maternal-child interaction and
+other environmental differences (Walfisch et al., 2013); or are a result of a combination of the two (Reynolds, 2001). Our study does not attempt to resolve this quandary, but does provide important support for the role of early nutrition, with specific emphasis on nutrients either involved in myelin synthesis or compositional components of myelin. This is particularly evidenced by the differences in development observed amongst the exclusively formula-fed children.
+In this work, we have focused on de novo myelination given its fundamental role in learning and cognition (Nagy et al., 2004; Fields, 2008) and its previously demonstrated sensitivity to nutrition (Lozoff and Georgieff, 2006; Innis, 2007; Bruno and Tassinari, 2011; Kodama et al., 2008). However, other developmental processes, including synapto-genesis and synaptic pruning are also important contributors to brain connectivity, brain function, and cognition throughout this age period. Like myelination, these processes may also be differentially influenced by nutrition (Kafouri et al., 2013; Luby et al., 2016). An MR imaging measure related to synapse density is cortical thickness, commonly quantified using conventional anatomical imaging and brain segmentation (Fischl, 2012). Analysis and examination of cortical thickness differences was not performed here owing to the challenges of accurate gray-white matter segmentation in young children, particularly those under 1 year of age. A further possibility is that the increase in myelin content measured here
+does not reflect more myelin per axon, but more myelinated axons overall. One approach to investigating this further is the use of neurite orientation dispersion and density imaging (NODDI) (Zhang et al., 2012) and myelin g-ratio imaging (Dean et al., 2016), which would inform on both axonal density and mean axon myelin thickness. High resolution structural and NODDI imaging data were, unfortunately, not collected across all children included in this analysis and, thus, investigations of these additional metrics remains a topic for future investigations.
+Conclusions
+While the exact mechanisms that underlie the previously demonstrated brain myelination and cognitive advantages differences in children, adolescents, and adults who were breastfed as infants remain unclear, our results presented here add to the growing evidence and consensus that early and exclusive breastfeeding is associated with improved neurodevelopment, including de novo myelination, and cognitive outcomes. Our longitudinal findings further suggest that early developmental differences persist into childhood and may predict changes previously identified in adolescents and adults. Furthermore, different compositions of infant nutrition appear to result in different
+658
+patterns of myelin development, with some being closer to the myelin trajectory associated with breast-fed infants than others. With respect to potential nutritional contributors, our analysis highlights the importance of known neuro-associated nutrients, including long-chain polyunsaturated fatty acids as well as the important myelin components phosphatidylcholine and sphingomyelin to early neurodevelopment.
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+The debate about the impact of the economy on suicide risk has progressed from untested theories to more complex epidemiological studies and much idiographic data in between. The subject illustrates the interaction of societal effects with the individual’s personal risk profile and vulnerability. Early theorists proposed that economic recession could increase suicide rates because of the stress and hardship that poverty creates (Brenner, 1979; Stack, 1981) as well as the potential loss of social status and connectedness (Durkheim, 1897). Some attribute suicide to the interplay of economic and social factors (Lester, 2001), while others focus solely on the economic contribution such as higher income decreasing the opportunity cost of suicide (Hamermesh & Soss, 1974). Conversely, it has also been proposed that the suicide rate would decline during times of economic hardship, because individuals could blame the macro-economy for feeling miserable instead of themselves (Henry & Short, 1954). Much research has examined the relationship between personal income level, the macro-economy, and suicide rates, and a few studies have also examined the interaction of regional or national economic state with psychiatric illness.
+How does an economic crisis affect suicide rates and which economic variables play a role? Does an economic downturn impact all social groups, regions within a nation, and different countries similarly? How does psychiatric illness and its treatment interact with economic variables and potentially affect suicide rates? An examination of the available literature answers some of these questions and illuminates the consideration of the best approaches for addressing the increased suicide rates seen during recent times of economic hardship as in the 2008 recession.
+The initial literature involves idiographic studies that consist of case reports of suicide apparently related to eco
+© 2017 Hogrefe Publishing
+nomic factors. In the 1997 financial crisis in South Korea (Watts, 1998) a woman indicated that she was going to kill herself because the devaluation of the Korean currency meant she could no longer afford her son’s college tuition. This case and many other reports of so-called economic suicides lack a systematic review of the person’s psychiatric condition and other factors that contribute to risk, meaning we do not know why this specific life stress was so deadly for this person.
+Reports on groups at particularly high risk of suicide are sometimes more informative since more general factors can be identified. Suicide rates in Canadian Inuit people, which are three-to-four times higher than Canada’s average suicide rates, have also been considered in the context of multiple socioeconomic factors (Leenaars, Anawak, & Taparti, 1998). The impact of the fur trade collapse and high unemployment rates are mentioned as potential contributing factors but are not studied systematically in this idiographic report. Increases in Australian farmer suicide rates - which are higher than those of the average rural population and male national rates - correspond with declines in trade over the same time period (Page & Fragar, 2002). An increase in male unemployment - among increases in violence, substance misuse, and alcohol consumption - was postulated as a potential psychosocial stressor to explain the increase in male suicide rates in England and Wales during 1975-1990 (McClure, 2000). Unemployment and worsening poverty associated with neoliberal structural adjustment following the 1998 economic crisis in South Korea were posited as contributing factors of the increased suicide rates observed (Khang, Lynch, & Kaplan, 2005). Similarly, benefits of economic growth - such as decreased unemployment - were proposed as explanatory factors of the observed decrease in suicide rates in China during 2009-2011 (Wang, Chan, & Yip, 2014). Unemployment, change in income, and household debt were posited as fac-
+Crisis (2017), 38(3), 141-146
+DOI: 10.1027/0227-5910/a000487
+tors contributing to the immediate rise in suicide following the 2008 European financial crisis (Karanikolos et al., 2013). Studies of this sort can suggest links between factors and suicide, but cannot estimate the attributable risk of the suggested economic factors because other factors may be contributing to the higher suicide rates.
+Analysis of suicide rates and economic variables at an epidemiological level may allow consideration of more variables like psychiatric disorder rates, treatment, regional income levels, unemployment rates, and demographics, but this analysis carries the risk of the ecological fallacy and does not permit conclusions to be drawn about causality. An examination of whether differences in the structure of agricultural production explained inter-state variation in suicide rates in India found positive relationships between the percentage of marginal farmers, cash crop production, and indebted farmers and suicide rates (Kennedy & King, 2014). Increased suicides in rural Japan led to the consideration of the effects of industrialization and urbanization as contributing factors; higher suicide rates were found in areas with a sparse population and a non-prosperous economy (Kurosu, 1991). Higher suicide rates and economic variables - unemployment and urbanization - have been studied in the context of other epidemiological studies, and relationships have been observed between negative economic impact and increasing suicide rates (Álvaro-Meca, Kneib, Gil-Prieto, & Gil de Miguel, 2013; Otsu, Araki, Sakai, Yokoyama, & Voorhees, 2004; Preti & Miotto, 1999; Thomas & Gunnell, 2010; Yip, Law, & Law, 2003). By contrast, an epidemiological study of Ontario farm suicides failed to find any associations between economic indicators - including number of farm bankruptcies, net farm income, and loan and unemployment rates - and farm suicide rates (Pickett, Davidson, & Brison, 1993). However, such studies cannot answer the question of whether there is a causal relationship nor do they provide any useful estimate of an economic factor’s potential importance in terms of attributable risk for suicide as an outcome.
+More helpful are time series studies and correlative studies of regional differences in suicide rates, per capita income, unemployment rates, and economic state. Time series studies have the advantage of examining the subsequent impact of changes in potentially relevant variables to seek a possible causal relationship. A causal change must precede an outcome attributable to that cause. The difficulty in concluding that such a relationship exists is due to the need to have measured all the relevant variables and interactions.
+A decline in the economy at a national or regional level is generally associated with higher suicide rates in time series studies and cross-sectional regional correlative studies. Associations between suicide rates and economic
+variables have been studied in many countries. National suicide rates in the United States during business cycles, 1928-2007, rose during economic recessions and fell during economic expansions (Luo, Florence, Quispe-Ag-noli, Ouyang, & Crosby, 2011). Greater increases in suicide rates were observed in the countries most affected by the Asian economic crisis of 1997-1998, with higher unemployment being most strongly associated with suicide rate increase (Chang, Gunnell, Sterne, Lu, & Cheng, 2009). Similar findings have been reported in other time series studies (Andrés, 2005; Brenner, 1979; Ceccher-ini-Nelli & Priebe, 2011; Corcoran & Arensman, 2010; Corimer & Klerman, 1985; Kwon, Chun, & Cho, 2009; McKeown, Cuffe, & Schulz, 2006; Motohashi, 1991; Park, Lee, & Kim, 2003; Pompii et al., 2014; Ruhm, 2000; Stuckler, Basu, Suhrcke, Coutts, & McKee, 2009; Tapia Granados, 2005; Vigderhous & Fishman, 1978; Zhang et al., 2010). A minority of studies found the opposite relationship (Neumayer, 2004) or no association between economic fluctuations and suicide rates (Hintikka, Saarinen, & Viinamaki, 1999; Rancans, Salander Renberg, & Jacobsson, 2001).
+Other perspectives have emerged from comparison of the effect of economic variables on suicide rates across different countries. The association between unemployment and suicide rates has been shown to be stronger in the United States compared with other countries, in which the effect of unemployment is weak or nonexistent (Yang & Lester, 1995). Economic factors were found to play a role in influencing US suicide rates but not Taiwan suicide rates in the period 1952-1984 (Yang, Lester, & Yang, 1992). The authors attribute this difference to the fact that being poor in Taiwan is not shameful, whereas in the United States being poor or becoming poor involves more of a loss of place in society and has a greater effect in a consumer-oriented society. Similarly, a lack of common socioeconomic predictors - unemployment rates, annual percentage change in gross national product, female labor force participation, and divorce rates - of suicide rates has been observed in the United States and Japan (Lester, Motohashi, & Yang, 1992). While a negative impact of unemployment on overall suicide rates was found in both Japan and the United States, the relationships between suicide and the other socioeconomic variables differed in the two countries. The correlation of GNP with suicide rates is negative in Japan and positive in United States. Female labor force participation correlation with suicide rates is negative in the United States and positive in Japan. The impact of employment conditions on suicide differed between Hong Kong and Taiwan: Suicide rates fell in Hong Kong but increased in Taiwan as employment conditions improved (Chen, Yip, Lee, Fan, & Fu, 2010). Such national differences have been attributed to national income
+level: unemployment having a positive relationship with suicide rates in high-income countries, but a negative association in low-income countries (Noh, 2009). Annual growth rates for industry and health-care expenditures are additional economic factors that distinguished European countries with higher suicide rates (Ferretti & Coluc-cia, 2009). Countries belonging to the high suicide rate group had lower health-care expenditures, a lower at-risk-of-poverty rate, higher percent total unemployment, and higher annual growth rates compared with the countries belonging to the low suicide rate group.
+How income level plays a role in affecting suicide rates during economic hardship is unclear. Some studies find that higher income is associated with higher suicide rates (Hamermesh, 1974; Jungeilges & Kirchgãssner, 2002) while others find that higher income is associated with lower suicide rates (Brainerd, 2001; Chuang & Huang, 1997; Hamermesh & Soss, 1974; Neumayer, 2003). Others report suicide rates are insensitive to income levels (Andrés, 2005). Findings for unemployment as an economic predictor are similarly mixed. Some studies find that higher unemployment rates are associated with higher suicide mortality (Blakely, Collings, & Atkinson, 2003; Brainerd, 2001; Chuang & Huang, 1997; Hamermesh & Soss, 1974; Neumayer, 2003), while others find no impact of unemployment rates on suicide rates (Andrés, 2005; Hamermesh, 1974; Kunce & Anderson, 2002).
+Noneconomic factors such as rates of psychiatric illness and their treatment levels should also be considered since untreated psychiatric illness is known to be present in most suicide decedents, and psychiatric illness can affect employment status and income. US county-level suicide rates are inversely related to median income with wealthier counties having lower suicide rates (Gibbons, Hur, Bhaumik, & Mann, 2005). Importantly, authors noted that higher suicide rates in rural areas were associated with fewer antidepressant prescriptions, lower income, and relatively more prescriptions for older antidepressants, tricyclic antidepressants, a possible index of how up to date doctors were in terms of medical education on new medications. The findings suggest an impact of access to affordable, adequate medical care of major depression on suicide rates. Lower per capita income reduces health-care resources available to people. Lower per capita income may also mean fewer and poorer health-care resources in a community. Higher-income areas may also have better emergency medical care, increasing the chance of survival after a suicide attempt (Neumayer, 2003). Higher suicide rates correlate with higher levels of rurality (Singh & Siahpush, 2002). Rural areas may be vulnerable owing to a smaller tax base as a result of both fewer people and lower per capita income, in addition to being less attractive to doctors as a place of work and living.
+How demographic subgroups are differentially affected can reveal other factors that affect suicide rates such as social cohesion, religion, and the stigma of psychiatric illness. Until recently the main breadwinner in a household has been a male aged 25-65 years. Because the responsibility for supporting the family falls most heavily on this demographic subgroup, it is the group that may feel the most stress when an economic decline adversely affects their capacity to earn the same level of income as before the recession. Suicidal behavior of older people has been shown to be more sensitive to fluctuations in unemployment compared with the suicidal behavior of younger people (Hamermesh & Soss, 1974), perhaps because the chances of re-employment are lower for older people, and the loss of income and social status is greater. More broadly, an economic decline may force a change in the social group to which the family belongs and impact the family’s housing, schooling for their children, vacations, automobile ownership, clothing, and many other social-defining characteristics. It is therefore of note that many studies identify spikes in suicide rates to be more pronounced in males (Aihara & Iki, 2002; Berk, Dodd, & Henry, 2006; Brainerd, 2001; Corcoran & Arensman, 2010; Huang, 1996; Inoue et al., 2007; Pompii et al., 2014; Preti & Miotto, 1999; Rancans et al., 2001; Schapiro & Ahlburg, 1982; Yang, 1992), and recently particularly in middle-aged males (Andrés, 2005; Corcoran & Arensman, 2010; Jungeilges & Kirchgãssner, 2002; Khang et al., 2005; Luo et al., 2011; Pompii et al., 2014; Schapiro & Ahlburg, 1982).
+Life stressors can trigger a major depressive episode or other psychiatric disorders in vulnerable individuals (Van Heeringen, 2012). An economic downturn may be such a stressor (Dooley, Catalano, & Wilson, 1994). Significant increases in the prevalence of major depression corresponding with economic hardship have been observed in cross-sectional studies (Economou, Madianos, Peppou, Patelakis, & Stefanis, 2013; Lee et al., 2010). Measures of low-economic status, such as low income and unemployment, have been found to be associated with a higher incidence of suicidal thoughts (Gunnell, Harbord, Singleton, Jenkins, & Lewis, 2004), increased risk of suicide (Gerdtham & Johannesson, 2003), and higher attempted suicide rates (Economou, Madianos, Peppou, Theleritis, et al., 2013; Ostamo, Lahelma, & Lonnqvist, 2001). Along with unemployment, fear of losing one’s job has adverse effects on psychological health (Reichert & Tauchmann, 2011) and exacerbates depression and suicidal thinking (Gunnell, Platt, & Hawton, 2009). An economic recession can also trigger a review of workforce needs by companies, and individuals with impairment due to a psychiatric illness may be more likely to be laid off at such times. This process would be detected as a disease by economic
+decline interaction. Some studies have examined such an effect. For example, about half of the association between unemployment and increased suicide risk was attributable to a confounding mental illness in a New Zealand sample (Blakely et al., 2003).
+Evidence exists to support a complex relationship between economic conditions and suicide. Outcome depends on both (a) adverse economic factors that can reduce per capita income and (b) the reduced tax base that can degrade quality and quantity of health care that a community can offer its citizens. From the other perspective, individuals with psychiatric illness can find it more difficult to find and hold better-paid jobs or any job. Finally, an economic downturn can have a disproportionately adverse economic effect on certain demographics like males 25-65 years of age, who are the household’s main income source, and on those with psychiatric illness, or older individuals, whose capacity to compete in the job market is not as good and who are also more vulnerable in terms of stress-triggered psychiatric or other medical disorders.
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+Introduction
+Worldwide suicide is a public health problem causing almost one million deaths every year [1]. Among countries, Brazil ranks eighth in the incidence of suicide, with an age-standardized rate of 5.8 per 100,000 inhabitants, in 2012 [1].
+Inside Brazil, variations may be observed between regions, with higher rates in the Southern region (9.8/100,000 in 2012), followed by the Centre-West (7.6/100,000 in 2012) [2]. Incidence is approximately three times higher amongst men than women, and there is a higher rate amongst old people (8.0/100,000 in 2012) [2].
+The association between suicide and socio-economic factors has been well-documented in the literature [3, 4]. A recent review assessed the effects of poverty on the entire spectrum of suicidal ideation and behaviour in low and middle-income countries and found that 62% of studies reported an association of suicide with worst economic conditions, income shocks and unemployment [5]. At the aggregate level, studies have found an inverse association between public spending on social policies and suicide mortality in Europe [6] and the United States [7]. In Brazil, a positive association has been seen between the suicide rate and the income inequality, where every 10-point decrease in the Gini Index results in a 5.5% reduction in the suicide rate. An inverse association of municipal suicide rates and average
+per capita income was also reported [2]. It is known that suicide is associated with mental health, in particular, mood disorders, such as depressive conditions [8]. As poverty is associated with mental health [9], this could be a possible mechanism to explain the link between suicide and poverty.
+It seems that poverty and income inequality may have some important impact on suicide rates [2, 10, 11] and therefore, actions focused on decrease poverty such as cash transfer programmes can impact on suicide [12]. In Brazil, the conditional cash transfer programme (CCT), branded as Programa Bolsa Familia (PBF), established in 2004, is an important socio-economic intervention that aims to attenuate the effects of absolute poverty through a minimum cash transfer for beneficiary families, and to break the intergen-erational cycle of poverty through investment in education and health conditionalities [13].
+The effects of the PBF on population health in Brazilian municipalities have been investigated, and the PBF has been associated with the improvement of nutritional status [14], and health outcomes such a as infant mortality [15] and leprosy [16]. However, no studies have yet been conducted to evaluate its effects on suicide. It is important to investigate whether a conditional cash transfer programme, such as the PBF, helps to attenuate the incidence of suicide to support the adoption of poverty reduction strategies, enabling potential changes and improvements to the programme, such as encouraging the inclusion of mental health
+care in its conditionalities. This study, therefore, aims to assess the effect of PBF coverage on suicide rates in Brazil. The mechanisms linking BFP to suicide are conceptualized in Fig. 1.
+Method
+We conducted an ecological longitudinal study, which combines an analysis of multiple observation units with a temporal trend design. We used panel data for the 5507 Brazilian municipalities which existed at the time of analysis. All the municipalities were examined through repeated observations over the 9 years from 2004 to 2012.
+All data came from the Health Informatics Department of the Brazilian Ministry of Health [17], including the mortality data for each municipality that was collected from the Mortality Information System (Sistema de Informagao sobre Mortalidade: SIM). Socio-economic and demographic variables were obtained from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatistica: IBGE) [18] and the Primary Care Information System (Sistema de Informagao da Atengao Basica: SIAB) [17]. PBF coverage was obtained from the Ministry of Social Development’s database [19].
+Suicide, the outcome variable, was defined as the cause of death recorded as “intentional self-harm” according to the
+10th Edition of the International Classification of Diseases (ICD-10), codes X60-X84. The suicide rate was calculated at the municipal level and standardized for age in 5-year intervals using the direct method and taking the World Health Organization (WHO) population as a reference. For each municipality and year of analysis, we calculated a general and gender-stratified suicide rates. Suicide rates were calculated for individuals aged 10 years or above since this event is infrequent before this age.
+The Programa Bolsa Familia (PBF)—the main exposure—is a conditional cash transfer programme, established in 2004. In 2012, cash transfers were made through a basic benefit of 70 Brazilian Reals (R$), aimed at extremely poor families, that is families with a monthly family income of up to R$ 70.00. Furthermore, there was a variable benefit of 32 Brazilian Reals granted to families with children, adolescents, pregnant and breastfeeding women, who had a per capita household income of up to R$ 140.00 [13]. Health conditionalities include the monitoring of vaccinations and nutritional surveillance of children under 7 years old, as well as pre-natal care for pregnant and postpartum women. In education, these include 85% school attendance for children and adolescents aged 6-15 years old and 75% attendance for young people aged 16 and 17 years [13]. In our study, we used the PBF coverage of the target population, calculated by dividing the number of families who receive PBF by the total number of eligible families, transformed into percentages and multiplied by 100 [18]. The coverage was then categorized according to three levels (< 30%; > 30% and < 70%; and > 70%). Then we analysed whether the persistence in the time of high coverage would have an effect on the rates of suicide and hospitalization for suicide attempts. Thus, the duration of high PBF coverage of 70% or more was categorized into 3 time periods (coverage < 70% for all years; > 70% for 1 year; > 70% for 2 years; > 70% for 3 or more years).
+We included socio-demographic, economic and social welfare variables as controls: percentage of employed population—percentage of the employed population aged 16 years or above; rate of urbanization—percentage of people living in cities; percentage of people with low education levels—percentage of people with incomplete primary education (up to 8 years of schooling); percentage of people on a low income—percentage of population resident with a monthly per capita household income of up to R$ 140.00; percentage of separated people—percentage of people who declared themselves divorced; percentage of households with one resident—percentage of private households occupied by only one resident; percentage of individuals who declared themselves to be Pentecostal. We also included a health care variable as control: Family Health Programme (Programa Saude da Familia: PSF) coverage—the ratio between the total number of people registered on the
+programme divided by the municipal population. The selection of all these variables was based on evidence of their association with suicide, as seen in the literature [4, 8, 9].
+Statistical analysis
+We used models of negative binomial regression to assess the association between PBF coverage and suicide rates. The time variable was introduced into the models to control for the effect of time on political changes and secular trends which might affect all the municipalities [20]. The estimated regression was (Yit) = ai + p1BFit + pXnit + yt + uit, where, Yit refers to the number of suicides divided by the population residing in municipality i in year t; ai was the fixed effect for municipality i which captures all the unobserved time-invariant factors; BFit was Bolsa Familia Programme coverage for municipality i in year t; Xnit the value for each co-variable in the model, including all the socio-economic, demographic and social welfare determinants, in municipality i in year t; yt was the specific effect of time; and uit the error. To assess whether municipality size might influence the results, we conducted stratified analysis by municipalities with a population lower than or equal to 10,000 inhabitants, between 10,000 and 50,000, and greater than or equal to 50,000 inhabitants.
+The Hausman specification test was conducted to analyse the robustness of results in relation to the regression model selected for the panel data. We used the Akaike (AIC) and Bayesian Information Criteria (BIC) to establish the model that best fitted the data was produced by Poisson or negative binomial regressions [20]. All the statistical analyses were conducted using Stata (version 12).
+This study used exclusively secondary data from the public domain, therefore; approval from a Research Ethics Committee was not a requirement.
+Sensitivity analysis
+We checked the robustness of our results performing several sensitivity analyses. First, we tested using several alternative model specifications with random and fixed effects and Poisson models (S1 Appendix, Table A). Second, models specifications with sequential addition of covariates were tested (S2 Appendix, Table B). Third, we repeated the best-fit model including only municipalities with accurate vital information [21] (S3 Appendix, Table C). Fourth, we tested different classifications of PBF coverage (S4 Appendix, Table D). Fifth, because of the potential for misclassification of external causes and suicide, we tested the effect of BF Programme over suicide rates adjusted to ill-defined causes of death (S5 Appendix, Table E). Sixth, we conducted analyses on hospitalizations for attempted suicide to verify common data trends between mortality and suicide attempted,
+given that the literature suggests that suicide and suicide attempts have similar phenomenological characteristics [8] and may have common contextual determinants (S6 Appendix, Table F). Seventh, we have also performed sensitive analyses including “hospitalizations rates due to psychiatry problems” as a controlling variable (S7 Appendix, Table G).
+Results
+From 2004 to 2012, there was a 4% increase in suicide rates. The PBF coverage increased by 46.64%, accompanied by improvements in socio-economic conditions, with the percentage of people on low incomes falling by 26.76%, the employment rate rising by 7.26% and those with low education levels falling by 21.83%. About the socio-demographic data, the average urbanization grew over this period (7.02%), as did the proportion of divorced people (59.75%). About healthcare, the average Family Health Programme coverage in the municipalities rose from 60% in 2004 to 77.23% in 2012 (Table 1).
+Table 2 shows the crude and adjusted associations between levels of municipal PBF coverage and suicide rates, presenting a statistically significant dose-response relationship, even after the adjustment for the socio-economic, demographic and social welfare co-variables. Suicide rates were significantly lower in municipalities with coverage between 30 and 70% (RRcrude 0.966; CI 95% 0.960-0.972) and over 70% (RRcrude 0.942; CI 95% 0.936-0.947) compared with low coverage municipalities (less than 30%).
+An increase in the percentage of the population with low incomes, with low educational levels, separated, in families with one resident was associated with an increase in suicide rates. The percentage of people employed, declared to
+be Pentecostal and urbanization rate by municipality were negatively associated with suicide rates. Similar results were obtained when the analysis was repeated stratifying by the size of municipal population (less than 10,000; between 10,000 and 50,000; and over 50,000), maintaining the magnitude and direction of association found between suicide rates and PBF coverage. There was no statistically significant association between suicide rates and Family Health Programme coverage (Table 2).
+When we assessed the association between the duration of high PBF coverage of 70% and suicide rates, the results demonstrated that an increase in the duration of high PBF coverage of 70% or more was associated with a fall in suicide rates. The effect increased as the number of years with the persistent high coverage increased (Table 3).
+In relation to gender, an increase in PBF coverage duration of 70% or more was associated with a fall in suicide rates amongst women (Table 3).
+Sensitivity analyses
+Sensitivity analyses demonstrate that our findings are robust as none of these sensitivity analyses changed our main findings. Alternative model specifications (S1 Appendix, Table A) demonstrate the stability of the results. We found that controlling for different factors, such as fixed or random effects and different covariates, did not change our results (S1 and S2 Appendix, Table A and B). Different classifications of PBF were tested, and our results were the same for all classifications (S4 Appendix, Table D). Evaluating proportion ill-defined cause adjusted in the model to control by mistakes in the classification of suicide, demonstrated that an increase in PBF coverage is associated with a reduction in suicide rates in Brazilian municipalities, even following
+adjustment for this variable (S5 Appendix, Table E). Repeating the analyses only including municipalities considered to have accurate vital information the results remained similar to the analyses including all the Brazilian municipalities (S3 Appendix, Table C). Evaluating the effect of PBF coverage on hospitalizations for attempted suicide rates in Brazil, demonstrated that an increase in PBF coverage is also associated with a reduction in hospitalizations for attempted suicide rates in Brazilian municipalities (S6 Appendix, Table F). We have added in the model the hospitalizations due to psychiatry problems rates, and our results remained similar (S7 Appendix, Table G).
+Discussion
+The results of our study demonstrate that an increase in PBF coverage was associated with a reduction in suicide rates in Brazilian municipalities, even following adjustment for socio-economic, demographic and social welfare factors. The effect of the PBF increased when, alongside high coverage (equal to or greater than 70%), this level of coverage was maintained for several years. We also conducted robustness sensitivity testing, and the main results were maintained in both suicide and hospitalizations for attempted suicide rates. Furthermore, this effect was also maintained following stratification for municipal population size. In relation to gender, PBF had an effect on reducing suicide rates in women, but not in men. These findings support the hypothesis that a well established and with high coverage CCT
+programme have an effect on reducing deaths from intentional self-harm. Our study main finding, that high PBF coverage is associated with lower suicide rates, is in line with a previous finding, suggesting that a cash transfer programme in Indonesia reported a reduction of approximately 10% in suicide incidence in the sub-districts that implemented the programme [12].
+Cash transfer may attenuate the effects of poverty, by improving better mental health and consequently reducing suicide [22]. Several studies have suggested that poverty may influence the incidence of suicide [2, 10], with one study undertaken in Brazil reporting that both absolute poverty, measured by average per capita household income, and income inequality, measured by the Gini Index, are related to an increased suicide rates in Brazilian municipalities [2]. Greater investment in public welfare is associated with reduced suicide rates [6, 7], and the population’s confidence in social welfare policies exercise a protective effect on suicidal behaviour [6]. Cash transfer programmes not only reduce poverty, but has also been associated with other factors that influence suicide, such as a reduction in depression symptoms [22] and common mental health disorders [23], and in perceived hope and optimism [22] among beneficiaries.
+In Brazil, the PBF may influence suicide rate by fulfilling its main objectives of immediate poverty alleviation through the transfer of benefits to poor and extremely poor families and via investment in human capital through education and health conditionalities. Transferring money may provide greater financial stability, which helps to reduce the stress
+related to economics [24] and increased well-being [23, 24]. Regular income transfers may also support a reduction in those factors that may precipitate the occurrence of suicide, such as alcohol consumption and diagnosis of other mental health disorders [23-25]. Education-related conditionalities may act in a prospective manner, supporting increased schooling, increasing social empowerment and inclusion in the job market. There is evidence that low educational levels and unemployment are associated with increased risk of suicide [4]. In this way, programmes aimed at mitigating these factors, such as the PBF, may also contribute to reducing suicide rates. Furthermore, health conditionalities lead to increased access to health services (Fig. 1).
+Another important finding was that the observed reduction of suicide rates increased with the duration of high PBF coverage at the municipality level, indicating the importance of the continuity of social interventions to strengthen its effect [11]. Regarding gender, duration of PBF coverage at municipality level only reduced suicide rates among women, but not among men. Women are the principal beneficiaries of many social protection programmes, underpinned by the notion of the greater vulnerability of women in economic crises, and the fact that studies demonstrate that cash received by women tends to be invested in resources that promote family well-being [26]. In Brazil, PBF is preferentially awarded to women [13]. Benefits received by women may influence the family dynamic, strengthening female self-esteem and decision-making power [25]. In Brazil, qualitative evidence has shown that PBF may contribute to female empowerment, promoting greater autonomy and visibility for women in the community [27, 28] as legitimated representatives and family spokespeople [27]. An association between cash transfer programmes and reduced intimate partner violence suffered by women have also been reported [25, 29]. Cash transfer programmes have an impact on increased overall well-being and in the self-reported happiness of women [29].
+In our study, socio-economic factors—percentage of people with low levels of education and percentage on a low income—were associated with higher suicide rates in the Brazilian municipalities, supporting the findings that suicide rates may be reduced by anti-poverty polices, such as PBF, given that this aims to increase family income and to break the poverty cycle. Furthermore, higher employment rates had a protective relationship with suicide rates.
+As we used municipal level data, we evaluated the effect of social policy at an aggregate level and our inferences cannot be extrapolated to the individuals, under the risk of committing an ecological fallacy. However, we are aware of the possible spillover effects of BFP. Besides the effect on the target population receiving BF, the economic improvement in the municipalities given a high percentage of inhabitants receiving the benefit, possibly affect the overall
+economic situation of these municipalities [30]. The quality of SIM data is one possible limitation, due to potential under-recording of suicide data. However, a previous study showed that SIM data is of good quality for about 80% of Brazilian municipalities [31]. We also tested the robustness of the PBF effect on suicide rate restricting our analysis for municipalities with accurate vital information, and we found similar results.
+One strong feature of the study was its use of longitudinal, panel data analysis, than traditional cross-sectional data analysis, which enabled us to explore the influence of the PBF and the social and economic contextual characteristics in the same municipalities over time, strengthening the evidence for the relationship we found [20]. We also realized sensitivity analysis done gave much the same effect estimates, suggesting that our findings are robust. Suicide contributes to increasing the burden of mortality from causes that have been gaining prominence in Brazil’s mortality hierarchy and our findings also have the potential to produce robust evidence on the impact of poverty and social interventions on suicide rates, not limited to health care. Our results suggest that economic circumstances may be associated with suicide incidence in Brazil and that the implementation of conditional cash transfer programmes has the potential to decrease self-inflicted deaths, particularly in programmes which use conditionalities to break the poverty cycle. However, as our results cannot be extrapolated for the individual level, we suggest that new researches at the individual level test the association between cash transfer and suicide rates, to strengthen the evidence of the potential role of PBF in reducing suicide.
+Compliance with ethical standards
+Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.
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+1.	Introduction
+Underage drinking is a major public health problem in Western society. In the Netherlands, adolescent alcohol use ranks among the highest in Europe. At the age of 14, 39% of Dutch adolescents are recent drinkers, i.e., they had at least one drink in the month prior to investigation (Van Dorsselaer et al., 2010). A young age of onset is associated with a greater risk of alcohol abuse 10 years later (Behrendt et al., 2009). Moreover, there are several risks involved in drinking alcohol at an early age, such as unprotected sex, accidents and brain damage (Bonomo et al., 2001; Hingson et al., 2003a,b; Tapert et al., 2002). Therefore, from a public health viewpoint, prevention of alcohol use in young adolescents is crucial.
+Especially in the Dutch Achterhoek region, a rural area in the eastern part of the Netherlands, the prevalence of alcohol use among adolescents was high. Health monitors performed by the Community Health Service in 1997 and 2002 showed a negative trend in the Achterhoek: the age of onset became lower, adolescents drank more often and they drank more alcohol consumptions per occasion (De Rover et al., 1998, 2002, 2003). Drinking alcohol was part of the culture at that time, and drinking alcohol by adolescents was considered normal by the community. Therefore, in 2005, the local authorities and several local organisations decided to develop the community intervention “Alcohol moderation among adolescents in the Achterhoek”. The aim was to promote alcohol moderation among adolescents aged 10-19 years, in order to reduce the harmful effects. This has been the start of one of the first community-based interventions for alcohol reduction among adolescents in the Netherlands.
+The effect of the intervention on knowledge, attitude and social norm of parents has already been demonstrated (De Vlaming
+et al., 2008). Moreover, the intervention has been acknowledged by the Dutch Centre ofHealthy Living as theoretically well-founded (Database Healthy Living, 2015). However, until now, the effect of the community intervention “Alcohol moderation among adolescents in the Achterhoek” on the drinking behaviour of adolescents has not been examined.
+Worldwide, the scientific literature on community-based interventions for prevention and reduction of alcohol use among adolescents is relatively scarce and shows mixed results (Anderson et al., 2009; Bagnardi et al., 2010; Foxcroft et al., 2003; Giesbrecht, 2003; Hallgren and Andreasson, 2013). Evaluation studies of community-based interventions do face difficulties regarding the time frame and scientific standards. For example, community interventions are often initiated by local organisations instead of researchers, which reduces the influence of the researcher in creating a ‘controlled’ setting, and increases the risk ofbias. In addition, measuring long-term effects (i.e., 4 years or longer) is important since it takes a long time before community interventions are developed and implemented, and it takes even longer before changes in behaviour or health status can be demonstrated. To this end, it has been argued that more methodically sound research is required, measuring both short- and long-term effects.
+Therefore, the aim of this study was to evaluate the effectiveness ofthe communityintervention“Alcoholmoderation amongadoles-cents in the Dutch Achterhoek region” on alcohol use by adolescents in the second grade and fourth grade of Dutch high school. It was hypothesised that the community intervention would be superior to the reference condition in reducing the prevalence of recent drinking and binge drinking on the short and long term (1 and 5 years, respectively). Superiority was expected, in particular, in adolescents in the second grade compared to adolescents in the fourth grade of high school, as Dutch adolescents in the second grade are all underage, whereas adolescents in the fourth grade are a mixture of underage adolescents and adolescents who already reached the legal drinking age (16 years at that time). In addition, we performed stratified analyses for age, gender, educational level and ethnicity to gain more insight into possible sources of heterogeneity.
+2.	Methods
+2.1.	Design and data collection
+In order to evaluate the effectiveness of the community intervention “Alcohol moderation among adolescents in the Achterhoek”, a quasi-experimental (non-randomised) pretest posttest design was used, based on three independent cross-sectional surveys in the intervention and reference region. The change in adolescent alcohol use in the Achterhoek region (intervention region) was compared to the change in the Noord-Veluwe region (reference region) in the same period. The repeated cross-sectional surveys were part of the regular electronic health monitor system (E-MOVO), performed in October/November, 2003, 2007 and 2011 by the Dutch Community Health Service as described elsewhere (Croezen et al., 2009). Data were collected in the second and fourth grade of Dutch high schools using a detailed Internet questionnaire, under supervision of instructed teachers following a standardised protocol.
+The questionnaire contained approximately 100 standardised questions concerning social-demographic factors, school, health-status and lifestyle, including alcohol use (Dutch National Health Monitor, 2015). Ethnicity was measured by asking where the parents were born, in accordance with the definition of Statistics Netherlands (2015a). Educational level was measured as type of education that adolescents were following at the time of the survey and classified as low (VMBO) or high (HAVO/VWO). Recent alcohol use was measured by asking how many times adolescents had consumed an alcoholic beverage in the past four weeks, with 13 predefined response categories ranging from 0 times to 20 times or more. Recent binge drinking was measured by asking how many times adolescents had consumed 5 or more alcoholic beverages at one occasion in the past four weeks, with 7 predefined response categories ranging from never to 9 times or more. Self-report measures of adolescents on alcohol use are reliable and valid methods to measure alcohol use (Del Boca and Darkes, 2003), although they might underestimate heavy alcohol consumption (Northcote and Livingston, 2011). We had no data available on the onset of alcohol use.
+2.2.	Intervention “Alcohol moderation among adolescents in the Achterhoek”
+The Dutch community intervention “Alcohol moderation among adolescents in the Achterhoek” was one of the first large-scale, intensive and long-lasting interventions inthe Netherlands which aimed to stop the trend of increasing alcohol use in adolescents. This intervention has been described in detail elsewhere (Izeboud et al., 2008). In short, the community intervention was comprised of a range of activities in orderto promote alcohol moderation among adolescents aged 10-19 years, targeting their environment and adolescents themselves. Health education, regulation, and enforcement were integrated and implemented in multiple settings, i.e., homes, schools, sport clubs, youth work, bars and dance clubs. The intervention was developed and carried out by the eight municipalities in the Achterhoek region, the regional Addiction Service, the Police and the Public Prosecution Service, underthe guidance of the Community Health Service. The Community Health Service and the regional Addiction Service selected evidence-based programmes (such as “Alcohol: another story”) or developed intervention activities based on scientific knowledge in close collaboration with the National Institute of Mental Health and Addiction and local communities. Some examples of intervention activities are mass media campaign (radio broadcast, posters, TV commercials etcetera), parent-child evenings at school, regulations at schools and at sport clubs, instruction of barkeepers of community centres, sport clubs, bars and dance clubs, health education by the school nurse, cartoon battle at high schools and the “fine or chance card” for adolescents who were fined for an alcohol-related crime. Substantial attention was paid to preventing the onset of alcohol use under the age of 16, the legal drinking age at that time. Several prevention strategies were focused on raising awareness among parents on the relation between braindevelopment and alcohol use of adolescents, as well as parenting skills, e.g., rule setting. The implementation of intervention activities started in 2006 and, after two prolongations, ended in December, 2012. The aim of this study was to assess the overall impact of the combined interventions and not the effects of individual strategies. The primary target population consisted of approximately 37,000 adolescents aged 10-19 years living in the eight municipalities of the Achterhoek region in January, 2006 (Statistics Netherlands, 2015b).
+2.3.	Reference region
+The reference region was a rural area west of the intervention region, with enough distance to avoid contamination from the intervention region to the reference region (Fig. 1). In the reference region, which consisted of six municipalities, “regular policy” was continued throughout the study period. This also included the regular national Dutch alcohol legislation and policy of that time (2003-2011), including the development of local initiatives for alcohol prevention. We do not consider this as a threat tothe results of our study, as most alcohol initiatives inthe Netherlands hadasmallerscale, a lowerintensity andashortertime framethan our intervention “Alcohol moderation among adolescents in the Achterhoek”.
+2.4.	Analyses
+Our hypothesis was that the change in alcohol use of adolescents would be significantly larger in the intervention region compared to the reference region. In addition, we expected that the effect would be more prominent in the second grade than in the fourth grade. Therefore, all analyses were stratified by grade. Data were analysed using SPSS, version 21. Overall, the response to the repeated cross-sectional surveys was high. As shown by a response study performed in 2007, 82% of schools participated inthe surveys andwithin participating schools, 95% ofthe adolescents participated (Croezen et al., 2009). This resulted in an analytical sample of 5881, 5502 and 5920 adolescents in the intervention region and 3122, 3053 and 3211 adolescents in the reference region in 2003, 2007 and 2011 respectively. Missing data varied from 0 to 606 missings (1.5%) per variable and consequently subjects with missing data were not included in the analyses. Descriptive analyses per region were conducted to identify possible differences in gender, educational level and ethnicity. For the main analyses, ‘recent alcohol use’ was defined as at least one drinking occasion in the past four weeks and ‘recent binge drinking’ was defined as at least one drinking occasion with 5 or more alcoholic beverages in the past four weeks, in accordance with national standards (Dutch National Health Monitor, 2015). To this end,the scales were recoded intodichotomous variables 0 = ‘no recent alcohol use’ versus 1 = ‘recent alcohol use’ and 0 = ‘no recent binge drinking’ versus 1 = ‘recent binge drinking’.
+For the main analyses, we compared the change in alcohol use in the period 2003-2007 and 2003-2011 in the intervention region with the reference region. Linear regression was used to obtain (adjusted) percentages as the outcome. Although logistic regression is the common method for binary outcomes, we primarily applied linear regression to obtain (adjusted) effect estimates; this enhances straightforward interpretation and it has been argued that this is statistically appropriate for the limited range of percentages and effect estimates in our data (Hellevik, 2009). The model used to obtain (adjusted) effect estimates contained an indicator variable for intervention (I =1 for intervention region, I =0 forcontrol region) and time period (T with subscript for the year 2007 and 2011; 2003 served as reference). The covariates gender, educational level and ethnicity were added as potential confounders as indicated below. In this model, the intervention effect is estimated by the coefficient ^12 and $13 of the product terms region*year, for the short and long term effects,
+respectively:
+3. Results
+Y = $0 + $1 * I + $2 * T2007 + $3 * T2011 + $12 * I * T2007 + $13 * I * T2011
++ $4 * gender + $5 * educational level + $6 * ethnicity.
+Crude estimates were obtained using the model without covariates, and stratified analyses were done to obtain adjusted percentages forthe second and fourth grade separately. Adjusted estimates were obtained from the predicted values from the model, using the mean values of the confounders as predictors. Logistic analyses weredone insimilarways,usingthe product term region x yearforthe intervention effect and adjusting for the same variables. Additionally, to gain insight into possible sources of heterogeneity of effect estimates, the analyses were repeated using strata for age, gender, ethnicity and educational level, adjusting for confounders where appropriate.
+3.1.	Characteristics ofthe study population
+The sociodemographic characteristics of the study population are presented in Table 1. The mean age was slightly over 14 years (SD 1.2), with more than half of the sample in the second grade. In the fourth grade, 41% of adolescents was 16 years of age or older, the legal drinking age of that time. For all three survey years, age, gender, educational level and ethnicity were similar in the intervention and control group; although some of the differences were statistically significant, adjustments for these covariates did not substantially alter the effect estimates and these small
+differences do not raise serious concerns on confounding by these factors.
+3.2.	Effects on alcohol use
+Table 2 and Fig. 2 present the prevalence of recent alcohol use and binge drinking in the second grade in 2003, 2007 and 2011. Generally, a strong decline in alcohol use could be seen. Over the whole period of 2003-2011, the prevalence of recent alcohol use in the intervention region declined from more than 50% to less than 20% (crude percentages). After one year of intervention, the change in the adjusted prevalence of recent alcohol use was significantly stronger in the intervention region (-26%), compared to the reference region (-15%). On the long term, these results remained, but were not strengthened: after five years of intervention, the change in prevalence of recent alcohol use in the intervention region was -39%, which was significantly stronger, compared to the reference region (-30%). The same pattern was seen for recent binge drinking. After one year of intervention, the change in the adjusted prevalence of recent binge drinking was significantly stronger in the intervention region (-14%), compared to the reference region (-8%), and this effect remained until 2011 (albeit non-significant in the logistic analysis). In fact, the high prevalences of alcohol use and binge drinking, which were observed before the start of the community intervention “Alcohol moderation among adolescents in the Achterhoek” in 2003, were ‘normalised’ to the same level as the reference region by the year 2007 and further declined similarly to the reference region until 2011. However when looking at the fourth grade (Table 3 and Fig. 2), the change in the intervention region was not significantly different from the change in the reference region - both regions showed a substantial, but similar decline in recent alcohol use and binge drinking in the period 2003-2011.
+3.3.	Stratified analyses
+Figs. 3 and 4 show the effect estimates of the intervention on recent alcohol use and binge drinking stratified by several variables which are possible sources for heterogeneity. It is clear that the
+effect of the community intervention “Alcohol moderation among adolescents in the Achterhoek” is concentrated in the second grade; as mentioned above, in the fourth grade no significant effects can be observed. The picture for age is in line with this. A significant effect can be seen for 13- and 14-year-olds, which is consistent with the effect in the second grade. The picture for 15- and 16-year-olds is, on average, also consistent with the fourth grade: generally no effect can be observed, although the effect estimate for alcohol use of 15-year-olds on the short term seems somewhat increased. For ethnicity, gender and educational level the effect estimates in the strata are similar to the overall effect estimates.
+4. Discussion
+This quasi-experimental evaluation study provides evidence that the community intervention “Alcohol moderation among adolescents in the Achterhoek” was effective in reducing the alcohol use of adolescents in the second grade of Dutch high school. After one year of intervention, the decline in the prevalence of recent alcohol drinking and binge drinking was 11% (P<0.01) and 6% (P<0.01) stronger in the intervention region as compared to the reference region. This effect was restricted to the second grade and remained, but was not strengthened, after five years of intervention. No clear subgroup effects or confounding were observed for ethnicity, gender or educational level.
+During the study period, there was an overall decline in alcohol drinking. This decline in alcohol use over the past years is a well-known phenomenon in the Netherlands. National data of 12- to 18-year-olds, gathered in similar ways as our data and including similar outcome variables, also show a decline in recent alcohol use: from 58% in 2003 to 51% in 2007 to 43% in 2011 (Van Laar et al., 2011; Verdurmen et al., 2012). This is comparable to the decline in our reference region: from 60% in 2003 to 48% in 2007 to 37% in 2011 (crude percentages of the second and fourth grade combined). The decline of alcohol use in the Netherlands in the past decade can be attributed to the Dutch policy towards adolescent drinking. In general, the policy has been lenient and it is only since 2006 has adolescent drinking become an issue on the public health agenda
+in the Netherlands, and has national policy become more stringent. Our community intervention was initiated as one of the first large initiatives and with our quasi-experimental evaluation study we were able to demonstrate an effect on top of the national trend.
+Most evaluation studies on alcohol prevention and reduction describe family- and school-based interventions rather than community-based interventions. A meta-analysis of family interventions on alcohol initiation and frequency of alcohol use (Smit et al., 2008) suggests family interventions to be effective in reducing adolescent alcohol consumption. However, just three of the studies in the meta-analysis reported the long-term effect of the intervention and all studies were conducted in the US. More recently, Foxcroft and Tsertsvadze (2012) reviewed multicomponent alcohol interventions, defined as interventions conducted in multiple settings, for example in both school and family settings. Out of 20 multicomponent alcohol interventions, 12 were effective in preventing alcohol abuse in young people, up to three years of follow-up. Also the majority of these studies (17 out of 20) were conducted in the US. In the Netherlands, the Preventing heavy Alcohol use in Adolescents (PAS) study showed that the combined school- and family-based intervention reduced the likelihood of onset of weekly drinking and the frequency of drinking after 10 and 22 months (Koning et al., 2009). Generally, community-based alcohol interventions tend to be scarce and less well described. Two examples of relatively well described community interventions
+are Project Northland, one of the first community interventions which was effective in reducing alcohol use of adolescents (Perry et al., 1996, 2002) and the Italian ‘Alcohol, less is better’ community project (Bagnardi et al., 2010), which found a reduction of 1-2 drinks per week in the intervention communities compared to the control communities after 2.5 years of intervention activities. Our study is a promising supplement to the current, still modest, evidence on community-based alcohol interventions.
+The majority of studies, including the Dutch PAS, found that alcohol interventions are primarily effective in underage adolescents. This is partly in line with our results. As hypothesised, our stratified analysis showed that the effect of the intervention was clearly concentrated in the second grade (13 and 14 year olds) and there was no effect in the fourth grade (15 and 16 year olds). Possibly, underage adolescents in the fourth grade were subject to peer pressure of classmates who had already reached the legal drinking age of that time (16 years). Peer pressure is a well-known factor influencing alcohol use of adolescents (Bot et al., 2005).
+In our study, the 1-year effect of the community intervention remained, but was not strengthened, after 5 years. Although in theory a stronger effect could be expected after a longer follow-up time (see also Section 1), the literature shows mixed results. In the above mentioned meta-analysis of Smit et al. (2008), a stronger effect was found after a longer follow-up time. However the opposite has also been reported, e.g., the Dutch PAS study found that the effect on
+one of their outcome measures (heavy drinking) disappeared on the longer term (Koning et al., 2009). More research is needed to understand the mechanisms that lead to stability, strengthening or reduction of the effects of alcohol interventions on the longer term.
+There are some limitations of our study which should be mentioned. Firstly, the use of existing data of the Dutch regular electronic health monitor system caused a time gap between the baseline measurements in 2003 and the start of the intervention in 2006. Because of this, other factors may have influenced the measurements in 2007, however this is unlikely to be different in the intervention and reference region. Another disadvantageous implication of using existing monitor data is that we did not have all relevant variables available such as alcohol related harms (Hallgren et al., 2012) and the frequency and quantity of specific beverages. The latter is a more specific measure to assess changes in the estimated volume of alcohol consumed and might have been more adequate for demonstrating important changes in the distribution of drinking.
+Secondly, our quasi-experimental study design lacks randomisation at the individual level and the community intervention could not be cluster-randomised over more regions. Therefore we are not completely sure that the effects found in this study are due to the intervention, and not to differences (inequivalence) between the intervention and reference region. Various region-level factors of environmental or cultural nature may influence trends in alcohol consumption. However to date, it is well known that randomisation is often unfeasible for community interventions and therefore the use of quasi-experimental designs has been advocated (DesJarlais et al., 2004; Victora et al., 2004). Moreover in our study, the baseline characteristics of the intervention group and the reference group were very similar and appeared not to be strong predictors or modifiers, therefore we do not expect that bias due to inequivalence has occurred.
+Thirdly, a multi-level design was not applied since adolescents in this region are, besides school, part of many other settings where alcohol is consumed i.e. homes, sports, night life and youth work. However by treating each pupil as an independent observation unconnected to the class and school environment, we may have underestimated the standard errors to the estimates.
+Fourthly, it is a limitation that our study design did not allow to disentangle the effect of the individual components of the community intervention. This makes it difficult to clarify why the intervention worked and which mixture of intervention activities was most effective.
+Finally, there are some potential limitations to the generalizability of our results. The intervention effects as observed in this study may depend on region- or country-specific characteristics such as a highly tolerant drinking culture or a lenient policy towards adolescent alcohol consumption. Therefore, it is unclear to which extent the results would hold for other regions or countries.
+There are also some important strengths to our study. Firstly, the use of a reference region made it possible to isolate the effects of the intervention from other influences in the time-period. The selected Noord-Veluwe region is a reliable reference, since ‘regular policy’ was provided throughout the study period, and the geographical distance to the intervention region was large enough to prevent contamination.
+Secondly, the main programme strategies were theoretically well founded; for example, the integration of health education, regulation and enforcement (Alcohol and Public Health Policy Group, 2010), the implementation in multiple settings (Foxcroft and Tsertsvadze, 2012) and the focus on adolescents as well as their environment. Especially strategies for parents were considered important and included knowledge transfer, raising awareness and increasing parenting skills (Van der Vorst et al., 2006; Smit et al., 2008). Although such evidence was scant at the start of
+our community intervention (2005), several publications during the past decade have demonstrated the effectiveness of these strategies.
+Thirdly, the high response of adolescents in the repeated cross-sectional surveys is a strength, since it yielded a high and representative number of cases for our study.
+Fourthly, we measured effects on the short term as well as the long term, after five years of intervention. This is much longer than the time frame of most studies.
+Finally, our study was part of a comprehensive evaluation of the community intervention “Alcohol moderation among adolescents in the Achterhoek”, which also included an extensive process evaluation (Database Healthy Living, 2015) and an effect evaluation among parents of adolescents (De Vlaming et al., 2008). Our results fit well into the greater picture of these evaluations.
+Some processes might have facilitated the favourable outcomes. One of these is the joint decision making between health promoters and local communities. Substantial effort was put in building relationships, lobbying and explaining the new scientific knowledge to the local communities. Although the health promoters initiated most plans, local communities were involved at an early stage, and they implemented and financed a large part of the community intervention.
+Our community-based intervention contributes to the growing body of evidence on community efforts aiming at reducing alcohol use. The evidence-base of community approaches for alcohol reduction has been debated in the past (Anderson et al., 2009). However, our study provided evidence that the prevalence of alcohol use and binge drinking can be substantially lowered by such efforts, even in communities where drinking alcohol at a young age is part of the culture and is considered normal. This broadens the perspective to community approaches, i.e., organised bottom-up by the initiative of local authorities or other local organisations, and combining the strategies of health education, regulation and enforcement. As suggested elsewhere, alcohol policy seems to be most effective on behavioural change when the three approaches are mixed and combined integrally (Alcohol and Public Health Policy Group, 2010). Especially in environments where drinking alcohol is the norm, a broad and integrated approach is important in order to be able to turn the tide. Therefore, we think that our results are of great importance for policymakers and local organisations who want to reduce alcohol use of adolescents in an effective and efficient manner.
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+BACKGROUND
+Access to housing is an important determinant of health, with homeless people having substantially increased morbidity and mortality compared with the housed population.1 2 For instance, a recent systematic review found that all-cause mortality in homeless populations in high-income countries is between 3 and 11 times higher than their housed counterparts.2 This excess mortality appears to persist even after accounting for socioeconomic
+deprivation and comorbidity.3 Homelessness may have a direct impact on health, through the physical and psychosocial hazards associated with rough sleeping or temporary accommodation (such as excessive cold, heat or damp; physical and sexual violence and other forms of crime); lack of basic amenities and social goods (such as washing facilities); stigma and social isolation and difficulties in accessing healthcare services.1 5 It is also strongly associated with other experiences deleterious to health, such as poverty (especially child poverty), adverse childhood experiences and substance misuse.6 7 The association between homelessness and health is also bidirectional, since poor physical or mental health can increase the risk of unemployment, relationship breakdown and housing loss.8
+Homelessness is increasing across Europe.10 Recent increases in homelessness may be linked to economic trends, cuts to public services and welfare benefits and changes in the availability and affordability of housing.11 Rehousing homeless (roofless or houseless) persons, or persons at risk of homelessness (insecure housing),12 may therefore be an important health intervention.13-15 One approach to increasing housing stability is Housing First (HF).
+HF is defined in contrast to the traditional ‘Treatment First’ model, which provides temporary accommodation alongside services to address health needs, particularly substance use. The client then progresses to transitional housing before achieving permanent housing; this is conditional on adherence to treatment for mental health and problematic substance use.16 17 The ‘HF’ approach aims to assist clients to access permanent housing as an initial step in addressing homelessness. Housing provision is not contingent on compliance with health treatment or substance abstinence. Additionally, HF includes ongoing support, through Intensive Case Management or Assertive Community Treatment approaches.17 18 There are a number of established HF projects in North America and Scandinavia, and governments in other countries, including France and the UK, have shown interest in rolling out the model.19-24
+HF may improve health, via the mediating factors of increased housing stability and access to support services (figure 1). However, critics have suggested that HF may adversely affect health, since engagement with health services is not compulsory
+and, it is argued, there is therefore a lack of incentive to adhere to treatment or abstain from problematic substance use.
+Although prior literature reviews of the impacts of HF have been conducted,26-29 these reviews did not meet the reproducibility standards of a systematic review and did not undertake meta-analysis. Moreover, new data on the health impacts of HF are now available. This paper reports the findings of a systematic review of the health effects of the housing provision aspect of HF. The review addresses a current gap in the literature by using a clear definition of the intervention, including recent studies, and conducting the first meta-analyses of health outcome data.
+METHODS
+We constructed an initial logic model linking HF to health from relevant literature sources (figure 1).17 25 We then systematically reviewed evidence of the health effects of HF to test the hypothesis that rapid provision of permanent, non-abstinence-contingent housing to homeless people, leads to health improvement in this vulnerable population compared with housing provision without these features.
+The scope, inclusion criteria and methods of the review are outlined below and in box 1. The review protocol was registered on the PROSPERO database.30 The intervention was defined in this review as ‘rapid provision of permanent, non-abstinence-contingent housing’. The inclusion of additional supports (such as Intensive Case Management or Assertive Community Treatment) was not used to define the intervention here, as our aim focused on housing. We had intended to compare interventions adhering with the wider principles of HF with interventions providing only housing; however, all studies found included some form of additional support, so this subgroup analysis was not possible. Given all interventions included both rapid provision of permanent, non-contingent housing and additional support, they are therefore labelled ‘HF’, whether or not they were identified as such in the literature.
+We restricted study types to randomised controlled trials (RCTs), to minimise risk of bias and allow synthesis of data from directly comparable studies. Given a number of RCTs were known to have been conducted, we focused on these as the best available evidence. Primary outcomes were quantitative measures of health, well-being and quality of life; a secondary outcome was housing stability.
+Search strategy
+The search strategy was developed in collaboration with a University of Glasgow librarian. The following databases were searched: EMBASE, MEDLINE, PubMed, PsycINFO, Cochrane Central Register of Controlled Trials (CENTRAL), Social Sciences Citation Index and Biosis. Databases were searched using Homeless Persons, Housing and Public Housing as MeSH terms, alongside keywords homeless*, housing and ‘housing first’. Filters were used to select RCTs.31 32 The full search strategy for each database is found in online supplementary file 1.
+Searches were restricted to studies published from 1992 (when Pathways to Housing was founded and the intervention first initiated) up to the date of the search (15 May 2017) in peer-reviewed journals. Reference lists of previous reviews were checked for additional studies.
+Screening and selection of studies
+Only studies published in English in peer-reviewed journals were included. Only studies which reported a primary health outcome (box 1) were included. Search results were screened by title by one reviewer (AJB) to remove obviously irrelevant citations. Abstracts and full texts were screened independently by two reviewers (AJB and ET). Any discrepancies were resolved by consensus.
+Data extraction and risk of bias assessment synthesis
+Data on key study characteristics, intervention details and reported outcome data were extracted by one reviewer (AJB) and checked by a second (ET). Outcome measures from studies were grouped by domain: mental health; quality of life; substance use; non-routine use of healthcare services; housing stability and other health-related outcomes.
+To avoid double counting of data, where sampling overlap was stated or suspected for any single outcome or where findings were reported in multiple papers, data were selected to prioritise larger combined samples or allow calculation of standardised effect estimates for comparison with other papers.33
+The Cochrane Risk of Bias Tool V2.0,34 was used by one researcher (AJB) to assess potential bias for each of the outcomes, and checked by a second (ET). If high risk of bias was reported in at least one domain of bias for an outcome, the outcome was given an overall ‘high’ rating.
+Population: adults (16 years and older) who meet at least one of the European Typology for Homelessness and Housing Exclusion (ETHOS) criteria: roofless, houseless, living in insecure housing, living in inadequate housing.
+Intervention: providing the homeless person with access to housing through:
+►	Assistance in locating and entering housing.
+►	Subsistence of rental costs to maintain permanent tenancy.
+The housing provided was defined as:
+►	Intended to be permanent—no intention by providers to end or transfer tenancy, counting sustained tenancy as the intended outcome.
+►	Not contingent on adherence to treatment or substance abstinence.
+►	Rapid, with the process of securing and entering housing initiated at first contact with the homeless person and with the aim of beginning tenancy promptly.
+Comparators: treatment as usual groups; although we note that this includes many diverse alternative homeless services and interventions.
+Outcomes: the primary outcomes, chosen to reflect the aim and research questions, were quantitative measures of health and well-being. These were grouped into five domains:
+►	Mental health—including self-reported mental health and clinical assessment of mental ill health.
+►	Self-reported health and quality of life—questionnaires and interviews recording perspectives.
+►	Substance use—including self-reported occasions of substance use and self-reported problematic substance use.
+►	Non-routine use of healthcare services—including episodes of hospitalisation and use of emergency services.
+►	Other, unanticipated measures of health and aspects of well-being associated with health and mental health.
+Secondary outcome: housing stability. This included any measure of housing which reflected the stated goals of the intervention of ending homelessness. The use of this domain in the review was based both on the hypothesised causative mechanism leading to changes in health and also its expected availability in almost all studies.
+Study design: randomised controlled trials
+Data synthesis
+Calculations of standardised effect sizes were conducted manually in Microsoft Excel.35 Standardised mean differences were calculated to compare continuous variables. These were interpreted as ‘small’ to ‘large’ effect size using Cohen’s classification.36. Incidence rate ratios were calculated for counts of use of health services, a risk ratio for attaining stable housing and ratios of rate ratios for the substance use subgroup outcome. Where effect sizes were reported only by subgroups and not the whole trial population, data were pooled where possible, otherwise subgroups were presented separately in forest plots.
+Forest plots were used to present standardised effect estimates for each outcome domain using Review Manager V5.3.37 A random effects model was used to calculate pooled effect size estimates, 95% CIs and heterogeneity, as we assumed that effect sizes and variation would differ across studies. Where meta-anal-ysis was not possible these were reported narratively in the relevant domain.
+Findings were summarised using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance to assess certainty of results for each meta-analysed outcome.38-42
+RESULTS
+Searching returned 494 records after removal of duplicates (figure 2). Following full-text screening, 25 eligible papers were identified for inclusion; these papers report results from four studies, all based in Canada and the USA (see online supplementary file 2 for included papers and online supplementary file 3 for exclusions).
+The four studies included in this review are outlined in table 1. The context and ‘treatment as usual’ provision varied across the cities and nations represented in these studies but were not always clearly and fully reported. All participants were homeless or insecurely housed; inadequate housing was not included in the studies retrieved. Beyond the inclusion criteria, there was
+some variation in the implementation of the HF model. All studies reported a measure of housing stability alongside one or more primary outcome measures. All results are summarised in table 2.
+Risk of bias
+The overall risk of bias was assessed as high for each outcome reported across all four studies (see online supplementary file 4 for all domains and table 2 for overall rating). Bias due to missing outcome data was rated as high if there were no data to
+evaluate how effectively the effect of loss to follow-up had been addressed.
+Primary outcomes
+Mental health
+All four studies reported mental health outcomes; these were categorised as ‘self-rated mental health’ (n=3: At Home, Chicago Housing for Health Partnership (CHHP) and Housing Opportunities for Persons with AIDS (HOPWA)) and ‘severity of mental health symptoms’ (n=3: At Home, Pathways Housing First (PHF) and HOPWA). Two studies provided data eligible for meta-anal-ysis of self-rated mental health (At Home and CHHP)43 44; a very small improvement was seen in intervention groups compared with treatment as usual (SMD = 0.07; 95% CI -0.19 to 0.33; p = 0.60, I2=82%; figure 3A). Additionally, HOPWA reported
+no statistically significant difference between groups.45 Both groups saw improvements in all studies.43-45 A small improvement in mental health symptom severity at 24 months in the At Home study was reported (SMD=-0.05; 95% CI -0.31 to 0.22; p = 0.73; I2 = 82%).46 47 Pathways HF participants saw no significant differences between groups in symptoms over 24 months (F=0.348; p=0.85; no effect direction reported).48 Improvements were seen in both intervention and TAU groups of the HOPWA study in depression and perceived stress, with no statistically significant differences between the two conditions.45
+Self-reported health and quality of life
+Several measures were reported in the domain of self-reported health and quality of life. Self-rated physical health was reported in three studies (At Home, CHHP and HOPWA).43-45
+Meta-analysis of two studies showed no detectable difference (SMD = 0.00; 95% CI -0.09 to 0.09; p = 0.94; I2 = 0%; figure 3B). Participants in both intervention and TAU groups of the HOPWA study reported improvements in self-rated physical health, with no statistically significant difference between groups.45 Two measures of quality of life were found in the At Home/Chez Soi study, but not repeated elsewhere. Pooling the two age group subgroups showed a small difference in mean change of generic quality of life between treatment and control groups from baseline, favouring TAU (SMD=-0.03; 95% CI -0.13 to 0.06; p = 0.50; I2 = 0%) and a small difference in condition-specific quality of life, favouring intervention (SMD = 0.18; 95% CI -0.09 to 0.46; p=0.19; I2 = 83%; not shown).43
+Substance use
+Two studies reported substance use outcomes (At Home and PHF).43 46-49 Data from PHF were reported as showing no significant differences in either alcohol or drug use at 24 months, but no direction of effect was indicated and so these could not be used in meta-analysis.48 Across 48 months, a greater reduction of heavy alcohol use (defined as using alcohol on >28 days in 6 months) in intervention groups compared with control is reported in the study by Padgett et al,49 with no clear difference in drug use. Pooling the two age group subgroups of the At Home/Chez Soi study showed a very small overall difference in self-reported problematic substance use, favouring HF (ratio of rate ratios = 0.96; 95% CI 0.72 to 1.28; p = 0.77; I2=61%; not shown)43; both groups saw decreases in reported problems.46 47
+Health service use
+All studies reported a measure of health service use. In meta-anal-ysis (n=2: CHHP and HOPWA), intervention participants experienced fewer hospitalisations (incidence rate ratio (IRR) = 0.76; 95% CI 0.70 to 0.83; p<0.00001; I2 = 0%; figure 3C).44 45 A small difference was seen in time spent hospitalised, also favouring intervention (n=3: At Home, CHHP and PHF; SMD = -0.14; 95% CI -0.41 to 0.14; p = 0.32; I2 = 83%; figure 3D).8 44 47
+A greater reduction was seen in intervention groups over control groups in number of emergency department visits (n=2: At Home and CHHP; IRR=0.63; 95% CI 0.48 to 0.82; p = 0.0006; I2 = 95%; figure 3E).44 46 HOPWA participants saw no significant difference between intervention and control groups in likelihood of one or more emergency department visit in each of three 6-month time periods (F = 0.63; p = 0.5977),45 and the CHHP intervention group saw a small reduction in likelihood of one or more emergency department visit in the 18-month period over control (risk ratio (RR) = 0.92; 95% CI 0.81 to 1.04).44
+Housing stability
+All four studies reported measures of housing stability, either recording a proportion of total days reported as ‘stably housed’ or a proportion of the population in stable housing at the end of the trial period. In all four studies, the intervention group was found to have large increases in housing stability over TAU.43-48 The combined effects estimate indicated that participants receiving HF are two and a half times more likely to be stably housed after 18-24 months (n=3: At Home, CHHP and HOPWA; RR=2.46; 95% CI 1.58 to 3.84; p<0.00001; I2 = 94%). A large standardised mean difference for time spent housed during trial was also seen, favouring intervention (n=2: At Home and PHF; SMD = 1.24; 95% CI 0.86 to 1.62; p<0.0001; I2=90%; see online supplementary file 4).
+Subgroups reported
+Subgroup comparisons were only conducted in the At Home/ Chez Soi study (see online supplementary file 4). In age comparisons, the older group (aged >50 years) had better outcomes than the younger group (18-49 years old) in a number of areas, such as self-rated mental health, mental health symptom severity, substance use and quality of life.43 50 Participants with less severe mental health and problematic substance use experienced slightly better outcomes.46 47 Participants housed together in dedicated accommodation blocks (referred to as the ‘congregate model’) experienced greater improvements than those in ‘scattered site’ housing, in mental health, quality of life and problematic substance use, among other outcomes.51 Across all subgroups reported, intervention participants saw large increases in housing stability.
+Other outcomes
+Several further outcomes that were related to health were recorded. These are listed in online supplementary file 5. Several small, uncertain effect sizes were observed, favouring HF in most cases, with two of the At Home subgroups experiencing small, uncertain effects favouring treatment as usual.43 51 Two studies reported HIV survival and viral load but the findings were conflicting.45 52
+DISCUSSION
+Summary of findings
+Our systematic review found that HF resulted in large improvements in housing stability; with unclear short-term impact on health and well-being outcomes. For mental health, quality of life and substance use, no clear differences were seen when compared with TAU. HF participants showed a clear reduction in non-routine use of healthcare services, over TAU. This may be an indicator of improvements in health.
+Comparison with existing literature
+The combination of a strong, positive impact on housing with little additional impact on mental health and substance use, compared with TAU, is consistent with the findings of other reviews.26-29 Our meta-analyses provide a clear picture of improvements in hospitalisation and emergency department visits, which has not yet been reported in other reviews. Inclusion of only RCTs gives greater confidence that these results are less susceptible to bias. Previous reviews have questioned whether abstinence-contingent housing may lead to greater reductions in problem substance use than HF, although at the cost of housing stability.28 However, our results found reductions in problem substance use for both HF and TAU, with no clear difference between them. This is consistent with non-randomised observational evidence suggesting greater effectiveness of HF than TAU in this respect.16 53
+Prior research on HF suggests that the consumer choice framework allows homeless clients greater perceived control, security and mastery of circumstances, leading to greater improvements in mental health and quality of life.54 55 A lack of clear difference seen across the RCTs analysed here may be due to several factors, including the heterogeneity of sample participants, differences in provision of attached services, differences in application of consumer choice and the relatively short-term observation period.
+Strengths and limitations of this review
+Our systematic review has several strengths. We conducted a comprehensive search across several databases, which aimed to include all of
+Review
+the relevant studies. The strict use of a clearly predefined protocol, with explicit inclusion and exclusion criteria, has allowed us to bring together all relevant evidence in a transparent manner. This includes drawing on theoretical understanding to define a clearly identifiable and replicable intervention. The use of the logic model allowed testing of the theoretical impact of HF on health through housing stability as a mediator.
+This systematic review had some limitations. The scope of this review was primarily limited by the focus on quantitative data from RCTs, and the largest study, a trial of At Home/Chez Soi, carried substantial weight and was the main determinant of effect estimates in rate of emergency department visits, and time spent stably housed. Although trials are underway elsewhere (eg, the Un Chez-Soi d’abord study in France56 57), the data included in this review were exclusively from North America and the participants were all selected on the basis of complex health needs (such as mental illness, substance abuse or chronic physical illness) as per the principles of HF.16 17 This may limit the generalisability of our findings internationally, as well as to homeless people without complex health needs. Other published data from non-randomised studies are available and may provide further insights into health outcomes, but these studies are at a higher risk of bias. Future qualitative enquiry to identify mechanisms associated with changes in health outcomes could help optimise the benefits of HF.
+Across all studies there were high ratings of risk of bias in several areas. Available data were limited to a 24-month follow-up period, providing observations of only short-term outcomes (figure 1). The uncertainty of effect size and direction of the primary health outcomes prevents accurate testing of the hypothesised intermediate and long-term effects of housing stability.
+A further systematic review, comparing HF with other interventions, for example, abstinence-contingent housing, housing vouchers, residential treatment and case management (without housing), was published after the completion of this review. This did not consider health outcomes but reported similar results for housing stability.58
+Implications for research and implementation
+Further questions are prompted by this review which could be addressed by ongoing evaluation of the HF model. Clear reporting of the intervention characteristics (for primary research) and inclusion criteria (for systematic reviews) should be a starting point in future research to ensure testing of an identifiable and replicable model.59 Further observations of longer follow-up periods would give greater confidence of impacts on long-term health.
+The subgroup analyses of the At Home/Chez Soi study showed several differences in effects for different age groups and health needs. It is unclear if these findings reflect genuine differences60; further research would be required to determine if there is greater effectiveness of the intervention for particular groups of homeless persons.
+To address some of these concerns, a further systematic review could synthesise the wider evidence base and allow generation of hypotheses about explanations for heterogeneity in reported effects. These data could then be used to refine aspects of HF with the aim of optimising potential beneficial impacts of HF investment. Evaluation of the relative contribution of key principles of HF to its effectiveness would be an important next step. In addition, a clearer differentiation and comparison of the treatments broadly grouped under treatment as usual in this review could show whether better interventions exist for certain groups.
+This review adds strength to the calls to adopt HF as an ‘evidence-based’ housing model, having shown consistent improvements in the housing stability of vulnerable homeless persons. Concerns that HF could result in higher rates of problematic substance use than treatment as usual are contradicted by these data. Alongside this, HF could reduce use of non-rou-tine health services, with potential cost savings. Subgroup analysis, although only reported in one study, suggests that housing stability is improved regardless of the age or health needs of the clients, while improvements in health might be differentially seen across groups. According to the logic model in figure 1, the improvements in housing stability associated with HF might be expected to result in intermediate and long-term positive impacts on these and other health outcomes, beyond the timescales considered in this review.
+CONCLUSION
+HF approaches appear to be highly effective in reducing homelessness among vulnerable participants. However, in several direct measurements of short-term health outcomes, the impact of HF is not clear. HF can be seen to reduce non-routine use of healthcare services, which may be an indicator of better health outcomes. Further evidence could be valuable in assessing the long-term effects of improved housing stability on health. HF could be implemented with strong confidence in its success as a housing intervention, alongside some confidence in a lack of immediate adverse effects on health, but with caution in relying on this model for certainty in improved health outcomes.
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+Introduction
+The way that media reports about suicide has received considerable attention in suicide prevention research over the past 5 decades. Most of the research has focused on harmful media impacts—the Werther or imitation effect.1,2 The Werther effect refers to the relationship between sensationalist or repetitive reporting of suicide and subsequent increases in suicide in the population. There is now strong empirical support for the Werther effect. A 2020 systematic review and meta-analysis found
+that media reports about celebrity suicides were associated with an 8-18% increase in suicides within 2 months of the reporting.3 This highlights that media reporting of suicide is a powerful environmental exposure that can have an impact on suicides. But importantly, poor reporting of suicide is potentially amenable to intervention through the implementation of media guidelines. For this reason, recommendations about news reporting on suicide are now a standard component of national suicide prevention strategies.4
+Research in context
+Evidence before this study
+Three systematic reviews and meta-analyses on harmful media reporting about suicides and subsequent suicides in the general population have been published (the Werther effect). One was published in 2005, another in 2012, and the third one in 2020. The most recent meta-analysis found that media reporting of celebrity suicides was associated with an 8-18% increase in suicides in the population in the following 1-2 months.
+The increase was even stronger for the same suicide method as reported in the media (18-44%). No systematic reviews or meta-analyses have assessed the effects of media messages designed to be protective against suicidal behaviour (the Papageno effect). We searched PubMed, Embase, PsycInfo, Scopus, Web of Science from inception up to Sept 6, 2021, using the search terms ((suicid* OR self-harm OR help-seeking) AND (Werther* OR Papageno* OR ripple* OR copycat OR imitat* OR contagio* OR suggesti* OR lived-experience) AND (media OR newspaper* OR print OR press OR radio* OR televis* OR film* OR movie OR book* OR documentar* OR internet OR cyber* OR web* OR music* OR drama* OR message* OR news* OR announcement* OR video OR broadcast* OR song* OR play* OR theat* OR story* OR narrative)). No language restrictions were applied. The first study investigating protective media information—an observational study—was published in 2010. This study showed a small negative association between protective media reports and suicide. Eight subsequent studies tested different types of protective interventions using a randomised controlled trial design. The interventions that were tested included media stories featuring individuals mastering their suicidal crises, and stories featuring prevention experts and peers of suicidal individuals speaking about suicide prevention and help seeking. All the eight studies included some baseline indicator of vulnerability of participants, and four of them
+explicitly examined protective effects among people with some degree of vulnerability to suicide. The individual studies showed differing results—four showed no statistically significant changes in suicidal ideation after the exposure to a narrative of hope and recovery whereas another four showed some beneficial effect. Among six studies assessing help-seeking attitudes, one reported a statistically significant beneficial effect while five reported no changes. There are no existing evidence syntheses on this topic.
+Added value of this study
+To our knowledge, this is the first systematic review and metaanalysis to pool together studies examining the association between protective media and suicidal behaviour. Our pooled analysis showed evidence that exposure to narratives of hope and recovery resulted in a small, but statistically significant reduction in mean suicidal ideation when compared with active controls. There was insufficient evidence to conclude that narratives of hope and recovery were associated with differences in help-seeking attitudes and intentions between groups.
+Implications of all the available evidence
+To our knowledge, this is the first study providing combined evidence across published trials that media narratives of hope and recovery from a suicidal crisis have a small protective effect on suicidal ideation on the short term (up to 4 weeks after exposure) among individuals with some degree of vulnerability to suicidal ideation or behaviour. These narratives pose little risk in populations with some degree of vulnerability to suicide. Large-scale trials to test the efficacy of these types of interventions on suicidal and help-seeking behaviours are needed.
+Although the harmful effects of media are documented, less is known about protective effects. In the past 10 years, researchers have begun to investigate how media exposure might be used to reduce suicide risk.5-10 If some specific media portrayals serve as a basis for subsequent suicidal behaviours, as indicated by the Werther effect, other narratives, particularly those featuring individuals who tell stories of overcoming suicidal crises without engaging in suicidal behaviour might reduce suicidal behaviours by decreasing the risk of acting out suicidal thoughts. Findings from the first study6 in the topic area suggested that news items featuring individuals who survived suicidal crises were associated with a small decrease in suicides. This suicide-protective media effect has been termed the Papageno effect.6,7
+Nearly all the studies of the Papageno effect have been randomised controlled trials (RCTs), which differ from ecological research designs used to study the Werther effect and provide a stronger basis for making causal inferences. These studies have frequently used ideation
+as the main endpoint of interest, which is a pragmatic endpoint for trials especially for assessing the safety of suicide-related messaging.8,10-14 A further important endpoint in these studies has been help-seeking attitudes and intentions.5 Some of these studies suggest that stories of hope and recovery have the strongest effect on individuals with some degree of vulnerability.10,12 This might be due to stronger identification with the stories, or higher perceived usefulness among individuals with personal experience of suicidality or suicide attempts.15 Effects on individuals with some degree of vulnerability are of particular importance to suicide prevention. Stories of suicide have been shown to be particularly harmful among those with some vulnerability,16 which raises the question of whether narratives of hope have similar harmful effects. Considering the small samples analysed in previous studies, meta-analytic approaches, particularly analyses using individual participant data (IPD), can answer the question about harmfulness in vulnerable audiences.
+In this systematic review and IPD meta-analysis,17 we combined participant-level data from RCTs on the Papageno effect to quantify the effect of personal stories of mastery of suicidal crises on individuals with some degree of vulnerability. We focused on two endpoints which have received the most attention: first, suicidal ideation (the primary outcome); and second, helpseeking attitudes and intentions (the secondary outcome). In two sensitivity analyses, we assessed whether findings for vulnerable individuals were generalisable to the entire intervention groups including people with low vulnerability, and whether findings were generalisable to narratives featuring peers and professionals rather than individuals speaking about their own recovery process.
+Methods
+Search strategy and selection criteria
+In this analysis, we searched PubMed (including MEDLINE), Scopus, Embase, PsycInfo, and Web of Science using the following search terms ((suicid* OR self-harm OR help-seeking) AND (Werther* OR Papageno* OR ripple* OR copycat OR imitat* OR contagio* OR suggesti* OR lived-experience) AND (media OR newspaper* OR print OR press OR radio* OR televis* OR film* OR movie OR book* OR documentar* OR internet OR cyber* OR web* OR music* OR drama* OR message* OR news* OR announcement* OR video OR broadcast* OR song* OR play* OR theat* OR story* OR narrative)). No language restrictions were applied. We searched titles, abstracts, and keywords from inception until Sept 6, 2021, for each database. The search was intentionally broad to capture all related studies. Google Scholar was used to identify grey literature, using the search terms “suicide and media”. Furthermore, we checked the Canadian Agency’s for Drugs and Technologies in Health (CADTH) Grey Matters guidance for a comprehensive list of clinical trials registries to identify any unpublished or further trials of interest.18 We also screened the reference lists of identified reviews and recent editorials or comments for further references. Finally, we searched the reference lists and did a cited-reference search for all included studies using Google Scholar. For a detailed overview of the search strategy see the appendix (p 3).
+Titles and abstracts were screened independently by two authors (TN and SK) using Mendeley. Eligible studies were then selected by full-text articles review by the same authors. At both stages of screening, disagreements were resolved by consensus.
+Our eligibility criteria were framed using the Population, Intervention, Comparison, Outcomes, and Study (PICOS) design tool. Studies were included that used data from the general population (P), analysed a media intervention illustrating hope and recovery from a suicidal crisis (I), used active controls not exposed to suicidal media content (C), measured suicidal ideation or help-seeking attitudes or intentions as an outcome (O), and used an RCT design (S).
+The media interventions needed to satisfy all the following criteria: first, have a focus on suicidal ideation in the absence of near-fatal or fatal suicidal behaviours; second, feature a personal narrative of hope and recovery; and third, involve media exposure only and not include any other components (eg, skills training). We were primarily interested in stories featuring hope and recovery from the perspective of an individual experiencing a suicidal crisis or ideation, but we also included stories from other perspectives (eg, stories emphasising recovery but featuring peers or professionals). Regarding the content of the control group, restrictions were applied in that it had to be a non-suicide related intervention and comparable to the intervention (eg, similar length, style, and format).
+Studies were excluded if they did not feature a clearly positive story of hope and recovery, had no control group, or had a control group exposed to suicide-related stimulus material. Furthermore, we excluded studies if they did not measure suicidal ideation or help-seeking attitudes or intentions. No restrictions were placed on the instruments used to assess outcomes, the publication dates, or follow-up periods.
+Data collection and analysis
+We contacted the lead or senior authors of all original studies to obtain participant-level data for our metaanalysis. All authors provided their data and the data documentation. These authors were included as coauthors in this study and approved the estimates from their original data.
+From each original dataset, we extracted participantlevel data on age, gender, trial group allocation, baseline scores for suicidal ideation, help-seeking attitudes or intentions, suicide vulnerability, and follow-up scores for suicidal ideation and help-seeking attitudes or intentions. Datasets were checked for consistency and completeness, and where appropriate, data recoding was done to ensure consistency (such that the vulnerability and outcome variables were scored in the same direction). We also extracted study-level data on the number of trial groups, the content of the intervention and control narratives, their length (in time), the scales used to measure ideation and help-seeking attitudes or intentions, how baseline suicide vulnerability was defined, where the trial was done, and follow-up times. Extractions were done independently by SK, TN, and MJS. Discrepancies were discussed and resolved.
+Risk of bias assessment
+Risk of bias within studies was based on the Cochrane risk-of-bias tool for randomised trials.19 This tool assesses bias in RCTs. It includes five domains: first, bias arising from the randomisation process; second, bias due to deviations from the intended interventions; third, bias due to missing outcome data; fourth, bias in outcome measurement; and fifth, bias in selection of reported
+results. Each domain is coded into low, some, or high risk ofbias. Studies were coded as being overall at low risk of bias if all five domains were coded as low risk. Studies were coded as being at some risk of bias if at least one domain was coded to be at some risk, but no domains were coded as being at high risk. Studies were coded as high risk if any domain was at high risk. Domain three largely determined the overall assessment. The coding of bias was done by two independent researchers (unconnected with this study or any of the original studies) for both outcomes. Any discrepancies were discussed and resolved by consensus.
+Risk of bias across studies, primarily due to missing results in the synthesis (publication bias), was assessed visually using contour-enhanced funnel plots and statistically with Egger’s regression test for funnel plot asymmetry of the study-specific estimates.
+Synthesis methods
+For studies with multiple intervention groups, we recoded the data to make a two-group comparison (intervention vs control). We combined intervention groups because we were primarily interested in a direct comparison between Papageno interventions and active controls (rather than examining whether some types of Papageno interventions have greater efficacy than others). We did a primary analysis and two sensitivity analyses using studies rated at low or some risk of bias. The primary analysis was done on studies that tested personal narratives of how to cope with a suicidal crisis and was done using participants experiencing vulnerability. Our first sensitivity analysis used the same set of studies as the primary analysis but included all individuals (ie, those not experiencing vulnerability in addition to those with vulnerability). Our second sensitivity analysis took a broader view of the narratives— that is, including studies testing professional as well as peer narratives of hope and recovery. In this analysis, we included only individuals experiencing vulnerability.
+For all analyses, we estimated the pooled standardised mean difference (SMD). We did this using the two-stage IPD meta-analysis approach. In the first stage, we extracted from the intervention and control groups of each study the mean, its standard deviation, and sample size of each outcome using complete-case methods. We then calculated study-specific SMDs and standard errors (the difference in means between groups divided by the pooled standard deviation). In the second stage, we used these data to estimate the pooled SMD using randomeffects restricted maximum likelihood estimation. Heterogeneity for all models was assessed using the 12 statistic. Values around 25%, 50%, and 75% were interpreted as low, moderate, and high heterogeneity, respectively.20 All analyses were done using Stata (version 16.1) and R (version 3.6.1).
+This study is registered with PROSPERO,21 number CRD42020221341. We made one amendment to the design
+of the study after registration. Instead of stratifying the analyses by vulnerability of study participants, we decided to focus solely on individuals with some degree of vulnerability in the primary analysis. This decision was due to the high relevance of findings in this subsample. Findings for all participants are reported as sensitivity analysis. A further protocol change was that we removed laboratory experiments from the eligible designs to focus only on RCTs. No changes in terms of studies included resulted from this change. We report our study using the Preferred Reporting Items for Systematic Review and Meta-Analyses of Individual Participant Data (PRISM A-IPD) guidelines.22 Ethical approval was obtained from the Ethics Review Board of the Medical University of Vienna (review number 1481/2020).
+Role of the funding source
+There was no funding source for this study.
+Results
+Our search yielded 7347 records, and after removing the duplicates, 3920 records remained for screening (figure 1). After the removal of ineligible records, we retained 25 records that we assessed for eligibility by reading the full text; 17 of these were excluded for specific reasons (figure 1). The appendix (pp 4-6) contains a complete description of the excluded studies. No additional ongoing or completed trials of possible relevance were identified in the search of clinical trials registries as listed in CADTH’s Grey Matters. This left eight unique studies8,10-14’23’24 for our qualitative synthesis and meta-analysis (table and appendix p 7). IPD data were sought and obtained from all eight studies. No issues of data integrity were identified.
+Two studies23,24 were done in Australia and six studies in Austria.8,10-14 The studies had a total of2350 participants randomly assigned to either the intervention or control groups. At baseline, participants had a mean age of 32 years (SD 14, range 18-97) and 60% were female. Because five studies had multiple intervention groups,8’11’12’14’23 there was an imbalance in the allocation to intervention and control groups. In total, 1518 participants (65%) were allocated to the intervention groups and 832 (35%) to the control groups. Detailed information on each study (ie, number of participants, demographic profile, and unavailability of outcomes) is available in the appendix (p 8).
+Vulnerability to suicide before the intervention was recorded in a variety of different ways (table 1). In four studies,8,10’23’24 vulnerability was measured using a suicidal ideation questionnaire and recorded as a binary variable (low vs high) that was split into two groups at the median. One study12 measured vulnerability using a question about suicide attempts in the past year. One study13 measured vulnerability using the Patient Health Questionnaire-9, with scores above 14 indicating vulnerability. One study14 assessed vulnerability with a question about current suicidal thoughts. A final study11
+had no baseline assessment of vulnerability but measured identification with the suicidal person featured in the media story, which has been shown to constitute a relevant factor in vulnerability and media effects.9,15
+Suicidal ideation was measured using the Adult Suicidal Ideation Questionnaire (two studies23,24), the Reasons for Living Inventory (one study10), its subscale, the Survival and Coping Beliefs subscale (four studies8,12-14), or the Implicit Association Test (one study11). Helpseeking intentions were measured using the General Help-Seeking Questionnaire (four studies13,14,23,24) and help-seeking attitudes with the Short Attitudes Towards Seeking Professional Help Scale (two studies10,12). Details on the interventions are in table 1. Follow-up outcome data were collected immediately after the media exposure for four studies,8,11,13,14 1 week after for two studies,10,12 and 4 weeks after for two studies.23,24 Four studies8,10,12,24 were judged to be at low risk of bias and four11,13,14,23 at some risk. No studies were at high risk of bias (appendix p 9), and therefore all studies were included in our analyses.
+For suicide ideation, scales in the original studies were scored so that lower scores were associated with lower levels of suicidal ideation. For the primary analysis, six studies met the inclusion criteria and follow-up data were available for 569 (90%) of 633 participants with baseline ideation scores above the median (345 [55%] participants were allocated to the intervention group and 288 [45%] to the control group). The pooled SMD for this group indicated a small reduction in mean suicidal ideation in the intervention group of -0-22 (95% CI -0-39 to -0-04, p=0-017, six studies; figure 2A). Low levels of heterogeneity were observed in this analysis (12=5%). For the first sensitivity analysis that used all participants regardless of the baseline vulnerability, data were available from the same six studies for 1138 (86%) of 1317 participants (717 [54%] allocated to the intervention group and 600 [46%] to the control group). The pooled SMD for this group was -0-06 (95% CI -0-24 to 0-11, p=0-49, six studies; figure 2B). Moderate heterogeneity was observed (12=49%). For the second sensitivity analysis, which broadened the types of narratives under investigation, all eight studies were included, and baseline data were available for 876 (87%) of 1009 participants who had baseline ideation scores above the median (643 [64%] allocated to the intervention group and 366 [36%] to the control group). For this group, the pooled SMD was -0-13 (95% CI -0-28 to 0-01, p=0-064, eight studies; figure 2C) and the heterogeneity was low (12=0%).
+For help-seeking attitudes and intentions, scales were scored so that higher scores indicated stronger help-seeking attitudes or intentions. For the primary analysis, four studies met the inclusion criteria and follow-up data were available for 362 (86%) of 420 participants who had baseline ideation scores that were above the median (247 [59%] allocated to the intervention group and 173 [41%] to the control group). The
+Figure 1: Study profile
+pooled SMD showed no significant difference between the groups (SMD=0-14, 95% CI -0-15 to 0-43, p=0-35, four studies; figure 3A). Moderate heterogeneity was observed for this set of studies (12=36%). For the first sensitivity analysis that included all participants, follow-up
+data were available for 739 (79%) of 939 participants (556 [59%] allocated to the intervention group and 383 [41%] to the control group). The pooled SMD for this analysis was 0-12 (95% CI -0-10 to 0-35, p=0-28, four studies; figure 3B) and the heterogeneity was moderate (12=50%). For the second sensitivity analysis, follow-up data were available for 583 (82%) of 710 participants who had baseline ideation scores above the median (459 [65%] participants were allocated to the intervention group and 251 [35%] to the control arm). The
+pooled SMD was 0-04 (95% CI -0-16 to 0-25, p=0-69, six studies; figure 3C). The heterogeneity in this analysis was low (12=24%).
+We found no evidence of publication bias in any of the analyses. For the outcome suicidal ideation in the primary analysis, scores were to the left of the null line, but none were outside the contours representing the 1% significance level (figure 4A). All other estimates were distributed symmetrically around the null line with none falling outside the 1% significance level.
+Egger’s test for asymmetry was non-significant for all analyses.
+Discussion
+To the best of our knowledge, this is the first systematic review and meta-analysis about media portrayals of stories of hope and recovery from suicidal crises on suicidal ideation and help-seeking attitudes and intentions. The evidence is that, among individuals with some degree of vulnerability, personal stories of hope have a small protective effect on suicidal ideation up to 4 weeks after exposure.
+The protective effect highlights that these narratives are unlikely to be harmful for individuals with some degree of vulnerability to suicide in the general population. This is
+important because this group is the key target for many media-based suicide prevention efforts. There are several examples of well intentioned media narratives that sought to educate the public and reduce suicide, but which have tragically resulted in an increase in suicides.25,26 These narratives have not typically focused on hope and recovery, but featured a specific suicide or suicide method. This highlights the importance of identifying narratives that do not put individuals at risk of harm.
+Our IPD meta-analysis did not find evidence of an association between personal stories on help-seeking attitudes and intentions. The wide confidence interval of the combined estimate pointed in the expected direction but was based on a small number of studies. Thus, we lack sufficient evidence to make a reliable determination
+about Papageno messages on this outcome. The only original study that found a statistically significant improvement in help-seeking intentions used a narrative that was specifically set up to tackle gender stereotypes to enhance help seeking in men,24 who have higher suicide rates but show less help seeking than women.27 To influence help-seeking attitudes and intentions, a focus on barriers to help seeking in the context of surviving a crisis might be necessary.
+Although the efficacy of messages of hope and recovery from a suicidal crisis was small, there are some reasons to be optimistic about their real-world impacts when implemented at scale. First, universal interventions with low efficacy can still be important if they can be delivered to a large proportion of the population. Media interventions are a good example of this because they can have substantial reach into the population. Narratives of hope and recovery are already readily available, and they are widely acceptable to stakeholders including individuals bereaved by suicide or with personal experience of suicidal ideation and attempts.28 Development and implementation of this type of intervention requires fewer resources than other interventions.29
+Another reason for optimism is that effects were observed in randomised trials where the outcomes were
+measured within a short period of time. Demonstrating efficacy at this stage should be a necessary precondition to further develop public health messages. It is unlikely that media messages will be effective in the real world if they have no effect in the short term. But in contrast to the included RCTs measuring single exposures, in the real world, successful public health advertising works through repetition and over prolonged periods.30 Finally, it is increasingly common to target advertising to a specific population using social media. Many media consumers are self-selecting themselves into media streams that reflect their interests, meaning it is possible to deliver relevant media messages to many people in the target audience at the same time.
+Another key implication of our study is the issue of harm. There are several examples of well intentioned media messages that have caused more harm than good.25,26,31-34 In contrast to this, stories of hope and recovery do not appear to show harmful effects among those who are vulnerable from the general population and could have some benefits. The Papageno effect cannot replace media narratives about fatal suicides if the topic meets the media criterion of newsworthiness, but it provides a potentially novel and safe way forward to educate the public about suicide prevention that can help
+to shift the focus from narratives of despair to a more focused portrayal of how to cope with adversity. Many media guidelines are now rightfully cautious about reporting on suicide.4 Our findings suggest that these guidelines could safely be revised to support the reporting of stories of mastery of a suicidal crisis. Further studies specifically analysing the effects of media stories of mastery on a wide range of audiences, using a variety of narratives that are tailored to the specific target groups and to the primary impact domain under investigation (eg, suicidal ideation or help-seeking intentions), are needed, as are large-scale trials that test any effect of such stories on suicidal and help-seeking behaviour.
+Strengths of this meta-analysis were the specific focus on stories of hope and recovery from a suicidal crisis and the inclusion of a broad set of interventions consisting of stories of different lengths and media types. The inclusion criteria were intentionally conservative in that they focused on evaluating studies with a message of hope and recovery rather than including studies with broad narratives aiming to promote suicide prevention (specifically those studies featuring suicides or details about suicide methods). Most importantly, the narratives all avoided presenting information on specific near-fatal or fatal suicidal behaviours, something which is known to cause harm. Another strength was that we selected only those studies that used active controls. This is a conservative approach because it isolates the effect of the message from other factors such as the way the message was delivered. None of the included studies were at high risk of bias. Finally, cross-study heterogeneity effects were low and there was no evidence of publication bias as evidenced by contour enhanced funnel plots and Egger’s tests.
+There are several limitations of our study. First, this meta-analysis relied on suicidal ideation and helpseeking attitudes and intentions as outcomes, rather than suicidal and help-seeking behaviours. It is not possible to generalise findings to suicidal behaviours due to the low specificity of suicidal ideation. It appears likely, however, that an intervention that reduces suicidal ideation in a substantial proportion of a population would reduce some suicides.35 To the best of our knowledge, there are currently only four published studies that have assessed associations between portrayals of suicidal ideation (typically covering stories of recovery) with suicides.6,32,36’37 Three ofthese studies6,36,38 suggested fewer suicides following stories about suicidal ideation, whereas one study32 did not identify any association. Second, the study could only examine those interventions that have been tested in RCTs, most of which were short, one-off interventions. Third, all source studies included in the meta-analyses were done by members of the study team. We attempted to address the limitation of evaluating our work by using established guidelines for the conduct and reporting and by being transparent about this. Furthermore, the quality
+assessment of included studies was done by independent researchers. Fourth, we assessed effects in a group of individuals with some degree of vulnerability to suicide, but it remains unclear how vulnerable these individuals were. Fifth, although we found no evidence of publication bias, our analyses included a maximum of eight studies. Egger’s test might have had insufficient power to detect publication bias if it were present. Nonetheless, the contour enhanced funnel plots showed good symmetry, which strengthens the argument that publication bias is not a factor in the retrievable literature. Sixth, all the studies we identified were done in either Australia or Austria and, therefore, the results might not generalise beyond these settings. Seventh, the outcome related to help seeking included attitudes and intentions, which are not the same constructs. For future studies, we recommend investigating help-seeking intentions (rather than attitudes) with broadly applicable questionnaires (eg, the General Help-Seeking Questionnaire). Eighth, data on race and ethnicity were not available. Finally, media portrayals can affect various other domains beyond help-seeking attitudes and intentions and suicidal ideation (eg, stigmatisation or prevention-related knowledge), which we did not study.
+To our knowledge, this is the first meta-analysis on the effect of portrayals of personal mastery of suicidal ideation on suicidal ideation and help-seeking attitudes and intentions. Our results suggest that these narratives reduce suicidal ideation in audiences with some vulnerability to suicide in the general population, providing a strong case for their use for suicide prevention.
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+WHO. Preventing suicide: a resource for media professionals.
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+Niederkrotenthaler T, Reidenberg DJ, Till B, Gould MS. Increasing help-seeking and referrals for individuals at risk for suicide by decreasing stigma: the role of mass media. Am J Prev Med 2014;	23
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+Niederkrotenthaler T, Voracek M, Herberth A, et al.
+Papageno v Werther effect. BMJ 2010; 341: c5841.
+Till B, Arendt F, Scherr S, Niederkrotenthaler T. Effect of educative	25
+suicide prevention news articles featuring experts with vs without personal experience of suicidal ideation: a randomized controlled trial of the Papageno effect. J Clin Psychiatry 2019; 80: 17m11975.
+Till B, Strauss M, Sonneck G, Niederkrotenthaler T. Determining 26 the effects of films with suicidal content: a laboratory experiment.
+Br J Psychiatry 2015; 207: 72-78.	27
+Till B, Tran U, Voracek M, Niederkrotenthaler T. Papageno vs.
+Werther effect online: randomized controlled trial of beneficial and	28
+harmful impacts of educative suicide prevention websites.
+Br J Psychiatry 2017; 211: 109-15.
+Arendt F, Till B, Niederkrotenthaler T. Effects of suicide awareness material on implicit suicide cognition: a laboratory experiment.	29
+Health Commun 2016; 31: 718-26.
+Niederkrotenthaler T, Till B. Effects of suicide awareness materials on individuals with recent suicidal ideation or attempt: online	30
+randomised controlled trial. Br J Psychiatry 2020; 217: 693-700.
+Niederkrotenthaler T, Till B. Effects of awareness material featuring individuals with experience of depression and suicidal thoughts on 31 an audience with depressive symptoms: randomized controlled trial. J Behav Ther Exp Psychiatry 2020; 66: 101515.
+Till B, Tran US, Niederkrotenthaler T. The impact of educative news articles about suicide prevention: a randomized controlled trial.	32
+Health Commun 2020; 36: 2022-29.
+Niederkrotenthaler T, Arendt F, Till B. Predicting intentions to read suicide awareness stories: the role of depression and characteristics 33 of the suicidal role model. Crisis 2015; 36: 399-406.
+Niederkrotenthaler T, Stack S. Media and suicide: international perspectives on research, theory, and policy. NY: Routledge, 2017.	34
+Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med 2017; 36: 855-75.
+Niederkrotenthaler T, Spittal MJ, Till B, et al. Effects of stories of 35 hope and recovery on suicidal ideation and help-seeking intentions: systematic review and meta-analysis. 2020. https://www.crd.york. ac.uk/prospero/display_record.php?ID=CRD42020221341 (accessed Sept 17, 2021).	36
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+Deeks JJ, Higgins JP, Altman DG, Cochrane Statistical Methods 37 Group. Analysing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, et al (eds). Cochrane handbook for systematic reviews of interventions. 2nd edn. Chichester, UK:	38
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+Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement. JAMA 2015; 313: 1657-65.
+Ftanou M, Ross A, Machlin A, et al. Public service announcements to change attitudes about youth suicide: a randomized controlled trial. Arch Suicide Res 2021; 25: 829 44.
+King KE, Schlichthorst M, Spittal MJ, Phelps A, Pirkis J. Can a documentary increase help-seeking intentions in men?
+A randomised controlled trial. J Epidemiol Community Health 2018; 72: 92-98.
+Niederkrotenthaler T, Kirchner S, Till B, et al. Systematic review and meta-analyses of suicidal outcomes following fictional portrayals of suicide and suicide attempt in entertainment media. EClinicalMedicine 2021; 36: 100922.
+Schmidtke A, Hafner H. The Werther effect after television films: new evidence for an old hypothesis. Psychol Med 1988; 18: 665-76. Canetto SS, Sakinofsky I. The gender paradox in suicide.
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+Bridge JA, Greenhouse JB, Ruch D, et al. Association between the release of Netflix’s 13 Reasons Why and suicide rates in the United States: an interrupted time series analysis.
+J Am Acad Child Adolesc Psychiatry 2020; 59: 236 43.
+Niederkrotenthaler T, Stack S, Till B, et al. Association of increased youth suicides in the United States with the release of 13 Reasons Why. JAMA Psychiatry 2019; 76: 933 40.
+Sinyor M, Williams M, Tran US, et al. Suicides in young people in Ontario following the release of “13 Reasons Why”. Can J Psychiatry 2019; 64: 798-804.
+Cooper MT Jr, Bard D, Wallace R, Gillaspy S, Deleon S. Suicide attempt admissions from a single children’s hospital before and after the introduction of Netflix series 13 Reasons Why.
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+Sinyor M, Schaffer A, Nishikawa Y, et al. The association between suicide deaths and putatively harmful and protective factors in media reports. CMAJ 2018; 190: e900-07.
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+www.thelancet.com/public-health Vol 7 February 2022
+e168
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+Background
+Mental disorders are on the rise globally, and are often co-morbid with other chronic conditions, being two to five times more prevalent in people with chronic physical health conditions than the rest of the population [1-4]. Mental-physical comorbidities are associated with greater decrements in health outcomes [1] and increased health care utilization costs [5]. The need for chronic disease management to include treatment for co-existing common mental disorders (CMDs) is thus increasingly viewed as a priority in the global challenge of care for multi-morbidity [6].
+South Africa is one of a growing number of low- and middle-income countries (LMICs) experiencing a rising burden of multi-morbid chronic conditions [7]. This is a consequence of the transition of HIV to a chronic condition with the scale-up of antiretroviral treatment; as well as the intensifying non-communicable disease (NCD) burden. In response the South African Department of Health has pioneered an Integrated Clinical Services Management (ICSM) approach that strives to service the majority of patients in primary health care (PHC) at a single delivery point using integrated clinical chronic care guidelines [8]. Integration of mental health care is part of ICSM in South Africa, but has been shown to be inadequate [9]; with a treatment gap of 75% for CMDs in South Africa [10]. While there is evidence of the effectiveness of collaborative care models for the treatment of common mental disorders (CMDs) comor-bid with chronic physical conditions from high-income countries [11], there is little evidence of the effectiveness of task-shared collaborative care models for physical and mental multi-morbidity from LMICs.
+The aim of this study was to evaluate an integrated collaborative care package of care for chronic patients with co-existing depressive and alcohol use disorder (AUD) symptoms that strengthened identification and management of these CMDs under real world conditions through strengthened referral pathways in one case study district in South Africa [12]. The study forms part of the PRogramme for Improving Mental Health CarE (PRIME) research consortium concerned with the development, implementation and evaluation of integrated packages of care for priority mental disorders at PHC level in five LMIC countries [13]. The specific objectives of this study were to assess primary outcomes of whether the collaborative care package: i) improved provider identification of depressive and AUD symptoms in chronic care patients and ii) reduced depressive symptoms and improved functioning in screen positive chronic patients identified and referred for care within the task shared stepped up collaborative care model. Secondary outcomes assessed effects in relation to health equity criteria.
+Methods
+Setting
+The study site was in the Matlosana municipality in Dr. Kenneth Kaunda District (DKK) in the North West Province. The district population was estimated to be approximately 796,823 at the time of the study, while the catchment areas where this study was located comprised over 90,000 people serviced by four primary health care facilities varying in size, serving between 2353 and 6058 chronic care patients per month. Further details of the DKK district can be found elsewhere [12].
+Description of the collaborative mental health care package
+Details of the collaborative care package that was developed through the formative phase and evaluated by this study are described in greater detail in Petersen et al. [12]. In brief, it comprised the following five components: i) PHC nurses functioned as case managers and were oriented to the ICSM, trained in clinical communication skills to facilitate person-centered care, and provided with supplementary mental health training in basic adult care guidelines (known as Adult Primary Care in South Africa) [14]; ii) Doctors were oriented to the importance of mental health and upskilled to prescribe antidepressant medications; iii) Referral pathways for psychosocial counselling for patients with mild to moderate depressive symptoms were strengthened with the introduction of clinic-based lay counsellors trained and supervised to deliver individual and group-based counselling drawing on cognitive behavioural therapy techniques which have international evidence of effectiveness [15]; and v) A referral form to monitor nurse referrals to the counsellor was introduced.
+Research design
+The research design was pragmatic with the intervention delivered independently of the evaluation. Given the complex nature of the collaborative care package, the evaluation comprised two main components: i) a Facility Survey to assess effects of the intervention on provider detection of depression and AUD; and ii) a non-randomly assigned comparison group cohort study to assess changes in symptom severity and functioning among screen positive patients identified and referred for further care by the PHC nurses compared to those not identified.
+Facility detection survey (FDS)
+Study procedure, sample and measures
+The primary objective of the Facility Detection Study was to estimate the change in detection of depression and of AUD by clinicians serving the adult chronic care population in intervention clinics. The design and power
+calculations for the sample sizes are described in greater detail elsewhere [16]. The FDS was conducted in three of the four facilities where the mental health APC module had not yet been implemented. The study procedures used at baseline and follow-up FDS were the same, with independent samples recruited in each study round. The baseline FDS was conducted from February-April 2014, before the implementation of the intervention began in April 2014. Training and embedding continued to September 2014. The follow-up FDS was 12 months after completion of the embedding period (October-December 2015) (see Fig. 1).
+Adult patients were recruited from the chronic care waiting areas of the three PHC facilities by trained recruiters who gave short oral presentations on the study. No reference to mental health was made to minimize sampling bias and limit stigma. Eligibility of patients who volunteered was confirmed in a private space and study objectives discussed. Eligibility criteria were: 18 years or older; attending the clinic for treatment for a chronic illness (e.g., HIV, tuberculosis, hypertension, etc) and capacity to understand the questions posed in either seTswana (the dominant local language) or English. Written informed consent was obtained from literate participants and illiterate participants consented by marking the form with a cross; with a witness countersigning.
+Trained fieldworkers administered a structured questionnaire, programmed into mobile devices. Questions included items on demographic characteristics, treated chronic condition(s), and screening instruments for probable AUD and depression. The Alcohol Use Disorder Identification Test (AUDIT), validated for use in South Africa [17], was used to screen for probable AUD, with participants scoring >16 considered positive for probable AUD, given nurse guidelines to provide advice for harmful drinking and refer patients with dependent drinking. Cronbach’s alphas were 0.78 (baseline) and 0.74 (follow-up). For probable depression, we used the Patient Health Questionnaire-9 (PHQ-9), with a cut-off of >10 being previously validated on a primary care population in South Africa [18]. Cronbach’s alphas were 0.88 (baseline) and 0.86 (follow up).
+All screen positive participants on either screening tool, as well as 15% of randomly selected screen-negative participants (both AUDIT and PHQ-9) were asked to return for an exit interview immediately following their clinical consultation.
+Participants’ exit interview data were used to assess clinical detection on the day of the interview. Broad criteria were used to classify participants as detected for AUD given our experience of nurse reticence to make a diagnosis of AUD (patients who reported a diagnosis of harmful or dependent drinking, and/or a referral to specialist alcohol services, and/or who received advice
+about managing problems with drinking alcohol). Two classifications (narrow and broad) were used for detection of depression: narrow - a diagnosis of depression was reported; broad - diagnosis of depression and/or referral for psychosocial counselling reported. The latter was included given our experience that nurses did not always inform patients of a diagnosis of depression when referring patients. Anti-depressant medication prescribed was also assessed. Participants who reported being on current treatment for either condition were excluded from the analysis.
+Statistical analyses
+The socio-demographic and clinical characteristics of the participants recruited over the two study rounds were analyzed using means and standard deviations for continuous measures and counts and proportions for categorical measures. For each study round we report the number of participants who screened positive on AUDIT and PHQ-9, the number of screen positive participants who completed the exit interview, and the proportion who were classified as having been detected for AUD (among AUDIT positive), or for depression (among PHQ-9 positive) using both narrow and broad criteria. For the equity analysis, where sufficient data were available, we assessed whether change in detection over time was equitable by sex and by household food security. Both these demographic variables were shown as having the highest odds ratios for depression/AUD comorbidity in the same population in a previous study [19]. For the inequity analysis, we used binomial regression models to estimate change in detection, and included the relevant interaction term into the models, with an interaction term p-value< 0.20 suggestive of inequity. Second, for the screen negative PHQ-9 participants who were randomly selected to complete the exit interview, we tabulated the number of depression diagnoses and anti-depressant medication prescription. When it was not possible to estimate the change in detection because of zero counts in the baseline round, we reported the proportion detected and 95% confidence interval for the follow up round only. Procedures to estimate proportions, 95% confidence intervals and p-values incorporated weights to adjust for the imbalance in clinic-level sample sizes between rounds [20].
+Depression cohort study
+Study procedure, sample and measures
+Details of the sample size calculation, recruitment, questionnaire design, data collection procedures, and analysis plan for the PRIME cohort studies have been described in detail by Baron et al. [21] A cohort study for AUD patients was not conducted in South Africa because of
+the very low levels of AUD identification by the providers in the baseline round of the FDS.
+Eligible participants were at least 18 years old; receiving care for a chronic physical condition; screened positive for probable depression and/or had been identified and referred for depression care by the providers; had the capacity to provide informed consent and comprehend the interview; and did not have a diagnosis of AUD or psychosis (see Fig. 2).
+Chronic care patients were informed about the study in the waiting areas of the clinics prior to their clinic consultations and informed written consent was obtained from volunteers. The same informed consent procedure was followed for low-literacy patients as described for the FDS. Immediately after their clinical consultations, individuals who had given their consent were screened using the PHQ-9, and asked whether, during the consultation, they had been identified as having depression and/ or had been referred for care to a provider within the collaborative care model. Individuals who provided affirmative responses to these questions were enrolled into the depression treatment group, regardless of their PHQ-9 score. Individuals who did not provide affirmative responses to these post consultation questions, but who scored 10 or more on the PHQ-9 were enrolled into a comparison group. Comparison group participants who later received a depression diagnosis were re-enrolled in the treatment group, and only the treatment group data of these participants were analyzed (see Fig. 2).
+Cohort study participants completed three assessments: at enrolment (baseline); 3 months and 12-months post-enrolment. Cohort recruitment and enrolment occurred from August 2014 to July 2015, and follow-up was conducted from November 2014 to September 2016 (see Fig. 1).
+Mobile devices were used by trained seTswane/English speaking fieldworkers to administer the questionnaire in private spaces at the clinic or participants’ homes. Each assessment comprised a range of demographic, clinical, health care use, social, economic, food security, and stigma-related measures. These are described in greater detail by Baron et al. [21]. Only measures pertaining to the socio-demographic and clinical characteristics of the sample are reported here.
+Primary outcomes of the cohort study were response on the PHQ-9, defined as at least 50% reduction in score from baseline to 3 months and 12 months follow-up; and remission on the PHQ-9 at both follow-up visits, defined as a score of 5 or less - used as measures of clinically significant improvement in treatment trials using the PHQ-9, e.g. Huijbregts et al. (2013) [22]. Functional impairment, was assessed at the three time points using the 12-item WHO Disability Assessment Schedule (WHODAS 2.0) - previously used in South Africa
+amongst older and HIV populations [23]. Item response theory (IRT)-based scoring was used, with scores ranging from 0 to 100, with a higher score indicating greater functional impairment.
+Analysis
+Given the non-normal distribution of the sample’s demographic and clinical characteristics, baseline characteristics of participants in the treatment and comparison groups were compared using non-parametric tests -the Mann-Whitney U test for continuous measures, and Exact Fisher’s test for categorical variables. The mean symptom severity and functioning were compared between groups at each follow-visit. Given that neither measure was normally distributed at follow-ups, a multilevel mixed effect negative binomial regression was used, controlling for HIV status and recruitment clinic, as these were imbalanced at baseline [21]. Risk ratios for the primary outcomes (i.e. 50% reduction in scores at 3 months and 12 months follow-up, as well as remission) were assessed using a modified Poisson regression, with robust variance estimator, as binomial models failed to converge [24]. Again, the models were adjusted for demographic or other clinical differences between the comparison and treatment groups at baseline. To assess equity in primary outcomes across gender and household food security, the same negative binomial regressions were conducted, this time including either gender or household food security at baseline as an interaction term.
+Results
+Facility detection survey
+In the first round, 1322 participants were eligible and consented to participate in the study. Twelve of these were on current treatment for depression and excluded from the study, resulting in a total of 1310 participants at baseline. During the second round, 1257 were eligible and consented to participate in the study; of these 11 were found to be on current treatment for depression and excluded, resulting in a total of 1246 participants at follow-up. The demographic and clinical characteristics of the Facility Detection Survey participants are presented in Table 1.
+Across both survey rounds, the mean age of the sample was 46 years; approximately three-quarters were women; the majority were receiving care for HIV or hypertension. There were significant between-round differences in participants’ employment status, food security, clinic site, and AUDIT screening.
+For depression, as seen in Table 2(a), the pre-imple-mentation diagnosis of depressive symptoms was 5.2% using the narrow definition, and 14.2% using the broader definition. Post-implementation, using the
+narrow definition, detection of depressive symptoms reached 16.2%, an increase of 11.0% (95% CI 3.3, 18.6); using the broad definition, detection increased to 26.7%, an increase of 12.5% (95% CI 1.9, 23.0). In the inequity analysis, the change in detection for food secure participants, using the broad definition, increased to 33.8%, compared to 23.0% for food insecure participants (P = 0.080). There was no evidence of inequity (P = 0.656) for men versus women participants.
+For AUD, no AUDIT-positive participants were detected in the pre-implementation round. In the
+post-implementation round, 11.7% (95% CI 0.6, 22.8) were detected. There were insufficient data for conducting inequity analyses for AUD detection and treatment.
+As seen in Table 2(b), of the 1310 participants enrolled in the pre-implementation round, 1084 screened negative on both PHQ-9 and AUDIT. Of the 1084, 118 were selected randomly to complete the exit interview, and 110 were successfully interviewed after their consultations. Of the 110, 0.9% had been detected and treatment initiated for depression using the narrow definition; and 6.7% using the broad definition. For AUD, 1.6% were prescribed anti-depressant medication and 1.6% were
+diagnosed with AUD. These proportions did not change significantly (all P > 0.05) between the pre- and post-implementation surveys.
+Depression cohort study
+Of 2602 patients screened, a total of 453 participants were enrolled in the cohort study. An initial 205 patients were diagnosed with depression and recruited into the treatment group. Another 248 patients were not diagnosed but screened positive on the PHQ-9, and were recruited into the comparison group; of these, 12 participants were subsequently diagnosed with depression at a follow-up visit at the clinic, and re-enrolled into the treatment group. The final sample was 236 for
+the comparison group and 217 for the treatment group (see Fig. 2).
+There were 82 participants (18.1%) lost to follow-up, mostly because of relocation and refusals. In the intervention group, 88.9% (n = 193) participants were followed at 3 months and 81.5% (n = 177) completed the 12-month assessment. In the comparison group, 88.6% (n = 209) completed the midline assessment and 82.6% (n = 195) the end-line assessment (see Fig. 1).
+Of the 217 participants in the treatment group, 80 participants screened below the clinical cut-off of 10 on the PHQ-9 at baseline. These participants were excluded from the analysis, to ensure that both treatment and control groups were comparable. These participants did not differ from those included in the analyses in terms
+of demographic characteristics at recruitment, besides food insecurity (Table 3). The final sample included in the analysis comprised 373 participants: 137 and 236 in the treatment and comparison groups, respectively.
+Participants in the comparison and treatment groups who were included in the analyses differed in terms of clinic of recruitment and in HIV status (Table 3). Also, participants recruited into the treatment group had significantly higher PHQ-9 scores (mean = 14.5, SD = 3.47), compared to participants in the comparison group (mean = 12.8, SD = 3.01).
+Results of the modified Poisson regressions are presented in Table 4. The proportion of participants showing at least a 50% reduction in PHQ-9 scores from
+baseline to the 3-month follow-up was greater in the treatment group (N = 69, 55.2%) than in the comparison group (N =49, 23.4%; RR = 2.10, p <0.001). The rate of participants who showed remission on the PHQ-9 (score < 5) was also greater in the treatment group (N = 40, 32.0%) compared to the comparison group (N = 25, 12.0%; RR = 2.78, p < 0.001). The same significant trends were found at the 12-month follow-up, with 57 (47.9%) participants in the treatment group reporting at least 50% reduction in PHQ-9 score compared to baseline, and 32 (26.9%) participants scoring below 5, compared to 60 participants (30.8%; RR = 1.52, p = 0.006) and 33 participants (16.9%; RR = 1.72, p = 0.016) in the comparison group, respectively.
+Change in mean scores on the PHQ-9 and WHODAS over time are also presented in Table 4. After controlling for HIV status and clinic of recruitment, the mixed effects analyses reveal a significant difference between the two groups at the 3-month follow-up; a greater decrease in PHQ-9 scores in the treatment group (M = -5.05, 95%CI: -6.02 to - 4.08) compared to the comparison group (M = -2.63, 95%CI: -3.34 to - 1.92; fi = -2.42, p < 0.001) and a greater decrease in WHODAS scores from baseline in the treatment group (M = -11.11, 95%CI: -15.18 to - 7.05) compared to the comparison group (M = -4.65, 95%CI: -8.02 to - 1.28; fi = - 6.46, p = 0.010). A similar trend was seen for the PHQ-9 scores at the 12-month follow-up compared to baseline (treatment: M = -5.07, 95%CI: -6.05 to - 4.09; comparison: M = -3.10, 95%CI: -3.83 to - 2.37, fi = - 1.97, p <0.001). The change in WHODAS scores at the 12-month follow-up was not significantly different between the two groups.
+Inequity analyses of the impact of the intervention on outcomes by gender and household food security are presented in Table 5. There is no evidence of inequity by gender at either follow-up time points. At 3 months follow-up, however, the intervention was found to have a significantly more positive effect on reducing depressive scores among participants who reported being food secure at baseline (fi = - 3.73, 95%CI -5.30 to - 2.15) (M = - 6.62, 95% CI: -7.95—5.28) compared to those reporting being food insecure (fi = - 1.23, 95%CI -2.65 to 0.20; 0 = - 2.50, p < 0.05). This trend persisted at 12 months follow-up.
+Discussion
+The results of the FDS suggest an improvement in nurse-detection and treatment initiation of depressive and AUD symptoms following implementation of the integrated collaborative care package in the district. Notably, identification remained essentially absent for individuals who were probable non-cases. In other words, the positive predictive value of nurse identification and treatment initiation was high for both depression and AUD.
+The results of the cohort study indicate that patients correctly identified and referred for further care for their depressive symptoms had a greater chance of having a clinically significant reduction in depressive symptoms to the point of being in remission at both 3 and 12 month follow-up than those not referred. They also showed a significant reduction in functional disability at 3-month follow-up compared to the non-intervention group, although this effect was not sustained at 12 months. The intervention was significantly more successful with food secure participants, suggesting that food insecurity, may serve as a barrier to improvement in depressive symptoms; being associated with poverty in more urban areas in South Africa [25]. This adds to
+the growing body of evidence suggesting the need for accompanying income generating initiatives for people with depressive symptoms from poor socio-economic contexts [26].
+While an improvement in the correct identification and treatment initiation of patients with depressive and AUD symptoms was noted, the treatment gap post training still remained large, with 75% of probable cases of depression and 84% of probable cases of AUD still not detected at 12 months follow-up. Previous studies suggest that training alone may be insufficient to improve identification of CMDs in PHC [27]. Other factors contributing to this gap resonate with our understanding of possible reasons and include individual provider level factors - with psychiatric stigma as well as providers’ own personal unresolved problems previously shown to act as barriers to the identification of emotional problems in patients [28, 29]. The inclusion of anti-stigma interventions [28], stress management and debriefing sessions to assist PHC personnel to engage in emotional labour has been previously suggested to assist integration efforts [29]. Further, the need for change management processes to accompany organizational changes associated with integrated care has also previously been highlighted [8].
+Limitations
+There are a number of limitations of this evaluation. With regard to the FDS: i) Non-random sampling compromised the representativeness of the sample. An imbalance in the demographics between the two rounds was noted - with an upward trend in food insecurity, unemployment and alcohol screen positives in the second round - partially explained by retrenchments on the mines between the two survey rounds, with mining being a major industry and source of employment in the study site; ii) There were no control clinics involved in the FDS - with the possibility that the some of the improvements in detection were due to other factors in the health system; iii) An upwards bias in detection may have been introduced given patients’ heightened awareness of their potential symptoms through exposure to the survey interview prior to their consultation - although offset by being the case for both rounds as well as having screen-negative cases; and iv) The FDS only assessed clinical detection on the day of the interview -with diagnosis of a CMD generally taking several visits [27] - thus potentially under-representing detection rates. In relation to the cohort study, recruitment into the intervention and control arm was not randomized, thus increasing potential for bias from unknown con-founders, although partially mitigated by multi-variable analyses.
+Conclusion
+This evaluation shows that the PRIME-SA intervention package assisted PHC teams to identify and manage comorbid CMDs in chronic patients under real world conditions using a collaborative stepped care model. There was an improvement in accurate detection of CMDs by PHC nurses, and a reduction in depressive symptoms in patients identified with comorbid depression and referred for care. Further research is required to assess patient outcomes in patients with comorbid AUD. In the face of the negative impact that comorbid CMDs have on treatment adherence and overall health outcomes in chronic patients, this collaborative task shared model provides a potential model for mental physical multi-morbid disease management in South Africa and other LMICs in the context of specialist resource shortages. Additional findings will be provided by two parallel pragmatic cluster randomized control trials that are currently underway, testing effectiveness of this model on depression and physical outcomes (viral load suppression in HIV patients and reductions in blood pressure in hypertensive patients) in chronic care patients [30, 31].
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+The British Journalof Psychiatry (2013)
+202, S45-S48. doi: 10.1192/bjp.bp.112.112896
+Claire Henderson (pictured) is Clinical Senior Lecturer in Psychiatry, King's College London, Institute of Psychiatry, and a consultant psychiatrist at the South London & Maudsley NHS Foundation Trust. Graham Thornicroft is a consultant psychiatrist at the South London & Maudsley NHS Foundation Trust and Professor of Community Psychiatry, King's College London, Institute of Psychiatry.
+Time to Change (TTC) is the largest-ever programme in England designed to reduce stigma and discrimination against people with mental health disorders (http://www.time-to-change.org.uk/).1 The first phase of this initiative was run by three charities: Mental Health Media, Mind and Rethink Mental Illness. It was funded in the first phase with £16 million from the Big Lottery Fund and £4.5 million from Comic Relief. The programme has also benefited from the secondment of two members of staff from the Department of Health to work on stakeholder management and policy. The Department of Health also funded the annual national Attitudes to Mental Illness survey.2 The programme went on to run two sports-related programmes: the Sport and Mental Health Project (funded by the Department of Health with £83 000) and Imagine Your Goals (funded by Sport Relief and the Premier League with £620 000).
+Evaluation of the Time To Change programme
+The outcomes set by the Time To Change programme were:
+(a)	significantly increased public awareness of mental health (an estimated 30 million English adults would be reached), a 5% positive shift in public attitudes towards mental health problems and a 5% reduction in discrimination by 2012;
+(b)	100 000 people with mental health problems to have increased knowledge, confidence and assertiveness to challenge discrimination by 2012;
+(c)	provision, through physical activity, of greater opportunities for 274 500 people with a range of mental health problems to come together, both to break down discrimination and to improve well-being, by 2012.
+Time To Change was aimed both at the general population and at specific target groups (identified by people with experience of mental health problems) as well as at people with mental health problems themselves. To maximise its reach - and thus its value for money - it engaged individuals, communities and stakeholder
+organisations such as statutory health services and professional membership groups in distributing social marketing campaign materials, collaborating in staging public relations events and holding events to promote social contact between people with and without experience of mental health problems.3-10
+Evaluation of the TTC programme was based on a conceptual framework that understands stigma as consisting of difficulties of knowledge (ignorance or misinformation), attitudes (prejudice) and behaviour (discrimination).1,11 Changes in public attitudes were measured every year from 2008 to 2012 using the Department of Health’s national Attitudes to Mental Illness general population survey in England.2,12 Since its inception the survey has used a shortened list of items from the Community Attitudes toward the Mentally Ill (CAMI) scale and the Opinions about Mental Illness Scale,13,14 providing data on attitudes from 1993. In collaboration with SHiFT, which commissioned this survey between 2008 and 2011, we also developed and from 2009 added the Mental Health Knowledge Schedule (MAKS) and the Reported and Intended Behaviour Scale (RIBS) to the pre-existing attitude questions,15,16 in line with our conceptual model.
+To assess progress towards the target of a 5% reduction in discrimination we conducted an annual survey from 2008 to 2011 of discrimination as experienced by people using mental health services across England (‘Viewpoint’),17,18 using the Discrimination and Stigma Scale.19 The results are reported by Corker et al (this supplement).20Any impact of the social marketing campaign (budget £8 311066) was likely to be influenced by concurrent reporting on mental health-related topics in the mass media.21 The nature and balance of media coverage are of concern to anti-stigma campaigns internationally,22,23 leading to increasing interest in methods of content analysis.24 Analyses of English press coverage are presented by Thornicroft et al (this supplement).25
+Employers were a specific target for stakeholder engagement, and were intended users of the Time to Challenge online resource (budget £196 049), which explained good practice in the field of employment and mental health, and the rights of employees with mental health problems. Henderson et al (this supplement) report evidence of changes in employers’ knowledge, attitudes and practice in this field,26 from the repeated survey in 2009 and 2010 of a survey originally undertaken by the Shaw Trust in 2006.27,28
+Two aspects of the social marketing campaign are reported by Evans-Lacko et al (this supplement).29 First, the national TTC
+social marketing campaign used bursts of mass media advertising and public relations exercises twice a year from 2009 to 2011. The key messages of the first two bursts addressed knowledge important in reducing stigma, i.e. that mental illnesses are common and that people with such disorders can lead meaningful lives. Bursts three and four addressed prejudicial attitudes, i.e. mental illness is our last taboo, such that the accompanying discrimination and exclusion can affect people in a way that many describe as ‘worse than the illness itself’. The last two campaign bursts addressed behaviour change; i.e. we can all do something to help people with mental illness, such as maintaining social contact. Selected knowledge, attitudes and behaviour questions from the three measures used in the Attitudes to Mental Illness survey were used to evaluate the impact of each burst on the pre-identified targeted demographic group of people aged 25-45 years in middle-income groups, and this showed a positive impact on those aware of the campaign for five of the six bursts. Second, a strikingly original component of TTC involved the attendance of large numbers of people with experience of mental health problems at a series of one-day events designed to deliver social contact, addressing the second and third TTC targets (budget £1 077 214). Although the evidence for social contact in reducing prejudice towards people with mental health problems largely concerned its short-term impact,9,30 these events also increased awareness of the social marketing campaign, and together this may have created a cumulative and more sustained effect. Our data suggest a positive relationship between the quality of social contact and a reduction in prejudice (both of improved attitudes and greater confidence to tackle stigma). Time to Change similarly delivered social contact through other programme components; 32 small-scale anti-discrimination initiatives (‘Open Up’, budget £1407 243) aimed to empower people through awareness-raising and confidence-building groups and anti- discrimination projects, many of which involved the use of the creative arts. Another set of projects comprised exercise programmes for people with mental health problems in community leisure facilities delivered by local Rethink and Mind associations (budget £4431 705).
+For specific target groups (medical students, trainee teachers, trainee head teachers and social inclusion officers), the Education Not Discrimination (END) component of TTC again used social contact (budget £1 310201).9,10,31 Friedrich et al (this supplement) described the effect of END on the knowledge, attitudes and intended behaviour of medical students at four English medical schools.32 We present the results for this target group only because it is of greatest interest to this journal’s readership and because we were able to include a control group in the design, which was not the case for the other groups. The results suggest initial positive effects that were no longer present at the 6-month follow-up assessment.
+It is vital that this investment has clear national economic benefits,33,34 and so an economic evaluation was applied to most of the TTC components. In view of the high advertising costs of social marketing, Evans-Lacko et al (this supplement) present the results of an evaluation of the TTC social marketing campaign costs in relation to outcomes.35 This applied an innovative model,36 in conjunction with social marketing campaign evaluation data, to investigate the economic impact of the campaign, including the potential effects on the wider economy.
+Strengths and limitations of the evaluation
+One wholly innovative aspect of the TTC programme is its annual measurement of discriminatory experiences on the part of those using mental health services, rather than evaluating only public
+knowledge and/or attitudes.37-39 Economic analyses have been lacking in previous campaign evaluations and analysis of changes in press coverage over time has been more limited.22 The evaluation is thus relatively comprehensive, as well as informed by the involvement of people using mental health services in the development and administration of new measures.1 -
+The main limitation of this evaluation was the inability to determine the exact contribution of TTC to the changes reported in annual survey results compared with other influences on public attitudes and behaviour, newspaper coverage and employers, owing to the lack of a control population.2,20,25,26 Nevertheless, it is possible to be fairly confident that pre-burst to post-burst changes seen for the anti-stigma campaign bursts were due to the programme per se.29 Further, the Viewpoint study suffered from low response rates.20 However, after weighted analysis of the Viewpoint samples to take account of the overrepresentation of participants of White ethnicity, female gender and older age the main findings were unaffected.
+Implications of the results
+Among our assessments of knowledge, attitudes and behaviour, the most marked change between 2008 and 2011 was the significant overall reduction in the levels of experienced discrimination reported by people using mental health services.20 This survey is the first of its kind so we cannot compare these findings with previous research. However, the results are in clear contrast to the lack of improvement in public attitudes found in England, Scotland and the USA during the previous 10-15 years.12,40
+After the positive change between 2008 and 2010 there was a negative shift both in public attitudes and in some Viewpoint items.2,20 The contemporaneous national economic problems might have exacerbated inequality in access to employment for people with mental health problems,41 despite and/or since the improvements found in the survey of employers between 2006 and 2010.26 There is also evidence that hostile and stigmatising behaviour towards groups with other disabilities has increased since 2010, for example towards people with cerebral palsy (http:// www.scope.org.uk/news/attitudes-survey). This hostility might also affect people with mental health problems.42 However, although reported discrimination in terms of safety, benefits and transport appears to have increased, these increases are not significant after allowing for multiple testing of Viewpoint items.
+The patterns of changes in the Viewpoint items, taken with the positive effect of social contact on outcomes among the campaign target group, suggest that reducing stigma and discrimination might depend increasingly on more social contact, which should be explored in future work. Newspaper coverage changes also suggest such a polarisation, in that fewer articles in 2012 were neutral compared with 2008.25 Journalists and editors may themselves have become more polarised and/or be catering for more polarised attitudes in their readership. These findings raise a key question for phase 2 of TTC: that is, whether individuals with lived experience of mental illness and those close to them can, through greater disclosure, contribute to higher levels of social contact at the population level with those with mental health problems, thus reducing public stigma. The results presented by Evans-Lacko et al (this supplement),2 concerning greater levels of reported contact among the respondents of the Attitudes to Mental Illness survey, offer some support for this view.7
+The lack of change in levels of experienced discrimination from health professionals among Viewpoint participants is of concern;20 whereas initial help-seeking for mental health problems might
+increase if public attitudes and behaviours improved, a lack of reduction in the rate of negative experiences with health professionals might deter people from seeking further help. It may be that the campaign lacked market penetration among health professionals, or that the ‘clinical fallacy’ means their attitudes and behaviour are more resistant to change, i.e. the accumulated experience of staff is that they most often see people with the worst course and outcome. Medical students are also exposed to this bias, which may mitigate the impact of END.32 In contrast with this finding, evaluation of the TTC programme components was on the whole positive, including the economic evaluation.29,35
+Stigma and discrimination against people with mental illness are global challenges,19,43 and the evidence of our evaluation of phase 1 of TTC is that they can be successfully tackled with a focused, determined and long-term approach.44 With this British Journal of Psychiatry supplement we intend to communicate the results of the first phase (2008-2011) of the TTC programme to those who need to know how to intervene most effectively for the greater social inclusion of people with mental health problems worldwide.
+Henderson & Thornicroft
+36	McCrone P, Knapp M, Henri M, McDaid D. The economic impact of initiatives to reduce stigma: demonstration of a modelling approach. Epidemiol Psichiatr Soc 2010; 19: 131-9.
+37	Crisp A, Gelder MG, Goddard E, Meltzer H. Stigmatization of people with mental illnesses: a follow-up study within the Changing Minds campaign of the Royal College of Psychiatrists. World Psychiatry 2005; 4: 106-13.
+38	Akroyd S, Wyllie A. Impacts of National Media Campaign to Counter Stigma and Discrimination Associated With Mental Illness: Survey 4. Phoenix Research, 2002.
+39	See Me. See Me So Far. A Review of the First Four Years of the Scottish Anti Stigma Campaign. Scottish Executive, 2007.
+40	Pescosolido BA, Martin JK, Long JS, Medina TR, Phelan JC, Link BG. ‘A disease like any other’? A decade of change in public reactions to
+schizophrenia, depression, and alcohol dependence. Am J Psychiatry 2010; 167: 1321-30.
+41	Warner R. Recovery from Schizophrenia: Psychiatry and Political Economy. Brunner-Routledge, 2004.
+42	Clement S, Brohan E, Sayce L, Pool J, Thornicroft G. Disability hate crime and targeted violence and hostility: a mental health and discrimination perspective. J Ment Health 2011; 20: 219-25.
+43	Ucok A, Brohan E, Rose D, Sartorius N, Leese M, Yoon CK, et al. Anticipated discrimination among people with schizophrenia. Acta Psychiatr Scand 2012; 125: 77-83.
+44	Sartorius N. Short-lived campaigns are not enough. Nature 2010; 468: 163-5.
+®OPEN
+ACCESS
+s48
+https://doi.org/10.1192/bjp.bp.112.112896 Published online by Cambridge University Press
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+First-episode psychosis (FEP) usually refers to the initial psychotic episode of a primary psychotic disorder, which often results in fear, confusion, and significant disruption in the individual’s life and that of the family. Over the past decade, specialized FEP programs, which combine antipsychotic treatment with psychosocial treatments, have become more widespread in the United States. Individual, group, and family psychotherapy components of comprehensive programs are critical in helping clients and families understand and process the experience of psychosis and learn strategies to promote recovery and well-being.
+Coordinated specialty care (CSC) programs (referred to as early intervention services [EISs] in Europe and Australia) provide team-based, comprehensive, evidence-based care, education, and support to engage clients and their families early in the course of illness.1 The goals of these programs are to reduce the duration of
+untreated psychosis (DUP; ie, the period between onset of symptoms and initiation of antipsychotic medication treatment), prevent further disability, and promote recovery and well-being.
+There are differences in the eligibility criteria across programs,2 but typically CSC programs treat individuals between 15 and 40 years of age (although the United Kingdom has begun offering FEP services to anyone regardless of age)3 who are within the first few years of the onset of their psychosis. CSC programs are intended for individuals with primary psychosis and are not meant for individuals whose psychotic symptoms are judged to be secondary to substance use (eg, substance-induced psychosis), a mood disorder (eg, bipolar disorder, major depression), a developmental disability (eg, autism), or posttraumatic stress disorder. The most common diagnoses of persons treated in CSC programs are schizophreniform disorder, schizophrenia, and schizoaffective disorder.
+RESEARCH SUPPORT
+CSC programs have been shown to yield better outcomes than treatment as usual, including fewer symptoms, more school/work participation, less treatment dropout, and reduced use of inpatient services.4 Studies have consistently highlighted both the importance of well-resourced EISs,5,6 and of shortening DUP to improve outcomes in persons with FEP.7-9
+United States
+Recovery After an Initial Schizophrenia Episode (RAISE) was a large-scale research initiative, funded by the National Institute of Mental Health (NIMH), which involved 2 studies in the United States, the RAISE-ETP trial7,10 and the RAISE Connection Program Implementation and Evaluation study.11 The RAISE-ETP trial enrolled 404 participants in a cluster randomized controlled trial involving 34 community mental health centers in 21 states to deliver 24 months of the NAVIGATE program (a CSC program) or usual care. At 2-year follow-up, participants who received NAVIGATE remained engaged in treatment for a longer period, and demonstrated greater reductions in symptoms, greater improvement in quality of life, better interpersonal relationships, and more involvement in work/school. Outcomes were moderated by DUP, such that those with a shorter DUP (<74 weeks) benefited more from NAVIGATE than those with longer DUP (>74 weeks).12 Client and clinician family therapy manuals (and other resources from the NAVIGATE program) are available online: https:// navigateconsultants.org/manuals/.
+The RAISE Connection Program Implementation and Evaluation study enrolled 65 persons with FEP to receive a CSC program and demonstrated the feasibility of delivering CSC, including high rates of engagement.11 Another study, the Specialized Treatment Early in Psychosis (STEP) in Connecticut,13 demonstrated that those engaged in CSC, compared with usual or community care, were less likely to be hospitalized (40.0% in CSC compared with 63.1% in usual care), had significantly fewer inpatient bed days, and showed improvements in vocational engagement.14
+Across the World
+In the United Kingdom, the standard of care requires individuals with FEP be engaged with EISs within 2 weeks of the initial referral, or offered an assessment if considered to have an “At-Risk Mental State,” shown to reduce DUP.15 EISs in the United Kingdom have been linked to reduced hospital admission rates, lower relapse rates
+and symptom severity, and overall improved access to treatment.16 Superior effects of CSC programs also have been demonstrated in other countries in Europe, Canada, Australia, and Hong Kong.17-20
+COMPONENTS AND DELIVERY OF COORDINATED SPECIALTY CARE
+CSC is composed of pharmacologic management (once a month), individual psychotherapy (weekly),21 family psychoeducation,22 supported employment and education (SEE; weekly),23,24 case management (weekly), and when available, peer support services.25 Although there is some variability, CSC programs generally offer time-limited care over a period of 2 to 3 years. However, immediately stopping care at 2 years may be detrimental to an individual’s recovery, particularly if the patient has built a good therapeutic relationship.26 If treatment is required beyond those 2 years, the individual may step down to a lower level of care, with a transition into regular adult services.27
+CSC involves a multidisciplinary team ,and each team member has a distinct role. For example, the team psychiatrist/prescriber uses a shared decision-making approach in collaboration with the individual with FEP to identify the most effective and tolerable medication(s) at the lowest possible dose.28 Using an adapted Individual Placement and Support model of supported employment for serious mental illness,29 the SEE specialist works closely with the client to identify goals related to returning to work and school and provides support across all phases of the employment or education process. Case management focuses on providing resources for basic needs (eg, transportation, insurance) ideally using assertive outreach to promote engagement, respond to crises, or provide services when necessary.30 Although the peer support role is newer and therefore less well-defined, individuals with lived experience of mental health illness provide valuable support for individuals with FEP,31 for example, by increasing hopeful attitudes about recovery through sharing their own recovery story, providing support, and facilitating the client’s personal goals around community engagement (eg, exercise, going to coffee shops, or becoming more involved in extracurricular school activities). Team meetings are also key to optimal sequencing and coordination of treatment components based on the client’s goals. The team typically meets weekly for assessment and treatment planning and communicates closely with outside organizations to provide appropriate community support.
+The following section elaborates on the content, goals, and strategies used in individual, group, and family therapies for persons with FEP.
+INDIVIDUAL, GROUP, AND FAMILY THERAPIES
+The overarching objectives of group, individual, and family therapies in CSC are to 1. Help the client and family understand and cope with the experience of psychosis 2. Promote symptomatic and functional recovery and improve quality of life 3. Support the pursuit of personally meaningful goals of the client32
+A positive alliance not only helps to engage clients and families in therapy but is also related to improved symptoms and functioning in persons with FEP.33-36 As such, therapies delivered as part of CSC share several common elements in terms of their primary objectives and focus on promoting engagement and a strong alliance. Psychoeducation about psychosis and its course serves as the backbone for many of these therapies to empower clients and their families to make informed decisions both about treatment and other important aspects of clients’ lives (eg, returning to school/work). Further, given that engagement in treatment can be challenging, it is critical for therapists to prioritize developing and maintaining a strong therapeutic
+alliance with clients and families, which involves agreement on goalsand tasks of therapy, as well as the presence of a supportive bond.37 The use of reflective statements as well as an emphasis on collaboration, shared decision-making, and autonomy can foster a supportive bond as well as improved therapy engagement.38
+Individual Therapies
+Cognitive behavioral therapy for psychosis (CBT-p) aims to help clients understand and cope with symptoms, prevent relapse, and identify and work toward meaningful goals through an improved understanding of how thoughts and beliefs shape emotional reactions and behaviors in response to events.39-41 CBT-p has garnered substantial support for its use with persons with established schizophrenia and FEP.16,42,43 Although CBT-p is often delivered as an individual therapy, there is evidence that it also can be effectively delivered as a group psychotherapy.44,45 Therapists delivering CBT-p often use Socratic questioning techniques to explore clients’ understanding of their experiences and to help them identify stressors and vulnerabilities, and the stress-vulnerability model46 is often used as a framework to discuss precipitants of the initial psychotic episode as well as to identify protective factors to prevent relapse. This therapy includes both psychoeducation about psychosis and collaborative exercises aimed to help clients generate and test out alternative methods for coping with symptoms and appraising current past and present experiences, including the experience of psychosis. CBT-p is typically delivered as 16 weekly sessions over 6 months.21,47 Persons with FEP are encouraged to complete homework between sessions to promote continued understanding and practice of CBT-p exercises.43
+Individual Resiliency Training (IRT) served as the individual therapy component of NAVIGATE in the RAISE-ETP study and has been identified as a valuable intervention for persons with FEP.7,32,48 IRT is rooted in CBT-p, training in illness selfmanagement, and psychiatric rehabilitation. IRT is a manual-based therapy that emphasizes the enhancement of resiliency and strengths to support individuals’ pursuit of meaningful goals and to improve their illness management, social functioning, quality of life, and well-being. IRT draws from the structure of the Illness Management and Recovery program49 and earlier psychotherapeutic approaches for FEP emphasizing positive psychology.50 IRT contains 7 “standard” modules that are considered foundational for all persons with FEP in CSC as they help to frame the therapy, support the person in setting goals and preventing relapse, provide psychoeducation about psychosis, offer a structure to process the episode of psychosis, and promote resiliency. The standard modules cover the following:
+1.	Orientation
+2.	Assessment and goal-setting
+3.	Education about psychosis
+4.	Relapse prevention planning
+5.	Processing the episode
+6.	Developing resiliency: part one
+7.	Building a bridge to your goals
+IRT also contains 7 “individualized” modules that cover the following:
+1.	Dealing with negative feelings
+2.	Coping with symptoms
+3.	Substance use
+4.	Having fun and developing good relationships
+5.	Making choices about smoking
+6.	Nutrition and exercise
+7.	Developing resiliency: part two
+The decision to offer the content of the individualized modules is made collaboratively between the therapist and client based on the client’s personal goals.48 IRT is typically delivered on a weekly or biweekly basis for as long as needed (eg, delivered for 2 years in the RAISE-ETP trial7,32,48).
+Group Therapies
+Group-based therapies that target social cognition and social skills are effective in promoting functioning51,52 and negative symptoms53 among those with established schizophrenia. A few studies have examined their use in FEP populations,44,54,55 and these studies demonstrate considerable promise given the social cognitive difficulties that persons with FEP experience.56 Social Skills Training (SST57) is an evidence-based intervention that focuses on helping individuals learn and practice skills involved in social interactions (eg, making requests, expressing positive feelings). SST groups often include a discussion of the rationale for a skill, specific steps of the skill, role-play exercises, feedback from the group, and homework assignments. This intervention has been shown to help individuals learn and practice social skills within the group setting and, subsequently, use them effectively in the community. Number of sessions per week and total weeks depend on the needs of the clients and the setting in which it is delivered (eg, mean number of weeks = 19.3 with a range of 2-104 weeks reported in one study).52
+Cognitive enhancement therapy (CET58) has shown benefits in schizophrenia and in early psychosis. CET is composed of computer training (focused on attention, memory, and problem-solving) group therapy (focused on perspective-taking, managing emotions, reading nonverbal cues, and interpreting social situations).59 CET typically consists of 60 hours of computer training and 45 weekly group sessions. This integrated intervention has been shown to improve social cognition and neurocognition in those with established schizophrenia60 and in those with FEP.61
+Stand-alone social cognition training interventions aim to improve individuals’ capacity to understand, interpret, and use social information effectively, often targeting one or more of the primary domains of social cognition: theory of mind, emotion perception, social perception, and attributional style.62 For example, Social Cognition Interaction Training (SCIT), has been examined extensively in established schizophrenia and has been piloted in a sample of persons with FEP.63 SCIT is delivered as a 20-session to 24-session group psychotherapy typically delivered weekly64 that includes 3 phases: emotion training (eg, identifying emotions from photos of faces, relationship between emotions and thoughts), figuring out situations (eg, distinguishing between facts and guesses in social situations), and integration (discussion of how information can be applied to salient situations). Individuals learn effective social cognitive strategies, practice them within the groups, and ultimately use them in everyday interactions.
+Family Therapies
+Historically, family interventions have been underutilized in the treatment of individuals with schizophrenia,65 despite their clear benefit in reducing relapse and rehospitalization.66-68 Over recent years, however, family interventions have occupied a more central role in the treatment of FEP.12 Family intervention, as part of CSC, typically includes education, validation of the impact of psychosis on the family, communication, problem-solving, and goal-setting skills training.
+Family education about psychosis and its optimal management serves a number of purposes:
+1.	Developing a shared language for the treatment team, individual, and the family to talk about psychosis and associated symptoms
+2.	Providing information so that individuals with psychosis and their families can make informed choices about illness management
+3.	Orienting the family to how they can support the management of their relative’s illness and pursuit of personal goals
+Educational topics include information about psychosis and associated symptoms, the stress-vulnerability model of psychosis, diagnosis and prognosis, the role of the family in treatment, early warning signs monitoring, and relapse-prevention planning. Family education provides the opportunity for family members to observe how the clinician talks to the individual with psychosis about symptoms, diagnosis, treatment, and recovery.
+Families are often put under tremendous stress due to the disruptions in the family system that result from an episode of psychosis. A key underlying aspect of family interventions is the validation of this stress for the entire family system. Before the onset of the psychosis, the young adult may have been living independently, such that the onset of an illness represents a shifting of roles and worry for the entire family system. Communication, problem-solving, and goal-setting skillstraining can be important for families during this period of adjustment and heightened stress. Communication skills are aimed at reducing stressful interaction styles characterized by strong displays of negative affect or ambiguous messages and emphasize the use of direct “I statements,” reference to specific behaviors, and specific feeling statements taught using the principles skills training (eg, modeling, role playing). Common targets for communication include medication, symptoms, and disclosure of information about the illness with the immediate, as well as the extended, family. In addition, it may be important for family members to reestablish how they will make requests of one another, which dovetails with the question of reasonable expectations of the individual with psychosis during the immediate period following illness onset and beyond.
+Fostering problem-solving and goal-setting skills in the family serves the dual purpose of minimizing strife and facilitating recovery through each family member’s identification of meaningful goals. Early in treatment, families often work toward the goal of increasing shared pleasant activities, which can increase family connection, shift the focus from illness to enjoyment and fun, and help remediate negative symptoms and demoralization that are commonly associated with the experience of psychosis. Later in treatment, families often take on more challenging goals, such as assisting the young person in returning to school or work, living independently, or traveling for educational or leisure purposes.
+Multifamily group (MFG) interventions typically include 5 to 7 families who meet with 2 clinicians on a biweekly basis,69 following “joining” sessions in which each family meets individually with the clinician to form a relationship and provide information about their family’s specific needs. Each MFG session lasts approximately 90 minutes and the content of the sessions map onto 4 treatment stages corresponding to the phases of an episode of psychosis: (1) engagement between client and their family, (2) education about the psychotic disorder, (3) development of strategies, such as stress reduction, to cope with the challenges of psychosis recovery, and (4) social and vocational rehabilitation.69 Elements of MFG considered to be particularly effective include access to a social network, reduction in perception of stigmatization, availability of mutual aid, and the opportunity to hear similar experiences and
+solutions.69 Although there can be some initial challenges with establishing a critical mass of families willing to attend a group, MFG is cost-effective70 and has been demonstrated to increase perception of ability to cope with a relative’s psychosis,71 and reduce FEP program dropout rates.72
+CLINICAL CHALLENGES
+Individuals with FEP vary tremendously from one another in terms of the severity of positive symptoms, negative symptoms, and cognitive and social functioning. The most significant challenges in therapy with individuals with FEP arise from the need to adapt the treatment to each individual’s specific symptom presentation and understanding of his or her problems. Problematic substance use73 and history of trauma or posttraumatic stress disorder74 also add complexity to the treatment of some individuals. Furthermore, significant stressors beyond coping with FEP (eg, limited income, transportation barriers, homelessness) can interfere with the feasibility of delivering treatment and, thus, should be considered when trying to engage and maintain persons in therapy. In these instances, mobile teams and/or case management supports (eg, transportation paid for by health insurance or access to disability pay-ments75) are essential ingredients to involve the individual and family in care and reduce strain on poorer families.
+Negative symptoms, cognitive deficits, and impaired social and occupational functioning tend to co-occur in primary psychotic disorders and are defining features of FEP. Clinicians may struggle to engage individuals with negative symptoms and families may blame these individuals for being “lazy” or “unmotivated,” which can amplify familial stress and impede recovery. Education about negative symptoms, spending more time getting to know the individual (eg, befriending techniques76,77), and slowing down the pace of therapy as well as breaking goals into small steps can be useful. An important part of the educational process involves dispelling the myth that negative symptoms indicate a lack of distress, because in fact, individuals with negative symptoms are often bothered by these symptoms and this is related to poor quality of life.78 Therefore, recognizing and labeling this distress can serve as a rationale to build coping skills for these symptoms. Another important discovery has been the identification of common dysfunctional beliefs expressed by individuals with negative symptoms,79,80 such as beliefs about self-efficacy (eg, “I don’t have enough energy or I don’t have anything to say”) and anticipatory pleasure (eg, “I won’t have a good time”), which are thought to impair effortful responding and can be addressed through cognitive restructuring and behavioral experiments.81,82 Further, given the variability in cognitive functioning among persons with FEP (eg, due to age, effects of medication/ electroconvulsive therapy, symptoms), psychosocial interventions should be appropriately tailored to the cognitive capacity of each individual.
+Another challenge when working with persons with FEP and their families is sensitively and effectively addressing the role of trauma in therapy. Many persons with FEP have had traumatic experiences in their lives,83 which may have been associated with the experience of psychosis and psychiatric treatment (eg, involuntary hospitalization, coercive treatment, use of restraints, and/or police involvement).84 IRT includes a module called “processing the episode,” which aims to help individuals integrate, process and understand the trauma of experiencing psychosis, and interventions designed to facilitate cognitive processing of traumatic experience in FEP have shown positive outcomes.85
+Cultural and religious factors can also impact the willingness of clients and families to engage in treatment. For example, individuals of some cultural and religious
+backgrounds may not believe that psychological or psychiatric medication approaches to treatment are appropriate and may seek out alternative options (eg, shaman, exorcism, religious practices). Therapists should try to work within the cultural context of the given client and family to best support the recovery of the person with FEP. Therapists should use a curious attitude about these alternative approaches and better understand how they fit within the cultural context of the family and, importantly, assess any potential risk for the person. However, people may also be open to alternative explanations of their experience, especially when they are less distressing or more helpful. Therapists may also offer an “open door” policy so that individuals and families know that they are welcome to reconnect in the future.
+SUMMARY
+CSC programs provide team-based, comprehensive, evidence-based care, education, and support across 2 to 3 years to individuals experiencing their first episode of psychosis and their families. A collaborative clinical approach within CSCs are important. Individual, group, and family therapies represent critical aspects of CSC as they are aimed at helping individuals with FEP and their families navigate the distressing experience of psychosis and to promote recovery and well-being. Several individual (eg, CBT-p, IRT), group (eg, SST, SCIT, CET), and family (eg, family psychoeducation) therapies have demonstrated benefits for this population and are guided by the individual’s goals and long-term vision of recovery. However, there are many clinical challenges that often accompany FEP therapy delivery that warrant significant attention. Therapists should be aware of these challenges and develop strategies to engage and maintain clients and their families in therapy. It is through awareness of challenges, prioritization of the therapeutic alliance, and effective delivery of evidence-based therapies that therapists can help clients and their families work toward recovery.
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+Introduction
+Findings from the Global Burden of Disease 2010 (GBD 2010) study have reinforced our understanding of the significant impact that mental, neurological and substance use disorders (MNSDs) have on population health (Murray et al. 2012; Whiteford et al. 2013). One of the key findings of GBD 2010 was the global health transition from communicable to noncommunicable diseases and this is particularly rapidly in low- and middle-income countries (LMICs) (Murray
+et al. 2012). The proportion of burden attributable to non-communicable disease in LMICs has risen by more than one-third, from 36% in 1990 to 49% in 2010. In contrast, the share of non-communicable disease burden in high-income countries (HICs) has raised only 3-4% over the same time period (from 80 to 83%) (Institute of Health Metrics and Evaluation, 2013). These findings hold particular public health importance for MNSDs in LMICs for the coming decades.
+GBD 2010 estimates the majority of disease burden due to MNSDs is from non-fatal health loss; only 15% of the total burden is from mortality, in terms of years of life lost (YLLs) (Institute of Health Metrics and Evaluation, 2013). This may erroneously lead to the interpretation that premature death in people with mental and neurological disorders is inconsequential, whereas evidence shows that people with MNSDs experience a significant reduction in life
+expectancy (Chang et al. 2011; Wahlbeck et al. 2011; Crump et al. 2013; Lawrence et al. 2013). In Australia and the UK males with a mental disorder die, on average, 15 years earlier than the general population and females die on average 12 years earlier (Crump et al. 2013; Lawrence et al. 2013). It is estimated that about 80% of premature deaths in people with MNSDs are due to physical illnesses, particularly cardiovascular disease, including stroke and cancer (Crump et al. 2013; Lawrence et al. 2013). Dementia is an independent risk factor for premature death with increased risk found in those patients with physical impairment and inactivity, and medical comorbidities (Park et al. 2014). Excess mortality in people with epilepsy is reported to be two- to threefold higher compared with the general population; with an increased risk of up to sixfold higher in LMICs (Diop et al. 2005). A significant proportion of these deaths are preventable, resulting from falls, drowning, burns and status epi-lepticus (Diop et al. 2005; Jette & Trevathan, 2014). A recent review has shown the highest standardised mortality ratio (SMR) among mental and substance use disorders was 14.7 for opioid use disorders (Chesney et al. 2014). In HICs the life expectancy gap is widening with the general population now enjoying a longer life while the lifespan for those with a mental disorder has remained static (Lawrence et al. 2013).
+Mortality associated with a disease can be quantified using two different, yet complementary, methods which are employed as part of GBD analyses. First, cause-specific mortality draws upon vital registration systems and verbal autopsy studies which identify deaths attributed to a single underlying cause using the International Classification of Diseases (ICD) death-coding system. Second, GBD creates natural history models for each disease, including its distribution across age and sex. This involves estimation of a range of epidemiological parameters, including excess mortality - that is, the all-cause mortality rate in a population with the disorder compared with the all-cause mortality rates in a population without the disorder. By definition, estimates of excess deaths include causespecific deaths.
+Although often arbitrary, the ICD conventions are a necessary attempt to deal with the multi-causal nature of mortality and avoid 'double-counting' of deaths. However, despite the systems clear strengths, causespecific mortality estimated via the ICD obscures the contribution of other underlying causes of death; for example, suicide as a direct result of major depressive disorder coded as injury, and will likely underestimate the true number of deaths attributable to a particular disease. On the other hand, estimation of excess mortality using natural history models will often comprise deaths from both causal and non-causal origins and
+will likely overestimate the true number of deaths attributable to a particular disorder. The challenge is to parse out causal contributions to mortality (beyond those already identified as cause-specific) from the effects of confounders.
+Quantification of the contributions of multiple causal factors to excess mortality associated with a particular disease is challenging and requires approaches such as the comparative risk assessment (CRA), which is now an integral part of the GBD studies. The fundamental approach for the GBD CRA is to calculate the proportion of deaths or disease burden caused by specific risk factors - e.g., lung cancer caused by tobacco smoking - while holding all other independent factors unchanged. A key concept when attempting to quantify causal relationships is that of 'counterfactual burden' which compares the burden associated with an outcome with the amount that would be expected in a hypothetical situation of 'ideal' risk factor exposure (e.g., zero prevalence). This approach provides a consistent method for estimating the changes in population health as a function of decreasing or increasing the level of exposure to risk factors (Lim et al. 2012). Importantly, the flexibility within counterfactual analysis allows the sum of death counts attributed to different risk factors for a particular cause to sum to more than 100% which is not permissible by ICD registry data.
+In this paper, we explore the cause-specific and excess mortality of individual MNSDs estimated by GBD 2010. We also present the additional attributable burden that can be ascribed to disorders using GBD results for CRA's assessing MNSDs as risk factors for other health outcomes. Disorders included in the analyses are grouped by: mental disorders (schizophrenia, major depression (MDD), anxiety disorders, bipolar disorder, childhood behavioural disorders (attention-deficit/hyperactivity disorder (ADHD) and conduct disorder (CD)), autistic disorder and intellectual disability); substance use disorders (alcohol, opioid, cocaine and amphetamine use disorders); and neurological disorders (dementia, epilepsy and migraine).
+Methods
+YLLs and cause of death
+The GBD 2010 methodology uses a time-based metric, YLLs, to quantify the fatal burden by underlying cause (Lozano et al. 2012). YLLs are computed by multiplying the number of deaths attributable to a particular disease at each age by a standard life expectancy at that age. The standard life expectancy represents the normative goal for survival and for 2010 was computed based on the lowest recorded death rates in
+any age group in countries with a population greater than 5 million (Salomon et al. 2012).
+Cause-specific death estimates in GBD 2010 were produced from available cause of death data for 187 countries from 1980 to 2010. Data sources included vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records and mortuaries (Lozano et al. 2012). Cause of death ensemble modelling (CODEm) was used for all MNSDs. In summary, CODEm uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models and model performance was assessed with rigorous out-of-sample testing of prediction error and the validity of 95% uncertainty intervals (UIs). Details relating to CODEm and the method for how these models were used in calculating YLLs are described in detail elsewhere (Lozano et al. 2012).
+YLLs for GBD 2010 were computed from causespecific mortality estimates for seven of the 15 MNSDs: schizophrenia; opioid, amphetamine, cocaine and alcohol use disorders; dementia and epilepsy (Lozano et al. 2012). As the ICD does not permit the other mental and neurological disorders to be recorded as the 'primary' cause of death, YLLs were unable to be calculated for the remaining eight disorder groups (World Health Organization, 1993; Lim et al. 2012).
+Excess mortality from a natural history model
+Drawing on a series of systematic reviews, we collated comprehensive sets of epidemiologic data for each disorder. Data were pooled, adjusting for between-study variance, and then an internally consistent epidemiologic model derived using the relationship described in the generic disease model (see the appendix) (Vos et al. 2012). To do this we used DisMod-MR, a Bayesian meta-regression tool which estimates a natural history of disease model producing age-, sex- and region-specific estimates for prevalence, incidence, remission and excess mortality (Vos et al. 2012). Where data were scarce, DisMod-MR was able to impute information with associated uncertainty ranges based on epidemiologically and geographically similar populations. Excess mortality estimates based on this natural history model are reported for MNSDs in terms of global deaths for 2010. Further details of the GBD 2010 methods for developing a natural history model of disease using DisMod-MR have been described in detail elsewhere (Vos et al. 2012).
+Counter-factual burden and CRA
+Prince et al. (2007) have summarised the evidence where a causal relationship between mental and
+substance use disorders and other health outcomes have been proposed. In GBD 2010, a series of reviews were conducted to assess the strength of evidence for MNSDs as independent risk factors for other health outcomes (Degenhardt et al. 2009a; Rehm et al. 2010a; Charlson et al. 2011; Degenhardt & Hall, 2012). Risk factor studies were identified through systematic searches of published and unpublished data with information on effect sizes and study characteristics extracted and collated (Charlson et al. 2013; Degenhardt et al. 2013; Ferrari et al. 2014). A metasynthesis was used to calculate a relative risk (RR) for MNSDs (the exposure) as a risk factor for other health outcomes. The RR was then applied to prevalence distributions of the specific exposures by sex and age-group for each geographic region to derive population attributable fractions (PAFs). More detail on the calculation of PAFs in GBD 2010 is provided by Lim et al. (2012). In some cases, for example suicide, ceiling values were calculated and applied for joint PAFs to ensure the sum of proportional contribution for all risk factors did not exceed 100% (Ferrari et al. 2014). The additional burden (YLLs and YLDs) attributable to MNSDs is the product of the PAFs and the burden for the health outcome as estimated in GBD 2010.
+Here we compare the number of cause-specific deaths reported (YLLs) for MNSDs, calculated as part of the GBD 2010 study with the number of allcause deaths derived from natural history models. We explore differences in these estimates across the age-span for each disorder. Additional YLLs attributed to disorders as underlying causes and quantified in the CRA are reported.
+Results
+YLLs and causal mortality
+Globally, the seven disorders (dementia, epilepsy, schizophrenia, alcohol use disorders, opioid use disorders, cocaine use disorders and amphetamine use disorders) for which YLLs were estimated were directly responsible for 1.3 million deaths in 2010, equating to about 12 million YLLs (see Fig. 2 in the appendix). Epilepsy and then dementia contributed the greatest proportion of YLLs within this group.
+Age-standardised YLL rates vary considerably across the seven geographical super-regions primarily due to differences in patterns of alcohol and drug use, and mental and neurological disorder prevalence. There are several regions with substantial deviations from global YLL average rates (Fig. 1). (Details of which countries are in each sup-region can be found
+on the IHME website (Institute for Health Metrics and Evaluation, 2014).)
+In 2010, YLL rates were highest in the sub-Saharan Africa region (604 YLLs per 100 000 population) and the region comprising Central/Eastern Europe plus Central Asia (593 YLLs per 100 000); the causes for which vary considerably (Fig. 1). In sub-Saharan Africa the YLL burden was driven by epilepsy which was fourfold higher than the global average and approximately 85% of all YLLs attributed to MNSDs in sub-Saharan Africa. Although the substance use disorders YLL rates appear unremarkable for this region, their YLL burden has increased 3.0% from 1990 to 2010, almost double the average global increase and the highest of all regions (Degenhardt et al. 2013). In contrast to sub-Saharan Africa, the high fatal burden in Central/Eastern Europe and Central Asia was largely caused by deaths attributed to alcohol use. High mortality due to illicit substance use disorders also contributed to the YLL rate in Central/Eastern Europe and Central Asia with all substance use disorders together explaining 73% of YLLs in the region. Countries within East Asia and the Pacific exhibit very low YLL rates across all MNSDs with little change observed between 1990 and 2010.
+Globally, neurological disorders accounted for 58% of the all MNSD YLLs in males and 81% of YLLs in females. Substance use disorders explained 39% of YLLs in males and 16% of those in females. The contribution of schizophrenia to total MNS disorder YLLs was similar for both genders (3% each). Differences in YLL patterns between the genders were influenced in part by the differing contribution to YLLs by substance use disorders compared with neurological disorders across regions (Fig. 2).
+Excess mortality from a natural history model
+The only mental disorder for which cause-specific deaths and YLLs were estimated in GBD was schizophrenia; however, several mental disorders, such as major depression and bipolar disorder, exhibit significant and documented excess-mortality (Roshanaei-Moghaddam & Katon, 2009; Baxter et al. 2011b) (Table 1). There were four disorders for which sufficient evidence of excess all-cause mortality could not be found in the literature (anxiety, childhood behavioural disorders, cannabis dependence and migraine) and therefore excess mortality was not included in the natural history of disease for these disorders.
+Mental disorders
+Figure 3 shows the estimated number of cause-specific and excess deaths for each of the five mental disorders with estimated excess mortality by age and with uncertainty bounds. While cause-specific deaths were attributed to only one mental disorder (schizophrenia),
+excess mortality was present in natural history models for five: schizophrenia, bipolar disorder, MDD, autistic disorder and intellectual disability.
+Although schizophrenia is one of the few mental disorders with cause-specific deaths permissible by ICD,
+the numbers of cause-specific deaths globally (approximately 20 000) are noticeably lower compared with allcause deaths (approximately 700 000) ascribed by the disorder’s natural history. Around 1.3 million excess deaths are estimated in the natural history model of
+bipolar disorder but there are no cause-specific deaths attributed to the disorder. The natural history of the disease suggests, however, that bipolar disorder is associated with a higher number of excess deaths globally than schizophrenia. No deaths were coded to depressive
+disorders in GBD 2010. Natural history models of MDD suggest there were more than 2.2 million excess deaths in persons with MDD, with a particularly high rate of death in older persons that is not observed in schizophrenia or bipolar disorder.
+Intellectual disability was modelled as an ‘envelope disorder for GBD 2010, meaning that the intellectual disability ascribed to all underlying causes including meningitis, Down’s syndrome, and chromosomal defects, were captured under a single disorder category. After modelling, the contributions of each specific underlying cause were separated out and a ‘rest’ category of idiopathic intellectual disability was created. There were no deaths causally attributed to intellectual disability; however, excess deaths in people with idiopathic intellectual disability were estimated to be substantial at over 900 000 deaths globally in 2010.
+At this time there is insufficient information available to determine whether premature mortality is significantly raised across the spectrum of anxiety disorders (Baxter et al. 2014) and in childhood
+behavioural disorders (Erskine et al. 2013). In GBD 2010 there were no YLLs or excess mortality associated with the natural history of disease applied to anxiety disorders or childhood behavioural disorders.
+Substance use disorders
+GBD estimates indicate more than 110 000 deaths were causally attributed to alcohol use disorders worldwide in 2010, but indicative of the true impact of alcohol dependence as an underlying cause of death in many is that over 5 million excess deaths were estimated in the same year. Over 700 000 excess deaths occurred in dependent illicit drug users in 2010 compared with only 44 000 deaths which were coded as the cause of death. The majority of these deaths can be ascribed to opioid dependence (43 000) (Fig. 4).
+Neurological disorders
+Cause-specific death estimates are more substantial for neurological disorders (Fig. 5) resulting in a less
+dramatic gap between cause-specific and excess deaths. This is likely indicative of neurological disorders being recognised more readily as the primary cause of death.
+Similar to intellectual disability, epilepsy was modelled as an envelope disorder in GBD 2010 with idiopathic epilepsy and epilepsy secondary to a range of causes, including meningitis, neonatal tetanus, iodine deficiency and a variety of birth complications, being modelled as one disorder. Cause of death modelling estimated nearly 180 000 deaths due to epilepsy in 2010 while natural history models show us about 300 000 excess deaths in fact took place. The proportion of deaths attributable to different causes differ by region and GBD 2010 showed sub-Saharan African populations had the highest death rate due to epilepsy. Around 2.1 million excess deaths worldwide were estimated from dementia for 2010, yet less than 500 000 were attributed to dementia as the primary cause of death.
+Table 2 shows that the cause-specific deaths and excess deaths directly coded to MNSDs are relatively similar up to 4 years of age but then rise sharply: in children aged 5-9 years there were 7420 cause-specific deaths compared with more than 91 000 excess deaths in the same age group. Alcohol use disorders explained the highest number of excess deaths (5.2 million): 38% of all excess deaths due to mental and neurological disorders in 2010. Considered together, the mental disorders for which no cause-specific deaths were attributed (bipolar disorder, major depression, autism and intellectual disability) explained more
+than 4.5 million deaths, equating to one third of all excess deaths in 2010.
+Counter-factual burden and CRA
+The reviews conducted as part of GBD 2010 collectively yielded sufficient evidence for several CRAs (see Table 3). Neurological disorders were not assessed as risk factors in GBD 2010.
+For mental disorders, a number of associations were investigated but data limitations meant that only suicide and IHD were able to be included as outcomes for mental disorders (suicide) and MDD (IHD) (Baxter et al. 2011a; Charlson et al. 2011; Ferrari et al. 2014). Collectively, mental and substance use disorders are estimated to be responsible for about 22 million YLLs due to death by suicide (Ferrari et al. 2014). The CRA of major depression as a risk factor for ischaemic heart disease estimated an attributable to burden of about 3.5 million YLLs (Charlson et al. 2013).
+Injecting drug use was considered as a risk factor for a number of outcomes, including blood borne viruses and liver disease, and collectively accounted for over 7 million YLLs in attributable burden. Interestingly, and despite common preconceptions, GBD results did not show any mortality-related burden from schizophrenia that could be attributed to cannabis dependence. Alcohol use was the biggest contributor with nearly 80 million attributable YLLs estimated across a number of health outcomes.
+Figure 6 shows the considerable additional burden when MNSDs are considered as underlying
+contributors to other health outcomes. Given the large estimate of mortality-related burden attributed to alcohol dependence, it is expected that, when aggregating YLLs, the regions with the largest attributable burden will be those which have highest rates of alcohol dependence, i.e., Eastern Europe and Central Asia. Sub-Saharan Africa experiences large communicable disease YLLs attributable to alcohol dependence as a
+result of the continuing high prevalence of communicable disease in relation to other regions.
+By incorporating the additional YLLs estimated using CRAs into the overall contribution of mental, substance use and neurological disorders to all cause YLLs (Table 4) we can see a dramatically different picture to that painted in Appendix 1 where YLL contributions appeared negligible in many cases.
+Contributions across regions vary in accordance with the epidemiological profile of disorders within each region, not only of mental, substance use and neurological disorders but also the health outcomes assessed in CRAs. For example, the relatively large contribution in HICs and Central/Eastern Europe and Central Asia likely to be reflective not only of high prevalence of substance use, but also of cardiovascular disease (CVD) which was assessed as an outcome of major depression and alcohol use. In contrast, the relatively lower contribution in sub-Saharan Africa is likely reflective of comparatively lower rates of both substance use disorders (risk factors) and chronic diseases such as CVD (health outcomes). If neurological disorders were assessed as risk factors for other health outcomes using CRAs this picture may have looked different for sub-Saharan Africa where YLLs attributable to neurological disorders is higher than in other regions in the world.
+Discussion
+A relatively small YLL burden was attributable to MNSDs in GBD 2010; however, numbers of excess deaths derived from natural history of disease models clearly demonstrate the high degree of mortality associated with these disorders. Quantifying the independent contributions of mental and substance use disorders to poor health outcomes through methods such as the CRA is restricted by data availability and methodological challenges such as establishing causal relationships (Baxter et al. 2011a); nevertheless, there is a growing body of literature which can help us develop hypotheses around these contributions by observing the risks associated with excess death in individuals with mental and substance use disorders.
+The relationship between mental disorders and suicide has long been recognised (Li et al. 2011). Mental disorders have also been linked to higher rates of
+death due to coronary heart disease, stroke, type II diabetes, respiratory diseases and some cancers (Hoyer et al. 2000; Crump et al. 2013). The relationship between mental disorders and physical disease, leading to premature death, is complex. People with mental disorders have an increased risk of death in several ways, for example people with MDD are more likely to develop CVD (Charlson et al. 2011). Psychotropic medications can negatively impact on cardiovascular and metabolic health (De Hert et al. 2012). Obesity and metabolic disturbances are primary risk factors for CVD and type II diabetes, and are two- to threefold more common in people with mental disorders compared with the general population (Scott & Happell, 2011). Major modifiable risk factors for chronic disease, such as smoking (Lawrence et al. 2009), poor diet and physical inactivity (Kilbourne et al. 2007; Shatenstein et al. 2007) and substance abuse (Scott & Happell, 2011), are overrepresented in people with mental disorders and these may be consequences of symptoms of MNSDs, medication effects and poor emotional regulation (Scott et al. 2013).
+Interestingly, while schizophrenia was the condition among these mental disorder among for which YLLs were attributed, the number of YLLs were very small compared with the excess mortality associated with the disorder. Our finding of high excess mortality in people with schizophrenia is in line with that found in the previous research (Laursen, 2011; Crump et al. 2013; Lawrence et al. 2013). Data linkage studies have shown that the majority of deaths in people with schizophrenia are due to chronic disease with CVD accounting for more than one-third of all premature deaths, while unnatural causes, including suicide, homicide and accidents account for just under 15% of excess deaths (Crump et al. 2013; Lawrence et al. 2013). Despite concerns over the side-effects of antipsychotic medication, lack of antipsychotic treatment has been linked with higher all-cause mortality rates (HR 1.45, 95% confidence interval (CI) 1.20-1.76),
+132 F. J. Charlson et al.
+with highest risks attributed to cancer (HR 1.94, 95% CI 1.13-3.32) and suicide (HR 2.07, 95% CI 0.73-5.87; Crump et al. 2013). Poly-pharmacy and discontinuation of medication also appear to increase risk of allcause death (Joukamaa et al. 2006; Haukka et al. 2008).
+Research from the UK suggests that the excess mortality rate in schizophrenia and bipolar disorder are comparable (Chang et al. 2011). In a recent study, it was estimated that about 80% of premature death in people with bipolar disorder is due to physical disease, almost half of which is explained by CVD (Westman et al. 2013). Just under 20% of premature deaths were explained by unnatural causes (suicide, homicide and unintentional injuries; Westman et al. 2013).
+People with developmental disorders are at twice the risk of premature death compared with the general population (Mouridsen et al. 2008). Elevated death rates in autistic spectrum disorders (ASD) are due to several causes, including accidents, respiratory diseases and seizures (Shavelle et al. 2001; Mouridsen et al. 2008). The elevated mortality risk associated with ASD may be due more to the presence of comorbid medical conditions, particularly epilepsy, and intellectual disability rather than ASD itself (Lee et al. 2008; Bilder et al. 2013).
+Individuals with intellectual disability are expected to have, on average, a life expectancy of 7-12 years less than the general community and life expectancy is dramatically lower in those more severe disability and those with a genetic disorder (e.g., Down syndrome) (Bittles et al. 2002). Intellectual disability is associated with greater tendency towards obesity and physical inactivity compared with the general population, and enhanced predisposition to mental disorders, osteoporosis, thyroid disorders, non-ischaemic heart disease and early onset of dementia (Bittles et al. 2002). In HIC, causes of death in people with intellectual disability are generally coded under congenital abnormalities, diseases of the nervous system and sense organs, mental disorders and respiratory disease (Tyrer & McGrother, 2009). Information on causes of death in LMIC populations is sparse.
+Children with ADHD or CD are two to three times more likely to experience unintentional injuries requiring medical attention compared with children without behavioural disorders (Rowe et al. 2004; Lee et al. 2008). The injuries most commonly reported included burns, poisoning and frac(Rowe et al. 2004). Adolescents and young adults with inattention disorders are more likely to be involved in traffic accidents (Jerome et al. 2006). Adults who were identified with behavioural disorders in childhood are at higher risk of cigarette smoking, binge-drinking (ADHD) and obesity (CD) (von Stumm et al. 2011) in later life. Despite the strong evidence for an association between childhood
+behavioural disorders and poorer health outcomes, there is insufficient information available to model the natural history of disease and thus no estimates quantifying excess mortality in this group at population level.
+Another important disorder demonstrating an apparent absence of excess-mortality in GBD 2010 is the umbrella anxiety disorders group. This was a necessary choice as the information on excess mortality in anxiety disorders was found to be inconsistent with some anxiety disorders; however, severe presentations such as post-traumatic stress disorder (PTSD), have previously been associated with increased deaths caused by ischaemic heart disease (IHD), neoplasms and intentional and unintentional injuries (Ahmadi et al. 2011; Lawrence et al. 2013).
+While light-to-moderate alcohol consumption has been associated with lower rates of some disease such as diabetes mellitus and coronary heart disease, heavy consumption has been associated with increased rates of chronic disease, including cancer, MNSDs, cardiovascular disease, liver and pancreas diseases (Rehm et al. 2010a). There is evidence for alcohol as a carcinogen in humans, with particularly strong causal links established between alcoholic beverage consumption and oral cavity, pharynx, larynx, oesophagus, liver, colorectal and female breast cancers (Rehm et al. 2010a). A consistent relationship has also been found between heavy alcohol consumption and epilepsy (Rehm et al. 2010a) and it is also implicated in development of depression and personality disorders, although the direction of causality and effect of confounding factors remains uncertain (Rao et al. 2000; Rohde et al. 2001). Risk of diabetes mellitus, hypertension, stroke, sudden cardiac death and other cardiovascular outcomes is elevated in those with alcohol use disorders (Rehm et al. 2010a). The relationship between alcohol consumption and liver cirrhosis is well recognised, but alcohol use disorders appear more strongly related to cirrhosis mortality v. morbidity as it negatively affects the course of existing liver disease (Rehm et al. 2010b). Heavy alcohol use is also related to higher rates of infectious diseases, such as tuberculosis, and unintentional and intentional injury, with strong evidence for a dose-response relationship (Rehm et al. 2010a).
+Excess and premature deaths in illicit drug users occur in several ways. Most obvious is the acute toxic effects of illicit drug use which may lead to overdose - the cause-specific deaths generally captured by the ICD-coding system. In addition, a substantial number of deaths are likely due to the more indirect effects of intoxication resulting in accidental injuries and violence. There are a plethora of adverse health outcomes with elevated risks of premature mortality for which illicit drug dependence is an important contributor. These outcomes are often chronic and include
+cardiovascular disease, liver disease and a range of mental disorders including psychosis. Suicide is an important outcome, particularly for opioid users where an SMR of approximately 14 has been reported in two separate reviews (Degenhardt et al. 2011; Chesney et al. 2014). Injection of drugs, most common in opioid dependence, carries a high risk of bloodborne bacterial and viral infections, notably HIV, Hepatitis B and Hepatitis C (Mathers et al. 2010; Nelson et al. 2011).
+Epilepsy is associated with two- to threefold higher than mortality in the general community, particularly in childhood onset epilepsy, with the highest standardised mortality ratio encountered in the first year or two after diagnosis (Preux & Druet-Cabanac, 2005; Sillanpaa & Shinnar, 2010; Neligan et al. 2010; Trinka et al. 2013). Common causes of premature mortality in epilepsy include acute symptomatic disorders (e.g., brain tumour and stroke), sudden unexpected death, suicide and accidents (Hitiris et al. 2007). Roughly 85% of people with epilepsy live in LMICs and here the risk of premature mortality is highest (Carpio et al. 2005; Diop et al. 2005; Newton & Garcia, 2012; Jette & Trevathan, 2014) from status epilepticus, drowning and burns associated with poor access to and/or compliance with medical treatment, cognitive impairment and age (Jilek & Rwiza, 1992; Kamgno et al. 2003; Mu et al. 2011; Ngugi et al. 2014).
+As with mental disorders, excess mortality in dementia has been associated with functional disability leading to lifestyle factors (e.g., poor eating behaviours, physical inactivity and poor hygiene) and comorbid or underlying physical conditions, including cardiovascular disease, diabetes mellitus and neoplasms (Guehne et al. 2005; Llibre Rodriguez et al. 2008). Infections, particularly pneumonia and the complications of urinary tract infections, frequently lead to death in people with dementia (Mitchell et al. 2009).
+Strategies for reducing mortality associated with MNSDs are primarily related to preventing onset of disorders, reducing case fatality, and preventing onset of fatal sequela. There is growing evidence that excess mortality in people in mental and substance use disorders can be reduced through existing evidence-based treatments and improved screening and treatment for chronic disease. There is some evidence that collaborative care by community-based health teams has the potential to reduce overall death as well as suicide deaths (Malone et al. 2007; Dieterich et al. 2010). The use of collaborative care models to improve physical health in people with MNSDs is growing in developed countries and these have demonstrated a range of positive health outcomes including reduced cardiovascular risk profiles (Druss et al. 2010). The effectiveness of these strategies
+in preventing premature mortality in LMIC populations has yet to be tested but this may be a costeffective approach to treatment where trained mental health clinicians are scarce.
+To improve life expectancy in people with comorbid mental and physical health issues requires proactive screening and adequate care for chronic disease. Screening and prevention of metabolic risk factors is essential. Strategies for early cancer detection should be prioritised and models of care developed to ensure that people with MNSDs receive the same level of physical health care and treatment as the rest of the population.
+Psychiatric treatments, specifically pharmacotherapies, may have some protective effect against excess mortality (Weinmann et al. 2009) although evidence suggests that this depends on use of medications according to best practice guidelines (Cullen et al. 2013). In contrast, some second generation antipsychotics may actually pose an elevated risk mediated by metabolic side effects (Newcomer, 2005; Smith et al. 2008; Rummel-Kluge et al. 2010).
+Much of the disease burden due to opioid dependence and injecting drug use could be averted by scaling up needle and syringe programs (NSPs), opioid substitution treatment and HIV antiretroviral therapy (Degenhardt et al. 2010; Turner et al. 2011). Both methadone and buprenorphine (the two most commonly used medications) have been listed on the WHO's List of Essential Medicines (World Health Organization, 2005) as core medications for the treatment of opioid dependence (Mattick et al. 2008, 2009). OST reduces mortality among opioid-dependent people (Davoli et al. 1994; Caplehorn & Drummer, 1999; Brugal et al. 2005; Darke et al. 2006; Gibson et al. 2008; Degenhardt et al. 2009b), with time spent in treatment halving mortality compared with that in time spent out of treatment (Degenhardt et al. 2011). A large evaluation study in multiple countries, including LMICs, has demonstrated that OST is effective in reducing opioid use and injecting risk behaviours and improving physical and mental wellbeing (Lawrinson et al. 2008). There is increasing evidence that not only HIV (Degenhardt et al. 2010) but also HCV (Turner et al. 2011) burden can been reduced through NSPs; HCV burden can also be decreased by effectively treating chronic HCV (Turner et al. 2011). The release of more effective and less toxic HCV drugs is expected to dramatically improve what have been extremely low rates of HCV treatment uptake by people who inject drugs (Swan, 2011).
+There is also scope for reducing the risk of overdose among people who continue to use opioids. There is increasing evidence that the provision of the opioid antagonist naloxone to opioid users enables peers to effectively intervene if overdoses occur (Galea et al.
+134 F. J. Charlson et al.
+2006; Sporer & Kral, 2007). Additional strategies may include: education of users about the risks of overdose (especially high risk periods such as post-release from prison or after a period of abstinence), and motivational interviews with users who have recently overdosed (Sporer, 2003). Safe injecting rooms have been proposed as an additional strategy to reduce overdose, although their population reach is likely to be more limited (Hall & Kimber, 2005). There is evidence that psychosocial interventions including self-help programmes and cognitive behavioural therapy are effective in psychostimulant dependence (Baker et al. 2005; Knapp et al. 2007).
+In low-income regions, mortality in epilepsy patients is largely due to preventable causes (Diop et al. 2005; Jette & Trevathan, 2014). Yet, the treatment gap is more than 75% in low-income countries, and more than 50% in many lower and upper middle-income countries (Jette & Trevathan, 2014). Legislation to ensure availability of affordable and efficacious antiseizure medications, clinician education in prescribing antiepileptic medications, and patient education regarding the importance of medical adherence is critical to alleviate the epilepsy treatment gap. Cost-effective epilepsy treatments are available and accurate diagnosis can be made without costly technical equipment. Targeting epilepsy risk factors, including more common structural and metabolic causes of epilepsy will likely decrease mortality risk as well. In addition, education and information provision on safe lifestyle habits in epilepsy patients (i.e., avoiding fires, swimming and driving in those with active convulsive epilepsy) will clearly be beneficial. Education to dispel myths associated with epilepsy among employers and teachers may empower those with epilepsy to seek treatment.
+Mortality in dementia patients is commonly by preventable medical conditions, including infections. Caregiver education and support services regarding proper care of patients with cognitive decline will likely decrease infection rates and thus, mortality. Government financial support for healthcare services and caregiver support would also benefit this population. Strategies to enhance nutrition, as well as monitoring and treatment of vascular risk factors including high blood pressure, hypercholesterolemia, smoking, obesity and diabetes, are important measures as well.
+Limitations of the study
+Quantifying mortality presents several challenges. Cause of death data is affected by multiple factors, including: certification skills among physicians, diagnostic and other data available for completing the death certificate, cultural variations in choosing and
+prioritising the cause of death, and institutional parameters for governing mortality reporting (Lozano et al. 2012). In LMIC populations, where many deaths are not medically certified, different data sources and diagnostic approaches are used (e.g., from surveillance systems, psychological autopsy work and disease registries) to derive cause of death estimates (Lozano et al. 2012). The implication is that cause of death assessments are subject to uncertainty; a good illustration is the widely debated difference in maternal death estimates by GBD and by the United Nations (Byass, 2010).
+Cause of death data also provided estimates of deaths due to MNSDs that were not captured within the main GBD 2010 categories. The decision was taken to create residual categories to reflect the additional mortality not captured within specific disorders. Deaths and YLLs were calculated for: 'other' mental disorders (16 140 deaths equating to 5.4 YLLs per 100 000 persons); 'other' drug use disorders (33 ,561 deaths equating to 22.5 YLLs per 100 000 persons); and 'other' neurological disorders (481142 deaths and 231.6 YLLs per 100 000). The modelling strategies for these residual groups do not allow calculation of excess mortality for comparison as done throughout this paper however the YLL estimates for these groups are exceptionally high.
+Mortality directly related to MNSDs is particularly difficult to capture in cause of death data due to the complex web of causality which link them with other physical disorders. Thus it becomes very important to identify and quantify the not inconsiderable excess premature mortality in people with MNSDs through understanding the pathway between these disorders and fatal sequelae.
+Although valuable, the CRAs undertaken as part of GBD 2010 provide an incomplete picture. There are almost certainly deaths where we may not have enough information to parse out what is causally related or what is due to confounding. Assuming multiple risk factors are independent of each other is also a limitation as done in CRA methodology. A more accurate quantification of the joint effects of multiple risk factors, that is what explains the difference between excess and cause-specific deaths, is an important area for future research.
+Conclusion
+Despite the challenges in quantifying causal mortality in MNSDs it is abundantly clear that the mortality-associated disease burden of mental, substance use and neurological disorders is significant. The continuing life expectancy gap in persons with these disorders
+represents a lack of parity between this portion of the population and the community in general (Thornicroft, 2013). People with MNSDs face additional barriers to physical health care because of stigma within the healthcare system, the 'silo' effect between mental and physical health care caused by overspecialisation, and diagnostic overshadowing of physical health issues by presence of mental disorders (Bailey et al. 2013). Differential access to 'usual' care for this group leads to poorer outcomes in terms of health loss and mortality and incurs high costs in health care provision (Centre for Mental Health, 2010). Further research and development of new strategies for reducing mortality associated with MNSDs is needed.
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+Premature mortality is a well-established adverse outcome of severe mental illness (SMI), most notably for schizophrenia, bipolar disorder and depressive disorder.1 Nonetheless, investigating mortality patterns remains important for: (a) for monitoring the profile of changing risk factors over time;2 (b) for evaluating the impact of sociocultural and geographic settings on mortality; (c) for reviewing the contribution of SMI to the global burden of disease; (d) for advocating for the inclusion of SMI in the global health agenda; and (e) to establish the mortality profile in population samples, which has not been demonstrated adequately to date. Moreover, the contribution of SMI to the global burden of disease has increased in the most recent analyses3,4 but is still likely to be underestimated substantially. For example, although mental disorders, particularly SMI, are the strongest predictors of mortality from self-harm,5 self-harm is calculated separately in the global burden of disease estimations.6 In addition, the indirect yet substantial contributions of mental disorders to mortality related to physical conditions are underrecognised in calculations of the global burden of disease.6,7 This underestimation has the potential to perpetuate the low prioritisation of mental disorders and the underinvestment in research and services related to SMI, particularly in low-income country settings, where policy-makers have to prioritise disorders with the highest burden and best outcome returns for their investment. Furthermore, our knowledge about mortality associated with SMI derives from clinical samples recruited in the context of service receipt or hospital admission, mostly from high-income countries, although over 80% of the world’s population lives in low- and middleincome countries with limited access to treatment. The little knowledge we have about the mortality of people with SMI from low- and middle-income countries comes from anecdotal accounts8 and the pioneering studies of the World Health Organization, which are now over three decades old.9-11 For example the International Pilot Study of Schizophrenia was initiated over 45 years ago, in 1966.11 Many changes have occurred
+in our understanding of mental disorders since: more refined methods of illness classification, case identification and monitoring have evolved; new methods for ascertainment of causes of mortality applicable in low-income settings have enabled researchers to define causes of death in more precise ways. Additionally, virtually no data exist on the mortality outcomes in bipolar disorder and severe depressive disorders in low-income country settings, which are also important contributors to premature mortality alongside schizophrenia. There is, therefore, a pressing need for up-to-date, methodologically rigorous, population-based studies from low-income countries. The aim of this report is to present the mortality outcomes of people with SMI from the Butajira-Ethiopia study on SMI, a recently completed large-scale, population-based cohort study.
+Method
+The cohort
+The study cohort and the methods for follow-up have been described in detail previously.12-14 A summary description of the cohort is presented below with the focus on the conduct of the mortality assessment. The Butajira cohort on SMI was established between March 1998 and May 2001 in the Butajira district, located 132 km from Addis Ababa, the capital of Ethiopia. At the start of the study, the area was administratively divided into 46 subdistricts and all except one inaccessible subdistrict were included in the study. Demographically, the district contained predominantly rural sites with only four of the subdistricts, representing 10.9% of the population, being urban. This is similar to the urban-rural balance seen in the southern region of Ethiopia.15
+The cohort was identified through a two-stage sampling design. In the first stage, potential participants with SMI (schizophrenia, bipolar disorder and severe depression) were identified through a supervised door-to-door survey and the key
+Fekadu et al
+informant method.16 The door-to-door survey consisted of lay interview with the Composite International Diagnostic Interview (CIDI), version 2.117 targeting adults aged 15-49 years, estimated at the beginning of the study to be 83 282.18 The CIDI was administered to 68 378 individuals (82.1% of the target population) and the CIDI case identification was augmented by trained key informants, community leaders and elders selected from each village.16 In the second stage, people with potential disorder were assessed using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN),19 a structured instrument to confirm diagnosis. The SCAN was administered by trained clinicians and the ratings were validated against clinical diagnosis by psychiatrists,2 with excellent agreement between the SCAN diagnosis and clinical diagnosis. The first cohort identification lasted 3 years (1998-2001) and 844 participants were recruited. Using similar methodology, a leakage study identified 75 additional participants in the next 3 years from the same catchment area (Fig. 1). Thus, the total cohort consisted of 919 participants with SMI, composed mostly of men (n = 572, 62.2%). This gender difference was primarily because the majority of individuals identified with schizophrenia were men (n = 296, 82.7%). There was no gender difference in bipolar disorder and the difference in severe depression was in the expected direction; women were overrepresented (n = 132; 61.4%). Details of the baseline demographic characteristics are presented in Table 1.
+Monitoring of cohort and confirmation of survival status
+The cohort was monitored serially over a median of 11.3 years (interquartile range 10.7-11.9), ending in February 2012. Three
+monitoring mechanisms were established. First, each subdistrict was allocated to trained field workers who were involved in the initial data collection. These field workers developed intimate knowledge of the patients and the family and provided a report on the status of patients about once per month. Second, patients had a monthly clinical follow-up at which time their clinical status, medication changes and any other relevant indicators were recorded. Third, all participants were assessed annually for diagnostic stability and symptom and functional status. Through this continuous monitoring the survival status of participants was ascertained prospectively for all entrants into the cohort.
+Death was confirmed within 1 month of death by field workers who completed the verbal autopsy using the WHO verbal autopsy questionnaire that was previously adapted for use in the Butajira area.22,23 Verbal autopsy is described as the ‘most promising’ method of ascertaining causes of death in settings where most deaths occur at home and are not attended by a doctor as is the case in our study setting.24 The verbal autopsy method has also informed the latest global burden of disease data on causes of death from low- income settings.4,25 The verbal autopsy questionnaire is prepared for completion by lay interviewers based on information from close family members who have knowledge of the terminal illness.26-29 The verbal autopsy questionnaire starts by asking about basic sociodemographic and personal habits (smoking and alcohol use). This is followed by details about the circumstances leading to death (signs and symptoms of illness, duration of the identified signs and symptoms, and explores potential external causes).
+The lay field workers of the Butajira study on SMI who administered the verbal autopsy questionnaire to close relatives of the deceased were trained in the administration of the questionnaire. Once the questionnaire was completed, decision on diagnosis of the cause of death was made by consensus of two physicians. The verbal autopsy data were supplemented by the Broad Rating Schedule (BRS).30 The BRS was originally developed to summarise the findings during follow-up of patients with a diagnosis of schizophrenia but the principles of the BRS are applicable to all disabling disorders. The symptomatic and functional status of the participants is rated for the last month. The rating is made based on all available information, including information from the participant, other informants and records. The BRS also assesses symptoms and disabilities using a modified version of the Global Assessment of Functioning scale (GAF). The score ranged from 1 (persistent inability to function in almost all areas) to 90 (good function in all areas). Generally a score of 60 and above is considered a reasonable level of functioning. The BRS contains sections on participants lost to follow-up and deceased. The causes of death were then grouped into broad ICD-10 disease classes32 as presented in Table 3.
+Analytic methods
+Data analysis was conducted with SPSS version 21 and Stata version 11 for Windows. Mortality was standardised using the latest (2007) census of the Southern Nations Nationalities Peoples Region of Ethiopia,15 which includes Butajira. In the absence of appropriate dates of birth, we followed the method proposed by Breslow & Day33 to calculate person-years of follow-up, and relied on age at entry, date of entry and date of exit. We estimated the amount of time contributed by each individual to each 5-year age category and summed up all those contributions for all cohort members and obtained the total number of person-years of observation in that category. Each participant was assumed to contribute 0.5 years to the age category of the participant at commencement in the study, but the precise follow-up duration
+was calculated for the exit year. A full 1 year was given to each intervening year. For participants exiting the study within a year of entry into the cohort the exact length of contributed time was calculated. We estimated the potential years of life lost (YLL) using the 2009 national data on life expectancy.34 As was the case in the global burden of disease estimation,4,35 YLL were computed by multiplying the number of deaths at each age (x years) by the standard life expectancy of the reference population (the Ethiopia population) at age x. Put slightly differently, YLL was computed by estimating the difference between the actual age at death of an individual in the cohort who died from any cause, and the expected age at death. This may be represented with the following formula:3 YLL = £di(E — i'), where i = actual age at death; d = number of deaths at age i; and E = Expected age at death estimated according to the 2009 life tables for Ethiopia, based on age and gender. The sum of the YLL was then divided by the number of deceased individuals to derive the mean YLL. Standardised mortality ratios were calculated as the ratio of the number of observed deaths in the sample with SMI to the number expected if the sample with SMI had the same mortality rate as the population within Southern region. Factors associated with premature mortality were computed using the Cox proportional hazards model. We also estimated the life expectancy at birth of the cohort based on the life expectancy of the population of the Southern region using Chiang’s method of abridged life tables in 5-year groups.37-39 This method has been recommended for its application to relatively small populations.38 Since those under 15 years of age would be unlikely to receive a diagnosis of SMI, the mortality rates of the Southern Region for the under-15-years age groups was applied in substitution.40 Additionally, we substituted the mortality rates of the Southern region for mortality above 60 because only a small number of participants had been above 60 (n = 6) and using the SMI cohort would lead to unstable estimation. Life expectancy was estimated for the whole cohort and by gender as well as by the three disorders. The differences in life expectancy at birth between those with SMI and that of the Southern region were calculated.
+Ethical considerations
+The study was initially approved by the ethics committee of the Department of Community Health and then by the Institutional Review Board of the Faculty of Medicine and the College of Health Sciences of Addis Ababa University. Treatment was made available free of charge for all patients needing treatment.
+Results
+In total 121 participants (13.2% of the initial cohort) died during the follow-up period. Details of the demographic characteristics are presented in Table 1. Nearly twice as many patients with schizophrenia died (n = 65, 18.2%) compared with those with bipolar disorder (n = 33, 9.5%, w2(1) = 10.9, P =0.001) or severe depression (n = 23, 10.7%; w2(1) = 5.7, P = 0.016). When all diagnostic categories were considered together, more deaths occurred among men (n = 88, 15.4%) than women (n = 33, 9.5%, w2(1)=6.5, P = 0.011). However, the difference was not statistically significant when comparison was stratified by diagnostic groups even though comparatively more men died in all categories: 19.3% v. 12.9% for schizophrenia, 10.9% v. 7.8% for bipolar disorder and 12.0% v. 9.8% for severe depression.
+Overall, mortality was twice that of the standard population (SMR = 213.9, 95% CI 177-256) (Table 2). The SMR was highest for schizophrenia (302.7, 95% CI 234-386), which was significantly higher than that of bipolar disorder (SMR =150.1,
+95% CI 103- 211) although not that of severe depression (SMR = 169.9, 95% CI 108- 255).
+The commonest cause of death, based on categories of the ICD-10,3 was related to infectious conditions (49.6%, n = 60) prevalent in the study area (Table 3). Unnatural causes accounted for a quarter of all causes of death (24.8%, n = 30), most arising from suicide (n = 19, 15.7%); the rest from other unnatural causes such as road traffic accidents and homicide (n =11). Proportionately, those with bipolar disorder had the highest mortality from suicide (24.2%, n = 8/33) although the difference
+was not statistically significant compared with both those with schizophrenia (13.8%, n = 9/65) and severe depression (8.6%, n = 2/23). Deaths from the various causes peaked in the first 5 years of follow-up and were more or less constant afterwards (Fig. 2).
+On average the YLL per person for all patients with SMI was 28.4 years. The YLL was slightly higher among women (30.0 years) compared with men (26.9 years). The YLL was also slightly higher for those with severe depression (29.4 years) compared with that for those with schizophrenia (27.7 years) and bipolar disorder (29.0 years). However, because of the larger number of patients who died, schizophrenia made the largest contribution to the overall YLL (52.4%), followed by bipolar disorder (27.9%) and severe depression (19.7%). Whereas deaths related to injuries accounted for 22.4% of YLL, suicide alone contributed to 8.7% of all YLL. Overall, those with SMI had a life expectancy gap of about 6 years and it was nearly 10 years for those with schizophrenia and depression (Table 4). The life expectancy gap for men and women was 5 and 6 years, respectively.
+The two factors associated independently with mortality were male gender, and shorter time in remission (Table 5). Thus, those who were in remission for less than 50% of the follow-up period had double the risk of dying (Hazard Ratio (HR) = 2.02, 95% CI 1.31-3.12, P = 0.002). Men also had increased mortality (HR= 1.67, 95% CI 1.04-2.66, P =0.032). Older age at enrolment into the study did not significantly increase the risk although there may be a trend (HR= 1.85, 95% CI 0.98-3.12, P = 0.060). For all
+disorder subtypes, longer periods in a symptomatic state were associated with shorter survival time (Table 6). For each of the diagnostic groups, spending a higher percentage of follow-up time in episode was associated with an increased risk of premature mortality. For those with depressive disorder (but not the other disorders), subthreshold symptoms were also associated with risk of premature mortality.
+In the month prior to death, most of the deceased were symptomatic (70.2%, n = 85/121) and functionally impaired (75.2%, n = 91/121) because of their mental illness (Table 7). Impairment was consistently high across diagnostic groups: schizophrenia (78.5%), bipolar disorder (75.8%) and severe depression (65.3%). Similarly, high percentages of the deceased were symptomatic across the diagnostic groups: schizophrenia (n = 44, 68.8%); bipolar disorder (n = 26, 78.8%) and severe depression
+(n = 15, 64.2%). Moreover, mental disorders were considered to be relevant to the death in over half of the deaths either because they were presumed to have led directly to death (n = 34, 28.8%) or were linked to the death (n = 29, 24.6%).
+Discussion
+To our knowledge this is the largest single-site study describing mortality in SMI from a low-income country setting. It is also one of the very few studies in the world describing mortality experience of community-ascertained cases with minimum treatment exposure. The study has several novel features that contribute to addressing the gap in our knowledge about mortality associated with SMI. First, the study employed rigorous community-based case identification and diagnostic methods, screening a population of over 68 000 people, and has monitored the individuals identified continuously over an average of 10 years of follow-up. Second, the cohort had very low treatment exposure at recruitment. Third, the report provides data on mortality associated with bipolar disorder and severe depression, where we have virtually no data from low-income countries. Finally, the causes of mortality were established close to the time of death and used validated methodologies. Moreover, by using prospective clinical and functional measures, our study also contributes to the evidence on the potential role of psychopathology in causing premature mortality.
+Main findings
+The study confirms that people with SMI, irrespective of setting, are at increased risk of mortality. Specifically, in this particular setting, the risk of mortality among those with SMI was double that of the general population, which is consistent with previous reports of mortality and SMI in high-income countries,41-49 However, the mortality figures were lower than what we had anticipated. This may be partly because of the increased mortality in the general population related to the high burden of preventable causes other than injuries.2 ’ 3 The SMR is also lower than the figure we previously reported based on a 5-year follow-up study of schizophrenia.50 This is likely to be because of higher mortality in the early parts of the follow-up as demonstrated in Fig. 2. Moreover, increased risk of early mortality has been
+demonstrated in service contact samples of both schizo-phrenia51,52 and mood disorders,45 particularly for unnatural causes.53 Thus, the previous report may have suffered from the short-term nature of the study given the higher burden of mortality in the early phases of the follow-up period. This is of interest from both a research and service perspective. In terms of research, longer term studies of mortality are more likely to provide more stable and more accurate estimation and should be encouraged. From a service perspective, given the mixed nature of our cohort, which was composed of both individuals with chronic and recent-onset disorders, the finding underscores the need for vigilance in the early periods of service provision irrespective of the duration of illness at the time of first-service contact. However, we cannot rule out the possibility that the reduction in mortality might have been as a result of better care provision related to recent restructurings in the healthcare system, for example, expansion of primary healthcare, although this would be expected to have a greater impact on the population without SMI. A distinct methodology would be required to detect the impact of health system changes on mortality. Based on the current findings, however, we recommend that provision of enhanced care in the early phases of the illness may be essential to improving the mortality outcomes of people with SMI.
+Deaths from infectious diseases
+Most patients in the study died from infectious causes prevalent within Butajira.23 This is partly a reflection of the limited care available for the population. Therefore, improving the general health of the public is likely to have an impact on the survival of people with SMI. Patients with SMI may also be at a particular disadvantage because they may not complain when they have symptoms, and family support may have been already overstretched when patients develop infections. Further exploration of the mechanisms underlying death from infectious conditions is warranted.
+Deaths from unnatural causes
+A large proportion of individuals have also died from unnatural causes, primarily suicide. Studies of suicide in Ethiopia have reported the rate to be between 6 and 8 per 100 000 per year.54,55 The rate among patients with SMI in the Butajira cohort was
+about 200 per 100000 per year, a substantially higher rate. The overall mortality rate from all unnatural causes was also high, accounting for 24.8% of all causes (n = 30/121) compared with the rate in the Butajira area which has been estimated at 1.7%.2 In a study from Addis Ababa, the capital of Ethiopia, unnatural causes of death other than suicide accounted for a higher rate (11.2%)55 although still around 50% lower than that of the Butajira sample with SMI.
+Contribution of psychopathology to mortality
+Given the high rate of deaths from unnatural causes and the high level of psychopathology in the deceased group, it is likely that psychopathology contributed to the death of many patients. This is supported by the finding that SMI was likely to have contributed directly to the deaths of at least a third of all deaths and contribute substantially to the other deaths. Additional evidence for the potential contribution of psychopathology to mortality comes from the survival analyses in which better survival was predicted by longer periods in remission, while longer duration in a symptomatic state was associated with increased mortality in all disorder categories. Two recommendations follow from these findings. First, it is reasonable to suggest that improving mental healthcare provision may improve mortality outcomes. Second, the contribution of psychopathology to mortality has to be accounted for in the estimation of the global disease burden although this may call for a new technology.
+preventable causes. The study adds new data not only on mortality in people with SMI in low-income countries, but also to the worldwide database on mortality among those with SMI identified and living in the community. The study highlights the need to improve the mental healthcare as well as the physical healthcare of people with SMI. The study also makes a case for the inclusion of mortality directly attributable to mental disorders in the estimation of the global disease burden. This is important to increase the visibility of mental health in the public health agenda, reflecting its rightful place.
+Life expectancy
+People with SMI lost about three decades of their life to premature death in this rural Ethiopian setting. Extrapolating this nationally, people with SMI lose a substantial amount of potential life years annually. Despite overlap in confidence intervals with the Southern region, the findings regarding the life expectancy gaps are important. A life expectancy at birth for people with schizophrenia and depression (46 years) is very low. Given the low life expectancy of the population in general, the life expectancy figures are worth paying attention to and have to be taken as evidence of the substantial neglect of people with SMI in such settings. Moreover, this study will serve as a baseline for future studies of the life expectancy gap in people with SMI in Ethiopia, which is likely to grow with improvement in the life expectancy of the general population.
+Limitations
+There are a few limitations worth mentioning. First, because of the lack of data on national cause-specific mortality, we were unable to present cause-specific mortality ratios for people with SMI. Second, cause of mortality confirmed by a physician would have been the best approach to determine causes of mortality; however, short of a physician determination, we have used the next best approach, the verbal autopsy method. The historical nature of the verbal autopsy assessment may have led to random misclassification of cause of mortality. Finally, information related to self-harm is not always volunteered. Therefore, the figure for suicide may be an underestimate.
+Implications
+This relatively large-scale cohort study from rural Ethiopia confirms the increased risk of mortality associated with SMI irrespective of setting. The study re-establishes mortality as a very important outcome for people with SMI in low-income settings despite the high burden of premature mortality from diverse
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+BJPsych Open (2020)
+6, e62, 1-10. doi: 10.1192/bjO.2020.40
+Background
+The elevated mortality risk associated with a diversity of mental disorders when contrasted with the general population is an accepted finding.1-3 The number of years of life lost in relation to all-cause mortality varies from 7 to 24 depending on the nature of the condi-tion.2 Substance use disorder (SUD) conveys the highest potential number of years lost (9-24); however, this is closely followed by the ranges for personality disorders (13-22 years), schizophrenia (1020 years) and bipolar disorder (9-20 years) demonstrating a psychophysiological influence (which we define as a physiological response mediated by biochemical pathways to psychological distress) upon morbidity/mortality beyond the physical impact of illicit substances.
+In relation to dual diagnosis the risk of death among individuals with severe mental illness (SMI) and concurrent SUD is in excess of those with SMI alone.4 Evidence suggests all-cause mortality rates for individuals with schizophrenia may also be increasing,1,5,6 but high rates of suicide, particularly after discharge from psychiatric hospital admissions7 and unnatural deaths8 do not entirely account for the observed disparity in mortality compared with the general population.1
+All-cause mortality in forensic settings
+Although research primarily focuses on mainstream services, higher rates of all-cause mortality have also been reported within prison settings where the burden of mental health is also in excess of general population comparisons,9 with mortality risk further compounded by the high prevalence of SUD reported among prison-ers.10 Similar high rates of all-cause mortality are apparent among the forensic psychiatry literature.1 - In 2011, Clarke et al12 noted the deaths of 9.6% of their cohort (n = 595) over a maximum 20-year follow-up, Fazel et al11 reported 29.9% of their population (n = 6520) dying over a mean of 15.6 years and Coid
+et al13 found 4.9% of their cohort (n = 409) died over a mean of 6.2 years. Although they report differing mortality rates they all exhibit high rates of suicide, 32%, 22.7% and 50% of the reported deaths, respectively. In addition, Fazel et al11 noted the death of 14.2% of their cohort and Clarke et al12 22.8% of deaths from acci-dental/unnatural causes.
+There is limited literature regarding morbidity/mortality among forensic patients who represent a specific subset of individuals experiencing SMI.14 Findings from mainstream populations cannot be safely generalised because of differing recovery and treatment pathways which, in the case of forensic patients, require to be balanced against the risk to the public.15 To address this and from within the context of recovery, a cohort from The State Hospital, Carstairs, the high secure hospital for Scotland and Northern Ireland, has been explored 20 years from their involvement in The State Hospital Survey.16 As part of this follow-up morbidity and mortality has been examined to explore influencing factors at a local level.
+Aims
+The aims of this paper are to:
+(a)	explore morbidity among this group;
+(b)	delineate which patients are at greatest risk of premature mortality;
+(c)	assess the extent of suicide and unnatural deaths;
+(d)	establish which factors, if any, appear protective to cohort members.
+Method
+Cohort
+The State Hospital Survey16 identified a whole population cohort of 241 patients (male n = 213, mean age 36 years, female n = 28, mean
+https://doi.org/10.1192/bjo.2020.40 Published online by Cambridge University Press
+age 32 years) resident in the high secure State Hospital, Carstairs, at some point between 25 August 1992 and 13 August 1993. Detention was under civil (n = 92, 38.2%) and criminal procedures (n = 149, 61.8%). Baseline data were collected from case notes and clinical interview. At baseline, the mean lifetime psychiatric stay was 9.3 years (range: 0.08-45). Following the baseline study the mean high security admission was 6.8 years (median 4.3, range: 0.0922.4). Individuals who were subject to restrictions on discharge, excluding prison transfers (n = 81), experienced a mean 9.4 years (median 6.1, range: 0.15-22.4) in high security. Non-restricted individuals and individuals who were prison transfers (n = 160) spent a mean of 5.4 years (median 3.5, range: 0.09-22.4) in high security. Additional detailed cohort characteristics have been reported elsewhere.16
+In 2014 follow-up was initiated with clinical and morbidity data collected for a mean of 19.2 years and mortality data only, for a mean of 21.1 years (median 25.1, range: 0.61-25.4) with 5093.61 person-years at risk (PYAR).
+The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study was approved by South East Scotland Regional Ethical Committee 01, reference 15/SS/0015. Supplemental approvals and the wider study protocol are described elsewhere.1 Written informed consent was obtained from all living participants.
+Data sources
+The Electronic Data Research and Innovation Service (eDRIS) utilised the cohort’s unique Scottish Community Health Index (CHI) numbers (CHI is a population register used for healthcare purposes) to search the data-sets of National Services Scotland (NSS), the Scottish Health data controller. NSS provided Scottish morbidity/ mortality information and emigration data. The National Health Service Central Register (NHSCR), using full name and date of birth, supplied UK mortality information and indicated general practitioner registration out with Scotland. Robust mortality status information was unavailable for seven individuals traced to Northern Ireland and one individual who was overseas therefore these eight male participants are classed as mortality status unknown.
+NSS provided date of death and ICD-9/10 codes for causes of death. NHSCR corroborated that data and supplied all death certificate information, identified deaths undetected by NSS and deaths within the rest of the UK. NSS provided ICD-9/10 codes recorded during general hospital in-patient and day-case admissions (Scottish Morbidity Record 01), event duration, partial date (month/year) and admission type with data requested for all deceased and consented individuals.
+Years of birth for the deceased cohort were applied to the expectation of life, by gender and selected age, Scotland, 1861 to 2011 table.18 Deaths were categorised as premature; defined as dying before the age listed based on year of birth/closest period or as post-expected, where death occurred at any point beyond the specified age. Using indirect standardisation19 we calculated standardised mortality ratios (SMR) and 95% CI comparing the risk of death among the cohort with the risk of death within the Scottish population.
+Data analysis
+We used Cox regression analysis to examine variation in risk of premature death over time according to baseline characteristics. The time in the study ran from 1 September 1992 (or recruitment to baseline study) until 31 December 2017 or date of death. Cases of
+individuals experiencing death post-expected age were filtered from analysis. The eight individuals with mortality status unknown were right censored20 with time in study as recruitment until discharge from The State Hospital when they were lost to follow-up. Independent variables were based upon previous literature and where preliminary analysis yielded statistically significant results.
+Kaplan-Meier plots were constructed for each covariate to assess the assumption of proportional hazard. Where the assumption was violated a time-dependent covariate was included. Cox regression models were constructed for each covariate while controlling for age at baseline. All analysis were conducted using SPSS version 22.21
+Results
+Mortality
+Eighty-nine individuals (36.9%) were deceased as of 31 December 2017 providing an all-cause crude death rate (CDR) of 1747/100 000 PYAR (95% CI 1403-2150). At point of death: 51.7% (n = 46) were resident in the community, 30.3% (n = 27) within low secure/open wards, 15.7% (n = 14) in high security and 2.2% (n = 2) were detained in prison.
+Mean age of death was 55.6 years (range: 30.5-84.7). Seventyseven (36.2%) men died and 12 (42.9%) women at average ages of 56.6 years (range: 30.5-84.7) and 48.9 years (range: 36.2-66.1) respectively.
+Table 1 outlines mortality status by primary diagnosis. As detailed in Table 2, 52 (67.5%) men died prematurely, and 11 (91.7%) women died prematurely. The mean years of potential life lost were 14.9 years (range: 0.09-35.7) for the men and 24.1 years (range: 5.2-35.8) for the women (10 women; one woman was born, lived primarily and died overseas). Excluding prison transfers, 22.2% (n = 18/81) prematurely deceased individuals were subject to restrictions at baseline (specific mortality rate 1052/100 000 PYAR, 95% CI 623-1663) with 28.1% (n = 45/160) non-restricted individuals having prematurely died (specific mortality rate 1330/100 000 PYAR, 95% CI 970-1780).
+The SMRs by gender are detailed in Table 3. The SMR for allcause deaths (SMR = 397, 95% CI 321-487) indicates a mortality rate almost four times that observed within the Scottish population. The ratio for the males (SMR = 297, 95% CI 236-370) returns a threefold increase in mortality whereas the females exhibit an SMR (SMR= 1000, 95% CI 542-1700) ten times the population rate. A high proportion of deaths (91.0%) occurred as a result of natural causes (SMR = 401, 95% CI 321-496).
+Suicide was defined by explicit codes; E950-959 (ICD-9) and X60-X84 (ICD-10). In addition, National Records Scotland include ‘event of undetermined intent’ among probable suicides. There were five suicides representing 5.6% of the 89 deaths (CDR 98/100 000 PYAR, 95% CI 32-229). Suicide mortality (SMR 625, 95% CI 229-1385) was six times the population rate. Accidental deaths occurred in three individuals (3.4%) (SMR = 375, 95% CI 95-1021) at almost four times the population rate. Re-categorising substance misuse deaths (n = 4, 4.5%) as accidental raises unnatural mortality to almost nine times (SMR= 875, 95% CI 382-1731) the population rate.
+Cause of death
+Underlying cause of death was assessed using ICD-9/10 classifications and categorised as premature/post-expected age as detailed in Table 4. Premature death occurred in 63 (70.8%) people. The primary cause of premature death (n = 20, 31.7%) was respiratory
+disease/cancer, with circulatory disease/events (n = 12, 19.0%) forming the next largest group with other cancers responsible for 11.1% (n = 7). There were no significant differences between premature and post-expected age deaths by cause.
+As noted there were n = 5 suicides. For these people baseline primary diagnoses were mixed: three had schizophrenia, one had antisocial personality disorder (ASPD) and one alcoholism. One had comorbid ASPD. At baseline four reported a history of heavy/abusive alcohol misuse and all confirmed polysubstance drug use. At death, two were under mental health legislation, four lived in the community and one in a low secure ward. All accidental
+deaths occurred in the community, two because of assault and one asphyxiation with food.
+Statistical analysis
+Cox regression analysis (Table 5) suggests that men detained under civil provisions at baseline are more likely to die prematurely than those detained under criminal provisions. Males experience a lower risk of premature death and higher survival than females. Diagnosis of intellectual disability at baseline is associated with lower hazard and increased survival. Being female and having a
+comorbid diagnosis of ASPD significantly increased the likelihood of premature death, a finding not replicated in the males. In general, and for the males, receiving antipsychotic medication by depot injection was associated with higher hazard and shorter survival. Substance misuse at baseline (alcohol misuse or illicit drug use) was significantly associated with an increased hazard of premature death both overall and among males.
+accidental harm. Prematurely deceased individuals received significantly more diagnoses (11.59, P = 0.022) compared with consented participants (5.8 diagnoses).
+Those who died prematurely spent on average significantly more days as general hospital in-patients in relation to total trauma (14.1 days, P = 0.007), urgent (12.4 days, P = 0.011) and routine admissions (15, P < 0.001) than living participants.
+Morbidity
+Data relating to Scottish general hospital in-patient admissions for the 89 deceased and 66 consented participants were obtained from NSS (n = 154 as one participant died following consent). A nil return was obtained if the individual had never had a general hospital in-patient admission in Scotland between baseline and 31 December 2014/date of death. Data were acquired for 115, with a further 39 individuals receiving a nil return. Table 6 reports the mean number of unique ICD-10 codes allocated for each ICD-10 block. Table 7 outlines the number of individuals in receipt of an ICD-9/10 classification by ICD-10 block description and Table 8 the number of days spent as a general hospital in-patient.
+Examination of the tables presented provides an overview of the physical health of the alive, prematurely deceased and post-expected age deceased groups. No significant differences were observed in terms of mean endocrine, nutritional and metabolic disease diagnoses between the living participants and those who prematurely died. Similarly, no significant differences were observed for diseases of the circulatory system. In relation to respiratory system diseases, a significant difference was observed in mean diagnoses between prematurely deceased (1.44 diagnoses, P = 0.002) and living participants (0.48 diagnoses). In terms of ICD-10 blocks S-Y ‘Injury, poisoning & certain other consequences of external causes’, a significant difference was observed between prematurely deceased (4.05 diagnoses, P = 0.005) and living participants (1.31 diagnoses). Unfortunately, injuries resulting from self-harm cannot be distinguished from
+Discussion
+This study investigated morbidity and mortality findings from a 20year follow-up of a cohort of high secure forensic patients first recruited as a whole population survey and followed through the context of recovery.
+Unnatural and suicide deaths
+In stark contrast to previously reported forensic6,22 and mainstream psychiatric findings2,23 we did not observe exceptional rates of suicide and accidental/unnatural deaths. We described a sixfold (SMR = 625) increase in suicide and an almost fourfold (SMR = 375) increase in unnatural deaths against the general population. In comparison, Clarke et al12 reported a SMR for suicide of 3231, and for all unnatural deaths of 1898 in relation to a cohort followed for a maximum of 20 years (n = 595, 5593 PYAR) after first admission to a medium secure unit. Including the few drug/alcohol deaths within accidental (unnatural) deaths we only returned an almost ninefold increase. No suicide/accidental deaths occurred within high secure care, and only one of our eight suicide/accidental deaths happened within the psychiatric hospital environment. Of the n = 31 suicide/open verdicts and accidental deaths reported by Clarke et al12 almost half (n = 15) the preventable deaths occurred within high/medium security and the wider hospital environment.
+We considered a number of factors in exploring this finding of a low rate of suicide and accidental deaths within our cohort. First, was the Scottish suicide rate lower than elsewhere in the UK? The 2018 Scottish suicide rate (16.1/100 000 persons) was greater than the comparably reported English rate (10.3/100 000).24 Therefore Scotland does not have a naturally low rate of suicide.
+Second, was our low rate of suicide reflected in the general adult psychiatry population in Scotland? This was refuted by a historical Scottish population study of discharged, long-term (>1 year) psychiatric in-patients that reported an increased suicide risk 13 times the population rate.25
+Third, was the diagnostic profile of this Scottish cohort impactful? Unlike other UK regions,13 within Scotland offenders with a primary diagnosis of personality disorder generally remain within the criminal justice system. At baseline only 5% received a primary diagnosis of personality disorder whereas 70% attracted a schizophrenia diagnosis. This compares with 25.8% of individuals with personality disorder in a Swedish forensic cohort,11 26.6% in an English medium secure cohort12 and 13.5% in an English community cohort.1 Only the Clarke et al12 study splits suicide between mental illness and psychopathy at 72.2% and 16.6%, respectively, which is reduced in the personality disorder group given that it
+was 26.6% of their cohort. The different diagnostic profiles may be a factor in explaining our findings but this is not supported by the Clarke et al12 study and would not account for the extent of the difference. Suicide rates are generally high among individuals experiencing personality or psychotic disorder26 with risk of accidental death higher for personality disorder compared with schizophrenia.8 Contextualising our suicide deaths within our diagnostic profile we observe a similar pattern; 60% of people who died by suicide had schizophrenia at baseline, one person had ASPD and another alcoholism. A total of 80% were male and the same proportion had a history of heavy/abusive alcohol use, all displayed polysubstance drug misuse, known risk factors for non-natural death.26 That description differs from the overall cohort, 47% of whom reported substance misuse at baseline and 29% had comorbid ASPD.
+Fourth, does the year the cohort was established or the length of follow-up influence findings? Compared with other studies11-13 our cohort was followed for the longest time therefore increasing rather than decreasing suicide likelihood. Alternatively, have more recently established cohorts observed behavioural change, making suicide/accidental in-patient death more likely? Only one suicide has been noted in our cohort source hospital (The State Hospital)
+since 1996 (email from Health Records, The State Hospital, tsh. Health_Records@nhs.net, November 2019).
+Finally, is engagement with the Scottish forensic mental health system protective against suicide or accidental death? An examination of suicides (n = 14) occurring within high secure care in Scotland (The State Hospital, 1972-1996)27 noted treatment-resistant schizophrenia and having committed violent offences as suicide risk factors. Greater liberalisation of the hospital, increased activities and reintroduction of clozapine were suggested for the suicide reduction observed over time. This continuing developmental process alongside estate renewal may influence our observed low suicide/accidental death rate while patients resided in high secure care. At death two individuals were under mental health legisla-tion/restrictions. Examining CDR figures, restrictions made little difference to the premature death rate. Also considered was if a longer period of contact and mandated relationship with services may protect against suicide; alternatively, lack of autonomy may be a negative influence. Neither was apparent within our findings.
+We hypothesise that factors within the Scottish forensic inpatient environment; physical, procedural and/or relational are protective in terms of suicide prevention or in deterring behaviour leading to accidental death and that these may have an ongoing effect on patients’ relationships with services in the community. There may also be organisational factors that reduce avoidable deaths. Scottish forensic services have advantages in terms of size and cohesion, with few independent secure beds, and the strategic lead of the Forensic Network.
+Risk of premature mortality
+A large proportion of our cohort died (36.9%) demonstrating an almost fourfold increase (SMR=397) in all-cause mortality. This represents a slightly higher CDR than reported for forensic services in England/Wales,22 mirrors an English general adult community-recruited cohort experiencing psychosis28 and is lower than reported internationally.22 In contrast, 91% of our cohort died of natural causes four times (SMR = 401) the rate of the general population. Natural deaths within the discharged Scottish general adult psychiatry in-patient population has been reported at SMR = 16929 indicating a difference in mortality between Scottish forensic and general adult in-patients.
+Overall, 70.8% of deaths were premature and naturally occurring at an average age 55.6 years. Respiratory disease/cancer was the underlying cause of almost a third of premature and 36% of all deaths, with cardiovascular disease related to 19% of premature and 21% of all mortality. Study recruitment occurred during 1992/3 when almost all patients smoked or were passive smokers. Since December 2011, our cohort source hospital, The State Hospital, Carstairs has been entirely smoke free.
+Rates of respiratory related deaths, in excess of fourfold the expected level have been noted in an early population study of discharged Scottish general adult psychiatry long-term (>1 year) in-patients25 with rates of death related to cardiovascular disease being slightly raised at SMR = 160. A later examination29 capturing all discharged general adult psychiatric in-patients reported that cardiovascular disease, despite displaying only a small rise in relative risk (SMR =170) was responsible for 67% of total mortality and 54% of the total years of potential life lost. A similar English study examining death within a year of discharge of patients with schizophrenia reported SMR of 470, and 250 for respiratory and circulatory disease deaths, respectively, with increased rates from 1999 to 2006.6
+The cohort of 28 females represents around 50% of Scottish female forensic patients, reported as n = 56 in 200430 and n = 60 in 2017.31 There is an obvious disparity between the male and female groups. Almost all women died prematurely aged under 48
+years with mean loss of 24 years of potential life. Their SMR for natural death was three- to fourfold the male rate and that held for females with schizophrenia, rising to fivefold for a primary diagnosis of ASPD. Receiving a comorbid diagnosis of ASPD was also associated with premature death. This suggests that ASPD may have a unique impact on the physical health of women in a manner not reflected in risky behaviour or suicide completion.
+There is a lack of published literature regarding gender differences in mortality among patients located within forensic services with papers and reviews generally commenting on the percentage of males within the cohort.1 , Where data does exist,12 our results replicate those findings, namely that SMR for death because of natural causes, unnatural causes and suicide are higher for the female than the equivalent male cohort. However, as with those presented here, findings must be interpreted with caution because of the extreme gender imbalance evident within forensic psychiatric services and reflected in small numbers of females in research cohorts.
+A similar picture has, however, been observed within mainstream services, specifically in relation to individuals with schizophrenia. A population follow-up of all in-patients admitted with schizophrenia diagnosis from 1980 to 2006 indicated that up to 1992 the SMR for males in relation to all-cause, unnatural and natural deaths exceeded that of females; however, for individuals admitted post 1992 the opposite was observed: females displayed greater all-cause, unnatural and natural SMR.3 More recently an American population study reported that female SMR for allcause mortality exceeded the male equivalent; however, the SMR for unnatural death was higher among males.5
+Almost 68% of males died prematurely at mean age 50 years. Males detained under civil measures were significantly more likely to die prematurely than those males under criminal procedures. Similarly, males in receipt of depot antipsychotics at baseline were significantly more likely to have died prematurely. It is conceivable that as civil patients transfer to high secure care because of acute morbid positive symptoms and/or locally unmanageable levels of violence or aggression16 and by utilising depot medication as a proxy for illness severity, we may be observing those who experienced the severest symptoms and psychological distress, dying prematurely. Specifically, that is, individuals who may be more greatly influenced by psychophysiological factors.
+Scottish forensic services acknowledge that female patients represent a heterogeneous group, often more chaotic and challenging with differing needs to male patients,30 with higher rates of mortality33 and generally poorer outcomes.3 Although Scotland lacks female high secure care and exclusively single gender medium secure provision (with some services provided within England), because of the general complexity of female patients, providing appropriate relational security and support can be more important than physical security.31 Regardless of shortcomings in the Scottish female forensic estate, the high levels of premature mortality of both genders cannot be overlooked.
+Despite spending on average longer as high secure in-patients, with exposure to the health benefits that offers, there was little difference in CDR for those subject to restrictions on discharge and those not. Fazel et al22 pointed out that mortality rates among forensic inpatients are high but more closely reflect the general adult psychiatry population than prisoners. They postulate that greater than any factors negatively influencing mortality within forensic environments are the poor lifestyle choices evident among general psychiatric populations that can compound psychotropic medication side-effects. Given our respiratory findings, however, the impact of a heavy smoking environment must be acknowledged. Although poor lifestyle choices have an impact on morbidity/mortality among individuals experiencing SMI again the increased morbidity risks and resultant mortality are not fully explained by behavioural patterns.35,36
+Protective factors
+Within this cohort some protective factors appeared evident. Although both genders died at early ages the males lived on average almost 8 years longer compared with the females. Males with a primary diagnosis of intellectual disability appeared less likely to die (SMR= 147, 95% CI 53.9-326) and it could be that those patients with intellectual disability received less psychotropic medication and accompanying side-effects. One reason suggested for high rates of morbidity among individuals with mental ill health is the propensity for diagnostic overshadowing to occur. This is when disparities occur in the treatment and diagnosis of physical disorders as a result of misattribution of physical symptoms to mental illness.37 Although we interpreted experiencing primary intellectual disability as a protective factor, diagnostic overshadowing has been presented as a particular problem within the intellectual disability population with symptoms related to physical or mental ill health being misattributed to their intellectual disabilitiy.38 We suggest that in line with our assertion that engagement with the Scottish forensic mental health system may be protective against unnatural death and death by suicide, for individuals with intellectual disability, location within services almost exclusively under National Health Service operational control may confer advantages to forensic patients with intellectual disability in terms of staffing, their training and support and patient services offered. Higher levels of in-patient and community support to avoid offending and foster appropriate behaviour may also reduce stress and encourage patients with intellectual disability to adopt heathier lifestyles, with a potential reduction in drug and alcohol use. More attention may be paid to their physical health by staff, and general practitioner referrals encouraged and supported, leading to earlier physical health intervention and a reduction in diagnostic overshadowing.
+It is also acknowledged that individuals with intellectual disability represent a particularly vulnerable subpopulation within the prison environment, being subject to high rates of mental disorder39 and possibly greater risk of suicidal ideation than the general population.40 We propose that a contributing factor to this poor outlook is a lack of equivalence, equality and equity for people with intellectual disability within the UK prison system.41 Again we propose that our intellectual disability cohort were protected from premature mortality precisely because they were, where appropriate, diverted from the prison environment and supported by specialist forensic psychiatric services within hospital and community settings, designed to promote and provide equality of life experience. Further research is required to address the paucity of literature robustly identifying and exploring the journey of individuals with intellectual disability through services.
+Morbidity
+Antipsychotic medications, a reliable mechanism for easing symptoms, reducing distress and therefore enhancing recovery carry with them a diversity of side-effects: activating, sedating and metabolic. Unsurprisingly antipsychotics have been targeted as a possible reason for the premature mortality associated with schizophre-nia.1,42 Population studies43 indicate that individuals with schizophrenia treated with antipsychotics or antidepressants have a lower risk of death compared with individuals not receiving such medications. A primary diagnosis of schizophrenia was applied to 70% of this cohort, and receiving depot neuroleptics was significantly associated with premature death; however, there were no significant differences in mean diagnoses of cardiovascular or endocrine, nutritional and metabolic disease applied to the living participants and the prematurely deceased. What is apparent is the significant difference in terms of respiratory disease, with those dying prematurely receiving more diagnoses. Although
+undoubtedly the legacy of an era, smoking remains an issue for forensic patients after discharge from controlled in-patient environments.
+Those who died prematurely attracted significantly more diagnoses related to injury, poisoning and other external causes; however, these did not translate into high numbers of traumatic deaths or completed suicides. Overall, those dying prematurely received more physical health diagnoses across all listed ICD-10 blocks and spent significantly more days as general hospital inpatients than those who remained alive evidencing the poorer physical state of that group.
+Scotland does not have the healthiest national population, indeed the ‘Scottish effect’ is much examined with 17 hypotheses proposed to account for excessive premature mortality.44 We suggest that something akin to the ‘Scottish effect’, specifically in terms of biopsychosocial stress and the resultant physiological stress response, is being observed among mainstream and forensic psychiatric populations. High levels of adverse life events observed among Scottish forensic patients45 together with the dose-response association observed between adverse life events/psychological distress and a negative impact upon subjective and objective physical health46 may be evidenced within our reported cohort. There is increasing physiological evidence of heightened levels of oxidative stress and inflammation within anxiety, depressive, bipolar disorder and schizophrenia.36,47 These processes provide mechanistic pathways by which leucocyte telomere length can be shortened. Metaanalysis has evidenced shortened telomere length across a range of psychiatric disorders36 with length being an indicator of cell ageing and short length associated with age-related morbidities, for example immune dysregulation, cancer, diabetes and cardiovascular disease.48 Interpreting the morbidity and excess mortality observed in cohorts such as this through the lens of the ‘Scottish effect’ may lead to better targeted local interventions.
+Limitations
+This study has several limitations. Follow-up physical health information could only be requested for deceased or consented participants. The wider study adopts a gatekeeper approach therefore all cohort members whose gatekeeper denied access because of their inability to provide ‘fair’ consent49 or they were not physically/mentally well enough to be approached were excluded. This removed individuals with the greatest physical health needs and/or the severest psychiatric symptomatology. The mortality status of eight individuals remained unknown although of these seven resided in Northern Ireland. We could not locate or engage with appropriate Northern Ireland services to confirm their status or locate their current care team. It proved impossible to access information without consent. Accessing cohort member gatekeepers within England was equally difficult but mortality information moved between NHSCR and their English counterparts. Some individuals with mortality status unknown could have died because of suicide or accidental causes, raising our mortality profile, but it would remain lower than the reported studies. We were also as reliant on the general hospital clinicians accurately recording and/or applying the most appropriate ICD-10 codes as we were of the cohort member and their accompanying staff providing a precise physical health history.
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+INTRODUCTION
+Suicide is one of the most important causes of death among young people in Europe.1 Parental risk factors, including parental suicidal behaviour, 2—4 parental psychiatric morbidity2 4 and parental non-suicidal death,4 contribute to suicidal behaviour.
+A life-course approach may add to our understanding of the aetiology of suicidal behaviour.5—8 This approach is based on the assumption that the impact of biological and social risk factors as well as protective factors may vary with age at exposure due to a differential impact at different developmental stages.8 Any variations in risk for suicidal behaviour
+in the offspring with age at first exposure to parental factors may be due to the accumulation of exposures during the life span, due to a latency in outcome manifestation, or due to specific developmental mechanisms that act exclusively (‘critical period hypothesis’) or predominantly (‘sensitive period hypothesis’) during a specific age window.8 In recent years, the life course paradigm has reached a considerable resonance in chronic disease epidemiology.8 In spite of this development, applications related to suicidal behaviour in the offspring remain sparse.7 In order to explore the effects of age at exposure to parental mortality and markers of morbidity on the risks of suicide and attempted suicide in offspring, we conducted the present study.
+METHODS
+Study design
+We conducted a matched case—control study through record linkages between Swedish national registers. The study base consisted of all individuals, born in Sweden between January 1973 and December 1983, who were singletons and for whom information on both biological parents was available. The cases comprised all individuals recorded in the National Patient Register (NPR) or in the Causes of Death Register (CDR) due to attempted or completed suicide (E950—E959 in the International Classification of Diseases ICD-8 and ICD-9, X60—X84 in ICD-10). The National Board of Health and Welfare provided us with information on the equivalence of ICD-8/ ICD-9 and ICD-10 codes.
+Cases included suicides and attempted suicides with uncertainty about intention (E980—E989 in ICD-8 and ICD-9, Y10—Y34 in ICD-10). Uncertain and certain diagnoses were combined to limit temporal and regional variation in ascertainment routines.2 A sensitivity analysis of certain and uncertain suicidal behaviours established the comparability of the estimates.
+Attempted suicide in offspring was also analysed with regard to the method used. We distinguished violent methods, including hanging, use of firearms and knives, jumping from heights and in front of moving objects, and drowning (ICD-8 and ICD-9: E953-957 and E983-987; ICD-10: X70-X82 and Y20-Y32) from non-violent methods, which included all forms of poisoning (ICD-8 and ICD-9: E950-952 and E980-982; ICD-10: X60-X69 and Y10-Y19). This classification strategy was in accordance with
+related literature investigating violent and non-violent suicide methods.9
+Cases comprised 1407 individuals with suicide completion and 17159 individuals with attempted suicide. They were each matched by sex, month, year and county of birth to up to 10 randomly selected controls. Only individuals who were alive and living in Sweden at the time of the index event were eligible to serve as controls. Events coded as attempted suicides and suicides before the age of 10 years might be misclassified and were excluded from the analyses. Suicide victims were assessed from 1 January 1983 until 31 December 2004, and were up to 31 years of age at the end of follow-up. Suicide attempters were assessed from the same date up until 31 December 2006.
+Data sources
+In Sweden, all residents are identified by a unique identification number. This enables merging of individual information from different national registers.
+Children were linked to their biological parents using the Multi-Generation Register (MGR). Of the individuals born after 1950, less than 2% could not be linked to their parents.10
+We derived data on completed suicides in parents and offspring and parental deaths due to other causes from the Causes of Death Register (CDR). The National Patient Register (NPR) provided information on attempted suicides in parents and offspring, and also data on the dates and diagnoses of hospital care based on clinical assessments. The Register of the Social Insurance Agency (RSIA) provided information on diagnosis-specific disability pension. All diagnoses in the NPR, CDR and RSIA were classified in accordance with ICD-8, ICD-9 and ICD-10.
+The Population and Housing Censuses (PHC) provided data on parental socioeconomic status, maternal marital status, and parental education in 1970. Data on parental education in 1990 were retrieved from the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA). Information on emigration and immigration was extracted from the Register of the Total Population. Table 1 summarises details of the variables and registers.
+Exposure variables
+The exposure assessment for each case and control refers to the time up until the event involving the index subject. Parental markers of morbidity, namely diagnosis-specific disability pension, attempted suicide, and inpatient care due to mental disorder, were categorised into age windows corresponding to offspring’s age at first exposure. The categories were exposure before the age of 3 years, between 3 and 10, and over the age of 10. We considered only the main diagnosis, and the first date of
+hospital inpatient care or receipt of disability pension. Also, diagnoses before the birth of the child were allocated to the youngest exposure window, since it was deemed likely that chronic illness underlying the markers of morbidity would result in early exposure of the child. Parental markers of mortality, namely suicide completion and death due to other causes, were categorised into exposures before and after the age of 10 years due to relatively small numbers of cases. Whenever the child was exposed to a marker that was positive for both parents, the age window corresponding to the earlier exposure was used. Absence of exposure was employed as the reference category.
+Covariates
+Maternal and paternal age at offspring’s exposure were used as continuous variables. Parental education was measured as education up to 10 years (primary education), 10 to 13 years (secondary education), and >13 years (university education, reference category). We used either maternal or paternal education, whichever was the higher, in accordance with the dominance principle which has been shown to perform well in classifying families.15 Whenever both parents were born in 1940 or earlier we used educational data from 1970. These parents were at least 30 years old at time of measurement. For all other parents, we used educational data from 1990. Categories for parental socioeconomic status were unskilled workers, skilled workers, low level salaried employees, intermediate or high level salaried employees (reference category), and others. We used either maternal or paternal socioeconomic status, whichever was the higher. Data from the 1980 census were used for events that occurred between 1983 and 1990, 1985 census data for events between 1991 and 1998, and 1990 data for events from 1999 to 2006. In the case of internal missing data, we took information from the preceding census. This approach ensured that all measurements were taken at a point in time preceding offspring’s suicidal behaviour.
+Maternal marital status was dichotomised into married and cohabiting versus other status (unmarried, divorced, widowed, non-cohabiting), using the same approach as for socioeconomic status with regard to the choice of census year.
+There were missing data on covariates in 4.0% of cases of attempted suicide, and in 4.2% of cases of completed suicide. Missing data were coded as a separate category. A sensitivity analysis showed similar patterns of suicide and attempted suicide risks in cases with complete information and in cases with missing data.
+Statistical analysis
+The outcome variables were completed suicide and attempted suicide in offspring. Univariate and multivariate ORs for the exposure variables were estimated using conditional logistic
+234
+J Epidemiol Community Health 2012;66:233-239. doi:10.1136/jech.2010.109595
+regression. Interactions between offspring’s sex, offspring’s age at onset of suicidal behaviour as well as maternal marital status and the exposure variables were subjected to partial likelihood ratio tests by introducing corresponding product terms into the adjusted models.
+Attributable proportions for the fully adjusted model were calculated using the formula AP=S(1—(1/OR)), where S is the number of cases with exposure divided by the total number of cases.
+Further, in order to test for a possible linear trend across offspring’s age at exposure, we included offspring’s age at exposure as a continuous variable in the fully adjusted models. These analyses were restricted to exposed individuals only.
+Data processing was performed using SPSS V15.0 for Windows.
+RESULTS
+The numbers of suicide victims and suicide attempters, respectively, were 1019 (72.4%) and 6003 (35.0%) boys/men, and 388 (27.6%) and 11 156 (65.0%) girls/women. Mean age was 22.3 years (SD 3.7) at suicide completion, and 21.1 years (SD 4.4) at first registered attempted suicide. With regard to suicide methods, for certain suicides (N—1077), the main method was hanging (41.0%), followed by poisoning (22.1%) and jumping (19.0%). For uncertain suicides (N—330), poisoning (67.3%) was followed by other methods (10.4%) and jumping (10.0%). For certain attempted suicides (N—13 976), poisoning (88.9%) was the most frequent method, followed by cutting (4.2%). For uncertain attempted suicides (N—3183), the main method was poisoning (76.2%), followed by other methods (17.2%). Of attempted suicides, 14 853 (86.6%) were coded as non-violent attempts, and 2306 (13.4%) as violent.
+A majority of all exposures to disability pensions attributed to parental mental disorder (N—9145) were coded under the headings neurotic, stress-related and somatoform disorders (48.0%), and affective disorders (22.9%).
+In exposures to disability pensions granted due to somatic disorders (N—25432), disorders of the musculoskeletal system and connective tissue were the most frequent diagnoses (69.2%).
+Tables 2—5 show the results of the univariate and multivariate analyses.
+Parental suicidal behaviour
+Exposure to parental attempted suicide was associated with a 3.4-fold increase in suicide risk (table 2), and a 3.5-fold increase
+in risk of attempted suicide (table 3) in offspring. The ratios decreased to 2.6 and 2.6, respectively, in the fully adjusted models.
+Exposure to parental suicide was associated with a 3.5-fold increase in suicide risk (table 2), and a 2.6-fold increase in risk of attempted suicide (table 3). The ratios decreased to 2.5 and 1.8, respectively, in the fully adjusted models. Adjustment for parental age alone had little effect on the estimates (tables 2 and 3). At the second adjustment step, parental education and marital status had the largest effects.
+The risk of completed suicide was most pronounced among offspring exposed to parental completed suicide in the earliest age window (table 4). There was a constant and an increasing risk of attempted suicide among offspring across exposure windows in relation to parental attempted suicide and parental completed suicide, respectively (table 5). Separate analyses of violent and non-violent attempted suicides in offspring revealed that there was one slight alteration to the general pattern: the risk of violent attempted suicide after parental suicide was 1.6 (95% CI 1.0 to 2.7) when exposed up to the age of 10 years, and 1.4 (95% CI 0.9 to 2.3) when exposed over the age of 10, indicating a more pronounced risk of violent attempted suicide in offspring in the case of early exposure to parental suicide.
+Parental diagnosis-specific disability pension
+Parental disability pension due to a psychiatric disorder was associated with a 2.9- and 2.7-fold risk of completed suicide (table 2) and attempted suicide (table 3) in offspring, respectively. The estimates fell to 1.9 and 1.7, respectively, after full adjustment. Exposure to parental somatic disability pension had a smaller effect than exposure to psychiatric disability pension, and was associated with a 1.5-fold risk of suicide completion (table 2), and a 1.7-fold risk of attempted suicide (table 3). The estimates decreased to 1.3 and 1.5, respectively, in the adjusted models. Offspring’s risk of completed and attempted suicide showed a similar pattern with regard to the timing of first exposure to parental diagnosis-specific disability pension. The earlier the exposure, the greater was the risk of suicidal behaviour (tables 4 and 5). There was a significant increase in suicide risk with decreasing age at exposure to psychiatric disability pension (p—0.027) (table 4). Further, there was a significant general increase in attempted suicide risk with decreasing age at exposure to somatic disability pension (p—0.001, table 5).
+Parental inpatient care due to mental disorder
+Parental inpatient care due to mental disorder was associated with a 2.6-fold risk of completed suicide (table 2) and a 3.0-fold risk of attempted suicide (table 3). The estimates decreased to 1.8 and 2.1, respectively, in the fully adjusted models, largely due to the effects of parental education and maternal marital status. The risks of both completed and attempted suicide were highest in
+the case of exposure in the youngest age window (tables 4 and 5). There was an increase in attempted suicide risks with decreasing age at first exposure (p<0.001, table 5).
+Parental death due to causes other than suicide
+Parental death due to causes other than suicide was associated with a 1.7- and 1.6-fold risk for suicide and attempted suicide
+among offspring, respectively (tables 2 and 3). ORs decreased to 1.3 and 1.3, respectively, in the adjusted models. In contrast to other markers of parental morbidity, exposure to parental non-suicidal death after the age of 10 years was associated with an increased risk of attempted and completed suicide only in offspring exposed over the age of 10 (tables 4 and 5). There was a significant increase in attempted suicide risk with increasing age at exposure to parental non-suicidal death (p=0.018) (table 5).
+Interaction effects of offspring’s sex, offspring’s age at onset of suicidal behaviour and maternal marital status with the exposure variables
+Partial likelihood ratio tests indicated that exposure to parental death due to other causes than suicide increased the risk of attempted suicide more in boys/men than in girls/women (p=0.006). In comparison to non-exposed girls/women, the estimates for exposed boys/men and exposed girls/women were 1.40 (1.26 to 1.55) and 1.15 (1.05 to 1.26), respectively.
+Furthermore, exposure to parental somatic disability pension increased the risk of attempted suicide more when onset of suicidal behaviour was before the age of 20 years, than in the case of later onset (p=0.012). Compared with unexposed offspring with onset after the age of 20 years, the respective estimates were 1.66 (1.54 to 1.80) for exposed offspring with onset before 20 years and 1.46 (1.39 to 1.55) for exposed offspring with onset after the age of 20 years.
+With regard to suicide attempt in offspring, there were several interaction effects between parental markers of morbidity and
+maternal marital status. Specifically, exposure to parental attempted suicide (p<0.001), parental inpatient care due to mental disorders (p=0.002), parental psychiatric disability pension (p=0.05) and parental somatic disability pension (p=0.001) interacted with maternal marital status. In comparison with unexposed offspring of married and cohabiting mothers, the suicide attempt risk for offspring of unmarried, divorced, widowed and non-cohabiting mothers exposed to parental attempted suicide were 2.51 (2.11 to 2.99), and the estimates for exposed offspring of married and cohabiting mothers were 1.87 (1.56 to 2.24). The respective estimates for exposure to parental inpatient care due to mental disorders, parental psychiatric and somatic disability pension were 3.17 (2.99 to 3.35), 2.69 (2.49 to 2.91) and 2.33 (2.18 to 2.49) for offspring of unmarried, divorced, widowed and non-cohabiting mothers, respectively, and 2.22 (2.10 to 2.36), 1.83 (1.67 to 2.02) and 1.58 (1.49 to 1.68) for exposed offspring of married/cohab-iting mothers, respectively.
+DISCUSSION
+Main findings
+A general pattern of increasing risk of suicide and attempted suicide in offspring with decreasing age at exposure to parental risk factors emerged. This pattern was present for parental somatic disability pension, parental inpatient care due to mental disorders and parental psychiatric disability pension. With regard to offspring’s risk of completed suicide, the pattern was
+also present for parental suicide and attempted suicide. For parental non-suicidal deaths, the pattern was the opposite.
+Strengths and limitations
+The main strengths of the present register study were the large number of participants, the population-based design, the full coverage of cases, the opportunity to control for potential confounders, the high quality of the data10-14 and the very low dropout. Difficulties, such as recall bias, which is often present in studies based on data from clinical settings, could be avoided due to the use of national registers.
+The study also has some limitations. For the Register of the Social Insurance Agency, which provided data on disability pension, misclassifications within the group of psychiatric diagnoses, entailing an under-reporting of psychotic disorders, have been discussed previously.16 This register has not yet been evaluated in detail. Furthermore, we could not address the effect of cumulative exposure to morbidity and mortality in both parents due to the relatively small number of cases in several exposure windows. A recent study identified a clear increase in attempted suicide risk in the offspring when suicidality or psychiatric disorders occurred in several family members.2 Further, the present data on attempted suicides and mental disorders only covered individuals who were hospitalised. It is estimated that approximately 25% of suicide attempters receive inpatient care.17 The findings, therefore, only apply to attempts that resulted in hospitalisation.
+Comparison with previous studies
+Parental suicidal behaviour2 4 7 and parental inpatient care due to mental disorders,2 4 as well as parental non-suicidal death,4 have previously been identified as risk factors of suicidal behaviour in offspring. Knowledge of possible consequences of being on disability pension is limited.18 Receiving a disability pension seems to increase an individual’s own risk of suicide beyond the effects of his or her psychiatric hospitalisation and socioeconomic status.18 19 A recent study identified an increased suicide risk in offspring exposed to parental disability pension, but did not consider the nature of the parental diagnoses involved.6 In the present study, exposures to both parental psychiatric and somatic disability pension were associated with an increased risk of suicide and attempted suicide.
+Concerning possible impacts of age at exposure to parental risk factors on the risk of suicidal behaviour in offspring, earlier studies found that the risk of bipolar disorder, which is an important risk factor of suicidal behaviour,20 was most pronounced when exposure to maternal suicide21 or parental loss22 occurred before the age of 10 years. A recent study analysed the impact of offspring’s age at exposure to parental suicide on the risk of completed suicide and identified an increased risk for offspring exposed under the age of 17 years compared to offspring exposed later in life.7 The present findings add that children exposed at the very youngest ages show an important vulnerability that may lead to suicidal behaviour later in life.
+Interpretation
+The present study was the first to identify an association between parental somatic disability pension and offspring’s risk of suicidal behaviour. The association was present for both sexes and remained after controlling for parental suicidal behaviour, parental socioeconomic conditions and marital status. This finding may be related to the genetic transmission of parental somatic disorders, or to uncontrolled co-morbidity of mental
+disorders in this group, or to the psychosocial consequences of having a parent on disability pension.
+The finding of more pronounced risks of attempted suicide in offspring of unmarried, non-cohabiting, divorced and widowed mothers stresses the importance of a specific awareness and support from the environment and healthcare system.23 Other socioeconomic indicators, specifically the parental socioeconomic index and parental education, did not significantly alter the effects of exposure to parental morbidity and mortality.
+In accordance with the hypothesis of sensitive life periods,5 8 exposure to parental somatic disability pension, parental inpatient care due to mental disorders and psychiatric disability pension, seem to increase the susceptibility to attempted suicide and suicide in offspring most when exposure appears early in life.
+Similarly, exposure to parental suicide may increase the susceptibility to completed suicide. Of note, the present risk estimates were only slightly attenuated by controlling for parental age at offspring’s exposure, which is a crude marker of parental age at onset of morbidity. In previous research, early-onset major depression in offspring, which is an important risk factor of suicidal behaviour, has been discussed to be caused by a stronger genetic disposition that may be reflected in early onset of major depression in parents.24
+Compared to the risk pattern in completed suicide, the risk of attempted suicide in offspring exposed to parental suicidal behaviour was more pronounced for later exposure windows. This stronger effect in adolescence and young adulthood may underscore the importance of parental suicidal behaviours as triggering events for non-fatal suicidal behaviour in offspring. Of note, imitation has been discussed as an important factor in suicidal behaviour, particularly among young people.25 A triggering effect may also be reflected in the association of parental non-suicidal death and suicidal behaviour in offspring exposed after the age of 10 years.
+The risk patterns of attempted suicides varied somewhat with suicide method. The risk patterns for violent attempt in offspring exposed to parental suicide resembled more the patterns estimated for completed suicide than for attempted suicide in general. Related research suggests that violent attempts constitute a further step in the suicidal process, and that medically serious attempted suicides and suicides are two overlapping populations that share common psychiatric diagnostic and history features.9 26 Future research is warranted to further scrutinise the present findings in this context.
+CONCLUSION
+The present findings comply with the ‘sensitive period’ hypothesis in life course epidemiology8 and add to our understanding of suicidal behaviour in the familial context during the life span. Parental disability pension, parental psychiatric inpatient care and parental suicide seem to increase the offspring’s susceptibility to suicide, particularly when they occur early in life. Parental non-suicidal death has most detrimental effects when occurring in adolescence or young adulthood. Early interventions in families with parental morbidity or suicidal behaviour seem necessary to prevent suicide in offspring. Evaluations of such interventions suggest possible preventive effects. An early intervention for families at psychosocial risk resulted in short-term improvements in motherechild relations.27 28 The six-week intervention focused on strengthening the mothers in their care giving skills and improving the motherechild relationship and interaction. Another study, which targeted early intervention through a five-year family counselling programme
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+Suicide is a global public health issue that causes around 800,000 deaths every year (World Health Organization, 2014). Many suicides could be prevented if individuals with suicidal thoughts or behavior sought help from appropriate health services that met their needs (Trueland, 2014). However, a significant proportion of individuals with suicidal thoughts or behavior do not seek help (Bruf-faerts et al., 2011; Luoma, Martin, & Pearson, 2002; Pitman & Osborn, 2011). Two published reviews examined the factors that influence help-seeking for suicidal behavior and self-harm, but focusing exclusively on adolescents (aged from 11 to 19) and young adults (aged up to 26; Michelmore & Hindley, 2012; Rowe et al., 2014). Several factors such as stigmatization, fear of confidentiality being breached, and high self-reliance have been highlighted as barriers to help-seeking. However, it is not clear if the factors identified among young people are applicable to all age groups. Exploring a wider age cohort is important since low rates of help-seeking for suicidal ideation and behaviors (suicidality) are not specific to young people, but are also evident in older adults (Lee, Lin, Liu, & Lin, 2008).
+Previous reviews exploring help-seeking factors for suicidality have also examined the factors that influence both
+© 2017 Hogrefe Publishing
+professional and nonprofessional help-seeking sources. The current review focuses specifically on professional help-seeking to refine the findings, as professionally trained health workers are more likely to provide evidence-based treatments and accurate information than family or friends are, who may find it difficult to recognize potentially unhealthy thinking states that may lead to suicide (Owens, Lambert, Donovan, & Lloyd, 2005). In line with the World Health Organization, the current review defines professional help-seeking as use of professional support such as health services, formal social institutions, or professional care providers, either in the public or private sector (Gary, 2007). In consideration of the benefits and low rates of professional help-seeking for suicidality internationally, a review to summarize the current findings may help inform researchers and health professionals of the knowledge gaps and barriers to professional help-seeking, and help develop high-quality suicide prevention campaigns.
+This review aims to explore the existing literature of factors that influence professional help-seeking along the progression of suicide, from suicidal ideation to suicidal behavior and death by suicide. For this purpose, this review presents three distinct research foci: (a) professional
+Crisis (2018), 39(3), 175-196
+https://doi.org/10.1027/0227-5910/a000485
+help-seeking intentions for perceived suicidal ideation, among people with or without suicidality; (b) professional help-seeking behavior among people with suicidality; and (c) suicidal decedents’ health services use. To our knowledge, this is the first review to summarize the factors influencing professional help-seeking for suicidality across these multiple study designs.
+Method
+Search Strategy
+Relevant studies in English were identified using Medline and PsycInfo, accessed through the Ovid interface. Databases were searched between January 1, 1995, and December 31, 2015. Search terms used were: suicid* AND (‘seek*’ OR ‘service use’ OR ‘service util*’ OR ‘getting’ OR ‘care util*’) AND (‘help*’ OR ‘treat*’ OR ‘service*’ OR ‘care*’) within abstract or title. References from identified studies were also reviewed for completeness.
+Selection of Studies
+A total of3,183 research papers were identified through the search strategy. Of these, 1,912 abstracts were
+screened, after removing non-human studies, duplicate abstracts, and non-English language articles. The first author (JH) examined all titles and abstracts, and 109 articles were retained based on the following inclusion criteria: (a) published in a peer-reviewed journal; (b) not an editorial or a review; (c) examined the factors that influence professional help-seeking intentions for suicidality, or examined the factors that influence professional help-seeking behavior among individuals with suicidality or suicide decedents. Studies containing both professional and nonprofessional help-seeking sources such as family, friends, and intimate partners were included, but only the results from professional help-seeking sources were coded.
+The full text of the 109 articles was obtained and double-coded by the first author and one of the coauthors independently (either PJB, ALC or RR). Disagreements between reviewers were resolved through discussion with a third coder. This process resulted in 54 additional research papers being excluded, and 55 total studies being included in the review. Figure 1 depicts the PRISMA flow diagram for inclusion.
+Data Extraction
+All papers were coded using a pro-forma coding sheet to identify a range of study characteristics. Factors that influence professional help-seeking were coded based on a
+narrative synthesis instead of a meta-analysis due to the diverse nature of the study designs and outcomes included in this review. Factors were marked as adjusted if potential confounding variables were accounted for in statistical analysis. Otherwise, factors were marked as unadjusted. To evaluate the quality of the included studies, the assessments of quantitative studies (Glasziou, Irwig, Bain, & Colditz, 2001) and qualitative studies (Mills, Jadad, Ross, & Wilson, 2005) were used. Studies that used both quantitative and qualitative methods were coded with respect to the relevant assessments. The first author and one of the coauthors independently (either PJB, ALC or RR) coded all the studies. Any disagreement was resolved by discussion. The results are presented in Table A1 and A2 in the appendix. The majority of the included studies were of sound quality.
+Results
+Study Characteristics
+In total, 55 publications met the inclusion criteria for the review. Studies were classified based on three distinct research foci; specifically 15 (27%) studies examined professional help-seeking intentions for perceived suicidal ideation, among people with or without suicidality (Type I), 21 (38%) examined professional help-seeking behavior among people with suicidality (Type II), and 19 (35%) studies examined suicidal decedents’ health services use (Type III).
+The sample size ranged from 107 to 1,896 participants (Mdn = 302) in the Type I studies, from 15 to 5,100 (Mdn = 543) in the Type II studies, and from 49 to 19,426 (Mdn = 370) in the Type III studies. The proportion of females ranged from 0% to 78% (Mdn = 65%) in the Type I studies, from 0% to 87% (Mdn = 63%) in the Type II studies, and from 3% to 40% (Mdn = 25%) in the Type III studies. The characteristics of the included studies are presented in Table B1 in the appendix. Only the factors influencing professional help-seeking were coded and analyzed. Table 1 presents a summary of the factors being reported five times or more across the three types of studies.
+Professional Help-Seeking Intentions for Perceived Suicidal Ideation Among People With or Without Suicidality
+Of the studies, 15 examined professional help-seeking intentions for perceived suicidal ideation (Table B1); 13 (87%) were conducted in Australasia, one in Japan, and one
+in the United States. Twelve studies (80%) were among a student population. All the studies used self-reported measurements; 11 studies (73%) used the General Help-Seeking Questionnaire (GHSQ; Wilson, Deane, Ciarrochi, & Rick-wood, 2005) to measure help-seeking intentions for perceived suicidal ideation. Another four studies used similar questions asking participants’ willingness to seek help for perceived suicide ideation. It is noticeable that the definition of professional help varies across studies, from psychologist only, to mental health professionals, or to a broader range of professionals including family doctor, physician, phone helpline, religious/spiritual leader, and social workers.
+Age was identified as a factor associated with professional help-seeking for perceived suicidal ideation with mixed results. Older age was associated with lower help-seeking intentions among elderly Japanese adults (Sakamoto, Tanaka, Neichi, & Ono, 2004), while younger Australians had lower help-seeking intentions (Calear, Batterham, & Christensen, 2014). In a study of 527 New Zealand prisoners with a wider age range from 16 to 72 years, older age was positively associated with greater intentions to seek psychological help in prison (Skogstad, Deane, & Spicer, 2006). Male subjects were usually found to have lower help-seeking intentions than their female counterparts (Calear et al., 2014; Ciarrochi & Deane, 2001).
+The severity ofmental health issues including depression (Calear et al., 2014; Wilson & Deane, 2010) and psychological stress (Wilson, Deane, Marshall, & Dalley, 2010) was negatively associated with professional help-seeking intentions, while the effect of anxiety symptoms was in the opposite direction - it facilitated help-seeking intentions (Calear et al., 2014). Seven studies (47%) supported the existence of a help-negation effect of suicidal ideation on help-seeking intentions - the presence of suicidal ideation is associated with lower help-seeking intentions (Calear et al., 2014; Carlton & Deane, 2000; Deane, Wilson, & Ciarrochi, 2001; Wilson & Deane, 2010; Wilson, Deane, & Ciarrochi, 2005; Wilson et al., 2010; Yakunina, Rogers, Waehler, & Werth, 2010).
+Increased stigma toward people who die by suicide measured by the Stigma of Suicide Scale (SOSS) (Batterham, Calear, & Christensen, 2013) was significantly associated with lower help-seeking intentions toward mental health professionals among Australian adults in the community (Calear et al., 2014); however, the effect of stigma was not significant in a study of 321 US university students measured by a different scale (the Stigma of Suicide Scale, SSS; Yakunina et al., 2010), nor in a study of Australian university students (measured by SOSS; Chan, Batterham, Christensen, & Galletly, 2014). In addition, stigma toward mental illness was addressed as an important barrier to help-seeking in a qualitative study among eight New Zealand university students (Curtis, 2010).
+Positive attitudes toward psychological help measured by the Attitudes Towards Seeking Professional Psychological Help Scale (Fischer & Farina, 1995) were found to facilitate help-seeking intentions (Carlton & Deane, 2000; Deane & Todd, 1996; Skogstad et al., 2006; Yakunina et al., 2010), while high self-stigma measured by the Self-Stigma of Seeking Help Scale (SSOSH; Vogel, Wade, & Haake, 2006) and perceived stigma of seeking help measured by the Stigma Scale for Receiving Psychological Help (SSRPH; Komiya, Good, & Sherrod, 2000) were significantly associated with low help-seeking intentions (Yakunina et al., 2010). Prior treatment was reported as a facilitator to help-seeking intentions (Carlton & Deane, 2000; Ciarrochi & Deane, 2001). Skogstad et al. (2006) suggested, however, that the characteristics of prior treatment may influence whether it has a positive, negative, or null relationship with help-seeking.
+Although willingness to seek help from nonprofessionals such as a partner and family is associated with higher intentions to seek help from professionals (Wilson, Rick-wood, Bushnell, Caputi, & Thomas, 2011), a quantitative study conducted by Yakunina et al. (2010) among US college students suggests that social support only facilitates help-seeking from nonprofessional sources, but not from professional sources.
+Self-reliance was found to be a barrier ofhelp-seeking intentions for suicidal thoughts among a sample of New Zealand university students (Curtis, 2010). A quantitative study among Australian university students found a significant but weak negative relationship between autonomy and formal help-seeking intentions (Wilson et al., 2011). Additional potential barriers of help-seeking intentions include hopelessness (Ciarrochi & Deane, 2001) and difficulty in identifying and describing emotions (Ciarrochi, Wilson, Deane, & Rickwood, 2003; Ciarrochi & Deane, 2001).
+Professional Help-Seeking Behavior Among People With Suicidality
+In all, 21 studies examined professional help-seeking behavior among people with suicidality (Table B1). Of these, 16 (76%) were conducted in the United States or Canada, three (14%) in Australia, and two (10%) in Europe. Only eight studies (38%) specified that the purpose of service use was relevant to suicidality. The other studies did not give information on whether people had disclosed their suicidality to professionals or not. Only one study (5%) used actual service use records, while the majority (95%) of the findings were based on self-reported service use.
+In the identified studies, older age was generally associated with higher self-reported service use among people
+with suicidality (Ahmedani et al., 2012; De Leo, Cerin, Spathonis, & Burgis, 2005; Encrenaz et al., 2012). Females were more likely to talk about suicidal ideation to health professionals (Encrenaz et al., 2012) and use health services (Ahmedani et al., 2012; De Leo et al., 2005; Routhier, Leduc, Lesage, & Benigeri, 2012; Wong, Brownson, Rutkowski, Nguyen, & Becker, 2014) than males were. Individuals of ethnic minorities including Latino (Ahmedani et al., 2012; Downs & Eisenberg, 2012; Freedenthal, 2007; Meyer, Teylan, & Schwartz, 2015; Wu, Katic, Liu, Fan, & Fuller, 2010), Black (Ahmedani et al., 2012; Freedenthal, 2007; Meyer et al., 2015; Wu et al., 2010), and Asian (Ahmedani et al., 2012; Downs & Eisenberg, 2012; Wong et al., 2014) tended to have less help-seeking behavior compared with Caucasian.
+The presence of mental health issues such as depression (Ahmedani et al., 2012; Arria et al., 2011; Encrenaz et al., 2012; Husky et al., 2012; Vasiliadis, Gagne, Jozwi-ak, & Preville, 2013; Wu et al., 2010), anxiety (Ahmedani et al., 2012; Arria et al., 2011; Encrenaz et al., 2012; Wu et al., 2010), and substance use (Encrenaz et al., 2012; Freedenthal, 2007; Routhier et al., 2012) was associated with greater help-seeking behavior among people with suicidality. In addition, severe suicidal intent (De Leo et al., 2005; Wong et al., 2014), history of suicidal ideation (Husky et al., 2012; McKibben et al., 2014; Pagura, Fotti, Katz, Sareen, & Tea, 2009), suicidal plans (Encrenaz et al., 2012; Husky et al., 2012), and suicidal attempts (Ballard et al., 2014; De Leo et al., 2005; Encrenaz et al., 2012; Freedenthal, 2007; McKibben et al., 2014; Milner & De Leo, 2010; Pagura et al., 2009) also facilitated help-seeking behavior.
+Four qualitative studies highlighted the negative influence of stigma toward mental health issues or suicide on help-seeking behavior (Czyz, Horwitz, Eisenberg, Kramer, & King, 2013; De Leo et al., 2005; Freedenthal & Stiffman, 2007; Strike, Rhodes, Bergmans, & Links, 2006). In addition, stigmatizing attitudes toward mental health services also impeded people’s help-seeking behavior (Czyz et al., 2013; De Leo et al., 2005; Downs & Eisenberg, 2012). Two quantitative studies reported prior treatment as a facilitator to help-seeking behavior (Arria et al., 2011; Milner & De Leo, 2010), although other studies suggested that the relationship between these factors depended on the specific setting of treatments such as hospital emergency department (Routhier et al., 2012), level of satisfaction with prior treatment (Czyz et al., 2013; Strike et al., 2006), and whether the relationship between clients and medical helpers was consistent (Osvath, Michel, & Fekete, 2003).
+Family and friends’ influence on help-seeking behavior was complicated. On one hand, disclosure of suicidal intentions to surrounding people (De Leo et al., 2005; En-crenaz et al., 2012; Wong et al., 2014) and being referred
+to professionals by family and friends (De Leo et al., 2005; Wong et al., 2014) are associated with higher likelihood of service use. On the other hand, findings also suggest that warm and trusting relationships may impede therapy or medication as they may decrease the subjective perception of distress (Downs & Eisenberg, 2012) and the need for professional help (Czyz et al., 2013).
+In addition, no perceived need for treatment was identified as the most frequently perceived reason for not seeking professional help in two studies ofUS students: 66% (Czyz et al., 2013) and 29% (Freedenthal & Stiffman, 2007) of students cited this reason as the primary motivation for not seeking help. Furthermore, in a third US study suggested that perceived need for help significantly facilitated treatment use among students with serious previous suicidal thoughts (Downs & Eisenberg, 2012).
+Self-reliance was reported to be a barrier for help-seeking behavior in two of the US qualitative studies (Czyz et al., 2013; Freedenthal & Stiffman, 2007) identified in the current review. Religion was also found to influence youth suicide attempters’ mental health service use in a qualitative study of Canadians, although it is unclear whether it facilitates or impedes health service use (Bullock, Nadeau, & Renaud, 2012). Pragmatic barriers including lack of time, long waitlists, financial difficulties, and lack of transportation or practicing GPs (Ahmedani et al., 2012; Czyz et al., 2013; Freedenthal, 2007; Osvath et al., 2003) were also identified as factors impacting help-seeking among the identified studies.
+Suicidal Decedents’ Health Services Use
+Professional help-seeking behavior among suicidal decedents (Table B1) was examined in 19 studies. Four studies (21%) were conducted in Canada, four (21%) in the UK, two (11%) in the United States, two (11%) in Australia, two (11%) in Hong Kong, two (11%) in Taiwan, one (5%) in Sweden, one (5%) in Korea, and one (5%) in Singapore. Six studies (31%) collected survey or interview data from decedents’ family, friends, carers, or community members. Other studies used the data from administrative data or patient records. All the studies did not specify whether decedents had disclosed their suicidal intentions to professionals during service use.
+Females were generally reported to have greater service use compared with males (Chang et al., 2012; Cho et al., 2013; Hamdi, Price, Qassem, Amin, & Jones, 2008; Law, Wong, & Yip, 2010; Lee et al., 2008; O’Neill, Corry, Murphy, Brady, & Bunting, 2014; Rhodes et al., 2013; Vasili-adis, Ngamini-Ngui, & Lesage, 2015; Vassilas & Morgan, 1997). Being single (Basham et al., 2011), employed (Hamdi et al., 2008; Law et al., 2010; Loh, Tai, Ng, Chia,
+& Chia, 2012), having a low income (Law et al., 2010), problem gambling (Seguin et al., 2010), and being in a rural area (Vasiliadis et al., 2015) were associated with less service use before suicide.
+Previous diagnosis of mental disorders (Hamdi et al., 2008; Law et al., 2010; Law, Wong, & Yip, 2015; Vasiliadis et al., 2015), anxiety (De Leo, Draper, Snowdon, & Kolves, 2013), mood disorders (De Leo et al., 2013), or problematic alcohol use (Sveticic, Milner, & De Leo, 2012) was significantly related to increased contact with health services among suicidal decedents. A family history of suicide facilitated mental health service use among young suicide decedents in Singapore (Loh et al., 2012). A low level of suicide intent (Law et al., 2010), and history of suicidal attempts (Hamdi et al., 2008; Kisely, Campbell, Cartwright, Bowes, & Jackson, 2011; Loh et al., 2012; Sveticic et al., 2012) also increased help-seeking behavior among suicidal decedents. Previous contact with primary care services and a GP was found to facilitate mental health service use among suicidal decedents in a UK study (Hamdi et al., 2008). In addition, shame and stigma surrounding mental health issues (Moskos, Olson, Halbern, & Gray, 2007; Tornblom, Werbart, & Rydelius, 2015) were described as barriers to seeking professional help by the informants in two of the qualitative studies identified.
+In another of the qualitative studies, 36% of suicidal decedents had been persuaded to seek medical help by a close friend or relative (Owens et al., 2005), suggesting that members of the family and immediate social network may play a key role in determining whether or not suicidal individuals would seek help from a medical practitioner. Furthermore, suicidal decedents who did not seek help were depicted as self-reliant and resourceful characters who were expected to be able to solve their own problems by informants (Owens et al., 2005).
+Discussion
+To our knowledge, this is the first systematic review delineating the factors that influence professional help-seeking for suicidality across three stages: professional help-seeking intentions for perceived suicidal ideation, among people with or without suicidality, help-seeking behavior among people with suicidality, and service use of suicidal decedents. Several potentially important barriers were identified. To sum up, low perceived need for treatment, high self-reliance, and stigmatizing attitudes toward suicide and/or mental health issues were identified as barriers to professional help-seeking across the three subgroups of studies, although much of the evidence was from the studies using qualitative design. The presence of suicid
+ality and other mental health issues, such as depression and psychological stress, generally reduced help-seeking intentions for perceived suicidal ideation, while facilitating help-seeking behavior among people with suicidality or suicidal decedents. The influence of social support on professional help-seeking was more complex. People with good social support seemed to favor informal sources, which might reduce professional help-seeking. However, there was also evidence that referrals to professionals by family or friends facilitated service use among individuals with suicidal thoughts and behavior. Other factors such as treatment history, exposure to suicide, and knowledge of suicide were found to influence help-seeking intentions and behavior; however, evidence was scant or too diverse to draw robust conclusions. The findings on demographic factors were somewhat inconsistent, suggesting local factors such as culture, ethnicity, and religion may need to be taken into consideration.
+Suicidality and Mental Health Issues
+Suicidality and a number of mental health-related factors were significantly associated with professional help-seeking. Higher levels of suicidal ideation, depression, and psychological stress were associated with lower help-seeking intentions for perceived suicidal ideation, while a history of suicidality and mental health issues tended to facilitate help-seeking behavior among people with suicidality and suicidal decedents. The underlying basis has not been identified. One possible explanation is that people with a history of suicidality and mental health issues may have to receive physical or mental health treatments in medical settings regardless of whether they are willing to seek care or not.
+Attitudes and Knowledge of Suicide and Mental Health Issues
+Stigmatizing attitudes and a limited knowledge of suicide and mental health issues were identified as barriers to seeking professional help, although much of the research was qualitative in nature. The lack of quantitative studies exploring these factors may reflect the limited number of validated scales that are available to measure suicide attitudes and knowledge (Batterham et al., 2013). Further investigation of these relationships in quantitative studies is warranted, and in particular there is a need for research to distinguish between attitudes and knowledge, and to differentiate between self-stigma and personal and perceived stigma.
+Attitudes Toward Professional Treatments, Perceived Need for Treatment, and Treatment History
+Attitudes toward health professional treatments were associated with both help-seeking intentions and behavior across the three subgroups of studies. Positive attitudes toward professionals facilitated professional help-seeking. Meanwhile, a lack of perceived need for treatment was the most frequently self-reported barrier to professional help-seeking among people with suicidality. However, the relationship between treatment history and help-seeking was not consistent across all of the identified studies. The quality and satisfaction level of previous treatment may therefore need to be taken into consideration when studying the effects of previous treatment on help-seeking. All of these results highlight the important role that professional treatments play in the help-seeking process.
+Self-Reliance and Social Support
+Self-reliance was a frequently reported barrier in the studies identified in the current review for both help-seeking intentions for perceived suicidal ideation and actual help-seeking behavior for suicidality. Extreme views on the value of self-reliance may contribute to self-stigma, whereby people’s negative attitudes about help-seeking combined with self-reliance prevent them from disclosing their symptoms or engaging in professional treatment even in the face of dangerous mental health symptoms (Labouliere, Kleinman, & Gould, 2015). However, only one quantitative study suggested a significant but weak negative effect of self-reliance on predicting intentions to seek help from a mental health service among a student sample (Wilson et al., 2011). All other studies reporting on self-reliance are qualitative.
+The influence of social support on professional helpseeking was complex. In some studies, seeking support from informal sources and having better social support facilitated professional help-seeking for suicidality, while in others people with good social support could rely on their social network instead of professional services (Downs & Eisenberg, 2012). It is also of note that encouragement from others was suggested to be an important motivation for seeking professional help: 80% of people with suicidality used health service because others thought it was important for them to (De Leo et al., 2005), while 36% of suicidal decedents had been persuaded to seek medical help by a close friend or relative (Owens et al., 2005).
+Limitations
+There are potential limitations of this review. First, different types of research, such as quantitative and qualitative studies, were included in the current review in order to report a greater coverage of findings. Although the majority of the included studies were of sound quality, the variability of the findings may relate to the inclusion of a variety of research designs in the review. Second, only results from published English-language studies were coded in this review, which may limit the generalizability of the findings. Third, many studies included in this review were based on students or selected samples such as prisoners and soldiers. Further investigation in broader community samples, particularly on perceived help-seeking intentions for suicidal ideation, may allow for greater generalization of the findings. Fourth, as noted throughout, some of the factors identified in the review were tested in relatively few studies, and most of the included studies were conducted in industrialized countries. As culture may have a considerable influence on help-seeking, further studies among developing countries and comparative studies are needed.
+amine whether participants had disclosed their suicidality to professionals or not. Future studies are therefore recommended to better delineate the purposes of help-seeking.
+Acknowledgments
+PJB and ALC are supported by National Health and Medical Research Council (NHMRC) Fellowships 1083311 and 1122544. This research is also supported by the NHMRC Centre of Research Excellence (CRE) 1042580 in Suicide Prevention, and PJB and ALC are Chief Investigators on this CRE.
+The authors declare no competing interests.
+Conclusion
+This review highlights several potentially important barriers to professional help-seeking across all types of studies. They include high self-reliance, lack of perceived need for treatment, and stigmatizing attitudes toward suicide, toward mental health problems, and toward seeking professional treatment, which warrant additional high-quality studies using quantitative and qualitative methods, especially in developing countries. The positive influence of suicidality and mental health issues on help-seeking behavior but not on intentions suggests people with suicidal thoughts or behavior may be passively engaged with health services, where stigmatizing attitudes and unpleasant previous treatment experience could discourage subsequent help-seeking behavior. Efforts to make mental health services more approachable, less coercive, and less stigmatizing are strongly encouraged. In addition, the findings of this review also suggest that enabling factors might be lacking among those without a history of suicidality or mental health issues. Future suicide prevention initiatives may need to target the broader community, connecting with individuals who have never used mental health services. The complex influence of social support on professional help-seeking also needs further investigation, especially in relation to the gatekeeper effects of family and friends. It is also notable that most of the studies among people with suicidality and suicidal decedents failed to ex
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+Introduction
+People with severe mental illnesses (SMI), including schizophrenia and bipolar affective disorder, are known to be at significant risk of premature morbidity and mortality. In the UK, individuals with SMI have a life expectancy around 12 years less than the general UK population, [1] and similar disparities are seen in the US.[2] Cardiovascular disease is a major contributor to this health inequality.[3]
+The UK Quality and Outcomes Framework (QOF) aims to improve quality in primary care through linking financial incentives to performance against indicators.[4] From its inception in 2004, the QOF has incentivised annual physical health review for people with SMI (S1 Appendix). The nature of this review was unspecified until 2011, when more explicit cardiovascular risk factor indicators were introduced; the majority of indicators were withdrawn in 2014.
+To date there has been little evaluation of the impact of the QOF on the recognition and management of cardiovascular risk factors in people with SMI. One study found the QOF incentives reduced inequalities in cardiovascular risk factor testing between those with and without SMI,[5] although the nature and time period of the analysis was relatively limited and did not evaluate risk factor detection. Elsewhere the QOF has been shown to have increased consultation rates[6] in people with SMI and also to have coincided temporally with an increase in recording of comorbidities[6,7].
+The current study builds on the above findings by exploring whether the QOF indicators have been associated with improvements in the identification and management of cardiovascular risk factors in people with SMI.
+Methods
+Anonymised data detailing diagnoses, prescribing, test results and demographics were extracted from the Clinical Practice Research Datalink (CPRD). CPRD captures data recorded by the general practitioner (GP) as part of routine care, and covers a 6.9% representative sample of the UK population.[8] Patients were included in the analysis during continuous periods of registration at those participating practices which met CPRD's internal quality standards. The study was approved by the CPRD Independent Scientific Advisory Committee (protocol reference 15_110RMn).
+Study design
+A retrospective open cohort design was used with cases having a lifetime SMI diagnosis and an unmatched population comparison group without SMI. The study period included the consecutive 'financial years' from 1st April 1995 to 31st March 2014. Two interventions were considered: intervention 1 from April 2004 (introduction of the QOF SMI annual review indicator), and intervention 2 from April 2011 (change to specific cardiovascular indicators).
+Data from April 1995 to March 2003 were used to ascertain trends in the outcome before the introduction of QOF. The 2003/04 year was excluded as per previous studies[6], as some practices were preparing for the introduction of the QOF in the year prior to its introduction.
+Within each year of analysis case and comparison group members could be eligible for the full financial year or for a proportion of the year. The first day of inclusion in the analysis was
+the latest of the patient reaching age 35, first day of continuous registration, the patient's GP practice meeting CPRD's data quality criteria, or (for cases) the date of SMI diagnosis. Eligibility for inclusion in the analysis ended at the first of having the outcome of interest, leaving the practice, death, or the practice ceasing to submit data to CPRD.
+Case and comparison group
+Cases included all available patients aged >35 years with a life-time diagnosis of SMI. SMI was defined as schizophrenia, bipolar affective disorder, psychotic depression and other non-tran-sient, non-organic psychoses. We identified Read codes corresponding to these conditions. Code lists were based on previously published lists from the clinicalcodes.org repository[9], supplemented by free-text searches for the aforementioned clinical terms. A snowballing approach was then employed to identify additional terms similarly categorised in the Read code hierarchy. Additional QOF codes were included to capture any change in coding practice post-QOF. The resulting list of codes was reviewed by two clinicians to confirm the appropriateness or otherwise of included codes, as well as identifying excluded diagnoses. As way of validation, the prevalence of the resulting outcomes was checked against existing literature and alternative published code lists. Patients with codes related to prodromal schizophrenia, to a “single episode” or to a “reactive” episode were excluded, unless they also had a valid diagnostic SMI code recorded. Code lists are available from the University of Bristol Research Data Repository.[1Q]
+The comparison group consisted of unmatched, randomly selected patients aged >35 years without SMI who had a period of continuous registration during the study period, aiming for a minimum ratio of controls to cases of 5:1. Patients were excluded if they had ever been prescribed medication used in the treatment of SMI, with the exception of those who had ever had an epilepsy Read code recorded (i.e. when there is a likelihood of medication being used as an anticonvulsant rather than a mood stabiliser).
+We placed a restriction on the lower age limit of our population as outcomes are rare in those under 35 years, and this improved our power to detect differences.
+Clinical outcomes
+We assessed four outcomes related to diagnosis and two related to treatment, which we considered of clinical importance and straightforward to measure using routine health-record data: first ever recording of elevated serum cholesterol >5.0mmol/L, first ever diagnosis of diabetes mellitus, first ever diagnosis/recording of obesity, first ever diagnosis of hypertension, first ever prescription of anti-diabetic medication, and first ever prescription of lipid-modify-ing medication.
+Hypertension and diabetes mellitus outcomes were identified by diagnostic and administrative Read codes, elevated cholesterol by test results, obesity by Read codes or body mass index values >30.0kg/m2, and medications from product code lists identified from the CPRD dictionary. A similar approach to code list development was employed as for the identification of SMI.
+Statistical analyses
+Annual incidence rates were calculated for all six outcomes for the case and comparison groups. The proportion of the financial year spent eligible for inclusion in the analysis by each patient was combined to create a denominator of 'person-years' active for each financial year for both groups.
+For all outcomes an interrupted time series analysis (ITSA) was performed. This approach is recognised to be amongst the strongest quasi-experimental approaches to intervention anal-ysis.[11,12] ITSA utilises data collected over equally spaced time intervals before and after an intervention. It assumes that, in the absence of the intervention, trends prior to the intervention could have been extrapolated to predict future trends.
+A mixed effect segmented logistic regression model was used for the ITSA, with the inclusion of a random intercept to allow for variation between general practices. The ITSA model has been described elsewhere[13| and can be extended to incorporate a control group, as shown in S2 Appendix. Additional terms were added to allow analysis of the second intervention (change to QOF SMI indicators in 2011) and potential confounding by age (categorised using 40, 50 and 60 year cut-offs) and gender. Data manipulation was undertaken using Stata13.0[14] and analyses were conducted in R (version 3.2.4) using the package lme4.[15] Step changes occurring immediately after the intervention's introduction are reflected in the model intercept, with subsequent effects on the temporal trend reflected by the model slope. Model fit was assessed by plotting the predicted probability of the outcome from the fitted model against the observed probability of the outcome for the SMI and non-SMI groups (S3 Appendix).
+We hypothesised that outcome measurements closer together in time may be more similar than outcomes further apart. We used the Cumby-Huizinga test to explore the data for the presence of this issue (autocorrelation), which may have resulted in part due to the grouping of patients by general practice. The autocorrelation was almost zero and appeared to be negligible (the test for lag orders 1 to 5 strongly accepted the null hypothesis of independence in the series, as did the test at the individual lag). On account of the size of the dataset and complexity of the statistical models, we therefore decided to take a parsimonious approach and not account for potential autocorrelation in subsequent analyses.
+Results
+The number of patients and practices included varied with the growth of the CPRD dataset, with 232, 595 and 530 practices contributing data in 1995/96, 2004/05 and 2013/14 respectively. Numbers of patients and demographic characteristics are summarised in Table 1. A total of 67,239 people with SMI and 359,951 people without SMI were included in the analysis, with the former group typically providing fewer days of continuous data (1936 vs. 2703 days) in part due to increased mortality.
+Fig 1 shows the ITSA results. Numerical results are reported in Table 2 (detection of risk factors) and Table 3 (treatment of risk factors). Following the incentivisation of annual reviews for people with SMI in April 2004, there is strong evidence of an immediate increase (i.e. intercept change) in the recording of elevated cholesterol.
+Immediately after the intervention in 2004, the odds of an SMI patient having elevated cholesterol test results are 1.21 (95% CI: 1.10±1.33) times higher, after the intervention in 2004. These odds are 37% (95% CI: 24%-51%) higher than for a non-SMI patient. The results similarly show increased recognition of diabetes (OR 1.21, 95% CI: 0.99±1.49), obesity (OR 1.21, 95% CI: 1.06±1.38) and hypertension (OR 1.19, 95% CI: 1.04±1.38), in the SMI compared to the non-SMI group (Table 2).
+A relative change over time coinciding with the 2004 QOF incentives was seen only for elevated cholesterol (OR 1.03, 95% CI: 1.00±1.05), whereas the remainder saw no relative change in gradient from 2004 to 2010, suggesting the immediate effects observed were sustained.
+The introduction of cardiovascular specific SMI indicators in 2011 was associated with further immediate increases (i.e. changes in intercept) in recognition of cases of elevated
+cholesterol (OR 1.84, 95% CI: 1.72-1.97) and obesity (OR 1.39, 95% CI: 1.26-1.53) relative to the non-SMI group. However, this increase was not sustained over the following two years that the incentives remained in place, with significant reducing trends relative to the non-SMI group. No added improvements in case-finding of diabetes or hypertension were found in association with the 2011 incentives.
+Prescribing of both anti-diabetic and lipid-modifying medications increased significantly over the study period, and to a greater extent in the SMI group. There was no evidence that these changes were related to the QOF indicators introduced in 2004 (Table 3). The SMI indicator introduced in 2011 was also not found to be associated with an increase in prescribing of anti-diabetic medications in the SMI group relative to the non-SMI group.
+Following the 2011 QOF SMI incentives, there was also no strong evidence of an immediate change in prescribing of lipid modifying medications. There was some indication that the change in the effect of time attributable to the 2011 QOF indicator differed for the SMI group compared to the non-SMI group (OR 0.94, 95% CI: 0.89-0.99).
+In the early years of the study there was a second peak in age of diagnosis of SMI in older patients (>60 years) which was absent following the introduction of QOF, raising concerns that a change in coding practice had influenced the findings. As a sensitivity analysis, we removed patients whose age at first diagnosis of SMI was at least 60 years; this had little impact on our findings (S4 Appendix). In addition, we conducted a falsification analysis, using fabricated policy indicator years of 2000 and 2008 (S5 Appendix). There were no significant changes in outcome observed in relation to these dates. The only exception was an apparent change in recording of elevated cholesterol in 2008 but this was in the opposite direction to that observed in the main 2011 analysis. These findings provide reassurance that the observed changes in outcomes in the main analysis are indeed related to the QOF interventions.
+Discussion
+We have found strong evidence that primary care incentives promoting physical health reviews in patients with SMI can result in improvements in the identification but not treatment of cardiovascular risk factors.
+This study benefits from the use of robust methodology and external generalizability. Interrupted time series analysis with a comparison group is a powerful, pseudo-experimental methodology that allows both the temporal trends prior to the intervention and any change coinciding with the intervention to be taken into account. However, the study's limitations also require consideration. First, it is not possible to distinguish improved case finding from
+genuine changes in incidence, although the latter are likely to happen gradually. It is also possible that the increases in diagnoses reflect improved recording as opposed to detection per se. Nevertheless, we believe that recording of clinical information in electronic health records is still likely to facilitate delivery of care, such as through improved case finding, and thus failure to record a risk factor is for all practical purposes equivalent to failing to identify it in the first place. In addition, laboratory values (e.g. cholesterol) are automatically populated from the laboratory systems, and as such reflect genuine measurement and not simply recording. Secondly, we found differences in the age distribution of our SMI population in the earlier years of the study, potentially reflecting changes in coding practice; a fall over time in the age at diagnosis of SMI has also been described elsewhere.[6] Nevertheless, we adjusted for age in our analyses, and also found no notable differences when age was not accounted for by the models, reassuring us that our findings are robust to this issue. Additional supplementary analyses, excluding those with a first diagnosis of SMI at age 60 years or older, supports these findings. Thirdly, ITSA may be rendered invalid by the presence of a confounder that changed in a time period coinciding with the intervention. This is pertinent to the current study as the QOF targeted other broad key health outcomes in the general population, some of which may also have been relevant to people with SMI. This is accounted for by the inclusion of a comparison group in the study design, although the assumption must be made that other health service changes would impact on both groups equally. We are unaware of other major interventions (e.g. clinical guidelines) that coincided temporally with the QOF changes. Although National Service Frameworks were introduced in the early 2000s to set minimum standards for care in areas including diabetes, we expect that the impact of this is likely to be small, as firstly our study focuses on case identification rather than quality of care, and secondly there is no reason to suspect it to have a significant differential impact on the two study groups. Fourthly, the QOF SMI indicators introduced in 2011 relating to lipids and blood glucose/HbA1c testing applied only to those aged 40 and over whilst cases aged 35 and over are included in this study. This could have resulted in a slight underestimate of the true effect. Finally, the study is limited by the small number of data points following the 2011 changes to the QOF SMI indicators. As these indicators were in place for only 3 years, a more complete analysis including longer term trends is impossible, although we believe our analysis probably still provides valid and useful insights into the effect of the later intervention.
+Under-recording of cardiovascular risk factors in the SMI population has been previously reported[16] and it is thus reassuring that incentives appear to help address this. When specific cardiovascular SMI indicators were introduced in 2011, people with SMI had already been subject to annual reviews for seven years, meaning those most willing to attend reviews who were at highest cardiovascular risk may already have been identified. This contextual difference makes it difficult to know whether there are differences in the effectiveness of the 2004 and 2011 indicators. Of note, the sharp increases in recording of both obesity and elevated cholesterol in 2011 despite the seven preceding years of annual reviews perhaps suggest the later cardiovascular-specific indicator resulted in a “catch-up” of previously undetected risk factors; the subsequent drop in recording may reflect patients being tested in earlier years or being otherwise harder to reach. Despite this, the levels of detection do not fall below the pre-2011 trend for either elevated cholesterol or obesity. One must therefore be cautious about both interpreting the initial improvement in 2011 as evidence of the superiority of a more specific incentive, and of interpreting the post-2011 decrease as meaning this indicator was either not sustained or less effective than the indicators introduced in 2004. The majority of indicators were withdrawn in 2014 meaning a more complete analysis of the 2011 indicators will not be possible: it is not clear whether they were in place for sufficiently long to become usual practice.
+The analysis suggests that the QOF SMI indicators have not affected prescribing of either anti-diabetic or lipid-modifying medications. This is despite longstanding (i.e. pre-QOF), readily available UK clinical guidance on pharmacotherapy for the associated risk factors in the general population.[17] National guidance[18] advising clinicians to be aware of and treat increased cardiovascular risk in SMI (specifically schizophrenia) patients has only been available since 2009, but further work is nonetheless required to explore the reasons why the increase in detection of risk factors is not matched by increased treatment. The findings are consistent with studies describing under-treatment in this population in the presence of dysli-pidaemia and hyperglycaemia[19] and in other areas such as stroke[2Q] and arthritis[21]. A number of potential explanations for this have been proposed, including at the patient-level (e.g. cognitive impairment, poor adherence), physician-level (e.g. stigmatization, complexity of care), and service-level (e.g. fragmentation of care, lack of resources).[22] It is unclear whether the increase in medication use due to the QOF prescribing incentives that has been observed in the general population[23] would translate to an increase in prescribing if targeted specifically at the SMI population.
+A number of issues raised by this study merit further investigation. These include whether or not more specific cardiovascular indicators offer additional value, patients' and clinicians' views on the role of SMI indicators, the reason that case detection was not followed by improvements in treatment, and whether there is value in specific incentives to encourage treatment of physical problems in this population. However, what remains clear is that financial incentives for GPs improve the detection of cardiovascular risk factors in a challenging patient group in which identification of physical health problems is known to be poor. Incentives may well have a broader role in reducing health inequalities and improving the care and treatment of patients with severe mental illness.
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+1.	Introduction
+Suicide is a global health problem, and although suicide affects people across the lifespan, it is the second leading cause of death of 16-29 year olds worldwide (World Health Organisation, 2014), as well as being the leading cause of death among people under 50 in the UK (Snowcroft, 2017). Recent research has identified a wide range of social, psychological and biological factors that act to increase suicide risk (O’ Connor and Nock, 2014), although these factors often do not distinguish between those who will think about suicide and those who will go on to act on suicidal thoughts (Klonsky and May, 2014). With around 60% of transitions from suicidal ideation to a first attempt occurring
+within a year of ideation onset (Nock et al., 2008), it is crucial that we identify factors that distinguish those whose suicidal thoughts may transition into suicidal behaviours (Kessler et al., 2005).
+In light of this, recent models of suicidal behaviour have adopted an ideation-to-action framework, where the development of suicidal ideation and the transition to a suicide attempt are viewed as distinct processes (Klonsky et al., 2017). The first theoretical model to emphasise this distinction was the interpersonal-psychological theory of suicide (IPT; Joiner, 2005), proposing that suicidal desire (comprised of perceived burdensomeness and thwarted belongingness) alone was insufficient to lead to a serious suicide attempt/death by suicide. A suicidal individual must also have the capability to act upon that desire
+characterised by a lowered physical pain sensitivity and high fearlessness about death that overrides the instinct towards self-preservation (Joiner, 2005). Although there has been considerable evidence for the key premises underpinning the IPT (Chu et al., 2017), a recent systematic review of IPT studies found limited evidence for an interaction between perceived burdensomeness, thwarted belongingness and acquired capability in association with suicide attempts, with the authors concluding that the relationships between the variables may be less straightforward than originally presented (Ma et al., 2016). Therefore, models of suicidal behaviour may need to account for a more complex relationship between suicidal ideation and the transition to a suicide attempt.
+In this vein, the integrated motivational-volitional model of suicidal behaviour (IMV; O'Connor, 2011) was proposed in 2011 and refined in 2018 (O’Connor and Kirtley, 2018). The IMV model is a tri-partite framework (Fig. 1) that builds upon previous theories to map the context in which suicide may occur (the pre-motivational phase), the development of suicidal ideation (the motivational phase) and the transition of suicidal thoughts into suicidal behaviours (the volitional phase). Building upon the cry of pain hypothesis (Williams, 1997), the motivational phase focuses on feelings of defeat and entrapment as the key drivers of suicidal ideation. Importantly for the present study, within the final phase of the model (volitional phase), it is argued that a group of factors, labelled volitional moderators, governs the transition from thinking about suicide to attempting/dying by suicide. In addition to Joiner's concept of acquired capability, these factors include im-pulsivity, planning, exposure to the suicidal acts of others, access to means, past suicidal behaviour and mental imagery about death (O’Connor and Kirtley, 2018).
+There has been support for the main facets of the IMV model (e.g., Dhingra et al., 2016; O'Connor et al., 2013; Wetherall et al., 2018), including a growing body of evidence demonstrating that volitional
+moderators do indeed differentiate between those who think about suicide and those who engage in suicidal behaviour (O'Connor et al., 2016; O’ Connor and Kirtley, 2018). For example, in one study of adolescents, only volitional phase variables (self-harm by friends and family, thinking about peers’ self-harm, impulsivity) and stress differentiated between those with thoughts of self-harm and those who engaged in self-harm (O'Connor et al., 2012). Similarly, in a test of the IMV facets with students, within a multivariable model, only the volitional phase factors (exposure to suicide, impulsivity and fearlessness about death) distinguished between those who had experienced suicidal ideation and those who had attempted suicide (Dhingra et al., 2015). Additionally, in a recent cohort study, exposure to the self-harm of others (alongside psychiatric disorder) was key to differentiating between adolescents who had made a suicide attempt compared to those who had thought about but not attempted suicide (Mars et al., 2018).
+A final model utilising the ideati.on-to-acti.on framework is the more recent three-step theory (3ST; Klonsky and May, 2015). The initial steps tap the development and escalation of suicidal ideation with a combination of pain, hopelessness and a lack of connectedness, and in the final step ideation progresses to an attempt when the capability for suicide is present. The concept of acquired capability has been a consistent component across all three models explored, with recent evidence suggesting that when those high on capability become agitated, suicidal intensity increases, thereby facilitating suicidal behaviour by providing sufficient energy and arousal (Ribeiro et al., 2015). Therefore, this concept, along with the additional volitional factors of im-pulsivity, exposure to suicide and mental imagery about death, are key variables to be explored more fully as factors that can differentiate those who think about suicide from those who will make a suicide attempt.
+1.1.	Current study
+This study aimed to investigate a key premise of the IMV model; namely that volitional phase variables govern the transition from suicidal ideation to suicide attempts when motivational phase variables are controlled for (O’Connor & Kirtley, 2018). Although a small number of studies have investigated the psychological factors associated with behavioural enaction (e.g., Dhingra et al., 2015), to our knowledge this is the most detailed study of its kind and the first study to do so in a nationally representative sample. To this end, the Scottish Wellbeing Study (O’ Connor et al., 2018), a nationally representative interviewbased survey of young adults aged 18-34 years across Scotland, was conducted. In short, we hypothesised that (i) motivational and volitional phase factors would differentiate non-suicidal controls from those who had a history of suicidal ideation or suicide attempts, and (ii) only volitional phase factors would differentiate between those who had a history of suicidal ideation and those who had attempted suicide in a multivariable model.
+2.	Method
+2.1.	Sample and procedure
+The data are from the Scottish Wellbeing Study (O’Connor et al., 2018) which is a nationally representative sample of young people aged 18-34 years (n = 3508) from across Scotland. Recruitment was conducted by Ipsos MORI, a social research organisation, between 25th March 2013 and 12th December 2013. A quota sampling methodology was utilised; quotas were based on age (three quota groups), sex and working status (for more details, see O’ Connor et al., 2018). Following written consent, participants completed an hour-long interview, carried out face-to-face in their homes, using Computer Assisted Personal Interviewing (CAPI), with confidential completion of sensitive questions (including suicidal history) on a personal computer. Participants were compensated £25 for their time. Ethical approval was obtained from the University of Stirling (Psychology Department) ethics committee as well as from the US Department of Defense Human Research Protections Office.
+2.2.	Measures
+2.2.1.	Outcome measure: lifetime history of suicidal ideation and attempts
+This was assessed with two items drawn from the Adult Psychiatric Morbidity Survey (APMS; McManus et al., 2007): “Have you ever seriously thought of taking your life, but not actually attempted to do so?” and “Have you ever made an attempt to take your life, by taking an overdose of tablets or in some other way?”. Responses to these questions were “no”, “yes” or “would rather not say”. These items were used to create a 3 category variable indicating if participants had (i) no history of suicidal ideation/ attempt (control group), (ii) had experienced suicidal ideation but had never attempted suicide (suicidal ideation group), or (iii) had reported having attempted suicide in the past (suicidal attempt group).
+2.2.2.	Motivational phase risk factors
+2.2.2.1.	Defeat. The Defeat Scale (Gilbert & Allan, 1998) is a 16-item self-report measure of perceived failed struggle and loss of rank (e.g., “I feel that I have not made it in life”). This scale has good psychometric properties and is significantly correlated with depressive symptoms (Griffiths et al., 2014). In the present study the measure had high internal reliability (Cronbach's a = 0.96).
+2.2.2.2.	Entrapment. The 16-item Entrapment Scale (Gilbert & Allan, 1998) is a measure of the sense of being unable to escape feelings of defeat and rejection (e.g., I am in a situation I feel trapped in). This measure consists of 10 items reflecting external entrapment
+(entrapment by external situations), and 6 items tapping internal entrapment (entrapment by one's own thoughts and feelings). The scale has good psychometric properties (Griffiths et al., 2014) and demonstrated high internal consistency in the present study (Cronbach's a = 0.96).
+2.2.2.3.	Perceived burdensomeness and thwarted belongingness. These were assessed using the 12-item Interpersonal Needs Questionnaire (INQ; Van Orden et al., 2012). The INQ includes 7-items to tap burdensomeness (e.g., “I feel like a burden on the people in my life”) and 5-items to assess belongingness (e.g., “I feel disconnected from other people”). The scales have been shown to have good internal consistency and construct validity (Van Orden et al., 2012), including in this study (perceived burdensomeness Cronbach's a = 0.87, thwarted belongingness Cronbach's a = 0.84).
+2.2.2.4.	Goal disengagement and goal reengagement. The 10-item goal adjustment scale (GAS; Wrosch et al., 2003) consists of a 4-item goal disengagement (e.g., “If I have to stop pursuing an important goal in my life its easy for me to stop thinking about the goal and let it go”) subscale and a 6-item goal reengagement (e.g., “If I have to stop pursuing an important goal in my life I start working on other new goals”) subscale. Both subscales have shown good validity (Wrosch et al., 2003), and in the present study they had adequate to good internal consistency (goal disengagement Cronbach's a = 0.70, goal reengagement Cronbach's a = 0.87).
+2.2.2.5.	Social support. The 7-item ENRICHD Social Support Instrument (ESSI; Mitchell et al., 2003), taps four defining attributes of social support: emotional, instrumental, informational, and appraisal (e.g., “Is there someone available to give you good advice about a problem?”). It has been found to be a valid and reliable measure of social support (Vaglio et al., 2004), and displayed good internal reliability in the present study (Cronbach's a = 0.87).
+2.2.2.6.	Resilience. Resilience was measured using the 10-item Brief Resilience Scale (BRS; Campbell-Sills and Stein, 2007), adapted from the 25-item Connor-Davidson Resilience Scale (CD-RISC; Connor and Davidson, 2003). This 10-item version (e.g., “Coping with stress can strengthen me”) has good psychometric properties and is highly correlated with the original 25-item version (Campbell-Sills and Stein, 2007), and in the present study it displayed excellent internal consistency (Cronbach's a = 0.90).
+2.2.3.	Volitional phase risk factors
+2.2.3.1.	Acquired capability. The Acquired Capability for Suicide Scale (ACSS; Van Orden et al., 2008) is a 5-item measure designed to assess one's fearlessness about death and physical pain sensitivity (e.g., “The pain involved in dying frightens me”). The scale has demonstrated convergent and discriminant validity (Van Orden et al., 2008), and in this study the ACSS had a relatively low internal consistency of 0.63 (Cronbach's a).
+2.2.3.2.	Impulsivity. This was assessed using the 30-item Barratt Impulsiveness Scale Version 11 (BIS-11; Patton et al., 1995); a selfreport questionnaire that accounts for the multi-faceted nature of the construct (i.e., attentional, motor and non-planning impulsiveness) that provides a general impulsiveness score (e.g., “I act on the spur of the moment” ). The BIS is a commonly used scale that has been shown to correlate with behavioural measures of impulsivity (Martins et al., 2004), and it displayed good internal validity in the present study (Cronbach's a = 0.83).
+2.2.3.3.	Mental imagery. Eight questions were asked to establish the frequency with which participants imagine death related imagery when they feel down or distressed, including engaging in self-harm or suicidal
+behaviour (e.g., “...images of yourself planning/preparing to harm yourself or make a suicide attempt”). Greater presence of suicide-related imagery has been linked to higher levels of suicidal ideation (Holmes et al., 2007). The scale displayed good internal reliability (Cronbach's a = 0.84).
+2.2.3.4.	Exposure to suicide. Participants were asked three items to establish whether they had friends or family who attempted or died by suicide (e.g., “Has anyone among your family attempted suicide?”). These items have been used in previous research (O'Connor et al., 2012) and have been shown to differentiate between those who think about suicide and those who attempt suicide (Dhingra et al., 2015).
+2.2.4.	Covariates: demographic characteristics and mood
+2.2.4.1.	Demographic characteristics. We recorded the following demographic information: age, gender, marital status (married vs. not married), ethnicity (white vs. non-white) and economic activity (employed, inactive and unemployed).
+2.2.4.2.	Depressive symptoms. The Beck Depression Inventory-II (BDI-II; Beck et al., 1996) is a well-established measure tapping a range of depressive symptoms (e.g., self-dislike, loss of energy) containing 21 items. It has been shown to yield reliable, internally consistent, and valid scores in many different populations (e.g., Dozois et al., 1998), and in this study, it displayed high internal reliability (Cronbach's a = 0.95).
+2.3.	Statistical analysis
+Data analysis was conducted using SPSS version 22. The missing data included items missed by participants and participants selecting ‘would rather not say’. We used every participant's data as long as they had completed 75% or more of a psychological scale, this resulted in minimal missing data, < 1% on any variable (range 0.31-0.86%; including those who had refused). These small amounts of missing data were checked against demographic characteristics and as there were no significant associations, expectation maximisation (EM) was applied to replace missing items for each scale. The multinomial regression model included only those who completed > 75% of every measure (n = 3330; 95% of total sample), with a small proportion of the data EM replaced. More information on the EM replacement method is included in the supplementary materials.
+Additionally, the data were weighted to ensure that the attained sample based on the quota variables was in line with the population in the sample frame using rim weighting. Overall, as the quotas were almost always met (30-34 year olds, full-time students and full-time workers were slightly under-represented) the effect of the weights was small. All analyses and reporting of data were conducted with the weights on. More information on the rim weighting is included in the supplementary materials.
+To investigate the respective influence of the motivational and volitional phase variables, initial univariate multinomal regression analyses were conducted. To control for the number of comparisons the Holm-Bonferroni correction method (Holm, 1979) was applied. In order to identify which variables independently distinguished between the groups, a multivariable multinomial logistic regression was performed. Specifically, demographic and mood variables were entered as covariates (age, gender, marital status, ethnicity, economic activity and depressive symptoms), followed by the motivational phase variables (defeat, entrapment, perceived burdensomeness, thwarted belongingness, goal disengagement, goal reengagement, social support and resilience) and then the volitional phase variables (acquired capability, impulsivity, mental images, exposure to suicide death (family & friend), exposure to suicide attempt by friend, exposure to suicide attempt by family) were entered. Odds ratios (OR) indicating the likelihood of each variable's association with the higher risk group were reported (i.e., the
+ideation and attempt groups relative to the controls, and the attempt group relative to the ideation group), with those greater than one indicating increased risk and less than one decreased risk. To estimate the variance explained by the volitional variables in distinguishing between the suicide ideation and attempt groups, a binary logistic regression was conducted with only the volitional variables.
+To better understand how well the volitional phase measures distinguish between those who have thought of suicide only and those who have made a suicide attempt at an individual level, the sensitivity (i.e., proportion of the sample high on a volitional phase variable that were correctly identified as having made a suicide attempt) and specificity (i.e., the proportion of the sample that were low on a volitional phase variables and had not made a suicide attempt) of each of the volitional phase variables is reported, along with their positive predictive value (i.e., the probability that the individual high on a volitional phase variable had attempted suicide) and negative predictive value (i.e., the probability that the individual low on a volitional phase variable had not attempted suicide). A cut-off score (mean + 1SD) was created for the continuous variables to indicate those ‘high’ and ‘low’ on a particular volitional phase variable.
+3.	Results
+3.1.	Sample characteristics
+In the primary analysis (n = 3330), the majority of the sample had no suicidal history (n = 2470; 74.6%), 14.3% (n = 481) had experienced suicidal ideation in their lifetime but had never made a suicide attempt, and 11% (n = 379) had attempted suicide in their lifetime. The descriptive statistics by group membership (i.e., ideation vs. attempt vs. control) and univariate differences for those who responded to the suicidal history questions (n = 3435) are provided in Table 1. With demographics, the univariate multinomial regression analyses indicated that those with suicidal ideation were more likely to be male, not married and unemployed compared to controls, and those who had reported a suicide attempt were more likely to be female, older and unemployed than both the controls and those in the suicidal ideation group.
+Members of the control group scored significantly lower on all of the psychological risk factors compared to those in the suicide ideation and suicide attempt groups; this included depressive symptoms, defeat, entrapment, acquired capability and impulsivity. Those in the suicide attempt group reported more frequent exposure to the suicidal behaviour of others, with almost 50% having been exposed to a friend making a suicide attempt, compared to just 16% for the control group. The control group reported higher levels of protective factors such as resilience and social support. A similar pattern emerged between the two suicidal history groups; those in the suicide attempt group more strongly endorsed the motivational and volitional phase risk factors compared to those in the suicide ideation group.
+3.2.	Multivariable multinomial regression analyses
+The results of the multinomial regression analyses are presented in Table 2. The model was statistically significant (%2 (42) = 1528.60, p < 0.001; pseudo R-square (Cox and Snell) = 0.37). Those in the control group were significantly lower than both suicidal history groups on a combination of motivational (defeat and burdensomeness) and volitional phase factors (acquired capability, mental images, exposure to suicide attempt by family or friend). Additionally, those in the suicide attempt group were more likely to be female, older, and higher on impulsivity than controls. Depressive symptoms did not distinguish between any of the groups when all motivational and volitional factors were accounted for.
+Similarly, those who reported a suicide attempt were older (OR = 1.07 [95% CI = 1.03-1.10]) and more likely to be female
+(OR = 0.49 [95% CI = 0.36-0.67]) than those in the ideation group. However, consistent with the IMV model, the only psychological factors that distinguished those in the suicide attempt group from those in the suicidal ideation group were volitional phase variables; none of the mood or motivational phase variables significantly differentiated between these groups. In comparison to those in the suicidal ideation group, those who reported a suicide attempt scored significantly higher on levels of acquired capability (OR = 1.10 [95% CI = 1.06-1.14]), impulsivity (OR = 1.02 [95% CI = 1.01-1.04]), mental images about death (OR = 1.07 [95% CI = 1.03-1.10]) and they were significantly more likely to have been exposed to a suicide attempt of a friend (OR = 1.49 [95% CI = 1.09-2.06]). In a binary logistic regression, the volitional phase factors accounted for 11% of the variance in distinguishing between the suicide ideation vs. the suicide attempt groups (Nagelkerke R Square = 0.112).
+3.3.	Sensitivity and specificity of the volitional phase variables in differentiating between suicide ideation and suicide attempt groups
+The findings of the sensitivity and specificity analyses are displayed in Table 3. Being high on acquired capability, impulsivity and mental images, as well as each of the exposure variables, identified those who had made a suicide attempt over half of the time, with acquired capability being the most sensitive (56.9% correctly identified). The specificity of the individual variables was higher overall (range 57.9-62.6%), indicating that being low on the volitional phase variables was more specific at identifying those who had not made a suicide attempt. All the volitional variables, when taken together, identified around 46% of those who had made a suicide attempt, and three
+quarters of those who had not. The positive predictive values (PPV) ranged from 37.1-54.5%, with mental imagery having the highest PPV. The negative predictive values (NPV; range 61.9-77.4%) were higher; indicating being low on a volitional variable was a better predictor of who had not attempted suicide than being high was a predictor of those who had. The PPV increased when all volitional variables were taken into account, with approximately 60% of those predicted to have made a suicide attempt correct, with almost two-thirds for the NPV.
+4.	Discussion
+We tested a key premise of the integrated motivational-volitional model (IMV, O’ Connor, 2011; O’Connor & Kirtley, 2018), namely that
+volitional phase factors are key to governing the transition from suicidal ideation to a suicide attempt. We hypothesised that (i) motivational and volitional phase factors would differentiate non-suicidal controls from those who had a history of suicidal ideation or suicide attempts, and (ii) only volitional phase factors would differentiate between those who had a history of suicidal ideation and those who had attempted suicide in a multivariable analysis. Findings yielded clear evidence in support of both hypotheses. Specifically, a combination of motivational and volitional phase variables distinguished the control group from both the suicide ideation group and the suicide attempt group. Whereas, apart from some demographic differences (those in the attempt group being older and female), only volitional phase variables differentiated between those with a history of suicidal ideation and those who had reported a suicide attempt; with the latter group reporting higher levels of acquired capability, impulsivity, mental imagery about death and they were more likely to have been exposed to the suicide attempt of a friend.
+This study adds to the growing literature highlighting the importance of the volitional phase factors within the IMV model (e.g., O'Connor et al., 2012; Dhingra et al., 2015) and the ideation-to-action framework more generally (Klonsky et al., 2017). It is also unique as it is the first study of its kind to investigate the role of volitional phase factors in a large, nationally representative sample. Although motivational phase variables, including key components of the IPT (e.g., perceived burdensomeness) and the IMV model (e.g., defeat), are useful to identify who may think of suicide, they are not the key drivers of behavioural enaction. In light of the recent concerns that most risk factors do not distinguish between those suicidal individuals who are/are not at increased risk of making a suicide attempt (Klonsky and May, 2014), the present volitional phase findings are important as they address this dearth in the research literature. Crucially though, they highlight potential targets for interventions and therapies, consistent with a recent call to action to identify better markers of suicide risk (Holmes et al., 2018).
+Our study adds to the recent research on sensitivities and specificities in the context of risk assessments, showing that the latter fail to accurately predict suicidal behaviour over time (Quinlivan et al., 2017; Steeg et al., 2018). In the present study, the sensitivity of the volitional phase variables in differentiating between the suicide ideation vs. suicide attempt groups was relatively low (46% correctly identified), therefore potentially limiting their utility in assessing risk at an individual level. However, given that our study design is investigating lifetime suicidal ideation and attempts, low sensitivities are not unexpected because the measures were assessed retrospectively; in many cases individuals had thought about suicide or attempted suicide many years before taking part in the study (indeed the overwhelming majority of participants had attempted suicide more than 12 months ago). Moreover, as our measures are not diagnostic tests nor were they designed as such (they are theoretically derived constructs), the utility of reporting sensitivities and specificities is at best only informative. Nonetheless, as noted above, the associations identify key parameters that could be targeted in interventions to reduce suicide risk. One could also argue that the volitional phase variables are actually quite powerful as they still identify those who have attempted suicide compared to those who have thought about suicide years later (albeit that the effect sizes are low). Taking the findings in context, therefore, we believe that the volitional phase variables are important treatment targets which routinely should form part of a clinical formulation.
+Consistent with previous findings (e.g., Dhingra et al., 2015; Mars et al., 2018), exposure to suicide in others, in particular to the suicide attempt of a friend, was most strongly associated with belonging to the suicide attempt group. Contrary to our predictions, the other exposure variables of suicide attempt by family member or death by suicide of either a family member or a friend, did not significantly differentiate between those in the suicidal ideation and the suicide attempt groups. It would be useful to explore why these other types of exposure did not
+differentiate between the groups. Interestingly, Mars et al. (2018) found a dose response effect with adolescents, whereby exposure to self-harm in both family and friends was 5 times higher in their suicide attempt group compared to those reporting suicide ideation only. A number of mechanisms have been suggested to explain this relationship; including that exposure to suicidal peers increases risk due to suicide modelling via social learning (Insel & Gould, 2008) and cognitive accessibility (Biddle et al., 2012). Contagion may also be more likely due to assortative relating processes whereby similar individuals are more likely to associate (Joiner, 2003), and there may even be evidence for a genetic basis to imitation (Brent and Melhem, 2008). Although further research is needed to better understand the mechanisms behind this phenomenon, ultimately the present study highlights the importance of exposure to suicide as a key risk factor for a suicide attempt.
+Additionally, recent research suggests that exposure to suicidal or self-harming behaviours may act as painful and provocative life experiences which feed into acquired capability (Klonsky et al., 2017). Although measures of acquired capability were only weakly associated with suicide attempt history in a recent meta-analysis (Chu et al., 2017), the concept of having to override an innate instinct for survival appears important in understanding the transition to a suicide attempt (Klonsky and May, 2015). Specifically, having fearlessness about death and reduced pain sensitivity appear to be important mechanisms in increasing the ability to act upon one's thoughts of suicide (Smith et al., 2010). Indeed, Kirtley et al. (2016) in a systematic review found a pervasive relationship between lower pain sensitivity and self-harm more generally but highlighted the dearth of research in this area (Kirtley et al., 2016). A better understanding of how capability for suicide develops requires urgent attention, in particular whether its effects can be buffered by protective interventions such as safety planning (Stanley and Brown, 2012).
+Impulsivity could also increase acquired capability through more exposure to painful events (Anestis et al., 2014). Although impulsivity is an established risk factor, traditionally thought to facilitate suicidal behaviours by increasing the likelihood of enacting suicidal thoughts (Mann et al., 1999), more recent findings have questioned the nature of this relationship. As in this study, a meta-analysis found the relationship between trait impulsivity and suicidal behaviour was relatively small (Anestis et al., 2014). Arguably, the research fails to differentiate between state and trait impulsivity; as an individual high in trait im-pulsivity may plan a suicide attempt (and vice versa) (Gvion and Apter, 2011). Therefore, impulsivity remains a problematic concept that may be difficult to target in interventions; trait impulsivity may not accurately reflect the individual's suicidal intentions, but from a clinician's perspective it may be useful to be aware of this.
+The finding that mental imagery related to death distinguishes those who have made a suicide attempt from those who have not is important and novel. It is consistent with Holmes et al. (2007) who found that ‘flash forwards’, defined as imagined future acts of suicide or self-harm are associated with suicide risk. They may be important targets for intervention, with evidence showing that a reduction in suicidal imagery is associated with less suicidal thoughts over time (Ng et al., 2016). However, to be effective, the key mechanisms need to be explored further as there is competing evidence. For example, it has been suggested that imagery increases the cognitive availability of powerful images (Florentine and Crane, 2010), potentially leading to more distress (Holmes and Mathews, 2005); however, for some the images may also function as a deterrent for suicidal behaviour (Crane et al., 2012). In contrast, it is also suggested that habituation may occur, whereby the fear of the (suicidal) act is reduced thereby facilitating behavioural enaction (Crane et al., 2012). In short, we need to advance our understanding of how experiencing suicide ‘flash forwards’ increases suicide risk, and then how best to intervene to reduce suicide risk.
+4.1.	Limitations
+Although this study had many strengths, a number of potential limitations should be noted. First, the data were cross-sectional; therefore causality or directionality cannot be inferred. Second, as with much psychological research, the measures here are reliant on self-report, therefore they are subject to memory and reporting biases. Indeed, suicidal ideation in particular may be subject to mis-reporting (Mars et al., 2016), and as the former was assessed using a single item, we were not able to tap the intensity or severity of thoughts. Third, although the sample was representative of young people across Scotland, it may not be generalisable to other populations, in particular to clinical groups who are at increased risk of suicidal behaviour. Finally, and as noted earlier, the effect sizes of the volitional phase variables were relatively small but given the retrospective study design this is perhaps not surprising as many of the suicide attempts occurred several years ago. Therefore, future research should investigate the extent to which such factors predict suicide attempts over time. Furthermore, Prentice and Miller (1992) set out clear guidelines when small effect sizes should be considered as important. This occurs under two conditions; (1) when the intervention is minimal or (2) when the outcome is difficult to influence. Here the outcome (suicidal behaviour) is relatively hard to predict or manipulate and the predictors here are minimal (scores on a scale). This is why within medicine when a minimal intervention (e.g., aspirin) that has a small (r = 0.034, which converts to an OR of 1.13) but significant effect in reducing a difficult to influence outcome (e.g., risk of future cardiovascular events) it has important public health implications (Steering, 1988). Thus while the effect sizes are small this does not necessarily negate their importance.
+Despite these limitations, the current research is unique and represents the most robust test to date of the volitional phase of the integrated motivational-volitional model of suicidal behaviour (O’Connor and Kirtley, 2018). In the multivariable analyses, only volitional phase factors (acquired capability, exposure to a friend's suicide attempt, mental imagery and impulsivity) differentiated those who reported suicide ideation from those who reported a lifetime suicide attempt. It extends our understanding of the factors which aid the transition from suicidal thoughts to attempts and it provides strong support for the ideation-to-action framework (Klonsky et al., 2017). As highlighted, future research would benefit from more prospective studies with high-risk populations, as well as further exploration of how these particular volitional factors emerge, how best to incorporate them into risk assessment protocols and how to optimally target them in interventions.
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+Quinlivan, L., Cooper, J., Meehan, D., Longson, D., Potokar, J., Hulme, T., Marsden, J., Brand, F., Lange, K., Riseborough, E., Page, L., Metcalfe, C., Davies, L., Connor, R., Hawton, K., Gunnell, D., Kapur, N., 2017. Predictive accuracy of risk scales following self-harm: multicentre, prospective cohort study. Br. J. Psychiatry 210, 429.
+Ribeiro, J.D., Bender, T.W., Buchman, J.M., Nock, M.K., Rudd, M.D., Bryan, C.J., Lim, I.C., Baker, M.T., Knight, C., Gutierrez, P.M., Joiner Jr., T.E., 2015. An investigation of the interactive effects of the capability for suicide and acute agitation on suicidality in a military sample. Depress. Anxiety 32, 25-31.
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+Stanley, B., Brown, G.K., 2012. Safety planning intervention: a brief intervention to mitigate suicide risk. Cogn. Behav. Pract. 19, 256-264.
+Steeg, S., Quinlivan, L., Nowland, R., Carroll, R., Casey, D., Clements, C., Cooper, J., Davies, L., Knipe, D., Ness, J., O'Connor, R.C., Hawton, K., Gunnell, D., Kapur, N., 2018. Accuracy of risk scales for predicting repeat self-harm and suicide: a multicentre, population-level cohort study using routine clinical data. BMC Psychiatry 18, 113.
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+Van Orden, K.A., Witte, T.K., Gordon, K.H., Bender, T.W., Joiner Jr, T.E., 2008. Suicidal desire and the capability for suicide: tests of the interpersonal-psychological theory of suicidal behavior among adults. J. Consult. Clin. Psychol. 76, 72-83.
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+483
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+Background
+The severe mental disorders (SMD), defined as schizophrenia-spectrum, psychoses and bipolar disorders as well as moderate to severe depression, are associated with markedly reduced life expectancy [1]. Worldwide, reductions in life expectancy amongst people with SMD are stark, ranging from 11 to 17 years in the UK [2], 15-20 years across Nordic countries [3], and up to 30 years reduced in low- and middle-income country (LMIC) settings such as in Ethiopia [4]. In particular, this decrement in life expectancy has been noted to be increasing over time [5].
+Although deaths from suicide and other unnatural causes may be more likely in this group compared to general populations, the majority of deaths are in fact due to preventable physical causes, such as cardiovascular disease, respiratory disorders, cancers and infectious disease [6]. In addition, lowered life expectancy may also be because comorbid substance use disorders (harmful substance use and dependence) are the most prevalent psychiatric conditions associated with SMD. Lifetime alcohol use disorders may affect up to 20% of people with schizophrenia [7] and between 24 to 35% of people with bipolar disorders [8, 9]. Comorbid substance use disorders such as cannabis use disorder [10], opioid and other drug use disorder are also known to be more prevalent in these populations compared with the general population [9]. Tobacco use has also been noted to be elevated more than five-fold in people with schizophrenia compared to reference populations [11, 12] and is a leading preventable cause of death in this group of people. Global successes in reducing tobacco use in the general population have not been mirrored by similar reductions in populations with SMD [11, 13].
+A history of substance abuse in populations with SMD has been shown to be associated with an increased risk of death from all-causes and from unnatural causes [14-16]. In addition, findings from a recent study indicated that in general, the presence of substance use disorders (across a broad spectrum of substance types) in SMD was associated with an increased risk of psychiatric admissions, psychiatric emergency department presentations and longer in-patient stays [17]. People with SMDs probably do not just use one substance in particular but are more likely to engage in
+polysubstance use [17]. Factors which make people with dual diagnoses (comorbid mental and substance use disorders) particularly vulnerable to poor health and social outcomes, include the mutually detrimental effect on the course of illness, its identification, diagnosis and treatment; double stigma and barriers to both mental and physical health care, as well as the contribution of substance use to negative health and social outcomes. For tobacco use, the prevalence of tobacco use in people with SMD is higher, and people with SMD are known to start smoking earlier and smoke more heavily [18] compared with the general population [19]. Potential aetiological pathways for premature mortality in SMD populations with these comorbidities are complex and interlinked. Some basic pathways are summarised in Table 1.
+To improve the management of comorbid conditions in adults with SMD and support the reduction of individual health behaviours constituting risk factors for these illnesses, with the aim of decreasing morbidity and premature mortality amongst people with SMD, in 2018 the World Health Organization (WHO) launched guidelines for the “Management of physical health conditions in adults with severe mental disorders” [20]. Prior to the launch of these guidelines it was recognised that whereas there are WHO guidelines addressing mental and substance use disorders as well as physical health conditions in general populations, there was an absence of guidelines specifically targeting those with SMD having comorbid conditions. The target audience for the guidelines are health care practitioners across all specialisms and levels of health care system, as well as policy makers, healthcare planners/providers, programme managers, and people living with SMD as well as their families and carers, and organisations representing the interests of people living with SMD.
+In this paper, we present the findings of a detailed comprehensive overview of existing systematic reviews on the topic areas of tobacco cessation and management of comorbid substance use disorder in SMD, which eventually led to the recommendations in the WHO guidelines on management of physical health conditions in adults with severe mental health disorders. The full guidelines and supporting materials can be accessed
+from the WHO website (https://www.who.int/mental_ health/evidence/guidelines_physical_health_and_severe_ mental_disorders/en/).
+Methods
+The methodologies used to inform the WHO recommendations for the management of tobacco and substance use disorders among people with SMD followed the GRADE (Grading of Recommendations Assessment, Development and Evaluation) process [21].
+A key outcome of the initial phase in developing the guidelines was in the identification of target areas which eventually informed the a priori research questions which followed the PICO [Population, Intervention, Comparison group, Outcomes] format. The research questions guided which physical health conditions and risk factors were to be addressed in the final disseminated guidelines [20]. This
+Table 3 Research questions- substance (drug and/ or alcohol) use disorders
+For people with SMD and substance (drug and/or alcohol) use disorder, are pharmacological and/or non-pharmacological interventions for substance use disorder effective to support reduction in substance use-related outcomes?
+Population/ Intervention / Comparison / Outcome (PICO)
+Population: people with SMD and substance (drug and/or alcohol) use disorder
+Intervention:
+pharmacologicaland/or non-pharmacological interventions for substance use disorders:
+-	Pharmacologicalinterventions
+-	Non-pharmacological interventions: e.g. motivationalinterviewing and/or CBT, psychoeducation, brief assessment interview, dual-focus interventions
+Comparison: care as usual / placebo or one treatment vs another Outcomes:
+Critical
+-	Levelof consumption
+-	Frequency of use
+-	Abstinence
+-	Relapse rates
+Important:
+-	Frequency of adverse events / side-effects
+process was informed by scoping reviews and consultation with a Guideline Development Group (GDG) of externally appointed international experts, engaged by the WHO. Selected PICO questions reflected areas of uncertainty which the GDG felt should be prioritised to inform final recommendations. The final research questions for informing systematic evidence searches were then ratified by the WHO Guideline Review Committee (GRC), which led to the formulation of specific research questions relevant to tobacco and substance use disorders among people with SMD (Tables 2 and 3).
+Figure 1 highlights the comprehensive processes which were followed, leading to the identification of relevant systematic reviews to inform the research questions relating to tobacco cessation, and treatment of substance use disorders in SMD. The retrieval, appraisal and synthesis of evidence closely followed the WHO handbook for guideline development [22]. Databases searched included: the
+Cochrane Library (including DARE), PubMed/Medline, Embase, Psychinfo, Epistemonikos and the Global Health Library. In addition, where searches had to be expanded (see step 3 in Fig. 1) the National Guideline Clearing House was also searched. Search terms employed for the research questions are displayed in supplementary material, and reflected the majority of substances listed in chapters F10-F19 of the tenth revision of the International Classification of Diseases and Related Health Problems (ICD-10) [23]. (Supplementary material: Table 1); these were informed through consultation with guideline methodologists and subject-specific experts at the WHO. Supplementary searches highlighting relevant drug-drug interactions were also employed (Supplementary material: Table 2). Searches between medicines used for tobacco cessation or treatment of substance use disorders and those used for SMDs were carried out using the drugdrug interaction software Lexi-Interact [24]. Lexi-Interact was selected for its clinical utility and the fact that it scored well on both accuracy and comprehensiveness in a review comparing drug-drug interaction software databases [25]. Searches were performed to February 2018 for the tobacco PICO question and to June 2018 for the substance use disorders PICO question.
+Systematic reviews selected for inclusion into GRADE tables conformed to the following inclusion criteria: (1) Timelines- Published within the last 5 years, preferably within the last 3 years; (2) Quality- Papers included for GRADE assessment had sufficiently high methodological quality ratings on the ‘Assessment of Multiple Systematic Reviews’ tool (AMSTAR) [26-28] (see below for further details); (3) Relevance- Retrieved papers were closely relevant to the PICO population. However, where relevant evidence could not be identified these criteria were relaxed, leading to ‘indirect evidence’ to inform recommendations (Fig. 1, step 3). Cochrane reviews or comprehensive meta-analyses and systematic reviews were given preference, wherever possible in this process.
+In order to inform the development of evidence based guidelines in a transparent manner, the GRADE approach was used [21]. An advantage of GRADE is that the certainty of the evidence can be summarised and assessment of the evidence can be separate to the strength of the recommendations which inform the final guidelines [21].
+Prior to selection for GRADE assessment, retrieved articles had to meet sufficiently high quality ratings on the AMSTAR tool [26-28]. The AMSTAR tool leads to a score across 11 domains according to which the quality of each retrieved systematic review is rated. Papers were initially assessed by a member of the team and then cross-checked by another member of the team (MS, JD, PCG). Systematic reviews fulfilling inclusion criteria with a sufficiently high AMSTAR quality rating (a positive rating on more than 6 out of 11 domains) were then assessed using the GRADE approach using
+the GRADEpro tool by a member of the team (MS), with all GRADE assessed papers subsequently rated by a second rater (JD and CB). Discordant ratings between team members on the AMSTAR and the GRADE were resolved through discussion in the team. Key attributes of studies relating to each of the PICO questions were extracted from each included study using a structured form by one member of the team and cross-checked by another. WHO guidelines for rating studies in terms of certainty of evidence, according to the GRADE were followed, to assess each study for limitations, inconsistency, indirectness, imprecision and the reporting of bias, leading to a final GRADE assessment of the certainty/ confidence of the findings reported in the review [29]. For each included study a relevant summary measure was extracted, which was either a Relative Risk (RR) or Mean Difference (MD).
+GRADE evidence profiles for each of the PICOs were presented and discussed over a series of roundtable meetings convened at the WHO in Geneva in May 2018. GDG members were selected internationally across UN member states for their expertise within the topic areas. In addition, the meetings were also attended by a guideline methodologist, the evidence review team and the WHO secretariat. The final recommendations resulted from a consideration of the background evidence for each of the PICO questions, summarised as GRADE profiles and the certainty of evidence for these, as well as taking into consideration other aspects such as whether the problem was considered a priority, how substantial desirable and undesirable anticipated effects were, whether the balance between desirable/undesirable effects favoured the intervention over the comparator, the value attached to the outcomes and the certainty of evidence relating to likely resource requirements, cost effectiveness, impact on health equity, acceptability and feasibility of the intervention. In addition the acceptability of the intervention to healthcare providers in LMICs, feasibility of the intervention and the impact of the intervention on equity and human rights were considered.
+Results
+After consultation with the GDG and WHO GRC agreed research questions specific to tobacco cessation and substance use disorders were:
+1. For people with SMD who use tobacco, are pharmacological (including nicotine replacement therapy, bupropion, varenicline) and/or non-pharmacological interventions effective to support tobacco cessation?
+2. For people with SMD and substance (drug and/or alcohol) use disorder, are pharmacological and/or
+non-pharmacological interventions for substance use disorder effective to support reduction in substance use-related outcomes?
+In total 1434 records were initially identified through the systematic searches for SMD and tobacco cessation; after screening for eligibility and removal of duplicates, 4 reviews were included in the GRADE tables for this PICO with 18 reviews in total contributing evidence through narrative synthesis. For SMD and substance use disorders, a total of 4268 records were identified. After screening and checking against eligibility criteria, 4 studies were included in the GRADE tables on this topic with a total of 16 studies included in the narrative synthesis. Figures 2 and 3 display PRISMA flow charts of relevant articles retrieved for SMD and tobacco use and with substance use disorders, respectively.
+For tobacco use in SMD, GRADE evidence profiles were compiled for: the use of Buproprion, Varenicline and Nicotine Replacement Therapies (NRT) (all versus placebo). In
+addition, GRADE profiles for non-pharmacological interventions (which included: motivational enhancement, psy-choeducational approaches, Cognitive Behavioural Therapy (CBT)), supplementing NRT were compared to standard care approaches, and the use of contingent reinforcement (using money/money plus NRT) compared to care-as-usual was assessed with GRADE [30-33] (For full recommendations with supporting evidence, including relevant drugdrug interactions for Buproprion, Varenicline and NRT see: https://www.who.int/mental_health/evidence/guidelines_ph ysical_health_and_severe_mental_disorders/en/). The GDG recommended combination pharmacological with behavioural interventions, as behavioural interventions alone have been shown to result in a relatively low abstinence rate for tobacco use in SMD.
+The certainty of evidence derived from GRADE, relating to specialised smoking cessation interventions versus standard approaches in people with SMD, was very low. There was insufficient evidence to suggest the superiority of specialised smoking interventions over standard
+smoking cessation approaches for SMD populations. In addition, the certainty of evidence relating to contingency reinforcement approaches compared with care-as-usual for tobacco cessation in SMD populations was very low.
+Pharmacological interventions identified for tobacco cessation in SMD populations were: NRT, Bupropion and Varenicline. Evidence for the efficacy of these interventions in SMD populations mostly derived from high income settings with a few exceptions (e.g. studies for Bupropion which had been conducted in China and Iran as well as in the USA). These pharmacological interventions for tobacco cessation are already recommended by the WHO in general populations, although only NRT is on the WHO essential medicines list [23]. Searches of pharmacological interactions indicated the possibility of interactions between Bupropion and psychotropic medications commonly prescribed in SMD, particularly related to lowering seizure threshold and enzyme inhibition or induction (see https://www.who.int/
+mental_health/evidence/guidelines_physical_health_and_se vere_mental_disorders/en/for full list of interactions).
+For substance use disorders and severe mental disorders, assessment of evidence using the GRADE approach included a review of evidence relating to psychological interventions such as CBT plus motivation interviewing (MI) versus care-as-usual, CBT versus care-as-usual, MI versus care-as-usual and contingency management versus care-as-usual for people with SMD and substance use disorders [34]. Brief interventions, specifically delivered in four or fewer sessions [35], were also assessed. Although these types of interventions may have a basis simply in providing education and advice [35], the brief interventions which were identified and assessed according to GRADE for these guidelines all compared motivational interviewing with CBT approaches, delivered over shorter time frames [35]. In addition, evidence relating to the efficacy of antipsychotic medications in reducing psychotic symptoms alongside other outcomes such as frequency of
+substance use, in dual diagnoses populations were also assessed [36, 37] as well the prescribing of antidepressants in depression comorbid with alcohol use disorders to improve outcomes [38].
+All of the main recommendations relating to each of the PICO questions are presented in Table 4. For dual diagnoses populations, there was a lack of evidence to support the
+superiority of any of the psychological interventions in improving outcomes related to SMD comorbid with substance use disorders. Furthermore, the review team were unable to identify any studies which had specifically assessed these populations within LMIC settings, further limiting generalisabilty. Of those studies retrieved, most were of very low certainty. The GDG reflected that the
+relative lack of evidence to support the efficacy of these interventions in people with SMD comorbid with substance use disorders may partly be due to these populations being more likely to be excluded from research [39].
+In general, the assessment of evidence using GRADE methods indicated low to very low certainty evidence from randomised controlled trials of pharmacological interventions for the management of mental disorders (whether through the use of antipsychotics or antidepressants), which did not indicate the superiority of any of the surveyed medications, when prescribed for people with SMD comorbid with substance use disorders [36-38]. Moderate side effects were noted for these interventions, which need to be taken into account when prescribing for this patient population. In addition, it was noted that medicines which may be used for the management of opioid use disorders such as Methadone and Buprenorphine have interactions with many of the commonly used psychotropic medications, including cardiac effects such as QTc prolongation, central nervous system depression and serotonergic effects (see Annex 6 of guidelines for details: https://apps.who.int/ iris/bitstream/handle/10665/275718/9789241550383-eng. pdf?ua=1).
+For both comorbid tobacco use and substance use disorders, where retrieved evidence was of very low certainty, the expertise of the international GDG was sought, who applied their expertise to the topic area. As a result of the low/very low certainty of evidence retrieved, resultant recommendations were conditional. A ‘conditional’ recommendation by the GDG indicates that GDG members concluded that beneficial effects of the intervention probably outweighed undesirable effects but with insufficient evidence for the GDG to support a ‘strong’ recommendation (with ‘strong’ recommendations indicating that the GDG felt confident that beneficial effects outweighed undesirable effects for the recommended intervention). For people with SMD and substance (drug and/or alcohol) use disorder, the low certainty of evidence led to the recommendation that the mhGAP guidelines for the management of substance use disorders should be followed (Table 4).
+The full GRADE evidence profiles are displayed in the supplementary materials (supplementary tables 1-2) and can also be accessed online. PRISMA checklist has also been provided in supplementary materials (see additional material: PRISMA checklist).
+Discussion
+These evidence-based recommendations, based on detailed and comprehensive reviews of systematic reviews, as well as consultation with an international body of experts and WHO specialists, represent a positive and important step towards tackling the 15-20 year reduction in life
+expectancy, experienced by people with SMD compared to the general population, globally. These guidelines highlight the need to adequately manage tobacco and other substance use disorders in people with SMDs, alongside optimally managing the mental disorder.
+Evidence synthesis highlighted a general lack of high-quality evidence detailing effective interventions for tobacco cessation in SMD and/or for dual diagnoses populations. This reflects a systematic exclusion of people with SMD and/or dual diagnoses from clinical trials, despite evidence indicating that mental disorders are highly comorbid with substance use. There is a need to consider and include these populations in future research [39].
+Do the guidelines go far enough? The guidelines retain a practical emphasis to inform clinicians, healthcare providers and other professional groups on best-practice recommendations and acknowledge the importance of wider multi-level interventional frameworks to address the inequalities impacting on SMD populations [40]. Within this framework, a consideration of health system factors as well as broader social determinants which include social support, stigma and attempts to reduce social exclusion play a major role [40]. In addition, although not directly addressed by the guidelines, public health actions to prevention implemented at country-level form the backdrop to recommended interventions at a whole populationlevel [41], irrespective of group-specific evidence; for example recommended interventions for tobacco cessation or harmful alcohol use could be read within the context of country-level increased taxation/pricing policies on tobacco or alcohol, restrictions on the availability of alcohol, measures to restrict drink-driving, restricted tobacco or alcohol advertising as well as population-level educational campaigns on tobacco cessation, and access to screening and brief interventions [42, 43] or other cost effective interventions [44]. In addition, the guidelines should be read in conjunction with public health/systemic interventions at country-level to address and support population-level mental health [45].
+Our searches revealed a scarcity of evidence particularly relating to dual diagnoses populations, which impacted on the ability to make strong recommendations relevant to people with SMD and comorbid substance use. The scarcity of good quality evidence to inform the recommendations reflects the experience of authors of a previous systematic review, whereby it was found that more than half of clinical randomised controlled trials on the pharmacological treatment of opioid dependence excluded people with psychiatric disorders [39]. The systematic exclusion of people with mental disorders from randomised controlled trials has also been noted in one other review in which the authors assessed the presence of psychiatric exclusion criteria in randomised controlled trials [46]. The exclusion of people with mental
+disorders from trials may in part be due to a number of factors, including trialists’ concerns that decisional capacity to take part is more likely to be impaired in people with SMDs, or concerns that the stress or unintended consequence of taking part in a trial may lead to an exacerbation of mental disorder [46]. In addition, pharmaceutical companies may stipulate extensive exclusion criteria to ensure a smoother pathway to regulation and approval for pharmaceutical products [46]. However, these practices lead to “scientific neglect” [46], and as we have highlighted in this paper, serve to perpetuate the inequalities which people with SMDs experience further. For those systematic reviews which were retrieved, there was also an absence of high-quality evidence relating to psychological interventions to address substance use disorders in dual diagnosis populations. This presents a major limitation, as there is a high co-morbidity of psychiatric and substance use disorders in clinical practice, and for practical purposes it is difficult to address one without the other. In future, research which actively includes people with SMD and comorbid substance use are needed particularly to avoid perpetuating further social exclusion and marginalisation.
+Most of the evidence which informed the development of the guidelines came from well-resourced settings. This may mean that specific issues relevant to low resource settings may impact on implementation. Issues relating to cost and capacity will need to be taken into account for some recommended interventions. The availability of certain medications- such as Varenicline (which does not currently appear in the WHO essential medicines list) may be restricted in certain contexts, although other interventions (such as NRT) are more widely available. Other factors relating to acceptability of the guidelines and longer term sustainability across countries will need to be monitored. Future guidelines may reflect feedback from people on the ground at the forefront of implementing these guidelines on tobacco use and substance use disorders in SMDs- for example following feedback from health care practitioners, policy makers and public health practitioners.
+Conclusions
+Tobacco use and substance use disorders play an important role in heightening the risk of premature mortality in people with SMDs. Our search of the evidence highlighted gaps in the evidence base, which may in part be due to the systematic exclusion of people with SMDs from clinical trials. Despite the challenges described in this paper, these guidelines may mark an important step towards addressing premature mortality in people with SMD. The recommendations may help to inform policy and decision makers globally and in LMIC settings in ensuring more equitable access to tobacco cessation and substance
+use disorder services for these populations. However the dearth of high-quality evidence and evidence from LMIC settings must inform the future research agenda.
+Das-Munshi et al. BMC Psychiatry (2020) 20:237
+Page 12 of 13
+Columbia in Canada; John Saunders, The University of Sydney, Australia; Najma Siddiqi, University of York, UK; Isolde Sommers, Danube University Krems, Austria; Charlene Sunkel, Central Gauteng Mental Health Society, South Africa; Hedinn Unnsteinsson, Prime Minister's Office, Iceland; Pieter Ventevogel, UNHCR, Switzerland Lakshmi Vijaykumar, Voluntary Health Services, Chennai, India; Inka Weissbecker, International Medical Corps, Washington DC, USA.
+Members of the WHO STEERING GROUP: Tarun Dua, Programme Manager, Department of Mental Health and Substance Abuse; Neerja Chowdhary, Technical Officer, Department of Mental Health and Substance Abuse. WHO headquarters members: Bernadette Cappello, Department of Essential Medicines and Health Products; Meg Doherty, Department of HIV/AIDS; Alexandra Fleischmann, Department of Mental Health and Substance Abuse; Dongbo Fu, Department of Prevention of Noncommunicable Diseases;
+Ernesto Jaramillo, Global TB Programme; Dzmitry Krupchanka; Department of Mental Health and Substance Abuse; Shanthi Pal, Department of Essential Medicines and Health Products; Vladimir Poznyak, Department of Mental Health and Substance Abuse; Shekhar Saxena, Department of Mental Health and Substance Abuse; Mark van Ommeren, Department of Mental Health and Substance Abuse; Cherian Varghese, Department of NCDs, Disability, Violence and Injury Prevention. We also acknowledge the contribution of other colleagues: Marco Antonio De Avila Vitoria, Department of HIV/AIDS and Chantal Mignone, Department of HIV/AIDS. WHO regional office advisors: Nazneen Anwar, WHO Regional Office for South-East Asia; Dan Chisholm, WHO Regional Office for Europe; Devora Kestel, WHO Regional Office for the Americas; Sebastiana Da Gama Nkomo, WHO Regional Office for Africa; Khalid Saeed, WHO Regional Office for the Eastern Mediterranean; Martin Vandendyck, WHO Regional Office for the Western Pacific.
+Parental consent for participation
+Not applicable.
+Disclaimer
+The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
+Authors’ contributions
+JD wrote the first draft of the manuscript, using materials prepared by MS. All authors (JD, MS, CB, NC, PG, KK, DK, TD, GT) contributed to the writing of the manuscript. All authors (JD, MS, CB, NC, PG, KK, DK, TD, GT) participated in the consensus meeting or reviewed the evidence and its interpretation for the development of the final recommendations (or a combination of these) and contributed to the interpretation. All authors (JD, MS, CB, NC, PG, KK, DK, TD, GT) agreed with the final version of the paper. All authors (JD, MS, CB, NC, PG, KK, DK, TD, GT) read and approved the final version of the manuscript.
+Funding
+JD is funded by the Health Foundation working together with the Academy of Medical Sciences and by the ESRC in relation to the SEP-MD study (ES/ S002715/1) and part supported by the ESRC Centre for Society and Mental Health at King's College London (ESRC Reference: ES/S012567/1). MS is supported by the NIHR Global Health Research Unit for Neglected Tropical Diseases at BSMS. PCG is supported by the UK Medical Research Council in relation the Indigo Partnership (MR/R023697/1) award. GT is supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King's College London NHS Foundation Trust, and the NIHR Asset Global Health Unit award. GT receives support from the National Institute of Mental Health of the National Institutes of Health under award number R01MH100470 (Cobalt study). GT is supported by the UK Medical Research Council in relation the Emilia (MR/ S001255/1) and Indigo Partnership (MR/R023697/1) awards.
+The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the MRC, the Department of Health, the ESRC or King's College London.
+Availability of data and materials
+All supporting documents which informed the development of this manuscript and the guidelines are freely available through the web links provided in this manuscript or through contacting the authors.
+Ethics approval and consent to participate
+Ethical approvals and consent to participate were not required for this study/ not applicable.
+Consent for publication
+Not applicable.
+Competing interests
+The authors have no competing interests to declare.
+Author details
+department of Psychological Medicine, Institute of Psychiatry Psychology & Neurosciences, King's College London, South London & Maudsley NHS-Trust, De Crespigny Park, London SE5 8AF, UK. 2Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK. 3WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy. department of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland. 5Centre for Global Mental Health, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. 6The Chester M. Pierce, MD Division of Global Psychiatry, Massachusetts General Hospital, Boston, USA.
+Received: 28 January 2020 Accepted: 26 April 2020 Published online: 14 May 2020
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+Acyl coenzyme A (CoA) binding protein (ACBP) is also called diazepam-binding inhibitor (DBI). In humans and mice, this small (10 kDa) protein plays a dual role, reflecting its double name. As an intracellular protein, ACBP/DBI binds to medium- and long-chain acyl-CoA esters, reducing their toxicity and facilitating their transport through different subcellular compartments, hence stimulating lipid metabolism (1-3).
+As an extracellular protein, ACBP/DBI binds to the peripheral benzodiazepine receptor (hence displacing the benzodiazepine diazepam from its binding site), which is the ionotropic gamma-aminobutyric acid type A (GABAA) receptor (GABAAR) possessing another endogenous ligand, yaminobutyric acid, the major inhibitory neurotransmitter (4, 5). In the central nervous system, ACBP/DBI can be proteolytically cleaved to yield several neuropeptides, one of which, octadecaneuropeptide (ODN), interacts with a G protein coupled receptor (GPCR) in the central nervous system (6, 7).
+ACBP/DBI is ubiquitously expressed and can be released from cells through an unconventional, autophagy-dependent pathway (8). It then acts as a paracrine mediator to inhibit autophagy through an action on GABAAR, which is expressed in many cell types outside the central nervous system (9). Hence, antibody-mediated neutralization of extracellular ACBP/DBI offers the possibility to stimulate autophagy by interrupting a paracrine feedback inhibition loop. In humans, obesity and metabolic syndrome are associated with elevated ACBP/DBI levels in the plasma (10), while anorexia nervosa is characterized by abnormally low concentrations of circulating ACBP/DBI (9, 11). In mice, injection of recombinant ACBP/DBI protein into the peritoneal cavity or the tail vein causes a GABAAR-dependent increase in feeding. This appetite-stimulatory effect of ACBP/DBI is also observed for proteins in which the acyl-CoA binding moiety has been mutated. Conversely, injection of a neutralizing antibody blocks feeding responses and counteracts weight gain or favors weight loss in multiple experimental conditions. These findings suggest that ACBP/DBI is involved in the pathophysiology of human obesity (12).
+ACBP/DBI is a phylogenetically conserved protein, as ACBP/DBI homologs have been described in all eukaryotic phyla and even in some bacterial species (13, 14). In the nematode Caenorhabditis elegans and in the insect Drosophila melanogaster, ACBP/ DBI orthologs stimulate pharyngeal pumping and mouth hook movement, which are the functional equivalents of mammalian mastication (15). In the yeast Saccharomyces cerevisiae, ACBP/DBI is the only protein known to be released in response to nutrient or oxidative stress (16, 17). Extracellular ACBP/DBI stimulates sporulation of yeast in a GPCR-dependent fashion, hence allowing yeast cells to swarm out to find new food resources (15, 18). Thus, the appetite-stimulatory function of ACBP/DBI appears to be phylogenetically conserved (19-21).
+Reportedly, the genomes of some bacteria code for ACBP/DBI orthologs (22, 23). It is well known that human obesity is associated with major shifts in the composition of the intestinal microbiome (24, 25). Moreover, fecal microbial transplantation (FMT) of the stools from obese (but not lean) individuals into mice can transfer features of obesity and metabolic syndrome, establishing cause-effect relationships between alterations in the gut microbiome and the obese phenotype (26, 27).
+Intrigued by these observations, we wondered whether specific microbial species in the human gut might encode and express ACBP/DBI-like proteins, thus potentially influencing human metabolism and eating behavior. Here, we report a detailed bioinformatics analysis of ACBP/DBI-like genes within the human gut microbiome and analyze their possible implication in obesity. We found that ACBP/DBI is mostly encoded by eukaryotes, that its presence in bacteria is mostly limited to pathogenic taxa, and that its rare presence in the human gut is not associated with alterations in the body mass index (BMI).
+RESULTS
+ACBP/DBI-like proteins are rarely encoded in members of the human microbiome. To assess whether microbial ACBP/DBI ortholog genes could potentially contribute to microbiome-dependent gut metabolism, we first looked for their presence in 99,211 microbial genomes from NCBI as of January 2019. Using an initial set of 1,098 UniRef-annotated orthologous ACBP/DBI sequences (see Materials and Methods) to search these genomes, we found ACBP to be present in 3,635 of them, encompassing 1,668 unique TaxIDs, with the majority belonging to Proteobacteria (89% of genomes). Species with the largest number of genomes encoding ACBP showed it to be part of
+the core genome of several known pathogens from the Burkholderia genus, as well as those from Saccharomyces cerevisiae and Ralstonia solanacearum (Table 1). While Saccharomyces cerevisiae can be found in the human gut (28, 29), although usually at low abundance, the bacterial taxa in NCBI containing ACBP are at best very rare members of the human microbiome.
+Because genomic sequencing captures only a limited fraction of the human microbiome diversity (30-33), we proceeded by searching homologous sequences of known ACBP genes in metagenome-assembled genomes (MAGs). We screened 154,000 MAGs previously recovered from the human microbiome sampled from almost 10,000 individuals spanning diverse geography and lifestyle (Table S1). We found only 129 out of the 154,000 MAGs (0.08%) to encode ACBP, belonging to 14 species-level genome bins (SGBs). One of these SGBs was classified as Deinococcus-Thermus and another as Chitinophagaceae, whereas the remaining 12 all belonged to Proteobacteria, with the closest known taxa being again Burkholderia or taxa linked with sample-processing contamination such as Ralstonia or Acidovorax (34). This exploration of microbial genomes and MAGs thus highlights a lack of ACBP/DBI ortholog genes in microbes of putative relevance in the human microbiome.
+Phylogenetic modeling of ACBP/DBI is highly taxonomically consistent. To better assess the sequence diversity of the ACBP/DBI gene, we phylogenetically modeled its sequence variants found in human MAGs and reference genomes from NCBI across different organisms. This analysis revealed very distinct eukaryotic versus microbial ACBP/DBI sequences, despite the relatively short alignment length used for phylogenetic inference (Fig. 1). This distinct pattern between the two domains was also seen when we used pairwise nucleotide identities calculated from multiple sequence alignments (Fig. S1). We found ACBP to be widespread across the domains of life, with ACBP sequences found in eukaryotic phyla including Streptophyta, Arthropoda, Nematoda, Ascomycota, and Chordata and present in 10 different bacterial phyla. Some taxa such as the genera Daphnia and Variovorax exhibited clearly defined clades, while other taxa such as the phyla Arthropoda and Actinobacteria displayed more diverse and paraphyletic phylogenies. The bacterial genera Burkholderia and Paraburkholderia showed a clearly defined subtree. ACBP sequences belonging to MAGs recovered from the human microbiome were widespread across the phylogeny but always maintained a consistent taxonomic structure. This adherence between phylogeny and taxonomy for ACBP/DBI suggests vertical evolutive trajectories for this gene, as a comparison between prokaryotic phylogenies built at the whole-genome level was highly consistent with the phylogenetic tree constructed for the ACBP/DBI gene (Fig. S2), with very limited evidence (if any) of horizontal transfer events and
+June 2021 Volume 87 Issue 12 e00471-21
+consequently a low likelihood that yet-to-be-characterized taxa not captured by our analysis carry ACBP/DBI ortholog genes.
+ACBP/DBI is rarely found in human gut microbiomes. To further investigate whether the few ACBP/DBI-positive genomes and MAGs recovered from the human microbiome could potentially contribute to gut metabolism, we evaluated their prevalence across 7,698 human gut metagenomes present in the curatedMetagenomicData R package (35), spanning different countries, age categories, and health conditions (Fig. 2A; Table S2). We found that the majority of MAGs belonging to these SGBs were very rarely found in samples across different data sets, with two known SGBs classified as Acidovorax sp. 12322_1 (kSGB 12676) and Cupriavidus metallidurans (kSGB 12928) achieving the highest overall prevalence (0.3%).
+Since MAGs rely on the success of metagenomic assembly and binning and thus may miss some low-abundance or hard-to-assemble taxa, we further screened unbinned contigs as well as the raw reads for each sample. The use of unbinned contigs (assembled reads) indeed led to an increase in the overall prevalence of ACBP/ DBI-positive samples, but this number remained low (0.6%) (Fig. 2B). When we aligned raw metagenomic reads to the set of retrieved ACBP/DBI sequences, we further observed an increase in the overall relatively low prevalence across samples (1.79%),
+although we cannot exclude that some of the hits are false positives that inflate the prevalence estimation. Notably, some data sets, such as CM_madagascar from a nonWesternized society (30) and VincentC_2016 comprising fecal microbiome of 98 hospitalized patients treated with antibiotics and that used laxatives (36), showed a higher prevalence of ABCP/DBI in their raw metagenomes compared to others, 19.64% and 25.76%, respectively. On the contrary, 35 data sets (83%) had a prevalence of 0%.
+This analysis thus reinforces the very low prevalence of ABCP/DBI-positive taxa and of the ABCP/DBI gene in the human gut microbiome, which appears inconsistent with a hypothesis of a role of this microbial gene variant in human metabolism. Moreover, the taxonomy assignments of the species (from MAGs and NCBI genomes) found to encode ACBP/DBI and occasionally present in some gut microbiome data sets (Fig. 2A) point at sample contamination as a potential source for those taxa. Indeed, Pseudoxanthomonas, Acidovorax, Comamonas, Delftia, Ralstonia, and Cupriavidus have been previously described as common reagent and laboratory contaminants (34).
+Lack of correlation between ACBP/DBI-positive species and body mass index. Although we found a low prevalence of ACBP/DBI-encoding members in the human gut microbiome, theoretically there could still be a possibility that low-prevalent low-abundance taxa can somehow contribute to human gut metabolism. To evaluate a possible link between microbial ACBP/DBI ortholog genes and obesity, we performed a meta-analysis of correlations between species-level abundances and BMI as a readout using 1,899 gut samples from healthy individuals curated within the curatedMetagenomicData (35) effort (Fig. 3; Table 2). We found 14 taxa to be significantly associated with BMI (random effects model false-discovery rate [FDR] < 0.1) (Table S3), with species such as Flavonifractor plautii, Coprococcus comes, and Blautia
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+hydrogenotrophica associated with increased BMI, in line with previous reports (37, 38). We also found species associated with decreased BMI, which included Oscillibacter sp. 57_20, Alistipes shahii, and Odoribacter splanchnicus, as previously described (39). However, these 14 species significantly associated with BMI were all ACBP/DBI-negative. Within the limited panel of ACBP/DBI-positive species at least occasionally found in the gut microbiome, only Saccharomyces cerevisiae, Lautropia mirabilis, and Comamonas kerstersii were sufficiently prevalent in these samples to perform the meta-analysis but showed no significant associations (q values > 0.8) (Fig. 3). These results indicate that species found to encode ACBP/DBI in the human gut microbiome do not show associations with BMI.
+DISCUSSION
+ACBP/DBI plays a major role in the control of appetite and metabolism through a phylogenetically conserved pathway that is conserved in yeast, nematodes, insects, and mammals (15, 20, 21, 40). Intrigued by the observation that ACBP/DBI is a highly conserved protein that is even encoded by some bacterial genes, as well as by the link
+between human obesity and the gut microbiome, we investigated the prevalence of ACBP/DBI in intestinal commensals and their potential correlation with the body mass index.
+The bioinformatic analyses presented in this paper based on extensive available metagenomic data sets suggest that ACBP/DBI-producing bacterial species are rather rare in the human microbiome and are mostly produced by eukaryotic species (as exemplified by the yeast S. cerevisiae) and environmental or potentially pathogenic bacteria (exemplified by Comamonas kerstersii that can cause peritonitis, bacteremia, and sepsis [41-43]), as well as potential sample contaminants. Indeed, the presence of ACBP/DBI-producing species in the human gut appears relatively rare. Moreover, we did not find any correlation between the presence of ACBP/DBI-encoding species and BMI across a large cumulative data set comprising 1,899 samples from healthy gut metagenomes. These results refute the hypothesis that the production of ACBP/DBI by the gut microbiome might affect whole-body metabolism, at least in the context of the normal microbiome.
+Despite our findings, it could still be possible that microbes that are strongly associated with the mucosal tissue in the upper intestinal tract (and that hence would be grossly underrepresented in fecal samples) might have some local or systemic effects. It is also noteworthy to mention that the lack of an association between ACBP/DBI gene carriage and obesity found here did not take into account gene expression levels, which could be relevant as they might not mirror gene presence and/or abundance patterns. Moreover, in the context of infections, bacterial ACBP/DBI might exert some physiological effects on the host. However, it is unclear whether prokaryotic ACBP/DBI orthologues possess similar functions as those present in yeast or other eukaryotes, despite previous work showing strong conservation of amino acids at the majority of sites determined to be important for ACBP structure and function across phyla (22). ACBP/DBI inhibits autophagy (9, 19), and autophagy is a potent mechanism to eliminate intracellular bacteria (44), meaning that the subversion of autophagy (also called xenophagy) might contribute to the virulence of pathogenic species. Thus, Streptococcus pneumoniae degrades the essential autophagy protein ATG14 to ensure its survival in host cells (45), while Salmonella enterica serovar Typhimurium targets the V-ATPase-ATG16L1 axis to avoid xenophagy (46), just to mention a few examples. In view of these premises, it might be interesting to generate recombinant bacterial ACBP/DBI proteins and to evaluate them for their autophagy-inhibitory and metabolic effects.
+The appetite-stimulatory effects of ACBP/DBI are lost in mice that bear a phenylalanine (F) to isoleucine (I) substitution at position 77 in the N-terminal domain of the gamma2 subunit of GABAAR (10, 47), supporting the contention that this neurotransmitter receptor is responsible for the obesogenic activity of DBI. ACBP/DBI is a GABAAR antagonist, while GABA is a GABAAR agonist. Of note, GABA, the natural agonist of GABAAR, can be produced by a series of bacteria. Reportedly, oral administration of GABA-producing Lactobacillus brevis strains reduces the abundance of mesenteric adipose tissue, enhances insulin secretion following glucose challenge, and improves plasma cholesterol clearance (48). Hence, it is possible that, beyond their documented effects on depression (49, 50), GABA-producing bacteria might affect whole-body metabolism, including appetite control. This hypothesis will be actively investigated by our laboratories.
+MATERIALS AND METHODS
+Identification of ACBP/DBI sequences and phylogenetic tree reconstruction. To obtain a more comprehensive set of ACBP/DBI sequences, we downloaded amino acid sequences that matched the keyword “ACBP” from UniProt90 (51), mapped their identifiers to those of the European Molecular Biology Laboratory's coding sequences using UniParc, and used the resulting DNA sequences to search, using BLASTn (52), all 99,211 microbial genomes available in NCBI, that included the whole set of 17,607 microbial species (16,959 bacteria, 648 archaea) available as of January 2019 and 154,723 metagenome-assembled genomes (MAGs) from reference 30. Matching queries were filtered to include only June 2021 Volume 87 Issue 12 e00471-21
+alignments with >70% identity, alignment length of >100 nt, and an E value of <1 x 1025. We found no evidence that more permissive minimum alignment lengths lead to increased ACBP/DBI detection.
+To build a phylogenetic tree of the known and metagenomically retrieved sequences, we clustered sequences at 97% sequence identity using UCLUST (parameters: “-id 0.97”) (53) and aligned centroid cluster sequences using MAFFT (parameters: “-localpair -maxiterate 1000”) (54). We removed “gappy” regions and ACBP/DBI sequences with insufficient aligned positions from the multiple sequence alignment using Jalview (55), resulting in 240 nucleotides of aligned positions and 1,223 sequences. The tree was built using fastTree (parameters: “-mlacc 2 -slownni -spr 4 -fastest -mlnni 4 -no2nd -nt”) (56) and refined with RAxML (parameters: “-m GTRGAMMA -t”) (57). GraPhlAn (58) was used for tree annotation and visualization.
+We used PhyloPhlAn 3 (59) to build a phylogeny on 3,490 reference prokaryotic genomes and 129 MAGs (which we found to contain ACBP/DBI) using the parameters “-diversity high -accurate -force_nucleotides” and the set of up to 400 PhyloPhlAn genome markers. We compared trees built using PhyloPhlAn 3 and ACBP/DBI (with the aforementioned methods) in terms of their normalized pairwise branch lengths and used the tqDist (60) function available in the R quartet package to compare their quartet distances using a random sampling of 477 genomes repeated 1,000 times.
+Search of ACBP/DBI sequences in human gut metagenomes. The prevalence of both known and unknown species-level genome bins (kSGBs and uSGBs) that were found in a repository (https:// opendata.lifebit.ai/table/SGB) (30) with ACBP/DBI-encoding MAGs was calculated using 7,698 human gut metagenomes present in the curatedMetagenomicData (cMD) version 1.16.0 R package (35). A given sample was deemed positive if a MAG belonging to the ACBP/DBI-encoding SGB was found.
+We used the set of retrieved ACBP/DBI sequences to search, using BLASTn, all contigs assembled from human gut metagenomes available in cMD. Samples were considered to be positive for ACBP if any of their contigs had a significant hit (>70% identity, alignment length of >100 nt, and an E value of <1 x 1025).
+We aligned raw reads from these gut metagenomes to the set of retrieved ACBP/DBI sequences using bowtie2 (61). Resulting BAM files were filtered to keep only alignments with more than 50 nt of matching positions and were used to calculate the breadth of coverage of each sequence using Samtools (62) and VCF utils (63). Samples whose metagenome presented ACBP/DBI sequences with breadth of >80% were considered positive.
+Correlations between BMI and species’ abundances. We used the PREDICT 1 data set comprising 1,001 healthy individuals from the UK and 97 from the US (38), as well as publicly available data sets collected in cMD and profiled with version 3 of MetaPhlAn (64, 65). Of the 57 data sets available, we selected those that had samples with the following characteristics: (i) gut samples collected from healthy adult individuals at first collection (“days_from_first_collection” = 0 or not available [NA]) and (ii) samples with age, sex, and BMI data available. Outlier samples were removed if their BMI value was outside 3.5 and 7.5 times the interquartile range (IQR) of samples meeting the above criteria (IQR = 5.03). Only data sets with at least 50 samples were considered: Asnicar_2020_UK (953 samples out of 1,001), Asnicar_2020_US (92 samples out of 97) (38), CosteaPI_2017 (82 samples out of 279) (66), DhakanDB_2019 (80 samples out of 110) (67), HansenLBS_2018 (57 samples out of 208) (68), JieZ_2017 (140 samples out of 385) (39), SchirmerM_2016 (437 samples out of 471) (69), and ZellerG_2014 (58 samples out of 199) (70).
+For each species, Spearman's correlations with BMI were computed using the pcor.test function from the ppcor R package controlling for age and sex. Resulting correlations were used as input to the metacor function from the meta R package using Fisher's Z transformation of correlations and the Paule-Mandel estimator of between-study variance in the random effects model. P values from the random-effects model were corrected using false discovery rate (FDR) through the Benjamini-Hochberg procedure, which are reported in the figure as q values. We report q values of ACBP/DBI-carrying taxa found in these data sets, as well as those of species with FDR of <0.1.
+SUPPLEMENTAL MATERIAL
+Supplemental material is available online only.
+SUPPLEMENTAL FILE1,PDF file, 2.7 MB.
+SUPPLEMENTAL FILE 2, XLSX file, 0.1 MB.
+SUPPLEMENTAL FILE 3, XLSX file, 0.01 MB.
+SUPPLEMENTAL FILE 4, XLSX file, 0.01 MB.
+ACKNOWLEDGMENTS
+This work was supported by the European H2020 program (ONCOBIOME-825410 project) to A.M.T. and N.S. and by the European Research Council (MetaPG-716575 ERC-STG project), the MIUR (“Futuro in Ricerca” RBFR13EWWI_001), the European H2020 program (MASTER-818368 project), a LEO Pharma research award to N.S., the National Cancer Institute of the National Institutes of Health (1U01CA230551 to N.S.), and the Premio Internazionale Lombardia e Ricerca 2019 to G.K. and N.S. G.K. is supported by the Ligue contre le Cancer (équipe labelisée), Agence National de la Recherche (ANR) -Projets blancs, Association pour la recherche sur le cancer (ARC), Association “Ruban
+June2021 Volume87 Issue12 e00471-21
+Rose”, Cancéropôle Ile-de-France, Fondation pour la Recherche Médicale (FRM), a donation by Elior, Gustave Roussy Odyssea, the European Union Horizon 2020 Project Oncobiome, Fondation Carrefour, High-end Foreign Expert Program in China (GDW20171100085), Institut National du Cancer (INCa), Inserm (HTE), Institut Universitaire de France, LeDucq Foundation, the LabEx Immuno-Oncology (ANR-18-IDEX-0001), the RHU Torino Lumière, the Seerave Foundation, the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE), and the SIRIC Cancer Research and Personalized Medicine (CARPEM). This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001.
+We declare that we have no competing interests.
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diff --git a/Global-incidence-of-suicide-among-Indigenous-peoples-A-systematic-reviewBMC-Medicine.txt b/Global-incidence-of-suicide-among-Indigenous-peoples-A-systematic-reviewBMC-Medicine.txt
new file mode 100644
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@@ -0,0 +1,62 @@
+Background
+Globally, suicide accounts for approximately 800,000 deaths annually [1] and is the second leading cause of mortality among adolescents [2]. According to the World Health Organization (WHO), low- and middle-income countries
+* Correspondence: nathaniel.pollock@med.mun.ca
+’Division of Community Health and Humanities, Faculty of Medicine, Memorial university, Prince Philip Drive, St. John’s, Newfoundland and Labrador A1B 3V6, Canada
+2Labrador Institute of Memorial university, P.O. Box 490, Stn. B, 219 Hamilton River Road, Happy Valley-Goose Bay, 'Newfoundland and Labrador A0P 1E0, Canada
+Full list of author information is available at the end of the article
+and high-income countries have similar annual age-standardized suicide rates at 11.2 and 12.7 per 100,000 respectively; however, low- and middle-income countries account for 75% of suicide deaths worldwide [1]. National suicide rates range from less than one to 44 per 100,000 population, though there is often a disproportionate burden among specific subgroups within countries, such as Indigenous peoples [1]. Studies from high-income countries including Australia [3, 4], New Zealand [5], the USA [6, 7], Canada [8-10], and other Arctic nations [11-14] consistently find elevated suicide rates among Indigenous
+populations, with substantial rate disparities compared to non-Indigenous populations. Several studies have shown that regional suicide rates vary greatly among Indigenous peoples, and that some Indigenous populations have low rates or no incidence of suicide [15, 16].
+Indigenous peoples and nations differ vastly in culture, language, political autonomy, and relative wealth, yet many face similar social disadvantages and health disparities as a result of colonization [17-19]. Colonial governments have used discriminatory legislation and policies to deny rights and economic opportunities, and have attempted to acculturate Indigenous people into non-Indi-genous societies [17, 19, 20]. Structural violence meted out by governments has taken many forms including dispossessing Indigenous peoples from traditional and sovereign lands, forced settlement and relocation, and outlawing cultural practices and languages [17-21]. This violence is grossly evident in the twentieth century assimi-lationist policies of former British colonies such as Canada and Australia. Indigenous children were systematically removed from their communities and placed in non-Indigenous institutions or families with the policy mandate to “weaken family ties and cultural linkages, and to indoctrinate children into a new culture” ([20], p. v). The contemporary legacy of this type of social engineering manifests in differential exposures to health threats and in inequitable outcomes that show up across generations [20, 22]. Intergenerational trauma from institutionalized abuse and racism experienced by Indigenous peoples has been linked to persistent social and mental health problems in some communities [19, 20, 23].
+Although evidence has shown a disproportionate burden of suicide among Indigenous populations in national and regional studies, a global and systematic investigation of this topic has not been undertaken to date. Previous reviews of suicide epidemiology among Indigenous populations have tended to be less comprehensive or not systematic, and have often focused on subpopulations such as youth [24, 25], high-income countries [9, 26], or regions such as Oceania [27] or the Arctic [24, 28]. Given that approximately 80% of the world’s more than 300 million Indigenous people live in Asia, Latin America, and Africa [17, 18], a comprehensive study of global suicide rates that includes low- and middle-income countries is needed. Our aim was to examine the published findings on the incidence of suicide among Indigenous peoples worldwide, and to compare rates with non-Indigenous or general populations to assess relative disparities.
+Methods
+Search strategy
+We systematically reviewed findings on the incidence of suicide in Indigenous populations worldwide. We searched
+for studies that analyzed population-based data on suicide deaths, and included papers that reported crude or standardized mortality rates. Health science librarians were consulted about the design of the search strategy with the aim to capture all peer-reviewed literature. The search combined terms related to three concept areas: population (Indigenous), outcome (suicide mortality rates), and study design (observational). Term selection was based on previous systematic reviews and combined key terms adapted for each database and also Medical Subject Headings (MeSH) as applicable. The study protocol is available in Additional file 1: Supplement 1. Additional details about the methods are reported in Additional file 1: Supplement 2, including citations for previous reviews, a list of included terms, a description of the procedures used for study selection and eligibility criteria, and a complete list of databases and hand-searched review articles.
+One author (NJP) performed online text word and MeSH searches for articles indexed in PubMed, MEDLINE, Embase, Cumulative Index of Nursing and Allied Health (CINAHL), PsycINFO, Latin American and Caribbean Health Sciences Literature (LILACS), and Scientific Electronic Library Online (SCiELO). A second author (KN) replicated the search in PubMed and obtained the same number of articles as the first author. We searched for studies in any language, indexed from database inception until June 1, 2017. We conducted a secondary search with a comprehensive list of terms for specific tribal groups, nations, and populations identified in previous reviews. As no additional studies were identified, this approach validated the primary search. We also searched the WHO’s regional medical literature indexes, Indigenous-specific online research portals, and journals focused on Indigenous health. We hand-searched the reference lists of included articles and previous reviews to identify other eligible studies. Additional file 1: Supplement 2 includes a list of all databases and hand-searched sources.
+One author (NJP) imported the results into a reference management program and removed duplicates. Two authors (NJP and KN) read the abstracts and screened in papers if they (1) reported a population-based crude and/or standardized suicide rate, or count and population data; (2) reported a rate for an Indigenous population; and (3) used an observational design. We excluded articles that did not include an Indigenous population, focused only on a specific age, gender, clinical subgroup, or deaths from a specific cause (for example, firearms), or were not peer-reviewed. Articles were also excluded if they were iterations, program evaluations or experimental studies, not primary studies, from the gray literature, or used identical data sources as prior studies.
+Although there is no international consensus on the definition of Indigenous, we used the United Nation’s working definition to assess study population eligibility
+[17, 18]. The UN's conceptualization of Indigenous involves self and group identification; a special attachment to and use of traditional land, distinct knowledge, language, and culture; distinct social, economic, and political systems; common ancestry with original territorial occupants; participation in maintenance and reproduction of distinct ethnic identity; and a non-dominant socio-political status [17, 18]. A paper was eligible based on this criterion if it reported an outcome for an Indigenous population, tribe, community, nation, or group, including papers that used the geographic proxy method. For the proxy method, census data is used to detect areas where Indigenous people are a majority population [29, 30]. We considered an area to be a proxy identifier if 80% or more of the population self-identified as Indigenous.
+Two authors reviewed the full text of each paper and assessed eligibility based on inclusion criteria. At this stage, we excluded papers that did not report rates for the majority of the population (aged 15-65 years), did not conduct the primary data analysis, or provided rates in figures only and did not report count and population data. If two eligible articles used the same data source with a period of overlap, we included the article with the longer study period. During screening, full text review, and data extraction, we resolved disagreements through discussion or consultation with a third author. Translators helped assess non-English language articles and assisted with data extraction for four included studies. The following data was independently extracted by two authors (AL and NJP), then compared: citation, study design, country and region/community, Indigenous population, data source, standard population, number of suicide deaths, population count, crude and standardized suicide rates (overall and by gender and age group), comparative rates for a non-In-digenous or general population, and the measure of relative effect (incidence rate ratio).
+Data analysis
+We summarized all included studies in a table and reported counts, population, crude and standardized suicide mortality rates, and rate ratios. We calculated crude suicide mortality incidence rates for articles that reported only count and population data, and we estimated rate ratios when not otherwise reported by dividing the Indigenous population rate by the comparison population rate. To identify global patterns, we presented rates and rate ratios in tables and figures grouped by WHO region, country, population, and gender; we did not pool the data due to heterogeneity. We also reported on trends in suicide mortality over time and by age group; reported time trends reflect results from included studies, not pooled and recalculated rates. We modified the Newcastle-Ottawa Scale and used it to
+assess the quality of included articles. Additional file 1: Supplement 2 includes a description of the quality assessment procedures and scoring, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist is provided in Additional file 1: Supplement 4 [31].
+Results
+The search identified 13,736 papers; after removing duplicates, screening abstracts, and full text review, we included 99 in our analysis (Fig. 1). Included studies examined suicide rates in Indigenous populations in 30 countries and territories across six decades (Table 1), though the majority focused on those in high-income countries such as American Indian and Alaska Natives in the USA (n = 35) and Inuit and First Nations in Canada (n = 14). Studies in low- and middle-income countries (n = 22) were mostly from Brazil (n = 4), China and Taiwan (n = 6), and Fiji (n = 5). Coverage included circumpolar Indigenous peoples such as Sami (n = 3) and Nenets (n = 1), and populations from the Western Pacific region including Aboriginal and Torres Strait Islanders in Australia (n = 6) and Maori and other Pacific peoples (n = 16). Four studies were transnational comparisons [32-35], though numerous papers included multiple Indigenous groups within a single country. Studies were mostly of moderate quality (mean 2.79 on a 4-point scale) based on our assessment of study characteristics, as reported in Additional file 1: Supplement 3, Tables S1 and S2.
+Incidence
+We extracted population-based suicide mortality rates from 93 papers (Table 2) and included gender-specific incidence data from six additional studies [5, 10, 36-39]. Overall, suicide rates among Indigenous peoples varied at all levels of aggregation in both high-income and low-and middle-income countries, and spanned from zero to 187.5 deaths per 100,000 person-years (PY; Table 2). In high-income countries, national and provincial suicide rates among Indigenous peoples ranged from 1.7 per 100,000 in Brunei Darussalam [40] to 50.4 per 100,000 among Aboriginal and Torres Strait Islanders in Northern Territory, Australia [41]. Rates in high-income countries were highest among rural Indigenous populations and in sparsely populated regions such as the Arctic. Among low- and middle-income countries, Palawan communities in the Philippines had the highest crude suicide rates (134 per 100,000) [42], while Indigenous peoples in Malaysia [43] and some Pacific small island states such as Fiji had crude rates under 7 per 100,000 population. The number of suicide deaths used for rate calculations ranged from zero to 4219 (Table 2).
+Measure of relative effect
+Incidence rate ratios were reported or calculated for 102 Indigenous populations in 69 studies. The results showed rate disparities in the majority of studies (Fig. 2), though 22 reported rate ratios below one. The rate ratios ranged from 0.04 in China [44] to more than 20 in Brazil [45] and Canada [30] (Additional file 1: Supplement 3, Table S4). Most Indigenous populations had higher suicide rates than comparison groups; disparities were widest in studies with small populations. One study reported a suicide rate of zero for an urban Indigenous population in Brazil compared the general population rate of 4.8 per 100,000 in the same city [46].
+Time trends
+Suicide rates appeared to increase over time, especially in the latter half of the twentieth century, though reports were limited. Among studies with reported time series (n = 24), most (83%, n = 20) had fewer than 10 data points and covered an average of
+19 years. A study in Greenland was the exception; it reported longitudinal data that showed a steady suicide rate increase among Inuit that began with the near absence of suicide in the early part of the twentieth century (2.4 per 100,000) and climbed exponentially to a rate of 110.4 per 100,000 in 2010-2011; the average number of suicides per year changed from less than one to 55 during this period [12]. Aboriginal and Torres Strait Islanders in Northern Territory, Australia experienced similar rate accelerations (6.1 per 100,000 in 1981 to 50.4 per 100,000 in 2002) [41], while incidence among Alaska Natives was relatively stable, though high, from the 1980s to the early 2000s [47, 48]. Indigenous peoples in the Micronesian islands experienced a sixfold increase in suicide rates between the 1960s and the late 1980s (from 4.3 to 25.8 per 100,000) [35], and one study reported slight rate declines for both Maori and non-Maori in New Zealand from 1996 to 2002 [5]. Annual rates tended to fluctuate in studies with small populations.
+Age differences
+Age-specific rates were reported in 39 studies; various age categories were used, and rates were often only available for select strata. Youth less than 30 years old, especially those aged 15-24 years old, had the highest suicide rates of any age group in 89% of studies (n = 34) that reported age-specific rates. In the larger studies (> 100 total suicides) with age-specific incidence, youth suicide rates ranged from 15.9 to 108 per 100,000 population. Very few studies reported deaths or rate estimates for adults more than 60 years old.
+Gender differences
+Men accounted for the majority of suicide deaths in all but four studies; only two of these four studies reported a greater number of suicide deaths among women [49, 50]. Studies with gender-specific crude and age-standardized rates (n = 35) ranged from zero to 75.5 per 100,000 among
+Indigenous women (Additional file 1: Supplement 3, Table S3). Suicide rates were higher among Indigenous men compared to Indigenous women, though rate differences were marginal among some Pacific populations [33, 51]. Suicide rates were also higher among Indigenous men than for men in comparison populations in all countries except Israel and Fiji. Outside of the relatively low rates among Indigenous men in these countries, estimates ranged from 19.5 among Sami [13] to 248.7 per 100,000 among Inuit [30].
+Discussion
+This study showed that the rate of suicide is elevated in many Indigenous populations globally, but that rate variation is common (Fig. 1). The evidence of substantial rate disparities for Indigenous peoples in Australia, Brazil, Taiwan, and circumpolar countries is notable. Equally important, we found that disparities were marginal or non-existent in some US territories and Pacific nations; we also identified 21 studies in which Indigenous populations had lower suicide rates than non-Indigenous populations. These results demonstrate that the high incidence of suicide and large rate disparities are not universal among Indigenous peoples. This confirms and extends findings from prior research that reported variation in localized estimates in the USA [52] and Canada [16].
+Worldwide variation in the incidence of suicide among Indigenous peoples has complex and place-based social origins. These origins are traceable to regional differences in the impact of colonization, which is widely recognized as a major determinant of Indigenous health [17-19, 53]. Colonial governments have historically threatened the well-being of Indigenous peoples through chronic and often state-sanctioned discrimination and human rights abuses, and continue to do so in many countries [18, 20, 23]. Until 2016, several high-income countries had not ratified the United Nations Declaration on the Rights of Indigenous Peoples, and therefore legislative reforms to recognize Indigenous self-determination lagged. As a result, many Indigenous nations have yet to attain political sovereignty over lands and natural resources, education, or health care.
+Globally, Indigenous peoples commonly experience social and economic marginalization and, as a consequence, some of the most disparate health outcomes [17, 18, 53]. In this context, the extent and the persistence of high suicide rates and rate disparities reveal a striking deficit in the global effort to prevent suicide and achieve social and health equity. This is further challenged by overlapping barriers to accessing health care and community supports, especially in rural areas and low- and middle-income countries. Barriers include fragmented care networks, lack of access to services due to geography, discriminatory attitudes from health care providers, and services that are not culturally safe or provided in the necessary language [18, 54, 55]. In resource-
+Pollock et al. BMC Medicine (2018) 16:145
+Page 12 of 17
+limited and conflict settings in particular, mental health services are inadequate in scope and quality, chronically under-funded, and in some places non-existent [18, 54].
+Challenges in accessing mental health care are compounded by the limited relevance and generalizability of some “best practice” interventions in Indigenous contexts [56, 57]. Recent clinical trials with gatekeeper training [57], hospital-based interventions [58], and mobile self-help applications [59] reported adverse and limited effects on suicide-related outcomes for Indigenous peoples. Overall, intervention studies with Indigenous populations are rare, and community-based programs are often not evaluated or have weak study designs [60-63]. These challenges point to a need to expand efforts to generate Indigenous-specific evidence [23, 56, 60]. Indeed, many communities have developed contextualized and complex approaches to suicide prevention that respond to local priorities. There is emergent evidence that such programs increase protective factors and reduce suicide-related behavior [63-65]. However, knowledge about programs’ effectiveness, implementation, and capacity to scale up is limited, and many programs are not sustainably funded [56, 60-62].
+Indigenous organizations and governments in New Zealand, Canada, and several Arctic states have moved beyond programmatic approaches and designed Indigenous-specific suicide prevention strategies [23, 55, 66]. These strategies integrate evidence-based public health and clinical interventions with Indigenous knowledge about the consequences of colonization, institutionalized violence and racism, and the value of culture. They also recognize that social conditions have an important role in shaping mental health, especially during the early years of life, and that improving these conditions can have a positive impact on population mental health and suicide-related outcomes. The path to lowering the incidence of suicide among Indigenous peoples and achieving health equity requires broader social transformation both within states and globally. This transformation must be collaborative, with Indigenous organizations and communities as leaders and rights-holders in knowledge production and decisionmaking [23, 29, 53, 56, 66, 67]. Public health systems can also enhance capacity for Indigenous suicide prevention with efforts to increase the visibility of community-level differences in health status and by accurately tracking changes in suicide mortality over time.
+Limitations
+This study is a comprehensive synthesis of the published evidence on the global epidemiology of suicide among Indigenous peoples. Although it is the first review of this scale, our study has several important limitations. First, included studies varied their methods of identifying Indigenous populations. Self-identification is the gold
+standard in administrative and registry data [67]. However, this is a recent benchmark. Its uptake has varied internationally, and some countries do not identify Indigenous populations in health data at all [53, 67]. The majority of included studies relied on linkages with census or registry data, geographic proxies, or observer-determined assessments. These procedures are useful approximations, but they use varied definitions and tend to under-count Indigenous people, especially groups without legal recognition [29, 53, 67]. This can lead to ascertainment bias and underestimation of inequities [53, 67]. A second and related limitation is the under-representation of studies from low- and middle-income countries. In our review, we may have missed studies, particularly from the Global South, due to the conceptualization of Indigenous and the search terms used, which do not necessarily apply in all contexts. We attempted to limit this bias by searching databases focused on low- and middle-income countries and including non-English language papers.
+The third limitation was that it was difficult to compare suicide rates between countries. Included studies were heterogeneous in population size, number of cases, aggregation, data source and outcome assessment, method of identifying Indigenous peoples, and coverage period. Many papers provided crude estimates only and did not report numerator and denominator data by age group, gender, or ethnicity. For studies with adjusted rates, different standard populations were used, and confidence intervals were rarely reported. Differences in analytic and reporting practices made it challenging to directly and reliably compare suicide rates across studies. To address this, we examined rate ratios to assess relative differences between Indigenous and non-Indigenous/general populations. This allowed us to estimate rate disparities, which were compared globally.
+The fourth limitation was that studies reporting low suicide rates may be under-represented, which is a potential publication bias. It is unclear whether the lack of low incidence populations is related to the common finding of elevated rates of suicide among Indigenous peoples compared to non-Indigenous populations or, as we suspect is more likely, to the possibility that suicide rates are rarely studied when they are low. Additional low incidence reports may exist outside of peer-reviewed studies; however, these were not identified because we did not search the gray literature. The primary reason for excluding gray literature reports was the extensive volume of sources with variable quality and also the risk of over-including data from high-income nations where public reporting of mortality data is common and vital statistics infrastructure is of high quality. Nonetheless, we identified 23 papers that reported rate parity or had a rate ratio below one, but these tended to use older data. A related problem is that case studies tended to examine suicide clusters in small populations [42, 68]. The
+Pollock et al. BMC Medicine (2018) 16:145
+Page 14 of 17
+(See figure on previous page.)
+Fig. 2 Globalsuicide mortality incidence rate ratios among Indigenous and comparison populations. a Western Pacific Region (Oceania and Australia). b Western Pacific Region (East Asia). c European Region. d Region of the Americas (Canada and Brazil). e Region of the Americas (USA, National). f Region of the Americas (USA, Alaska). g Region of the Americas (Lower 48 states and Hawaii). NWT Northwest Territories, IHSA Indian Health Services Area. The dotted line indicates a rate ratio of one (RR = 1). This means that there is rate parity (no difference) between the incidence of suicide in Indigenous and comparative populations. Rate ratios to the left of the dotted line (RR< 1) indicate that rates are comparatively higher in the non-Indigenous population. Conversely, rate ratios to the right of the dotted line (RR> 1) show that the Indigenous population has a comparatively higher rate. Citations for each study are reported in Additional file 1: Supplement 3, Table S4
+advantage of using localized data is the ability to contextualize a complex health issue. The disadvantage is that the potential to compare health status between multiple groups, across regions, and over time is reduced.
+Strengthening surveillance in Indigenous suicide prevention
+Our results substantiate previous work [16, 52] to demonstrate that elevated suicide rates are not universal among Indigenous people and debunk notions that Indi-geneity increases risk for suicide. Our results also point to several gaps in knowledge about the epidemiology of suicide in Indigenous populations globally. The lack of published suicide data on Indigenous populations in low- and middle-income countries is a glaring absence. Previous studies noted a scarcity of Indigenous-specific data in the Global South overall [18, 53]. Poor infrastructure for death registration is a key limitation [1]. In the context of suicide, this is especially problematic, because countries in Asia, Africa, and Latin and South America are the homelands for the majority of the world’s Indigenous populations [18] and, at a national level, account for more than three quarters of all suicide deaths [1]. Suicide data in high-income countries tends to be of better quality than that in low- and middle-income countries; however, many governments do not include Indigenous or other ethnic identifiers in administrative health data, and do not routinely link census or Indigenous registries with national health datasets such as vital statistics. In Canada for example, the federal government does not know how many Indigenous people die by suicide in a given year. Globally, there is a critical need to strengthen capacity for surveillance in Indigenous suicide prevention.
+National governments can take several steps to improve suicide surveillance in Indigenous populations. Actions should include efforts to enhance suicide data quality and standardized classification by improving vital registration infrastructure, especially in low- and middle-income countries, and integrating mortality data with monitoring of suicide attempts [1]. Countries should adopt an equitybased approach to data collection that includes Indigenous identifiers derived from self-reported sources and linked to registries or census data to address gaps in identification, and align Indigenous identification procedures
+with recommendations from the International Group for Indigenous Health Measurement, adapted for each national context [1, 53, 56, 67, 69]. Building inclusive, Indigenous-centered models of data governance in suicide prevention will be a critical element of strengthened surveillance. To achieve this will require national statistical agencies to not only consult Indigenous communities, organizations, and leaders about priorities, but to respect Indigenous rights to determine the parameters of data ownership, custodianship, access, and use [29, 32, 67].
+Future research and global suicide surveillance efforts will be further strengthened with longitudinal and up-to-date national and state-level datasets that allow disaggregation and comparisons of outcomes in small areas and subpopulations by ethnicity [1, 17, 53, 56]. Overall, these actions will help maintain robust public health surveillance systems in order to monitor health status, increase knowledge about the social determinants of suicide, target interventions, and evaluate strategies aimed at reducing the incidence of suicide among Indigenous peoples worldwide [1, 56]. Increasing the visibility of populations that bear the greatest burden from suicide can help drive efforts to achieve the WHO and Sustainable Development Goals of reducing national suicide rates by up to 30% [1, 69].
+Conclusions
+Suicide among Indigenous peoples is not a universal or intractable problem. Our study showed substantial global rate variation, with striking disparities in some countries. Efforts to understand these differences and to continue to build the knowledge base for effective interventions will require sustained political and financial investments in Indigenous communities, health systems, and governments. Across sectors and countries, Indigenous peoples have called for suicide prevention strategies that are community-led, strengths-based, and trauma-informed, and that redress intersecting forms of structural discrimination, social inequity, and their downstream consequences. Global efforts to reduce suicide rates among Indigenous peoples must include actions focused on communities that experience the most profound disparities, while also seeking to promote population mental health and improve health equity.
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diff --git a/Help-seeking-behavior-for-problematic-substance-uses-in-northWest-EthiopiaSubstance-Abuse-Treatment-Prevention-and-Policy.txt b/Help-seeking-behavior-for-problematic-substance-uses-in-northWest-EthiopiaSubstance-Abuse-Treatment-Prevention-and-Policy.txt
new file mode 100644
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@@ -0,0 +1,47 @@
+Background
+Mental, neurological and substance use disorders are common. Approximately one in four families has at least one member with a mental disorder. It also contributed for 14% of the global burden of disease. Globally, about 190 million drug users were reported and of them 40 million serious drug related illnesses or injuries were identified each year [1].
+Alcohol alone contributes for 7.6 and 4% of deaths females and males respectively. It also contributes to more than 200 alcohol related diseases [2, 3]. However, 76 to 85% of people with mental, neurological and substance use disorders in low- and middle-income countries did not receive treatment for their behavioral problems [4-6].
+Help-seeking is an adaptive coping process in which the individual attempts to obtain external assistance to deal with a mental health concern formally or informally. Formal help-seeking is assistance from professionals whereas informal help-seeking is assistance from informal social networks, such as friends and families [7].
+Based on different circumstance help-seeking behavior for behavioral disturbance is varied a across regions and populations. Among people with mental disorders including, alcohol use disorders, only 31.7% of them had sought help. and 15.7% of sought help was from mental health providers, 8.4% from general practitioners, and 7.6% from religious/ spiritual advisors or other healers [8]. Professional help seeking behaviors among problematic drug user lesbian and bisexual women was only 41.5% [9]. Among people with alcohol use disorders 7% of them seek help from any mental health professional; 5.8% from medical health professional; 7.2% from a
+social support setting and 4.5% from any religious or spiritual advisor/healer [8]. Among patients who visited traditional healers for their mental health problems, 41% was visited to seek help for substance use disorders [10].
+There are factors which serve as barriers to seek help for substance related behavioral disturbances [11]. From WHO report factors like, personal motivation, perception of need, lack of health insurance, internalized gender norms, and perceptions of social supports as positive, and others influence the help-seeking behaviour of adolescents’ [12]. Factors related to enrolling in problematic substance use treatment are multiple and researchers had blamed factors like lack of availability, transportation, and insurance [13]. Even though, men report higher levels of problematic substance use than women and are more likely to have psychosocial problems, but are less likely to seek help [14]. Older adults’ positive attitudes and treatment beliefs [15]; negative beliefs about the quality and effectiveness of treatment [16]; reluctance to give up the substance and to admit the need for help, and inability to afford treatment [17] were the determinant factors to use mental health services.
+In Ethiopia, substance use in the community [18] and in university students has many social and academic related problems [19-21]. Despite the high prevalence of substance related problems, most people do not access professional help seeking. For instance, WHO reported that universally alcohol use disorder has a widest treatment gap (78.1%). Even though problematic substance use is remaining a major public health concern, barriers to treatment have not been well searched in developing world [22]. In Ethiopia, magnitude of problematic substance use reaches up to 22.8% in the general population [23] and health care service has been given with different modern health care settings, from holy water and traditional medicine. In Ethiopia, people with behavioral disturbance always seek traditional or/and modern health care as culturally acceptable manner. Currently, there is a growing awareness about availability of treatment options for substance misuse in Ethiopia. In one study, among lifetime khat chewer high school students, 70% of them want to stop their habit of chewing [24]. However, as per our best knowledge there is no publishing data to show prevalence and factors that associated with help seeking behaviour among people with problematic substance use in Ethiopia. Therefore, the purpose of this study was to see the level of help seeking behaviour and its contributing factor among people with problematic substance uses in urban residents.
+Methods
+Study settings
+We have conducted a community based cross-sectional study in Bahir Dar town Northwest Ethiopia which is 565 k meters from Addis Ababa. The town has a total of
+180,174 populations in 6 sub-cities; of these 93,014 are females. Currently, there are four hospitals, ten health centers and a number of other private health institutions (clinics, pharmacies and drug shops). In this town alcohol and khat (a green stimulant leaf) use are common among adolescents [25, 26]. Psychiatric service is delivered through formal settings (private and governmental hospitals, clinics) and informal settings (holy water and traditional medicines).
+Participants
+The study was targeting on the adult residents in Bahir Dar town. Participant’s age 18 years and above were included and those participants who couldn’t communicate well for data collectors were not included. Participants who were positively screened for problematic substance use were considered for further interview.
+Sampling, selection and data collection
+The sample size was determined with a single population formula by using proportion of help seeking for problematic substance uses among residents 41% [10], 4% margin of error, and 95% confidence interval. From total samples from the calculation 9 was withdrawn, 13 were unable to complete the interview and 11 refuse to continued. Finally, 548 participants were complete the interview. Since the study was two stage; we had screened a total of 2400 individuals for problematic substance use by using CAGE AID screening, until we had reach up 548 participants with problematic substance use (Khat, alcohol, tobacco and cannabis) and then assessed for help seeking. The CAGE AID screening was pre-tested for our study and has good internal consistency (@ = 0.78). Multistage sampling technique was used to select the sub-cities (three) from the total of six and to select respective administrative kebeles (the smallest administrative unit) (four) from total of seventeen. The households in the administrative kebeles were selected by simple random, and only one adult member of the house hold was selected by lottery method for the interview towards for problematic substance uses. Data was collected by degree holder nurses with interview by semi-structured questionnaire which was translated into Amharic version (local working language).
+Measurements
+CAGE AID (Cut down, Annoyed, Guilty, and Eyeopener) questionnaire was used to screen for problematic substance uses. Scoring of two or greater positive answers from the four questions to social drugs (khat, alcohol, tobacco and cannabis) was considered as problematic substance uses [27]. CAGE-AID is able to address for other problematic drug uses (khat, tobacco and cannabis) in addition to screen problematic alcohol uses.
+Each has yes and no response which is valued one point (1) and zero (0).
+Help seeking was assessed if individuals sought assistance for their substance related problems from delineated formal and informal source [7]. Help seeking behavior for problematic substance use was assessed by using modified General Help Seeking Questionnaire (GHSQ). It assesses whether help was sought or not and the potential sources of that help for the last 12 months. Participants were asked whether they did seek help or not for their substance use problems; and if they did seek help, they were interviewed for the source of help. GHSQ has good validity, reliability, and reliability (chronbach’s alpha = 0.83) [28] and we did a pre-test for our study and has internal consistency (chronbach’s alpha = 0.80). Social support assessed by using oslo-3 social support scale that consist three items [29]. Common mental disorder was considered when adults who score 11 or more symptoms of the 20 self-reporting questionnaire in the last one month. The self- reporting questionnaire developed to screening the presence of common mental disorders among community participants and developed by WHO [30]. Income was assessed by using relative income by leveling their own income as less than others; similar to others and better than others. Education was categorized in uneducated (who were unable to read and write); and educated (which were able to read and write). Clinical variables like, co morbid diagnosis medical illnesses were assessed by asking the participants, if they had a diagnosis of medical illnesses before the survey.
+Analysis
+After the data was checked for completeness and consistency, it was coded and entered in the Epi-Data 3.1 software. Data was exported to SPSS for analysis and p-value less than 0.05 was declaration of a statistically significant. Bivariate and multiple logistic regression analyses had done to identify determinants of help seeking behavior for substance use related problems.
+Ethical clearance
+Ethical clearance was obtained from Ethical Review committee of college of medicine and health sciences, Bahir Dar University. Formal permission letter was taken from administrative of the city and written consent was taken from the participants. All participants who were screened with a problematic substance use were referred to clinic for better screening to mental and medical illnesses. Those who were positive for screening of common mental disorders were link with psychiatric clinics.
+Results
+From the total of 548, 422 (77%) of them were males. The median age of participants was 27 years and
+substantial number of them were living alone, 241 (44%) (Table 1). One hundred sixty-one (29.4%) had substance user family members; two hundred forty (37.2%) had substance user friends and three hundred nineteen (58.2%) had reported history of substance use among their grandparents. Two hundred three (37%) participants with a problematic substance use had found with poor social support and one hundred forty-nine (27.2%) had common mental disorders (Table 2).
+Magnitude of help seeking behavior
+Only one hundred and sixty-eight (30.7%) with 95% CI (27, 35%) sought help for their substance related behavioral disturbance among total five hundred forty-eight participants with problematic substance use. Most of them were sought help from the informal source. Among these 124 (22.6%) sought help from their love;
+Table 1 Socio-demographic characteristics and help seeking behavior among participants with problematic substance use (n = 548)
+Multiple logistic regression analysis
+During bivariate analysis advanced age, having substance user friends, comorbid medical illnesses, had grandparents with history of substance use, and positively screened for common mental disorders were candidates for multiple logistic analysis. However, after multiple logistic regression of help seeking behavior in relation to all independent variables; advanced age, comorbid medical illnesses, had grand-parents with history of substance use, and positively screened for common mental disorders were found to be statistically significant (Table 3).
+Discussion
+In the current study 30.7% of the participants sought help for their substance related problems. The result is similar with the global report and a study done in Singapore 31.7% [8], but less than Los Angeles study 41.5% [9] and South African study 41% [10].
+Formal help seeking behavior in this study (16.4% from mental health professionals) found similar with Singapore study (15.7% from mental health providers) [8]. However, help seeking from general medical practitioner found higher in this study (16.2%) than Singapore (8.4%). Even though many psycho-social determinants accountable for the discrepancy of magnitude of help seeking behavior, generally modern help seeking behavior looks low in developing countries. However, a high formal help-seeking behavior has been reported in this
+one hundred and ten (20.1%) from their friends; one hundred and three (18.8%) from their families; eighty-four (15.3%) from their relatives; and one hundred (18.2%) from religious institutes. From the formal help seekers ninety (16.4%) sought help from mental health professionals; and eighty-nine (16.2%) from general medical practitioner.
+Help-seeking for specific substance
+The proportion of help-seeking behavior across different substance has a certain variation. From the total participants who has a problematic alcohol use 134/391 (34.3%) has sought help, from a problematic cigarette smoking 109/277 (39.3%) has sought help, from a problematic khat chewing 136/432 (31.5%) has sought help, and from a problematic cannabis user 63/154 (40.9%) has been sought help for their behavioral problems.
+In terms on their mental health status among help seekers, 50.6% had common mental disorders, 42.3% had poor social support and 9.5% had strong social support. Among help seekers gender difference was examined and 32% male and 26.2% females were sought help.
+study in a developing country. This may be due to source of study population residency area was urban, and those participants may have awareness about the availability of modern health care through different medias.
+Participants’ age above 35 years old was negatively associated with help seeking behavior [AOR = .47 95% CI (.25, .90)]. This contradicted with a previous study that stated older adults were seeking more mental health services than youngers [15]. Elder adults may not have a good family support to motivate them to get assistance for their behavioral disturbance. Those elder adults may not consider others’ advice or recommendations like that of younger adults to seek help due to their experience or adaptation of their behavioral problems. Participants who screened positively for common mental disorders found four times more help seeker than those who did not have common mental disorders [AOR = 4.12, 95% CI (2.7, 6.3)]. This may be due to the co-morbid effect of common mental disorders with substance related behavioral disturbance on their daily activities. The dual effect on their mental and behavioral status has become worsen and may push them to seek help. This finding agrees with studies done in America [31, 32]. Comorbid diagnosis medical illness was identified as the determinant factor for help seeking behavior for problematic substance uses. Participants with problematic substance use and had comor-bid medical illnesses were three times more likely to sought help than participants who had not comorbid medical illnesses [AOR = 3.0, 95% CI (1.7, 5.3)]. This comorbidity may result perceived fear of death or other complications among participants and/or family members, which gives an alarm to sought help from others. This finding agrees with previous study in America [33].
+History of substance use in grand-families had a significant association with help seeking behavior among participants with problematic substance uses. Help seeking behavior found two times more common among participants who had grand-families with history of substance use than those who hadn’t [AOR = 2.18, 95% CI (1.4, 3.4)]. The historical impact of substance related behavioral disturbance in the family member (in terms of morbidity and mortality) may has been perceived as harmful by the family members and this might motivate individuals with a problematic substance use to sought help to prevent further disabilities in the family. The impact of the substance use on their grand-family’s health may be learned in the family members and due to the uses may alert to seek help for their problem.
+Strengths and limitations
+The sample size was large enough to assess all associated factors. The study had also its own limitation which must be considered in generalization. The major limitation of the study is its lack of representation of rural
+population. So, we recommended studies which includes rural settings.
+Conclusion and policy implication
+Help seeking behavior for problematic substance use was infrequent. Help-seeking behavior was significantly associated with age, symptoms of common mental disorders and family history of substance use problems. Interventions should be done to help people with problematic substance use by targeting with those associated factors of help-seeking behavior.
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+People with conditions such as schizophrenia or bipolar disorders experience marked reductions in life expectancy compared with the general population, ranging from 13 to 15 years (Hjorthoj, Sturup, McGrath, & Nordentoft, 2017) and in some contexts up to 20 years (Fekadu et al., 2018; Hjorthoj et al., 2017; Liu et al., 2017). This has been noted over time and is increasing (Lawrence, Hancock, & Kisely, 2013; Saha, Chant, & McGrath, 2007). There is some indication of marked heterogeneity in life expectancy in these populations internationally, with the largest reductions in life expectancy reported in studies from sub-Saharan Africa (Hjorthoj et al., 2017). A similar reduction has been reported for severe unipolar depression, ranging from 10 to 14-year reduction in life expectancy, compared with the general population (Laursen, Musliner, Benros, Vestergaard, & Munk-Olsen, 2016).
+It remains unclear whether life expectancy in serious mental disorders varies by ethnic group and deprivation. In the general population, there is concern that inequalities in population-level life expectancy are widening over time, with the largest reductions in life expectancy noted in the most deprived communities across the UK, relative to the least deprived (Bennett et al., 2018). The largest contributors to shortened life expectancy in the general population are from the same common preventable physical health conditions, such as respiratory disorders and ischaemic heart disease, that account for the majority of deaths in people with serious mental illness (SMI) (Bennett et al., 2018; Das-Munshi et al., 2017b). Although the UK operates a system of universal access to healthcare it has been suggested that in deprived areas, access to healthcare may still be inequitable and may contribute to observed differences in life expectancy (Bennett et al., 2018). The interplay of these inequalities has not been previously assessed, despite a concern that access to care is also inequitable for
+people with SMI (Mitchell, Malone, & Doebbeling, 2018), fuelled by the stigma accorded to mental disorders (Thornicroft, 2018).
+Common preventable conditions such as cardiovascular disease, type 2 diabetes mellitus and respiratory disorders account for the majority of deaths in people with serious mental disorders (Das-Munshi et al., 2017b; Saha et al., 2007). These are known to be elevated within certain ethnic minority groups, independent of the presence of serious mental illness (Health and Social Care Information Centre: HSIC, 2005). These conditions may become further elevated with the onset of mental illness (Das-Munshi et al., 2017a). Despite these important associations, there have been no studies which have examined differences in life expectancy at birth in SMI among ethnic minority groups (Hjorthoj et al., 2017).
+We, therefore, aimed to assess variations in life expectancy at birth in a large ethnically diverse sample of people with schizophrenia-spectrum, bipolar disorders or depression, in contact with a secondary mental healthcare provider in London, UK. We included a sample of people with depression, as we considered that people in contact with secondary mental healthcare with depression represent the more ‘severe’ end of the spectrum since most ‘milder’ common mental disorders (including depression) are generally managed within primary care (Das-Munshi, Chang, Schofield, Stewart, & Prince, 2018). The sample of depressed individuals within this study may, therefore, represent complex cases referred to secondary mental healthcare for further management (Das-Munshi et al., 2018). We also sought to assess the moderating role of area-level deprivation and ethnicity on life expectancy at birth among men and women with SMI. ‘Serious mental illness’ was defined as including schizophrenia-spectrum, bipolar disorders as well as (severe) depression in contact with secondary care.
+Method
+Setting, participants and linkage to death certificates
+We used electronic health records data from a large secondary care mental health service provider, South London & Maudsley NHS Foundation Trust (SLaM), providing near-complete secondary mental healthcare coverage to a well-defined catchment area covering approximately 1.3 million people in south-east London (Perera et al., 2016). Since 2006 SLaM has operated fully electronic health records for all its services. The SLaM Clinical Record Interactive Search (CRIS) system was established in 2007, allowing the search and retrieval of anonymised health records for the purposes of research (Perera et al., 2016).
+Mental health clinicians are required to assign International Classification of Mental Disorders-10 (ICD-10) (World Health Organization, 2011) codes for confirmed clinical diagnoses of mental healthcare service users. We used structured fields in CRIS, supplemented by natural language processing (NLP) algorithms developed through the Generalised Architecture for Text Engineering (GATE) software (Perera et al., 2016) to mine the free text in clinical records for diagnostic statements, supplementing clinician entries captured in structured fields (Perera et al., 2016). In previous work, these have been shown to enhance the detection of specific diagnoses of mental disorder with good sensitivity and positive predictive value for NLP-based approaches (Das-Munshi et al., 2018; Das-Munshi et al., 2019).
+We thus identified ‘at risk’ cohorts of individuals with ICD-10 diagnoses spanning schizophrenia-spectrum (non-affective)
+disorders (ICD-10 codes: F2*), bipolar disorders (F30 and F31) and depression (F32 and F33). Individuals with a comorbid diagnosis of dementia (F0*) prior to the diagnosis of SMI or depression diagnoses were excluded. We developed a hierarchical approach whereby people with schizophrenia-spectrum or bipolar disorder diagnoses were given this diagnosis, irrespective of prior or later depression diagnosis and people in the depression group could not have any mention of bipolar disorders or schizophreniaspectrum disorders. The samples used for this study have been previously described elsewhere (Das-Munshi et al., 2016; Das-Munshi et al., 2017b; Das-Munshi et al., 2018).
+For inclusion into the study, individuals had to have a relevant clinical diagnosis before or between 1 January 2007 and 31 December 2014. Individuals were followed from diagnosis date until the earliest of either: death or the end of the study, on 31 December 2014. For individuals with a diagnosis before the window, entry date was set as 1 January 2007. For individuals with a diagnosis after this time, the entry date was set as the first recorded date of their diagnosis. We also extracted data on month and year of birth (to derive age for the sample), gender and ethnicity. Ethnicity was classified according to the Office for National Statistics (ONS) as White British, Black Caribbean, Black African and Irish. A ‘South Asian’ group which comprised the ethnic minority groups of Indian, Pakistani and Bangladeshi groups was deprived, due to very low numbers in each of these groups, not permitting separate analyses. Certain other groups were excluded (e.g. people of Chinese ethnicity) due to small numbers across samples and concerns related to disclosure risks. People identifying as of mixed ethnicity were grouped with the ethnic minority identity stated. Information on area of residence was derived by linking postcodes closest to the time of diagnosis to the Index of Multiple deprivation, a multi-domain small area assessment of deprivation (Noble, Wright, Smith, & Dibben, 2006) at lower super output area level. Lower super output areas are geographical areas of adjacent postcodes in the UK, which typically comprise a mean population of 1500 individuals.
+Death/mortality outcome
+The primary outcome of the study was death from all causes. We used a linkage to death certificate information through the Office of National Statistics (ONS), to determine deaths and date of death, which was provided as a file for any deaths occurring over the observation period, in England and Wales.
+Statistical methods
+Deaths by relevant ICD-10 diagnoses in men and women within the sample in 5 year age bands were used as an outcome measure for the analyses. As schizophrenia-spectrum and bipolar diagnoses are uncommon below the age of 15 years, we substituted under-15 years’ mortality rates from the population of England and Wales from 2011, as this year was the midpoint of the cohorts; This methodology has been used previously for these populations (Chang et al., 2011). For comparability, we retained this approach for the sample with depression. We used Chiang’s method of abridged life tables (Chiang, 1984), with 5 year age band up to age 85+ years, to estimate life expectancy at birth and associated standard errors/95% confidence intervals, using an Excel spreadsheet, provided and recommended by Public Health England. This approach is recommended for deriving life expectancy at birth for smaller study populations (Eayres &
+Williams, 2004). As we used a cohort with 8 years’ data on follow-up and deaths, weights were then calculated taking the mean observation period contributed by individuals by age and sex, to derive the mean at-risk period for each age- and gender-band, applying to the denominator of each corresponding band.
+Deaths and the ‘at risk’ population were used to estimate life expectancy at birth with 95% confidence intervals for men and women by ICD-10 diagnosis, and then according to ethnicity. Death summary tables were available from the UK Office for National Statistics (ONS) website (http://www.ons.gov.uk) and were used to compare life expectancy at birth in our study populations with mental illness against life expectancy in the general population from England and Wales and against life expectancy in the general population residing in the most deprived areas in England (defined as a resident in the most deprived decile according to the Index of Multiple Deprivation). These data were taken for the period 2011-2013 which was closest to the midpoint of our study cohort.
+Results
+A total of 18 641 individuals contributed data to analyses relating to schizophrenia, schizoaffective, other schizophrenia-spectrum disorders and bipolar disorders and 20 203 individuals contributed data to analyses relating to depression. Table 1 highlights the demographic characteristics of the samples.
+Relative to the general population in England, life expectancy at birth among people with any SMI diagnoses was markedly lower. Among men, life expectancy at birth was on average 12.6 years (for bipolar disorders) to 15.0 years (schizophreniaspectrum and depressive disorders) lower compared to the life expectancy at birth among the general population; while among women, this was just over 13 years lower compared to women in the general population, across all serious mental disorder diagnoses assessed (schizophrenia-spectrum disorders, bipolar disorders and depression; see Tables 2 and 3). Across men and women with all SMI diagnoses, life expectancy at birth remained considerably lower than that of the general population residing in the most deprived areas nationally (Tables 2 and 3). All ethnic groups (including the White British group), across all mental illness diagnoses, experienced reductions in life expectancy at birth compared to the general population. The largest reductions in life expectancy were observed within schizophrenia-spectrum disorders for Irish men with 20.5 years lost, and in White British women with 16.3 years lost, compared to the general population. However, across some subgroups, 95% confidence intervals overlapped, indicating that smaller samples may have impacted precision.
+In general, trends were indicative of reduced life expectancy at birth among people with SMIs, irrespective of their ethnicity (Tables 2 and 3). In men with SMIs (defined as schizophrenia-spectrum or bipolar disorders combined) differences compared to the general population ranged from an 8.6 year (Black Caribbean) to a 17.4 year (Irish) reduction in life expectancy and in women this ranged from a 7.4 year (South Asian) to a 15.0 year (White British) reduction in life expectancy. A similarly adverse picture with respect to premature mortality was evident, with men with depression in contact with mental healthcare services experiencing reductions in life expectancy at birth ranging from 12.4 years (Black African) to 15.6 years (White British) and women with depression in contact with
+mental healthcare services experiencing reductions in life expectancy at birth ranging from 7.7 years (Black African) to 14.6 years (Irish), thus indicating a similar impact on life expectancy at birth across diagnostic groups as well as by ethnicity.
+We also assessed life expectancy at birth among people with SMIs living in the least and most deprived areas (Figs 1a and 1b also see online Supplementary Material: Table S1). The gap in life expectancy at birth was largest for men with schizophreniaspectrum disorders residing in the least deprived areas, this amounted to a life expectancy at birth of 59.6 years (95% CI 54.3-64.8), a 23.4-year reduction compared with men residing in comparable areas of affluence (online Supplementary Table S1). Life expectancy at birth was lower in people with SMIs across all diagnostic groups, compared with the general population residing in areas of comparable deprivation. Of note, across all SMI diagnoses, people with SMIs had a lower life expectancy at birth compared with the general population residing in the most deprived areas (Figs 1a and 1b).
+The supplementary tables (online Supplementary Tables S2a and S2b) highlight causes of death. As we have described previously, most causes of deaths (80% of deaths in schizophreniaspectrum and bipolar disorders and 85% of deaths in major depression) were from preventable physical causes, while 11% of deaths in schizophrenia-spectrum and bipolar disorders and 7% of deaths in major depression were from unnatural causes, including suicide (Das-Munshi et al., 2017b; Das-Munshi et al., 2018; Das-Munshi et al., 2019).
+Discussion
+Main findings and interpretation
+Our study indicates two key findings. First, in keeping with previous work (Chang et al., 2011; Hjorthoj et al., 2017), our findings indicate that men and women with SMIs, from a large urban sample from the UK, experience marked reductions in life expectancy at birth compared to the general population. In the most affluent areas, this amounted to a 20-year difference in men with any SMI. However, the excess risk was noted across all psychiatric diagnoses surveyed in men and women, and included schizophreniaspectrum disorders, bipolar disorders and depression managed in secondary mental healthcare. Marked reductions in life expectancy in people with SMIs were evident across all ethnic groups, including the White British group with SMIs, indicating an adverse impact of having a SMI on mortality outcomes.
+In stratified analyses, life expectancy at birth among people with SMIs remained lower than the general population resident in areas with equivalent levels of deprivation, indicating an effect of SMIs over and above deprivation effects on mortality outcomes. This effect of deprivation on life expectancy appeared to be stronger in women with SMIs compared to men. In the UK general population, large differences have been noted with individuals residing in the most deprived areas experiencing a shorter life expectancy than those residing in the most affluent areas; this follows a strong social class gradient and has been highlighted as a major area of public health concern (Bennett et al., 2018). Therefore, our second finding - that life expectancy at birth in SMI populations is substantially lower than the life expectancy observed for the general population in the most deprived areas - may suggest that people with SMIs are ‘off the scale of the social hierarchy completely (Marmot, 2018), akin to other groups known to experience excess mortality and other extreme
+health inequalities, due to being marginalised and socially excluded (Aldridge et al., 2018).
+People with SMIs may experience additional multiple adversities, beyond those experienced by the most deprived communities in the UK. Findings from previous research have indicated that people with SMIs are less likely to have timely access to healthcare resources (e.g. specialist treatment) (Mitchell & Lawrence, 2018). It was not possible to further test interactions between ethnicity and area deprivation in this study, as this would have led to samples below the recommended size for assessments of life expectancy (Eayres & Williams, 2004). As in the UK, ethnically dense areas also tend to be more deprived, area-level interactions with mortality in mental illnesses could be complex. Findings from previous studies have indicated that in ethnic minority groups with SMI, residency in areas of higher own group density may be associated with a reduced risk of death from a range of causes, which could be due to the health-protective effects of social networks, social support, community participation and protection from social isolation and social exclusion (Das-Munshi et al., 2019). Interactions with area-level deprivation for ethnic minority groups could be explored in future work.
+Strengths and limitations
+A limitation of our study related to the lack of information on ethnicity in death certificates, which is not recorded in the UK. As a result of this, other investigators had to use innovative methods -for example UK investigators previously used data linkages to derive these for ethnic minority groups in Scotland (Gruer et al., 2016), which could be an approach also used in this area in future. This limitation meant that we were only able to compare observed mortality in each of the ethnic minority groups in the sample with SMIs to life expectancy in the general population, without the benefit of being able to take into account life expectancy by ethnicity in the general population. One possibility is that the life expectancy of each of the ethnic minority groups was lower to start with. Although we should be cautious in comparing ethnic minority groups across the UK who may have differing histories of migration and settlement as well as experiences of health inequalities, in the study using linked data from Scotland, the life expectancy of Irish and South Asian groups was similar to the ‘population standard’ from England used in the present study (Gruer et al., 2016). This may suggest that the differences seen in our study reflect the over-riding harm of severe mental illness on life expectancy, although more work assessing these interacting
+factors will be needed, particularly because adverse mental and physical health outcomes in each of the ethnic minority groups in the present study have also been well described (Das-Munshi et al., 2017a; Oduola et al., 2019; Oduola et al.).
+A further limitation is that the data from this study were from a mainly urbanised location which may limit generalisability to other samples, for example covering rural areas. However, the broader
+evidence base indicates that premature mortality is still significant in people with SMIs irrespective of how urbanised their location of residence is (Das-Munshi et al., 2019; Phillips, Yang, Li, & Li, 2004), and so we may anticipate similar trends if the study could be repeated in populations residing in less urban locations. As this study used data from electronic health records, a further limitation may have been due to the quality of the data input into the
+care record by clinicians. We used a well-validated and reliable method to ascertain clinical diagnoses. However, for other measures such as ethnicity, although this had high levels of completion, it is possible that this was not always self-ascribed. We plan to assess this and other measures against other sources (e.g. through linkages) in future work. We did not assess comorbid alcohol and substance use disorders and did not have information on physical health comorbidities. Both issues may play an important role in accounting for premature mortality in people with severe mental illnesses and should be considered in future work.
+Finally, we did not have data on country of birth, therefore it was not possible to assess the impact of generational status in ethnic minority groups on life expectancy estimates. This is an important issue as health-related behaviours such as tobacco use, as well as exercise and dietary practices leading to weight gain, have been shown in previous work to converge to that of White British reference groups in first-generation migrants with a longer duration of residence, or show convergence across
+generations in ethnic minority groups (Alidu & Grunfeld, 2018; Hawkins, Lamb, Cole, & Law, 2008; Smith, Kelly, & Nazroo, 2011). Acculturation refers to the process whereby health-related behaviours, attitudes and beliefs change when people of one culture come into contact with another. It is possible that acculturation in certain British ethnic minority groups has led to the adoption of health-related behaviours usually more prevalent in White British groups; this issue could be explored in future work.
+Our study utilised 8 years of cohort data from an ethnically diverse region in the UK, and therefore the statistical power to detect differences by ethnicity, sex and diagnoses and by area-level deprivation was enhanced, albeit with the caveat that the methodologies utilised in this report result in estimates which were less precise with lower sample sizes (Eayres & Williams, 2004). The estimates and 95% confidence intervals for the smaller samples in this report should, as a result, be viewed with caution (Eayres & Williams, 2004) and may reflect random variations. The catchment area of the study represents a large and well-defined region
+within south London and as such, may have good generalisability to other metropolitan and urban samples elsewhere.
+In the UK, healthcare is free at the point of contact and delivery and within the catchment area at the time of the study, the hospital Trust provided near-complete coverage of secondary
+mental healthcare services. Therefore, we can be reasonably certain that our samples for schizophrenia-spectrum and bipolar disorders are fairly representative of all individuals with the condition living in the catchment area since it is highly likely that such individuals would have made contact with secondary
+mental healthcare services at some point over the course of their illness. However, as most cases of depression in the UK are managed in primary care, our sample of people with depression represent the more ‘severe’ end of the spectrum, who may present challenges to standard management in primary care, for example through treatment resistance or comorbidities (Das-Munshi et al., 2018). Therefore, the estimates for reduced life expectancy in the depression sample in this study should be viewed as those representing people with more complex/severe conditions in contact with secondary mental healthcare services and may not reflect estimated life expectancy for people with depression solely managed in primary care, or never treated.
+The linkage to death certificates would have meant that deaths occurring anywhere in England and Wales (even if outside of the immediate catchment area of the mental health Trust) would have been captured. In previous analyses we have also used the linked data on mortality and known emigrations to model the possibility that migrant groups may be more likely to emigrate and potentially die outside of the UK, leading to a numerator-denominator mismatch and possibly artefactually lowering standardised mortality rates (Razum, 2006). Our previous work which has taken this data on emigrations into account has indicated estimates for analyses relating to deaths to be robust to this possibility of out-migration (Das-Munshi et al., 2017b, 2018).
+Implications
+There are longstanding concerns around the corrosive effects of health inequalities across the social gradient or hierarchy in the general population (Institute of Health Equity, 2010; Marmot, Allen, Boyce, Goldblatt, & Morrison, 2020). Yet these findings highlight that life expectancy at birth among people with SMIs, irrespective of ethnicity, is lower and more adversely impacted upon than the most deprived communities in the UK. Put simply, people with SMIs experience excess mortality greater than those who are already on the lowest rung of the ‘social ladder’. The larger excess risk of death (around 20 years) in men with any SMI, schizophrenia-spectrum disorders and major depression, in the most affluent areas, compared to men in the general population, further brings into focus the extent of this stark inequality. Future work may need to broaden from a focus on traditional risk factors (smoking, weight and other cardiovascular risks) to approaches which enhance social inclusion and address multiple intersecting disadvantages and structural inequalities (Marmot, 2018).
+The findings, therefore, quantify the extent to which public health, as well as the health and social care systems, continue to fail to address this important measure of health inequality in people with SMIs. Addressing structural inequalities such as poverty, improving equitable access to healthcare (Bennett et al., 2018) and tackling causes with a focus on individuals, communities and health systems (Liu et al., 2017) and strengthening approaches towards evidence-based clinical practice for common preventable physical health conditions (World Health Organization, 2018) are required, alongside a consideration of those aspects which promote the social inclusion of people with SMIs.
+Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/S0033291720001087.
+Data. Data are owned by a third party SLaM BRC CRIS tool which provides access to anonymised data derived from SLaM electronic medical records. These data can only be accessed by permitted individuals from within a secure
+Psychological Medicine
+2589
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+Hawkins, S. S., Lamb, K., Cole, T. J., & Law, C., & Millennium Cohort Study Child Health, G. (2008). Influence of moving to the UK on maternal health behaviours: Prospective cohort study. BMJ (Clinical research ed.) 336, 1052-1055.
+Health and Social Care Information Centre: HSIC. (2005). Health Survey for England 2004: The Health of Minority Ethnic Groups- headline tables. In Health Survey for England, (ed. NHS Health and Social Care Information Centre). https://files.digital.nhs.uk/publicationimport/pub01xxx/pub01209/ heal-surv-hea-eth-min-hea-tab-eng-2004-rep.pdf.
+Hjorthoj, C., Sturup, A. E., McGrath, J. J., & Nordentoft, M. (2017). Years of potential life lost and life expectancy in schizophrenia: A systematic review and meta-analysis. The Lancet Psychiatry, 4, 295-301.
+Institute of Health Equity. (2010). Fair society: Healthy lives; The Marmit Review. Strategic review of health inequalities in England post-2010 London.
+Laursen, T. M., Musliner, K. L., Benros, M. E., Vestergaard, M., & Munk-Olsen, T. (2016). Mortality and life expectancy in persons with severe unipolar depression. Journal of Affective Disorders, 193, 203-207.
+Lawrence, D., Hancock, K. J., & Kisely, S. (2013). The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: Retrospective analysis of population based registers. BMJ: British Medical Journal, 346, f2539.
+Liu, N. H., Daumit, G. L., Dua, T., Aquila, R., Charlson, F., Cuijpers, P., ... Saxena, S. (2017). Excess mortality in persons with severe mental disorders: A multilevel intervention framework and priorities for clinical practice, policy and research agendas. World Psychiatry, 16, 30-40.
+Marmot, M. (2018). Inclusion health: Addressing the causes of the causes. The Lancet, 391, 186-188.
+Marmot, M., Allen, J., Boyce, T., Goldblatt, P., & Morrison, J. (2020). Health equity in England: The Marmot review ten years on. London: Institute of Health Equity.
+Mitchell, A. J., & Lawrence, D. (2018). Revascularisation and mortality rates following acute coronary syndromes in people with severe mental illness: Comparative meta-analysis. British Journal of Psychiatry, 198, 434-441.
+Mitchell, A. J., Malone, D., & Doebbeling, C. C. (2018). Quality of medical care for people with and without comorbid mental illness and substance misuse: Systematic review of comparative studies. British Journal of Psychiatry, 194, 491-499.
+Noble, M., Wright, G., Smith, G., & Dibben, C. (2006). Measuring multiple deprivation at the small-area level. Environment and Planning A, 38, 169-185.
+Oduola, S., Craig, T. K. J., Das-Munshi, J., Bourque, F., Gayer-Anderson, C., & Morgan, C. (2019). Compulsory admission at first presentation to services for psychosis: Does ethnicity still matter? Findings from two populationbased studies of first episode psychosis. Social Psychiatry and Psychiatric Epidemiology, 54, 871-881.
+Oduola, S., Das-Munshi, J., Bourque, F., Gayer-Anderson, C., Tsang, J., Murray, R. M., ... Morgan, C. (2019) Change in incidence rates for psychosis in different ethnic groups in south London: Findings from the Clinical Record Interactive Search-First Episode Psychosis (CRIS-FEP) study. Psychological Medicine, 1-10.
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+Phillips, M. R., Yang, G., Li, S., & Li, Y. (2004). Suicide and the unique prevalence pattern of schizophrenia in mainland China: A retrospective observational study. Lancet (London, England), 364, 1062-1068.
+Razum, O. (2006). Commentary: Of salmon and time travellers - musing on the mystery of migrant mortality. International Journal of Epidemiology, 35, 919-921.
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+Thornicroft, G. (2018). Physical health disparities and mental illness: The scandal of premature mortality. British Journal of Psychiatry, 199, 441442.
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+https://doi.org/10.1017/S0033291720001087 Published online by Cambridge University Press
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+B A C K G R O U N D
+Description of the condition
+Substance misuse among people with a severe mental illness is a major concern, with prevalence rates over 50%. This figure varies across studies, depending on location and methodologies and by the way substance misuse problems and severe mental illness are defined (Carra 2009; Green 2007; Gregg 2007; Lai 2012a; Lai 2012b; Lowe 2004; Regier 1990; Todd 2004). Improving services for these patients (often labelled as having a 'dual diagnosis') is a priority as using drugs or consuming alcohol, even at low levels, is associated with a range of adverse consequences, including higher rates of non-adherence, relapse, suicide, HIV, hepatitis, homelessness, aggression, incarceration, and fewer social supports or financial resources (Donald 2005; Green 2007; Hunt 2002; Schmidt 2011; Siegfried 1998; Tsuang 2006). Further co-morbidity places an additional burden on families, psychiatric and government resources and is particularly challenging to those providing services as these patients have lower rates of treatment completion and higher rates of relapse (Siegfried 1998; Tyrer 2004; Warren 2007).
+Description of the intervention
+It is important that co-occurring substance use is detected as early as possible and that appropriate and effective treatment is provided (Green 2007; Siegfried 1998). Treatment has traditionally been complicated by different approaches and philosophies among mental health and drug services as they may differ in their theoretical underpinnings, policies and protocols. Separate treatment programmes have been offered in parallel or sequentially by different clinicians, which may result in less than optimum patient care with the patient having to negotiate two separate treatment systems (Green 2007). Another approach to care is the integrated treatment model where mental health and substance use treatments are brought together simultaneously by the same service, clinician or team of clinicians who are competent in both service areas and place similar importance on both (Drake 2004; Green 2007). Basic elements include an assertive style of engagement, techniques of close monitoring, comprehensive services (including inpatient, day hospital, community team and outpatient care), supportive living environments, flexibility and specialisation of clinicians, step-wise treatment, and a long-term perspective and optimism (Drake 1993). Assertive Community Treatment (ACT) and residential programmes are generally longterm and can form a basis for integrated programmes.
+How the intervention might work
+As many substance users in the general population have benefited from a range of psychosocial interventions, it would follow that these same interventions should also benefit people with psychosis when their mental health problems are taken into account (Barrowclough 2006 a). Most, if not all, substances of abuse increase dopaminergic activity in the brain (Koob 2010). Given that schizophrenia and other forms of psychosis are characterised by heightened dopaminergic transmission and that neuroleptics decrease activity or block dopamine receptors (Kapur 2005), it stands to reason that most substances of abuse increase symptoms, the risk of relapse and compromise the beneficial effects of neuroleptics (LeDuc 1995; Seibyl 1993). This is especially true for stimulant drugs like amphetamine, cocaine and concentrated
+forms such as crack cocaine and methamphetamine ('ice') that can exacerbate or mimic psychotic symptoms (Callaghan 2012; McKetin 2013; Pluddemann 2013). Substance use is also related to poor compliance with treatment, further increasing the risk of relapse (Hunt 2002). Interventions that reduce substance use are likely to improve symptoms, relapse rates, recovery and other outcomes (Cleary 2009a; Drake 2008; Horsfall 2009). Common psychosocial interventions to reduce substance use and misuse include Twelve Step recovery, which adopts a supportive approach such as that used by Alcoholics Anonymous (AA); motivational interviewing, which aims to increase an individual's motivation for change; group and individual skills training; family psycho-education regarding the signs and effects of substance use; and individual or group psychotherapy involving cognitive or behavioural principles, or both, which aim to increase coping strategies, awareness and self-monitoring behaviour. All of these interventions can vary in intensity and duration, and can be offered in a variety of settings either individually or as part of an integrated programme. Integrated treatment ensures mental health and substance misuse services are available in the same setting and delivered in a coherent fashion.
+Why it is important to do this review
+While encouraging, results of trials assessing the effectiveness of these psychosocial interventions for mental health consumers are equivocal (for reviews, see: Bogenschutz 2006; Cleary 2009a; Dixon 2010; Drake 1998b; Drake 2004; Drake 2008; Horsfall 2009; Ley 2000; Mueser 2005; NICE 2011). Many studies have been hampered by small heterogeneous samples, poor experimental design (for example non-random assignment), high attrition rates, short follow-up periods, lack of accuracy of measuring substance use, skewed data, use of non-standardised outcome measures and unclear descriptions of treatment components (Barrowclough 2006 a; Cleary 2008; Ley 2000). When assessing integrated programmes, it can also be difficult to determine exactly which part of the programme is the most effective, and control groups (particularly in the USA) may involve a certain level of service integration, making interpretations difficult (Drake 1996). Moreover, study methodologies, interventions and outcome measures vary across studies, as do patterns of participants' readiness to change, severity and type of illness and substance use, all of which make combining results in a review problematic (Donald 2005).
+This current review updates the 2008 Cochrane review on "Psychosocial treatment programmes for people with both severe mental illness and substance misuse". The previous review included any programme of substance misuse treatment and located 25 randomised controlled trials. The authors from two previous reviews found no evidence to support any one substance misuse programme as being superior to another (Cleary 2008; Ley 2000). We felt an update of this review was warranted as there are several new studies that have been conducted in the last five years.
+O B J E C T I V E S
+To assess the effects of psychosocial interventions for reduction in substance use by people with a serious mental illness compared with standard care.
+M E T H O D S
+Criteria for considering studies for this review
+Types of studies
+We included all relevant, randomised controlled trials (RCTs) with or without blinding if they utilised a psychosocial intervention to reduce substance use in patients with severe mental illness and substance misuse compared with standard care. We excluded quasi-randomised trials, such as those where allocation was alternate or sequential.
+Types of participants
+We included people with severe mental illness (for example, schizophrenia, bipolar disorder and psychosis) and concurrent problem of substance misuse. We have defined people with 'severe' illness as those with a chronic mental illness like schizophrenia who present to adult services for long-term care. Those with an organic disorder, non-severe mental illness (for example, personality disorder, post-traumatic stress disorder (PTSD), anxiety disorders, depressive symptoms based on scores from a scale) or those who solely abused tobacco were, if possible, excluded. Trials that included a mixture of patients with a severe mental diagnosis were included if a large proportion had a schizophrenialike illness or psychosis (see Characteristics of included studies). For the current update, studies were excluded if all of the participants had a diagnosis of bipolar disorder or major depressive disorder, so they do not overlap with affective disorder reviews.
+Types of interventions
+We anticipated that studies included in the review would use a wide variety of psychosocial interventions for substance misuse, making direct comparisons difficult. In order to enhance the utility of the review, we developed a priori categories within which we made planned comparisons. These categories were developed from theoretical models of the types of behavioural and psychosocial interventions offered to clients and the context in which they are delivered. The types of interventions were grouped in two strata, based on duration and intensity of treatment. The first stratum describes long-term interventions for dual diagnosis patients that offered an array of services with different levels of integration and assertive outreach (taking place over years rather than weeks or months), and the second describes stand-alone psychosocial interventions that clients received over shorter periods. We did not include Interventions for informal carers (partner or family members) as separate categories, though we did sometimes include them as part of the treatments mentioned below.
+1.	Provider-oriented long-term interventions: integrated and non-integrated care by community mental health teams for dual diagnosis populations
+1.1	Integrated models of care with assertive community treatment (ACT)
+Integrated treatment models for patients with a dual diagnosis unify services at the provider level rather than forcing clients to negotiate separate mental health and substance abuse treatment programmes (Drake 1993). The range of services provided varies according to client needs and should be able to handle patients at differing stages of readiness to change (Tsuang 2006). Substance abuse treatments are integrated into an array of direct services,
+such as frequent home visits, crisis intervention, housing skills training, vocational rehabilitation, medication monitoring, and family psycho-education. Integrated treatment means that the same clinicians or teams of clinicians in the one setting provide long-term treatments in a co-ordinated fashion (Barrowclough 2006 a; Green 2007). Teams consist of three to six clinicians and attempt to remain faithful to a specified model of care. To the client, the services should appear seamless with a consistent approach, philosophy and set of recommendations. Usually the caseloads of dual diagnosis teams are lower (approximately 10 to 15 clients shared within a team) than for standard case managers (approximately 20 to 30). Integrated treatment is a process that takes place over years rather than weeks or months. Studies included in this category must have clearly demonstrated the following: 1) assertive community outreach to engage and retain clients and to offer services to reluctant or uncooperative clients, 2) staged interventions to reduce substance use, and 3) adherence to the integrated team philosophy. The intervention could be community-based or provided for special populations, such as homeless people or forensic patients.
+1.2	Non-integrated models of care or intensive case management
+Non-integrated treatment entails similar interventions by community teams, as described above, except the same members do not deliver them in a co-ordinated fashion and assertive community outreach is not included. Normally, case managers in this category are better trained and have higher clinical qualifications and better therapeutic skills than standard case managers. Intensive case management is defined as lower case load size (approximately 10 to 15 clients) than for standard case managers and tends to have a 'psychodynamic' flavour (see Marshall 1998). To be included in this category, part of the intervention had to address the client's drug and alcohol misuse.
+2.	Patient or client focused short-term interventions for substance misuse
+These interventions can be broadly grouped into individual and group modalities. They are offered in addition to routine care (treatment as usual, standard case management) and are based on different theoretical models. Although they could be part of the provider-oriented packages described above, studies included here were easier to evaluate since they described a simplified intervention that can be easily reproduced. As some studies used more than one intervention (for example, cognitive behavioural therapy combined with motivational interviewing), these were included in a separate category.
+2.1	Individual approaches
+2.1.1 Cognitive behavioural therapies
+Cognitive behavioural approaches include a variety of interventions (Rector 2012; Work Group 2007). The defining features are: 1) emphasis on functional analysis of drug use, understanding the reasons for use and consequences; and 2) skills training for recognising the situations where a person is most vulnerable to drug use and avoiding these situations. A cognitive behavioural intervention seeks to establish links between drug misuse, irrational beliefs, and misperceptions at a personal level and endeavours to correct the thoughts, feelings and actions of the recipient with respect to and the promotion of alternative ways of coping (Jones 2004; Jones 2012). The target symptom that is
+usually focused on is reducing problematic substance use or harm minimisation, such as reducing the risk of contracting HIV.
+2.1.2 Motivational interviewing
+Motivational interviewing takes a non-confrontational approach to treating substance misuse and is intended to enhance the individual's intrinsic motivation for change, in patients who often find it difficult to commit to change (Tsuang 2006). It matches the patient's level of problem recognition to change with specific strategies and goals and can be delivered in brief sessions or over a number of weeks. It is based on four key principles: 1) expressing empathy, 2) developing discrepancy, 3) supporting self-efficacy, and 4) rolling with resistance (Chanut 2005); and is directed at five stages: 1) pre-contemplation, 2) contemplation, 3) preparation, 4) action, and 5) maintenance (Tsuang 2006). A key hypothesis is that the patient's perspective on the importance of change is fundamental to the patient's readiness to address the problem. Developing the patient's confidence in their ability to achieve the desired change is also a key issue of motivational interviewing. This treatment is delivered individually or in small group settings.
+2.1.3 Contingency management
+Based on principals of operant conditioning, contingency management (CM) offers incentives or rewards to reinforce specific goals (reduced substance use, risky behaviours etc). Typically, rewards are provided if a negative substance test is provided (urine test or breath test). Rewards can vary widely, ranging from encouraging statements ('keep up the good work') to large or small financial prize (vouchers for food, cash etc). This approach has shown consistent success with various drug use disorders: cannabis, opiate and cocaine dependence and polysubstance use disorders (Dutra 2008). Contingency management has also been 'bundled' with other psychosocial interventions, for example, motivational interviewing plus cognitive behavioural therapies (Bellack 2006). Thus, contingency management was added to the current review due to the number of current and ongoing trials using this intervention.
+2.2	Group approaches
+2.2.1 Social skills training
+These groups are aimed at helping clients develop interpersonal skills for establishing and maintaining relationships with others, dealing with conflict, and handling social situations involving substance misuse (Mueser 2004). They are taught in a highly structured way by using role play, corrective feedback and homework. This usually occurs in a group format, although the methods can also be employed in individual work as a type of cognitive behavioural counselling.
+3.	Standard care or treatment as usual
+This was defined as the care that a person would normally receive had they not been included in the research trial. This could include standard case management (see Marshall 1998 for definition). Standard care varies between settings and can be supplemented by additional components, including psycho-educational material, family therapy, or referral to self-help groups (for example, Alcoholics Anonymous) or other agencies for substance abuse treatment.
+Types of outcome measures
+We intended to group data into short, medium and long-term outcomes. However, this would have resulted in much data loss as outcome periods varied and therefore, post hoc, we reported for the following time periods: 3, 6, 9, 12, 18, 24 and 36 months (where applicable).
+Primary outcomes
+1.	Numbers lost to treatment: this is a measure of stability and engagement.
+This is the number of participants who did not continue with the treatment following randomisation; however, some may have provided data for the study. This varies with study design as some treatments are ongoing for the study duration and some are shortterm. When studies reported exactly the same data for both lost to treatment and lost to evaluation (see below), and if there were no other studies with which to pool data, then we only reported the numbers lost to treatment (to reduce the number of comparison tables). We did not adjust numbers lost to treatment for death (see below).
+2.	Change in substance use as defined by each of the studies.
+3.	Changes in symptoms as defined by each of the studies.
+Secondary outcomes
+1.	Numbers lost to evaluation.
+This is the number of people lost to the study who did not provide data at particular time points.
+2.	Death (all causes).
+Some studies may not have reported on the number of participants dying over the treatment or evaluation period. If reported, we recorded death in a separate table but these cases were retained in the lost to treatment and lost to evaluation figures as it was often unclear when the death occurred or the cause of death was not stated as unlikely to be linked to the intervention.
+3.	Substance use (alcohol or drugs, or both).
+4.	Mental state.
+5.	Global functioning.
+6.	Social functioning.
+7.	Quality of life and life satisfaction.
+8.	Hospital readmissions (and days in the community).
+9.	Homelessness.
+10.	Compliance with treatment and medication.
+Summary of findings table
+We used the GRADE approach to interpret findings (Schunemann 2008) and used the GRADE profiler to import data from Review Manager (RevMan) to create 'Summary of findings' (SOF) tables. These tables provide outcome-specific information concerning the overall quality of evidence from each included study in the comparison, the magnitude of effect of the interventions examined, and the sum of available data on all outcomes that we rated as important to patient care and decision making. We selected the following main outcomes for inclusion in the SOF tables.
+1.	Numbers lost to treatment (medium-term: 12 months; if these data were not available we used the short-term data).
+2.	Death.
+3.	Alcohol use (as measured in the trials).
+4.	Drug use (as measured in the trials).
+5.	Mental state (as measured in the trials, and if no specific scale assessment was done we reported on relapse or hospitalisation).
+6.	Global assessment of functioning (as measured in the trials),
+7.	General life satisfaction (as measured in the trials).
+Search methods for identification of studies
+Electronic searches
+For previous search methods from prior review updates please see Appendix 1.
+Cochrane Schizophrenia Group Trials Register
+The Trials Search Co-ordinator searched the Cochrane Schizophrenia Group Trials Register (July 2012) using the phrase:
+[((*polydrug* or *substanc* or *alcoh* or *tranquiliz* or *narcot* or * abus* or *opiat* or *street drug* or *solvent* or *inhalan* or *intoxi*) in title, abstract and indexing terms REFERENCE) or ((*substance abus* or drug abus* or *alcohol* or *cannabis*) in health care conditions of STUDY)].
+The Cochrane Schizophrenia Group Trials Register is compiled by systematic searches of major databases, handsearches of relevant journals and conference proceedings (see Group Module). Incoming trials are assigned to relevant existing or new review titles.
+Searching other resources
+1.	Reference lists
+We searched all references of articles selected for inclusion, major review articles (Baker 2012; Dixon 2010; Drake 2008; Dutra 2008; Horsfall 2009; Kelly 2012) as well as recent guidelines (NICE 2011) on this topic for further relevant trials.
+2.	Journal databases
+Two further searches were completed (8 October 2012 and 15 January 2013) by the principal reviewer (GEH) using the Cochrane Database of Systematic Reviews, MEDLINE (daily update, PREMEDLINE), and PsycINFO. A separate search for randomised trials using contingency management was completed as this was an additional intervention category for this update. We also searched MEDLINE for recent articles (2008 to 2013) by the first authors of all included studies in order to get a more complete list of recent publications.
+We also did 'forward' searches to identify trials that cited previously included RCTs using Web of Science and Scopus. Scopus was used to identify trials that cited the most recent version of this review (Cleary 2008) up to 15 February 2013.
+3.	Trials registries
+In addition, websites and journals that list ongoing trials in the USA, UK, Australia and various European countries were searched for RCTs through the the Cochrane Schizophrenia Group Trials Register.
+The principal researcher (GEH) searched www.clinicaltrials.gov for protocols of current and previously included studies for proposed outcome measures to assess selective reporting bias.
+4.	Personal contact
+We contacted the first author (or corresponding author) of newly included studies for this update regarding their knowledge of ongoing or unpublished trials.
+Data collection and analysis
+For previous data collection and analysis methods see Appendix 2.
+Selection of studies
+For this update GEH inspected all citations from the new electronic search and identified relevant abstracts, full text articles and trials against the inclusion criteria. To ensure reliability, KM inspected all full text articles for inclusion. Where there were uncertainties or disagreements, two additional authors provided resolution (NS and MC). Where disputes could not be resolved, these studies remained as awaiting assessment or ongoing studies and the authors were contacted for clarification.
+Data extraction and management
+1.	Extraction
+For this update, GEH and KM extracted data from the included studies. We resolved disputes by discussion and adjudication from the other review authors (NS and MC) when necessary. If it was not possible to extract data or if further information was needed, we attempted to contact the authors. We extracted data presented only in graphs and figures whenever possible, but the data were included only if two review authors independently had the same result. When further information was necessary, we contacted authors of studies in order to obtain missing data or for clarification of methods.
+2.	Management
+2.1	Forms
+We extracted data onto standard, simple forms.
+2.2	Scale-derived data
+We included continuous data from rating scales only if:
+•	the psychometric properties of the measuring instrument have been described in a peer-reviewed journal (Marshall 2000); and
+•	the measuring instrument has not been written or modified by one of the trialists for that particular trial.
+Ideally the measuring instrument should either be: i) a self-report or ii) completed by an independent rater or relative (not the therapist). We realise that this is not often reported clearly; we have noted whether or not this is the case in Characteristics of included studies.
+2.3	Endpoint versus change data
+There are advantages of both endpoint and change data. Change data can remove a component of between-person variability from the analysis. On the other hand, calculation of change needs two assessments (baseline and endpoint), which can be difficult in unstable and difficult to measure conditions such as schizophrenia. We decided to primarily use endpoint data, and only use change
+data if the former were not available. We combined endpoint and change data in the analysis as we used mean differences (MD) rather than standardised mean differences throughout (Higgins 2011, Chapter 9.4.5.2).
+2.4	Skewed data
+Continuous data on clinical and social outcomes are often not normally distributed. To avoid the pitfall of applying parametric tests to non-parametric data, we aimed to apply the following standards to all data before inclusion:
+•	standard deviations and means are reported in the paper or obtainable from the authors;
+•	when a scale starts from the finite number zero, the standard deviation, when multiplied by two, is less than the mean (as otherwise the mean is unlikely to be an appropriate measure of the centre of the distribution (Altman 1996));
+•	if a scale started from a positive value (such as the Positive and Negative Syndrome Scale (PANSS) which can have values from 30 to 210), we modified the calculation described above to take the scale starting point into account. In these cases skew is present if 2SD > (S - S min), where S is the mean score and S min is the minimum score.
+Endpoint scores on scales often have a finite start and endpoint and these rules can be applied. We entered skewed endpoint data from studies of fewer than 200 participants as 'other data; within Data and analyses rather than into a statistical analysis. Skewed data pose less of a problem when looking at mean if the sample size is large; we entered such endpoint data into the syntheses.
+When continuous data are presented on a scale that includes a possibility of negative values (such as change data), it is difficult to tell whether data are skewed or not; we entered skewed change data into analyses regardless of size of study.
+2.5	Common measure
+To facilitate comparison between trials, we intended to convert variables that can be reported in different metrics, such as days in hospital (mean days per year, per week or per month) to a common metric (for example, mean days per month).
+2.6	Conversion of continuous to binary
+Where possible, we made efforts to convert outcome measures to dichotomous data. This can be done by identifying cut-off points on rating scales and dividing participants accordingly into ’clinically improved’ or ’not clinically improved’. It is generally assumed that if there is a 50% reduction in a scale-derived score such as the Brief Psychiatric Rating Scale (BPRS) (Overall 1962) or the PANSS (Kay 1986; Kay 1987) this could be considered as a clinically significant response (Leucht 2005a; Leucht 2005b). If data based on these thresholds were not available, we used the primary cut-off presented by the original authors.
+2.7	Direction of graphs
+Where possible, we entered data in such a way that the area to the left of the line of no effect indicated a favourable outcome for the treatment intervention. Where keeping to this made it impossible to avoid outcome titles with clumsy double-negatives (for example, 'Not improved') we reported data where the left of the line indicates an unfavourable outcome. This was noted in the relevant graphs.
+Assessment of risk of bias in included studies
+For this 2013 update, GEH worked independently by using criteria described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) to assess trial quality. This new set of criteria is based on evidence of associations between overestimate of effect and high risk of bias of the article, such as sequence generation, allocation concealment, blinding, incomplete outcome data and selective reporting.
+Where inadequate details of randomisation and other characteristics of trials were provided, we contacted authors of the studies in order to obtain additional information.
+We have noted the level of risk of bias in the text of the review.
+Measures of treatment effect
+1.	Binary data
+For binary outcomes we calculated a standard estimation of the risk ratio (RR) and its 95% confidence interval (CI). It has been shown that RR is more intuitive (Boissel 1999) than odds ratios and that odds ratios tend to be interpreted as RR by clinicians (Deeks 2000). The Number Needed to Treat or Harm (NNT or H) statistic with its CIs is intuitively attractive to clinicians but is problematic both in its accurate calculation in meta-analyses and interpretation (Hutton 2009). For binary data presented in the 'Summary of findings' tables, where possible, we calculated illustrative comparative risks.
+2.	Continuous data
+For continuous outcomes we estimated mean difference (MD) between groups. We would prefer not to calculate effect size measures (standardised mean difference (SMD)). However, if scales of very considerable similarity were used, we presumed there was a small difference in measurement, and we would have calculated effect size and transformed the effect back to the units of one or more of the specific instruments.
+Unit of analysis issues
+1.	Cluster trials
+Studies increasingly employ 'cluster randomisation' (such as randomisation by clinician or practice), but analysis and pooling of clustered data poses problems. Authors often fail to account for intra-class correlation in clustered studies, leading to a 'unit of analysis' error (Divine 1992) whereby P values are spuriously low, confidence intervals unduly narrow and statistical significance overestimated. This causes type I errors (Bland 1997; Gulliford 1999).
+None of the presently included trials used cluster randomisation. For the purposes of future updates of this review, where clustering is not accounted for in primary studies we planned to present data in a table with a (*) symbol to indicate the presence of a probable unit of analysis error. In subsequent versions of this review, should we include cluster RCTs, we will seek to contact first authors of studies to obtain intra-class correlation coefficients for their clustered data and to adjust for this by using accepted methods (Gulliford 1999). Where clustering has been incorporated into the analysis of primary studies, we plan to present these data as if from a non-cluster randomised study but adjusted for the clustering effect.
+We have sought statistical advice and have been advised that the binary data as presented in a report should be divided by a 'design effect'. This is calculated using the mean number of participants per cluster (m) and the intra-class correlation coefficient (ICC) (design effect = 1 + (m - 1)*ICC) (Donner 2002). If the ICC is not reported it was assumed to be 0.1 (Ukoumunne 1999).
+If we had identified cluster trials, we would have analysed them taking into account intra-class correlation coefficients and relevant data documented in the report. Synthesis with other studies would have been possible using the generic inverse variance technique.
+2.	Cross-over trials
+None of the presently included studies employed a cross-over trial design. For the purposes of future updates of the review, a major concern of cross-over trials is the carry-over effect. It occurs if an effect (for example, pharmacological, physiological or psychological) of the treatment in the first phase is carried over to the second phase. As a consequence, on entry to the second phase the participants can differ systematically from their initial state despite a wash-out phase. For the same reason cross-over trials are not appropriate if the condition of interest is unstable (Elbourne 2002). As both effects are very likely in severe mental illness, we proposed to only use the data of the first phase of crossover studies.
+3.	Studies with multiple treatment groups
+Where a study involves more than two treatment arms, if relevant, we presented the additional treatment arms in comparisons. If data are binary we simply added these and combined them within the two-by-two table. If data were continuous we combined data following the formula in section 7.7.3.8 (Combining groups) of the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011). Where the additional treatment arms were not relevant, we did not reproduce these data.
+Dealing with missing data
+1.	Overall loss of credibility
+At some degree of loss of follow-up, data must lose credibility (Xia 2009). We chose that, for any particular outcome, should more than 50% of data be unaccounted for we would not reproduce these data or use them within the analyses. If, however, more than 50% of those in one arm of a study were lost, but the total loss was less than 50%, we would address this within the 'Summary of findings' tables by down-rating quality. Finally, we would also downgrade quality within the 'Summary of findings' tables should loss be 25% to 50% in total.
+2.	Binary
+In the case where attrition for a binary outcome is between 0 and 50% and where these data are not clearly described, we presented data on a 'once-randomised-always-analyse' basis (an intention to treat analysis). Those leaving the study early were all assumed to have the same rates of negative outcome as those who completed, with the exception of the outcome of death and adverse effects. For these outcomes the rate of those who stay in the study - in that particular arm of the trial - was used for those who did not. We undertook a sensitivity analysis testing how prone the primary outcomes are to change when data only from people who complete
+the study to that point were compared to the intention to treat analysis using the above assumptions.
+3.	Continuous
+3.1	Attrition
+In the case where attrition for a continuous outcome is between 0% and 50%, and data only from people who complete the study to that point are reported, we reproduced these.
+3.2	Standard deviations
+If standard deviations are not reported, we first tried to obtain the missing values from the authors. If not available, where there are missing measures of variance for continuous data but an exact standard error and confidence intervals available for group means, and either a P value or t value available for differences in mean, we can calculate them according to the rules described in the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011). That is, when only the standard error (SE) is reported, standard deviations (SDs) are calculated by the formula SD = SE * square root (n). Chapters 7.7.3 and 16.1.3 of the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011) present detailed formulae for estimating SDs from P values, t or F values, confidence intervals, ranges or other statistics. If these formulae did not apply, we calculated the SDs according to a validated imputation method which is based on the SDs of the other included studies (Furukawa 2006). Although some of these imputation strategies can introduce error, the alternative would be to exclude a given study’s outcome and thus to lose information. We nevertheless examined the validity of the imputations in a sensitivity analysis by excluding the imputed values.
+3.3	Last observation carried forward
+We anticipated that in some studies the method of last observation carried forward (LOCF) would be employed within the study report. As with all methods of imputation to deal with missing data, LOCF introduces uncertainty about the reliability of the results (Leucht 2007). Therefore, where LOCF data have been used in the trial, if less than 50% of the data have been assumed we would present and use these data and indicate that they are the product of LOCF assumptions.
+Assessment of heterogeneity
+1.	Clinical heterogeneity
+We considered all included studies initially, without seeing comparison data, to judge clinical heterogeneity. We simply inspected all studies for clearly outlying people or situations which we had not predicted would arise. When such situations or participant groups arose, we fully discussed these.
+2.	Methodological heterogeneity
+We considered all included studies initially, without seeing comparison data, to judge methodological heterogeneity. We simply inspected all studies for clearly outlying methods which we had not predicted would arise. When such methodological outliers arose, we fully discussed these.
+3.	Statistical heterogeneity
+3.1	Visual inspection
+We visually inspected graphs to investigate the possibility of statistical heterogeneity.
+3.2	Employing the I2 statistic
+We investigated heterogeneity between studies by considering the I2 statistic alongside the Chi2 P value. The I2 provides an estimate of the percentage of inconsistency thought to be due to chance (Higgins 2003). The importance of the observed value of I2 depends on: i) magnitude and direction of effects, and ii) strength of evidence for heterogeneity (for example, P value from Chi2 test, or a confidence interval for I2). An I2 estimate greater than or equal to around 50% accompanied by a statistically significant Chi2 statistic was interpreted as evidence of substantial levels of heterogeneity (Higgins 2011). When substantial levels of heterogeneity were found in the primary outcome, we explored reasons for the heterogeneity (Subgroup analysis and investigation of heterogeneity).
+Assessment of reporting biases
+Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results (Egger 1997). These are described in section 10 of the Cochrane Handbook for Systematic Reviews of Intervention (Higgins 2011). We are aware that funnel plots may be useful in investigating reporting biases but are of limited power to detect small-study effects. We did not plan to use funnel plots for outcomes where there were 10 or fewer studies, or where all studies were of similar sizes. As no meta-analyses of more than five studies were undertaken, we did not conduct funnel plot analysis.
+Data synthesis
+We understand that there is no closed argument for preference for use of fixed-effect or random-effects models. The random-effects method incorporates an assumption that the different studies are estimating different, yet related, intervention effects. This often seems to be true to us and the random-effects model takes into account differences between studies even if there is no statistically significant heterogeneity. There is, however, a disadvantage to the random-effects model: it puts added weight onto small studies, which often are the most biased ones. Depending on the direction of effect, these studies can either inflate or deflate the effect size. We chose the random-effects model for all analyses. The reader is, however, able to choose to inspect the data using the fixed-effect model.
+Subgroup analysis and investigation of heterogeneity
+1.	Subgroup analyses - only primary outcomes
+1.1 Clinical state, stage or problem
+We proposed to undertake this review and provide an overview of the effects of psychosocial interventions for people with schizophrenia in general. In addition, however, we tried to report data on subgroups of people in the same clinical state, stage and with similar problems.
+2.	Investigation of heterogeneity
+If inconsistency was high, we have reported this. First, we investigated whether data had been entered correctly. Second, if data were correct, we visually inspected the graph and successively removed studies outside of the company of the rest to see if homogeneity was restored. For this review we decided that should this occur, with data contributing to the summary finding of no more than around 10% of the total weighting, we would present the data. If not, then we did not pool the data and discussed the issues. We know of no supporting research for this 10% cut-off, but we use prediction intervals as an alternative to this unsatisfactory state.
+When unanticipated clinical or methodological heterogeneity is obvious we simply stated hypotheses regarding these for future reviews or versions of this review. We do not anticipate undertaking analyses relating to these.
+Sensitivity analysis
+We conducted sensitivity analyses on outcomes of comparisons with four or more trials where studies with different quality were combined to ascertain if there were substantial differences in the results when lesser quality trials or those comprising patients with schizophrenia (or other psychoses) were compared to trials of higher quality or using mixed diagnostic groups. We applied all sensitivity analyses to the primary outcomes based on randomised sequence, allocation concealment and blinding of outcome measurement. We only conducted sensitivity analyses to comparisons with four or more studies as analyses with less than four trials would provide unclear decisions on whether there have been any possible biases in the estimate of effects.
+1.	Implication of randomisation
+We aimed to include trials in a sensitivity analysis if they were described in some way so as to imply randomisation. For the primary outcomes we included these studies and if there was no substantive difference when the implied randomised studies were added to those with a better description of randomisation then we entered all data from these studies.
+2.	Assumptions for lost binary data
+Where assumptions had to be made regarding people lost to followup (see Dealing with missing data) we compared the findings of the primary outcomes when we used our assumptions and when we used data only from people who completed the study to that point. If there was a substantial difference, we reported the results and discussed them but continued to employ our assumption.
+Where assumptions had to be made regarding missing standard deviation (SD) data (see Dealing with missing data), we compared the findings of the primary outcomes when we used our assumptions and when we used data only from people who completed the study to that point. A sensitivity analysis was undertaken testing how prone results were to change when completer-only data were compared to the imputed data using the above assumption. If there was a substantial difference, we reported results and discussed them but continued to employ our assumption.
+3.	Risk of bias
+We analysed the effects of excluding trials that were judged to be at high risk of bias across one or more of the domains of randomisation (implied as randomised with no further details available), allocation concealment, blinding and outcome reporting for the meta-analysis of the primary outcome. If the exclusion of trials at high risk of bias did not substantially alter the direction of effect or the precision of the effect estimates, then we included data from these trials in the analysis.
+4.	Imputed values
+A sensitivity analysis to assess the effects of including data from trials where we used imputed values for ICC in calculating the design effect in cluster randomised trials was not needed for this update as there were no cluster randomised trials.
+If we noted substantial differences in the direction or precision of effect estimates in any of the sensitivity analyses listed above, we
+did not pool data from the excluded trials with the other trials contributing to the outcome but presented them separately.
+R E S U L T S
+Description of studies
+Results of the search
+A total of 4866 citations were found using the search strategy devised for the original version of this review. The inclusion of the word 'drug' in the search strategy produced a vast number of irrelevant references. For the updated search, we found an additional 661 citations of which 52 appeared relevant. From this pool, 25 were considered for inclusion (Cleary 2008). For the current update (2012 search) 130 additional relevant references were scrutinised in October 2012, which resulted in an additional five studies considered for inclusion. Two further studies were considered for inclusion from an updated search in January 2013. See also Figure 1. One trial report was in German (Bechdolf 2011) and was translated into English for the purposes of data extraction.
+Psychosocial interventions for people with both severe mental illness and substance misuse (Review)
+Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+29
+Included studies
+In the previous review (Cleary 2008), 25 randomised controlled trials (RCTs) were selected for inclusion. Three studies (Godley 1994; Maloney 2006; Morse 2006) contained only skewed data (shown as 'other data' within the Data and analyses). The remaining 22 trials provided usable data (either dichotomous or continuous parametric data). For the current update, nine new trials were selected for inclusion. Two studies included in the previous review (Schmitz 2002; Weiss 2007) were excluded in this update as all of the participants were diagnosed with bipolar disorder (see Types of participants). In total, 32 RCTs were included in the current review.
+1.	Design
+Three trials were set exclusively in hospital (Baker 2002; Bechdolf 2011; Swanson 1999) and 19 in the community. Eight trials recruited patients or were conducted in both the community (outpatients) and in hospital (Bellack 2006; Bonsack 2011; Graeber 2003; Hellerstein 1995; Hjorthoj2013; Kavanagh 2004; Madigan 2013; Naeem 2005) and two were set in the community and in jail (Chandler 2006; Maloney 2006).
+Most studies randomly allocated participants to one of two treatment conditions; the exceptions were Burnam 1995; Jerrell 1995a; Jerrell 1995b; Maloney 2006; and Morse 2006. These trials randomly allocated participants to one of three or four (Maloney 2006) interventions. We have used only two of the intervention arms in Burnam 1995 as the other did not fit into any a priori category described for inclusion in this review. Data are shown in additional tables. Study durations ranged from three months to three years and the length of the interventions ranged from less than one hour to three years. There were 19 trials from the USA, six from Australia, three from the UK and one each from Denmark, Germany, Ireland and Switzerland.
+2.	Participants
+A total of 3165 people participated in the trials after giving informed consent and were randomised into one of the treatment arms. All participants were adults (aged 18 to 65 years) who were 'severely mentally ill' with the majority having a diagnoses of schizophrenia, schizoaffective disorder or psychosis. All had a current diagnosis of substance use disorder or had documented evidence of substance misuse. Some were homeless or had a history of unstable accommodation (Burnam 1995; Essock 2006; Morse 2006; Tracy 2007) and some were incarcerated at the time of the study (Chandler 2006; Maloney 2006).
+3.	Interventions
+•	Integrated models of care (4 RCTs).
+•	Non-integrated models of care (4 RCTs).
+•	Combined cognitive behavioural therapy and motivational interviewing (7 RCTs).
+•	Cognitive behavioural therapy (2 RCTs).
+•	Motivational interviewing (8 RCTs).
+•	Contingency management (2 RCTs).
+•	Skills training (2 RCTs).
+Three trials, containing unusable data, were not allocated to a comparison (Godley 1994; Maloney 2006; Morse 2006) although skewed data were noted in 'Other data' tables where available.
+4.	Outcomes
+Where possible, we included dichotomous data relating to loss to treatment, loss to evaluation, death, abstinence or reduced substance use, relapse, attendance at aftercare, and arrests.
+All of the outcome scales and their abbreviations are listed in Table 1 together with the reference of the source of the scale. See below for descriptions of the continuous data scales that reported data used in the analyses. For a full list of the scales mentioned in each of the studies see Characteristics of included studies.
+4.1	Substance use scales
+a.	Drug and alcohol scales from Addiction Severity Index (ASI)
+The ASI (McLellan 1980) provides two summary scores of problems of functioning in seven areas, including psychiatric problems, and those concerning drug and alcohol use. Severity ratings range from zero to nine and are assessments of lifetime and current problem severity derived by the interviewer. Composite scores are mathematically derived and are based on client responses to a set of items based on the last 30 days. Although difficulties have been reported concerning the use of the ASI with people who have severe mental illness (Corse 1995), the psychometric properties of the subscales with this population have been reported by a number of authors (Appleby 1997; Hodgins 1992; Zanis 1997). Given that the problems encountered by the scale are likely to be encountered by any other similar instrument based on self-reports of those with severe and persistent mental illness, it was decided to include data obtained with the ASI (used in Barrowclough 2001; Bechdolf 2011; Bellack 2006; Drake 1998a; Essock 2006; Hellerstein 1995 and Lehman 1993).
+b.	Alcohol Use Inventory (AUI)
+This inventory assesses alcohol use (Horn 1987) (used by Hickman 1997).
+c.	Alcohol Use Scale (AUS)
+A five-point scale based on clinicians' ratings of severity of disorder, ranging from one (abstinence) to five (severe dependence) (Mueser 1995). This was used in Drake 1998a and Essock 2006.
+d.	Cannabis and Substance Use Assessment Schedule (CASUAS) (modified from the SCAN)
+This measures cannabis use and includes similar information to the ASI, such as percentage of days using cannabis in the past four weeks, frequency of cannabis use, and an index of severity (range 0 to 4) with higher scores indicating greater severity (Wing 1990) (used by Edwards 2006).
+e.	Drug Use Scale (DUS)
+A five-point scale based on clinicians' ratings of severity of disorder, ranging from one (abstinence) to five (severe dependence) (Mueser 1995) (used in Drake 1998a and Essock 2006).
+f.	Opiate Treatment Index (OTI)
+The OTI has six domains reflecting treatment outcomes of: drug use, HIV risk-taking behaviour, social functioning, criminality, health status and psychological adjustment (Darke 1991; Darke 1992). The drug use domain consists of 11 items measuring drug use over the last three days (recent drug use) or previous month (28 days) for alcohol, cannabis, amphetamines, cocaine, opiates and other drugs. Clients are asked to estimate the number of drinks or usage of drugs on the two most recent use days in the previous month. The quantity over the two days (q1 + q2) is divided by day interval (t1 + t2). Thus, an OTI score of 1.0 indicates one drink, injection or joint per day; 0.14 to 0.99 more than once a week; 0.01 to 0.13 once a week or less, and 2.0 or more indicates use more than once a day. Higher scores indicate a greater degree of dysfunction or substance use. Baker 2002 and Baker 2006 used the OTI to measure substance use over the previous month.
+g.	Substance Abuse Treatment Scale (SATS)
+An eight-point scale indicating progression toward recovery ranging from one (early stages of engagement) to eight (relapse prevention). Higher scores indicate greater progression (McHugo 1995). This was used by Drake 1998a and Essock 2006.
+h.	Alcohol and drug use disorders section of the Structured Clinical Interview for DSM-III-R (Patient Edition) (SCID)
+Items relate to substance use in the past month (Spitzer 1990). Higher scores indicate a greater degree of dysfunction (used by Baker 2002).
+i.	Substance Use Severity Scale (USS)
+This is a five-point scale, ranging from one (not using) to five (meets criteria for severe use) (Carey 1996), used by Morse 2006.
+4.2	Mental state assessment
+a.	Beck Depression Inventory (BDI)
+This contains 21 self-report items which measure the severity of depression (Beck 1972). Each item comprises four statements (rated from 0 to 4) describing increasing severity on how they felt over the preceding week. Scores range from 0 to 84, with higher scores indicating more severe symptoms (used in Baker 2006; Edwards 2006 used the short form of this scale (BDI-SF)).
+b.	Brief Psychiatric Rating Scale (BPRS)
+Used to assess the severity of a range of psychiatric symptoms, including psychotic symptoms (Lukoff 1986), the scale has 24 items of which 14 are based on the person's self-report in the last two weeks and 10 on the person's behaviour during the interview. Each
+item can be defined on a seven-point scale from one (not present) to seven (extremely severe). Total scoring ranges from 24 to 168 and there are five subscales with minimum scores ranging from three to four depending on the subscale (used in Baker 2006; Drake 1998a; Edwards 2006; and Essock 2006).
+c.	Brief Symptom Inventory (BSI)
+This measures psychiatric symptomatology (Derogatis 1983a). A brief rating scale is used by an independent rater to assess severity of psychiatric symptoms. Scores range from 0 to 4 with higher scores indicating more symptoms (used by Baker 2002 and McDonell 2013).
+d.	Comprehensive Psychopathological Rating Scale (CPRS)
+This is an interview rating scale covering a wide range of psychiatric symptoms, and can be used in total or as subscales. The Montgomery Asperg Depression Rating Scale (MADRS), Brief Scale for Anxiety (BSA) and the Schizophrenia Change Scale (SCR) are all subscales of the CPRS. It comprises 65 items that cover the range of psychopathology over the preceding week (40 symptom items are rated by the participant) (Asberg 1978). Each item is rated on a 0 to 3 scale, varying from 'not present' to 'extremely severe', with high scores indicating more severe symptoms and a worse outcome (used by Naeem 2005).
+e.	Global Assessment of Functioning (GAF)
+The Global Assessment of Functioning is a revised version of the Global Assessment Scale (GAS) (Endicott 1976). The (GAF) scale allows the clinical progress of the patient to be expressed in global terms using a single measure. The GAF allows the clinician to express the patient's psychological, social and occupational functioning on a continuum extending from superior mental health, with optimal social and occupational performance, to profound mental impairment when social and occupational functioning is precluded. Developed by DSM-IV to report global assessment of functioning on the Axis V (DSM-IV) it ranges from 1 to 100 (zero is used to acknowledge inadequate information). Higher scores indicate a better outcome; scores ranging from 1 to 20 indicate a person unable to function independently; 21 to 40 indicate major impairment, severely impaired by delusions; 41 to 60 moderately impaired, having serious symptoms and these patients usually need continuous treatment in a partial hospitalisation or outpatient setting; 61 to 80 indicate slight or mild impairment with transient symptoms; and 81 to 100, good or superior functioning. Baker 2006, Barrowclough 2001, Barrowclough 2010, Bechdolf 2011, Bonsack 2011, Essock 2006 and Madigan 2013 used this scale.
+f.	Health of the Outcome Nation Outcomes Scale (HoNOS)
+HoNOS is a 12-item instrument on a scale of 0 to 4 used to rate patients' symptoms and progress towards health (Wing 1996). Item 3 can be used to rate drug and alcohol use (0 = no problem, 1 = some over-indulgence but within social norm, 2 = loss of control, 3 = marked craving, 4 = incapacitated by alcohol or drug problem) and other items can be used to assess social functioning. Thus, ratings range from 0 to 48 and higher scores indicate a poorer outcome (used by Naeem 2005).
+g.	Insight Scale
+This is used to assess the level of insight the patient has of his or her illness (David 1992). Seven self-report items are scored from 0 = no
+insight to 2 = full insight. One additional self-report item is scored 0 to 4 (used by Naeem 2005).
+h.	The Positive and Negative Syndrome Scale (PANSS)
+The PANSSt was developed from the BPRS and the Psychopathology Rating Scale (Kay 1987). It is used as a method for evaluating positive, negative and other symptom dimensions in schizophrenia. The scale has 30 items and each item can be defined on a seven-point scoring system, varying from one (absent) to seven (extreme), so total scores range from 30 to 210. This scale can be divided into three subscales for measuring the severity of general psychopathology (range 16 to 112), positive symptoms (PANSS-P, range 7 to 49) and negative symptoms (PANSS-N, range 7 to 49). A low score indicates low levels of symptoms. This was used by Barrowclough 2001, Barrowclough 2010, Bechdolf 2011, Bonsack 2011 and Kemp 2007.
+i.	Psychiatric scale from Addiction Severity Index (ASI-psychiatric)
+Psychiatric subscores (McLellan 1980) were reported in Lehman 1993 and Hellerstein 1995. See the ASI scoring above.
+j.	Scale for the Assessment of Negative Symptoms (SANS)
+The scale assesses negative symptoms for schizophrenia (Andreasen 1982). This assesses five symptoms complexes to obtain the clinical rating of negative symptoms over the preceding week. They are affective blunting, alogia, apathy, anhedonia and disturbance of attention. Each item uses a six-point scale ranging from 0 (not at all) to 5 (indicating severe). High scores indicate a worse outcome (used by Edwards 2006).
+k.	Symptom Checklist 90 (revised) (SCL-90-R)
+Used to measure psychiatric symptoms (Derogatis 1983a), the scale has 90 self-report items designed to measure nine symptom dimensions. Each item has a five-point Likert scale ranging from 0 (mild or not at all) to 4 (severe or extremely distressing), with higher scores indicating greater symptomatology (used by Hickman 1997).
+4.3	Quality of life and client satisfaction
+a.	The Quality of Life Interview (QOLI) and the Brief Quality of Life Scale (BQOL)
+The QOLI contains 153 items that measure global life satisfaction as well as objective and subjective quality of life (Lehman 1988; Lehman 1995). It has eight domains (for example, living situations, daily activities and functioning, family relations, social relations). Rated on a 7-point scale (1 = terrible, 2 = unhappy, 3 = mostly dissatisfied, 4 = equally satisfied and dissatisfied, 5 = mostly satisfied, 6 = pleased, and 7 = delighted) with higher scores indicating better quality of life. It was used by Baker 2006, Bellack 2006, Drake 1998a, Essock 2006 and Lehman 1993.
+b.	World Health Organization's Quality of Life scale (WHOQOL-BREF)
+The WHOQOL-BREF is a 26-item scale (Skevington 2004) assessing physical health, psychological well being, social relationships, and environmental factors (for example, home environment, recreation, access to health care, physical safety and financial resources). It also contains two general items and each item is rated on a 5-point scale (1 to 5, with higher scores = better quality) (used by Madigan 2013).
+c.	Client Satisfaction Questionaire (CSQ)
+The CSF questionnaire (CSQ) (Larsen 1979) is a self-report instrument that consists of eight items designed to measure global patient satisfaction of services provided and if they met their needs or approval. The items are rated on a 4-point scale (minimum of 1 = no definitely not to maximum 4 = very satisfied), with a minimum score of 8 and maximum of 32 and higher scores indicating greater satisfaction (used by Hjorthoj 2013).
+4.4	Social functioning
+a.	Role Functioning Scale (RFS)
+This is a self-report scale whereby the total of four subscales measures global role functioning (Green 1987). Scores reported are summary scores derived from four independent raters. Higher scores indicate better functioning (used by Jerrell 1995a and Jerrell 1995b).
+b.	Social Adjustment Scale for the Severely Mentally Ill (SAS-SMI)
+An abbreviated version of the Social Adjustment Scale II is used to assess social adjustment (Wieduwilt 1999), with a self-reported scale composed of 24 items covering seven areas including social, family and work functioning designed specifically for use with schizophrenic populations. Scores range from 1 to 7, with a high score indicating poor outcome (used by Jerrell 1995a and Jerrell 1995b).
+c.	Social Functioning Scale (SFS)
+A self-report scale developed for people with schizophrenia which enumerates basic skills necessary for community living and performance (Birchwood 1990), the SFS is a 79-item questionnaire that uses a 4-point rating scale (0 to 3) of frequency or ability. Items are grouped into seven domains. Raw scores for each subscale are converted to a standard score; overall functioning is based on the mean standard score (Burns 2007). Higher standardised scores indicate better functioning (range 55 to 135) (Birchwood 1990). This was used by Barrowclough 2001.
+d.	The Social and Occupational Functioning Scale (SOFAS)
+SOFAS was derived from the GAF scale and is used to assess levels of physical and mental functioning in social and work settings (Burns 2007; Goldman 1992). Scored similarly to the GAF (see above) by an observer it ranges from 0 to 100 with zero representing inadequate information. Higher scores indicate better outcomes (used by Bonsack 2011 and Edwards 2006).
+e.	Service Utilisation Rating Scale (SURS)
+This measures inpatient and outpatient attendance and medication usage (Mihalopoulos 1999) (used by Edwards 2006).
+Excluded studies
+In the current update, we excluded 46 studies or trials identified through the initial search (July 2012): five were not randomised, 30 did not include participants with a concurrent diagnosis of severe mental illness and substance misuse, 10 studies used a non-psychosocial intervention or did not include a specific substance misuse treatment programme, and one trial had no usable data. Five further full text articles that were identified through subsequent searches (Bagoien 2013; Jones 2011; Sigmon 2000; Smeerdijk 2010; Weiss 2009) were excluded.
+In the 2008 review, we excluded 68 studies (this did not include related studies, please see Characteristics of excluded studies). Twenty-six were not randomised or used a quasi-randomisation method, 18 did not have participants with a concurrent diagnosis of severe mental illness and substance misuse, and 14 used a nonpsychosocial intervention or did not include a specific substance misuse treatment programme. A further 10 RCTs were excluded either due to high attrition rates or unclear reporting (attempts were made to contact all authors for further information). One study previously listed in the 2008 review as ongoing (Sitharthan 1999) was excluded in the current review. Two studies previously included in the 2008 review (Schmitz 2002; Weiss 2007) were excluded as all the participants had bipolar disorder.
+Two studies are listed as awaiting assessment (Meister 2010; Odom 2005). Both are dissertations: one requires translation from German to English and the other has been requested.
+We found 12 ongoing studies and have tried to contact the authors for further information. Three trials each intend to assess cognitive behavioual therapu versus treatment as usual, motivational interviewing plus cognitive behavioural therapy versus treatment as usual and contingency management; one trial intends to assess motivational interviewing versus treatment as usual, one integrative therapy, and one will use an (undescribed) educational intervention.
+Risk of bias in included studies
+For a summary of the overall risk of bias in the included trials please see Figure 2 and Figure 3.
+Allocation
+1. Random generation
+All 32 studies were stated to be randomised. Some used blocking or stratification methods in the sequence to obtain evenly balanced groups or different proportions (2:1) for each intervention, site, or to control for various demographic variables (type or degree of substance use, gender or psychiatric diagnosis). Ten studies stated the sequence was computer generated (Barrowclough 2001; Barrowclough 2010; Bonsack 2011; Chandler 2006; Edwards 2006; Essock 2006; Hjorthoj 2013; Madigan 2013; Maloney 2006; Naeem 2005), but often it was unclear how the sequence was generated (for example, random number table). Six studies used urn randomisation or placed cards in envelopes that were shuffled to produce a random sequence (Bellack 2006; Jerrell 1995a; Jerrell 1995b; Kemp 2007; Lehman 1993; McDonell 2013). Four other studies mentioned that a numbered table or random sequence was used to generate the sequence or that the sequence was stratified (Kavanagh 2004; Nagel 2009; Swanson 1999; Tracy 2007) but it was not clear if a computer was involved as no further details were provided. The 20 studies listed above (with the exception of Maloney 2006) were classified as low risk of selection bias as they provided some details of the allocation process or further particulars were provided by the researchers. Some participants in the Maloney 2006 study were not randomised due to procedural difficulties. The remaining studies did not provide enough details of how the allocation sequence was generated to make a judgement so were classified as of unclear quality with a moderate risk of selection bias and an overestimate of positive effect.
+2. Allocation concealment
+One study provided a full description of the methods used to generate the random sequence and allocation concealment (Barrowclough 2010). Seven studies provided some details or made
+explicit their method used for allocation concealment. Four other studies used urn method randomisation (Jerrell 1995a; Jerrell 1995b; Lehman 1993; McDonell 2013), which has a low risk of bias if used properly, and some confirmed this via personal emails. Five trials stated that allocation concealment was achieved by a third party or researcher who was independent of the treating team (Barrowclough 2001; Chandler 2006; Edwards 2006; Hjorthoj 2013; Madigan 2013) but often no further details were provided. The 10 studies listed above were judged as low risk as it was implied that the allocation concealment was adequate. Two trials (Maloney 2006; Swanson 1999) were judged high risk of bias as the researcher or therapist was involved with allocating patients. Four studies used sealed envelopes or patients selected a card (Baker 2006; Bonsack 2011; Kemp 2007; Naeem 2005). However, it was not clear if the envelopes were opaque or if other measures were taken to ensure concealment, so these were judged as unclear risk. The remaining studies were classified as of unclear quality with a moderate risk of selection bias and overestimate of positive effect as no details were provided regarding allocation concealment, but this may be due to incomplete reporting and not how the study was conducted.
+Blinding
+1. Performance bias
+We classified blinding in respect to primary outcomes for performance and detection bias. Due to intervention characteristics, that is being a therapy or model of service, we assumed the participants and clinicians as being implicitly not blind to treatment assignment when considering performance bias. Therefore, we judged performance bias of all trials to be of unclear risk.
+2. Detection bias
+Overall, 15 studies stated that independent raters were blinded to allocation when assessing clinical ratings of mental state or substance use. For 13 other studies it was unclear if the raters were blind to treatment as this was not stated. Four studies (Graeber 2003; Kemp 2007; Maloney 2006; Nagel 2009) were judged at high risk of bias because it was stated that the outcome assessors were not blind to treatment allocation and it was therefore possible to assess the risk of bias in these studies with higher confidence for clinical-based ratings. In three of these studies blinding status would not influence the primary outcome data as these were administrative measures (hospital readmissions, convictions, time to first outpatient appointment etc), however they were still judged high risk as less effort may have been made to follow-up those in the control arm.
+Incomplete outcome data
+We only rated risk of incomplete outcome data in respect to the primary outcome. The number of participants lost to treatment or evaluation across studies ranged from 0% to 57%. Four trials were judged as adequately addressing incomplete outcome data and were rated as low risk of attrition bias because there were no missing outcome data (Hickman 1997; Lehman 1993; Swanson 1999) and for one study (Graeber 2003) there were no missing values for the primary outcome.
+The following trials were rated as high risk. Bellack 2006 excluded 46 of 175 participants after they were randomised, a further 19 participants because they did not become engaged in treatment, and a further 27 were lost to follow-up. Therefore, a significant proportion of participants (92/175, 53%) were excluded from the analysis, which may have a clinically relevant bias in intervention effect estimates. The attrition rate was greater than 50% for Bond 1991a, Godley 1994 (at 18 months) and Hellerstein 1995 (at 8 months) so data from these trials were excluded from the analysis as per protocol. In the Chandler 2006 trial the attrition rate for the primary outcome measure was 37% (68/182); and for the controls no interviews were conducted to ascertain their whereabouts (moved from area, reincarcerated or died) and this may have led to severe bias. Three further trials were rated as high risk as more than 40% of patients were lost to follow-up; no reasons were given for them being missing and a full intention-to-treat (ITT) analysis was not reported (Baker 2002; Jerrell 1995a; Jerrell 1995b).
+Many, but not all, included studies provided reasons for attrition. Reasons given included: some of the participants died during the trial, some could not be contacted or moved elsewhere, and some withdrew. Seven studies reported their results based on a full ITT analysis with all missing data imputed for primary outcomes using appropriate methods (Barrowclough 2010; Bechdolf 2011; Bonsack 2011; Edwards 2006; Hjorthoj 2013; Naeem 2005; Nagel 2009). These were rated as unclear as all imputation strategies can bias study results. The remaining trials were rated as 'unclear'. They either did not address this issue, presented insufficient information of attrition or exclusions to permit judgement (that is, no reasons for missing data provided or numbers lost to evaluation not stated for each group) or did not report a full ITT analysis with imputed missing values (Baker 2006; Barrowclough 2001; Bond 1991b; Burnam 1995; Drake 1998a; Essock 2006; Kavanagh 2004; Kemp 2007; Madigan 2013; Maloney 2006; McDonell 2013; Morse 2006; Tracy 2007).
+Selective reporting
+Four studies were rated as high quality in reporting outcomes with a low risk of reporting bias (Barrowclough 2001; Barrowclough 2010; Hjorthoj 2013; McDonell 2013) as the pre-specified outcomes listed in the trial protocol were fully reported (cases or means, SD and number (n) for each outcome at specific time points) in the study report. Conversely, four studies were rated as low quality with a high risk of reporting bias (Godley 1994; Maloney 2006; Morse 2006; Tracy 2007) as they presented data in a way we could not consider as free of suggestion of selective outcome reporting. For these studies, there were no usable data or data were reported incompletely for each treatment arm or in a way (for example, as correction matrix, graphically or in a mixed-methods model) that they could not be entered in a meta-analysis. For the rest of the studies the risk of bias was assessed as unclear with a moderate risk of reporting bias due to insufficient information to permit judgement of yes or no; there was no protocol to assess the presence of selective reporting.
+Other potential sources of bias
+The risk of other potential sources of bias was rated as low as no evidence of other bias was apparent. Most were publicly funded trials. No declaration of interest was made by authors, and we assume there was none to be made. However, many study authors were active pioneers in developing and the implementation of the experimental intervention model across the scientific community and clinical world. This raises the issue of how researcher beliefs could affect the entire process of evaluating an intervention in an RCT. Although conscious of this issue, we decided not to make any attempt to rate it as it is very difficult to judge, and erroneous quantification could drive bias into our conclusions.
+Effects of interventions
+See: Summary of findings for the main comparison INTEGRATED MODELS OF CARE compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 2 NON-INTEGRATED MODELS OF CARE OR INTENSIVE CASE MANAGEMENT compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 3 COGNITIVE BEHAVIOUR THERAPY + MOTIVATIONAL INTERVIEWING compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 4 COGNITIVE BEHAVIOUR THERAPY compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 5 COGNITIVE BEHAVIOUR THERAPY and PSYCHOSOCIAL REHABILITATION compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 6 COMBINED COGNITIVE BEHAVIOUR THERAPY and INTENSIVE CASE MANAGEMENT compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 7 INTENSIVE CASE MANAGEMENT compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 8 MOTIVATIONAL INTERVIEWING compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 9 SKILLS TRAINING compared to TREATMENT AS USUAL for both severe mental illness and substance misuse; Summary of findings 10 SPECIALISED CASE MANAGEMENT SERVICES compared to STANDARD CARE for both severe mental illness and substance misuse; Summary of findings
+11 CONTINGENCY MANAGEMENT compared to TREATMENT AS USUAL for both severe mental illness and substance misuse
+Comparison 1: integrated models of care versus treatment as usual
+See Summary of findings for the main comparison. Data for this comparison came from four trials (Burnam 1995; Chandler 2006; Drake 1998a; Essock 2006).
+1.1	Lost to treatment
+By the end of treatment (36 months) we found no significant difference in the likelihood of participants being lost to treatment from the pooled results of Chandler 2006; Drake 1998a and Essock 2006 (treatment group 24% lost, control group 21% lost; n = 603, RR 1.09 CI 0.82 to 1.45, Analysis 1.1). Statistical heterogeneity was not present (Chi2 = 1.95, df = 2 (P = 0.38); I2 = 0%).
+1.2	Lost to evaluation
+The control group for Burnam 1995 were 46% more likely to be lost to evaluation by 3 months (treatment group 15% lost, control 28% lost; n = 132, RR 0.54 CI 0.27 to 1.08), although not statistically significant. Six months data (Burnam 1995; Essock 2006) also did not reveal any significant difference between groups (n = 330, RR 0.69 CI 0.27 to 1.73, Analysis 1.2). Nine, 12, 24 and 36 months data were also not significantly different. For 36 month data we combined the results from three studies (Chandler 2006; Drake 1998a; Essock 2006) in a meta-analysis. There was considerable statistical heterogeneity (Ch|2 = 7.70, df = 2 (P = 0.02); I2 = 74%). Closer inspection of the forest plot indicated a higher retention rate in the treatment group in Drake 1998a, likely to account for this heterogeneity.
+1.3	Death
+We found no significant differences in the pooled results of Drake 1998a and Essock 2006 with regards to the likelihood of participants dying by the end of 36 months of treatment (treatment 3% died, control 3% died; n = 421, RR 1.18 CI 0.39 to 3.57, Analysis 1.3). Statistical heterogeneity was not present (Ch|2 = 0.68, df = 1, P = 0.40; I2 = 0%).
+1.4	Substance use
+We found no significant difference (Drake 1998a) between groups in the likelihood of participants not being in remission (alcohol -treatment 57%, control 50%; n = 143, RR 1.15 CI 0.84 to 1.56; drugs -treatment 58%, control 65%; n = 85, RR 0.89 CI 0.63 to 1.25, Analysis 1.4) or in their average SATS scores by 6 months (n = 203, weighted mean difference (MD) 0.07 CI -0.28 to 0.42) or 36 months (n = 203, MD 0.11 CI -0.41 to 0.63, Analysis 1.5). Further outcome data related to alcohol use (Analysis 1.6), drug use (Analysis 1.7) and general substance use attitudes (Analysis 1.8) contained skewed data and are reported in 'Other data' tables.
+1.5	Mental state
+We found that the relapse data (Analysis 1.9) and BPRS scores (Analysis 1.10) contained wide confidence intervals (skewed data) and reported these in 'Other data' tables.
+1.6	Service utilisation
+We found that the pooled results of two studies (Drake 1998a; Essock 2006) for average number of days spent in stable community
+residences (not in hospital) by 12 months were equivocal (n = 378, MD -10.00 CI -38.61 to 18.60), and also between 24 (n = 203, MD 7.40 CI -6.32 to 21.12) and 36 months (n = 364, MD 5.17 CI -9.20 to 19.55, Analysis 1.11). Statistical heterogeneity was not present (Ch|2 = 0.31, df = 1, P = 0.58; I2 = 0%). We found no significant difference (Essock 2006) in likelihood of hospitalisation by 36 months (treatment 42% hospitalised, control 48% hospitalised; n = 198, RR 0.88 CI 0.64 to 1.19, Analysis 1.12 ). Other measures (skewed data) of service use are reported in 'Other data' tables (Analysis 1.13).
+1.7	Functioning
+Only Essock 2006 reported data for functioning and we found no significant differences for average global functioning scores (GAF) at 6 months (n = 162, MD 1.10 CI -1.58 to 3.78), 12 months (n = 171, MD 0.70 CI -2.07 to 3.47), 18 months (n = 176, MD 1.00 CI -1.58 to 3.58), 24 months (n = 166, MD 1.70 CI -1.18 to 4.58), 30 months (n = 164, MD -0.60 CI -3.56 to 2.36) or 36 months (n = 170, MD 0.40 CI -2.47 to 3.27, Analysis 1.14). Forensic measures (Analysis 1.15), number of hours requiring medication (Analysis 1.16), per cent of time on the street (Analysis 1.17) and time in independent housing (Analysis 1.18) were skewed and are reported in 'Other data' tables.
+1.8	Satisfaction
+The pooled results of Drake 1998a and Essock 2006 revealed no significant difference in average general life satisfaction (QOLI) scores by 6 months (n = 361, MD -0.11 CI -0.41 to 0.20), 12 months (n = 372, MD 0.02 CI -0.28 to 0.32), 18 months (n = 377, MD 0.09 CI -0.27 to 0.44), 24 months (n = 370, MD 0.02 CI -0.29 to 0.33), 30 months (n = 366, MD 0.02 CI -0.27 to 0.32) and 36 months (n = 373, MD 0.10 CI -0.18 to 0.38, Analysis 1.19). Statistical heterogeneity was not present at any of the 6 time points (for example, 24 months: Ch|2 = 1.09, df = 1, P = 0.30; I2 = 8%).
+Comparison 2: non-integrated models of care (intensive case management) versus treatment as usual
+See Summary of findings 2. Four trials assessed this comparison (Bond 1991a; Bond 1991b; Jerrell 1995b; Lehman 1993).
+2.1	Lost to treatment
+Pooled results of Bond 1991a; Bond 1991b and Jerrell 1995b showed a 23% increase in the likelihood of patients being lost from the treatment group by 6 months (treatment 27% lost, control 22% lost; n = 134, RR 1.23 CI 0.73 to 2.06), which was not statistically significant. Longer-term evaluations at 12 months (treatment 28% lost, control 24% lost; n = 134, RR 1.21 CI 0.73 to 1.99) and 18 months (treatment 51% lost, control 37% lost; n = 134, RR 1.35 CI 0.83 to 2.19, Analysis 2.1) did not reveal any significant difference between groups. Statistical heterogeneity was not present at 6 months, 12 months, 18 months, 24 months, 30 months or 36 months.
+2.2	Lost to evaluation
+We found no significant difference in the pooled results (Bond 1991b; Jerrell 1995b; Lehman 1993) for lost to evaluation by 6 months (treatment 10% lost, control 10% lost; n = 121, RR 1.00 CI 0.38 to 2.60) and by 12 months (treatment 12% lost, control 12% lost; n = 121, RR 1.00 CI 0.43 to 2.35). Pooled results (Bond 1991b; Jerrell 1995b) at 18 months also revealed no significant differences between treatment groups (treatment 43% lost, control 33% lost; n = 92, RR 1.26 CI 0.48 to 3.30, Analysis 2.2). Statistical heterogeneity was not present at 6, 12, or 18 months.
+2.3	Substance use and mental state
+Data for substance use (Analysis 2.3) and mental state (Analysis 2.4) were skewed and are included in 'Other data' tables.
+2.4	Functioning
+We found no significant difference in the average role functioning (RFS) scores (Jerrell 1995b) by 6 months (n = 50, MD -0.78 CI -2.91 to 1.35) or 12 months (n = 50, MD 0.70 CI -1.56 to 2.96), although by 18 months the data favoured the control group (n = 29, MD -2.67 CI -5.28 to -0.06, Z = 2.00, P = 0.045, Analysis 2.5). The average baseline means (SD) on the RFS were similar between groups so did not explain this difference: baseline treatment 9.46 (4.11) to 10.77 (2.36) at 18 months; and baseline control 10.03 (3.87) to 13.44 (4.78) at 18 months. Note that higher scores indicate better functioning.
+We found no significant difference in average levels of social adjustment scores (SAS) by 6 months (Jerrell 1995b) (n = 50, MD -0.93 CI -6.34 to 4.48), 12 months (n = 50, MD 3.09 CI -2.71 to 8.89) or 18 months (n = 29, MD -3.75 CI -10.12 to 2.62, Analysis 2.6).
+2.5	Satisfaction
+Data for average life satisfaction (QOLI) were skewed so are reported in 'Other data' tables (Analysis 2.7).
+Comparison 3: cognitive behavioural therapy + motivational interviewing versus treatment as usual
+See Summary of findings 3. Data for this comparIson came from seven trials (Baker 2006; Barrowclough 2001; Barrowclough 2010; Bellack 2006; Hjorthoj 2013; Kemp 2007; Madigan 2013).
+3.1	Lost to treatment
+We found that the results from Baker 2006 indicated that the treatment group were 17 times more likely to be lost to treatment by 3 months (treatment 12%, control 0%; n = 130, RR 17.00 CI 1.0 to 288.56). In contrast, Madigan 2013 reported no significant group difference in lost to treatment by 3 months (treatment 29%, control 24%; n = 88, RR 1.19 CI 0.56 to 2.55). Combined, the treatment group was more likely to be lost to treatment by 3 months (treatment 20%, control 7%; n = 218, RR 3.37 CI 0.20 to 57.79) and there was considerable statistical heterogeneity (Chi2 = 3.95, df = 1, P = 0.05; I2 = 75%). Six month data (Barrowclough 2010; Bellack 2006; Hjorthoj 2013) revealed no significant difference for loss to treatment (treatment 29%, control 23%; n = 605, RR 1.02 CI 0.68 to 1.54, P = 0.91). Similarly, we found 9 to 10 month data (Barrowclough 2001; Hjorthoj 2013) were not significantly different in rates of loss to treatment (treatment 23%, control 32%; n = 139, RR 0.72 CI 0.42 to 1.23) nor were 12 month data significantly different (Barrowclough 2010) (treatment 17.7%, control 17.8%, Analysis 3.1). Statistical heterogeneity was not present at 6 or 9 to 10 months.
+3.2	Lost to evaluation
+We found all data to be equivocal between the treatment and control groups by 3 months (Baker 2006) (treatment 8% lost, control 6% lost; n = 130, RR 1.25 CI 0.35 to 4.45) and by 6 months (Baker 2006; Bellack 2006; Kemp 2007) (treatment 15% lost, control 14% lost; n = 259, 3 RCTs, RR 1.02 CI 0.35 to 2.94). Longer evaluation times also did not reach statistical significance, at 9 months (Barrowclough 2001) (treatment 11%, control 17%; n = 36, RR 0.67 CI 0.13 to 3.53), 12 months (Baker 2006; Barrowclough 2001;
+Madigan 2013) (treatment 31%, control 21%; n = 254, 3 RCTs, RR1.35 CI 0.87 to 2.08), 18 months (Barrowclough 2001; Barrowclough 2010); (treatment 20%, control 22%; n = 363, 2 RCTs, RR 0.92 CI 0.61 to 1.38) and 24 months (Barrowclough 2010) (treatment 21%, control 28%; n = 327, 1 RCT, RR 0.76 CI 0.52 to 1.11, Analysis 3.2). Statistical heterogeneity was not present for any of the above subgroup analyses.
+3.3	Death
+We found no significant difference in the pooled results (Baker 2006; Barrowclough 2001; Barrowclough 2010) for the likelihood of participants dying by about 1 year (treatment 2.4%, control 3.3%; n = 493, 3 RCTs, RR 0.72 CI 0.22 to 2.41, Analysis 3.3). Statistical heterogeneity was not present (Ch|2 = 2.18, df = 2, P = 0.34; P = 8%). Similarly, we found no significant difference for the likelihood of participants hospitalised or dying versus alive and not admitted to hospital by 24 months (Barrowclough 2010) (treatment 23%, control 20%; n = 326, RR 1.15 CI 0.76 to 1.74, Analysis 3.4).
+3.4	Substance use
+Substance use from polydrug usage was not significantly different by 3 months (Baker 2006) (n = 119, MD 0.37 CI -0.01 to 0.75), or by 6 months (n = 119, MD 0.19 CI -0.22 to 0.60, Analysis 3.5). Moreover, cannabis use in the last 30 days was not significantly different at 3 months, the end of treatment (Madigan 2013) (n = 50, MD -0.2 CI -2.54 to 2.14) or at 12 months (Madigan 2013) (n = 42, MD -0.3 CI -2.84 to 2.24, Analysis 3.6). Averages of various substance use measures that reported skewed data are shown in 'Other data' tables (Analysis 3.7; Analysis 3.8).
+3.5	Mental state
+We were only able to include limited data for relapse and found no significant difference in the likelihood of relapse between groups (Barrowclough 2001) by 9 months (treatment 28% relapsed, control 56% relapsed; n = 36, RR 0.50 CI 0.21 to 1.17), or by 12 months (treatment 33%, control 67%; n = 36, RR 0.50 CI 0.24 to 1.04), or 18 months (treatment 39%, control 67%; n = 36, RR 0.58 CI 0.30 to 1.13, Analysis 3.9). No significant differences were found for total PANSS scores between treatment groups by 6 months (Hjorthoj 2013; Kemp 2007) (n = 78, MD 0.99 CI -5.91 to 7.89), 9 to 10 months (Barrowclough 2001; Hjorthoj 2013) (n = 92, MD -5.01 CI -11.25 to 1.22), 12 months (Barrowclough 2010) (n = 274, MD 2.52 CI -0.68 to 5.72) and by 24 months (Barrowclough 2010) (n = 247, MD 2.71 CI -0.58 to 6.00, Analysis 3.10). Moreover, no significant differences were reported for the PANSS positive symptom (Analysis 3.11) nor the PANSS negative symptom (Analysis 3.12) subscales at 12 or 24 months. Statistical heterogeneity was not present for any of the above time points. Average scores for other measures of mental state that reported skewed data are presented in 'Other data' tables (Analysis 3.13).
+3.6	Functioning
+3.6.1 Arrests
+We found the number of reported arrests (Bellack 2006) were not significantly different between treatment and control group by 6 months (treatment 13%, control 27%; n = 110, RR 0.49 CI 0.22 to 1.10, Analysis 3.14).
+3.6.2 Global assessment of functioning
+Global assessment scores for functioning (GAF) were not significantly different by 3 months (Baker 2006; Madigan 2013) (n = 177, MD -1.17 CI -4.57 to 2.23), 6 months (n = 119, MD -0.09 CI -3.70 to 3.52), 12 months (Baker 2006; Barrowclough 2001; Barrowclough 2010; Madigan 2013) (n = 445, 4 RCTs, MD 1.24 CI -1.86 to 4.34), 18 months (n = 28, 1 RCT, MD 6.68 CI -5.24 to 18.60) or 24 months (n = 234, 1 RCT, MD -0.21 CI -2.93 to 2.51, Analysis 3.15). Lower scores indicate poorer functioning. Statistical heterogeneity was not present at 3 months or 12 months (Chi2 = 5.20, df = 3, P = 0.16; I2 = 42%).
+3.6.3 Social functioning
+We found no significant difference by 9 months (Barrowclough 2001) (n = 32, MD 5.01 CI -0.55 to 10.57) in social functioning scores. However, by 12 months (3 months following end of treatment) results favoured the treatment group (high scores = better) (Barrowclough 2001) (n = 32, MD 7.27 CI 0.86 to 13.68, Analysis 3.16).
+3.7	Quality of life
+Average general life satisfaction scores (BQOL) were higher for the treatment group (Bellack 2006) by 6 months (n = 110, MD 0.58 CI 0.00 to 1.16, P = 0.049, Analysis 3.17), although confidence intervals crossed the line of no effect. Differences in baseline means (SD) did not account for this finding (treatment 4.25 (1.65) to 4.79 (1.66) at 6 months, and control 3.96 (1.58) to 4.21 (1.43) at 6 months). Lower scores indicate less life satisfaction. However, no significant differences were found in overall quality of life scores (BQOL) by 6 months (Bellack 2006) (n = 110, MD -0.02 CI -0.61 to 0.57, Analysis 3.18). No significant differences in WHOQOL Bref scores were reported by Kemp 2007 (n = 16, MD -15.70 CI -36.19 to 4.79, Analysis 3.19) nor were there any significant differences in quality of life scores using the MANSA by 6 months (Hjorthoj 2013) (n = 64, MD -2.70 CI -7.01 to 1.61) or 10 months (n = 61, MD 0.90 CI -3.73 to 5.53, Analysis 3.20).
+3.8	Satisfaction
+One study (Hjorthoj 2013) reported client satisfaction was higher for the treatment group by 10 months (n = 62, MD 6.40 CI 3.87 to 8.93, P < 0.001, Analysis 3.21). The average direct cost subscale of the BQOL at 6 months reported by Bellack 2006 was skewed and is reported in 'Other data' tables (Analysis 3.22).
+Comparison 4: cognitive behavioural therapy versus treatment as usual
+See Summary of findings 4. Data for this comparison came from two trials (Edwards 2006; Naeem 2005).
+4.1	Lost to treatment
+We found that the data for being lost from treatment (Edwards 2006; Naeem 2005) by 3 months were not significantly different (treatment 18%, control 23% lost; n = 259, RR 1.12 CI 0.44 to 2.86, Analysis 4.1). Statistical heterogeneity was not present (Ch|2 = 0.00, df = 1, P = 0.95; I2 = 0%).
+4.2	Lost to evaluation
+The number of participants lost to evaluation (Edwards 2006) after 9 months were similar in each group (treatment 30%, control 29%; n = 47, RR 1.04 CI 0.43 to 2.51, Analysis 4.2).
+4.3	Substance use
+No significant differences were found in the use of cannabis (Edwards 2006) in the previous 4 weeks between groups at 3 months assessment (treatment 57%, control 54%; n = 47, RR 1.04 CI 0.62 to 1.74). Six month data were also not significantly different (n = 47, RR 1.30 CI 0.79 to 2.15, Analysis 4.3). Various measures of substance use reporting skewed data are shown in 'Other data' tables (Analysis 4.4).
+4.4	Mental state
+We found no significant difference on insight scores (Insight Scale) by 3 months (Naeem 2005) (n = 105, MD 0.52 CI -0.78 to 1.82, Analysis 4.5). Various measures of mental state reporting skewed data are shown in 'Other data' tables (Analysis 4.6).
+4.5	Functioning
+We found no significant difference in average social and occupational functioning scores (Edwards 2006) (SOFAS) by 3 months (n = 47, MD -0.80 CI -9.95 to 8.35) or 6 months (n = 47, MD -4.70 CI -14.52 to 5.12, Analysis 4.7). Average HONOS scores (Analysis 4.8) and outpatient medication (Analysis 4.9) are shown in 'Other data' tables due to skewed data.
+Comparison 5: cognitive behavioural therapy + psychological rehabilitation versus treatment as usual
+5.1 Functioning
+See Summary of findings 5. We were only able to add outcome data relating to functioning and these were all skewed data, which are reported in 'Other data' tables (Maloney 2006). There was no real indication that the number of arrests was less in the cognitive behavioural therapy + psychosocial rehabilitation group over all the time periods (Analysis 5.1), and this also applied to the number of convictions (Analysis 5.2). The number of days in jail for each group was also not really noticeably different (Analysis 5.3). It should be stressed that all data were skewed and not reanalysed, merely reported again in this review.
+Comparison 6: combined cognitive behavioural therapy + intensive case managementversus treatment as usual
+6.1 Functioning
+See Summary of findings 6. We were only able to add outcome data relating to functioning and these were all skewed data, which are reported in 'Other data' tables (Maloney 2006). There is some indication that the number of arrests was less in the cognitive behavioural therapy + intensive case management group over all the time periods (Analysis 6.1) and this also applied to the number of convictions (Analysis 6.2). However, the number of days in jail for each group was not noticeably different (Analysis 6.3). It should be stressed that all data were skewed and not reanalysed, merely reported again in this review.
+Comparison 7: intensive case management versus treatment as usual
+See Summary of findings 7.
+7.1 Functioning
+We were only able to add outcome data relating to functioning and these were all skewed data, which are reported in 'Other data'
+tables (Maloney 2006). There is no real indication that the number of arrests was less in the intensive case management group over all the time periods (Analysis 7.1) and this also applied to the number of convictions (Analysis 7.2). The number of days in jail for each group was also not noticeably different (Analysis 7.3). It should be stressed that all data were skewed and not reanalysed, merely reported again in this review.
+Comparison 8: motivational interviewing versus treatment as usual
+See Summary of findings 8. Data for this comparison came from eight trials (Baker 2002; Bechdolf 2011; Bonsack 2011; Graeber 2003; Hickman 1997; Kavanagh 2004; Nagel 2009; Swanson 1999).
+8.1	Lost to treatment
+Bonsack 2011 had an unusually long treatment period using motivational interviewing (6 months). There were no significant differences in lost to treatment at 3 months (n = 62, RR 0.89 CI 0.30 to 2.61) or 6 months (n = 62, RR 1.71 CI 0.63 to 4.64, Analysis 8.1).
+8.2	Lost to evaluation
+Pooled results from six studies (Baker 2002; Bechdolf 2011; Graeber 2003; Hickman 1997; Kavanagh 2004; Swanson 1999) revealed no significant difference in those lost to evaluation by 3 months (treatment 17% lost, control 16% lost; n = 398, RR 1.12 CI 0.64 to 1.96). Similarly, 6 month data (Bechdolf 2011; Graeber 2003; Kavanagh 2004; Nagel 2009) (n = 164, 4 RCTs, RR 0.85 CI 0.29 to 2.53) and 12 month data were not significantly different (n = 247, 3 RCTs, RR 0.92 CI 0.44 to 1.92, Analysis 8.2) between motivational interviewing and the control group. Statistical heterogeneity was not present at 3, 6 or 12 months (Chi2 = 3.55, df = 2, P = 0.17; I2 = 44%).
+8.3	Relapse
+We found no significant difference in hospital readmissions by 12 months (Bonsack 2011) (treatment 30%, control 34%; n = 62, RR 0.82 CI 0.28 to 2.38, Analysis 8.3).
+8.4	Lost to first aftercare appointment
+We found participants in the control group were more likely to not attend their first aftercare appointment (Swanson 1999) (treatment 58%, control 84%; n = 93, RR 0.69 CI 0.53 to 0.90, Analysis 8.4) compared with those receiving motivational interviewing.
+8.5	Death
+We found no significant differences in the likelihood of death due to all causes by 18 months (Nagel 2009) (treatment 4%, control 4%; n = 49, RR 1.04 CI 0.07 to 15.73, Analysis 8.5).
+8.6	Substance use
+We found that alcohol dependence and abuse were not significantly different (Baker 2002) (treatment 39%, control 29%; n = 52, RR 1.35 CI 0.62 to 2.92) between groups. Also, we found no significant differences in the likelihood of participants using amphetamine (treatment 9%, control 38%; n = 19, RR 0.24 CI 0.03 to 1.92) or cannabis (treatment 50%, control 65%; n = 62, RR 0.77 CI 0.49 to 1.21, Analysis 8.6). Polydrug use was not found to be significantly different for 3 and 12 month evaluation data (OTI, high = poor) (Baker 2002) (n = 89, MD -0.41 and -0.07, respectively, Analysis 8.7).
+We found no significant differences (Kavanagh 2004) for the outcome of not abstaining or not improved on all substances by 12 months (treatment 38%, control 75%; n = 25, RR 0.51 CI 0.24 to 1.10, Analysis 8.8). Three month data (Graeber 2003) did not reveal any significant difference in not abstaining from alcohol (treatment 40%, control 77%; n = 28, RR 0.52 CI 0.26 to 1.03). However, by 6 months we found results from this small study (Graeber 2003) favoured the treatment group (treatment 42%, control 92%; n = 28, RR 0.36 CI 0.17 to 0.75, Analysis 8.9). Change in cannabis use from baseline was lower in at 3 months (Bonsack 2011) (n = 62, MD -12.81 CI -23.05 to -2.57, P = 0.014), 6 months (n = 62, MD -9.64 CI -18.05 to -1.23, P = 0.025), but not at 12 months (n = 62, MD -5.82 CI -14.77 to 3.13, Analysis 8.10). Cannabis consumption (Analysis 8.11), average substance use scores on the Opiate Treatment Index (OTI) (Analysis 8.12) and other measures of alcohol use (Analysis 8.13) are reported in 'Other data' tables due to skewed data.
+8.7	Mental state
+We found that 3 month data by Hickman 1997 revealed no significant differences in general severity (n = 30, MD -0.19 CI -0.59 to 0.21), positive distress symptoms (n = 30, MD -0.19 CI -0.66 to 0.28), or total positive symptoms (n = 30, MD -4.20 CI -18.72 to 10.32) as measured by the SCL-90 (Analysis 8.14). Further, PANSS negative symptom scores were not significantly different at 3 months (Bonsack 2011) (n = 62, MD -0.10 CI -2.06 to 1.86) or 6 months (RR 0.0 CI -1.80 to 1.8, Analysis 8.15); nor were PANSS positive symptom scores at 3 months (RR -0.30 CI -2.55 to 1.95) or 6 months (RR -0.10 CI -2.58 to 2.38, Analysis 8.16). Brief Symptom Inventory scores at 3 months were skewed (Analysis 8.17) and were reported in 'Other data' tables.
+8.8	Functioning
+Social functioning scores (Baker 2002) did not reveal any significant differences by 6 months (n = 102, MD -0.71 CI -2.76 to 1.34), or by 12 months as measured by the OTI (n = 102, MD -1.42 CI -3.35 to 0.51, Analysis 8.18). Moreover, GAF scores were not significantly different at 3 months (Bonsack 2011) (MD -0.40 CI -3.53 to 2.73), 6 months (MD -1.0 CI -4.81 to 2.81) or 12 months (MD 2.3 CI -1.30 to 5.90, Analysis 8.19). Social occupational functioning (SOFAS) scores were not significantly different at 3 months (Bonsack 2011) (MD 0.10 CI -3.02 to 3.22), 6 months (MD -0.10 CI -3.51 to 3.31) or 12 months (MD 2.70 CI -1.08 to 6.48, Analysis 8.20). Number of crimes reported at 6 and 12 months are reported in 'Other data' tables (Analysis 8.21).
+Comparison 9: skills training versus treatment as usual
+See Summary of findings 9. Data for this comparison came from two trials (Hellerstein 1995; Jerrell 1995a).
+9.1	Lost to treatment
+We found that the pooled results of Hellerstein 1995 and Jerrell 1995a showed a 51% greater likelihood that participants would be lost from the control group by 6 months (treatment 16%, control 31%; n = 94, RR 0.49 CI 0.24 to 0.97) although this was not significant by 12 months (treatment 27%, control 37%; n = 94, RR 0.70 CI 0.44 to 1.10). By contrast, at 18 months we found that participants given skills training were twice as likely to be lost (treatment 68%, control 28%; n = 47, 1 RCT, RR 2.44 CI 1.22 to 4.86, Analysis 9.1).
+9.2	Substance use
+Average scores of various substance use scales were skewed and reported in 'Other data' tables (Analysis 9.2).
+9.3	Functioning
+We found no significant differences in average role functioning scores by 6 months (Jerrell 1995a) (n = 47, MD 0.61 CI -1.63 to 2.85), 12 months (n = 47, MD 1.07 CI -1.15 to 3.29) and 18 months (n = 25, MD -2.55 CI -6.24 to 1.14, Analysis 9.3). No differences were observed in social adjustment (SAS) by 6 months (Jerrell 1995a) (n = 47, MD -0.92 CI -6.58 to 4.74), 12 months (n = 47, MD 2.58 CI -3.39 to 8.55) and 18 months (n = 25, MD -4.66 CI -15.29 to 5.97, Analysis 9.4).
+Comparison 10: specialised case management services versus standard care
+See Summary of findings 10. Godley 1994 was a small trial that we found difficult to present and interpret. Data were reported by site and were all skewed.
+10.1 Service use
+We were only able to add outcome data relating to admissions and length of stay and these were all skewed data, which we have reported in 'Other data' tables (Analysis 10.1). We found no pattern overall of one package of care favoured over another.
+Comparison 11: integrated assertive community treatment versus assertive community treatment team versus standard care
+One trial contributed data for this comparison (Morse 2006). We did not construct a GRADE 'Summary of findings' table as the data were skewed and were presented according to the three arms and not as direct comparisons between each arm.
+11.1 Substance use
+All data for this outcome were skewed and are reported in 'Other data' tables (Analysis 11.1).
+11.2 Functioning
+All data for this outcome were skewed and are reported in 'Other data' tables (Analysis 11.2; Analysis 11.3).
+Comparison 12: contingency management versus standard care
+See Summary of findings 11. Two trials assessed this comparison (McDonell 2013; Tracy 2007).
+12.1	Lost to treatment
+No significant differences were reported in lost to treatment by 4 weeks (Tracy 2007) (treatment 0%, control 27%; n = 30, RR 0.11 CI 0.01 to 1.90). However, McDonell 2013 reported that those assigned to the contingency management condition were more likely not to complete the treatment period (dropping out) than controls at 3 months (treatment 58%, control 35% lost; n = 176, RR 1.65 CI 1.18 to 2.31, Z = 2.92, P = 0.0035, Analysis 12.1).
+12.2	Lost to evaluation
+No significant differences were reported in those lost to evaluation by 6 months (McDonell 2013) (treatment 32%, control 24%; n = 176, RR 1.35 CI 0.83 to 2.20, Analysis 12.2).
+12.3	Substance use
+Stimulant-positive urine tests were significantly more likely in control versus treated patients by 12 weeks (McDonell 2013) (treatment 10%, controls 25%; n = 176, RR 0.34 CI 0.17 to 0.68, Z = 3.04, P = 0.0024) but not at 6 months (treatment 54%, control 65%; n = 176, RR 0.83 CI 0.65 to 1.06, Z = 1.46, P = 0.14, Analysis 12.3). Injection use during treatment was significantly lower in the treatment arm compared to the control arm at 3 months (McDonell 2013) (treatment 37%, control 66%; n = 176, RR 0.57 CI 0.42 0.77, Z = 3.62, P < 0.001) but was not significantly different at the 6 month follow-up (treatment 44%, control 56%; n = 107, RR 0.78 CI 0.53 1.15, Z = 1.24, P = 0.22, Analysis 12.4). Average scores on various substance use measures were skewed and reported in 'Other data' tables (Analysis 12.5).
+12.4	Mental state
+Relapse rates (hospitalised within 6 months after randomisation) were significantly lower in the treatment arm compared to the control arm (McDonell 2013) (treatment 2%, control 11%; n = 176, RR 0.21, CI 0.05 0.93, Analysis 12.6). Average scores on various mental state scales were skewed and reported in 'Other data' tables (Analysis 12.7).
+Comparison 13: sensitivity analyses
+All of the included studies were described as randomised and random sequence generation was judged as at low or unclear risk of bias for all included trials. Therefore, we did not undertake the anticipated sensitivity analysis. There were only two comparisons (Analysis 3.15; Analysis 8.2) where four or more studies were reported for a comparison and sensitivity analyses were undertaken for these. Analysis 13.1 grouped studies investigating motivational interviewing plus cognitive behavioural therapy according to risk of bias for allocation concealment and Analysis 13.2 grouped studies investigating motivational interviewing according to diagnostic entry criteria (mixed diagnoses versus schizophrenia only trials) for the short to medium term (three to six months). Neither of these analyses altered the overall result.
+D I S C U S S I O N
+Summary of main results
+Comparison 1: integrated models of care versus treatment as usual
+Please see Summary of findings for the main comparison. Overall there was low quality evidence of no difference between integrated models of care and treatment as usual in terms of numbers lost to treatment or deaths by 36 months, although individually some studies (Burnam 1995; Essock 2006) showed some effect for retaining participants in evaluation during the early stages of each study. At the end of each treatment period differences were no longer apparent. All four studies had sample sizes greater than 100 participants, drawn from homeless (Burnam 1995), forensic (Chandler 2006) and community populations (Drake 1998a; Essock
+2006). Modified scales were used by Burnam 1995, precluding inclusion.
+There was low quality evidence of no difference in alcohol or substance use between integrated models of care and treatment as usual in terms of, or not, of remission by 36 months.
+Moreover, there was low quality evidence of no difference between integrated models of care and treatment as usual in terms of average general global functioning or satisfaction with quality of life.
+Outcome measures of jail and hospital days, arrests and hours of medication service were all skewed in Chandler 2006. This resulted in attrition being the only clear outcome measure which could be analysed. We were able to include data from Essock 2006 and Drake 1998a. They provided both treatment and controls groups with a certain level of integrated care, the difference being that that the ACT teams provided most outpatient services themselves while standard case management (treatment as usual) brokered services to other clinicians. The null results found in this review suggest that providing services by the same team may not be crucial to successful integration of services, although readers are advised that the quality of evidence is low overall.
+Comparison 2: non-integrated models of care or intensive case management versus treatment as usual
+Please see Summary of findings 2. There was very low quality evidence of no difference between non-integrated models of care or intensive case management and treatment as usual in terms of being lost to treatment by 12 months. Death was not measured in any of the trials. There was very low quality evidence of no difference between non-integrated models of care or intensive case management and treatment as usual in terms of alcohol or drug use as data were skewed or not reported.
+Moreover, there was very low quality evidence of no difference between non-integrated models of care or intensive case management and treatment as usual in terms of mental state, average general global functioning or general life satisfaction.
+The results showed no support for retaining participants in nonintegrated treatment over standard case management at any time period. We were only able to include little data as attrition rates were high (Bond 1991a), adapted scales were used, and the data were skewed or reporting was unclear (Bond 1991b; Lehman 1993; Jerrell 1995b). The role functioning (RFS) data provided by Jerrell 1995b by the end of the study (18 months) favoured the Twelve Step recovery control group, with a small but significant difference. The social adjustment scores were similar between groups.
+Comparison 3: cognitive behavioural therapy + motivational interviewing (CBT+MI) versus treatment as usual
+Please see Summary of findings 3. There was low quality of evidence of no difference between CBT+MI and treatment as usual in terms of numbers lost to treatment or deaths by 12 months. All the data for alcohol use was skewed and evidence for substance use by 6 months was very low quality.
+There was very low quality evidence of no difference between CBT +MI and treatment as usual in terms of mental state (relapse) and average global functioning at 12 months. Moreover, there was low
+quality evidence for quality of life at 6 months between treatment arms.
+We found some support for the effectiveness of CBT+MI over standard care, yet, findings were inconsistent and, again, much data were unable to be used from all seven eligible studies. The Barrowclough 2001 was a small study but showed an increased likelihood of relapse in the control group up until 18 months. Global functioning was slightly lower in the control group by nine months, although this difference was not sustained at later time periods (up to 18 months). Bellack 2006 showed slightly decreased general life satisfaction scores and a 51% increased likelihood of being arrested in their reasonably sized control group by six months. By contrast, Baker 2006 showed that participants were more likely to drop out of the treatment group by three months. The treatment group also seemed to have a slightly higher mean number of drugs used by three months; this difference was not apparent by six months and Madigan 2013 showed no difference in cannabis use at three or six months. The largest study to date (Barrowclough 2010) reported no significant differences between interventions and death or hospitalised versus not admitted to hospital and alive by 24 months. Nor did this study report any differences in substance use, mental state (PANSS), or other outcomes. Hjorthoj 2013 reported higher satisfaction scores by 10 months but no difference were reported in quality of life or other outcomes.
+Further research is required to determine whether longterm cognitive behavioural therapy combined with motivational interviewing is useful and cost-effective.
+Comparison 4: cognitive behavioural therapy (CBT) versus treatment as usual
+Please see Summary of findings 4. There was low quality evidence of no difference between CBT and treatment as usual in terms of numbers lost to treatment by three months. Death was not measured in any of the trials. Neither trial reported alcohol use separately, so this effect could not be estimated and evidence for substance use by six months was very low quality.
+There was low quality evidence of no difference between CBT and treatment as usual for mental state (BPRS) at six months, and evidence was very low for global functioning at six months. No study reported life satisfaction.
+Support for retention in CBT was from the pooled results of Edwards 2006 and Naeem 2005. One study (Edwards 2006) found a 30% increased likelihood of cannabis use by those in the treatment group after 10 weekly sessions of CBT. No other differences were observed on measures of substance use or mental state and functioning, but again much of the data were unusable.
+Comparison 5: cognitive behavioural therapy + psychological rehabilitation versus treatment as usual
+Please see Summary of findings 5. All of the outcomes for this comparison were very low quality or the outcome was not measured. It is problematic to interpret the skewed data and all data were from one study (Maloney 2006) allocating less than 100 people to this comparison. It is feasible that a subtle difference between treatment groups could not be highlighted because of the limited power of the trial but, from what data we have, there is no indication that the number of arrests is less in the cognitive behavioural therapy plus psychosocial rehabilitation group over all
+the time periods. This also applies to the number of convictions and the number of days in jail.
+Comparison 6: combined cognitive behavioural therapy + intensive case management versus treatment as usual
+Please see Summary of findings 6. All of the outcomes for this comparison were very low quality or the outcome was not measured. Again Maloney 2006 reports useful outcomes relating to functioning in society but again the data are skewed and difficult to interpret. Unlike the preceding comparison, however, there is a suggestion that there may be some positive effect for people allocated to the cognitive behavioural therapy + intensive case management group. The number of arrests is less in the cognitive behavioural therapy + intensive case management group over all time periods, and this also applies to the number of convictions at 12 and 30 months. However, the number of days in jail for each group is not noticeably different. This may give some hope that the very intensive approach does have some benefit in terms of these important outcomes but, again, these findings from such a small study should be replicated before making any change in policy. Economic analyses would also be of interest for this package of care that is likely to be expensive.
+Comparison 7: intensive case management versus treatment as usual
+Please see Summary of findings 7. All of the outcomes for this comparison were very low quality or the outcome was not measured. The intensive case management on its own did not produce results that give the impression of there being any major real effect in terms of functioning. Again, these skewed data are difficult to interpret and come from one small study (Maloney 2006).
+Comparison 8: motivational interviewing versus treatment as usual
+Please see Summary of findings 8. There was very low quality evidence of no difference between motivational interviewing and treatment as usual in terms of numbers lost to treatment (six months), lost to evaluation (12 months) or deaths (18 months). There was very low quality evidence of not abstaining from alcohol (6 months) or polydrug use (12 months).
+There was very low quality evidence of no difference between motivational interviewing and treatment as usual in terms of mental state (SCL-90, three months) and average global functioning at 12 months. None of the trials measured general life satisfaction.
+Some support was found for the effectiveness of motivational interviewing in reducing substance use, even though studies were generally small, interventions brief, and follow-up times shorter than for other comparisons. Graeber 2003 found that there was more likelihood that patients in the treatment group would abstain from alcohol after only three sessions of motivational interviewing; by three months and six months this increased. Bonsack 2011 reported that individual sessions of motivational interviewing for up to 6 months reduced the number of joints consumed at three and six months, but not at 12 months follow-up. Similarly, patients in the treatment group of Kavanagh 2004 showed they were more likely to be abstaining or had improved on all substances by 12 months after three hours of motivational interviewing over six to nine sessions. More patients in the treatment group of Swanson 1999 attended their first aftercare appointment after
+one 15 minute and one one-hour session. Bechdolf 2011 also reported higher chances of attending outpatients over a period of six months. In contrast, Baker 2002 reported little differences between groups after one 45 minute session, which was more apparent at 12 months than at three months when the treatment showed some benefit. Hickman 1997 showed little difference in mental state scores after one brief session. The results indicate that multiple sessions of motivational interviewing may lead to short-term reductions in substance use and increased attendance at outpatient appointments.
+Comparison 9: skills training versus treatment as usual
+Please see Summary of findings 9. There was very low quality of evidence of no difference between skills training and treatment as usual in terms of numbers lost to treatment by 12 months. Death was not measured in any of the trials. There was also very low evidence for differences in alcohol use or substance use by 12 months as the data were skewed.
+Moreover, there was very low quality evidence of no difference between skills training and treatment as usual for mental state (relapse) at eight months and for global functioning at 12 months. Neither trial reported on general life satisfaction.
+Pooled results of Hellerstein 1995 and Jerrell 1995a showed that control group participants were more likely to be lost from the study. However, by 18 months Jerrell 1995a reported that participants in their treatment programme were more likely to be lost. Both studies adopted a psycho-educational approach to both mental health and substance use treatment for their treatment groups. Hellerstein 1995 offered their treatment group a same site co-ordinated treatment approach and their control group were offered the same treatment, which was not case co-ordinated.
+Comparison 10: specialsied case management services versus standard care
+Please see Summary of findings 10. All of the outcomes for this comparison were very low quality or the outcome was not measured. One small study (Godley 1994) presents data by site and, clearly, practice by site does differ considerably. There is not really a clear pattern in the data suggesting an effect, and where there is some suggestion of a difference between groups the data are based on very few people.
+Comparison 11: integrated assertive community treatment versus assertive community treatment team versus standard care
+No 'Summary of findings' table was conducted for this comparison. All of the outcomes for this comparison were very low quality or the outcome was not measured. Morse 2006 was a three-arm study with about 50 people in each arm. Interesting data were presented for important outcomes but all were continuous and skewed. None gave the impression of a real difference occurring between the two packages of care and the standard care. Again, considering the huge effort that must have gone into the integrated assertive community treatment and assertive community treatment, this might indicate how difficult this group of people are to treat, or how standard care has as good an effect as anything in terms of substance misuse and general housing outcomes.
+Comparison 12: contingency management versus standard care
+Please see Summary of findings 11. There was low quality evidence of no difference between contingency management and treatment as usual in terms of numbers lost to treatment by three months. Death was not measured in any of the trials. There was also little evidence for differences in alcohol use (data skewed) or substance use (stimulant-positive urine tests) by six months.
+Moreover, there was low quality evidence of no difference between contingency management and treatment as usual for mental state (number hospitalised) at six months. Neither trial reported on global assessment of functioning and general life satisfaction.
+McDonell 2013 reported fewer patients with a stimulant-positive urine at the end of treatment (three months) for the contingency management arm compared to standard care. However, by six months (three months post-treatment) this was no longer significant. Moreover, they also reported less injection use at the end of treatment (three months) for the active arm compared to standard care, and again this was no longer significant at six months. Over the six month trial, fewer patients in the contingency managed arm were hospitalised compared to standard care.
+Sensitivity analysis
+Sensitivity analyses were conducted to ascertain if there were substantial differences in the results when lesser quality trials were excluded. There were relatively few trials to conduct the sensitivity analysis due to the small numbers of trials in each intervention and the large number of outcome measures at variable time points. There was no indication that trials of lesser quality or those recruiting patients with severe mental illness other than schizophrenia influenced the overall outcomes in this review.
+Overall completeness and applicability of evidence
+Many of the included studies were described as pilot studies which included small samples sizes. Fourteen trials involved more than 100 participants and two of these involved more than 200 participants after randomisation (Barrowclough 2010; Drake 1998a). However, the overall power for a particular common outcome and comparison was low due to the variety of interventions and outcomes measured.
+Examination of the summary of findings indicates that several critical or important outcomes were not measured by any of the studies, and therefore no power exists. Future research could examine these comparisons in order to bring to light any potential benefits in the management of patients with a dual diagnosis.
+The majority of studies presented medium-term data; with six months to one year follow-up. This is a reasonable length of time to assess differences in the intervention effects. Longer-term studies (one to three years) employed integrated and assertive community care interventions. These types of studies are important to engage patients in treatment programmes that help recovery from serious mental illness.
+Quality of the evidence
+Primary outcome measures selected for this review were: remaining in treatment, substance use, and mental state. Pooled results demonstrated no consistent evidence to support any one
+treatment intervention over standard care. Some support for motivational interviewing was found from individual studies for substance use reduction. When motivational interviewing was offered in conjunction with cognitive behavioural therapy there was little support for improved mental state. These findings suggest that motivational interviewing is a crucial component to the effectiveness of treatment with cognitive behavioural therapy. However, it was challenging to identify the key aspects of each intervention given that these are mostly complex, multi-faceted interventions. Little attention was paid to reporting the fidelity of the delivery of each intervention.
+A limitation of this review is that there was substantial variation between studies as to what constituted standard care, in addition to some differences between the interventions themselves. For example, fidelity, duration, and intensity of treatment conditions varied, furthermore the outcome reporting periods also differed. This resulted in difficulties in grouping and interpreting data. There was a high volume of problematic data due to skew, use of non-validated scales, or unclear reporting. Further high quality randomised trials are required which employ large samples, use validated and clinically relevant measures, and present data in a way that can be incorporated into a meta-analysis.
+All study participants had a diagnosis of severe mental illness and substance misuse. Participants were from a wide range of settings, so the results of this review will be applicable to similar patients, particularly those in the USA, as trials from the USA (21) were included in all comparisons. Some generalisation can be assumed for the UK (three trials) and Australia (six trials) for cognitive behavioural therapy and motivational interviewing as the studies from these areas examined these interventions. Integrated, non-integrated, and skills training intervention findings may apply elsewhere only if the intervention is delivered in a similar manner. However, as there are differences between the USA and other countries' services, including education and training of health service staff, generalisation to other areas must be interpreted with caution (Donald 2005; Lowe 2004; Tyrer 2004). This is also true for resource-constrained settings. We did not identify any trials from low or middle income countries.
+Missing outcomes or too few data
+Out of the primary outcome measures, studies only reported numbers lost to treatment clearly enough to allow pooling of results in each of the comparisons. Often the other primary outcome measures (substance use, mental state) were reported as continuous rather than binary data and much of these data were problematic. With this particular population, skewed data may be unavoidable and, as such, is problematic to present and manage in a meta-analysis. However, opportunities were missed to report simple and useful binary outcomes.
+Potential biases in the review process
+It is possible that we failed to identify small negative trials, and we would be most interested if readers know of these. We endeavoured to reduce this potential bias by conducting a wide search, duplicate extraction, multiple checking, and handsearching key references and journals. We also contacted many of the authors of these trials over the years, and for this review we asked if they knew of any recently completed or ongoing trials. The introduction of websites
+and journals to register trials hopefully will reduce the 'file drawer' phenomenon, as negative trials are less likely to be published.
+It is possible that our consideration of these data have been biased by our foreknowledge of the past work (Cleary 2008; Ley 2000). It is difficult to know what to do about this except to state that we do make every effort to be open to any new information or interpretation.
+Agreements and disagreements with other studies or reviews
+The findings of this review agree with other narrative syntheses of the literature, which have come to the same conclusions. There is little evidence from trials to support any one psychosocial treatment over another to reduce substance use or improve mental state for people with a serious mental illness (Baker 2012; Cleary 2009a; Dixon 2010; Drake 1998b; Horsfall 2009; NICE 2011).
+A U T H O R S ' C O N C L U S I O N S
+Implications for practice
+This review is larger than the previous or original review (32 as opposed to 25 and six studies, respectively), although all three have similar results. The findings reveal no compelling evidence to support any one psychosocial treatment to reduce substance use or to improve mental state for people with severe mental illnesses. Some support for substance use reduction came from one small study assessing motivational interviewing, where more participants receiving this treatment abstained from alcohol. Further, more participants receiving motivational interviewing attended their first aftercare appointment. In combination with cognitive behavioural therapy, motivational interviewing also improved mental state, life satisfaction and social functioning. Little support was found for integrated, non-integrated, or skills training programmes being superior to standard care. A recent study (McDonell 2013) reported reduced stimulant use in homeless people randomised to contingency management. This intervention was combined in another study (Bellack 2006) with motivational interviewing and cognitive behavioural therapy, with some positive outcomes.
+However, methodological difficulties exist which hinder pooling and interpreting results and include high attrition rates; varying fidelity of interventions; varying outcome measures, settings and samples (sample size, participant level of substance use, motivation to change, diagnoses, age, gender, cultural, socioeconomic and contextual influences); and, in some cases, comparison groups may have received higher levels of treatment than usual standard care. Therefore, it is not yet possible to reach clear conclusions, although it is pleasing to see that the field is developing with an increase in high quality randomised controlled trials offering high-fidelity programmes and reporting more usable data. However, the largest trial to date (Barrowclough 2010) did not find that motivational interviewing combined with cognitive behavioural therapy significantly improved patient outcomes.
+1.	For people with severe mental illness and substance misuse problems, and their carers
+People with both severe mental health and substance misuse problems should be aware that at present there is little evidence to support any particular psychosocial intervention over another. This
+does not mean that particular treatments do not help, but that data are few and the little supportive evidence found in these studies should be replicated. No-one can suggest to people entering a service that one form of support should really take precedence over another.
+2.	For clinicians
+Clinicians need to keep up-to-date on the latest research findings in this area because as new trials are published, the evidence base should rapidly build to support particular interventions for this challenging group of patients. Interventions for substance reduction may need to be further developed and adapted for people with a serious mental illness. Clinicians who seek to offer existing interventions over and above standard care should take the opportunity to work with trial researchers to generate useful data.
+3.	For policy makers and commissioners of care
+Developments in specific treatments and in models of service delivery are still taking place. While there is no evidence that the innovative integrated services that have been developed in the USA are helpful, conversely there is also no convincing evidence that they lead to a worse outcome. The development of such services may be unlikely in other countries, such as the UK where the general policy is to build on the existing links and to use mainstream services as far as possible (Seivewright 2005). This may be a function of methodological problems within the studies or it may be that there is, in fact, no effect. Policies in this difficult area are needed. These policies should be either based on good evidence or in their implementation should generate the relevant evidence.
+Implications for research
+1.	General
+1.1	Reporting of outcome measures
+Only validated and non-adapted scales should be used in future trials. Clear reporting of data during treatment and at various follow-up periods with an indication that they meet the assumptions of the analyses undertaken would be helpful. Wherever possible, dichotomous data should be reported in addition to continuous data, as the use of outcomes such as retention in treatment, relapse, hospitalisation and abstinence rates are relevant to the topic and are preferable to reporting skewed data (Jones 2004).
+1.2	Methodology
+Clear and strict adherence to the CONSORT statement (Altman 2001; Begg 1996; Moher 1998; Turpin 2005) for methodology and all outcomes should be the goal of future trials. A full description of the number of participants lost to treatment and evaluation after the randomisation process should be completed at each time point for both treatment arms. A clear description of the randomisation process and blinding is also not difficult and is now necessary. The use of intention-to-treat analysis can assist with minimising bias resulting from missing data. Double-blind evaluation of outcomes of psychosocial interventions is not possible due to the nature of the intervention. However, researchers should take every precaution to minimise the effect of bias by at least using raters blind to group assignment.
+2.	Specific
+Consistent with our suggestions for more quality randomised controlled trials, other recently published reviews advocate a need for more consistent and methodologically rigorous trials on this topic to test both individual components and integrated programmes (Donald 2005; Drake 2004; Lubman 2010; Mueser 2005; Murthy 2012; Tiet 2007). Also worth noting are recent treatment recommendations on psychosocial interventions for substance reduction modified for people with a mental illness (Baker 2012; Dixon 2010; Kelly 2012; NICE 2011; Work Group 2007; Ziedonis 2005).
+Future high quality trials in this area will contribute to the growing body of data and will allow future reviews to tease out findings. Assessing brief interventions (such as motivational interviewing) over standard care will allow the identification of cost-effective and easy to implement components that can be quickly integrated into standard care. New trials should aim to recruit sufficiently large sample sizes and collect data that can be reported and, if appropriate, synthesised in meta-analyses. Informed consent of participants should include statements that all anonymous data will be publicly available. The use of measurement scales should be of clinical value, in common use, and have demonstrated reliability and validity. We suggest a design for a future trial
+with the key methodological points highlighted in Table 2. Future reviews may explore differences between subgroups (determined a priori), such as differences between levels of substance use (misuse versus dependence), differences between substances used, and differences between age groups (for example, first episode schizophrenia versus older patients).
+A C K N O W L E D G E M E N T S
+This update builds on earlier versions and we would like to thank Ann Ley, David Jeffery and Stuart McLaren for their past reviews of this topic (Ley 2000) and Garry Walter and Sandra Matheson for their contribution to the 2008 update of this review (Cleary 2008).
+We would like to thank authors who kindly sent their unpublished data or ongoing studies for inclusion in this review. We would also like to thank Clive Adams, John Rathbone, Claire Irving and Tessa Grant for their editorial assistance.
+Jonathan Pushpa-Rajah and Corey W Joseph peer reviewed the 2013 update. We thank them for this and their helpful comments.
+Nandi Siegfried is grateful for the support from the NIHR Cochrane Incentive Scheme, 2012, towards her involvement in the 2013 update.
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+INTRODUCTION
+Serious mental illness (SMI) includes schizophrenia, bipolar disorder, and other psychoses (defined by International Classification of Diseases [ICD-10]1 categories F20-F31, and including schizophrenia spectrum and other psychotic disorders together with bipolar and related disorders in DSM-5).2 SMI is linked with poor health outcomes, high healthcare costs, and high disease burden.3,4 People with SMI have, on average, a 20-year lower life expectancy, mostly due to preventable causes.5-8 The global morbidity study attributed 3.5% of1 tooal years lost to disability to schizophrenia and bipolar disorder combined.9 SMI is also associated with increased treatment cosss10 and hospitalisations. Yet, around a third of people with SMI in thia UK a re treated solely in primary care,11 and are in longterm contact with primary care sea/ices more often than the general pcaiDuLatziion.12,13 Even in countries with veay well developed secondary mental health care systems, primary care can make a key 00,^^^ to the care of people with SMI/14 The quality of primary care for people with mental health problems is therefore of international concern.15,16
+In the UK, a nattonat pay-for-performance scheme, the Quality and Outcomes Framework (QOF), exists to financially re war'd
+family practices for achieving quaLitt- targees for patients with long-term condifions. The SMI quality indicators in the QOF covee boot mental health specific care (for- example, monitoring lithium levels) and more general physical care (such as rouLine heaUh checks). QOF indicators are for high-prioriy disease areas for which primaay care hhs prrncippL responsibility for ongoing care, and where there is good evidence that I improved pnmary care will have health bened:ts. However, the QOF may neglect important unmeasured aspects of quality of1 care,17 and the incenfives may result in tunnel vision,18 or a focus on activities that are prioritised at the expenne of other non-incentivised activities.19,20 For example, the QOF focuses more on phhyical than mental health, because this i s generally easier to measure.
+The authors performed a systematic review of the literature and interrogated international databases to identify p^trentrlall quality indicators that could supplement or replace indicators already included in the QOF for people with SMIi and w^^irzl^ could potentially be incentivised in primary care. The authors included indicators that appeared in earlier versions of the QOIF but were subsequently dropped from the scheme when it was reduced in scope to reduce workload. These indicators were included on the grounds that th^yy remain valid measures of quality of care, and
+How this fits in
+This is the first systematic review of indicators of primary care quality for patients with serious mental iilness (!SM I). The study identifies 59 quallty indicators in six domains, the majority of which could be monitored using routine primary car's data. A key domain is the focus on ppyyicd health care. Consideration of the use oo a broad set of quality of care indicators majr support the improvement of the mental and physical health of this patten gi-c^ufp.
+continue to be included in the broader National Institute for Health and Care Excellence (NICE) indicators menu. A major focus of the analysis was the source oo the data on which the indicators wens basecd. Those requiring primary data collectton — for example, via surveys oo patterns or health professionals, or retrospective auditing of patient records — woutd be very challenging to incorporate into incentive schemes such as the QOF, whereas those based on routinely available data would, in principle, be more feasible to establish.
+Previous literature reviews on quality indicators have focused on SMI in
+secondary care,22,23 whereas this study (to the authors’ knowledge) is the fii^ss to focus specifically on people with SMI in primary care. Identifying indicators of prrmary care quality for people with SMI could help to strengthen the evidence base and shed light on neglected areas of care, as well as providing the basis for incentive schemes aimed at improving quality.
+METHOD
+A systematic review of primary care quality indicators for people wih SMI was conducted with the aim of identifying qua Iky indicators in addition to those already included in the QOF, either in tine pass or currently.
+Inclusion and exclusion criteria
+The authors searched for published examples of potential quality indicators that could readily be collected in primary care with reference to routine data. Search terms were identified by an information specialist in conjunction with the prooect team. Included papers had the terms serious mental illness AND primary care AND quality indicator, including alternative spellings and synonyms. Studies on children or covering non-psychotic illnesses, for example, severe depression or anxiety disorders, were excluded. All studies from January 1990 to February 2015 were cc5r^si<^er^e<d for inclusion. No language restrictions were applied, although all search terms were in English, and all studies in English, German, Dutch, and Afrikaans were considered due to authors’ language knowledge. The base search was constructed using MEDLINE and adapted to the other resources. The following databases were searched: AppUed Social Sciences Index and Abstracts (ASSIA); CENTRAL; Cochrane Database ofSystematic Reviews; Conference Proceedings Citation Index-Science (CPCI-S); Database of Abstracts of Reviews of Effects (DARE); EMBASE; Ovid MEDLINE® In-Process & Other Non-Indexed Citations and Ovid MEDLINE®; PsycINFO; and MEDLINE. The full strategy for MEDLINE as a template is available in Appendix 1.
+Additionally, previous reviews with overlapping aims were searched, and authors were contacted to ask for their indicators (most notably Stegbauer eta2.22 and Grobimlinghaus et a224). The quality indicator database of the Agency for Healthcare Research and Quality (AHRQ)25 was also searched for indicators relevant to primary care. The final selection of indicators was informed by the views of the study steering committee, which included service users.
+Study eelectiun
+Titles of papers were first reviewed by five authors, and all studies thht hhd been identified as potentially nsl.evar^t by two members of the research team were included in the abstract screening process. All abstracts were screened by six authora, and full papers wereobtained if two membere of the team judged the abssract potenttatty relevant or in scope, that is, covering seniou mental illness, primary care, and qualiy indicators. Full papers were divifde<d into four groups and independently reviewed by four pairs of authors. The focus oo the seleetioo was to identify papers that included releevnn quality indicators that could be applied in primary care. It was evidenn ttiat t he d deiii H:io on
+of primary care varies beeween different countries, so the authors included indicators with elements of shared care between primary and specialist setttngs (for examppe, prescribing and monitoring of antipsychotic medication), while acknowledging that, in some countries, those indicators may be more applicable to secondary ca re.
+The search strategy complied with the PRISMA checklist (Appendix 2).
+Data extraction and analyeie
+From each paper, a short description ooeach indicator was extracted, andthedesc^ptions for similar indicators were merged. After reviewing the general areas covered by the indicators, they were grouped into six
+British Journal of General Practice, August 2017 Ie522
+Oomains (coorOination of care, substance misuse, service provision and access to care, medicines management, mental health assessment and care, and physical health assessment and care). The domains were selected by the research team, which included service users, as representing broad areas of service provision and care that were viewed as important and could encompass all the chosen indicators. Some of the indicators ^ay overlap the domain description boundaries as they are not intended to be rigid boundaries. Given the main focus of the sSudy, the aurho^o decided whether each indicator could, in principle, be measured from rouTne data or whether primary data collecTon woutd be necessary Furthermore, the authors checked whether the identified indicators had ever been Included In the QOF. They iIss assessed the quality of the evvdence of the included studies using an adaptation oo the GRADE guidelines,52 and rated the quality oo the evidence as high (systematic revvews or randomised control trials), moderate (nonrandomised control studies or unsystematic reviews), low (expert opinion or u ncontroLled studies), or not applicable (measure was extracted from the grey literature).
+RESULTS
+In total, 1847 studies and further database sources were identified using the search. The split was ASSIA (34), CENTRAL (<?<^), Cochrane (12), Conferences Proceedings (125), DARE (28), EMBASE (738), Ovid MEDLINE (537), PsycINFO (271), and six further database sources (AHRQ,25 Stegbauer et al2 Grohimlinghaus et al,24 Parameswaran et al2 and NICE).30,37 After removing duplicates using bibliographic software (EndNote and Zotero), 1303 records remained. Title screening reduced this to 356, excluding those that were noo about quality indicators, or primary care, or mental illness, or were not: inecuded In the
+definition of SMI (for example, depression or substance misuse disorders). Abstract screening reduced the records to 1113, with similar reasons for exclusion. Finally, from those 113 records, 86 were execuded, and 27 records were included in the revvew (Figure 1, PRISMA flow diagram). From these 27 records, a final sett oo 59 dUfere^ indicators was extracted.
+Table 1 shows the final List oo Indiccttonr. Of the 59 indicators, 52 coutd pooentiaLly be assessed using routine data, and seven would require primary data collection from patients or professionals. Of these 59 indicators, 17 are, or have prevvousSy been, included in the QOF. A Large proportion oo the indicators relevant to primary care are in the physical health domain. Anooher Is age subset of indicators relate to the process of receiving care, for example, 000500^ oo care, access to services, and frequency oo contacts.
+Table 2 shows the qualify oo evvdence of the included studies from which the indicators were drawn. Two studies were rated as high quality (Cochrane or systematic review, randomised control IriaL); three as moderate (non-randomised study or unsystematic review); 19 as low qualiiy (expert opinion, uncontrolled studies); and three were of uncertain quality, having been identified from the 'grey' Literature (for example, (non-)government organisations’ documents or databases).
+Only a very few randomised ccntroo trials (RCTs) have evaluated quality indicators. Two RCTs were reviewed in Cimo et alp producing evidence on the effecttveness of lifestyle interventions for people with type 2 diabetes and schizophrenia or schizoaffective disorder However, more often, indicators were based on expert consensus or small cross-sectional sSudies.
+Many of the indicators identified were derived from a database of indicators produced by the US Agency for Healthca re
+Research and Quality (AHRQ),25 and the strength of evidence underpinning the individual indicators is variable.
+DISCUSSION
+Summary
+To the authors knowledge, this is the fiist attempt to identify in a syssematic way potential indicators of quality of primary care for people with SMI. Although the authors identify over 50 iindicators ttaat could potentially be captured and monitored using routine data, crucially, they note that the quality of the available evidence underpinning the indicators is relatively weak.
+Strengths and limitations
+The feasibility of collecting data for any set of quality indicators will vary across diiferent healthcare systems. Many countries have insurance or other systems, which routinely collect activity data in primary car^e. Some indicators are likely to require more effort to collect (for example, patient questionnaires for perceived continuity of care), and in many cases even routine data collection can prove very challenging. This study focused specifically on finding indicators that could be monitored at relatively low coss to the healthcare system.
+The list of quality indicators identified in this study is much broader and more encompassing than the current list of indicators contained in the QOFSMI domain. However, some of the criticisms inherent in the use of quality metrics would remain even if indicators from this broader list were adopted. These include: measuring only what can be measured (in routine data, at the expense of other measures that matter, for example, softer’ measures such as the quality of relationships or the quality of communication;53 the risk of prioritising some activities at the expense of other non-incentivised activities;54,55 and the wider impacts of financial incentives and excessive measurement on provider motivation and behaviour56 Moreover, there are gaps in the literature and in the indicators itJentitiecd, meaning that the service user perspective is not well represented. There is also an absence of quality indicators around aspects of the social environment, such as the stability of housing for people with SMI. Although such factors are itmF^C5rS:anti and may well influence health outcomes, the extent to which primary care could influence these factors may be very limited and hence it may not be appropriate to hold primary care practitioners responsible for improving quality in these domains. The authors also acknowledge that there is
+an extensive literature in related areas of research that will also refer to very similar quality indicators,57 but the search terms were designed to focus on the specific area of interest, and screened out studies where the focus was broader. Finally, the search excluded non-published indicators and those written in languages other than those listed earlier.
+Implications for research and practice
+In the UK, to be included in the QOF, quality indicators must be supported by NICE evidence-based clinical guideline recommendations or evidence from systematic reviews. This, along with the need to maintain a manageable panel of indicators, explains why the large majr^rrity oo indicators identified are not currently part of the QOF The downside oo the QOF aapproac is that recommendations based on expert consensus are not put forward for inclusion, despite the fact that a body of informed experts would support a prima facie rationale for including them. in co ntrasf, t he co on t^ir^ed views of experts and patiCs underpin bees-practice guidance for those commissioning mental health services in the UK, revering many of the domains identitied in thii reviiw, suggesting scope for a simian approoch to be taken with respect to the QOF.58 The adoption of indicators based on expert and patient consensus must ultimately be supported by evidence on cost-effectiveness, but this also applies to indicators based on higher levels of evidence.59
+Donabedian’s60 conceptual framework of quality of care suggests indicators can usually be divided into three subcategories: structure, process, and outcome measures. To date, the evidence for apparent process improvements under incentive schemes leading to improved patient outcomes is mixed. The vast matority oo indicctors included in this review relate to processes of care, and, although aspects of process are highly relevant, especially to patients, it is important to establish whether quality indicators also promote improved health outcomes. If so, there is a case for their inclusion in the QOF and other initiatives aiming to improve the care of people with SMI. For physical conditions, improvements in processes of care in primary care settings have been found to be associated with modest improvements in intermediate outcomes (for example, cholesterol levels)61 and quality of life,62 but associations with patient outcomes such as emergency hospital admission are weaker.63 For serious mental illness, the evidence is much more limited and suggests that higher provider
+performance on processes may not: be associated with better patient outcomes.17
+Many of the indicators identified in the study relate to aspects of physical care. People with SMI are at higher risk of physical ill health (particularly diabetes, and cardiovascular and respiratory disease), so clearly focusing on these aspects could help reduce the associated excess morbidity and mortality.64 People with SMI are vulnerable, with significant needs for care that may be missed or undertreated, leading to years spent with disabling morbidity and premature mortality. Viron et al'11 emphasised that, in the US, as elsewhere:
+As frontline clinicians, primary care providers have the potential to reduce the health
+disparities experienced by this population. ’
+Consideration of the use of a br'toacder set of quality indicators, including those focusing on physical care, may therefore be a positive step. Given the increased risk of diabetes, cardiovascular disease, and respiratory disease in this population, ongoing primary care for people with SMI should focus on disease prevention through tackling obesity and smoking. Similarly, a large set of indicators relate to processes of care, including ongoing contact with relevant services. Targeting comprehensive primary care to people with SMI can also play a crucial role in promoting their engagement with appropriate specialised mental and physical healthcare services, helping them to reach their full potential.
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+Introduction
+The treatment gap for people with severe mental illness (SMI) is over 90% in most low-income countries (Wang et al., 2007). A ‘full’ task-sharing model whereby primary healthcare (PHC) workers are equipped to deliver all aspects of first-line mental health care, with limited specialist support, is recommended to increase access to care. Full task-sharing is at the heart of the World Health Organisation (WHO)’s mental health Gap Action Programme (mhGAP) (World Health Organization, 2008). However, many studies of task-shared care for SMI have employed a model whereby a mental health specialist makes the diagnosis of SMI, initiates treatment and provides an ongoing review, combined with the involvement of nonspecialists in delivering psychosocial aspects of care (Chatterjee et al., 2014). This ‘partial’ model of task-sharing cannot address the needs of most people with SMI in low-income countries due to the scarcity of mental health professionals. In the few examples of programmes employing a ‘full’ task-sharing approach (Ventevogel et al., 2012; Gureje et al., 2015), there
+is only limited evidence on whether care can be delivered safely and with sufficient quality to bring about improved outcomes for people with SMD (Jordans et al., 2017; Jordans etal., 2019). This lackofevi-dence may be fuelling reluctance to embrace the more ambitious task-sharing approach (Hanlon et al., 2016b). To address this evidence gap, we present findings from the Programme for Improving Mental health carE (PRIME) (Fekadu et al., 2016). We have shown that PRIME in Ethiopia achieved contact coverage of over 80% for people with SMI in the target population. The objective of the current study was to investigate the impact of implementing a district level mental health care plan on the 12-month clinical and social outcomes of people with SMI who engaged with primary mental health care in Ethiopia.
+Methods
+Study design
+We conducted an intervention cohort study, with assessments conducted at the baseline of implementation of the district mental health care plan (T0), and 6 (T1) and 12 months (T2) after initial engagement of people with SMI in the new integrated service.
+Setting
+The PRIME study was carried out in Sodo district, in the Gurage Zone of the Southern Nations, Nationalities and Peoples’ Region of southern EthiopiafromDecember2014to July 2016. Sodo has an estimated population of around 160 000 people (Hanlon etal., 2014). More than 90% of inhabitants live in rural areas and rely on subsistence farming and small-scale trading. At the time of the study, health services in Sodo comprised 54 health posts, most of which were staffed by two community health extension workers with 1 year of training in general health promotion and illness prevention, and eight health centres, staffed by nurses, health officers and midwives, who provide basic curative and obstetric care. There were no doctors or mental health specialists in the district. In the nearest town of Butajira, located 30-50 km from Sodo, there was a psychiatric nurse-led out-patient clinic. The nearest in-patient mental health services were 100120 km away in the capital city, Addis Ababa.
+Sample and recruitment procedures
+As described previously (Baron et al., 2018), people with possible SMI in the community were identified by community key informants and health extension workers who had received half a day of training in typical presentations of SMI (Shibre et al., 2002). People with possible SMI were referred to the nearest health centre and assessed by PHC workers who had been trained in mental health care for a total of 10 days: 5 days of classroombased teaching using adapted mhGAP training materials and 5 days of practical clinical training in the Butajira psychiatric clinic. The PHC workers assessed the referrals, made a diagnosis of SMI and initiated treatment according to the evidence-based clinical guidelines in the WHO mhGAP Intervention Guide (World Health Organization, 2016). Independent diagnostic review was carried out with standardised, semi-structured clinical assessments by research psychiatric nurses using the OPerational CRITeria for research (OPCRIT) (McGuffin et al., 1991). Diagnostic assessments were conducted for all people with PHC worker-diagnosed SMI and wherever the PHC worker was uncertain about the diagnosis. The research psychiatric nurses reviewed
+the initial treatment plan. Any changes made by the psychiatric nurses were communicated back to the PHC workers.
+Eligibility criteria
+•	Confirmatory research psychiatric nurse diagnosis of a psychotic disorder (including schizophrenia, schizoaffective disorder and depression with psychotic features) or bipolar disorder.
+•	Providing informed consent, or caregiver permission, to participate in the study if the person with SMI lacked capacity to consent.
+•	Able to converse in Amharic, the official language of the region.
+•	Planning to reside in the district for at least 12 months.
+•	No cognitive or sensory impairment that interfered substantially with the clinical assessment.
+•	Not acutely physically unwell.
+Sample size
+The sample size for the PRIME SMI cohorts across countries was calculated to detect a 20% reduction in the severity of symptoms at 12 months, with 90% power, two-sided alpha of 0.05 and 20% attrition rate (Baron et al., 2018), leading to a target sample size of 150. However, in the Ethiopia district, all people who received a confirmatory diagnosis of SMI were included to establish a completely ascertained population cohort.
+PRIME intervention for people with SMI
+The integrated district mental health care plan for the Ethiopian setting has been described in detail previously (Fekadu et al., 2016), and involved interventions at the level of the health system, PHC facility and community.
+Health system interventions
+The district health office staff were involved in participatory planning using Theory of Change methodology (Hailemariam et al., 2015). The district health office assigned a focal person for mental health co-ordination. PRIME provided ongoing technical support with medication supply management (including establishing a revolving drug fund and providing assistance with forecasting the amount of psychotropic medication required), building capacity in supervision of mental healthcare (training high-performing general health workers to supervise mental health care) and monitoring and evaluation activities (e.g. aggregating data on facility contacts and feeding back at the advisory board meetings).
+PHC facility interventions
+All frontline PHC workers in Sodo district (n = 128) were trained in mental healthcare. The PHC workers received monthly supervision from a psychiatric nurse trained using the mhGAP supervisor training manual. For rural health centres, supervision was conducted by telephone when weather conditions precluded travel to the facility. PHC workers could also consult the psychiatric nurse for advice. People with confirmed SMI were prescribed an antipsychotic and/or antidepressant medication and/or a benzodiazepine, as indicated. There were no mood-stabiliser medications available, so people with bipolar disorder received antipsychotic medication as per usual practice in this setting (Fekadu et al., 2015). Almost all service users had to pay for medication.
+In addition to the prescription of medication, PHC workers were trained to provide psychoeducation, activate social supports, address social stressors, monitor physical health, review response to treatment and refer to specialist mental health care if needed. As prescriptions were usually for a maximum of 1 month, follow-up appointments with the PHC workers were usually scheduled monthly.
+Community-level interventions
+A multi-sectoral ‘community advisory board’ was established to support community awareness-raising and mobilisation, to help with trouble-shooting during the implementation phase and to review project activities and outcomes. A total of 96 communitybased health extension workers were trained in case detection, outreach to re-engage people who dropped out of care, identification of medication side effects, community awareness-raising and supporting social reintegration and recovery of people with SMI. In half of the sub-districts, people with schizophrenia who had enduring symptoms or disability after 6 months received adjuvant community-based rehabilitation, delivered by trained lay workers (n = 75) or ongoing PRIME care (n = 87), as part of a nested cluster randomised trial (the RISE trial) (Asher et al., 2016). Aside from the additional contacts from CBR workers in the intervention arm (weekly for 2-3 months and 2-weekly for the subsequent 5-6 months), there were no additional trial-related contacts.
+Measures
+Primary outcomes
+•	Clinical symptom severity was measured using the Brief Psychiatric Rating Scale, expanded version (BPRS-E) (Burlingame et al., 2006). The BPRS-E is a 24-item, clinicianrated scale which has been translated into Amharic and shown to have robust psychometric properties and sensitivity to change in Ethiopia (Habtamu et al., 2017).
+•	Disability was measured using the World Health Organisation Disability Assessment Schedule (WHODAS), version 2.0, 36-item version (Üstün et al., 2010). The WHODAS has been validated for use in people with SMI in Ethiopia and is sensitive to change (Habtamu et al., 2017). The WHODAS was completed by a combination of responses from the person with SMI and the caregiver responses to the proxy-WHODAS at the post-baseline assessments (19.8% of WHODAS scores from caregivers at midline, 24.1% at endline). We used the poly-tomous summary score of the WHODAS scaled from 0 to 100.
+Secondary outcomes
+•	Experience of discrimination was measured using the ‘unfair treatment’ subscale of the discrimination and stigma scale-12 (DISC-12) (Brohan et al., 2013). The original DISC-12 subscale has 21 items. Four items lacked face validity or had a low frequency of endorsement, but the remaining 17 items loaded onto a single factor using exploratory factor analysis and were summed.
+•	Restraint was measured by self-report of whether the person had been ‘restrained, chained or confined’ in the preceding 12 months.
+•	Alcohol use disorder was measured using the lay interviewer-administered Alcohol Use Disorder Identification Test (AUDIT) (Babor et al., 2001). This 10-item scale has been
+adapted for local drinks in the Ethiopian setting. People scoring ^8 are considered to have a probable alcohol use disorder.
+•	Depression was measured using a locally validated version of the Patient Health Questionnaire (PHQ-9) (Kroenke and Spitzer, 2002). In the Ethiopian setting, a cut-off of 5 or more is indicative of major depressive disorder (Hanlon et al., 2015).
+•	Suicide attempts in the past 3 months were assessed using the Mini International Neuropsychiatric Interview (Sheehan et al., 1997).
+Potential effect modifiers
+Equity indicators
+•	Gender and residence (rural v. urban).
+•	Socio-economic status: a poverty index was constructed which loaded onto a unidimensional scale using exploratory factor analysis: roof material made of straw (v. corrugated iron), unimproved water source, unimproved sanitation, no electricity, no separate room for kitchen, no radio or television, no mobile phone.
+•	Time to access the nearest health facility, estimated in minutes, whatever the means of travel. This was dichotomised into <60 min v. 60 min or longer.
+Baseline characteristic
+•	Diagnosis: clinician-assessed diagnosis using criteria of the Diagnostic and Statistical Manual of the American Psychiatric Association, version IV) (American Psychiatric Association, 1994) from OPCRIT (McGuffin et al., 1991).
+Process indicators
+•	Receipt of minimally adequate treatment used the definition proposed by Wang et al. (Wang et al., 2002): prescription of medication on at least one occasion combined with at least four follow-up appointments with a health worker trained in mental health. We additionally required that psychotropic medication should be prescribed at therapeutic levels (World Health Organization, 2016). Data on the number of facility contacts and prescriptions were extracted from the clinical records, cross-referenced with a facility registration book.
+•	Receipt of community support (measured at T1 and T2): support received with returning to work, remembering to take medication, improving self-care, meeting people and social engagement. Responses were summed and binarised: 0-2 types of community support v. 3-5 types of community support.
+•	Receipt of in-patient care for mental health problems, contact with specialist mental health or general health facilities and traditional or religious healers, and type of care received during primary health care contacts were measured at T1 and T2.
+Descriptive baseline characteristics
+•	Socio-demographic characteristics (age, educational level, marital status).
+•	Social support was assessed using the three-item Oslo Social Support Scale (OSS-3) (Dalgard et al., 2006), which asks about number of close supports, extent of concern from supports and amount of practical support received. The OSS-3 total score was categorised as follows: 3-8 ‘poor support’, 9-11 ‘intermediate support’ and 12-14 ‘strong support’.
+•	Duration of illness, type of illness onset (acute/sub-acute v. gradual), psychiatric hospitalisation in the past 12 months, presence of co-morbid medical condition and receipt of psychotropic medication at baseline were obtained from the OPCRIT (McGuffin et al., 1991).
+Data collection
+The lay interviewers were individuals with an educational level of at least tenth grade who were recruited from the local area and trained for 12 days on the study questionnaires and protocols, including observed practice interviews. The trainers had master’s level qualifications. Degree-level supervisors monitored data quality in the field. The clinician assessments were conducted by research psychiatric nurses who were trained for 7 days by senior Ethiopian psychiatrists. The OPCRIT diagnoses were doublechecked by an Ethiopian psychiatrist by reviewing the OPCRIT responses and clinical documentation and conducting verification interviews (n = 2) where needed.
+Data management and analysis
+Data were double-entered using EpiData (Lauritsen and Bruus, 2003) and analysed using Stata version 13.1 (StataCorp LP, 2016). The data were summarised descriptively, with outcome data stratified by equity indicators (gender, residence, distance from the health facility and poverty status). Comparison of the characteristics of participants remaining in the cohort with those lost to follow-up at 12 months, as well as by equity indicators, was conducted using Pearson chi-squared for categorical variables, Student’s t-test for comparing means in normally distributed variables and Kruskal Wallis equality-of-populations rank test for non-normal continuous variables.
+Mixed effects linear regression with random intercept was used to model the change in symptom severity, disability and depressive symptoms over time. We tested for improvement in model fit using likelihood ratio tests after adding random slopes. We also tested for any significant difference in the mean change between T0 and T1 or between T1 and T2. Mixed effects ordinal regression was used to model change in discrimination score over time. A prevalence ratio was calculated for change from T0 to T2 in probable alcohol use disorder (AUDIT ^ 8), suicide attempt in the preceding 3 months or restraint in the preceding 12 months.
+For the primary outcomes, we calculated standardised mean difference (Borenstein et al., 2009) and examined effect modification by the equity indicators, diagnosis ( primary psychotic disorder v. affective disorder), process indicators (receipt of minimally adequate treatment and community support) and by the community-based rehabilitation intervention group for the RISE trial. This was done by adding an interaction term into the model and testing for improved model fit using a likelihood ratio test. Multiple linear regression analysis was conducted to examine the association between baseline duration of illness and type of illness onset with 12-month outcomes.
+Ethical considerations
+Ethical approval was obtained from the Institutional Review Board of the College of Health Sciences, Addis Ababa University (No. 084/11/Psy). Informed consent was obtained where possible. If the person lacked capacity and did not refuse,
+the accompanying caregiver was invited to give permission on the person’s behalf.
+Results
+Of the 294 people diagnosed by PHC workers as having SMI, 279 were confirmed to have SMI, giving a positive predictive value for PHC worker diagnosis of 94.9%. A further 21 people referred by PHC workers to psychiatric nurses for diagnostic review were also found to have SMI, giving a total of 300 participants. See Fig. 1.
+Baseline characteristics
+See Table 1. At baseline, there was no significant difference in symptom severity score, disability, discrimination or depressive symptoms by gender. The proportion with an alcohol use disorder (x2(1) 48.6750; p <0.001) or who had been restrained (X2(1) 5.1154; p = 0.024) was higher in men. Baseline disability (t = 2.1012; p = 0.04) and restraint (x2(1) 3.9058; p = 0.048) were significantly higher in those with low socioeconomic status. Perceived negative discrimination at baseline was higher in urban residents (x2(1) 3.986; p =0.049) and people of low socioeconomic status (x2(1) 4.195; p = 0.04).
+Cohort follow-up
+A total of 247 (82.3%) people were assessed at T1 (mean 7.4 months; s.d. 1.49) and 245 (81.7%) at T2 (mean 12.3 months; s.d. 1.12) (see Fig. 1). There was no evidence of differential loss to follow-up based on baseline characteristics (online Supplementary File 1). During the follow-up period, 11 participants died.
+Facility-based intervention
+After review by psychiatric nurses, the medication initiated by PHC workers was unchanged or changed within the same medication class for 184 (67.7%) participants. Psychiatric nurses increased the dose or added another medication for 46 (16.9%), stopped or reduced the dose in 15 (5.9%), changed the class of medication in 15 (5.5%) and stopped a prescription of depot antipsychotic medication in 5 (1.8%).
+Participants attended a median of two PHC appointments during the follow-up period (IQR 2-4; minimum 1 and maximum 12) (online Supplementary File 2). Minimally adequate treatment was received by 89 (29.8%) (online Supplementary File 3). There was no evidence of prescribing above recommended limits and only one occurrence of antipsychotic polypharmacy. Admission for in-patient care was very low (1.4% at T1 and 2.0% at T2) and <10% of participants had direct contact with a mental health specialist. A high proportion reported receiving psychosocial support and explanation about medication (online Supplementary File 4).
+Community-based interventions
+Most people with SMI reported receiving support with medication adherence and to improve self-care. More than half received support to get back to work, but less than a quarter were supported to get involved in social activities and <10% had support with meeting people. Almost all support was reported to come from the family. A small proportion reported contact with traditional or religious healers: 13.0% at T1 and 11.6% at T2.
+Impact on clinical and social outcomes
+There was a significant improvement in all clinical (symptom severity score, depressive symptoms, suicide attempts, alcohol use disorder) and social (functioning, discrimination, restraint) (Tables 2 and 3). The standardised mean difference for symptom severity was 0.18 (95% confidence interval (CI)) 0.02-0.34) between T0 and T1 and 0.28 (95% CI 0.13-0.44) from T0 to T2. For disability, the standardised mean difference was larger at both time-points: T0-T1: 0.27 (95% CI 0.13-0.41), T0-T2: 0.50 (95% CI 0.35-0.65). The test for interaction between the RISE trial intervention group and the main outcomes (symptom severity and functioning) at 12 months was non-significant.
+For symptom severity and functioning, the magnitude of the change did not differ between T0 and T1 compared to T1 and T2. The reduction in depressive symptoms was significantly greater between T0 and T1 than between T1 and T2 (x2(1) 4.26; p = 0.039); similarly for change in perceived discrimination (X2(1) 3.75; p = 0.053).
+There was no statistically significant effect modification by the equity indicators, diagnosis or process indicators for the primary outcomes. At T1, higher receipt of community support had a borderline statistical association with a greater reduction in symptom severity ( p = 0.19) and disability ( p = 0.09) (online
+Supplementary Files 5 and 6). There was no association between duration of illness or type of illness onset and the mean improvement in the primary outcomes.
+Discussion
+In this community-ascertained intervention cohort of clinician-confirmed people with SMI, there was a significant improvement in clinical and social outcomes after implementation of a district level mental health care plan. PHC workers diagnosed SMI accurately, prescribed psychotropic medication safely and were reported to have delivered psychoeducation and provided support to most people with SMI. The findings from this study are generalisable to similar rural settings of low-income countries.
+Although PHC workers prescribed safely (low polypharmacy and no doses above the recommended therapeutic range), the psychiatric nurses did consider that it was necessary to change the initial prescriptions of psychotropic medication in 30.1% of cases. This reinforces the need for task-shared care to be supported by input by mental health specialists, either through regular supervision or through timely review of newly diagnosed cases.
+We are only aware of two previous studies, both from Nepal, where the impact of a ‘full’ task-sharing model on clinical and
+social outcomes of people with SMI was evaluated (Jordans et al., 2017; Jordans et al., 2019). Both studies found a significant reduction in symptom severity, disability and caregiver burden but had sample sizes under 100 and included extensive community-based psychosocial interventions (Jordans et al., 2019). Our larger study, which included a more representative population of people with SMI and a task-sharing intervention more closely based on mhGAP, provides more definitive evidence of important clinical and social benefits from the recommended WHO mhGAP approach.
+In our study, the impact of the district mental health care plan care on psychotic symptoms was less marked than the reductions seen in the social outcomes, with a reduction of 4.8 on the BPRSE being less than that usually considered to be clinically significant (Hanlon et al., 2016a). In previous intervention studies for people with SMI in LMIC settings, a key factor for clinical improvement has been enhanced adherence to antipsychotic medication (Chatterjee et al., 2014), but only 30% of people with SMI in our study received ‘minimally adequate treatment’ over the follow-up period. Our process data indicate that engagement waxed and waned over time, rather than people dropping out of care altogether, and lends support to the acceptability of care provided as well as providing explanation for the limited impact on symptom improvement at cross-sectional assessment. In-depth interviews with study participants with SMI who had disengaged from care indicated that most had experienced symptomatic improvement, but that affordability of medication and side effects of medications was a barrier to continuous engagement. This is
+supported by quantitative data from the same sample, indicating high levels of poverty and out-of-pocket healthcare costs compared to the general population (Hailemichael et al., 2019). Mechanisms to reduce out-of-pocket healthcare costs for people with SMI are needed to achieve improved access to mental health care (Hanlon et al., 2019).
+Although disability is closely linked to symptom severity, our previous work in this community indicates that stigma, discrimination and poverty also make important contributions. The borderline significant effect modification indicating greater improvements in disability in people with higher receipt of community support may reflect the impact of reduced social exclusion. We observed a significant decline in perceived negative discrimination in people with SMI. The PRIME mental health care plan included a cascade model of training of communitybased health extension workers to raise community awareness and reduce stigma against mental health problems (Fekadu et al., 2016); however, the extent of implementation is not known. The growing community and family awareness of the treatability of SMI, arising from the local availability of a treatment service and bearing witness to the clinical improvement of people with SMI who were well-known to the community, might have also reduced stigmatising attitudes, social exclusion and the need for people to be restrained. Furthermore, the Community Advisory Board members were selected due to their level of community influence and their endorsement of mental health care helped to reduce stigma and misconceptions about mental illness in the study site. In a recent trial in
+Ghana, restraint was not reduced by short-term provision of psychiatric care to people with SMI who were receiving faith healing in prayer camps (Ofori-Atta et al., 2018). In our study, the follow-up period was longer, which may have allowed family members and the community to gain confidence in the beneficial effects of treatment and the PRIME intervention incorporated community and system-level interventions as well as task-shared facility-based care.
+Alternative approaches to expanding access to care for people with SMI in rural populations in LMICs have been reported. One model is to utilise outreach clinics staffed by mental health specialists who diagnose, prescribe and monitor clinical progress, combined with community-level interventions by non-specialists to promote social inclusion and functional recovery (Srinivasa Murthy et al., 2005; Chatterjee et al., 2009; Chatterjee et al., 2014). Another ‘back-referral’ model is for mental health specialists to provide initial assessment and development of a care plan which is then implemented by non-specialist health workers in the local area combined with task-shared psychosocial interventions (Xiang et al., 1994). Augmenting the PRIME model with more intensive and systematic community-based rehabilitation delivered by non-specialists may help to address some of the gaps identified in our study by strengthening engagement with PHC and supporting livelihoods (Asher et al., 2016), although the affordability and sustainability of such approaches need
+evaluation. There are no published reports of these task-sharing service models being successfully, safely and sustainably taken to scale and we have no evidence regarding their relative impact on effective treatment coverage for people with SMI. In the future, randomised controlled trials comparing task-shared models of care for people with SMI are needed to inform policy decisions (Hanlon et al., 2016a).
+Limitations
+PHC workers were informed of the research psychiatric nurse diagnosis and changes in treatment plan. For diagnosis the concordance was high and so this is unlikely to have affected the outcome, but the treatment plan reflected task-shared care with support from a mental health specialist. We only collected data on the positive and not the negative predictive value of the PHC worker diagnosis. Calculating the negative predictive value would be an important focus for future studies. Due to ethical concerns, there was no comparison group of people with SMI receiving ‘usual care’, which would have amounted to no access to evidence-based care for most people. However, outcomes in people with untreated schizophrenia (who formed the majority of our cohort) have been shown to be poor (Ran et al., 2001). Given the chronicity and severity of SMI at baseline, spontaneous improvement is unlikely. This is supported by the lack of an
+association between baseline duration of illness or type of illness onset and outcomes in our study.
+Conclusions
+An integrated district level mental health care plan employing a task-sharing approach safely addressed the large treatment gap for people with SMI in a rural, low-income country setting, resulting in improved clinical and social outcomes and reduced human rights abuses. Training and supporting PHC workers to provide mental health care has great potential as a sustainable and feasible approach to the care of people with SMI in resource-poor settings.
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+1.	Introduction
+Research over the past two decades has demonstrated beneficial effects of comprehensive treatment programs for people who have recently experienced a first episode of psychosis (FEP) (McGorry, 2015).
+This accumulation of evidence has led to efforts to identify the essential components of effective FEP treatment (Addington et al., 2013), referred to in the U.S. as Coordinated Specialty Care (CSC) programs (Heinssen et al., 2014). However, until recently most of research on the effectiveness of FEP treatment programs has been conducted in countries with universal (or near universal) healthcare and single payer healthcare systems such as Australia, Canada, and Europe, limiting the generalizability of the findings to the U.S. healthcare system (Kane et al., 2015).
+To address the gap in knowledge about effective treatments for FEP that can be implemented in the U.S. healthcare system, the National Institute of Mental Health developed the Recovery After an Initial Schizophrenia Episode (RAISE) initiative, and issued a request for research proposals in 2008. These proposals were required to develop, pilot test, and rigorously evaluate FEP treatment programs that were tailored to the unique needs of this population, and could be implemented in typical “real world” treatment settings in the U.S., as well as be funded through existing payment mechanisms. The RAISE Early Treatment Program (ETP) was funded by this initiative. RAISE-ETP developed a CSC program for FEP and evaluated it in a cluster randomized controlled trial (Kane et al., 2015,2016; Mueser et al., 2015).
+The treatment that was developed and evaluated in the RAISE-ETP project is called NAVIGATE. The program was named “NAVIGATE” in order to convey its goal of helping clients and their family members find their way through the confusing experience of an FEP and the complexities of the mental health system towards the desired outcome of recovery. As a standardized, team-based program, NAVIGATE was designed to be implemented by existing staff in community mental health centers serving persons with FEP. The program was compared to usual Community Care in a cluster randomized controlled trial with a two-year treatment and follow-up. Primary analyses showed that clients in NAVIGATE had significantly greater reductions in overall psychiatric symptoms and depression, and greater improvements in quality of life, social relationships, and involvement in work and school compared to those who received usual Community Care (Kane et al., 2016).
+The results provide strong evidence for the effectiveness of the NAVIGATE program in typical mental health treatment settings in the U.S. However, information has not yet been published on the methods used to implement NAVIGATE and to monitor site adherence to the treatment model, nor of the extent to which sites were able to successfully deliver the program. The systematic evaluation of clinicians' (or program') adherence and competence (or fidelity) at providing an intervention is important for several reasons.
+First, increasing attention has focused on improving access to empirically supported practices for people with severe mental illness. Fundamental to this trend has been a widespread recognition of the importance of establishing that interventions shown to be effective in rigorous research trials can be implemented into routine service settings with acceptable levels of adherence to the principles of the treatment model (Addington et al., 2018 Ahead of Print). Effective interventions can only be expected to produce positive outcomes in typical treatment settings if they are provided competently and with good adherence to the original treatment model, and there is evidence linking better intervention fidelity to better clinical outcomes (Bond et al., 2011; McHugo et al., 1999).
+Second, fidelity assessment can play an important role in training and quality improvement of services when the results are fed back to individual clinicians, supervisors, and teams. For example, the timely provision of fidelity feedback based on reviews of audio-recorded therapy sessions has been used to train clinicians in the cognitive behavioral treatment of posttraumatic stress disorder in people with severe mental illness, with accompanying client level evidence of improvement in targeted symptoms (Lu et al., 2012). Similarly, in the National Implementing Evidence Based Practices project, in which 53 mental health agencies each implemented two of five practices, fidelity assessments were conducted for each practice every six months, based on site visits, for two years, with the results reviewed with sites and
+incorporated into technical assistance plans for improving fidelity (McHugo et al., 2007).
+Third, in a cluster randomized controlled trial design such as the RAISE-ETP project, the demonstration of a treatment effect favoring the experimental intervention does not address the question of whether different sites were in fact implementing the same intervention. This is a critical question because it informs the field as to whether the research supports the effectiveness of a specific treatment model (e.g., the NAVIGATE program) or a more general approach (e.g., having designated teams of clinicians provide treatment to people with an FEP). Such information has implications for the training and dissemination of programs based on research findings, and whether efforts should focus on implementing the defining components of an intervention or on the more general principles that the program is based on. For these reasons, this article focuses on describing the methods used to train clinicians in implementing the NAVIGATE program, and to evaluate their fidelity to the intervention.
+2.	Methods
+Thirty-four community mental health agencies across 21 states were randomized to deliver either NAVIGATE (N =17 sites) or Community Care (N =17 sites) to persons with a first episode of non-affective psychosis. A total of404 clients aged 15-40 were recruited and assessed at baseline and every six months for two years by clinical interviewers who were masked to treatment assignment (Kane et al., 2015, 2016).
+2.1.	Study sites
+Potential study sites volunteered to participate after being made aware of the project and site requirements, which included: a) experience in treating schizophrenia; b) interest in offering early intervention services for FEP clients; c) sufficient staff to implement the NAVIGATE program; d) anticipated subject flow to recruit approximately 20 clients with FEP over a two-year period; and e) institutional assurance that research assessments would be completed. Exclusion criteria for sites were: a) academic programs (centers with academic affiliations were allowed); and b) centers with pre-existing specialty FEP programs. Descriptive characteristics collected in 2009 of the 17 sites randomized to provide the NAVIGATE program are provided in Table 1.
+2.2.	Participants
+For the purposes of the fidelity assessment discussed in this paper, the staff members at the 17 sites implementing the NAVIGATE program were the study participants.
+2.3.	NAVIGATE treatment program
+NAVIGATE was designed to provide comprehensive care to individuals who have recently experienced an FEP (Mueser et al., 2015). The NAVIGATE program contains four integrated interventions, which were offered within a shared decision-making approach with clients selecting treatments based on their own treatment preferences and recovery goals. These interventions included: Individual Resiliency Training (IRT), the Family Education (FE) program, Supported Employment and Education (SEE), and Personalized Medication Management (PMM). These interventions have a clear evidence base (Bird et al., 2010; Killackey et al., 2008; Onwumere et al., 2011; Penn et al., 2005), and are considered key components of FEP services (Addington et al., 2013). The component services are described in more detail in Mueser etal. (2015) and Robinson etal. (2018).
+2.3.1.	Individual Resiliency Training (IRT)
+IRT is an individual therapy program aimed at helping clients set and work towards personal goals, enhance wellness and personal resiliency,
+learn information about psychosis and treatment, and improve illness management (Meyer-Kalos et al., 2015). IRT is offered on a weekly or biweekly basis at enrollment in NAVIGATE and includes seven Standard modules recommended for all clients (e.g., Education about Psychosis), and six Individualized modules intended for delivery on an as-needed basis (e.g., Coping with Symptoms).
+2.3.2.	Family Education (FE) program
+The FE program was recommended for clients who were in regular face-to-face contact with family members (e.g., >4 h/week) or other significant people (e.g., girlfriend). The goals of the FE program are to teach families about psychosis and its treatment, to reduce relapses by monitoring of early warning signs, to provide support for the client's work towards personal goals, and to reduce family stress (Mueser et al., 2015). Single family sessions are offered to all family members, including the client, who can choose to opt out of sessions if he or she prefers. A series of 10-12 sessions of psychoeducation is recommended for all families. Additional optional components of the FE program include family consultation to address circumscribed problems (1-2 sessions), and skills training to improve communication and problem solving skills. Brief monthly “check-ins” with the family clinician are encouraged after families have completed the program (Glynn et al., 2014).
+2.3.3.	Supported Employment and Education (SEE)
+SEE was adapted from the Individual Placement and Support (IPS) model of supported employment (Becker and Drake, 2003) to include education, and is offered to all clients who want to work or resume their studies. SEE focuses on helping clients develop and pursue education and work goals, obtaining competitive jobs or enrolling in educational programs as rapidly as possible, and succeeding in work or school through provision of follow-along supports (Lynde et al., 2014). In order to ascertain whether clients have work or educational goals and want to receive SEE services, the SEE specialist endeavors to meet with each client at least once soon after their enrollment in the NAVIGATE program.
+2.3.4.	Personalized Medication Management (PMM)
+PMM was recommended for all clients. NAVIGATE medication prescription includes detailed FEP psychotropic medication guidelines and a computerized decision support system named COMPASS to facilitate shared decision-making regarding prescriptions (Robinson et al., 2018). Antipsychotic medications are grouped based upon their documented efficacy and side effect profiles from the FEP and adolescent treatment trial literature into suggested treatment stages. Recommended dosing guidelines are provided for each medication. Suggested
+treatment begins with a stage 1 medication. If the stage 1 medication is ineffective, medications from subsequent stages (e.g., next a stage 2 medication, then if stage 2 was ineffective a stage 3 medication) are suggested. Guidelines for side effect minimization and for health monitoring and medical referral/treatment when applicable are also provided. As a part of COMPASS, prior to meeting with their prescriber participants record their recent symptoms and side effects on a standardized computer-based questionnaire, and their responses are summarized to provided to the prescriber for their meeting with the client.
+2.3.5.	The NAVIGATE treatment team
+The four NAVIGATE interventions were provided by a multidisciplinary team that usually included five mental health professionals, who met regularly and worked together to work with clients towards achieving their personal goals (Mueser et al., 2014). The team was led by the director, who was usually a master's level clinician and who also provided FE program, and who supervised the IRT clinicians and SEE specialist. The prescriber, a psychiatrist or nurse-practitioner, provided PMM. Two clinicians, usually master's level, provided IRT, while one typically bachelor's level member provided SEE. Case management was sometimes provided by one of these five members, or by a separate case manager who also served as a member of the team. Because of the relatively low flow of FEP clients at study sites, most team members were not employed full time on NAVIGATE, and served other clients at their agency. NAVIGATE clients could also access other services available at their local center.
+2.4.	Training of NAVIGATE teams
+NAVIGATE teams received a combination of in-person training and phone, as well as occasional video consultation that focused on both working effectively together as a team and implementing each of the specific NAVIGATE interventions. IRT and FE clinicians also received feedback from experts based on audio-files of sessions rated for adherence to the manuals using standardized fidelity scales as part of the certification process for training clinicians to implement the interventions. Following the initial in-person training, a series of training videos was created to demonstrate the implementation of IRT skills, which was made available to all IRT clinicians, and was used to train new clinicians.
+2.4.1.	In-person trainings
+At the initiation of the project, members of eight or nine NAVIGATE teams from different sites participated in three-day in-person training sessions conducted at a central location. Manuals for the overall program and the individual NAVIGATE interventions were distributed and reviewed at these meetings. The training was divided into team-based and individual specialty-based training. The team training was conducted with all of the team members together, and provided education about unique aspects of FEP, an overview of NAVIGATE and the specific roles of each member, and guidelines and exercises to foster effective teamwork (e.g., role playing a treatment team meeting). Individual specialty training was conducted concurrently for each of the four interventions, and included an introduction to the intervention, a review of the critical components, and a combination of modeling and role-playing skills for delivering it. In addition, directors received a half-day of training on leading the team, outreach to educate the community about the service and engage clients into NAVIGATE, strategies to maintain client engagement in treatment, and methods for supervising IRT and SEE.
+Approximately two years after the initial training, an in-person two-day follow-up training meeting was conducted with all 17 teams. Half of a day was spent reinforcing team strengths and sharing success stories across the different teams. The remainder of time was devoted to concurrent advanced training in each intervention and the director role, which included reviewing common challenges, identifying solutions, demonstrating possible solutions, and engaging clinicians in role plays to practice specific strategies.
+2.4.2.	Expert consultation
+Ongoing expert phone consultation for each intervention and the director role was part of the implementation plan for NAVIGATE, and was described in the NAVIGATE Team Members' Guide (Mueser et al., 2014). This consultation was an extension of the in-person training, and provided an opportunity for team members to get ongoing support and guidance as they implemented the NAVIGATE interventions. A specific consultation approach was developed for each intervention and the director, designed to provide the clinical training and support needed to achieve and maintain competence.
+For IRT, as part of the training and fidelity evaluation, clinicians made audio-recordings of IRT sessions, which were uploaded to a secure website and listened to by an IMR consultant. IRT session fidelity was then rated by a consultant using a standardized form, and written quantitative and qualitative feedback was provided to the clinician and supervisor in a timely fashion (e.g., within a week of the session). This rapid turn-around time for providing feedback about the quality of sessions was designed to facilitate the shaping of clinicians' skills early in the process of learning IRT, which has been used successfully to train frontline clinicians in implementing other psychosocial interventions for persons with severe mental illness (Lu et al., 2012).
+Clinicians and the site IRT supervisor also received two 1 -hour group phone consultations from an IRT expert monthly, with two sites participating per call, for the first four years of the project, with the frequency of consultations decreasing to monthly for the fifth year. During these calls, the group reviewed the status of clients engaged in IRT, consultants provided recommendations for implementing it, clinicians practiced skills and strategies, and any questions about written fidelity feedback from the consultant on audio-files of IRT sessions were addressed. There were additional opportunities during the calls to review and practice advanced IRT skills such as cognitive restructuring.
+Clinicians providing the FE program also made audio-recordings of sessions, which were uploaded and listened to by a consultant who provided written quantitative and qualitative feedback in a timely fashion using a standardized fidelity scale. Consultation was offered weekly for 1-hour in a group format with 6-8 clinicians per call. FE clinicians were expected to join the calls at least twice a month for the first four years of the project, which was reduced to once per month for the fifth year. Clinicians could call in more frequently if needed to receive additional support or training. During these calls, the consultant reviewed with clinicians their active families in treatment, discussed strategies to engage new families in treatment, and provided additional training, support, and skills practice in FE as needed.
+SEE experts provided group consultation on a biweekly basis to three to six SEE specialists per call. NAVIGATE directors were strongly encouraged to participate in these calls. The focus of the calls was on reviewing client engagement with the SEE specialist, client involvement in school and work activities, provision of SEE services (e.g., assessment of work/school interests and preferences, job development or liaising with schools), and problem-solving barriers to clients' pursuit of work or school goals.
+Prescribers received individual training via teleconferencing on technical aspects of the COMPASS decision support program. A monthly group teleconference with the NAVIGATE Central Team was also open to all prescribers, which included group feedback about clinical challenges and treatment options for these and review of relevant FEP literature. The COMPASS program provided prescribers real-time information on recommended NAVIGATE strategies for the treatment of symptoms and the management of medication side effects and medical health issues. Towards the end of the project, a call approximately every six months occurred between the Central Team and individual prescribers to provide an opportunity for case-by-case review.
+Monthly group conference calls with three to four NAVIGATE directors per call were led by the consultants. Following identification of agenda items from directors on the call, these meetings followed a semi-structured agenda that included: review of recent NAVIGATE
+enrollees and dropouts, the number of IRT and SEE supervision meetings conducted, the number of team meetings conducted, and any challenges experienced implementing the components of the program. The remaining time was spent solving problems related to implementing NAVIGATE or addressing clinically challenging cases. The consultants also occasionally “sat in” (via phone) on different sites weekly NAVIGATE team meeting, and after provided feedback to the director (one to three times per site). Information gleaned from these informal consultations to the directors was not included in the fidelity assessments.
+2.4.3.	Training new team members
+New members of the NAVIGATE team were trained using a combination of strategies. Training for all new members included directed reading of manuals and related materials, individual or small group time with consultants to answer questions and ensure basic understanding of the intervention, and participation in ongoing consultation calls. For new IRT clinicians, IRT training tapes created early in the project were also employed in training new clinicians. Some new SEE specialists also took a 12-week online course on providing IPS supported employment as part of their training.
+2.5.	Fidelity assessment
+Specific methods for evaluating fidelity to each of the four NAVIGATE interventions were developed, as well as for evaluating the adherence of teams to the overall structure and staffing of the NAVIGATE program (Mueser et al., 2014). The methods used to measure fidelity were intervention specific, and depended on the types of information that could be readily accessed without imposing a significant burden on the sites. For IRT and FE, fidelity assessments were based on consultant reviews of audio-files of treatment sessions, using instruments that were adapted from fidelity measures of other psychosocial interventions for the severe mental illness population (Lu et al., 2012; McGuire et al., 2012). All IRT and FE sessions were audio-recorded, unless the client objected or there was equipment failure. Fidelity to SEE and the overall team were assessed through a combination of interviews with team members, participation on consultation calls, and records maintained by SEE specialists and directors. Fidelity to PMM was evaluated by examination of prescribing data. Copies of the IRT, FE, SEE, and NAVIGATE Team fidelity scales are included in the supplementary material for this article.
+2.5.1.	Individual Resiliency Training (IRT)
+Fidelity to IRT was evaluated through a certification process, based on consultants' ratings of audio-files of IRT sessions. Ratings were made on the IRT Fidelity Scale, a 14-item scale of critical components of IRT (e.g., agenda setting, goal setting/follow up, cognitive restructuring, skills training strategies), with each item rated on a 5-point Likert scale ranging from 1 (unsatisfactory) to 3 (satisfactory) to 5 (excellent) (Browne et al., 2016 Ahead of Print). Similar to some other scales for measuring fidelity to cognitive behavioral therapy (Muse and McManus, 2013), the IRT Fidelity Scale combines ratings of clinician adherence and competence, with low numbers for most items reflecting poor adherence to the treatment model (1, 2), and higher numbers reflecting level of competence for items that were adhered to (3-5). To ensure consistency of ratings, inter-rater reliability checks were conducted by having different consultants rate the same sessions, although these data were not analyzed.
+Two levels of certification were established to designate clinicians who had demonstrated adequate fidelity to implementing the Standard (Level 1) IRT modules and the Individualized (Level 2) IRT modules. For both levels, clinicians were required to demonstrate an overall rating of at least 3 (satisfactory) on the IRT Fidelity Scale on four out of five consecutively rated sessions. A “3” was selected to indicate acceptable fidelity to the IRT model based on other cognitive behavioral therapy scales
+that employ a similar threshold to designate satisfactory clinician fidelity or competence (Blackburn et al., 2001; Haddock et al., 2001; Young and Beck, 1980). If fewer than four of the initial session ratings met the criterion, additional sessions were rated, and certification was met when four consecutive sessions met the criterion level. Level 1 certification was required before a clinician could obtain Level 2 certification. Following certification, quality ratings were conducted on approximately 10% of randomly selected sessions, with feedback sent to the clinician and supervisor.
+2.5.2.	Family Education (FE) program
+A similar certification process was used to evaluate fidelity to the FE program. The FE Fidelity Scale included 13 critical components of the program (e.g., agenda setting, use of family educational handouts and worksheets) that were rated on 5-point Likert scales. Similar to IRT, inter-rater reliability checks on ratings by different consultants were conducted, although data were not analyzed. Certification required clinicians to achieve a rating of 3 (satisfactory) or higher on the overall fidelity rating for three out of four sessions with two families. If the clinician did not meet this criterion for one or both families, sessions for additional families were rated using the same criteria, until the criteria were met for two families.
+2.5.3.	Supported Employment and Education (SEE)
+The SEE Fidelity Scale (see Table 4) was developed to be completed based on a combination of program and administrative records (but not site visits), and included nine items scored by two raters on behaviorally anchored 4-point scales (1 = poor, 2 = limited, 3 = basic, and 4 = good) (Rosenheck et al., 2017). A score of “3” (“basic”) was considered the minimum for acceptable implementation.
+Four items on the scale were based on four of the eight principles of IPS supported employment (zero-exclusion for eligibility: #5; focus on competitive work or integrated school: #6; integration of SEE and clinical treatment: #4; follow-along supports: #8), and five items were based on other characteristics of IPS included in the IPS Fidelity Scale (caseload size: #1; SEE specialist role: #2; supervision: #3 and #9; community-based services: #7) (Bond et al., 2012). Benefits counseling, a principle of IPS, was not included in the SEE Fidelity Scale because it was a responsibility of the entire NAVIGATE team and not just the SEE specialist (Mueser et al., 2014). SEE adaptations of the other three principles of IPS, including attention to client preferences, rapid job search, and job development, were not included in the scale because of difficulty rating them based on available records, although these principles was incorporated into the SEE Manual (Lynde et al., 2014).
+2.5.4.	Personalized Medication Management (PMM)
+For the psychosocial interventions, sites were providing treatments that they had not provided before as these treatments had been developed or adapted from prior models specifically for NAVIGATE (e.g., sites could not have provided IRT before NAVIGATE as it did not exist before NAVIGATE was developed). In contrast, all sites had provided medication treatment to clients before NAVIGATE was developed and NAVIGATE medication recommendations only employed marketed agents available to all clients via prescription. Prescription for a particular client could be either 1) the site usual medication practice choice, or 2) a choice facilitated by the NAVIGATE guidelines (e.g., if a client received a prescription for risperidone, it might have been the prescription they would have received at the site outside of NAVIGATE treatment or it might have been from application of NAVIGATE guidelines).
+To assess the degree to which a site followed NAVIGATE medication principles, we estimated the degree that sites' prescriptions differed from usual practice. Medication prescriptions and dosage received by study participants were recorded monthly for both NAVIGATE and Community Care sites as part of study research procedures. Each month's treatment was coded as either conforming or not to
+NAVIGATE first-line principles. A detailed description of these principles has been published (Robinson et al., 2018). The site-specific metrics were averaged to determine the median percent months of first-line treatment across all sites (NAVIGATE and Community Care). Sitespecific rates were compared with the median all-sites rate to determine if first-line prescription rates at a site were greater or lesser than the overall median rate.
+2.5.5.	NAVIGATE team composition and activities
+Adherence of each team to the structural and process elements related to team activities was evaluated by two raters based on a combination of program and consultant records, with the 10-item behaviorally anchored NAVIGATE Team Fidelity Scale, which employs 4-point anchored scales with the same descriptors as the SEE Fidelity Scale. Team fidelity was assessed regarding the defining characteristics of the program described in the NAVIGATE Team Members' Guide, including continuity of staffing and services, participation of all staff at weekly team meetings, and director supervision of IRT and SEE.
+2.5.6.	NAVIGATE Fidelity Index
+The NAVIGATE Fidelity Index was developed in order combine the five component fidelity ratings into an overall measure of a team's adherence to the NAVIGATE model. Three-point scales were created to summarize the different fidelity components with respect to the adequacy of their implementation: 1 = not implemented, 2 = basic implementation, 3 = good implementation. A mean of these five scores was computed to form an overall NAVIGATE Fidelity Index score for each site ranging from 1 to 3. NAVIGATE programs with Index scores < 2 were designated “not implemented,” scores > 2 and <2.5 were designated “basic implementation,” and scores > 2.5 were designated “good implementation.”
+For IRT, sites that had no clinicians certified in IRT Level 1 were given a Fidelity Index score of 1, sites with at least 1 clinician certified in IRT Level 1 but no clinicians certified in IRT Level 2 were given an Index score of 2, and sites with at least 1 clinician certified in IRT Level 2 were given an Index score of 3. For FE, sites that had no clinicians certified in FE were given an Index score of 1, and sites that had at least 1 clinician certified in FE were given an Index score of 3.
+For both SEE and NAVIGATE team fidelity, the mean scores on each fidelity measure were used to assign Index scores as follows: 1 = mean score < 3 (“basic”), 2 = mean score 3-3.5, 3 = mean score > 3.5. For fidelity to PMM, sites in which the mean percentage adherence to the antipsychotic guidelines was above the median percentage adherence for all 34 study sites were given an Index score of 3, and sites with mean adherence below the median were given an Index score of 1.
+2.6.	Receipt of NAVIGATE program services
+For PMM, the COMPASS computer system recorded data from every visit. To evaluate participation in the three NAVIGATE psychosocial interventions, we examined client responses to selected items on the Service Use Reporting Form (SURF) (Rosenheck and Fontana, 2003). The SURF is a brief instrument that was administered to all study participants (including those at Community Care sites) on a monthly basis in order to obtain information about recent service utilization, and which included three questions designed to evaluate whether participants had received IRT, FE, and SEE, and if so how many sessions or meetings of each (Kane et al., 2016).
+2.7.	Statistical analyses
+We summarized the demographic (gender) and professional characteristics of the staff members on the NAVIGATE teams across the 17 sites, as well as the time spent on the study, by computing means or percentages for staff based on their primary role on the team. In order to
+evaluate whether staff members fulfilling different roles on the team (director, prescriber, IRT specialist, SEE specialist) differed significantly in the length of time they participated in the study, a one-way analysis of variance was performed.
+In order to evaluate the participation rate in PMM, we computed the percentage of clients who completed at least one visit based on the COMPASS computer system, and among those the mean number of visits. For participation in IRT, FE, and SEE we computed the percentage of clients who reported receiving at least one service for each intervention, and among those the mean number of services received over the two-year study period.
+For IRT, we computed the number of sites that had at least one clinician who was certified in IRT Level I, and the number of sites with a clinician certified at IRT Level 2. Similarly, for FE we computed the number of sites with at least one clinician certified in FE. For IRT certification at both Levels 1 and 2, and FE certification, we also calculated the mean number of sessions rated and the duration of time required for clinicians to achieve certification. For the SEE Fidelity Scale and NAVIGATE Team Fidelity Scale we computed the mean score and range for each item, and the mean rating across items for each site.
+In order to evaluate whether fidelity to the different components of NAVIGATE across sites were correlated with each other, Pearson correlations were computed between the three-point fidelity scores that comprised the NAVIGATE Fidelity Index.
+3.	Results
+A total of 129 practitioners served on NAVIGATE teams at the 17 sites participating in RAISE-ETP study. Table 2 summarizes the characteristics of these practitioners, organized according to their role on the team. Most of the SEE specialists had a bachelor's degree, whereas the majority of IRT providers, project directors, and family clinicians had master's degrees. Among the prescribers, 80.0% were medical doctors (MDs).The mean number of months on the project ranged from 30.4 months for SEE specialists to 38.8 months for directors, and did not differ significantly between the NAVIGATE staff roles, F(4,116) = 0.64, NS.
+Of the 223 study participants at NAVIGATE sites, according to the COMPASS program 211 (94.6%) completed one or more PMM visits, and among those they had a mean of 14.2 visits. Based on the monthly SURF reports, 205 (91.9%) participants reported receiving at least one IRT service (M = 24.1), 150 (71.3%) reported receiving an FE service (M = 13.7), and 187 (83.9%) reported an SEE service (M = 13.6).
+3.1.	Individual Resiliency Training (IRT) and Family Education (FE) Interventions
+The characteristics of certification for clinicians providing IRT and FE are summarized in Table 3. For IRT, 36 of 42 clinicians (85.7%) achieved Level 1 (Standard Modules) certification; at least one clinician was certified at each of the 17 sites. Fourteen clinicians (32%) achieved Level 2 (Individualized Modules) certification, including at least one clinician at 11 sites (65%). For FE, a total of 19 out of 22 clinicians (86%) achieved certification; 15 of the 17 sites (88%) had at least one certified clinician. For both IRT Levels 1 and 2, the number of sessions required to achieve the four acceptable sessions required for certification was 5.17 and 4.29 sessions, respectively. Clinicians required a mean of 9.63 sessions to achieve the eight acceptable sessions for FE certification. Thus, most of the sessions recorded by clinicians for certification in both IRT and FE were rated as satisfactory or higher quality.
+The results of the certification process leave open the question of how much FE was provided by clinicians who were not certified, and how much IRT was delivered by clinicians who were not certified at one or both levels of IRT. Among the 3 clinicians who were not certified in FE, a total of only 14 sessions were audio-recorded, suggesting that the vast majority of FE sessions were provided by certified clinicians. Similarly, among the 6 clinicians who were not certified in the Standard
+Modules of IRT, a total of only 13 sessions were audio-recorded, also suggesting that most Standard Module IRT sessions were provided by clinicians certified at that level.
+While relatively few Standard Module IRT sessions were provided by clinicians who were not certified at that level of IRT, more Individualized Module IRT sessions (N = 48) were provided by the 22 clinicians who never achieved that higher level of certification. We compared the fidelity of the Individualized Module IRT sessions between clinicians who were certified at that level and clinicians who were not certified at the same level by conducting a t-test on the overall session quality rat-ingofthe IRT Fidelity Scale. The t-testwas significant, t =2.75, df =105, p = .007, with certified clinicians having higher quality ratings (M = 3.63, SD = 0.72) than non-certified clinicians (M = 3.23, SD = 0.78). However, the average quality rating of the non-certified clinicians was nevertheless above the “satisfactory” rating of 3 on the IRT Fidelity Scale. Thus, while only a minority of sites had clinicians who were certified in the Individualized IRT Modules, this finding suggests that
+acceptable levels of quality were achieved even when non-certified clinicians delivered these modules.
+32.	Supported Employment and Education (SEE)
+Data for the SEE Fidelity Scale ratings for the sites are presented in Table 4. Four sites (24%) were in the upper range of basic to good fidelity (>3.5), 11 (65%) were in the lower range of basic to good fidelity (>3.0 and <3.5), two sites (12%) were in the range of limited to basic fidelity (>2.0 and <3.0). Considering a mean SEE score corresponding to “basic” fidelity as the minimum acceptable fidelity, 15 of the 17 sites (88.2%) implemented SEE with acceptable levels of fidelity.
+3.3.	Personalized Medication Management (PMM)
+As presented in Table 5, the median percentage of months across all sites that participants received first-line prescriptions over the potential 2-year follow-up was 41.37%. Twelve of the 17 NAVIGATE sites (70.5%) had a site-specific percentage of greater than the median and 5 were below the median. In contrast, only 5 of the 17 Community Care sites had a site-specific percentage above the median and 12 were below the median, a statistically significant difference (x2 = 5.76, N = 17 p = .016).
+3.4.	NAVIGATE team fidelity
+Data for the NAVIGATE Team Fidelity Scale are provided in Table 6. Eleven sites (65%) were in the upper range of basic to good fidelity (>3.5), 5 sites (29%) were in the lower range of basic to good fidelity (>3.0 and <3.5), and 1 (6%) was in the upper range of limited to basic fidelity (>2.5 and <3.0). If “basic” fidelity (M > 3) is considered the minimal acceptable level of adherence to the structure and staffing of NAVIGATE model, then only one of the 17 sites (6%) fell below an acceptable level.
+3.5.	NAVIGATE Fidelity Index
+The mean adherence of sites to the NAVIGATE model (i.e., the NAVIGATE Fidelity Index), including fidelity to each of the four treatments and the NAVIGATE staffing and structure is provided in Table 7. Nine sites (53%) were in the “good implementation” range (M > 2.5-3.0), eight (47%) were in the “basic implementation” range (M = 2.0-2.5), and no sites were in the “not implemented” range (M< 2.0).
+Two Pearson correlations between the five different fidelity ratings for each site included in the NAVIGATE Fidelity Index were significant: fidelity to IRT was correlated with fidelity to FE (r = 0.49, p = .04), and NAVIGATE Team fidelity was correlated with fidelity to SEE (r = 0.49, p = .04). None of the other correlations were significant.
+4.	Discussion
+The findings indicated that among the 17 NAVIGATE sites in the RAISE-ETP project, all demonstrated at least basic or higher levels of fidelity to the model, according to scores on the NAVIGATE Fidelity Index. Fidelity to NAVIGATE was measured using clear definitions for each of the four interventions included in the program, as well as the structure and staffing of the program. This is an important finding because one of the requirements stated by the NIMH Request for Proposals for the RAISE initiative was that the intervention could be delivered in real-world settings (Kane et al., 2015). More than 130 practitioners provided NAVIGATE treatment to clients and on average, they were part of a NAVIGATE team for more than three of the five years that the study was ongoing at their sites. This means that many participants saw continuity in their treatment providers.
+Among the five individual components of the NAVIGATE Fidelity Index, sites scored the highest on adherence to the NAVIGATE team structure and staffing, and the FE and IRT treatments; mean Index scores for these items were over 2.5. Sites were generally effective at hiring and replacing staff on the NAVIGATE team, offering the range of services in the model, and meeting regularly for team meetings and supervision.
+Further, all 17 sites had at least one clinician certified in the IRT Standard modules, 11 sites (65%) had a clinician certified in the IRT Individualized modules, and 15 sites (88%) had a clinician certified in the FE program, indicating high rates of fidelity to these psychotherapeutic components of the NAVIGATE program. In addition, the number of audio-files of IRT and FE sessions reviewed by fidelity raters for certification was only 5.15 sessions to achieve 4 acceptable sessions for the IRT Standard modules, 4.29 sessions to achieve 4 acceptable sessions of IRT Individualized modules, and 9.63 sessions to achieve 8 acceptable
+sessions of FE. These findings suggest that the combination of the training, manuals, supervision, and consultation for IRT and FE were sufficient for clinicians to rapidly demonstrate good clinical skills when providing each intervention for the first time. Anecdotally, the limited amount of time to implement NAVIGATE (i.e., a maximum of two years of study enrollment, and often less time), and the slow enrollment of clients at some sites, led to low caseloads of FEP clients and engagement of family members in treatment, making it more difficult for clinicians to achieve certification in the FE program and IRT Individualized modules.
+The model for assessment of PPM fidelity differed from the certification procedures for IRT and FE. Prescribers were either psychiatrists or nurse-practitioners and therefore licensed to prescribe marketed antipsychotics, which were the only ones used the study. As a result, the fidelity model here was to contrast the prescribing practices at NAVIGATE sites to an estimate of usual practice. NAVIGATE prescribers were significantly more likely to prescribe antipsychotics that were in the first tier of recommendations according to the NAVIGATE guidelines. These results suggest that support provided by written guidelines, the webbased COMPASS decision support system, and the training model had a valuable impact. However, it is important to note that for PMM a decision support system such as COMPASS is not readily available in routine practice.
+In contrast to the relatively strong implementation of IRT, FE, PPM, and the overall NAVIGATE team structure, the implementation of SEE was somewhat weaker, with the mean NAVIGATE Fidelity Index score for this item of 2.12, just above the “basic” implementation level. Some of the challenges in implementing the SEE program have been previously discussed (Rosenheck et al., 2017), including the lack of financing mechanisms for SEE at some sites. Despite supplementary research funds available to support SEE, some sites could not adequately support SEE services, making it challenging to implement with high fidelity to the model. As with supported employment (Drake et al., 2016; Mueser and Cook, 2016), more reliable funding mechanisms are needed to support the provision of SEE to the FEP population. Other factors may have also contributed to attenuated SEE fidelity, as discussed below.
+Among the correlations between elements of the NAVIGATE Fidelity Index, two were statistically significant: fidelity to the IRT and FE programs (r = 0.49), and fidelity to SEE and the overall NAVIGATE team (r = 0.49). These correlations could reflect shared method variance between how fidelity to the elements of NAVIGATE were measured. Fidelity to IRT and FE were evaluated with a certification process based on audio-files of sessions, whereas fidelity to SEE and the NAVIGATE team were evaluated by review of administratively collected data on
+services provided (SEE) and team staffing and activities. However, the ratings of NAVIGATE program elements were made by experts in each area and not by the same raters, somewhat attenuating this possibility.
+The correlation between IRT and FE fidelity could also reflect the effects of site-related factors on the implementation of these two psychotherapeutic interventions, such as caseload size, cross-training of clinicians in both interventions, the ability of stronger sites to hire better clinicians, and turnover of the director position, who usually provided FE and supervised the IRT clinicians. In addition, the significant association between fidelity to the SEE model and fidelity to the NAVIGATE team model points to the potential influence of systemic factors in successful implementation (Aarons et al., 2011). Difficulties implementing the SEE program due to limited funding for these services, and problems maintaining the continuity of staffing, services, and responsibilities of
+different team members, could reflect broader issues related to the resources available at sites to support the implementation of NAVIGATE, and the capability and will of leadership in commanding those resources. Access to resources and quality of leadership are frequently cited factors in contributing to the success of implementing novel psychosocial interventions in community settings (Lundgren et al., 2013; McGuire et al., 2015; Whitley et al., 2009).
+The method for evaluating fidelity to the NAVIGATE program differed in important ways from some other methods used for CSC programs (Radhakrishnan et al., 2017 Ahead of Print), including the First Episode Psychosis Services Fidelity Scale (FEPS-FS) (Addington et al., 2016). The FEP-FS was developed in order to identify the critical components of a range of empirically supported programs for persons with FEP through a systematic review of the research literature followed by a Delphi consensus process (Addington et al., 2013). The resulting scale was designed to be completed by two or three assessors based on a site visit, and to extract information through a combination of interviews, record reviews, and observations (Addington et al., 2016). In contrast, the assessment of fidelity to the NAVIGATE program was intended to evaluate adherence and competency to a specific set of interventions and defined program structure, standardized in a set of manuals. Thus, a more precise approach to measuring fidelity to this program was possible based primarily on evaluation of individual providers of the interventions.
+Assessments based on reviews of audio-files of IRT and FE sessions were the most time consuming methods used to evaluate fidelity. However, the review of these sessions also provided quantitative and qualitative feedback to clinicians, which was an integral part of their training. Therefore, the time required to implement this component of the fidelity assessment should not viewed in isolation as a program monitoring cost, but instead should be considered within the broader context as a cost related to the high-quality training and supervision of clinicians.
+Several limitations of this study should be noted. First, PPM included an extensive range of recommendations, including strategies for adherence enhancement, treatment for clients with varying degrees of treatment resistance, side effect minimization and general medical management. To include all recommendations in a fidelity measure would have resulted in a measure that would have been so complex that it would be difficult to interpret. Instead, for fidelity assessment we focused upon one key recommendation, prescription of a NAVIGATE first-line antipsychotic.
+Second, the 17 sites providing NAVIGATE were not necessarily nationally representative of mental health centers in the U.S. Rather, sites participating in the study were chosen following an open, national solicitation process in which potentially eligible and interested mental health centers applied to participate in the study (Kane et al., 2015). It is likely that participating centers were more open to innovation and interested in learning new service models than the average mental health center, which could have facilitated the implementation of NAVIGATE. While these “early adopters” may have been motivated to learn this new treatment model (Panzano and Roth, 2006), academic sites or those that already had an FEP program were excluded from participation. Thus, aside from having a sufficient number of clients and staff members to participate in the study, and potentially greater enthusiasm for innovative programs among the agency leadership, these sites had no special advantages over other community-based mental health agencies serving people with FEP.
+These limitations notwithstanding, the present results demonstrate that the NAVIGATE program can be implemented with acceptable levels of fidelity with existing staff at typical community mental health care centers. Considering that the primary findings from the cluster randomized controlled trial showed that over two years participants at NAVIGATE sites had substantially better clinical and psychosocial outcomes than Community Care sites, priority should be given to disseminating this program throughout the U.S. Research is needed to develop more efficient methods of implementing the NAVIGATE program, and
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+Introduction
+Mental disorders (MD) and use of substances such as illegal drugs or alcohol (SUD) together account for 7.4% of the global burden of disease and are the leading causes of years lived with disability (YLD).1 They frequently co-occur;2 among users of community mental health team (CMHT) and drug and alcohol services in four UK cities in 2001-2002, 44% of CMHT service users reported
+SUD, with 75% of drug service users and 85% of alcohol service users reporting one or more MD.3 In the UK during the 2000s, MD and SUD were found to be strongly associated with poorer than average health and greater risk of premature death.4 During the 1990s, prevalence of CC recorded in routine primary care data in England and Wales significantly increased.5 A study of birth cohorts from the UK (births in 1946) and New Zealand (births in 1972-73) and survey data from the USA in the late 1990s and early 2000s suggested that at least half of adult mental disorders began in adolescence, with anxiety disorders typically presenting earlier than substance use disorders and psychotic disorders.6 There may be long-term consequences for children and young people with these diagnoses, increasing the likelihood of poorer social, physical and mental health outcomes during the course of their lives.7 A range of individuals and services are involved in the provision of care for children and young people with MD or SUD in Wales, including parents and carers, schools, primary care, specialist child and adolescent mental health services (CAMHS) and children’s social care.8
+Studies from the late 1990s onwards have identified a complex epidemiological picture for MD and SUD in young people in the UK. In people under the age of 20 in England and Wales, incidence of anxiety and depression diagnosis is declining, but incidence of associated symptoms, and the prescription of antidepressants and anxiolytics, is increasing.9-12 Survey data show an increase in emotional disorders in young people up to the age of 19 in England, particularly older female adolescents.10 Between 2001 and 2016, the proportion of 8-24 year olds in England reporting that they drink alcohol has fallen,13 contacts with primary care relating to alcohol dependency in people under 25 in the UK have declined since 2005,14 abstinence in young people aged 16-24 in England increased between 2005 and 2015,15 and alcohol-related emergency admissions for 10 to 18 year olds in Wales decreased between 2006 and 2011.16 However following declining rates of reported drug use by young people in England between 2001 and 2014, rates since 2014 are increasing for both 11-15 year olds17 and 1624 year olds18 and poisoning events associated with alcohol and opioids (including prescribed opioids) increased between 1998 and 2014, particularly among females in the UK using opioids.19 People aged 10-19 years old in England between 1997 and 2012 were at significantly increased risk of death or further emergency admission in the 10 year period following a drug or alcohol-related
+hospital admission20 and SUD was a significant risk factor for progression to suicidal behaviour in young people under 22 in the UK who self-harm or express suicidal thoughts.21
+The authors are not aware of any recent studies using routine health data in the UK to examine trends and outcomes for children and young people with CC, and the National Institute for Health and Care Excellence (NICE), the body responsible for producing clinical guidelines covering the NHS in England, Wales and Northern Ireland, has identified a need for research in this area.22 The aims of this study were: 1) to use routine health data from primary care, inpatient admissions and death registrations to estimate first recorded incidence of CC in children and young people aged 11-25 in Wales, UK; 2) to estimate all-cause mortality rate and 10 year survival with CC in this population; and 3) to compare survival and mortality for individuals with codes for either CC, a record of either MD or SUD or no relevant codes recorded.
+Methods
+Design
+A retrospective population-based electronic cohort study was conducted using linked routine primary care, hospital inpatient admissions and mortality data.
+Data Source
+The data source for this study was Secure Anonymised Information Linkage (SAIL) Databank, a secure repository established and managed by Swansea University Medical School, Wales. It houses anonymised health and related datasets describing the Welsh population, which can be linked for research purposes.23,24 Datasets (Table 1) were prepared within the Adolescent Mental Health Data Platform.25 We used data for the period 2008-2017 inclusive; data were available for the full study period from all datasets.
+Measures
+Clinical Coding for Case Definitions
+Read V2 Codes: Substance Use and Co-Occurring Conditions
+With clinical input and based on published literature14,26,27 we compiled a list of SUD-related Read v2 codes, including diagnoses, symptoms, observations, medications, behaviours (eg “injecting drug user”), referrals and contacts with other services. We included codes for alcohol
+and illegal drugs but excluded tobacco, in keeping with similar studies.5,28 We included codes designating MD due to substance use, which were classified as CC without requiring the presence of a second MD or SUD code (for example Read v2 codes in section Eu%, designating “Mental and behavioural disorders due to psychoactive substance use”): this included codes for mental and behavioural disorders due to acute intoxication, as there is an association between contact with services for acute intoxication and subsequent suicide risk.29
+We included only those prescriptions relevant to treatment for substance use, and excluded those used primarily for pain management. We included disulfiram, naltrexone, lofexidine, acamprosate and methadone, as almost all recipients had a history of SUD. For buprenorphine we included only those Read v2 codes where 10% or fewer recipients had no history of SUD. We excluded alcohol Read v2 codes requiring an associated value of units relating to consumption volumes, because we could not be confident that on their own these codes denoted SUD.
+International Classification of Diseases (ICD-10) Codes: Substance Use and Co-Occurring Conditions
+ICD-10 codes30 were initially identified by cross-mapping with SUD Read v2 codes. We then searched the literature
+to identify any additional codes:26,27,31-33 these were cross-mapped and added to the Read v2 code list, to ensure consistency. As with Read v2 codes, ICD-10 codes designating MD due to substance use were classified as CC.
+Read V2 and ICD-10 Codes: Mental Disorders
+MD codes were sourced from the Adolescent Mental Health Data Platform (ADP) Concept Library.25 We included codes for depression, anxiety, severe mental illness (SMI; schizophrenia, schizotypal and delusional disorders, bipolar disorder, other mood-related disorders and other severe mental illness),9,12,34,35 eating disorders,36 Attention Deficit Hyperactivity Disorder (ADHD),37 Autistic Spectrum Disorder (ASD),37 conduct disorders37 and developmental disorders.38 Codes included both diagnoses of conditions and associated symptoms, but did not include prescription of medication associated with these conditions.
+All code lists can be found in Additional File A1.
+Factors and Covariates
+We obtained data on factors and covariates for age, sex, and Welsh Index of Multiple Deprivation (WIMD) 2011 quintile, an area-based measure of relative deprivation in Wales.39 We divided age into four groups; 11-14, 15-17,
+23
+18-21 and 22-25 years of age (collapsed into two groups; 11-17 and 18-25, where numbers were too low to report). Age was defined at the end of each reporting year for incidence and at the start of the study window for mortality and survival. Individuals with null or contradictory indicators for sex were excluded. WIMD 2011 was derived from the 2001 census Local Super Output Area (LSOA) in which individuals were registered at the end of each year (or next nearest available record, where a record at registration end had no WIMD) for incidence, and at the start of follow-up period (or nearest available record) for mortality and survival. We did not carry out any additional imputation for missing values.
+Analysis Methods: Incidence Individuals Included
+Using WDSD as the primary population, we identified individuals having their 11th - 25th birthdays between 1st January 2008 and 31st December 2017.9,12 We included only periods during which individuals were registered with a SAIL supplying GP practice. For analysis of WLGP data, we excluded the first six months of each GP registration period, to minimise the designation of prevalent cases as new incident cases due to re-recording of patient history when individuals move between GP practices.9,12 We did not apply this exclusion to the inpatient data, as there is no retrospective coding in inpatients. The data collection start date was therefore the latest of; SAIL GP registration start date (plus six months for WLGP data); first day of 11th birthday year or 1st Jan 2008. The data collection end date was the earliest of SAIL GP registration end date; last day of 25th birthday year, date of death or 31st December 2017. An individual could contribute more than one period of data; for example, where they had moved between SAIL and non-SAIL GP practices or migrated out of Wales and subsequently returned. The denominator for incidence was person years at risk (PYAR), to reflect individuals present in the data for only part of a year.5,9,12
+MD and SUD Indicators
+Incident cases were identified separately in primary care data (WLGP) and inpatient data (PEDW) using Read v2 and ICD-10 code lists. We excluded codes designating a history of a particular condition, as they do not distinguish between ongoing and historical conditions.
+Incidence Measures
+First recorded incidence was defined as the date of the first occurrence in the patient history of a CC code, or in the
+absence of such a code, the latter of the first MD or the first SUD code (the first of which could appear at any time in the patient history). An incident event was recorded only once for each individual, regardless of how many periods of data they contributed to the study population.
+We plotted annual first recorded incidence rates to describe trends over time. Poisson regression, with an offset allowing for comparison of rates, was initially undertaken to model counts of CC incidence by year, sex, age band and WIMD quintile. The degree of over-dispersion was estimated using the Quasi-Poisson method40 and as the data were found to be overdispersed, we ran the final analysis using Negative Binomial regression. Rates were reported as annual incidence per 1000 PYAR and incidence rate ratios (IRR) adjusted for sex, age and WIMD quintile, with 95% Confidence Intervals (CI).
+Analysis Methods: Mortality
+We extracted from the incidence cohort a subset of individuals born between 1983 and 1997 and registered with a SAIL-supplying GP practice on 1st January 2008. We followed these individuals for 10 years, from 1st January 2008 to 31st December 2017. Therefore, the oldest age cohort, (those born in 1983), was followed up from the year of their 25th birthday to year of their 34th birthday and the youngest age cohort (those born in 1997) was followed up from the year of their 11th birthday to the year of their 20th birthday. In this cohort each individual provided only one period of data; the start date of follow-up was 1st January 2008 and the end date was the earliest of death, 31st December 2017 or last date of registration with a SAIL-supplying GP practice (date of loss to follow-up).
+We searched the patient record to identify the first occurrence of MD, SUD and CC codes, at any time between birth and end of follow-up, including codes designating a history of a particular condition. Using the ONS Annual District Deaths Extract (ADDE) we identified individuals who had died during the study window. We compared the proportion of deaths among those with a history of CC, either SUD or MD, and neither SUD or MD (NC). We calculated observed unadjusted mortality rates per 1000 PYAR for each condition group, by age, sex and WIMD quintile.
+We included individuals with no prior history of SUD, who died following a single episode involving use of a substance, in either the SUD or CC groups (depending on the codes in their history). We carried out a sensitivity analysis examining the impact of designating these individuals as NC.
+Figure 1 Flow diagram of study cohorts.
+Analysis Methods: Survival
+Using the subset of individuals present in SAIL on 1st January 2008, we estimated survival from start of follow up time (1st January 2008); the outcome variable was death. The exposure variable was condition group (NC; MD only; SUD only; CC). We right censored follow up time to the earliest of data collection end date or end of follow up. We plotted Kaplan-Meier survival curves, with significance of difference assessed by log rank tests. We performed Cox regression to derive hazard ratios (HR) comparing risk of all-cause death for individuals with CC in their history with those with SUD or MD only and those with NC, adjusted for sex, WIMD quintile and age band at start of follow-up. We tested the proportional hazards assumption by plotting Schoenfeld residuals. We then repeated the analysis with condition group as a time-dependent variable (as first event in each condition group could occur at any time), WIMD quintile as a two-level group (60% least deprived; 40% most deprived) and age at start of follow-up as a continuous instead of a categorical variable.41
+We adopted an Alpha level of 0.05 for all statistical analyses.
+Results
+Study Populations
+Figure 1 shows a flow diagram of the study cohorts. The WLGP incidence cohort consisted of 923,941 individuals
+contributing 4,391,444 PYAR and the PEDW incidence cohort consisted of 958,603 individuals contributing 4,545,876 PYAR. The mortality cohort consisted of 465,242 individuals, contributing 3,746,991 PYAR (mean = 8.1 years, SD = 3.1 years), of whom 1416 died during the 10-year follow-up period.
+Table 2 summarises the proportion of the incidence cohorts with codes for SUD only, MD only or CC at any time in their history up to 2017. In the WLGP cohort, 75.4% were NC, 21.8% were MD only, 0.8% were SUD only and 2.0% were CC. About 70.4% of individuals with SUD also had a code for MD and 8.4% of individuals with MD also had a code for SUD. In the PEDW cohort, 94.8% were NC, 2.9% were MD only, 0.5% were SUD only and 1.9% were CC. About 79.7% of individuals with SUD also had a code for MD and 38.8% of individuals with MD also had a code for SUD.
+Table 3 summarises the condition groups (based on events at any time up to 2017) of the 923,941 individuals present in both the WLGP and PEDW incidence cohorts, by sex and across both settings (primary care and hospital admission). Overall, a greater proportion of females than males had a record of MD in either setting (26.0%, 95% CI 25.9-26.2 compared with 17.0%, 95% CI 16.9-17.1), whereas more males than females had a record for SUD or CC (1.0%, 95% CI 1.0-1.1 compared with 0.6%, 95% CI 0.6-0.6 for SUD and 3.9%, 95% CI 3.9-4.0 compared
+with 3.1%, 95% CI 3.0-3.1 for CC). A higher proportion of males than females had no record of a condition in either setting (78.0%, 95% CI 77.9-78.1 compared with 70.3%, 95% CI 70.2-70.4).
+A greater proportion of females than males had only a primary care record with an MD (22.6%, 95% CI 22.422.7 compared with 14.9%, 95% CI 14.9-15.0). In the WLGP SUD only and CC groups, the proportion of
+males with no PEDW record was greater than that for females; 0.8% (95% CI 0.8-0.9) compared with 0.4% (95% CI 0.4-0.4) for SUD only and 1.3% (95% CI 1.31.4) compared with 0.8% (95% CI 0.8-0.9) for the CC group. Across both sexes, 86.2% of the WLGP MD only group (173,167 out of 200,891), 74.0% of the WLGP SUD only group (5754 out of 7778) and 53.7% of the WLGP CC group (9933 out of 18,491) had not had a relevant PEDW admission. Of the 696,691 individuals in the WLGP NC group, 5754 (0.8%) were MD only in PEDW, 1505 (0.2%) were SUD only in PEDW and 4148 (0.6%) were CC in PEDW.
+Of the 1416 individuals in the mortality cohort who died during follow-up, 1020 (72.0%) were male and 396 (28.0%) were female. Six hundred and seven (42.9%) were NC, 417 (29.4%) were MD only, 60 (4.2%) were SUD only and 332 (23.4%) were CC (0.2% of the NC group, 0.3% of MD only, 0.9% of SUD only and 1.2% of CC). Of the 165,835 individuals with MD and/or SUD, 809 (0.5%) died during follow-up.
+Incidence
+Figures 2 and 3 summarise trends in CC incidence rate per 1000 PYAR between 2008 and 2017 by sex, age and WIMD quintile, presented separately for WLGP and PEDW. Table 4 summarises the incidence of CC by sex, age, WIMD and year, including IRRs adjusted for sex, age and WIMD quintile, derived from Negative Binomial regression.
+Overall incidence in WLGP significantly reduced over the period (2.49, 95% CI 2.35-2.64 in 2008 and 2.10, 95% CI 1.97-2.24 in 2017, IRR = 0.88, 95% CI 0.78-0.99). Incidence in PEDW was stable (2.27, 95% CI 2.13-2.41 in 2008 and 2.17, 95% CI 2.03-2.31 in 2017, IRR = 0.95, 95% CI 0.84-1.08).
+Incidence for males (WLGP = 2.53, 95% CI 2.46-2.60; PEDW = 2.37, 95% CI 2.31-2.43) was significantly higher than for females (WLGP = 2.07, 95% CI 2.01-2.13; PEDW = 1.94, 95% CI 1.88-1.99), IRR = 1.18 (95% CI 1.12-1.24) for WLGP and IRR = 1.17 (95% CI 1.10-1.24) for PEDW. Incidence among females in WLGP (but not PEDW) declined whereas for males it remained stable; incidence from WLGP for females in 2008 was 2.27 (95% CI 2.08-2.48) and in 2017 was 1.82 (95% CI 1.64-2.01, IRR = 0.82, 95% CI 0.71-0.94).
+Higher incidence was significantly related to increasing age: incidence in WLGP increased from 0.15 (95% CI 0.13-0.18) for 11-14 year olds to 3.81 (95% CI 3.70-3.91) for 22-25 year olds (IRR = 24.80, 95% CI 21.20-29.40); and in PEDW from 0.60 (95% CI 0.560.65) for 11-14 year olds to 2.77 (95% CI 2.69-2.86) for 22-25 year olds (IRR = 4.50, 95% CI 4.08-4.98). The association between higher incidence and increasing age was stronger for primary care than for hospital admissions, with rates in WLGP lower than in PEDW in the youngest age band but higher in the oldest; this was evident in greater IRRs in WLGP between age bands.
+Higher incidence was associated with greater deprivation; the lowest incidence rates were among the least deprived quintile (WLGP = 1.13, 95% CI 1.07-1.21; PEDW = 1.24, 95% CI 1.17-1.31;) with the highest among the most deprived quintile (WLGP = 3.75, 95% CI 3.63-3.87; PEDW = 3.20, 95% CI 3.10-3.32), IRR (WLGP) = 3.28 (95% CI 3.00-3.58) and IRR (PEDW) = 2.59, 95% CI 2.36-2.84, with rates declining in the intermediate quintiles as deprivation reduced. Between 2008 and 2017, the gap between most and least deprived quintiles reduced considerably in WLGP, with a significant reduction in the most deprived quintile and a significant increase in the least deprived quintile. In 2008, incidence was 4.52 (95% CI 4.12-4.94) in the most deprived quintile and 1.02 (95% CI 0.82-1.24) in the least deprived quintile; by 2017 incidence was 3.00 (95% CI 2.67-3.37) in the most deprived quintile and 1.39 (95% CI 1.14-1.66) in the least deprived quintile (IRR for change in most deprived quintile; 0.67, 95% CI 0.54-0.82; IRR for change in least deprived quintile; 1.38, 95% CI 1.01-1.89). This was not observed in PEDW. An interaction between WIMD quintile and year was significant for the most deprived quintile in 2014, (IRR = 0.64, 95% CI 0.44-0.93), 2016 (IRR = 0.52, 95% CI 0.36-0.76) and 2017 (IRR = 0.48, 95% CI 0.34-0.70), and for the second-most deprived quintile in 2016 (IRR = 0.63, 95% CI 0.43-0.93) and 2017 (IRR = 0.57, 95% CI 0.39-0.84), but not significant for any other year and quintile combination. Results for regression
+including interaction terms are shown in Additional File A2.
+Mortality
+Figure 4 summarises observed unadjusted mortality rates for each condition group per 1000 PYAR, by sex, age at start of follow-up and WIMD quintile. The highest rate was for individuals with CC (1.38, 95% CI 1.24-1.54), followed by those with SUD only (1.11, 95% CI 0.851.43); these rates were not significantly different but both were significantly higher than rates for MD only (0.36, 95% CI 0.33-0.40) and for NC (0.26, 95% CI 0.24-0.29); unadjusted rate ratios (RR) and 95% CIs were CC to MD; 3.84 (3.82-3.85), CC to NC; 5.21 (5.19-5.25), SUD to MD; 3.10 (3.07-3.12), SUD to NC; 4.21 (4.17-4.24). Rates were significantly higher for males than females for all condition groups except SUD only, and were significantly higher for those aged 18-25 at start of follow-up than those aged 11-17, for all condition groups except SUD only. Rates for the most deprived WIMD quintile were higher than any of the other quintiles, but other than in the NC group (most deprived = 0.33, 95% CI 0.28-0.39, least deprived = 0.20, 95% CI 0.16-0.24, RR 1.66, 95% CI 1.65-1.68) there were no significant differences by deprivation other than between the most deprived quintile (1.62, 95% CI 1.36-1.91) and the second least deprived quintile (0.93, 95% CI 0.62-1.35) in the CC group (RR 1.73, 95% CI 1.71-1.76).
+Of 392 deaths among the SUD only and CC groups, we identified six who died in hospital with no records for SUD until their final admission. Reclassifying these as NC in the analysis made no significant difference. We included MD and SUD events occurring at any age from birth to end of follow-up; average age at first recorded event in either data source was 19.6 years of age (SD 5.9) for MD and 20.0 years of age (SD 4.7) for SUD.
+Survival
+Figures 5-11 show plots of Kaplan-Meier survival curves with p-values derived from Log Rank tests, by condition group, sex, age band at start of follow-up and WIMD quintile. Due to risk of statistical disclosure arising from small counts, the curves for SUD only were excluded from Figures 6-11. To further prevent statistical disclosure, age at start of follow-up and WIMD quintile were collapsed to two levels (11-17 and 18-25; least deprived 60or quintiles 1-3 and most deprived 40%, or quintiles 4 and 5).
+Survival was significantly different for individuals with CC, NC or MD only, for both males and females
+(p<0.0001, Figure 5). Figures 6-11 show that survival for males was significantly lower than for females in all condition groups and in both age bands at p<0.0001, and for 11-17 year olds with CC at p<0.05. The group who were 18-25 at start of follow-up had significantly lower survival for all conditions (all significant at p<0.01) except females with NC where there was no significant difference by age. Results by WIMD group were mixed; survival for both males and females with NC was significantly lower for the more deprived group (females = p<0.05; males = p<0.001). Differences in survival between the least and most deprived females with MD only and CC, and between the least and most deprived males with CC were not significant; differences between the least and most deprived males with MD only were significant at p<0.05.
+Figure 12 summarises the results of a Cox regression with death from all cause as the outcome. Results showed that compared to the NC group, the risk of death during the study window was significantly higher for individuals with MD only (HR = 2.7, 95% CI 2.4-3.1), with SUD only (HR = 4.5, 95% CI 3.4-5.9) and with CC (HR = 8.7, 95% CI 7.5-10.0).
+Discussion
+Main Findings in the Context of Previous Studies
+In keeping with previous studies we found a high degree of overlap between cases of MD and SUD,1-3 particularly for SUD in secondary care where almost 80% with SUD also had an MD, as shown in Table 2. The overlap for MD, particularly in primary care, was lower, with around 8% of those with MD also having a record of an SUD; this may reflect the large proportion of patients with MD who are
+managed in primary care without ever being admitted to hospital.
+Incidence of CC in young people aged 11-25 between 2008 and 2017 was stable in secondary care and decreased in primary care, particularly for females and among 1117 year olds. Similar trends have been identified in studies using routine data to separately estimate incidence or prevalence of MD9,11,12,34 and SUD.14,16 The gap in primary care incidence rates between the most and least deprived quintiles has narrowed, due to a reduction in the most
+deprived quintile and a smaller but significant increase in the least deprived quintile. An interaction between WIMD quintile and year (visible in Figure 2, panel D) was nonsignificant for most terms until 2014, but with some significant results for the most deprived quintiles in the most recent years, suggesting a significantly greater reduction in incidence among the most deprived. However, there remains a strong positive association between greater incidence and greater deprivation, as well as male sex and older age, as shown in Figure 2, Figure 3 and Table 4.
+Observed unadjusted mortality was significantly higher among individuals with a diagnosis of CC, and to a lesser extent among those with a diagnosis of SUD or MD only, than among individuals with NC, as shown in Figure 4. Survival was significantly lower for individuals with CC, particularly for males and those in the older age band at start of follow-up, as shown in Figures 6-11. Compared to the NC group, the hazard ratio for death was 8.7 times greater in the CC group, 4.5 times greater in the SUD only group and 2.7 times greater in the MD only group, as shown in
+Figure 12. Alcohol and drug use have been shown to commonly precede suicide.42 Our findings are consistent with previous studies suggesting individuals with a history of alcohol use disorder are at significantly increased risk of death,29 even in the absence of a co-occurring MD.43 They may also suggest that there is undiagnosed or unrecorded MD among individuals with SUD-related service contacts. MD (particularly with comorbid SUD) is associated with allcause mortality rates significantly higher than those for the general population: as well as the inherent risk of death directly attributable to substance use, there may be greater
+medical morbidity, which is not always well recognised by service providers.44 There is a well-established association between deprivation, male sex and increased risk of death.45 Higher mortality but lower contact with services among males may indicate greater unmet need in this group, although no association can be assumed without further analysis.
+Strengths and Limitations
+This was a large-scale population study using linked routine health data comprising the records of nearly
+one million participants in Wales, providing a sufficiently large number of outcomes (CC cases and deaths) to support our estimations. We used the ONS ADDE to ascertain date of death, which is a near-complete record and is considered the gold standard for death records.46 Although the SAIL Databank dataset holds records for 80% of GP practices in Wales, the data in SAIL is broadly representative of the Welsh population in terms of sex, age and deprivation. Routine data may vary in quality between sources, and this may affect dataset linkage; to mitigate
+this we used only those records where there was sufficient level of confidence in matching quality.24
+Alcohol use disorders, particularly hazardous and harmful drinking (as opposed to dependent drinking) are under-recorded by GPs, particularly for men and younger people.47 This is also likely to be the case for illegal drug use.48,49 Rates of recording may vary over time or between GP practices, due to experience, training, practice protocols and government policies.50 The exclusion of codes relating to consumption levels may also mean that some
+33
+individuals with problematic but non-dependent alcohol consumption are not detected. Estimated rates of SUD derived from routine primary care data should therefore be considered as a minimum. The analysis should be interpreted as examining coding behaviour as much as clinical indicators.36
+The identification of cases within this study is limited by the availability of full patient history in the WLGP and PEDW datasets. We did not include individuals attending Emergency Departments; inclusion of this dataset would very likely increase the incidence of CC as it would include individuals not admitted to hospital and those who are reluctant to seek help from their GP. Incident cases are defined as the first recorded occurrence of a code, but we cannot be certain that these events genuinely represent the onset of a condition.51 The rates presented are therefore a measure of contacts with services.52
+We estimated mortality and survival for death from all causes, and did not consider specific causes. SUD and MD are (both individually and in combination) associated with an increased risk of death from specific causes such as suicide, as well as deaths from natural causes.20,26,29,35,42-44 Future studies should examine the relationship between CC and specific causes of death, and in particular the relationship between CC and death by suicide.
+We did not include personality disorders (PD) in our definition of MD, although PD commonly co-occurs with SUD;53 this is because SUD is considered a diagnostic criterion for borderline personality disorder.54 We grouped together use of alcohol and drugs, and did not consider the impact of specific substances, the severity of usage or the impact of using specific combinations of substances. We have included SUD codes indicating varying degrees of severity; for example we included as CC all episodes with codes for mental or behavioural disorders due to psychoactive substance use, which includes episodes of acute intoxication “resulting in disturbances in level of consciousness, cognition, perception, affect or behaviour”.30 We will be undertaking further studies to examine the role of (and interaction between) specific substances such as alcohol and cocaine. These studies will also consider the relationship between CC, mortality and specific types of mental disorder. As incidence of different types of mental disorder varies by age,10 we will consider the relationship between type of disorder, age at diagnosis and outcome.
+Policy, Research and Practice Implications Individuals who have had contact with primary care or inpatient services related to CC (as well as those with SUD or MD only) in their patient history are at significantly
+Dovepress
+increased risk of death; these contacts may offer an opportunity to identify particularly vulnerable individuals in need of specialist intervention.
+CC incidence rates for younger age bands were lower in primary care than in hospital admissions, which was unexpected, given that GP practices should receive and record notification of any inpatient admissions and that primary care may be the first place individuals turn to for help with SUD.50 This finding supports existing evidence of under-recording of SUD in primary care (but in this instance may relate to the recording of SUD, MD or both). There are well documented sensitivities about discussing and recording SUD in primary care50 which may be amplified for younger patients. Survival and mortality rates were significantly poorer for individuals with CC, but were also significantly worse for individuals with SUD only, suggesting that SUD (with or without co-occurring MD) is a key risk factor, particularly for males. Alternatively this may be due to undiagnosed MD among substance users. Mental health and substance misuse service providers should work in partnership to ensure that substance use does not create barriers preventing access to mental health support.55 Health, education and social care services in contact with young people should ensure they are discussing substance use and offering advice, support and onward referral to specialist services where required.56 Accessible and acceptable services need to be available to those who are at greater risk, or who are less likely to engage, such as young men and those living in the most deprived areas, and use co-produced approaches that are designed to meet their needs.57 However a “glass ceiling” effect may limit the value of studies identifying risk factors for low prevalence events, and it has been recommended that prediction rules should not be used in isolation.58 A contextual safeguarding approach59 may help to identify specific locations where at-risk young people are likely to be, allowing early intervention and prevention to be delivered by youth service hubs and detached youth workers, providing an opportunity to reach young people who would otherwise not engage with services. Further studies should also consider whether the reduction in incidence in the most deprived WIMD quintile is due to genuine decreases in MD and SUD, or is a consequence of increasing difficulty with accessing services.
+This study did not consider subcategories of death; we included deaths from all causes, as previous studies have indicated that MD and SUD increases the risk of natural as well as unnatural deaths. However it is likely that risks of natural and unnatural death (particularly suicide) are not equal, and are affected by the presence or absence of CC. This may also be the case for risk of non-lethal self-harm
+among individuals with CC, which was not considered in this study. Risk may vary according to the type and combination of substance used, particularly whether both alcohol and drugs are used. Future studies should examine the relationship between CC and different causes of death, including suicide, and should also consider the impact of and relationship between specific substances, such as cocaine and alcohol (where coding is sufficiently granular).
+Conclusion
+CC is associated with significantly greater mortality in children and young people. Incidence of CC in children and young people in Wales between 2008 and 2017 decreased in primary care and remained stable in secondary care, with significantly higher incidence associated with male sex, increasing age and greater deprivation. In primary care, the gap in incidence between the most and least deprived quintiles has reduced; rates remain highest in areas with greatest deprivation, but as well as a significant decrease in the most deprived quintile, rates significantly increased in the least deprived quintile. Mortality was significantly higher among individuals with a diagnosis of CC, and to a lesser extent among those with a diagnosis of SUD or MD only, compared with individuals with NC. The higher mortality rate for individuals with SUD (with or without mental disorder) may indicate substance use as a key risk factor, or alternatively may be indicative of undiagnosed or unrecorded mental disorder in substance using individuals. All services coming into contact with children and young people, including primary care, education, youth services and CAMHS, should be adequately resourced to provide advice, support or referral to appropriate services where there are concerns about mental health or substance use.
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+The stigma of mental illness is a complex construct with affective, cognitive, and behavioral components that affects attitudes and behavior patterns at both the individual and population levels. Its reduction requires a multidirectional approach.1 Measures such as federal antidiscrimination legislation have been shown to be an important cornerstone against stigmatization of mental illness, but multiple components of the stigma process are beyond the reach of legislation, and need to be coupled with preventive programs to positively impact
+people’s perceptions of mental illness or increase helpseeking across heterogeneous populations.1,2
+The reduction of stigmatization of mental illness is considered to be relevant to the prevention of a variety of adverse mental health outcomes, including suicide. From the perspective of a public health approach to suicide prevention, some suicide prevention advocates consider raising public awareness of the scope of the problem of mental illness and suicide as a first key step in reducing the public health problem.3
+However, there is also the counter-argument that targeting the broader public to raise awareness of the scope of the problem may adversely affect vulnerable individuals.4-6 Adverse effects may be due to an increase in norms that describe suicidal behavior as common or frequent. This may increase the likelihood that individuals will believe that engaging in suicidal behavior is widespread and therefore acceptable.7,8
+Suicide prevention researchers and practitioners alike are frequently torn between these two lines of thinking, and there is currently mixed evidence regarding
+beneficial and harmful effects of broad-scale awareness programs.4,5,9 A 2006 survey by Research!America10,11 found that 89% of the U.S. population believed that mental health was as important as physical health, and 48% strongly agreed that “many suicides and suicide attempts can be prevented.” Further, input from the National Action Alliance for Suicide Prevention’s (Action Alliance) Research Prioritization Task Force (RPTF) stakeholder survey highlighted the reduction of stigma and increased help-seeking as a priority, because of a prevalent perception that suicidality continues to be stigmatized.11 Persons bereaved by suicide describe isolation and misunderstanding of their loss as a result of this stigma.12,13
+This report focuses on various types of broad public health messaging/media-based approaches that aim at reducing the burden of suicide. These approaches include campaigns to reduce the stigma of mental illness and increase public awareness of suicide, media campaigns to increase help-seeking as well as efforts to prevent copycat suicides. The authors elaborate on how multilevel approaches are related to Aspirational Goal 10 and provide examples of research efforts that seem necessary to move the field of suicide prevention forward.
+Influences on Help-Seeking
+Individuals generally seek mental health services in a series of interactive stages that involve problem recognition, decision to seek help, and service selection. These stages can be influenced by a number of other factors, including attitudes and beliefs about suicide, health literacy, internal and external barriers, and perceived need for treatment.14,15 Studies on help-seeking often use heterogeneous definitions of help-seeking, and methodo-logic inconsistencies across studies have been noted in the literature.16
+One of the few available conceptual frameworks that may help increase consistency across studies is the framework proposed by Rickwood and Thomas,16 which takes into account the specific part of the help-seeking process to be investigated, the source and type of assistance, and the type of mental health concern. Studies have inventoried reasons why individuals with suicidal ideation do not frequently seek help, some of which are outlined below.
+Stigma—both self- and other-induced—is believed to reduce the likelihood that an individual will seek help to resolve a suicidal crisis.17,18 Men, who have the highest rate of suicide and lower rates of accessing care for many health problems, particularly mental health services, are assumed to have more stigma and resistance to helpseeking, as are people with less exposure to suicide, of
+older age, with less education, or from culturally diverse backgrounds.18
+According to Corrigan,19 stigma can be described “in terms of prejudice (agreement with stereotypic beliefs leading to hostile emotional responses, such as fear and anger) and discrimination (the behavioral consequence of prejudice, which leads to social distance and the loss of opportunity).” However, research on stigma of mental illness and suicidal ideation has been hampered by heterogeneous definitions of stigma. Furthermore, a shortage of validated scales to measure stigma has been noted in the literature.20 A scale to directly measure the stigma of suicide in the community has recently been proposed by Batterham and colleagues20 in Australia and was found to have robust psychometric properties that require international validation.
+A lack of problem recognition has been found to be one of the most prevalent reasons among teenagers and adults for not seeking help for suicidal ideation or mental health issues.14,21-24 It is a more prevalent barrier to helpseeking among callers to the National Suicide Prevention Lifeline than financial or personal barriers (e.g., shame) or barriers related to perceptions about mental health services.21
+Furthermore, maladaptive coping strategies, such as not considering external help, have been found most prevalent among high-risk youth,15 and help-seeking intentions seem to further decrease with increasing suicidal ideation (so-called help-negation).24
+News Media Approaches to Preventing Suicide
+The media play an important role in the stigmatization of mental illness, suicidal ideation, and persons bereaved by suicide. The reduction of stigmatization by influencing public perceptions of suicide has been an important target in media-related suicide prevention efforts over the last two decades. Unfortunately, there are many discrepancies between typical media reports of suicide and actual suicide in the population, which may generate and help maintain stereotypes of suicide.
+Suicide reports in news media are selective, frequently underreport the relationship between suicide and mental illness, and focus frequently on the reporting of homicide suicides.25-27 Repetitive reporting of suicide in the context of homicide may increase or contribute to maintaining the stigmatization of suicidal individuals and of those bereaved by suicide. Young people without past experiences of seeking professional help have been found to largely rely on inaccurate media stereotypes.24 The discrepancies between the realities of suicide prevention and the reality portrayed in the mass media
+therefore warrant attention in public education on suicide prevention.
+Following the publication of Goethe’s The Sorrow of the Young Werther in 1774,28 several suicides by young men of similar age and with suicide motives like the protagonist in Goethe’s novel were reported in the literature. There is strong evidence today that media portrayals of suicide can lead to additional suicides, the so-called Werther effect, but negative findings also continue to be reported.29-32
+The evidence of copycat behavior is strongest following media coverage of a celebrity suicide, and for other types of repetitive, high-quantity reporting.29,33,34 A recent meta-analysis29 identified an average significant cumulative increase of 0.26 suicides per 100,000 people in the month following reporting on a celebrity suicide. The effect seemed to vary with the type of celebrity involved, with entertainers having the largest impact.29 Effects have been shown to be most pronounced in subpopulations that resemble the portrayed suicide with regard to gender, age group, and selected suicide methods.34,35
+Although most research has focused on the impact of news media reporting, some studies also have detected potential copycat behavior following fictional media programs.36 For example, a fictional German TV series featuring the suicide of a teenager—which was produced in the 1980s with an aim to increase awareness of suicide—was associated with a strong increase in suicides among teenagers and young adults of similar age who used the same suicide method. An increase was witnessed again when the series was repeated later on.37
+Most studies rely on aggregate data to analyze potential copycat behavior. These studies cannot account for whether those who died by suicide after the broadcast were actually exposed to the broadcast. Ecologic studies may also be subject to ecologic fallacy. There are only a limited number of individual-level studies available that support the negative influence of some sensationalist suicide reports on actual suicidal behavior.31
+The consideration of potential copycat behavior and prevention thereof is essential in any public media discourse on suicide, including both media reporting on suicide and campaigns to increase awareness of the problem of suicide. Particularly with regard to news reporting, prevention efforts frequently involve the distribution of media recommendations for suicide reporting.
+The U.S. recommendations were revised and released in 2012 by several national and international suicide prevention organizations in partnership with journalism and media representatives and are available at reportingonsuicide.org (see also nimh.nih.gov/health/top
+ics/suicide-prevention/recommendations-for-reporting-on-suicide.shtml). Similar recommendations are available from the WHO.38 There is some evidence that media recommendations have resulted in improved and less sensational media reporting about suicide39-42 and may have even contributed to a decline in suicides.42
+A suicide-protective effect of news articles featuring someone overcoming a suicidal crisis has been termed the Papageno effect—after the character in Mozart’s opera The Magic Flute, who overcomes his suicidal crisis in the last minute because of three boys who remind him of alternative coping strategies.32 Reporting on individual mastery of crises is recommended in media guidelines for reporting suicide. In an Austrian sample, these news articles turned out to lack the sensationalist characteristics that were common in some articles on completed suicide and suicide statistics.32
+The problem of suicidal ideation and how to cope with it was raised in a responsible way in these articles, which may help reduce stigmatization of suicidal ideation and of individuals who suffer from suicidal thoughts. Moreover, publication of articles on coping with suicidal ideation was associated with a decrease in suicide rates in the area where they were widely distributed, suggesting that these articles may have a suicide-protective effect.
+A potential explanation for a protective effect of these media reports may derive from the inherent social normative messages in media reports on mastery of crisis, which present help-seeking and constructive behaviors as the outcome of psychosocial crisis and may thereby manage to increase the psychological availability (sometimes referred to as “cognitive availability”43) of alternatives to suicide. Portrayals of ways on how to actively cope with suicidal ideation, emphasizing other options than suicide, may help to broaden the perspective in some individuals, particularly those in the psychological state of cognitive and affective constriction that has frequently been used to describe the dangerous tunneling and narrowing of the range of opportunities in suicidal individuals.44,45
+Awareness Campaigns Using Mass Media as a Tool
+Media awareness campaigns comprise a heterogeneous set of prevention efforts that pursue the goals of either decreasing the stigma of mental illness, raising awareness of the problem of suicide, increasing help-seeking, or, most frequently, a combination of several of these goals. Some of the campaigns focus primarily on mental illness (particularly depression), whereas others focus primarily on suicide. Accordingly, the campaign structures and
+evaluation methods vary widely, but all of these efforts are based on the aspiration to ultimately help prevent suicide.
+In general, broad awareness campaigns can be considered a type of social advertising,46,47 which differs from conventional advertising by focusing on information that reminds people of their vulnerability and mortality, thereby triggering fear. Social advertising typically activates psychological defense mechanisms in the audience more so than conventional advertising, which may reduce the effectiveness of these messages.48 Broad awareness campaigns may require additional components to effectively enhance learning and motivation in the target group to adopt the advertised behavior.
+Many of the currently used awareness programs in suicide prevention apply a broad-scale approach. Yet, awareness campaigns that aim at increasing awareness or knowledge of suicide using media rarely apply the findings from media research. Moreover, studies on the effectiveness of awareness campaigns are currently scarce and provide mixed results, at best. In a review of 15 public campaigns about depression or suicide awareness between 1987 and 2007, Dumesnil and Verger49 found only a modest improvement in public knowledge of and attitudes toward depression or suicide. Most studies did not assess the durability of the attitude changes, and none of these programs demonstrated an impact on help-seeking.49
+For high-risk groups, such as individuals with major depression and suicidal ideation, no improvements in terms of attitudes toward treatment seeking and, more importantly, treatment-seeking behavior, were reported following an intensive community education program in Australia.50 Furthermore, studies failed to demonstrate an effect on important primary outcome measures such as suicidal ideation or behavior.
+A billboard study conducted by Klimes-Dougan et al. and the Suicide Awareness Voices of Education (SAVE) in 20095 indicated that when exposed to the public awareness message “Prevent suicide. Treat depression. See your doctor,” adolescents most vulnerable to suicide, but not those with low vulnerability, had an increase in maladaptive coping behaviors. The findings of this study were largely replicated in a young adult population6 and clearly suggest that caution is warranted when awareness campaigns are used to educate the public about suicidality.
+Such campaigns may have unwanted backlash effects, or may not reach the most vulnerable populations. For example, in Austria, a 20-fold increase in utilization of a crisis hotline after the promotion of the crisis line telephone number on national television was reported,
+along with a tripling of clients at the crisis center. However, the proportion of suicidal individuals among clients decreased considerably after the campaign.45 A significant increase of calls to an emergency mental health service was also reported following a mass media campaign in Cuyahoga County, Ohio.51 This campaign adopted the message “Suicide is preventable. Its causes are treatable. For immediate help call (emergency number).”
+Besides campaigns that primarily aim to increase knowledge of suicide risk or increase awareness of services, there are also examples of campaigns that focus directly on the stigma of mental illness with the aim of changing public attitudes to mental illness on a broader level.19 However, there is little evidence that supports that public service announcements addressing the stigma of mental illness are effective in reducing prejudicial attitudes and discriminatory behaviors.19
+For example, factsheets from the Royal College of Psychiatrists’ Changing Minds campaign in the United Kingdom on stigmatizing attitudes of the general public toward schizophrenia or substance use disorders were largely ineffective in changing these attitudes in the study participants.52 Another campaign targeting youth and young adults in British Columbia, Canada,53 featured a prominent male sports figure talking about mental health issues and used online social media to convey its message. It resulted in an increase of campaign and website awareness, and those who were exposed to the campaign were significantly more likely to talk about and seek information relating to mental health issues. However, attitudes toward mental health issues did not change.53 It has been noted that more evaluation of these types of campaigns is warranted, particularly regarding tangible positive impacts that go beyond the assessment of penetration in the population.19
+There are also campaigns and initiatives that aim at improving attitudes toward treatment and health services. Help-seeking attitudes are thought to be a key barrier to service use for mental health problems. A meta-analysis of studies on help-seeking attitudes revealed an increasingly negative attitude toward helpseeking between 1968 and 2008,54 which has been hypothesized as an unintended side effect of marketing biological therapies and medicalizing mental health problems.54
+The evidence for the effectiveness of related campaigns that address attitudes toward mental health services is mixed.54 For example, Jorm and colleagues55 conducted an RCT to assess the effect of evidence-based consumer guides on effective treatment options for depression in a randomly selected community sample of individuals who screened positive for depression. The results showed
+that attitudes to some treatment options improved. However, there were no increases in actual helpseeking.55
+Multilevel Approaches
+Multilevel approaches using individual-level strategies, such as gatekeeper training, to complement a campaign using media as a tool to distribute information to a smaller, well-defined audience has been used frequently in recent years, and some evaluations show promising results.4 A Germany-based awareness campaign focusing on depression has involved physician training, information and awareness campaign for the broad public (e.g., movie spots, flyers); educational training for gatekeepers including teachers, priests, or geriatric care staff; as well as support of self-help-activities.56,57
+There was a significant reduction of completed and attempted suicide combined following the program. Furthermore, there was some improvement in public knowledge of depression, which did not, however, include an improvement of negative attitudes toward antidepressant medication.56,57 In Australia, a multimedia campaign promoting mental health literacy and help-seeking behavior increased awareness of suicide risk, depression, and other mental health issues and reduced the perceived barriers to seeking adequate help in youth.9
+In the U.S., Boeke, Griffin, and Reidenberg58 reported that, following a 6-month awareness campaign on suicide prevention in Minnesota, knowledge of how to help a depressed or suicidal person was good among individuals who participated in the evaluation. They identified a need to involve physicians and other healthcare providers in such campaigns. Physician and other gatekeeper trainings that might be used to complement media campaigns may occur in a variety of settings (e.g., schools, military installations, community settings). They have yielded partially positive findings regarding their effects on knowledge of suicide and attitudes toward suicide, intent to seek treatment, and referral behaviors.4,59
+However, outcomes documenting behavioral changes are limited, particularly for the highest-risk individuals. Gatekeepers with professional responsibilities related to referral seem more likely to increase referral behaviors60 and intent to seek treatment,22 but it is not clear if their enhanced skills are sufficient to reach the individuals most in need of referral. Few programs have directly addressed the reduction of stigma as a goal.
+A program in the U.S. Air Force that focused on decreasing the stigma of help-seeking included several components such as education of leadership and staff, guidelines for commanders on the use of mental health services, the establishment of trauma stress response
+teams, and surveillance measures.61 An evaluation of this initiative indicated a statistically significant decline in suicide rates over time compared to baseline but did not include an evaluation of its impact on stigma associated with help-seeking.61
+Future Research
+Future research needs to focus on appropriate ways of providing information about suicidality in order to reduce stigma of suicidal ideation, mental illness, and stigmatization of those bereaved by suicide, to increase help-seeking behavior and referrals, and to ultimately reduce suicides. Awareness campaigns and multilevel intervention approaches, such as combinations of broad public health approaches using the mass media and individual-level approaches using gatekeeper training, need to be evaluated with regard to their overall effectiveness, and attempts should be made to identify which of the single components are most effective. Particular emphasis also needs to be placed on the evaluation of effects on individuals at risk for suicide.
+Most researchers agree that audience characteristics, sender characteristics, and the actual media content influence media effects; therefore, a consideration of several factors that may determine media effects will help guide this research.
+A paucity of research exists for individual audience characteristics, including risk status, which may impact media effects. A focus on these characteristics may shed light on the understanding of both protective and harmful media effects. For example, personal suicidal ideation may influence the reception and effects of media products. In a recent laboratory experiment, individuals with higher baseline suicidal ideation before watching a movie with suicidal content were more likely than audiences with lower suicidal ideation scores to get ideas about their own problem solving from the films.62
+From a sender perspective, qualitative research on journalist perspectives has identified commercial competition, willingness to address social problems, and reading interest as main drives for suicide reporting.63 Research on journalists’ attitudes about reporting on suicide and the published media recommendations may assist in the successful dissemination, implementation, and adherence to media recommendations.
+The question of how and what to report in order to reduce the stigma surrounding mental illness, suicidal ideation, and suicide decedents without promoting suicidal behavior, while still providing information on risk and protective factors and coping strategies, including treatment resources, remains the foremost public
+health challenge regarding the media’s role in suicide prevention and stigma reduction.
+More evaluation work is needed to determine the impact of media recommendations on the quality of reporting and suicide rates.42 Moreover, the specific recommendations require further scientific evaluation, as they are mainly based on expert opinions. Media recommendations also need some adaptation to meet the requirements of emergent media sources such as online news and social media.
+More research needs to focus on the underlying mechanisms of media effects.64 A recent review65 has identified a clear lack of studies on the protective effects of media reporting whereas there are many on harmful effects. Because this research may open up new opportunities for awareness campaigns and reporting on suicidal ideation and suicide in news media, a stronger emphasis on protective effects seems necessary in future research endeavors.
+For all types of media campaigns, including those that address public awareness of suicide risk, public awareness of services to prevent suicide, mental health issues on a broader level, or stigmatization of suicide and mental illness, more evaluation work is needed. The specific aims and objectives need to be defined well in advance, and predefined primary and secondary outcomes need to be evaluated.
+In anti-stigma campaigns, the ultimate question is how to talk about suicide and reduce the stigmatization of suicidal ideation and mental illness without additional risk to vulnerable groups. Stigma associated with suicidal ideation and mental illness frequently hinders individual disclosure of mental health issues and adequate responses to suicidal communication and thereby hampers suicide prevention efforts. Stigma reduction efforts should therefore promote communication and disclosure of suicidal ideation. Guidelines on how to develop a stigma reduction initiative are available from the Substance Abuse and Mental Health Services Administration (SAMHSA) and may assist in the development of anti-stigma campaigns.66
+Caution is needed to avoid normalizing the suicidal acts in these campaigns, which may have adverse effects. Research findings from media and communication studies need to be considered when developing awareness campaigns to reduce the risk of harm. In the short term, experimental studies that shed light on several core research questions related to the impact of the intervention need to be conducted before awareness campaigns on the community level are implemented. Some of these questions are outlined below.
+Priority should be given to RCTs and well-planned controlled research designs, which are currently scarce.4
+Vignettes or other stimuli and cognitive interviewing could be used to identify potentially useful or iatrogenic content for stigma-reduction and help-seeking interventions. Quantitative and qualitative research as well as combinations of both will be necessary.
+Some specific research questions may include the following: (1) What is the immediate impact of specific awareness/information messages (in news media or in awareness campaigns) in terms of actual help-seeking behavior? (2) What individual characteristics impact/ mediate any immediate media effect? For example, do media effects vary with regard to age, gender, personality characteristics, and suicide risk status of the audience? (3) How are messages interpreted in relation to how they are intended, with a particular focus on those vulnerable to suicide? (4) What are the effects of media campaigns focusing primarily on suicide or suicide prevention as compared to campaigns that address mental health issues or their prevention in terms of outcomes relevant to suicide prevention? and (5) How do vulnerable individuals use media to obtain information related to suicide and suicide prevention?
+Finally, owing to the documented shift in the media landscape from more traditional media types to online and other new forms of mass media, including social media,67 differences between effects of awareness messages delivered online and via traditional media types require evaluation.
+Social relationships based on trust and understanding are clearly established factors that facilitate help-seeking.24,45 It is therefore necessary to investigate how individuals can best establish these relationships in times of need. Conflict resolution training, which includes problem recognition training in various settings such as schools but also via online media, may help to increase problem-solving skills.
+Men and boys in particular need to be encouraged to express emotions in ways that are perceived as strength rather than weakness,24 and research should focus on groups known to show more resistance toward helpseeking. Whether findings from such studies can be used to shape future media campaigns is an empirical question. Individual-level or multilevel strategies may be best suited to facilitate the enhancement of social relationships and problem-solving skills that underlie help-seeking behavior.
+Multilevel interventions using several intervention approaches that may complement each other tend to show more promising results than single-level interventions and are increasingly used and recommended.60,68 However, substantially more research is needed to determine the effectiveness of multilevel interventions. Promising multilevel programs that should be examined
+further are educational programs that target the public and are combined with training of practitioners and primary care personnel in the diagnosis and treatment of depression and suicidality.59’69
+Novel analytic strategies are needed to compare the potential benefits of individual-level interventions targeting high-risk groups with those of more mass media/ public health approaches. Research is also needed to identify the optimal balance or combination of individual-level and public health-level approaches in order to achieve their maximum impact.
+Depending on the aims and target group of an education program using media’ researchers can select appropriate candidate media vignettes. For anti-stigma campaigns’ examples of outreach materials are available from SAMHSA.66 If the aim of the initiative is to encourage individuals to intervene if someone close to them is suicidal, the theory of planned behavior (TPB) has recently been proposed to guide the content of persuasive messages. The TPB posits that a person’s behavior can be predicted by attitudes toward the behavior, subjective norms related to the behavior, the intention to perform that behavior, and control beliefs that describe beliefs about being able to perform the action based on the presence of skills, absence of obstacles, and other factors.70 Salient relevant beliefs associated with the specific outcome can be assessed using open-ended interviews or focus group techiques.70
+If the media campaign targets individuals at risk for suicide or if at-risk individuals are to be exposed to the campaign, the selected media vignette should be tested regarding their effect on self- or perceived stigma, helpseeking attitudes, and suicidality (Figure 1). The vignettes should be tested for different types of audiences within the target population (e.g., groups with different suicide risk status) to determine their appropriateness as a suicide prevention initiative. It is essential that evidence from different settings be combined to identify the most promising elements and complementary components for suicide prevention programs.
+Conclusions
+Suicide is a significant public health problem for which all aspects should be addressed seriously, including awareness efforts. In this article, the authors have provided evidence for mass media as a powerful tool to address the stigma surrounding suicidal ideation and mental illness, although more research is needed before any definitive conclusions can be made about how this tool can best be used to increase help-seeking and prevent suicide, particularly in vulnerable populations. Recent findings such as the responsible reporting patterns in news articles on individual mastery of crisis, which were associated with a possible suicide-protective Papageno effect, provide an important basis for further research in the topic area.
+All suicide preventive interventions should carefully consider the recommendations for reporting suicide when using media as a tool. Because of the omnipresence of mass media in everyday life and their use by even the most vulnerable populations, research on how to provide the best suicide prevention possible via mass media constitutes a high priority and timely topic area for suicide research and prevention.
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+1	| INTRODUCTION
+Recent cross-national studies show that approximately one third of college students report mental disorders in the past 12 months (Auerbach et al., 2016; Auerbach et al., 2018). The occurrence of mental disorders during this critical period of development has profound consequences on academic outcomes (college attrition—Auerbach et al., 2016; grades—Bruffaerts et al., 2018), role impairment (e.g., dysfunctional relationships and inability to work or attend class; Alonso et al., 2018), and the occurrence of suicidal thoughts and behaviors (STBs; Mortier et al., 2017). Despite recent attention and awareness about the alarming rates of mental disorders among college students (Blanco et al., 2008; Cho et al., 2015; Eisenberg, Golberstein, & Gollust, 2007; Hunt & Eisenberg, 2010; Kendler, Myers, & Dick, 2015; Mojtabai et al., 2015), less research has focused on clarifying patterns of comorbidity (Eisenberg, Hunt, & Speer, 2013). Addressing this issue is essential given that most mental disorders do not emerge in isolation (Kessler, Chiu, Demler, Merikangas, & Walters, 2005), and perhaps more importantly, college campuses must determine how best to provide appropriate intervention services for students with diverse profiles of mental disorder comorbidity.
+Decades of psychiatric research across age groups have shown that comorbidity is the rule rather than the exception with comorbidity rates as high as 79% (Kessler et al., 1994; Kessler, Chiu, et al., 2005). Although it is unequivocal that mental disorders are highly comorbid, a critical question that remains in college students is whether specific patterns of disorders cooccur. Identifying mental disorder risk profiles is an essential next step for research in this population segment, as colleges are quickly pivoting from identifying
+widespread disorders to intervening (e.g., Harrer et al., 2018). Somewhat paradoxically, most approaches to treatment hinge on a singular diagnosis (e.g., presence of depression or anxiety), but given that comorbidity is commonplace, developing transdiagnostic interventions that target specific profiles may prove crucial in curbing escalating rates of mental disorders (Auerbach et al., 2016; Auerbach et al., 2018) and STBs (Mortier, Cuijpers et al., 2018) in college students.
+The current report includes data from the first phase of the World Health Organization (WHO) World Mental Health International College Student (WMH-ICS) initiative in which baseline surveys were completed by first year college students from 19 colleges across eight countries. In prior publications, we detailed the lifetime and 12-month prevalence of mental disorders (Auerbach et al., 2018) and STBs (Mortier et al., 2018) and, more recently, highlighted role impairment associated with internalizing and externalizing mental disorders (Alonso et al., 2018). Building on this research, our current aim was to assess 12-month psychiatric comorbidity by using a latent class analytic approach. In doing so, the goal was to identify multivariate disorder risk profiles (i.e., clarify patterns of cooccurring disorders) and, then, test whether these profiles were associated with sociodemographic and college-related factors and 12-month STBs.
+2	| METHOD
+2.1	| Samples
+The first wave of WMH-ICS surveys was administered to first year students in a convenience sample of 19 colleges and universities
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+(henceforth referred to as “colleges”) in eight middle- to high-income countries (Australia, Belgium, Germany, Mexico, Northern Ireland, South Africa, Spain, and the United States). Procedures for obtaining informed consent and protecting human participants were approved and monitored for compliance by the institutional review boards of the organizations coordinating the surveys in each country. Details about ethics approval for the WHO WMH-ICS Initiative countries is available in this link: http://www.hcp.med.harvard.edu/wmh/ftpdir/ IRB_EthicsApproval_WMH-ICS.pdf. Web-based self-administered questionnaires (SAQs) were administered to incoming first year students in each participating college (seven private and 12 public) between October 2014 and February 2017 (see Table 1).
+As noted in a prior report on this survey (Auerbach et al., 2018), 14,371 SAQs were completed, with sample sizes ranging from a low of 633 in Australia to a high of 4,580 in Belgium and response rates ranging from a low of 7.0% in Australia to a high of 79.3% in Mexico. The weighted (by achieved sample size) mean response rate across all surveys was 45.5%. The analyses reported here are based on the 14,348 respondents for whom poststratification weights could be computed.
+2.2	| Procedures
+All incoming first year students in the participating colleges were invited to participate in the web-based self-report health survey. Mode of contact varied widely across colleges, but in all cases, other than in Mexico, it consisted of an approach that attempted to recruit 100% of incoming first year students either as part of a health evaluation, as part of the registration process, or in a stand-alone survey administered to students via their student email addresses. Contact with initial nonrespondents were then made through a series of personalized reminder emails. Incentives such as a raffle for store credit coupons or movie passes were used in the final stages of recruitment in 10 of these colleges. An additional “end-game” strategy was used in Spain by selecting a random sample of nonrespondents at the end of the normal recruitment period to receive a financial incentive for one last chance at participation, with respondents recruited at that final phase given a weight equal to the inverse of their probability of selection to adjust for the undersampling of these hard-to-recruit students. The sampling scheme was quite different in Mexico, where 100% of entering first year students were invited to participate in the survey in conjunction with mandatory activities that varied from college to college, such as student health evaluations and tutoring sessions, with time set aside in these sessions for completing the surveys via computers or tablets handed out to students attending the sessions. No follow up of nonrespondents was carried out in Mexico because it was felt that students who failed to complete the survey when time was set aside for it during mandatory activities were firm nonrespondents. Informed consent was obtained before administering the SAQs in all countries. The text statement used to obtain informed consent varied across schools and was approved by the institutional review boards of the organizations coordinating the surveys in each country.
+AUERBACH et al.
+2.3	| Measures
+The SAQ was developed in English and translated into local languages using a translation, back-translation, and harmonization protocol that expanded on the standard WHO protocol using methods developed by cross-national survey methodologists to maximize cross-national equivalence of meaning and consistency of measurement (Harkness et al., 2008).
+2.3.1 | Mental disorders
+The SAQ included short validated self-report screening scales for lifetime and 12-month prevalence of seven common Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV mental disorders. These included four internalizing disorders (major depressive episode, mania/hypomania, generalized anxiety disorder, and panic disorder) and three externalizing disorders (attention-deficit/hyperactivity disorder, alcohol abuse or dependence, and drug abuse or dependence involving either cannabis, cocaine, any other street drug, or a prescription drug either used without a prescription or used more than prescribed to get high, buzzed, or numbed out). This is a larger set of disorders than used in previous college mental health surveys, most of which either focused only on depression (for a review, see Ibrahim, Kelly, Adams, & Glazebrook, 2013) or included only screening scales of current anxious and depressive symptoms (Mahmoud, Staten, Hall, & Lennie, 2012). Although a larger set of disorders is used in the face-to-face WMH surveys (Scott, Jonge, Stein, & Kessler, 2018), the need for a brief measure prevented the administration of student surveys that would be long enough to include all those disorders. The seven disorders in the core WMH-ICS surveys were a compromise that included the disorders associated with the highest levels of role impairment among college students in the WMH surveys (Auerbach et al., 2016). As an indication of the coverage of these disorders, 83% (unweighted) of the college students in the WMH surveys who reported suicidal ideation in the 12 months before interview met criteria for one or more of these seven disorders during that same time period.
+The assessments of five of the seven disorders were based on the Composite International Diagnostic Interview Screening Scales (CIDI-SC; Kessler et al., 2013; Kessler & Ustun, 2004). The exceptions were the screen for alcohol use disorder, which was based on the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993), and the screen for attentiondeficit hyperactivity disorder (ADHD), which was based on DSM-IV version of the WHO Adult ADHD Self-Report Scale (Kessler et al., 2005). The CIDI-SC scales have been shown to have good concordance with blinded clinical diagnoses based on the Structured Clinical Interview for DSM-IV (First, Spitzer, Gibbon, & Williams, 1994), with area under the curve (AUC) in the range 0.70-0.78 (Kessler et al., 2013; Kessler, Calabrese, et al., 2013). However, validation studies have not yet been carried out in samples of college students. The version of the AUDIT we used, which defined alcohol use disorder as either a total score of 16+ or a score 8-15 with 4+ on the AUDIT dependence questions (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001), has been shown to have concordance with clinical diagnoses in the range AUC = 0.78-0.91 (Reinert & Allen, 2002). Additional
+AUERBACH et al.
+items taken from the CIDI (Kessler & Ustun, 2004) were used to assess age-of-onset of each disorder and number of lifetime years with symptoms. The DSM-IV version of the WHO Adult ADHD Self-Report Scale was found to have good concordance with blinded clinical diagnoses based on a standard research diagnostic interview for adult ADHD in two separate clinical studies (Kessler, Adler, et al., 2005; Kessler et al., 2007).
+In addition to assessing lifetime prevalence of all the above disorders other than ADHD, brief screening assessments were made for lifetime prevalence of binge-eating disorder, intermittent explosive disorder, and post-traumatic stress disorder. A more thorough assessment would have also asked about 12-month prevalence of these disorders but that was not done in this initial round of the WMH-ICS surveys. This omission has been corrected in the more recent version of the survey that is currently being administered. For the purposes of the analyses reported here, these disorders were coded as lifetime but not 12-month disorders even though it is almost certainly the case that at least some of these disorders were active in the 12 months before interview. The inclusion of these disorders in the current analysis accounts for discrepancies in the proportion of students who are estimated to have lifetime disorders compared with the proportion presented in an earlier report (Auerbach et al., 2018).
+2.3.2 | Suicidal thoughts and behaviors
+As described in an earlier report from this survey (Mortier, Auerbach, et al., 2018), a modified version of the Columbia Suicidal Severity Rating Scale (Posner et al., 2011) was used to assess STBs, including suicidal ideation and suicide attempts (SA). In addition to lifetime prevalence, respondents were asked about number of months in the past 12 months with suicide ideation (SI) and about presence of a SA in the past 12 months.
+2.3.3 | Sociodemographic correlates
+Several basic sociodemographic variables were included in the survey. Gender was assessed by asking respondents whether they identified themselves as male, female, transgender (male-to-female, female-to-male), or “other.” Respondent age was divided into four categories (16-18, 19, 20-21, 22 or more years old). Parental educational level was assessed for father and mother separately (none, elementary, secondary, some postsecondary, college graduate, and doctoral degree) and was categorized into high (college graduate or more), medium (some postsecondary education), and low (secondary school or less) based on the higher-of-both parents' educational level. Parental marital status was dichotomized into “parents married and both alive” versus “parents either not married or at least one deceased.” Respondents were asked about the urbanicity of the place they were raised (large city, small city, town or village, suburbs, and rural area) and their religious background (categorized into Christian, other religion, and no religion). Sexual orientation was classified into heterosexual, gay or lesbian, bisexual, asexual, not sure, and other. Additional questions were asked about the extent to which respondents were attracted to men and women and the gender(s) of people they had sex with (if any) in the past 5 years.
+-------------------------------------Wiley 1 5of16
+Respondents were categorized into the following categories: heterosexual with no same-sex attraction, heterosexual with same-sex attraction, nonheterosexual without same-sex sexual intercourse, and nonheterosexual with same-sex sexual intercourse.
+2.3.4 | College-related correlates
+Respondents were asked where they ranked academically compared with other students at the time of their high school graduation (from top 5% to bottom 10%; categorized into quartiles) and what their most important reason was to go to university. Based on the results of a tetrachoric factor analysis reported elsewhere (Auerbach et al., 2018), respondents were classified into those whose most important reasons to go to university were extrinsic (i.e., family wanted me to, my friends were going, teachers advised me to, and did not want to get a job right away) versus intrinsic (to achieve a degree, I enjoy learning and studying, to study a subject that really interests me, to improve job prospects generally, and to train for specific type of job). Respondents were also asked where they were living during the first semester of the academic year (parents', other relative's, or own home, college hall of residence, shared house, apartment, or flat/private hall of residence, and other), and if they expected to work during the school year.
+2.4	| Analysis methods
+2.4.1 | Weighting
+We noted above that an “end-game” strategy was used in Spain in which a random sample of nonrespondents at the end of the normal recruitment period was offered a financial incentive for participation. Respondents in this end-phase were given a weight equal to 1/p, where p represented the proportion of nonrespondents at the end of the normal recruitment period included in the end-game, to adjust for the undersampling of these hard-to-recruit respondents. In addition, in an effort to make the WMH-ICS sample in each college as representative as possible of all first year students, the surveys were poststratified by weighting the data to adjust for differences between survey respondents and nonrespondents on sociodemographic information made available about the student body by college officials. Standard methods for poststratification weighting were used for this purpose (Groves & Couper, 1998). In the case of the Spanish survey, this meant that the data were doubly weighted, one to include the end-game weight and then with the poststratification weight applied to those weighted data. Each country was given an equal sum of weights, with the total sum of weights across countries set at 14,348.
+Item-level missing data in the completed surveys were imputed using the method of multiple imputation by chained equations (van Buuren, 2012). Four kinds of item-missing data were imputed simultaneously in this way. The first was a 50% random subsampling of the drug use section to reduce interview length in Belgium. The second was the complete absence of the panic disorder section due to a skip logic error in Mexico, Northern Ireland, and South Africa. The third was the complete absence of some sociodemographic variables in various colleges (sexual orientation, current living situation,
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+expected student job, and most important reason for going to college in Australia, Belgium, and South Africa; parent education and marital status in Australia and Belgium; religion in Australia; and self-reported high school ranking in Belgium) because of a decision not to assess those variables. The fourth was item-level skips or invalid responses to individual questions throughout the survey. The latter was less than 0.1% for lifetime disorders, 0.0-2.3% for 12-month disorders other than AUD, and in the range 3.0-9.3% (3.8-7.0% interquartile range) for AUD, 0.0-12.0% (interquartile range 1.9-2.7%) for disorder age-of-onset, 0.0-24.6% (interquartile range 2.4-8.8%) for disorder persistence, 1.8-25.4% (interquartile range 8.8-24.1%) for most important reasons for attending college, 1.0-10.8% (interquartile range 3.0-3.4%) for high school ranking, and 0.0-7.0% for the other sociodemographic and college-related variables.
+2.4.2 I Substantive analyses
+Latent class analysis (LCA; Magidson & Vermunt, 2004) was used to examine multivariate profiles among the seven 12-month DSM-IV disorders. Mplus software was used to estimate the models (Muthen & Muthen, 2012). LCA is a person-centered approach to define associations among discrete variables. LCA assumes the existence of two or more distinct unobserved classes of individuals that differ in prevalence of disorders, where presence versus absence of individual disorders is independent across disorders within classes and each person has a probability of class membership that sums to 1.0 within individuals across classes. Analysis consists of simultaneously estimating the vector of class membership probabilities associated with each observed multivariate disorder profile and prevalence of each disorder in each latent class for a fixed number of classes. A standard measure of model fit, the Lo-Mendell-Rubin adjusted likelihood ratio test with p-value of 0.05, was used to select a best model from among those estimated with different assumed numbers of latent classes. Once a final model is selected, survey respondents with a given disorder profile can be assigned to the class with the highest probability of membership for purposes of subsequent analysis.
+Once we defined and interpreted the latent classes, SAS version 9.4 (SAS Institute Inc., 2010) was used to examine associations of LCA classes with both 12-month sociodemographic variables and 12-month STBs using logistic regression analysis. Area under the receiver operating characteristic curve was calculated to characterize the strength of these associations. The LCA classes were treated as the outcomes in a multinomial logistic regression analysis of sociodemographic predictors. The LCA classes were then treated as the predictors in logistic regression analyses to predict STBs. The extent to which the LCA classes captured the multivariate associations of the seven disorders with STBs was then examined by estimating models that included disorders, classes, and both as predictors of STBs and comparing AUCs across models. Logistic regression coefficients and their 95% confidence intervals (CIs) were exponentiated to create odds ratios (ORs) and associated 95% CIs to facilitate interpretation. All results were pooled across countries. Due to the variable within-country sample sizes, no attempt was made to search for variation in associations across countries.
+AUERBACH et al.
+Statistical significance of individual coefficients was evaluated consistently using two-sided tests with multiple imputation significance level a set at 0.05. But another issue can be raised about the possibility that the significance of some individual predictors was due to chance in the analysis of such a large number of predictors. Our main concern about this issue focused on the LCA classes, as our previous research has documented global significance of sociodemographic variables predicting mental disorders (Auerbach et al., 2018) and mental disorders predicting STBs (Mortier, Auerbach, et al., 2018). We address the concern by reporting global significance tests for the associations of the LCA classes as a set with STBs controlling for the component mental disorders.
+3	I RESULTS
+3.1	I Sociodemographic distribution of the sample
+Sociodemographic information is summarized in Table 2. The majority of respondents (54.8%) were female. Most of the others were male (44.7%), and the small remaining number defined themselves as either transgender or “other” (0.5%). Most respondents were 16-18 years of age (51.1%), and the vast majority (96.5%) were full-time students.
+3.2	I Latent class analysis
+Prevalence of at least one 12-month disorder was 38.4% in the pooled cross-national analysis that weighted each country to have equal representation in the sample. This is somewhat different from the prevalence found in an earlier analysis in which we did not include ADHD diagnoses and excluded part-time and transgender students (Auerbach et al., 2018). The LCA found that a four-class solution provided the best fit to the data (Table S1). All students in three of the four classes met criteria for at least one 12-month disorder, whereas the largest class included both respondents with exactly one disorder or no 12-month disorders. We separated these two groups in our analysis and also distinguished between respondents with no 12-month disorders depending on whether or not they met criteria for any lifetime disorder, resulting in a total of six classes being included in the analysis.
+By far the smallest of these classes was Class 1 (C1; Figure 1). The 1.9% of respondents in C1 all met criteria for four or more 12-month disorders, the vast majority of them including mania/hypomania (77.9%). All had at least one internalizing disorder (especially major depression disorder and generalized anxiety disorder) and virtually all (95.2%) had either substance use disorder and/or ADHD. The next smallest class was C2. Nearly all the respondents in C2 (5.8% of respondents) met criteria for either two (72.0%), three (21.3%), or more (6.0%) 12-month disorders. The most striking differences between C2 and C1 were that respondents in C2 had much lower prevalence of mania/hypomania (14.7% vs. 77.9%) and anxiety disorders (6.8-12.8% vs. 49.3-98.3%) than respondents in C1. Prevalence of at least one externalizing disorder (i.e., substance use disorder or ADHD), in comparison, was relatively similar in C1 (94.8%) and C2 (95.2%).
+Note. The data are weighted so that each country has an equal weight.
+C3 was considerably more prevalent (14.6% of respondents) than C1 or C2. All C3 respondents met criteria for at least one 12-month disorder and 77.0% met criteria for two or more disorders. All C3 respondents met criteria for at least one internalizing disorder and a much smaller proportion (42.1%) met criteria for either alcohol use disorder (37.7%) or ADHD (6.9%). None of the C3 respondents met criteria for drug use disorder. The remaining respondents met criteria for either only one (16.1%) or none (61.6%) of the 12-month disorders assessed in the survey. We defined C4 for purposes of analysis as consisting exclusively of students who met criteria for only one disorder. By far the most common disorders in C4 were ADHD (40.3%) and major depression disorder (32.8%). It is noteworthy that the original C4 respondents without any 12-month disorders included roughly equal numbers with a lifetime history of at least one remitted DSM-IV disorder (C5; 29.2% of the total sample) and no lifetime history of any of the DSM-IV disorders assessed in the survey (C6; 32.4% of the total sample). (See Table S2 for a more detailed description of precise prevalence estimates of individual disorders within classes.)
+3.3	| Sociodemographic correlates of latent class membership
+A number of sociodemographic variables were significant correlates of being in C1, the high comorbidity class (vs. being in C6, the no lifetime disorder class), in a multivariate model that included all these predictors (Table 3). The highest ORs were associated with nonheterosexual orientation either with (OR = 14.5) or without (OR = 16.5) same-sex intercourse (compared with heterosexual without same-sex attraction) and self-identifying as transgender/other gender (OR = 12.6; compared with male). Other significant correlates included being female (OR = 1.6; compared with male), ages 20-21 or 22+ (OR = 2.5-3.2; compared with 16-18), parents either not married or deceased (OR = 2.1), heterosexual with some same-sex attraction (OR = 4.0; compared with no same-sex attraction), not graduating in the top 5% of one's high school class (OR = 2.0-3.5; strongest for the lowest class ranking), and having primary extrinsic reasons for attending college (OR = 2.4). The AUC of a multivariable model with all sociodemographic predictors and country dummies was 0.89. The 10% of respondents with the highest predicted probabilities of being in C1 in that model accounted for 56.4% of all C1 cases.
+As with C1, the strongest sociodemographic correlates of being in C2, the comorbid class characterized by higher prevalence of externalizing than internalizing disorders, were nonheterosexual orientation either with (OR = 8.2) or without (OR = 4.5) same-sex intercourse (compared with heterosexual without same-sex attraction). There was one fewer significant correlate of C2 than C1 and the ORs were less pronounced. These correlates included being male (i.e., significantly reduced relative-odds associated with being female; OR = 0.8), ages 20-21 and 22+ (OR = 1.5-2.1; compared with ages 16-18, weaker effect for ages 22+), parents not both alive and married (OR = 1.6), low parental education (OR = 0.7; compared with high parental education), living in a college residence hall (OR = 1.4; compared with living with parents), no religion (OR = 1.5), heterosexual orientation with some same-sex attraction (OR = 2.8; compared with heterosexual without same-sex attraction), and graduating outside the top 10% of one's high school (OR = 1.7-2.5; compared with being in the top 5% of the class). The AUC of a multivariate model with all sociodemographic predictors of C2 versus C6 was 0.76. The 10% of respondents with highest predicted probabilities of being in C2 in that model accounted for 33.2% of all C2 cases.
+The sociodemographic correlates of being in C3, the class associated with lower comorbidity and a much higher prevalence of internalizing than externalizing disorders, and C4, the class with pure 12-month disorders, were strikingly similar to those of C2. The correlates of C3 differed from those of C2 only in including selfidentification as transgender/other (OR = 10.0) compared with male, non-Christian (OR = 1.4), and lowest 70% of high school class ranking (OR = 1.4) rather than the two lowest levels in C2, and having generally somewhat weaker ORs with the other correlates than in the prediction of C2. The AUC of a multivariate model with all sociodemographic predictors of C3 versus C6 was 0.75. The 10% of respondents with highest predicted probabilities of being in C3 accounted for 23.6% of all C3 cases.
+The correlates of C4 differ from those of C2 only in including selfidentification of transgender/other (OR = 3.8; compared with male), age 19 (OR = 1.2), non-Christian religion (OR = 1.8; compared with Christian religion), and residence in group housing (OR = 1.3 compared
+with living with parents). The significant ORs of sociodemographics with C4 were generally somewhat weaker than those with C3. The AUC of a multivariate model with all sociodemographic predictors of C4 versus C6 was 0.66. The 10% of respondents with highest predicted probabilities of being in C4 in that model accounted for 17.7% of all C4 cases.
+The sociodemographic correlates of being in C5, the class associated with one or more lifetime disorders but no 12-month disorders, finally, were the weakest and most inconsistent of all, although with an overall pattern of significance similar to C2-C3 in that ORs were significantly elevated among respondents older than 19 (OR = 1.21.6), with parents not both alive and married (OR = 1.3), non-Christian religion (OR = 1.6), nonheterosexual orientation (OR = 1.4-1.7), and having been in the two lowest levels of high school class ranking (OR = 1.2-1.3). The AUC of a multivariate model with all sociodemographic predictors of C5 versus C6 was 0.62. The 10% of respondents with highest predicted probabilities of being in C5 in that model accounted for 13.6% of all C5 cases.
+3.4	| Associations of latent classes with 12-month STBs
+Pooled cross-national 12-month prevalence of STBs was 17.6% for SI, 9.2% for suicide plan (SP), and 1.1% for SA when the sample was weighted to give equal representation to each country. As shown in an earlier report from this survey (Mortier, Auerbach, et al., 2018), these pooled estimates were somewhat different because of the exclusion of ADHD as a diagnosis, part-time students, and transgender students. A generally monotonic association was found between complexity of comorbidity and prevalence of 12-month STBs across the 12-month LCA classes (Table 4). C1 had by far the highest prevalence of SI (68.6% vs. 17.6% in the total sample), SP (51.5% vs. 9.2% in the total sample), and SA (15.4% vs. 1.1% in the total sample). Prevalence was lower and roughly equal in C2-C3 and successively lower in classes C4, C5, and C6. It is noteworthy that the differences in STB risk across LCA classes differed for SP and SA compared with SI, a
+pattern that can be seen by inspecting the ORs in Table 4. Sociodemographics are not controlled in estimating these models. Results are especially striking for C1, where the OR relative to C6 increased from 43.3 in predicting SI to 61.6 for SP and to 175.5 for SA. This increase for C1 can be seen even in comparison with the classes with the next highest risks, C2-C3, where the ratio of ORs is roughly 3:1 for SI (i.e., 43.3 vs. 14.6-13.7) and SP (i.e., 61.6 vs. 21.1-23.1) but becomes 6-10:1 for SA (i.e., 175.5 vs. 17.7-25.4). An analysis of between-class differences in SA among respondents with SI (SA/SI) controlling for SP (detailed results not reported but available on request) shows that C1 had an elevated relative-odds (OR = 6.4; vs. C6) but that the ORs of C2-C5 were not significantly different from C6.
+3.5	| The joint associations of classes and disorders with STBs
+It is noteworthy that the AUCs of the models in which LCA classes predicted 12-month STBs (0.75-0.87) were roughly comparable with those of the models in which the disorders underlying the classes predicting the same outcomes (0.74-0.89; Table 5). The AUCs increased slightly, though, in most of the models that added disorders
+to the classes to predict the same outcomes (0.76-0.89). This indicates that disorders might predict within-class differences in STBs. We explored this possibility initially by investigating the extent to which disorders interacted with classes in predicting STBs. None of these interactions were statistically significant.
+On the basis of this result, we used stepwise logistic analysis to determine which disorders were significant predictors in overall models that controlled for classes. These associations were much less pronounced for SA than for SI or SP (Table 6). Five disorders were significant in the SI model, four in the SP model, two in the SA model, and only one in the SA/SI model. All 12 of these ORs were positive (in the range 1.3-5.2). The most consistently significant ORs were associated with mania/hypomania (in three of four models; OR = 1.4-2.1) and generalized anxiety disorder (in all four models; OR = 2.2-5.2). The other disorders were significant only in predicting one outcome, either SI (major depressive disorder, ADHD, drug use disorder; OR = 1.3-4.7) or SP (panic disorder, alcohol use disorder; OR = 1.4).
+Importantly, the latent classes were significant as a set in all four models. The 15 significant ORs in those models were all positive (in the range OR = 2.2-21.1). The ORs for all classes C1-C5 were roughly comparable in predicting SI (OR = 2.2-3.8), but the OR for CI has the highest in predicting both SP (OR = 8.4) and SA (OR = 21.1) as well as the only significant LCA predictor of SA/SI (OR = 7.3). The significant
+Note. ROC: receiver operating characteristic; SI: suicide ideation. Pooling across all multiply imputed observations based on models that include dummy predictors for country.
+OR for C5 was the lowest in predicting SP (OR = 2.7 vs. OR = 3.7-7.0 for C2-C4) and the only nonsignificant OR in predicting SA (compared with significant ORs = 4.7-7.5 for C2-C4). These significantly elevated ORs for class membership in models that also control underlying disorders are most plausibly interpreted as due to synergistic effects of comorbidity on STBs.
+4	| DISCUSSION
+The current report from the WHO WMH-ICS initiative provides results from first year college students in 19 colleges across eight countries. The unique contribution of this report is the documentation of the existence of four latent classes of students with multivariate disorder profiles across seven 12-month DSM-IV disorders. The smallest of these classes (1.9% of all students) was characterized by extremely high comorbidity. Two other comorbid classes were characterized, respectively, by primarily internalizing disorders (14.6%) and by a combination of internalizing and externalizing disorders (5.8%). These classes were found to be very strongly predictive of 12-month STBs. Although a number of disorders also predicted STBs, the ORs of the classes remained significantly elevated even after controlling for individual disorders. The latter result documents the existence of interactive predictive effects of the disorders in the classes.
+Interestingly, we found a number of sociodemographic and college-related variables that had statistically significant associations with LCA class prevalence. Two prominent correlates of comorbidity included transgender students and sexual minority students (i.e., heterosexual students with some same-sex attraction and nonheterosexual students both with and without same-sexual intercourse). In our previous publication (Auerbach et al., 2018), these students reported high rates of mental disorders compared with other college students, which is unsurprising given that prior to arriving on college campus, many are subject to family rejection, bullying, and social isolation (Dean et al., 2000; Heatherington & Lavner, 2008). Further, once on college campus, these students are frequently marginalized and harassed (Rankin, 2003; Tetreault, Fette, Meidlinger, & Hope, 2013). Despite the greater incidence of mental disorders and comorbidity, transgender and sexual minority students are often less likely to utilize counseling services (e.g., Beemyn, Curtis, Davis, & Tubbs, 2005). Doubtlessly, college campuses have made painstaking efforts to be more inclusive of students with varied needs and backgrounds. That said, there remain critical institutional barriers to clinical care. Some
+--------------------------------------------Wiley 1 11of16 transgender and sexual minority students continue to face insensitivity and discrimination from healthcare workers (e.g., Sperber, Landers, & Lawrence, 2005). And even well-intentioned counselors may feel that they lack the cultural competence or expertise to treat these students and, thus, do not take them on as patients (Shipherd, Green, & Abramovitz, 2010). Collectively, the current findings underscore the need for counseling centers to develop more diverse cultural competencies and outreach strategies to address patient populations that are presenting with escalating rates of mental disorders, the most complex psychiatric comorbidity, and the highest risk for STBs (Mortier, Cuijpers, et al., 2018).
+Several important findings emerged in our analysis of the LCA-STB relationship. Notably, the ORs of the LCA classes in predicting STBs were higher for SP than SI and higher for SA than SP (but also were significant for SA/SI). This is quite different from the pattern found in the numerous previous studies that examined individual mental disorders as predictors of STBs, as the most highly elevated ORs on those studies were usually associated with SI, were successively weaker predicting SP and SA, and were usually nonsignificant predicting SA/SI (Kessler, Borges, & Walters, 1999; Nock et al., 2008; but see Nock, Hwang, Sampson, & Kessler, 2010). This kind of successively weaker prediction pattern was found for the individual mental disorders in the models that controlled for LCA classes, with five disorders predicting SI, four predicting SP, two predicting SA, and only one predicting SA/SI. Additionally, the finding that ~15% of C1 respondents made an SA in the past 12 months is noteworthy. Prior research has shown that effectiveness of universal suicide prevention is limited (e.g., improving help-seeking behavior in suicide prevention efforts; Klimes-Dougan, Klingbeil, & Meller, 2013), and coupled with limited resources, it is essential for universities to strategically identify subsets of high-risk students and offer indicated prevention services. Our findings suggest that students reporting high comorbidity may be at elevated risk of SA, which is consistent with prior research (Nock et al., 2010) and highlights a specific role for (hypo)mania and generalized anxiety disorder in predicting SA, which is in line with recent theories on the importance of affective disturbance and overarousal (including core features such as insomnia and irritability) in predicting suicidal intent (particularly when combined with feelings of alienation or helplessness; Stanley, Rufino, Rogers, Ellis, & Joiner, 2016). More broadly, the results underscore that relatively low-cost web-based screening tools may be effective in reaching high-risk students in need of help (Mortier et al., 2017) and, if integrated with prevention and intervention services, may reduce the incidence of STB on college campuses.
+Given the limited mental health resources that exist on most college campuses relative to the scope of the problem and the importance of comorbidity for treatment planning, it might be prudent to think in terms of latent classes when targeting treatment outreach efforts. This is especially true given the finding that comorbidity becomes an increasingly important predictor of STB in the progression from SI to SA. Focusing on profiles of disorders rather than a specific diagnosis is consistent with recent transdiagnostic approaches to treatment (e.g., Unified Protocol; Barlow et al., 2017), which target common underlying factors that cut across disorders. Transdiagnostic therapeutic approaches have been designed to tackle the limitations
+of past psychotherapeutic approaches and to address issues of comorbidity (and subthreshold presentations), as these treatments intervene on core deficits that are common among disorders (e.g., behavioral avoidance and emotion dysregulation; Ellard, Fairholme, Boisseau, Farchione, & Barlow, 2010). A transdiagnostic approach to treatment also is in line with the focus on mechanisms of action to improve therapeutic outcomes. By targeting core therapeutic processes (e.g., alliance and adherence), phenotypes, and/or biological markers that are shared among a range of disorders, the goal is to determine why psychotherapeutic and pharmacologic interventions are effective as a means of improving outcomes that have remained relatively stagnant in recent decades (e.g., DeRubeis et al., 2005; Dimidjian et al., 2006). Nevertheless, as transdiagnostic approaches may disregard important differences between participants, a promising alternative might be a person-specific approach in which treatment modules—specifically using internet-based treatments—are based on the comorbidity, symptoms, and other characteristics tailored to each individual student (e.g., Weisel et al., 2018). As a whole, given high rates of comorbidity coupled with suboptimal treatment response rates with traditional tracks of care, there is an urgency to design and disseminate interventions that are effective across different profiles of disorders that are commonplace in college students.
+4.1 | Limitations
+Our findings should be considered in light of several limitations. First, the cross-national prevalence estimates are based on a convenience sample of colleges with relatively low and quite variable response rates, limiting generalizability of results. Second, not all common mental disorders were assessed in the surveys. Eating disorders, social anxiety disorder, phobias, post-traumatic stress disorder, conduct
+disorder, oppositional-defiant disorder, and intermittent explosive disorder are especially noteworthy because of their comparatively high prevalence in the WMH surveys (Auerbach et al., 2016), and therefore, the true prevalence of mental disorders among college students may be higher than those reported in the current study, particularly as we are only including first year students who are not yet through the high-risk periods for many common disorders. However, we have developed screening scales for those disorders, and we are experimenting with a design in which subsets of these screening scales are rotated in future iterations of the surveys at random to provide partial information about prevalence and correlates of a wider range of disorders. This approach, which is referred to in the survey methodology literature as matrix sampling (Merkouris, 2015), is becoming an increasingly popular approach to reduce respondent burden when the number of questions of interest in a survey exceeds the number that causes respondent burden (Hughes, Beaghen, & Asiala, 2015; Thomas, Raghunathan, Schenker, Katzoff, & Johnson, 2006). Third, although the surveys used well-validated screening scales calibrated to yield unbiased prevalence estimates in general population samples, calibration studies have just begun in samples of college students. That said, we do not know if calibration studies in separate countries will show that concordance of the structured questions in our diagnostic screens are equally valid in all countries. Fourth, the LCA is based on the assumption that true underlying classes exist that lead the disorders to be conditionally independent within classes. If this assumption is incorrect, it might be that other methods would yield more useful characterizations of the multivariate profiles among disorders. This possibility needs to be investigated in future analyses of the WMH-ICS data. Last, although the study provides key information related to the impact of comorbidity on STBs, there are other important issues at large. Namely, future research would benefit from investigating the societal costs (e.g., lost productivity) associated with
+different types of comorbidity. Additionally, for many of these disorders, it may be that different types of adversity and stress exposure may be driving the types of comorbidity students experience. Both of these issues remain critical, particularly as it relates to developing public health response plans.
+5	| CONCLUSIONS
+Consistent with prior epidemiological research, rates of comorbidity are high in college students (Auerbach et al., 2016). Presently, colleges around the world are faced with an increasingly challenging problem: There is a need to provide unparalleled access to cutting edge educational opportunities while contending with rising rates of mental disorders. Given finite resources, colleges will need to be strategic in how resources are distributed, particularly as this relates to prioritizing cases that are at highest risk for STBs. The current report coupled with recent national (e.g., Eisenberg et al., 2007; Kendler et al., 2015; Mojtabai et al., 2015) and cross-national (Auerbach et al., 2018; Mortier, Auerbach, et al., 2018) findings underscore the need to increase access to care, develop novel ways (e.g., internet-based therapies) to reach students in need, and generate ways to triage student mental health services on campus.
+AUERBACH et al.
+14of16 1 Wl LEY---------------------------------------------------
+Saal, (Department of Psychology, Stellenbosch University); Spain: The UNIVERSAL study Group (Universidad y Salud Mental) includes: Jordi Alonso (PI), Gemma Vilagut, (IMIM-Hospital del Mar Medical Research Institute/CIBERESP); Itxaso Alayo, Laura Ballester, Gabriela Barbaglia Maria Jesus Blasco, Pere Castellvi, Ana Isabel Cebria, Carlos Garcia-Forero, Andrea Miranda-Mendizabal, Oleguer Pares-Badell (Pompeu Fabra University); Jose Almenara, Carolina Lagares (Cadiz University), Enrique Echeburua, Andrea Gabilondo, Alvaro Iruin (Basque Country University); Maria Teresa Perez-Vazquez, Jose Antonio Piqueras, Victoria Soto-Sanz, Jesus Rodriguez-Marin (Miguel Hernandez University); and Miquel Roca, Margarida Gili, Margarida Vives (Illes Balears University); USA: Randy P Auerbach (PI), (Columbia University); Ronald C. Kessler (PI), (Harvard Medical School); Jennifer G. Green, (Boston University); Matthew K. Nock, (Harvard University); Stephanie Pinder-Amaker, (McLean Hospital and Harvard Medical School); Alan M. Zaslavsky (Harvard Medical School).
+ORCID
+Randy P. Auerbach® http://orcid.org/0000-0003-2319-4744
+Jordi Alonso® http://orcid.org/0000-0001-8627-9636
+Pim Cuijpers® http://orcid.org/0000-0001-5497-2743
+Jennifer Greif Green® http://orcid.org/0000-0002-3541-4989
+Ronald C. Kessler® http://orcid.org/0000-0003-4831-2305
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+of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey. Archives of General Psychiatry, 51(1), 8-19. https://doi.org/10.1001/archpsyc.1994.03950010008002
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+Kessler, R. C., & Ustun, T. B. (2004). The World Mental Health (WMH) Survey initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13(2), 93-121. https://doi. org/10.1002/mpr.168
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+Merkouris, T. (2015). An efficient estimation method for matrix survey sampling. Survey Methodology, 41(1), 237-262.
+Mojtabai, R., Stuart, E. A., Hwang, I., Eaton, W. W., Sampson, N., & Kessler, R. C. (2015). Long-term effects of mental disorders on educational attainment in the National Comorbidity Survey ten-year follow-up. Social Psychiatry and Psychiatric Epidemiology, 50(10), 1577-1591. https://doi.org/10.1007/s00127-015-1083-5
+Mortier, P., Auerbach, R. P., Alonso, J., Bantjes, J., Benjet, C., Cujipers, P., ... WHO WMH-ICS Collaborators (2018). Suicidal thoughts and behaviors among first year college students: Results from the WMH-ICS Project. Journal of the American Academy of the Child and Adolescent Psychiatry, 57(4), 263-273.e1. https://doi.org/10.1016Zj.jaac.2018.01.018
+Mortier, P., Cuijpers, P., Kiekens, G., Auerbach, R. P., Demyttenaere, K., Green, J. G......... Bruffaerts, R. (2018). The prevalence of suicidal
+thoughts and behaviours among college students: A meta-analysis. Psychological Medicine, 48(4), 554-565. https://doi.org/10.1017/ s0033291717002215
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+Mortier, P., Demyttenaere, K., Auerbach, R. P., Cuijpers, P., Green, J. G., Kiekens, G., . Bruffaerts, R. (2017). First onset of suicidal thoughts and behaviours in college. Journal of Affective Disorders, 207, 291-299. https://doi.org/10.1016/j.jad.2016.09.033
+Mortier, P., Kiekens, G., Auerbach, R. P., Cuijpers, P., Demyttenaere, K., Green, J. G.....Bruffaerts, R. (2017). A risk algorithm for the persistence
+of suicidal thoughts and behaviors during college. Journal of Clinical Psychiatry, 78(7), e828-e836. https://doi.org/10.4088/ JCP.17m11485
+Muthen, L. K., & Muthen, B. O. (2012). Mplus (statistical analysis with latent variables) user's guide (7th ed.). Los Angeles: Muthen & Muthen.
+Nock, M. K., Borges, G., Bromet, E. J., Alonso, J., Angermeyer, M., Beautrais, A., . Williams, D. (2008). Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. British Journal of Psychiatry,	192(2),	98-105.	https://doi.org/10.1192/bjp.
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+Nock, M. K., Hwang, I., Sampson, N. A., & Kessler, R. C. (2010). Mental disorders, comorbidity and suicidal behavior: Results from the National Comorbidity Survey Replication. Molecular Psychiatry, 15(8), 868-876. https://doi.org/10.1038/mp.2009.29
+Posner, K., Brown, G. K., Stanley, B., Brent, D. A., Yershova, K. V., Oquendo, M. A....... Mann, J. J. (2011). The Columbia-Suicide Severity
+Rating Scale: Initial validity and internal consistency findings from three multisite studies with adolescents and adults. American Journal of Psychiatry,	168(12),	1266-1277.	https://doi.org/10.1176/appi.
+ajp.2011.10111704
+Rankin, S. (2003). Campus climate for gay, lesbian, bisexual, and transgender people: A national perspective. New York: The National Gay and Lesbian Task Force Policy Institute.
+Reinert, D. F., & Allen, J. P. (2002). The Alcohol Use Disorders Identification Test (AUDIT): A review of recent research. Alcoholism, Clinical and Experimental Research, 26(2), 272-279. https://doi.org/10.1111/ j.1530-0277.2002.tb02534.x
+SAS Institute Inc (2010). SAS/STATR software. Cary, NC: SAS Institute Inc.
+Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction, 88(6), 791-804. https://doi. org/10.1111/j.1360-0443.1993.tb02093.x
+Scott, K. M., de Jonge, P., Stein, D. J., & Kessler, R. C. (2018). Mental disorders around the world: Global perspectives from the WHO World Mental Health Surveys. New York: Cambridge University Press. https://doi. org/10.1017/9781316336168
+Shipherd, J. C., Green, K. E., & Abramovitz, S. (2010). Transgender clients: Identifying and minimizing barriers to mental health treatment. Journal of Gay & Lesbian Mental Health, 14(2), 94-108. https://doi.org/ 10.1080/19359701003622875
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+15570657, 2019, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/mpr.1752 by CAPES, Wiley Online Library on [25/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+16of16 1 Wiley
+AUERBACH et al.
+Weisel, K. K., Zarski, A. C., Berger, T., Schaub, M. P., Krieger, T., Moser, C. T.......Ebert, D. D. (2018). Transdiagnostic tailored internet-and mobile-
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+SUPPORTING INFORMATION
+Additional supporting information may be found online in the Supporting Information section at the end of the article.
+How to cite this article: Auerbach RP, Mortier P, Bruffaerts R, et al. Mental disorder comorbidity and suicidal thoughts and behaviors in the World Health Organization World Mental Health Surveys International College Student initiative. Int J Methods Psychiatr Res. 2019;28:e1752. https://doi.org/ 10.1002/mpr.1752
+15570657, 2019, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/mpr.1752 by CAPES, Wiley Online Library on [25/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
\ No newline at end of file
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+South Africa has a 12-month prevalence estimate of 16.5% for common mental disorders (anxiety, mood and substance use disorders),1 with almost a third (30.3%) of the population having experienced a common mental disorder in their lifetime.2 These estimates are relatively high when compared with international prevalence estimates of the WHO (World Health Organization) World Mental Health surveys.3 As is the case internationally,4,5 the treatment gap in South Africa is also high, with only one in four people with a common mental disorder receiving treatment of any kind. For those with psychotic disorders, although identification and access to treatment is better, there are insufficient resources at community level for promotion of recovery. ,7 The aim of the formative phase of the PRogramme for Improving Mental health carE (PRIME) in South Africa was to (a) develop a mental healthcare plan (MHCP), customised to local conditions at district level that provides acceptable and feasible collaborative care packages for depression, alcohol use disorders and schizophrenia and can be integrated into existing service delivery platforms; (b) identify the human resource mix to deliver the MHCP and develop implementation tools to support and facilitate scale up of the packages; and (c) identify potential barriers to implementation at scale. The reason for the focus on these conditions is their relatively high burden of disease and disability and evidence of cost-effective interventions for their treatment.8
+Method
+Context
+Although South Africa is an upper-middle-income country, there are large disparities in wealth and access to resources. Within the
+health sector, these disparities are reflected in inequities between private and public health provision. Private healthcare, funded through private health insurance and out of pocket payments, serves approximately 16% of the population, compared with about 84% served by public healthcare. Yet gross domestic product spend on each is similar (4.1% and 4.2% respectively).9 To redress these inequities, South Africa is phasing in (over 14 years) a national health insurance system, to ensure universal access to appropriate, efficient and high-quality health services.9 The introduction of national health insurance involves an overhaul of services as well as systems to support service delivery. Notably, at the district-service level is re-engineering of primary healthcare. This includes the establishment of district specialist clinical teams to provide support to ward-based primary healthcare teams. The latter comprise primary healthcare staff at fixed primary healthcare facilities as well as community outreach teams consisting of a professional nurse and community health workers.10
+Embedded within the national health insurance system, is the introduction of integrated chronic disease management (ICDM) to meet the needs of the rising number of patients with multiple chronic diseases associated with the roll-out of antiretroviral therapy and a burgeoning non-communicable diseases epidemic.11 At the facility level, the ICDM aims to strengthen the quality of care for chronic conditions through: (a) consolidating services for all chronic care patients, including those with communicable and non-communicable diseases, into a single delivery point; and (b) strengthening clinical decision support through the adoption of an integrated set of nurse-led clinical guidelines developed for the identification and management of multiple chronic diseases, called Primary Care 101 (PC101).12 At community level, community outreach teams support clinically stable patients in
+order to promote self-management. At a population level, health promotion and population screening are envisaged to promote an informed and activated population.13 Within this context, mental health is gaining ground as a public health priority. It is increasingly understood to be integral to the delivery of chronic care given that depression and alcohol misuse compromise prevention efforts as well as adherence to treatment.14-16
+Legislative and policy developments specific to mental health include the introduction of a new Mental Health Care Act (No 17 of 2002) in 2004, as well as a new national mental health policy framework and strategic plan (2013-2020). Both promote decentralised and integrated care through task sharing. A noteworthy development that will enable implementation is the introduction of specialist district mental health teams expected to play a public mental health role.
+Against this background, the South African national Department of Health advised that PRIME in South Africa focus on integrating mental health services for depression and alcohol use disorders into the ICDM service delivery platform given departmental priorities to reduce mortality as a result of chronic conditions, including HIV/AIDS. In light of service gaps in community-based psychosocial rehabilitation for patients with schizophrenia,6 this became a further focus of PRIME in South Africa. Ethical approval for the formative phase of PRIME, including the pilot study, was obtained from the University of KwaZulu-Natal Ethics Committee (HSS/0880/011 and BE 317/13) and the Human Research Ethics Committee of the Faculty of Health Sciences, University of Cape Town (REC Ref: 412/2011). All participants involved in semi-structured interviews consented to participating in the studies using approved informed consent procedures.
+Study site
+The Dr Kenneth Kaunda District (DKK) in the North West Province was chosen as the study site by the Department of Health as it is one of three districts where ICDM is being piloted in the country, and is a pilot site for national health insurance and the re-engineering of primary heathcare. DKK is in the southern part of the North West Province, which is located immediately west of the populous Gauteng province. Please see online Fig. DS1 for a map of South Africa with the location of DKK highlighted. DKK comprises four subdistricts, with a population of approximately 796 823, the majority of whom (90%) are urban. The main economic activities are mining and agriculture. Public health facilities include regional hospitals, primary healthcare facilities and one specialist in-patient mental health facility (details are contained in online Table DS1). Private healthcare facilities are also available but were not the focus of study given the emphasis on integrating mental health into the public service ICDM service delivery platform.
+Research approach
+A mixed methods approach to the formative phase was used: a situational analysis; theory of change (ToC) workshops; qualitative interviews with service managers, service providers, patients and carers; and piloting of the preliminary MHCP in one clinic.
+Situational analysis
+A generic situational analysis tool developed for use across all PRIME country sites (downloadable from http://www.prime.uct. ac.za/images/prime/PRIME_Final_Situational_analysis_Tool.pdf) was adapted for the South African site. This tool required that information be gathered on a range of contextual issues such as
+burden of disease; mental health policies, plans and legislation; estimated treatment coverage for mental and neurological disorders; available resources and mental health information systems. Data were collected via secondary data sources at national and district level. The results of this situational analysis are reported elsewhere.17
+Theory of change workshops
+Participatory theory of change (ToC) workshops were held with key stakeholders including service managers, service providers and patients to develop a MHCP for the district. ToC provides a useful framework for guiding the development and ownership of complex health interventions such as an MHCP, starting with the intended impact and working backwards to identify the outcomes to achieve the impact, and the inputs and processes needed to achieve these outcomes.18 A more detailed account of the process is provided by Breuer et al.19
+Discussion in the ToC workshops centred on how key outcomes identified in the cross-country ToC map20 could be achieved in the district for each priority condition. At the organisational level the key cross-country packages comprised engagement and mobilisation, programme implementation and management and capacity building. At the facility level, they comprised mental health literacy, identification and diagnosis, drug treatment, psychosocial interventions and continuing care. The community level included mental health literacy, detection and referral, adherence support, rehabilitation and mobilisation.
+In total three ToC workshops were held over a period of 6 months from March to August 2012 to develop the initial plan. One workshop involved service providers and managers from facility level to the national level (n = 26); one was with community-based service providers and patients (n = 21); and one combined the above two groups (n = 31), which merged and refined the ToC maps into a final one, drawing on information from the formative qualitative interviews as well.
+Qualitative formative interviews
+Formative qualitative interviews were undertaken with key stakeholders to maximise the social and cultural fit of the emergent MHCP, including an acceptable human resource mix and associated tools to support implementation. All interviews were audiotaped. In total there were 79 individual interviews with patients (n = 70) and caregivers (n = 9); 47 with service providers made up of 25 individuals and 4 focus groups (n = 22). All interviews were audiotaped. The interviews were translated where necessary with back translation checks applied and transcribed verbatim. The transcripts were analysed with the help of NVivo 10 qualitative data analysis software, using framework analysis by stakeholder group.21 This involved a number of steps including: (a) reading and re-reading the transcripts; (b) the development of a coding framework based on the interview questions; (c) coding of the data, with emergent themes being added to the coding framework during this coding process; (d) summarising the responses from the respondents across each theme; and (e) interpreting the final themes in light of what they suggested for service planning and interventions. The results of the formative interviews are reported by stakeholder group in detail in a number of published articles.22-24
+Draft collaborative care packages
+The situational analysis, ToC workshops and formative interviews informed the development of preliminary collaborative care packages depicted in Fig. 1. At the community level, improved
+identification for all the disorders was to be achieved through the second phase of the Department of Health community health worker outreach team training programme, which includes training in identification and referral of people with mental disorders. These community health worker outreach teams are also responsible for tracing all patients with chronic care needs who default on treatment. At primary healthcare facility level, improved identification and diagnosis of comorbid depression, alcohol use disorders and schizophrenia by primary healthcare nurses and doctors was to be achieved through strengthening the mental health guidelines of PC101 to ensure alignment with the WHO’s Mental Health Gap Action Programme (mhGAP) algorithms (subsequently referred to as PC101+); adding a psychoeducational page for the ‘stressed/ miserable patient’; and adding additional mental health cases to the PC101 training.
+For the depression package, patients with mild symptoms were to be provided with basic psychoeducation using the ‘stressed/miserable patient’ page; patients with moderate to severe depression were to be referred to a primary healthcare doctor for initiation of antidepressant medication and/or to a facility-based lay counsellor for initiation of individual or group counselling using the PRIME South Africa depression counsellor guidelines adapted from a local intervention using evidence-based approaches of interpersonal therapy and cognitive-behavioural techniques.2 ’ (The term ‘lay counsellor’ is used in the South African context to differentiate this group from other counsellors who have formal qualifications in counselling. ‘Lay counsellors’ typically receive minimal training in counselling to provide a limited and prescribed service.) After 8 weeks of counselling
+(the optimum number used in an effective collaborative task sharing intervention for depression in India27) patients were to be reassessed by a primary healthcare nurse using PC101+ and referred onwards for specialist care if necessary. Patients on medication were expected to be monitored on a monthly basis when they come to collect their repeat medication.
+For the alcohol misuse package, primary healthcare nurses/ doctors were expected to provide psychoeducation on what constitutes acceptable levels and patterns of alcohol use for patients with harmful or hazardous drinking using guidelines introduced into PC101+. This has been shown to be an effective strategy for reducing alcohol consumption globally.28 mhGAP guidelines for the management of alcohol withdrawal at primary healthcare level were included in PC101+, with onward referral to the local district hospital for detoxification and specialised rehabilitation services.
+In the case of the schizophrenia package, according to the Mental Health Care Act,29 patients with acute conditions are admitted to the nearest designated district hospital that provides 72 h observation and stabilisation. If necessary they are then referred onwards to a specialist mental health facility for diagnosis and initiation of treatment. On being discharged, patients are expected to attend their nearest primary healthcare facility as a chronic care patient for ongoing medication. In the PRIME collaborative care package, these stabilised patients were to be referred to community-based psychosocial rehabilitation groups facilitated by auxiliary social workers from the Department of Social Development/local Mental Health Society (a Mental Health non-governmental organisation (NGO)). Recent evidence attests
+to the effectiveness of community-based task sharing approaches for reducing disability and symptoms of schizophrenia in India.30
+Pilot study
+These collaborative care packages were piloted from August to November 2013 in one clinic in DKK identified by the district management team. The identified clinic is situated in a peri-urban area of the district and saw an average of 3000 patients per month in 2013. Implementation involved a series of training workshops for personnel who formed part of the human resource mix required to deliver the packages of care.
+Evaluation and monitoring of the implementation of the pilot involved monitoring implementation via implementation logs as well as process evaluation interviews. Process evaluation interviews were conducted at 3-month follow-up with service providers trained to deliver the intervention packages and patients who received the interventions. The purpose of the qualitative process evaluation interviews was to gain an understanding of individual’s experience of delivering and receiving the interventions as well as bottlenecks that emerged and reasons for these. Participants included primary healthcare nurses (n = 4), lay counsellors (n = 4), auxiliary social workers (n = 2), patients who received counselling for depression (n = 6), patients who attended psychosocial rehabilitation groups (n = 6) and caregivers of patients attending psychosocial rehabilitation groups (n = 4). The interviews were translated and transcribed verbatim. Framework analysis, described previously, was used to analyse the data.
+Results
+For depression, over a 3-month period, only 15 patients were identified by the primary healthcare nurses and referred to the counsellors for depression. Two-thirds of patients referred (n = 10) presented for their counselling appointment and of these, 7 attended follow-up counselling sessions following the initial session. There were no recorded referrals to the primary healthcare doctor. No patients with alcohol use disorders were identified. For schizophrenia, 19 patients who had been down-referred from the psychiatric hospital to receive their follow-up medication from the clinic were identified from the clinic records. Of these, only nine were attending the clinic for their follow-up medication of which six eventually attended the psychosocial rehabilitation group on a regular basis, with one dropping out. Only one caregiver attended the caregiver sessions.
+The bottlenecks identified by the pilot study thus included a paucity of referrals by primary healthcare nurses to the lay counsellors for depression counselling; minimal identification of alcohol use disorders; poor follow-up of counselling referrals made by counsellors; a high default rate of patients receiving follow-up medications for mental illness at the primary healthcare clinic, which limited the number of patients who could be accessed for psychosocial rehabilitation groups; and poor uptake of the psychosocial rehabilitation intervention by caregivers of patients with schizophrenia.
+Reasons for these bottlenecks that emerged from the qualitative process interviews included the following.
+(a)	On the part of patients/caregivers, poor mental health literacy was a barrier to help-seeking for depression; defensiveness in divulging alcohol consumption was a barrier for identification of alcohol use disorders.
+(b)	On the part of nurses, barriers to identification and/or referral rates of depression and alcohol use disorders was a result of low self-confidence in ability to diagnose common mental
+disorders; unattended personal issues; focusing on underlying social problems and referral to social workers without attending to the presenting mental disorders; and lack of confidence in lay counsellor abilities.
+(c)	On the part of counsellors, in addition to unattended personal issues; marginalised status and unclear roles; low confidence; and poor suitability of some counsellors emerged as being reasons for poor follow-up of patients referred to them for counselling.
+(d)	Structural and organisational challenges that impeded identification and/or referral of depression or alcohol use disorders by nurses included high patient loads and space constraints that limited consultation.
+(e)	Space constraints also emerged as limiting confidentiality of counselling.
+(f)	With regard to the psychosocial rehabilitation group intervention, a high default rate and poor tracing of individuals who defaulted limited the number of patients who could be referred to the groups by the primary healthcare nurses.
+A feedback meeting was held with key stakeholders including district managers, facility managers, service providers and patients in February 2014 to discuss the results of the pilot study, collectively identify strategies to overcome emergent bottlenecks and adapt the MHCP following the approach used by the International Health Institute for health systems strengthening. This approach uses a continuous quality improvement approach to bring healthcare facilities together at regular intervals to jointly identify and solve bottlenecks that emerge in the improvement of health system innovations.31
+To aid roll-out, the implementation tools have been strengthened to include an implementation toolkit comprising implementation guidelines for district and facility managers, providing a step-by-step guide for the implementation and integration of mental health services at facility level. This toolkit also incorporates change management to orientate managers and service providers to the ethos and organisational needs of the collaborative chronic care, including providing more containing leadership and stress management; establishing targets for identification and treatment of priority mental disorders; and role clarification of the different team members where the primary healthcare nurse is designated as the case manager; lay counsellors designated to provide counselling for depression; auxiliary social workers are designated to provide psychosocial rehabilitation for patients with schizophrenia; and community health workers’ role in following up patients who have defaulted on treatment and/or psychosocial intervention is emphasised. The need to strengthen the existing employee wellness programme at an organisational level was also highlighted.
+The piloting process also informed how the guidelines and training materials in the toolbox needed to be strengthened. Noteworthy is modification of the alcohol use disorders guidelines to take into account patients’ defensiveness in acknowledging high levels of alcohol use as patients with chronic care needs are often told to abstain from drinking alcohol; the introduction of waiting room educational talks in addition to information leaflets to improve mental health literacy; and the strengthening of referral documentation and information collected on the priority conditions. All the training materials, guidelines and resources comprise an ‘implementation toolbox’ for integrating mental healthcare at district level in South Africa (see online Table DS2 for a comprehensive list). A summary of the findings of the piloting process and how they informed modifications to the
+MHCP and adaptation of the implementation tools is provided in Table 1.
+The final MHCP is depicted in Tables 2-4. It comprises core intervention packages at the organisational (Table 2), facility (Table 3) and community (Table 4) levels identified through the ToC process to achieve the identified outcomes along the continuum of care (see Method), with the addition of employee wellness at the organisational level. These packages comprise the human resources required to deliver each package as well as resources/mechanisms to aid implementation.
+Based on the collaborative care models for the different disorders, the implications for the human resource mix, their roles and responsibilities and associated resources/mechanisms required to implement the plan at scale are depicted in Table 5. Identification of the human resource mix is important to inform core competencies and curricula of training programmes; and the role of regulatory and accreditation bodies in ensuring
+the production of a workforce equipped with the necessary competencies for scale up.
+Discussion
+The advantages of integrating mental health into existing service delivery platforms include the opportunity for the provision of holistic care; reduction in stigma; and leverage of existing resources to promote efficiency and greater effectiveness of health interventions.32 Given synergies with chronic care, unlike other country plans in this supplement, South Africa is in the fortunate position of being able to scaffold off the introduction of ICDM, with chronic care embracing a collaborative patient-centred approach, central to integrated mental healthcare. We have structured our discussion using the framework suggested by Patel et al on how to integrate mental health into other healthcare platforms.32 This framework incorporates three aspects:
+assessment and customisation; tasks and human resources; and standardisation.
+First, assessment and customisation includes active collaboration of service managers and providers to accurately assess what can feasibly be delivered by the platform, and what lies beyond the scope of the platform and needs to be referred. The participatory nature of the ToC and quality improvement workshops helped facilitate customisation of the MHCP but a number of issues that emerged from the pilot were overlooked.
+These include first, that the collaborative care models adopted by the MHCP require a paradigm shift in the approach to care.33 Although ICDM provides a potentially enabling platform for integration of mental health, staff need to be orientated to this approach. Change management workshops to orientate facility managers and service providers to the ICDM and integrated mental health have thus been included in the revised MHCP at an organisational level.
+Second, with respect to the increased burden of emotional labour that accompanies mental healthcare, to minimise burnout and assist service providers who may experience mental health difficulties of their own, the revised MHCP includes (a) strengthening the employee assistance programme: and (b) instilling more containing leadership including stress management (included in the change management workshops).
+Third, in relation to role clarification, the human resource mix and associated skills sets and resources to achieve the plan are contained in Table 5. Notwithstanding evidence of the effective use of lay health workers in other low- and middle-income countries to increase access to psychosocial interventions,34 poor role clarification and marginalised status of existing lay counsellors in the South African healthcare system35 resulted in two major difficulties: a lack of confidence and reticence to take on additional counselling duties on their part; and low referrals by primary healthcare nurses who did not trust their competencies to counsel patients effectively. This has been addressed in the implementation toolkit of the revised MHCP through: (a) more clearly identifying a case manager (primary healthcare nurse) responsible for monitoring patient progress at primary healthcare level (which has been a need identified in the MHCP in India36 in this supplement); and (b) providing greater role clarification of lay counsellors.
+A further difficulty has been harnessing human resources from other sectors, particularly auxiliary social workers from the Department of Social Development and NGO sector to facilitate psychosocial rehabilitation groups. Greater formal collaboration between the Department of Health and Department of Social Development at national and provincial level has been identified as a strategy to address this.
+Standardisation refers to monitoring of patient progress to assess whether care needs to be adjusted or ‘stepped up’.32 This approach is commonly used in chronic care to monitor remission and obviate patients falling through the cracks. It has been incorporated into the collaborative care models for all three conditions but was not evaluated in the pilot study as no patient reached the point of needing upward referral.
+In relation to the human resource requirements and costing of the PRIME South Africa MHCP, readers are referred to the crosscountry costing paper in this supplement,37 where the number of additional full-time equivalent staff and associated costs for increasing coverage for the priority conditions in the MHCP are estimated. Given that mental health is part of ICDM in South Africa, the cost of increasing coverage of integrated mental healthcare in South Africa37 will have to be borne by the existing primary healthcare budget and thus have to compete with other chronic care priorities. For planners to see the value of integrated mental
+healthcare, the need for cost-benefit studies to show the cost savings and impact of integrated mental health on improved health outcomes in chronic care in South Africa emerged as a priority.
+Limitations
+An obvious limitation is the lack of integration into a maternal and child health service delivery platform. This is of concern given the high rate of maternal depression in South Africa38 and the negative impact on child developmental outcomes.39 Reasons for this include the current focus of the Department of Health on the ICDM and that in South Africa, maternal and child health occurs through a different service delivery platform in primary healthcare and would require the development of a different MHCP. This remains a challenge for the future. A further limitation is the difficulty in reaching men with alcohol misuse. Alcohol use is more prevalent in men in South Africa40 and given they comprise a minority (approximately a third) of primary healthcare clinic attendees in the North West province, the need to engage other health service delivery platforms such as private healthcare provided on the mines is indicated. An additional limitation of the MHCP is that there was minimal engagement with traditional healers in the development of the plan. Given that a large number of people with mental disorders consult traditional healers in South Africa, engaging traditional healers in the collaborative care models is an important task as we move forward.
+Recommendations
+The recent adoption of a national mental health policy framework that embraces decentralised care and task sharing, together with mental health gaining ground as a public health priority, bodes well for future scaling up of the integrated mental healthcare
+services in South Africa. Although the PRIME South Africa MHCP does not serve as a blueprint for scale up to other districts given the great variety in resources available across districts in South Africa, the approach and implementation tools developed should aid this process.
+An important consideration in scale up is the additional time demands on an already burdened system, reflected as an increase in the number of full-time equivalent’s required for scaling up integrated care in the costing of the plan.37 A recommendation would be to use the introduction of national health insurance in South Africa to leverage additional resources for mental healthcare. To strengthen this possibility, cost-benefit studies demonstrating the health benefits and cost savings of integrated mental health are needed. This is especially important, given the lack of a dedicated budget for mental health within ICDM and the need to compete with other priority conditions for resources.
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+1104 |	WASTLER et al.
+----LWl LEY----------------------------------------------------------------------------------
+1	| INTRODUCTION
+Approximately 12 million US adults had serious suicidal thoughts in 2019 (Center for Behavioral Health Statistics and Quality, 2020). Suicidal thoughts are one of the strongest predictors of suicidal behavior (Franklin et al., 2017; Nock et al., 2014, 2018; Ribeiro et al., 2016) and are often considered an initial step in the trajectory toward suicide (Klonsky et al., 2018; Klonsky & May, 2014). Despite this well-established finding, only a small portion of people who think about suicide actually engage in suicidal behavior and very little is known about the transition from suicidal thoughts to behaviors (Klonsky et al., 2018). One potential explanation for this lack of progress is the surprising paucity of research examining the nature of the relationship between suicidal thought content and suicidal behavior. Consequently, the field has made a number of assumptions about this relationship, including the presumption that some suicidal thoughts are higher risk and more likely to transition to suicidal behaviors than others. This assumption stems from the idea that suicide risk exists on a continuum, with individuals progressing through successive, hierarchical stages of risk. The continuum begins with passive suicidal thoughts, which progress to active suicidal thoughts, suicide planning, and eventually suicidal behavior (e.g., Baca-Garcia et al., 2011; Paykel et al., 1974; Simon & Crosby, 2000; Simon et al., 2013). As such, active suicidal thoughts (e.g., thoughts of killing oneself) are often considered higher risk and more likely to transition to suicidal behavior than passive suicidal thoughts (e.g., thoughts about death or a desire for death).
+Although the continuum model has been the foundation for a number of suicide theories (e.g., Joiner, 2005; Van Orden et al., 2010) and suicide risk assessments (e.g., Beck & Steer, 1991; Posner et al., 2011), there is emerging evidence that there are likely multiple pathways to suicidal behavior (Baca-Garcia et al., 2011; Bernanke et al., 2017; Bryan, May, et al., 2020; Bryan, Rozek, et al., 2020; Millner et al., 2017). For example, Millner et al. (2017) found 17 unique sequences of suicide planning among 30 individuals who recently attempted suicide. Additionally, Bernake et al. (2017) propose two distinct pathways: a stress reactivity pathway with fleeting suicidal thoughts that lead to unplanned suicidal behavior and a nonstress reactivity pathway with chronic suicidal thoughts that lead to planned suicidal behavior. Bryan, May, and colleagues (2020) have similarly described multiple potential pathways, some of which are characterized by large and sudden shifts from low-risk to high-risk states. In some cases, these shifts may be so rapid that individuals “skip over” intermediate levels of risk such as active suicidal ideation and/or planning.
+Further supporting the notion of multiple pathways to suicidal behavior, several studies have shown that individuals do not always follow a linear progression of suicide risk. For instance, some individuals with active suicidal thoughts deny experiencing a passive desire to die (Baca-Garcia et al., 2011; Millner et al., 2015). Similarly, approximately 25% of individuals who attempt suicide deny having suicidal thoughts in advance (Simon et al., 2013; Wyder & De Leo, 2007) and up to two-thirds deny making a plan before their attempt (Borges et al., 2000; Jeon et al., 2010; Kessler et al., 1999; Nock et al., 2014; Wyder & De Leo, 2007). There is also a growing body of literature demonstrating that passive suicidal thoughts confer similar risk for suicidal behavior as active suicidal thoughts (Liu et al., 2020), suggesting a potential pathway that does not involve severe, worsening suicidal thoughts. Taken together, these studies suggest that the transition from suicidal thoughts to behavior likely involves multiple pathways. However, additional research is needed to better understand whether specific types of suicidal thoughts have a stronger relationship with suicidal behavior than others.
+The purpose of the current study was to examine the possibility that there are multiple pathways to suicidal behavior by conducting a fine-grained investigation of the relationship between suicidal thought content and suicidal behavior (Baca-Garcia et al., 2011; Bernanke et al., 2017; Bryan, May, et al., 2020;. Bryan, Rozek, et al., 2020). First, we examined the relationship between lifetime suicidal ideation and attempts. We specifically sought to understand what types of suicide-related thoughts are experienced by individuals with lifetime suicide attempts. We also examined whether passive suicidal thoughts alone, active suicidal thoughts alone, and combined passive/active suicidal thoughts are associated with lifetime suicide attempts. To address potential concerns about recall bias and temporal sequencing, we also examined the relationship between past-month suicidal ideation and
+10974679, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/jclp.23268 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+WASTLER et al.	| 1105
+--------------------------------------------------------------------------Wl LEY-1---------
+attempts. Based on suggestive evidence from prior studies, we hypothesized that there would be a subgroup of participants who attempted suicide but denied experiencing active suicidal thoughts. We also hypothesized that the presence of passive suicidal thoughts would be associated with suicidal behavior.
+2	| METHOD
+2.1	| Participants and procedures
+Participants included 6200 US adults recruited via Qualtrics Panels from March to April 2020. Qualtrics Panels are an online survey panel company that maintains a list of several million US adults who have volunteered to participate in online surveys. The current study used quota-sampling methods to recruit a sample that approximates the 2010 US census demographic distributions (±10%) for biological sex, age, race/ethnicity, geographic region, and income level. Owing to the novel coronavirus (COVID-19) outbreak, data were collected online only. Eligibility criteria included: (1) ages 18 years or older and (2) the ability to speak and understand English. The current study had no additional exclusion criteria. Interested panel members received an email invitation with a hyperlink to complete the survey. Potential participants were provided with an information page describing the purpose of the study, risks and benefits, and investigator contact information. Interested participants provided consent by selecting a button that allowed them to begin the survey. After completing the survey, all participants were provided with information for the National Suicide Prevention Lifeline Network, the Crisis Text Line, and the Veterans Crisis Line. Participants were compensated in an amount that was agreed upon with Qualtrics when signing up for the panel. This study was approved by The University of Utah Institutional Review Board.
+Consistent with best practice recommendations (Bauer et al., 2020), we employed several strategies to ensure data integrity. First, we only allowed one response from each IP address to prevent participants from responding multiple times. Second, Captcha images were used to reduce the probability of bot responses. Third, a “soft launch” was conducted before full implementation to identify errors in survey construction (e.g., skip logic). Fourth, we eliminated responses with survey completion times that were under 4 min, which is considered improbably fast by Qualtrics. Fifth, we included reverse-scored items to identify straight-line responding and embedded identical and/or similarly worded items throughout the survey to identify inconsistent response patterns. Only 0.2% of participants evidenced both straight-line and inconsistent responding.
+2.2	| Measures
+2.2.1 | Self-Injurious Thoughts and Behaviors Interview-Revised (SITBI-R)
+An abbreviated, self-report version of the SITBI-R (Fox et al., 2020; Nock et al., 2007) was used to assess suicidal thoughts and behaviors. Prior research has demonstrated that online self-report formats of the SITBI-R are comparable to researcher-administered interview formats, demonstrating good psychometric properties for assessing suicidal ideation, nonsuicidal self-injury, and suicidal behavior (Fox et al., 2020). Participants were asked if they have ever experienced any of the following thoughts: (1) I wish I could disappear or not exist; (2) I wish I could go to sleep and never wake up; (3) My life is not worth living; (4) I wish I was never born; (5) I wish I were dead; (6) Maybe I should kill myself; (7) I should kill myself, (8) I am going to kill myself. Participants who selected yes were then asked when they most recently experienced the thought (within the past month, within the past year, more than a year ago). Responses to these items were used to create binary variables for lifetime and past-month presence of each suicidal thought. Consistent with prior studies distinguishing between passive and active ideation (e.g., Liu et al., 2020), we also created binary variables for lifetime and past month presence of passive suicidal thoughts only, active suicidal thoughts only, and both passive and active suicidal thoughts. The passive suicidal
+10974679, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/jclp.23268 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+1106 |	WASTLER et al.
+------LWl LEY---------------------------------------------------------------------------------------
+thoughts variable was defined as endorsing any of items 1-5, but none of the items 6-8. The active suicidal thoughts variable was defined as endorsing any of items 6-8, but none of the items 1-5. The active and passive thoughts variable was defined as endorsing at least one passive item (1-5) and at least one active item (6-8).
+The SITBI-R also includes a number of items that assess for suicidal behavior. To assess for suicidal behavior, participants were asked to select all of the behaviors they have done from the following list: (1) purposefully hurt yourself without wanting to die; (2) been very close to killing yourself, but at the last minute you decided not to do it before taking any action; (3) been very close to killing yourself but at the last minute, someone or something else stopped you before you took any action; (4) started to kill yourself and then you stopped after you had already taken some action; (5) started to kill yourself and then you decided to reach out for help after you had already taken some action; (6) tried to kill yourself and someone found you afterward; (7) tried to kill yourself and no one found you afterward. We also provided examples of each behavior to ensure that participants accurately interpreted and responded to each item. Consistent with the Centers for Disease Control and Prevention's definition of suicide attempts (Crosby et al., 2011), the current study defined a suicide attempt as any behavior that involved any intent to die and had the potential for injury or death. As such, we used items 4-7 to identify suicide attempts. Participants that endorsed any of these items were also asked when they most recently engaged in this behavior (within the past month, within the past year, more than a year ago), which allowed us to create binary variables for lifetime and past-month suicide attempts.
+2.2.2 | Positive and Negative Affect Scale (PANAS)
+The PANAS (Watson et al., 1998) is a 20-item self-report measure that assesses both state and trait positive and negative affect. Participants are asked to rate the extent to which they have felt a list of positive and negative affective states over the past week. Items are measured using a five-point Likert response scale (1 = Very slightly or not at all to 5 = Extremely), with higher scores indicating greater affect intensity. The current study used the international short-form PANAS (Karim et al., 2011) that included five positive (determined, attentive, alert, inspired, active) and five negative affective states (afraid, nervous, upset, ashamed, hostile). The negative effect subscale was included as a covariate in our analyses given the well-established relationship between negative affect and suicide risk (Franklin et al., 2017). The Cronbach's a for the negative affect subscale was 0.863 in our sample.
+2.3	| Statistical Analyses
+We calculated descriptive statistics to examine the frequency of suicidal thought content among individuals with lifetime suicide attempts and past-month suicide attempts. We then used logistic regression to examine the relationship between lifetime suicidal thoughts (passive only, active only, and both passive/active) and lifetime suicide attempts. Additionally, we used logistic regression to examine the association between past-month suicidal thoughts (passive only, active only, and both passive/active) and past-month suicide attempts. Sociodemographic variables (e.g., age, sex, race, ethnicity, education, and military service) and negative affect were included as covariates in both models. We used a combination of p values, odds ratios, and 95% confidence intervals to examine the significance of our findings.
+3	| RESULTS
+3.1	| Demographic characteristics
+The demographic characteristics of our sample are described in Table 1. Our sample included 51.0% females, 40.8% adults ages 25-44, 44.6% adults with a college education, and 14.2% adults who have served in the US military.
+10974679, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/jclp.23268 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+WASTLER et al.	| 1107
+--------------------------------------------------------Wl LEY—------
+Our sample was predominantly Caucasian (62.4%) and non-Hispanic (85.2%). We conducted x2 tests to examine demographic differences among individuals with and without lifetime suicide attempts (n = 6200). The two groups differed in age x2 = 128.23, p <0.001, race x2 = 53.99, p <0.001, ethnicity x2 = 17.51, p <0.001, military status X2 = 17.60, p < 0.001, and education x2 = 14.36, p = 0.001, but not sex x2 = 2.35, p = 0.125. We also conducted x2 tests to examine demographic differences among individuals with and without past-month suicide attempts (n = 6200). The two groups differed in age x2 = 41.47, p < 0.001, sex x2 = 6.75, p = 0.009, race x2 = 18.03, p = 0.003, ethnicity x2 = 4.50, p = 0.034, military status x2 = 34.88, p < 0.001, and education x2 = 40.80, p < 0.001. Based on these group differences, all demographic variables were included in our regression models.
+1108 |	WASTLER et al.
+------LWl LEY----------------------------------------------------------------------------------------------
+3.2	| Association of lifetime suicidal thoughts with lifetime suicide attempts
+First, we sought to examine what types of suicide-related thoughts are experienced by individuals who have ever attempted suicide. Four hundred forty-four participants (7.2%) reported a lifetime history of suicide attempts. Most participants with a lifetime suicide attempt reported having suicidal thoughts at some point in their life (n = 395, 89.0%). The most common lifetime suicidal thoughts were, I wish I could disappear or not exist (n = 281, 63.3%) and I wish I could go to sleep and never wake up (n = 266, 59.9%) (Figure 1a). Many participants with a lifetime history of suicide attempts reported experiencing multiple types of suicidal thoughts in their lifetime, with a mean of 4.11 ± 2.81 different thoughts: 25.0% (n = 111) reported passive suicidal thoughts only, 2.3% (n = 10) reported active suicidal thoughts only, 61.7% (n = 274) reported both passive and active suicidal thoughts, and 11.0% (n = 49) denied any suicidal thoughts (Figure 2a). Overall, 36.0% (n = 160) denied ever having any active suicidal thoughts in their lifetime.
+Table 2 summarizes results from the logistic regression examining the relationship between lifetime suicidal thoughts and lifetime suicide attempts. The full model including all predictors was significant (x2 (17) = 938.25, p < 0.001), correctly identifying 93.3% of cases and accounting for 14.0% (Cox and Snell R2) to 34.9% (Nagelkerke R2) of variance in lifetime suicide attempts. Regarding demographic variables, age, education, military service, and race were associated with lifetime suicide attempts (see Table 2 for details). Lifetime presence of passive suicidal thoughts only (OR = 4.84, 95% CI = 3.38-6.93), lifetime presence of active suicidal thoughts only (OR = 13.63, 95% CI = 6.16-30.15), and presence of both passive and active lifetime suicidal thoughts (OR = 25.03, 95% CI = 17.72-35.36) were all associated with significantly increased rates of lifetime suicide attempts.
+3.3	| Association of past-month suicidal thoughts with past-month suicide attempts
+Given concerns about recall bias and lack of clarity regarding temporal sequencing of suicidal thoughts and behaviors, we also examined what types of suicide-related thoughts were recently experienced by individuals who recently attempted suicide. Ninety-three participants (1.5%) reported attempting suicide in the past month. Similar to our lifetime analyses, most participants with a recent suicide attempt endorsed experiencing at least one suicide-related thought within the past month (n = 72, 77.4%). The most common past-month suicidal thoughts reflected passive suicidal ideation: I wish I could disappear or not exist (n = 36, 38.7%) and I wish I could go to sleep and never wake up (n = 35, 37.6%) (Figure 1b). Many participants reported experiencing multiple types of suicidal thoughts in the past month, with a mean of 2.45 ± 2.58 different thoughts: 31.2% (n = 29) reported passive suicidal thoughts only, 10.8% (n = 10) reported active suicidal thoughts only, 35.5% (n = 33) reported both passive and active suicidal thoughts, and 22.6% (n = 21) denied any suicidal thoughts (Figure 2b). Overall, 53.8% (n = 50) denied active suicidal thoughts in the month they attempted suicide.
+Table 3 summarizes results from the logistic regression examining the relationship between past-month suicidal thoughts and past-month suicide attempts among individuals with a lifetime attempt. The full model including all predictors was significant (x2 (17) = 127.97, p < 0.001), correctly identifying 82.2% of cases and accounting for 25.0% (Cox and Snell R2) to 39.0% (Nagelkerke R2) of variance in the past-month suicide attempts. Regarding demographic variables, race, education, military service, and sex were associated with past-month suicide attempts (see Table 3 for details). Past-month passive suicidal thoughts only (OR = 4.73, 95% CI = 2.33-9.60), active suicidal thoughts only (OR = 9.69, 95% CI = 2.84-33.08), and both passive and active suicidal thoughts (OR = 9.63, 95% CI =4.56-20.35) were all associated with significantly increased rates of past-month suicide attempts.
+We also conducted follow-up analyses to examine lifetime history of suicidal ideation among individuals who recently attempted suicide but denied any recent suicidal ideation (n = 21). This assessed the possibility that individuals who denied or “skipped” ideation within the past month, despite making a suicide attempt within that same timeframe, may have experienced these thoughts in the more distant past. Results indicate that 52.4% (n = 11) of these individuals denied a lifetime history of suicide-related thoughts (Figure 3). Only 23.8% (n = 5) of these individuals reported a history of active suicidal thoughts.
+10974679, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/jclp.23268 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+Suicidal ideation is a well-established risk factor for suicide (Franklin et al., 2017; Nock et al., 2014, 2018; Ribeiro et al., 2016). However, very little is known about the transition from suicidal thoughts to behaviors (Klonsky et al., 2018). One potential contributor to this limited progress is the assumption that suicide risk exists on a continuum, with individuals progressing through worsening stages of suicidal thoughts (Baca-Garcia et al., 2011; Paykel et al., 1974; Simon & Crosby, 2000; Simon et al., 2013). The current study sought to examine this assumption as well as the alternate possibility
+10974679, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/jclp.23268 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+that there are multiple pathways to suicidal behavior (Baca-Garcia et al., 2011; Bernanke et al., 2017; Bryan, May, et al., 2020; Bryan, Rozek, et al., 2020). Several noteworthy findings emerged. First, over one-third of individuals with a lifetime suicide attempt denied ever experiencing active suicidal thoughts in their lifetime and one in 10 denied ever having any suicide-related thoughts. Second, over half of the individuals with a recent suicide attempt denied experiencing active suicidal thoughts during the month they attempted suicide. One in five denied experiencing any suicide-related thoughts during the month they attempted suicide. Third, the sole presence of passive suicidal ideation was associated with increased rates of both lifetime and past-month suicide attempts. Taken together, these findings provide preliminary evidence that the transition from suicidal thoughts to behaviors likely involves multiple pathways, some of which might not involve worsening suicidal thoughts (Baca-Garcia et al., 2011; Bernanke et al., 2017; Bryan, May, et al., 2020; Bryan, Rozek, et al., 2020).
+According to the continuum model, suicide risk involves a linear, hierarchical process, which begins with passive suicidal thoughts and progressively worsens to active suicidal thoughts, planning, and suicidal behavior (Baca-Garcia et al., 2011; Paykel et al., 1974; Simon & Crosby, 2000; Simon et al., 2013). Results from the current study suggest that the continuum model likely represents only one of the multiple pathways to suicidal behavior. Specifically, our results indicated that approximately 36% of participants with a recent attempt experienced both passive and active suicidal thoughts during the month they attempted suicide, a finding that is consistent with the continuum pathway. However, 31% experienced passive ideation only and 23% denied experiencing any recent suicide-related thoughts, suggesting that some individuals might not experience worsening suicidal thoughts before attempting suicide. Additionally, 11% reported only experiencing active suicidal thoughts before attempting suicide, suggesting that some people might “skip” lower-risk states, such as passive suicidal ideation. Consistent with our findings, several other studies have raised questions about the continuum model of suicide, demonstrating that most individuals do not progress through each hierarchical stage before engaging in suicidal behavior (De Leo et al., 2005; Wyder & De Leo, 2007). Similarly, when directly asked about their own suicidal process, only 20% of individuals with a lifetime attempt report experiencing a linear pathway with worsening suicidal thoughts (De Leo et al., 2005; Wyder & De Leo, 2007). These findings suggest that there are likely multiple pathways to suicidal behavior and highlight the need for further research examining the complex relationship between suicidal thoughts and behaviors (Baca-Garcia et al., 2011; Bernanke et al., 2017; Bryan, May, et al., 2020; Bryan, Rozek, et al., 2020).
+Interestingly, the current study found that individuals who attempted suicide most frequently experienced thoughts that are typically classified as passive suicidal ideation (e.g., I wish I could disappear or I wish I could go to sleep and never wake up). Additionally, when examining the relationship between suicidal behavior and passive suicidal thoughts alone, active suicidal thoughts alone, and the combined presence of passive and active thoughts, we found that all three were significantly associated with increased odds of lifetime and past-month suicide attempts. Our finding that the sole presence of passive suicidal ideation was associated with suicide attempts is particularly noteworthy given the common assumption that passive ideation is lower risk and less likely to transition to suicidal behavior than active ideation. Though our results align to a degree with this perspective, passive suicidal thoughts alone were nonetheless a meaningful risk factor for suicidal behavior. Consistent with this notion, a recent meta-analysis showed that passive suicidal thoughts are highly prevalent and strongly associated with suicide
+attempts and death (Liu et al., 2020). Additionally, this meta-analysis found that the association between passive ideation and suicide attempts was comparable to the association between active ideation and suicide attempts (Liu et al., 2020). Despite its clinical relevance, passive ideation has received much less empirical attention than active ideation (Liu et al., 2020), limiting our understanding of how an individual might transition from experiencing only passive suicidal thoughts to engaging in suicidal behavior. Further research is needed to determine whether there is a “passive suicidal ideation only” pathway to suicidal behavior.
+Our finding that some individuals who attempted suicide denied experiencing any suicide-related thought warrants further discussion. Specifically, 11% of lifetime attempters denied suicidal ideation at any point in their lives and 23% of participants who made a suicide attempt within the preceding month denied experiencing any suicidal ideation within the same timeframe. There has been some debate about whether suicidal behavior can occur outside of the context of suicidal ideation, with some researchers arguing that prior suicide planning might be
+stored on a “mental shelf,” which can be easily accessed and acted upon without current suicidal thoughts (Jobes & Joiner, 2019). We sought to address this possibility by examining lifetime history of suicidal thoughts among individuals with a recent suicide attempt who denied recent ideation. Results showed that over half of these individuals denied ever experiencing any suicide-related thought in their lifetime, suggesting that past suicidal ideation/planning does not fully explain the occurrence of suicidal behavior without current suicidal ideation. Additional explanations discussed in the literature include unplanned, impulsive suicidal behavior (e.g., Wyder & De Leo, 2007), stigma or fear of disclosure (e.g., Frey et al., 2018; Ganzini et al., 2013; Jobes & Joiner, 2019; Richards et al., 2019), and inaccurate assessment of suicidal ideation (e.g., Millner et al., 2015). Interestingly, a recent qualitative study also found that some individuals deny suicidal ideation because the assessment language does not reflect their subjective experience of suicidal thoughts (Richards et al., 2019). Relatedly, Ammerman and colleagues (2021) demonstrated that subtle variations in the language used to assess suicidal ideation has a significant impact on item endorsement. Relative to other suicide risk assessments, the SITBI-R includes a broad range of commonly experienced suicide-related thoughts. However, it is still possible that our participants denied suicidal thoughts because they did not experience any of the specific thoughts included in our study. For example, some individuals might not experience traditionally defined “passive” and “active” suicidal thoughts, but may instead experiencing other types of negative thoughts that signal increased vulnerability to suicidal behavior, such as unbearability (e.g., I can not imagine anyone being able to withstand this kind of pain) and self-hatred (e.g., I am completely unworthy of love). Such thoughts have been shown to prospectively predict suicidal behaviors even when accounting for suicidal ideation (Bryan et al., 2014, Bryan, May, et al., 2020; Bryan, Rozek, et al., 2020; Bryan, Allen, et al., in press). This suggestion is consistent with our broader finding that suicide risk does not always involve a linear progression of worsening suicidal thoughts. Further research is needed to determine whether there are pathways to suicidal behavior that do not involve passive and active suicidal thoughts.
+Our findings have important clinical implications. As previously discussed, the continuum model is the foundation of many suicide risk assessments (e.g. Beck & Steer, 1991; Posner et al., 2011). Thus, most suicide risk assessments include a hierarchical structure with skip logic that is based on the assumption that suicide risk involves progressively worsening suicidal thoughts. For example, Tabares et al. (2020) recently found that although the hierarchical structure of the Columbia Suicide Severity Rating Scale (Posner et al., 2011) was generally supported, many respondents who endorsed higher level items did not endorse lower level items, suggesting the scale's hierarchical structure may be error-prone. The present results suggest that suicide risk assessments should involve a detailed inquiry about all aspects of suicidal ideation regardless of whether the individual endorses active suicidal
+1114 |	WASTLER et al.
+------LWl LEY--------------------------------------------------------------------------------------------
+thoughts. Additionally, our findings highlight the clinical relevance of passive suicidal ideation, suggesting that suicide-focused interventions, such as Brief Cognitive Behavioral Therapy for Suicide Prevention (Bryan & Rudd, 2018) and Crisis Response Planning (Bryan, 2010; Bryan et al., 2017; Rudd et al., 2006) might also be useful for individuals who only report passive suicidal ideation. Additional research is needed to examine whether specific interventions are more effective at targeting passive versus active suicidal ideation.
+The following limitations should be considered when interpreting our results. First, the current study was limited by our cross-sectional design. We sought to address concerns regarding the temporal sequencing of suicidal thoughts and behaviors by conducting analyses that examined both lifetime and past-month ideation and attempts. However, our cross-sectional, retrospective design impedes our ability to make inferences about the progression of suicidal thoughts in the days/moments before attempting suicide. Thus, our results only provide preliminary support for the multiple pathways model and longitudinal research is needed to examine the transition from suicidal thoughts to behaviors. For instance, ecological momentary assessment with long-term follow-ups would provide more fine-grained information about whether the transition from suicidal thoughts to behavior involves multiple pathways. Second, the current study used an abbreviated, self-report version of the SITBI-R that only included items about suicidal ideation and behavior. Therefore, we were unable to examine the suicide planning (e.g., method, specific time, and specific location) aspect of the continuum model. Given research demonstrating the suicide planning is heterogeneous and nonlinear (Millner et al., 2017), further research that examines the relationship between passive ideation, active ideation, suicide planning, and attempts, would provide further information about the continuum model and other potential pathways to suicidal behavior. For instance, such work could address the possibility that some individuals might skip traditional passive and active suicidal thoughts and only experience thoughts about specific methods (e.g., Maybe I should take all of these pills or I should step in front of this bus). Relatedly, our SITBI-R items assessed suicidal behavior as a binary outcome, which limited our ability to examine whether there are distinct pathways for individuals who make single versus multiple attempts. Future research that examines the multiple pathways model among multiple versus single attempters is essential given prior research demonstrating that multiple attempters exhibit nonlinear change in suicide risk, whereas ideators and first-time attempters exhibit linear change in suicide risk (Bryan & Rudd, 2018). In addition, our abbreviated version of the SITBI-R did not include an “other” option when inquiring about specific suicidal thoughts. Future studies that also include an open-text “other” option would provide further insight into the possibility that some individuals who attempt suicide deny suicidal ideation because of subtle language variations (Ammerman et al., 2021) or because the language does not reflect their subjective experience of suicidal thoughts (Richards et al., 2019). Further, the current study used a self-report assessment of suicidal thoughts and behaviors, which raises potential concerns about recall and response biases. Future research that utilizes the full-length clinician-administered version of the SITBI-R would lend additional support to our claims. Additionally, data collection for the current study took place from March to April 2020, which coincided with the US national emergency declaration for the COVID-19 pandemic. The current study did not include any items assessing the impact of COVID-19, which limits our ability to examine whether our findings were influenced by the pandemic. As such, our results should be interpreted with caution and further replication is needed. Finally, our sample included a slightly larger portion of adults with higher education (44.6% with a college degree and 14.5% with an advanced degree) and military experience (14.2%) than recent US Census Bureau data. Although these demographics are within the 10% error margin target for our quota sampling, additional research with a more representative sample would increase the generalizability of our findings.
+Despite these limitations, the current study provides preliminary support for the multiple pathways model (Baca-Garcia et al., 2011; Bernanke et al., 2017; Bryan, May, et al., 2020; Bryan, Rozek, et al., 2020). Our findings suggest that some individuals who attempt suicide experience progressively worsening suicidal thoughts, whereas others only experience passive ideation and some do not experience any suicide-related thoughts. Incorporating the multiple pathways perspective into existing suicide theories has the potential to advance our understanding about the transition from suicidal thoughts and behaviors. Future longitudinal research is needed to better understand the complex relationship between suicidal thoughts and behaviors.
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+The current spread of the novel SARS-CoV-2 (COVID-19) virus is a major threat to physical and, along with the national lockdown measures imposed, also to mental health (WHO, 2020). Although there has been much media speculation about the adverse impact of this crisis on mental health and family life, there has been little empirical investigation of this. Moreover, emerging research on the impact of COVID-19 on mental health (Moccia et al., 2020; Veer, Riepenhausen, Zerban, Wackerhagen, & Engen, 2020) is limited to adult studies, with a few notable exceptions (Green et al., 2021; Janssen et al., 2020; Magson et al., 2021; Widnall et al., 2020). However, adolescents are a population group who may be especially vulnerable to any mental health impacts of COVID-19.
+Adolescence is a developmental period where the vast majority of mental health conditions have
+their onset (Solmi et al., 2021). Large-scale US research suggests that 75% of adults who report ever having a mental health condition indicate they experienced their first symptoms during adolescence (Kessler, Petukhova, Sampson, Zaslavsky, & Wittchen, 2012). Along with significant physical and psychological changes, adolescence is a period of profound social transformation, where both peer and parent interactions are crucial for the development of adolescents into independent adults (Andrews, Ahmed, & Blakemore, 2020; Blakemore & Mills, 2014; Steinberg & Morris, 2001). During this period, adolescents strive to become independent and focus more on socializing and spending time with friends rather than with their families. Consequently, the drastic changes in daily social life due to the pandemic and associated lockdown measures may have particularly affected adolescents, as they are at a a critical stage of social development (Andrews et al., 2020; Orben, Tomova, & Blakemore, 2020). Adolescents’ enforced proximity to their families and the limitation of face-to-face contacts with peers may not allow their developmental needs to be met (Andrews et al., 2020; Grusec & Davidov, 2021; Orben et al., 2020; Steinberg & Morris, 2001). Findings from emerging research during the pandemic suggest an increased vulnerability for mental health problems in adolescents as compared to adults (Magson et al., 2021); however, some studies suggest a decrease in psychopathology symptoms
+© 2021 Society for Research on Adolescence
+DOI: 10.1111/jora.12657
+624 JANSSENS ET AL.
+(Widnall et al., 2020). To learn more about these conflicting findings on the impact of COVID-19 on adolescent mental health, we may need to focus more on mental health symptoms at a subclinical level.
+Several studies and viewpoint papers have suggested an increase in irritability, stress, and loneliness in adolescents due to the sudden global virus outbreak and government-imposed lockdown regulations, which may be precursors to later mental health problems (Hasking et al., 2021; Loades et al., 2020; Panda et al., 2021). For example, previous studies found that irritability was fairly common among quarantined adolescents (Panda et al., 2021), possibly due to the increased parental involvement, reducing adolescents' privacy and time alone (Hasking et al., 2021; Wang, Zhang, Zhao, Zhang, & Jiang, 2020). An increase in stress may also be expected, as adolescents worry about their own and loved ones' safety during the pandemic, as well as their school education, given the swift transition to online learning from home (Ellis, Dumas, & Forbes, 2020; Hasking et al., 2021). Additionally, limitation of face-to-face contact with peers potentially increased concerns about maintaining close social connections during a period where these are crucial for adequate development and mental well-being (Andrews et al., 2020; Grusec & Davidov, 2021; Smetana, Robinson, & Rote, 2015). Therefore, enforced physical distancing may have led adolescents to feel lonely during the COVID-19 pandemic (Ellis et al., 2020; Loades et al., 2020). The broader literature emphasizes how these daily-life outcomes, that is, irritability, stress, and loneliness, are related to negative physical and mental health outcomes, for example, anxiety disorders, depression, and suicidal behavior and mortality (Brotman, Kir-canski, & Leibenluft, 2017; Hawkley & Cacioppo, 2010; McClelland, Evans, Nowland, Ferguson, & O'Connor, 2020; Romeo, 2017; Stringaris, Vidal-Ribas, Brotman, & Leibenluft, 2018). As such, investigating proximal vulnerability and protective factors for irritability, stress, and loneliness could inform efforts to mitigate or prevent mental health problems in adolescents.
+In addition to the potential impact of the COVID-19 pandemic on adolescents' levels of irritability, stress, and loneliness, several review papers suggest an impact on the family system as a whole (Campbell, 2020; Prime, Wade, & Browne, 2020). To understand this, we can draw upon the Family Resilience Model (FRM; Henry, Sheffield Morris, & Harrist, 2015): a theoretical framework describing how families as systems navigate
+unexpected adversity, such as the COVID-19 pandemic, given certain pre-existing protective and vulnerability factors. The FRM includes four basic elements that play a role in family resilience: family risk, protection, vulnerability, and adaptation. Families each have their own set of vulnerabilities and protective factors, which combine with certain risks (i.e., stressors) to produce a unique response to adversity. Both protection and adaptation occur within the Family Adaptive Systems (FAS) that are described in the FRM as arising from family interactions, which develop and regulate key domains of everyday family life. The five FAS within the FRM include, but are not limited to, meaning, emotion, control, maintenance, and stress response systems. For example, from the perspective of the FRM, the Stress Response System regulates the level of change and stability in the family equilibrium on a meta-level. The other FAS influence how a family develops what is necessary to regulate family goals, structures, and interaction patterns, and adapt to the incoming adversity. For example, when the quality of parent-child relationships (an aspect of the Family Emotion Adaptative System) is poor before a new incoming challenge, this can limit family resilience and the adversity may further worsen the relationship. At the same time, when there is already a high level of conflict (an aspect of the Family Control Adaptative System), this may increase the distress during adversity and heighten the level of conflict in the family (Henry et al., 2015).
+Emerging research suggests COVID-19-related stress, fear, uncertainty, limited support networks, and social isolation are risk factors for family conflict during the pandemic (Campbell, 2020; Gues-soum et al., 2020). Another conceptual framework, recently proposed by Prime et al. (2020), posits that the social disruptions caused by the pandemic may heighten stress in parents, which in turn negatively affects parental, parent-child, and sibling relationships. Parents' stress increases during the pandemic because they are searching for a new workfamily balance (e.g., shift in routines and structures), dealing with job insecurity/loss, as well as concerns about their safety and that of their loved ones. When parents' mental and emotional resources are exhausted, ensuring the positive functioning of the family is difficult (Prime et al., 2020). This proposed cascading process suggests that family conflict is a particularly relevant risk factor for adolescent adjustment and well-being (Browne, Plamondon, Prime, Puente-Duran, & Wade, 2015). Therefore, it is of utmost importance
+to understand the consequences of the COVID-19 pandemic on adolescents and their families. High-quality parent-child relationships may buffer against the risk of negative outcomes in the context of COVID-19 by promoting resilience (Prime et al., 2020), whereas poor parent-child relationships within families may create vulnerability to the negative effects of the pandemic on family life, for example, family conflict (Henry et al., 2015).
+Previous research has shown that high-quality parent-child relationships can protect adolescents against the impact of stressors (Dimitry, 2012; Kro-nenberg et al., 2010; Wickrama & Kaspar, 2007) as this can provide them with the opportunity to more easily identify, describe and share feelings with others (Cerutti, Zuffiano, & Spensieri, 2018; Gandhi et al., 2019). Conversely, adolescents with lower quality parent-child relationships might lack the skills to find support from others and share difficulties, meaning their strategies for dealing with adversity fall short (Brumariu & Kerns, 2010; Shpi-gel, Diamond, & Diamond, 2012). During adolescence, the need for (physical) proximity from the parental figure changes to the need for (emotional) availability, as self-regulatory skills grow with age. Nevertheless, adolescents still need their parents to be available in times of need, because peer relationships are still developing and the intense emotions —inherent to adolescence—may be overwhelming (Bosmans & Kerns, 2015).
+The broader literature has shown that low-quality parent-child relationships are associated with more frequent and burdensome family conflict, whereas high-quality parent-child relationships are associated with less frequent and less burdensome family conflict (Hannum & Dvorak, 2004; Shpigel et al., 2012). Therefore, children with poor parent-child relationships and their families are potentially more strongly impacted by stressors such as the pandemic. However, no published research in adolescents so far has investigated the presumed impact on irritability, stress, loneliness, and family conflict during this pandemic, and how the quality of paternal and maternal relationships may be associated with these outcomes. More specifically, how and to what extent the quality of paternal and maternal relationships can mitigate or exacerbate the pandemic's potential impact on these daily-life outcomes, and family conflict. Insights into these associations may help us to identify adolescents and families at risk of adverse mental health outcomes or experiencing heightened conflict and to develop effective strategies to support them.
+Research on mental health and family dynamics during COVID-19 faces two major challenges. First, in order to investigate dynamic processes such as irritability, stress, and loneliness in adolescents, it is necessary to look at these outcomes in an ecologically valid manner by targeting them in the context where they naturally occur: daily life. The Experience Sampling Method (ESM; Csikszentmi-halyi & Larson, 1987; Myin-Germeys et al., 2018) enables data on adolescents' activities, thoughts, and experiences to be captured within the context of their natural daily life, by completing multiple brief questionnaires on a smartphone over a period of several days. The use of ESM to assess feelings of irritability, stress, and loneliness is expected to improve the accuracy of measurements by reducing recall bias and increasing ecological validity. Second, the vast majority of studies examining psychological and social effects of COVID-19 are cross-sectional. Consequently, we lack data on key predictors (and outcomes) from before the pandemic. Considering that prepandemic vulnerability factors may heighten risk of negative psychological outcomes and family conflict (Prime et al., 2020), longitudinal data which enables comparison of predictors and outcomes pre- and during-pandemic is essential. To this end, the current study leverages pre-COVID-19 data from an ongoing adolescent cohort study including ESM (SIGMA; Kirtley, Achterhof, et al., 2021; Kirtley, et al., 2020; Kirtley, Achterhof, et al., 2021) in combination with data collected from a subgroup of these adolescents during the first COVID-19 lockdown (Achterhof, Myin-Germeys, et al., 2021).
+Our study aims to investigate to what extent the quality of a parent-child relationship is associated with changes in adolescents' levels of irritability, stress, and loneliness in daily life from before to during the COVID-19 pandemic. We also examine whether the quality of the parent-child relationship is associated with adolescents' experiences of COVID-19-related family conflict and its perceived burden. First, we hypothesize that there will be an increase in adolescents' levels of irritability, stress, and loneliness in daily life from before to during the pandemic. Second, we predict that adolescents who report a lower quality of paternal and maternal relationships will be more likely to report higher levels of irritability, stress, and loneliness in daily life before and during the pandemic. Third, we expect that changes in adolescents' daily life levels of irritability, stress, and loneliness from before to during the pandemic will be moderated by the quality of the paternal and maternal
+626 JANSSENS ET AL.
+relationships, such that adolescents with a lower quality of relationships exhibit a larger increase in irritability, stress, and loneliness. Fourth, we hypothesize that adolescents who report a lower quality of paternal and maternal relationships will be more likely to experience more frequent and burdensome family conflict during the pandemic.
+METHOD
+Socio-Cultural Context
+For the current study, we used data from two waves of the SIGMA study: Wave I and Wave COVID-19. SIGMA is a large-scale, accelerated longitudinal study that investigates the mental health and development of adolescents. Wave I of the SIGMA study included data from 1913 adolescents from 22 schools in Flanders, the Northern, Dutchspeaking region of Belgium and took place between January 2018 and June 2019. Flanders counts approximately 6.6 million people (Statbel, 2020) of which 457,000 are in secondary education (Statistiek Vlaanderen, 2019). The sample was representative in terms of sex, education level, and geographical spread.
+Wave COVID-19 of the SIGMA study occurred during the first national lockdown from the 27th of April until the 10th of May. On the March 18, 2020, the Belgian government decided to impose restrictive measures to prevent the spread of COVID-19. There was a stay-at-home order, and schools and nonessential shops were closed. In the week of the 4th of May, some measures were lifted (e.g., outdoor activities with a friend) and it was announced that other regulations (e.g., schools) would be lifted in the near future (Belgische Federale Overheidsdi-ensten, 2020).
+Participants and Recruitment
+For a detailed description of the measures and sample from the full SIGMA study, see Kirtley, Achterhof, et al. (2021), Kirtley, et al. (2020), and Kirtley, Lafit, et al. (2021). For an overview of the complete COVID-19 Wave of SIGMA, see Achter-hof, Myin-Germeys, et al. (2021). See Appendix S1 for a full overview of the self-report measures used for the full Wave I of the SIGMA study and Wave COVID-19.
+Wave I took place between January 2018 and June 2019 and includes data from 1913 adolescents, recruited via 22 mainstream secondary schools. The majority of the schools were recruited via their
+existing relationship with Te Gek!?, a Flemish nongovernmental organization that aims to break taboos surrounding discussion of mental health, and a partner organization of the SIGMA study. After the board of the secondary school had agreed with participation, the research team visited the school to explain the study and recruit potential participants. The parents/caregivers and potential participants were sent an information letter with further details. The majority of the sample were female (n = 1207; 63%), n = 695 were male (36%) and 11 participants indicated ‘Other’ (<1%). Within Wave I, age ranged from 11 to 20 years (M = 13.76 years, SD = 1.86 years). Inclusion criteria were being in the first, third, or fifth year of mainstream secondary education, having an adequate command of Dutch and having provided informed consent, both from themselves and their parent/caregiver. Within the current study, we only included adolescents that have participated in both Wave I and Wave COVID-19.
+Wave COVID-19 occurred during the first national lockdown in Belgium due to COVID-19 from the 27th of April until the 10th of May. From the full Wave I sample, it was possible to contact 1581 of 1913 adolescents via email (for n = 239, there was no contact information available and for n = 93 the contact information was erroneous). Of those, n = 173 took part in this second follow-up wave, and n = 110 participated in the ESM part of the study. Regarding the family situation of this group, n = 146 indicated having both a father and a mother in their lives, n = 2 indicated having two fathers, while 2 other participants indicated having only one father or one mother. The other n = 17 indicated ‘Other’ (if none of the other options were relevant to them) or did not answer the question about their family situation. Inclusion criteria for the COVID-19 measurement were having participated in Wave I of SIGMA, providing contact information for follow-up contact at Wave I, being able to complete baseline questionnaires in REDCap (Harris et al., 2009), having provided informed consent and if younger than 18 years of age, providing informed consent from a parent/caregiver as well. Both SIGMA Wave I and COVID-19 received full approval from the UZ/KU Leuven Medical Ethics Committee (S61395).
+Procedure
+Wave I. The full procedure for Wave I of the SIGMA study is described in detail in Kirtley, Achterhof, et al. (2021), Kirtley, et al. (2020), and
+Kirtley, Lafit, et al. (2021). During school hours (100 min), instructions were given to the participants by the research team where they received an explanation about the purpose of the study, as well as where they had the opportunity to ask questions and were guided through a demo of the full ESM questionnaire. Participants completed self-report questionnaires in school on a tablet using the REDCap application (Harris et al., 2009). At the end of the testing session, all participants received a support sheet with contact details for relevant support services, including local and national crisis and advice phone lines.
+Daily-life data were collected using the ESM. To complete the ESM questionnaires via the MobileQ app (Meers, Dejonckheere, Kalokerinos, Rummens, & Kuppens, 2020), participants received a smartphone device (Motorola Moto E4) from the research team. The sampling scheme was semirandom signal-contingent with an ESM questionnaire 10 times a day, for six consecutive days. The questionnaire consisted of a minimum of 39 items and a maximum of 46 items with an average completion time of 162.8 s. The notification would buzz or beep for 90 s or until the participants opened the notification. To complete each item, they had 90 s. Participants were instructed to answer these items with the moment right before the notification in mind (e.g., ‘I feel irritated’). Compensation for participation was a 10-euro gift voucher for a physical or online store. In addition, schools received mental health-themed psychoeducation sessions, workshops or educational materials, delivered in cooperation with Te Gek!?. Participants received no feedback on their ESM compliance and were included irrespective of their rate. The average compliance during Wave I was 39.5% across all participants (N = 1913) and notifications. The MobileQ app did not allow partial responses to be saved until October 25, 2018.
+Wave COVID-19. The data collection procedure for Wave COVID-19 was kept as similar as possible to that of Wave I, but was conducted completely remotely. Participants were invited to take part via email and received instructions via a prerecorded video made by the research team. The self-report questionnaire battery was slightly adapted for this follow-up (e.g., inclusion of a questionnaire assessing experiences of COVID-19-related stressors, such as family conflict).
+As the MobileQ application used in Wave I, was not suited for remote data collection, participants installed another experience sampling application,
+SEMA3 (Koval et al., 2019) on their own smartphone device. The sampling scheme for Wave COVID-19 was the same for Wave COVID-19, but the momentary questionnaire consisted of 40-45 items (depending on conditional branching), as well as once-a-day morning (10 items) and evening questionnaires (21 items). The questionnaire expired after 10 min, even if participants were still completing items. All data until the moment of expiry were saved on the phone. If participants had not opened the questionnaire, a reminder was sent to the participants after five minutes. Compensation for participation in this wave was a 10-euro gift voucher for an online store. For the group of 110 participants with ESM data in the COVID-19 measurement, the compliance rate was 43.6%.
+Measures
+Self-report questionnaires. Relationship quality (Wave I). The quality of the parental relationship was assessed with a Dutch version of the Inventory of Parent and Peer Attachment (IPPA; Armsden & Greenberg, 1987; translated into Dutch by Noom, Dekovic, & Meeus 1999), a 36-item selfreport questionnaire at baseline in Wave I of the SIGMA study. Both paternal and maternal relationship qualities was assessed on three dimensions: trust, communication, and alienation. To assess relationship quality, a sum score was used that added the Trust and Communication item scores and subtracted the Alienation item scores. Items were rated on a 4-point Likert-type scale (from 1 = Almost never to 4 = Almost always). For example, ‘My mother respects my feelings’ and ‘I feel angry with my mother’. As both one- and two-factor models have been suggested for the IPPA (Gandhi et al., 2016; Murphy, Laible, & Augustine, 2017), we conducted a confirmatory factor analysis to test whether a two-factor (separate father and mother relationship quality scores) or a one-factor (composite parental relationship quality score) was most appropriate. The CFA in the present study showed that a two-factor model (y2 = 1404.3, AIC = 6479.7, BIC = 6597.0) was a better fit than the one-factor model (v2 = 1559.3, AIC = 6632.7, BIC = 6747.6). Paternal and maternal relationship qualities was weakly correlated (r = .17). Internal consistency was high (Cronbach’s a = 0.90) for both the paternal and maternal relationship qualities scales.
+Family conflict (Wave COVID-19). The experience and burden of family conflict during COVID-19 were investigated with three items from the 22-item COVID-19-related stressors questionnaire
+628 JANSSENS ET AL.
+adapted from the DynaCORE survey on resilience, conducted as part of the DynaMORE project (https://dynamore-project.eu/). Parental, parentchild, and sibling conflicts were assessed using the following three items: ‘In the following, some situations are listed that people may experience due to the current COVID-19 pandemic. Please indicate if you are currently experiencing the following situations or have experienced these during the past 2 weeks in connection to the COVID-19 pandemic, and how burdensome these are/were to you: (1) conflict between your parents, (2) conflict between you and your parents and (3) conflict between you and your siblings’. Presence vs. absence of family conflict in each of the three domains was measured using a binary ‘Yes’/‘No’ item. If participants indicated ‘Yes’ when asked about presence of family conflict, the follow-up item was administered to assess the burden of the family conflict, with five response options ranging from ‘Not at all burdensome’ to ‘Very burdensome’. Cronbach’s a for the family conflict scale was 0.54.
+Age and sex (Wave I and Wave COVID-19). Given that previous research has highlighted differences in irritability, loneliness, and stress as a function of age (Friberg, Hagquist, & Osika, 2012; Van Roekel, Scholte, Verhagen, Goossens, & Engels, 2010) and sex (Friberg et al., 2012), we included these as covariates in the analysis.
+Experience sampling. Irritability, stress and loneliness (Wave I and Wave COVID-19). To investigate momentary irritability, stress, and loneliness, the following items were used: ‘I feel irritated’, ‘I feel stressed’, and ‘I feel lonely’. These three items were rated on a 7-point Likert scale ranging from 1 (Not at all) to 7 (Very much). All ESM items were presented in a fixed order.
+Missing Data
+From the 173 participants who took part in both waves, 22 were excluded due to completely missing data on their relationship quality scales and 75 had incomplete data for parental relationship quality (at least one item filled out for paternal or maternal relationship quality) assessed with the IPPA at baseline in Wave I of the SIGMA study. Therefore, these incomplete data (for n = 75) were imputed using multiple imputation by chained equations (MICE). This particular multiple imputation technique operates under the assumption that the missing data are Missing At Random and
+is recommended to address larger numbers of missing data in psychiatric research (Azur, Stuart, Frangakis, & Leaf, 2011). Within the current study, 20 imputed datasets were used to perform the analyses, and parameter estimates were pooled using Rubin’s rule (Eekhout, van de Wiel, & Heymans, 2019). All participants were asked to indicate whether they had: 1. ‘One father and one mother’, 2. ‘Two mothers’, 3. ‘Two fathers’, 4. ‘One father’, 5. ‘One mother’, or 6. ‘Other’. This provided us with information about the family situation of those who had incomplete data for parental relationship quality. Supplementary analyses showed that 63 of the 75 participants with incomplete data indicated to have both a mother and a father in their lives, 3 of them indicated ‘Other’ and 1 of them indicated having 2 fathers. These 67 participants received both questionnaires for paternal and maternal relationship qualities. The other eight participants (of the 75 with incomplete data) indicated having only one mother or one father, meaning they received only one questionnaire regarding relationship quality with their available parent. We only imputed data for paternal relationship quality if participants had completed at least one item for paternal relationship quality, and similarly for the imputation of maternal relationship quality data. Imputation was carried out in R studio (RStudio Team, 2020) with R version 4.0.3 (R Core Team, 2020) using the mice package (van Buuren & Groothuis-Oudshoorn, 2011).
+Open Science Practices
+Hypotheses and planned analyses were postregistered on the Open Science Framework (OSF) after data collection but before data were accessed and analyses were conducted (Benning, Bachrach, Smith, Freeman, & Wright, 2019), using the registration template for ESM research (Kirtley, Achter-hof, et al., 2021; Kirtley, et al., 2020; Kirtley, Lafit, et al., 2021). The postregistration is available at https://osf.io/83evy/?view_only=8cd6772e331c4b 2595158186f70bdaf7. Please see Appendix S2 for changes that were made to the registration along with the full description of CFA results, list of ESM items, missing data procedure, R codes for all main and power analyses at https://osf.io/wdkxz/?vie w_only=661b8a0c433747e68aacfdb1d85b5ffe.	ESM
+items used in Wave I of the SIGMA study are also publicly available in the ESM Item Repository (Kirtley, et al., 2020).
+Data Analyses
+For the research questions on the associations between relationship quality at Wave I and dailylife irritability, stress, and loneliness at Wave I and Wave COVID-19, we estimated linear mixed effects models, as these data have a multilevel structure with repeated measurements (i.e., observations) nested within persons. Multilevel models, that is, linear mixed effects models, enable us to analyze data that are organized at more than one level (i.e., nested data) by taking into account that observations within any given cluster at any level (e.g., observations nested within a person) can be expected to be more similar to each other than to observations within other clusters. In all multilevel models, we accounted for autocorrelation with the corAR1() component. For the analyses on relationship quality and family conflict, we used logistic regressions as the variables included were all timeinvariant. All analyses were carried out in R Studio (RStudio Team, 2020) with R version 4.0.3 (R Core Team, 2020).
+Sensitivity power analysis. As there were no data available from a pilot study nor information in the literature on effect sizes, we conducted a sensitivity power analysis to calculate the effect size that could be detected within the COVID-19 sample (N = 173). For full details on the sensitivity power analysis, see https://osf.io/83evy/?view_ only=8cd6772e331c4b2595158186f70bdaf7. For the sensitivity power analysis, the following packages were used: future.apply (Bengtsson, 2020), r2glmm (Jaeger, 2017) and nlme (Pinheiro, Bates, DebRoy, & Sarkar, 2020).
+For the hypotheses regarding the increase in irritability, stress, and loneliness, the results show that the standard linear mixed models are sufficiently powered (>.99). For the hypotheses regarding relationship quality and irritability, stress, and loneliness at T0, the results show that the standard linear mixed models are sufficiently powered (□.88) in the case of partial R2 > .02. For the hypotheses relationship quality and irritability, stress, and loneliness at T1, the results show that the standard linear mixed models have sufficient power (□.82) in the case of an effect size of partial R2 > .03. For the moderation hypotheses regarding the change in irritability, stress, and loneliness from T0 to T1 and how this is associated with parent-child relationship quality, the standard linear mixed models perform with sufficient power (> .80) in the case of partial R2 > .06.
+For the hypotheses regarding the experience of family conflict, the results show that the binary logistic regression is underpowered (0.07-0.77). For the hypotheses regarding the burden of family conflict, power could not be calculated because of convergence issues arising due to the small sample size.
+Relationship quality and irritability, stress, and loneliness. To estimate the change in levels of irritability, stress, and loneliness in daily life from Wave I to Wave COVID-19, a standard linear mixed model was performed on each outcome variable (irritability, stress, and loneliness), allowing for varying intercepts. The timepoint (0 = ‘Wave I’, 1 = ‘Wave COVID-19’) was set as the predictor, while age and sex were included as covariates in separate models for each outcome variable. Of the N = 173 participants, N = 110 had ESM data during Wave I and Wave COVID-19 and were therefore included in these three standard linear mixed models.
+The associations between the relationship quality (paternal and maternal) and the levels of irritability, stress, and loneliness at Wave I were estimated with standard linear mixed models with the levels of irritability, stress, and loneliness at Wave I all set as a separate outcome variable, allowing for varying intercepts. Paternal and maternal relationship qualities were both simultaneously set as the predictor variables in each of the three standard linear mixed models, while age and sex were included as covariates. N = 151 participants had both relationship quality and ESM data during Wave I, and were therefore included in this analysis.
+The same analyses with relationship quality (paternal and maternal) as predictors simultaneously in each model were conducted for Wave COVID-19 data for each of the three outcome variables (irritability at Wave COVID-19; stress at Wave COVID-19; and loneliness at Wave COVID-19). Age and sex were included as covariates in all three standard linear mixed models, and intercepts were allowed to vary. For these three standard linear mixed models, N = 88 participants were included because they had relationship quality data at Wave I and ESM data during Wave COVID-19.
+The moderation of relationship quality (paternal and maternal) in the change in irritability, stress, and loneliness from Wave I to Wave COVID-19 was estimated with three standard linear mixed models with the levels of irritability, stress, and loneliness all set as the outcome in separate models. The timepoint (0 = ‘Wave I’, 1 = ‘Wave
+630 JANSSENS ET AL.
+COVID-19'), paternal and maternal relationship qualities and the interaction terms (timepoint x paternal relationship quality; timepoint x maternal relationship quality) were entered simultaneously as the predictor variables with random intercepts for persons. In all three models, we included age and sex as covariates. For these moderation analyses, N = 88 participants were included. For these ESM-based analyses, the following packages were used: r2glmm (Jaeger, 2017), readxl (Wickham & Bryan, 2019), mice (van Buuren & Groothuis-Oudshoorn, 2011), mitml (Grund, Robitzsch, & Luedtke, 2019), and nlme (Pinheiro et al., 2020). For more details on how the models are expressed, see the postregistration: https: / /osf.io/83evy/?view_ only=8cd6772e331c4b2595158186f70bdaf7.
+Relationship quality and family conflict. To investigate the association between relationship quality (paternal and maternal) and the experience of family conflict, a binary logistic regression with the presence/absence of family conflict (0 = ‘Absence of family conflict', 1 = ‘Presence of family conflict') was performed for each outcome variable (parental conflict; parent-child; and sibling). Paternal and maternal relationship qualities were both set simultaneously as the predictor variables in each of the three models. In all three binary logistic regressions, N = 151 adolescents were included, and age and sex were included as covariates.
+Finally, an ordinal logistic regression with the burden of family conflict (0 = ‘No burden at all', 1 = ‘Hardly burdensome', 2 = ‘Somewhat burdensome', 3 = ‘Quite burdensome', and 4 = ‘Very burdensome') was performed for each outcome variable (parental conflict burden; parent-child conflict burden; and sibling conflict burden) to investigate the association between relationship quality (paternal and maternal) and the burden of each type of family conflict. Paternal and maternal relationship qualities were both set as the predictor variables simultaneously in each of the three models. In all three ordinal logistic regressions, N = 151 adolescents were included, and age and sex were included as covariates. Analyses from these six models were carried out in R Studio (RStudio Team, 2020) with R version 4.0.3 (R Core Team, 2020) using the following packages: r2glmm (Jaeger, 2017), readxl (Wickham & Bryan, 2019), mice (van Buuren & Groothuis-Oudshoorn, 2011), mitools (Lumley, 2019), ordinal (Christensen, 2019), and miceadds (Robitzsch & Grund, 2021). For more details on how the models are expressed, see the
+postregistration: https://osf.io/83evy/?view_only= 8cd6772e331c4b2595158186f70bdaf7.
+RESULTS
+Descriptive statistics for demographics, family conflict, relationship quality, and ESM variables in Wave I and Wave COVID-19 are provided in Table 1.
+Relationship Quality and Irritability, Stress, and Loneliness
+Results for irritability, stress, and loneliness in daily life in Wave I compared to Wave COVID-19 are presented in Table 2. Analyses revealed a significant decrease in daily-life irritability scores, and a significant increase in daily-life loneliness scores from Wave I to Wave COVID-19. Results showed no significant change in daily-life stress scores from Wave I to Wave COVID-19.
+Associations between parent-child relationship quality and the levels of irritability, stress, and loneliness are presented in Table 3. Results showed that paternal relationship quality was significantly associated with irritability at Wave I and Wave COVID-19; lower paternal relationship quality was linked to higher daily-life irritability. In addition, results showed that both paternal and maternal relationship qualities was significantly associated with loneliness at Wave I; lower paternal and maternal relationship qualities was linked to higher daily-life loneliness.
+Changes in irritability, stress, and loneliness from Wave I to Wave COVID-19 as a function of paternal and maternal relationship qualities are presented in Table 4. The interaction effect of timepoint with paternal and maternal relationship qualities was significant for loneliness; the increase in loneliness scores from Wave I to Wave COVID-19 was greatest when paternal and maternal relationship qualities was low. Figures 1-3 visualize all associations between relationship quality (paternal and maternal) and irritability, stress, and loneliness.
+Relationship Quality and Family Conflict
+Associations between paternal/maternal relationship quality and the experience and burden of family conflict are provided in Table 5. The analyses showed that the associations between paternal/ma-ternal relationship quality and the experience of family conflict were not significant. However,
+results showed significant associations between relationship quality and the extent to which family conflict was experienced as a burden; lower paternal relationship quality was associated with experiencing parental and sibling conflict as more of a burden and lower maternal relationship quality was associated with adolescents experiencing parent-child conflict as more of a burden.
+DISCUSSION
+In line with the hypotheses, the current study found an increase in daily-life loneliness from before to during COVID-19. However, the results
+showed no change in daily-life stress scores and a decrease in daily-life irritability scores from before to during COVID-19. In addition, we found that low paternal relationship quality was associated with irritability scores in daily life during Wave I and Wave COVID-19. Both low paternal and maternal relationship qualities was associated with loneliness scores during Wave I. Results confirmed the expected buffering effect of paternal and maternal relationship qualities for loneliness. Nevertheless, while associations between relationship quality and these outcomes were statistically significant, the amount of variance explained by relationship quality was small. Given the small effect sizes,
+not all of our models had sufficient power to reliably detect these effects. Therefore, results should be interpreted with caution and require replication in more highly-powered studies. Regarding the expected associations of father and mother relationship quality with experiences of COVID-19-related family conflict and its perceived burden, our results showed that low paternal relationship quality was positively associated with experiencing parental and sibling conflict as a burden, while low maternal relationship quality was positively associated with experiencing parent-child conflict as a burden.
+Irritability, Stress, and Loneliness
+The finding that adolescents reported feeling lonelier during the COVID-19 pandemic, in comparison with before the pandemic, is in line with emerging literature and suggests that adolescents are vulnerable to the detrimental effects of the COVID-19 pandemic and its associated physical distancing measures (Van Bavel et al., 2020; Brooks et al., 2020; Gunnell et al., 2020; Nelson, Pettitt, Flannery, & Allen, 2020). However, the effect size for the increase in loneliness was small. Coupled with our results showing no significant increase in stress and decreased irritability during COVID-19 relative to before the pandemic, this may suggest that increases in psychological distress during the early phase of the pandemic were minimal and specific. While this may bring some relief, we must be careful not to be complacent—our results only reflect the situation in the early phase of the pandemic and the small observed increase in loneliness may have grown as the pandemic progressed. Previous
+research in adolescents showed that loneliness is associated with negative mental health outcomes months or even years in the future (Hawkley & Cacioppo, 2010; McClelland et al., 2020) and as many of the pandemic’s potential negative consequences are anticipated to follow later, after the initial acute phase of the pandemic (Brooks et al., 2020; Gunnell et al., 2020), continued monitoring of loneliness and other indicators of psychological distress is essential.
+The lack of an increase in stress and the decrease in irritability is consistent with Achterhof, Myin-Germeys, et al.s’ (2021) findings that indicate a decrease in anxiety symptoms in adolescents from Wave I to Wave COVID-19, within the same sample used in the current study. This may indicate a ‘positive’ side-effect of the national lockdown, as this eliminated two well-known triggers for stress and irritability in adolescents, that is, school and social contact. For example, adolescent studies show that the pressure of high demands at school—much more than those at home—is a major source of stress in their lives (Modin, Ostberg, Toivanen, & Sundell, 2011; Wiklund, Malmgren-Olsson, Ohman, Bergstrom, & Fjellman-Wiklund, 2012). Another study showed that adolescents’ irritability is mainly triggered in a social environment (Toohey & DiGiuseppe, 2017). Consequently, while some aspects of the pandemic might have increased stress (i.e., worrying about their own and others’ safety, as well as their education ), others might have decreased stress (i.e., not being at school and reduced social interaction), resulting in stress scores remaining stable between Wave I and Wave COVID-19. Additionally, this finding, along with the increase in loneliness and decrease in
+irritability, highlights the complexity of social interactions and experiences during adolescence. During this age period, social contact is indispensable, but at the same time, these interactions are accompanied by stress and irritability in growing adolescents (Steinberg & Morris, 2001). Consequently, lockdown measures could increase loneliness in adolescents because their social needs are not being met, but on the other hand, may bring some relief due to reducing the stress and irritability that come with school and social interaction.
+However, this should not be interpreted as indicating that closing schools and limiting social contact benefits adolescents by eliminating stress. In fact, both ‘stressors’ are important for adolescents’ development and the stress they elicit is adaptive for the development of social and stress-regulating skills (Andrews et al., 2020; Steinberg & Morris, 2001). The timing of our study should also be considered when interpreting the results; it took place during the first national lockdown, and at the time, there was the prospect of relaxation in the restrictions. While adolescents who were feeling more stressed and irritable may have felt unable to participate in the COVID-19 wave of this study, our results suggest this was not the case. Adolescents from the COVID-19 sample did not significantly differ in terms of irritability and loneliness from adolescents in the full Wave 1 sample who did not take part in the COVID-19 study—they even felt less stressed at Wave I in comparison with adolescents who did not participate in the COVID-19 wave. See the OSF project page for the study for these supplementary analyses: https://osf.io/wd kxz/?view_only=661b8a0c433747e68aacfdb1d85b 5ffe. Although another study found that adolescents in the COVID-19 sample scored significantly higher on psychopathology at Wave 1 compared to participants who did not take part in the COVID-19 study, once age and sex were taken into account, this was no longer the case (Achterhof, Myin-Germeys, et al., 2021).
+Relationship Quality and Irritability, Stress, and Loneliness
+Our finding that adolescents with a lower-quality relationship with their father reported higher levels of irritability in daily life during Wave I and Wave COVID-19, converges with findings from previous cross-sectional literature (Brumariu & Kerns, 2010; Shpigel et al., 2012). Given that irritability may be a precursor symptom of developing mental health problems (Brotman et al., 2017; Stringaris et al.,
+b
+2018), these findings may point toward low paternal relationship quality as a vulnerability factor for psychopathology, as it may increase feelings of irritability in adolescent daily life. However, we found no significant association between maternal relationship quality and irritability.
+We also found that adolescents with a lower quality relationship with their father or mother reported higher levels of loneliness during Wave I, which is consistent with previous research that suggests a negative effect of low parental relationship quality on loneliness in middle and late childhood (de Minzi, 2006). Conversely, the perception of acceptance from both parents, as well as trust in their love, protects children against loneliness (de Minzi et al., 2006).
+In addition, our findings suggest a small buffering effect of high paternal and maternal relationship qualities for the increase in loneliness scores from Wave I to Wave COVID-19. This indicates that adolescents with higher quality paternal and maternal relationships were slightly more protected against an increase in loneliness during the first national lockdown in comparison with adolescents with a lower paternal and maternal relationship qualities.
+These findings may add to a growing body of literature on the association between relationship quality and emotional experiences in everyday life (Sheinbaum et al., 2015; Torquati & Raffaelli, 2004) and on the buffering effect of high-quality relationships on the mental well-being of adolescents (Bowlby, 1973; Shpigel et al., 2012). This increases
+insights into the vulnerability and protective factors for prediagnostic precursors of actual psychopathology, that is, loneliness. Moreover, these results seem to support the importance of a high-quality relationship with parents to help adolescents weather personal adversity.
+Relationship Quality and Family Conflict
+Results within this sample show no significant associations between parental relationship quality and the experience of COVID-19-related family conflict. However, the amount of reported family conflict appeared high in this sample: 36.8% of adolescents reported parental, 61.4% parent-child and 57.9% sibling COVID-19-related conflict. Therefore, it is possible that the pandemic has increased the conflict in all families irrespective of relationship quality. Unfortunately, as there was no measure of family conflict at Wave 1, we could not investigate whether family conflict had increased from pre- to mid-pandemic. This hypothesis should be addressed in future studies with family conflict data across multiple time points.
+On the other hand, results showed significant associations between parental relationship quality and the burden of COVID-19-related family conflict. Low paternal relationship quality was associated with greater experienced burden of parental (between-parents) and sibling conflict, while low maternal relationship quality was associated with higher burden of parent-child conflict. These findings converge with recent research on parenting
+638 JANSSENS ET AL.
+and the experience of social interactions in the SIGMA Wave I sample (N = 1913) that showed that paternal autonomy support, which is related to higher paternal relationship quality, was linked to the experience of nonparent social interactions, that is, interactions with individuals other than their parents (Achterhof, Myin-Germeys, et al., 2021). Additionally, maternal responsiveness, a parenting style that increases maternal relationship quality, was related to adolescents' experiences of interacting with their parents. As a consequence, our findings fall in line with this as also in the present study the paternal relationship is associated with the experience (i.e., the burden) of nonparent interactions, namely sibling and between-parent interactions (i.e., conflict), while the maternal relationship is associated with parent-child interactions (i.e., conflict). Our findings may be explained by the distinct but complementary caregiving roles that fathers and mothers often adopt (Kerns, Mathews, Koehn, Williams, & Siener-Ciesla, 2015), which may influence different domains—and interactions —of adolescents' life (Palm, 2014). Whereas mothers generally function as a safe haven (i.e., listens, comforts, and shows availability) that the child seeks in times of need, fathers are generally experienced as a play mate that functions as a secure base (i.e., sets boundaries, gives trust and supports autonomy) from which the child explores the world and engages in social relationships and interactions outside the parent-child relationship. These findings support the importance of both paternal and maternal relationship qualities for the extent to which family conflict is experienced as burdensome (Hannum & Dvorak, 2004; Shpigel et al., 2012). This highlights the relevance of improving both the quality of the paternal and maternal relationships (i.e., holistic family approaches) when family conflict occurs and is experienced as burdensome in adolescents.
+Strengths and Limitations
+The current study has several strengths. First, the data used within this study originate from an ongoing longitudinal cohort study with unique data from a subgroup of adolescents from before and during the COVID-19 pandemic, which enables the delineation of pre-existing (i.e., prepandemic) vulnerabilities for irritability, stress, loneliness, and family conflict during COVID-19. Second, using ESM to assess daily life levels of irritability, stress, and loneliness in adolescents increases ecological validity and reduces recall bias. We did not
+assess family conflict in daily life, primarily in order to minimize participant burden due to an already long ESM questionnaire. While momentary assessments of family conflict may have yielded different results, research by Chung, Flook, and Fuligni (2009) suggests that episodes of family conflict are rather rare events in adolescents' daily life, and may not be best captured by momentary assessments. Third, the study was postregistered, a form of preregistration occurring after data collection (Benning et al., 2019). All research questions, hypotheses, and analysis plans were determined and documented prior to data access, reducing the chances of data-dependent decision-making and as such, researcher degrees of freedom. Additionally, all analysis code has been made available on the OSF, further increasing the transparency of this research. Given that routine use of open science practices in clinical psychology and developmental psychology is still the exception rather than the rule (e.g., Tackett, Brandes, & Reardon, 2019), we feel the open science approaches used in the current study are a major strength.
+Although the current study has several strengths, the findings should be interpreted within the context of its limitations. First, parent-child relationship quality was only assessed during Wave I and not during Wave COVID-19. Although, traditionally, parent-child relationship quality is hypothesized to be a stable characteristic in children (Bowlby, 1973), more recent research on this topic challenges the stability of relationship quality (Davila & Sargent, 2003). This raises the possibility that parent-child relationship quality may have changed between Wave I and Wave COVID-19. Therefore, it might be fruitful to assess the dynamic nature of the relationship quality over both the long term (using longitudinal studies) and the short term (using Experience Sampling or daily diaries). Additionally, the analyses involving relationship quality were somewhat underpowered, due to a moderate level of missing data for this variable at Wave I.
+Second, family conflict was measured by asking participants about the presence or absence of family conflict in relation to the COVID-19 pandemic. As a result, participants may have interpreted the question differently and assumed that questions were about conflicts about or directly related to the pandemic. This narrows our measurement of family conflict, and therefore, the level of family conflict reported may be an underestimation. Also, the items used to assess family conflict were only included in Wave COVID-19, precluding
+comparison with family conflict in Wave 1. Further, the family conflict items were taken from a larger scale assessing COVID-related stressors and internal consistency for these items was low. Future research would benefit from employing well-validated measures of family conflict, which assess the construct more fully.
+Third, although it is common in ESM research to use single items to minimize participant burden (Wright & Zimmermann, 2019), irritability, stress, and loneliness may be better captured with multiple items. Optimal items and combinations of items for assessing these constructs should be substantively investigated in future research. Fourth, the ESM compliance rates were lower in both studies (39.5% in Wave I with N = 1913 and 43.6% in Wave COVID-19, with N = 110) than would be expected from previous ESM studies conducted with adults, in both general and clinical samples (Rintala, Wam-pers, Myin-Germeys, & Viechtbauer, 2019). There may be several reasons for this, including the short time in which ESM questionnaires were available to participants (questionnaires had to be started within 90 s of the notification). The length of the questionnaire may also have played a role, as recent research by Eisele et al. (2020) in young adults demonstrated that questionnaire length negatively impacts compliance. Moreover, participants were also asked to complete ESM during school hours, and even though schools agreed to this, there may still have been barriers to completion of ESM questionnaires during lessons. We also did not incentivize compliance which may result in lower ESM compliance rates, but—in comparison with other ESM studies—we believe it enhanced our data quality. Last, nonsignificant results should be interpreted within the context of the effect sizes, as some hypotheses were not sufficiently powered to detect small effects while these may have been detected in a larger sample.
+FUTURE RESEARCH
+Given that the current study is limited only to the period of the first national lockdown and the COVID-19 pandemic appears to be not only invasive but also long-lasting, understanding how this crisis affects adolescents' mental health and family relationships over time is important. Further insights into the impact of COVID-19 on adolescents' daily life outcomes and family conflict, and the specific roles of father and mother relationship quality, require well-powered, longitudinal studies, with multimethod approaches, to investigate
+whether adolescent and family well-being worsens or recovers from this global crisis, for example, large ongoing cohort studies of youth mental health, for example, SIGMA (Kirtley, Achterhof, et al., 2021; Kirtley, et al., 2020; Kirtley, Lafit, et al., 2021). ALSPAC (ALSPAC Study Team, 2001; Kwong et al., 2021) and ABCD (Karcher & Barch, 2021) can provide opportunities to look at the evolution of psychosocial distress over time.
+IMPLICATIONS
+Findings from the current study can provide researchers, clinicians, parents, adolescents and policy makers with insights into the impact of the COVID-19 pandemic and its measures on adolescents' daily life experiences and their families during the first national lockdown. Even though during this first lockdown, adolescents were not as affected as we would have expected, we caution against complacence regarding young people's mental health and well-being, given that the most deleterious effects of the pandemic may only emerge much later (Brooks et al., 2020; Gunnell et al., 2020). Our findings indicated a small but statistically significant contribution of both paternal and maternal relationship qualities to adolescents' daily-life experiences, and we emphasize the need for holistic family therapy approaches—including both fathers and moth-ers—to improve relationship quality in adolescents facing adversity. Before these findings are translated into practice, further replication is essential.
+CONCLUSIONS
+The current study provides small, but positive support for the prediction of an increase in daily-life levels of loneliness from before to during COVID-19 and for the role of both paternal and maternal relationship qualities as a moderator in this relationship. In addition, the current study provides no evidence for change in daily-life stress and finds a decrease in daily-life irritability from before to during COVID-19. Also, an association between paternal relationship quality and irritability is demonstrated as well as an association between both paternal and maternal relationship qualities and daily-life loneliness. No significant associations were found between parental relationship quality and the frequency of family conflict; however, findings from the study do provide evidence for a link between both paternal and maternal relationship qualities and how burdensome family conflict was experienced. The findings of this study may
+suggest that the impact of the pandemic on the daily lives of adolescents during the first national lockdown is not as bleak as what was expected. However, continued monitoring of young people’s well-being and mental health is still warranted, as our results only reflect the situation during the early phase of the pandemic, which may have changed as the pandemic progressed. Future research on the link between relationship quality and adolescent’ daily life experiences and family conflict in large cohort studies is needed to see how this evolves as the pandemic progresses.
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+644 JANSSENS ET AL.
+health complaints in older adolescents are related to perceived stress, anxiety and gender - a cross-sectional school study in Northern Sweden. BMC Public Health, 12 (1). https://doi.org/10.1186/1471-2458-12-993
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+Appendix S1. Overview of self-report questionnaire measures used in the full SIGMA Wave I and Wave COVID-19 studies.
+Appendix S2. Deviations from post-registration.
+Supporting Information
+Additional supporting information may be found online in the Supporting Information section at the end of the article.
+15327795, 2021, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jora.12657 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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+On March 31, 2017, Netflix released its 13-part show 13 Reasons Why. The show describes the events leading up to and the aftermath of the suicide of a character, 17-year-old Hannah Baker, who left her personal story and reasons for her suicide on audiotapes. The tapes are directed at specific people, explaining their roles in Hannah’s death, and each of the tapes provides the context for an episode. The show was one of the most watched shows in 2017, generating more than 11 million Tweets within 3 weeks of its release alone.1,2 It also sparked immediate criticism from mental health and suicide prevention organizations for not following recommendations on responsible media portrayal of suicide.3 In particular, concerns were raised that the graphic depiction of Hannah cutting her wrists in the bathtub, and the implication that seeking help for suicidal thoughts is futile, might trigger imitation acts and additional suicides.3
+Little evaluation has been conducted of the consequences of 13 Reasons Why, largely owing to the lags in availability of suicide data. In general, fictional portrayals of suicide have not been found to be consistently associated with suicides. Specifically, a recent meta-analysis of studies did not support contagion by fictional media.4 However, the conclusion in that meta-analysis appeared to be too strong, given that some studies do suggest that entertainment media can be a factor in subsequent suicides.5-7
+The 7 published studies and reports into 13 Reasons Why focused on suicide attempts, suicidal ideation, and some other outcomes and had mixed results.8-14 They generally suggested that the show placed vulnerable members of the audience at excess risk.8-13 In particular, the show appeared to be associated with increased hospitalizations for suicide attempts and self-harm.8 By contrast, a study commissioned by Netflix suggested that the show was associated with improvements in empathy toward others in some segments of the audience who were potentially struggling with depression.14
+An overview of all 6 available studies that present quantitative findings is provided in Table 1. Any observational study examining the potential associated effects of a suicide depiction, such as in 13 Reasons Why, across a population carries a substantial risk ofconfounding.Nevertheless,effortstodescribe the associations between exposures (such as the show) and health outcomes in different regions are important because consistent findings across studies may help to clarify if the associations maybe causal.
+The current study is crucial to that effort as it overcomes the limitations of previous studies by explicitly examining the association between the release of 13 Reasons Why and actual suicides and doing so in the country (United States) in which the show takes place. Observers have called for nationwide analyses of death data given the widespread belief that 13 Reasons Why could trigger suicides in the vulnerable younger population.3,15,16 Such studies had not been possible until the recent release of 2017 suicide data by the Centers for Disease Control and Prevention.
+Methods
+No protocol approval was needed for this study in accordance with the Declaration of Helsinki.17 The data used were deidentified mortality data obtained from a secondary source.
+Key Points
+Question Was the release of the Netflix show 13 Reasons Why associated with excess suicides in the United States?
+Findings In this time series analysis ofmonthly suicide data from 1999 to2017, an immediate increase in suicides beyond the generally increasing trend was observed among the target audience of10- to 19-year-old individuals inthe3 months after theshow’s release. Age- and sex-specific models indicated that the association with suicide mortality was restricted to 10- to 19-year-old individuals, and proportional increases werestronger in females.
+Meaning The increase in suicides in only the youth population and thesignalof a potentially larger proportional increase in young females all appeared to be consistent with media contagion and seem to reinforce the need for safer and more thoughtful portrayal of suicide in the media.
+Viewership Over Time
+Viewership data for 13 Reasons Why can strengthen models of the show’s possible associated effects; however, Netflix does not publicly share statistics that would allow a direct measurement of the viewership of 13 Reasons Why in the United States.18 However, it is possible to use a proxy to estimate the amount of attention the show received through social media, namely Twitter and Instagram, which are 2 of the most popular platforms frequented by US adolescents. In particular, 72% of US adolescents aged 13 to 17 years reported using Instagram.19
+In January 2019, we used the advanced search interface on Twitter to retrieve original Tweets in the English language that contain references to the show or its main characters. Our search terms were 13RW, 13 Reasons Why, Thirteen Reasons Why, Hannah Baker, and Clay Jensen. This search allowed us to generate an exhaustive data set with all mentions of the show, excluding Tweets produced by accounts that Twitter considered malicious bots, up to the retrieval date. This method was used to gather 1416175 Tweets, generated by 870 056 users, for the period April 1, 2017, to June 30, 2017.
+To measure the attention received on Instagram, we used data from InfluencerDB, a company that owns a database that includes an exhaustive record of metadata of media posted on Instagram by influencers (ie, users with at least 15 000 followers). We processed the data for April to June 2017, selecting content with mentions of the show similar to those on Twitter. We further filtered non-English content with the textcat R package (R Foundation for Statistical Computing), yielding a data set of 26 322 Instagram posts produced by 7875 influencers.
+Figure 1 shows the weekly number of Twitter users and Instagram influencers who posted about 13 Reasons Why for the first time between April 1, 2017, and June 30, 2017. Social media attention peaked in April, in which 84% of initial Tweets and 74% of initial Instagram posts about the show occurred. This general trend is supported by Netflix, which reported that the show was the third most binge-watched on Netflix in 2017.20 Thus, this analysis consideredthe exposure to the show to be sudden during April 2017. Because of the absence of social media attention after June 2017, we defined the exposure window as April to June.
+Suicide Data and Statistical Analysis
+We downloaded monthly suicide data from the Centers for Disease Control and Prevention WONDER (Wide-ranging Online Data for Epidemiologic Research) system21 for the period January 1,1999, to December 31,2017. Suicide data were extracted for the age groups 10 to 19 years (the main target audience for 13 Reasons Why), 20 to 29 years, and 30 years or older for both males and females. Identification with the life circumstances of a high school student like Hannah Baker and related issues such as school bullying were expected to be most prominent among individuals aged 10 to 19 years. Therefore, the prespecified hypothesis of this study was that any potential associated effects of 13 Reasons Why would be most pronounced in the 10- to 19-year age group. Similarly, we expected the consequences to be stronger in females, owing to the show’s focus on Hannah’s suicide. We also extracted data on suicide methods for the 10-to 19-year age group, including cutting (the method of suicide used by Hannah), hanging, and shooting with firearms.
+Time series models were fitted to the data, according to the analysis of the pre-April 2017 period. For the selection of models, we used SPSS Expert Modeler function, version 25 (IBM), to choose the model with the lowest Bayesian information criterion value, highest stationary R2 value (the variance accounted for by the fitted time series model), and a not sig-
+nificant Ljung-Box Q statistic (indicating whether residuals couldbe assumedwhite noise, with stated df). The models were subsequently fitted to the full time series. On the basis of social media data shown in Figure 1, we investigated a temporary association of the release of 13 Reasons Why with suicides (1) for April 2017, which was consistent with the period of strong interest in the show, and (2) for April to June 2017,
+which included the total period with some indication of public interest in the show. We used dummy variables to model these associations as discrete pulses and calculated the number of excess suicides for each model. Two-sided tests of significance were performed. P < .05 was considered significant.
+Results
+Observed suicides from April to June 2017 exceeded the 95% CIs of model forecasts fitted to pre-April 2017 data for 10- to 19-year-old males and females (Figure 2B, D). This observa
+tion was also true for the suicide method of hanging in this age group (Figure 2F).
+Models including a discrete pulse for April (Figure 2B, D, and F) indicated 38.2 (95% CI, 10.5-65.9) excess suicides among 10- to 19-year-old individuals of both sexes (14.6% increase; 95% CI, 4.0%-25.3%). Gender-specific models indicated 27.9 (95% CI, 2.3-53.5) excess suicides among males (14.2% increase; 95% CI, 1.2%-27.3%) and 16 (95% CI, 3.5-28.4) excess suicides among females (27.1% increase; 95% CI, 6.0%-48.2%).
+Models testing discrete pulses from April to June 2017 indicated 94.4 (95% CI, 39.3-149.6) excess suicides among 10-to 19-year-old individuals in the 3-month period after the
+show’s release, corresponding to an increase of 13.3% (95% CI, 5.5%-21.1%) when compared with the expected number of suicides. For 10- to 19-year-old males, the model indicated 66 (95% CI, 16.3-115.7) excess suicides (12.4% increase; 95% CI, 3.1%-21.8%). Among females, 37 (95% CI, 12.4-61.5) excess suicides were estimated (21.7% increase; 95% CI, 7.3%-36.2%). No associated differences in suicide mortality were seeninthe 20-to 29-year and the 30-year-or-older age groups (Table 2).
+With regard to suicide methods, cutting (the method portrayed in the show) was rare, with typically no more than 2 cases per month among individuals in the 10- to 19-year age group. Because of the low number of suicides by cutting, these data were not amenable to time series analysis. Increases in suicide by hanging were found. The model testing a discrete pulse in April 2017 indicated 34.7 (95% CI, 16.8-52.7) excess suicides by hanging (33.6% increase; 95% CI, 16.2%-51.0%) in the month with the highest volume of public attention to the show. The model testing 3-month associated suicide mortality estimated 79.8 (95% CI, 45.6-114.1) excess suicides by hanging (26.9% increase; 95% CI, 15.3%-38.4%). No associations were seen for suicide by firearm.
+Robustness Analysis
+The skewness of the time series data ranged from 0.33 (females >30 years) to 1.11 (all 10- to 19-year-olds; males 10-19 years of age). When a square root transformation was applied to reduce the possible consequence of nonnormality, all associations reported in Table 2 retained statistical significance,
+except for the 1-month period of April 2017, among the 10- to 19-year-old males and females, which only closely missed nominal significance. The specific parameter estimates (with SEs; all on a square root scale) of discrete pulses were as follows: All aged 10 to 19 years 1-month estimate, 1.08 (0.54; P = .045), and 3-month estimate, 3.01 (1.10; P = .007); males aged 10 to 19 years 1-month estimate, 0.91 (0.56; P = .11), and 3-month estimate, 2.48 (1.09; P = .02); females aged 10 to 19 years 1-month estimate, 0.86 (0.53; P = .10), and 3-month estimate, 2.24 (1.04; P = .03); hanging among all youths aged 10 to 19 years 1-month estimate, 1.13 (0.52; P = .03), and 3-month estimate, 4.05 (1.55; P = .01).
+Discussion
+To our knowledge, this study is the first to investigate the association between 13 Reasons Why and suicides in the United States. Although these results must be interpreted with substantial caution, they do identify a rise in youth suicides above and beyond the generally increasing trend inthe country.22 This increase was concurrent with the period of strongest interest in the show, as reflected by Instagram and Twitter data, and occurred only in the age group targeted by the show. Time series modeling from April to June 2017 suggested the magnitude of increase was 13.3% in those aged 10 to 19 years, which would be meaningful from a clinical and public health standpoint at any value within its 95% CI (5.5%-21.1%).
+Ecological studies have inherent limitations; however, we believe this method is the best available to answer the research questionposed here. A detailedexaminationofthe findings may help to clarify the degree of confidence with which to conclude that the association between 13 Reasons Why and increased suicides is causal. The immediate increase in suicides after the release of 13 Reasons Why among this age group is consistent with the prespecified expectation. Studies on how people self-select for online content strengthen the argument that most viewings of the show (and therefore potentially harmful exposures) occurred in April 2017, when attention on social media was greatest.23 Previous research on suicide contagion subsequent to fictional media portrayals has generally found that the associations were strongest in the first month after public release.5,6 However, 13 Reasons Why was a media phenomenon, which remains available on Netflix, that generated unusually intense press interest for months and was expected to have implications beyond the first month. As indicated by social media data, the associations might have been present for at least 3 months, until June 2017, when social media interest inthe show was reduced. Therefore, the timing of the observed associations is consistent with possible contagion by media.
+With regard to the specificity of these associations, young people were the clear target demographic of 13 Reasons Why, which portrayed issues such as bullying at schools and life problems in adolescence. Increases in suicide were seen only in this age group with no associations observed for individuals aged 20 to 29 years and 30 years or older, and this finding is potentially consistent with contagion by media.
+Potentially greater proportional increases in suicides among females were noted. Previous research indicated that contagion by media most likely (but not exclusively) occurs among individuals of the same sex and age as fictional characters who die by suicide.5 There is no expectation that this association would be exclusive to females, given that some of the life problems presented as causes of Hannah’s suicide and discussed in the show (eg, bullying) similarly adversely affect both female and male adolescents.24 The increase in male suicide may, in part, reflect that suicide deaths are more prevalent in male adolescents, whereas females have higher rates of suicide attempts, which were not analyzed in this study.25
+Hanging stood out as the method associated with increased suicides among 10- to 19-year-old individuals in the months after the release of 13 Reasons Why. If the association were causal, the inference may be that suicide increases should occur by cutting (the suicide method depicted in the show) rather than hanging. However, cutting is a method with generally low lethality and may be more likely to rise in suicide attempt rather than suicide death data. Research indicates that cutting has the lowest case fatality rate among suicide methods.26 In contrast, hanging is one of the most lethal methods,26 and the availability of hanging is high. Furthermore, research conducted immediately after the release of 13 Reasons Why indicated that web searches for suicide methods and queries on how to kill oneself increased immediately after the release of the show in the United States.1 Hannah’s controversial suicide scene was discussed on social media, and
+the discussions highlighted that the method was difficult to carry out.27
+Taken together, the findings may reflect a form of selection bias, highlighting only the increases in the most common method of suicide death in adolescents but offering no information on changes in low-lethality methods that would have been present in suicide attempt data. In support of this conjecture, public mass media that speculated on the potential association between youth suicides and the show repeatedly reported about teens who died by hanging in the aftermath of the release of the show.28-30
+Implications for Suicide Prevention
+This study does not provide definitive proof that 13 Reasons Why is associated with harmful outcomes, but the findings are sufficiently concerning so as to warrant greater care and attention by Netflix and other entertainment producers. These findings support the urgent necessity for active engagement between those in the entertainment industry and mental health and suicide prevention experts to minimize or avoid potentially harmful suicide portrayals. In particular, media recommendations for responsible reporting of suicide in the news are readily available,31,32 but few resources are provided for those who create content in the entertainment industry.33,34 National recommendations for depicting suicide with a specific focus on the entertainment industry were recently released by the National Action Alliance for Suicide Prevention.35 Strong collaborations between different sectors could result in on-screen portrayals that not only do no harm but also act as a force for good in suicide prevention.
+Strengthsand Limitations
+A strength of this study was the length of the time series analysis data set: It used monthly data of 19 years to estimate expected suicide counts. Time series models can produce accurate estimates without measuring exogenous variables, and they control for issues such as autocorrelation and seasonal changes in suicide. The structural characteristics of the time series, including trends, temporal fluctuations, and seasonality (eg, known spring peaks in adolescent suicides) were adequately adjusted for in autoregressive integrated moving average time series models, as applied here.
+The main limitation of the study was that it was based on ecological data. Thus, it was not possible to ascertain whether the excess suic ide decedents had actually watched 13 Reasons Why. Furthermore, viewership data of the show were not available, and therefore the timing of exposure was modeled only through the proxy of interest on social media. The ecological nature of the study also meant that this study could identify only associations and not causation. Many factors are associated with suicide across any population, let alone a country the size of the United States. The wide CIs of the time series analyses underscore this point. The models could not account for other suicide-related media events that occurred during the study period that might have affected suicide counts. For example, on April 28, 2017, the rapper Logic released his song 1-800-273-8255, which shared the telephone number for the National Suicide Prevention Lifeline. The
+release was followed by the second-highest call volume in the history of the service, and overall calls to the hotline rose approximately 33% over the corresponding time in 2016.36 This outcome might have helped mitigate any harmful consequences of 13 Reasons Why. Furthermore, mental health and suicide prevention organizations shared material for educating teachers, adolescents, clinicians, and parents about how to discuss the show in schools,3 and Netflix added content warnings to the show in May 2017.37
+Although it is impossible to account for all potential confounding variables, it is notable that the timing, specificity, and magnitude of the associations observed here are all consistent with a potential contagion by media. This finding would be strengthened by other well-designed studies in other countries with high Netflix viewership. Because it was not possible to do a randomized clinical trial of 13 Reasons Why to examine outcomes such as suicide, for practical and ethical reasons, ecological studies like the present study (in which it is unknown whether those who died from suicide actually watched the show) or individual-level studies that use an
+alternative outcome to suicide will remain necessary in informing researchers and policymakers.
+Conclusions
+To our knowledge, this study is the first to examine the associations between suicides and the release of 13 Reasons Why in the United States. The associations identified here must be interpreted with a substantial degree of caution, but they do appear to demonstrate an increase in suicides that is consistent with potential contagion by media. Specifically, excess suicides of approximately 15% occurred in the first month after the show’s release in the main target group, 10-to 19-year-old individuals. Significant associations were present for all of the 3 months in which the show was discussed on social media. Our findings appear to point to the need of engagement by public health and suicide experts to engage with members of the entertainment industry to prevent further harmful suicide portrayals.
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+sexual abuse. More prospective studies are needed to elucidate the relative importance of risk accumulation and risk specificity for youth suicide.
+Keywords Suicidal behavior • Adolescence • Life adversities • Abuse • Maltreatment
+Introduction
+Adolescence is a period of changes that identifies the transition from childhood to adulthood. The need for independence and the acquisition of new abilities associated with several physical and brain changes during this critical period prepare the individual to assume adult roles [1]. Adolescence and early adulthood is also a period of increased vulnerability to mental ill health partly because of biologically based changes in brain structures involved in emotional/motivational functions that contribute significantly to risk-taking behaviors and sensation seeking [1, 2]—and a period of increased exposure to adverse life events, which may raise independently the risk of mental ill health [1]. A life event may be generally defined as “a detectable occurrence representing discrete changes in the subject’s social or personal environment that is external and verifiable rather than internal or psychological” [3].
+Adverse life experiences during development may induce significant biological changes (biological embedding) and modify the maturation and responsiveness of allostatic systems, thus exerting long-term effects on nervous, endocrine, and immune systems [4]. This is one of the reasons why exposure to adverse life events has been implicated not only in the development of several psychopatho-logical disorders during adolescence and early adulthood— such as major depression, anxiety, disruptive behavior
+[5], antisocial behavior and substance abuse/dependence [6], psychosis [7], and suicidal behavior [8, 9]—but also in physical ill health [10, 11]. For example, Flaherty and colleagues [10] found that more than 90 % of young adolescents in their sample had experienced some adversity, such as physical abuse, sexual abuse, psychological abuse, neglect, parental substance use, parental depression, or parental criminality, and more than 25 % had at least one health problem.
+Suicide is the second most common cause of death during this period of life, the third cause of death in male adolescents (after car accidents and violence) and the first in female adolescents aged 15-19 years [12]. Major risk factors for youth suicidal behavior not only include sociodemographic, educational, psychiatric, and psychological vulnerabilities, but also family adversity, interpersonal difficulties among peers, and adverse life events [12] including specific adverse experiences, such as sexual or physical abuse [13-15] and maltreatment or neglect [8]. The association between these experiences and youth suicidality has received much attention. Several cross-sectional studies have found that sexual abuse is an independent predictor of suicidality in adolescence/early adulthood even after controlling for the presence of risk factors, such as major depression, hopelessness, and other life adversities [16, 17]. There are also longitudinal studies demonstrating a link between physical/sexual abuse and neglect and youth suicidality [18-24]. Some studies also investigated the roles of abuse and neglect relative to other adverse experiences. For example, Thompson et al. [23] reported the existence of a significant link between cumulative lifetime adversities and suicidal ideation. Individually, however, the most predictive adversities of suicidal ideation were childhood physical abuse, childhood neglect, childhood family violence, childhood residential instability, adolescent physical abuse, adolescent sexual abuse, adolescent psychological maltreatment, and adolescent community violence. Nonetheless, the role of specific as opposed to cumulative life adversities in youth suicidality is still poorly understood. With this systematic review, we sought to investigate the association between the type and number of adverse life events and experiences and suicidal behavior in young people.
+Methods
+Eligibility criteria
+To achieve a high standard of reporting, we adopted the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ (PRISMA) guidelines [25]. Adverse life events were as follows: (1) maltreatment and violence (sex-ual/physical/emotional abuse, emotional/physical neglect,
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+witnessing home/community aggression); (2) loss events (separations, death of a parent or close friend); (3) intra-familial problems (parental divorce, family instability, social or economic problems); (4) school and interpersonal problems (failure of grade in school or in exam, breaking up with a close friend, and poor social relationships).
+We included studies that explicitly mentioned the association between negative/adverse life events and suicidal behavior (OR suicidal ideation OR suicidal thoughts OR suicide attempts, and excluding completed suicides) in clinical and non-clinical samples (for more details see below) aged 10-25 years.
+We excluded studies on completed suicides through the psychological autopsy method because assessments of life events relevant to the decedent are frequently dependent on second-hand reporting. Based on the current literature, prospective surveys registering suicide victims have been also excluded as not focused on suicidality among those aged 10-25 years.
+When a title or abstract seemed to describe a study eligible for inclusion, the full-text article was obtained and carefully examined to assess its relevance for our review.
+Specifically, our exclusion criteria were as follows: (1) studies using adult (>25 years) samples; (2) studies published before 1980; (3) studies without abstracts or with abstracts that did not explicitly mention the association between suicidal behavior in adolescence/early adulthood and negative/adverse life events or life adversities; (4) studies that were not published in English; (5) studies including subjects who died by suicide and using the psychological autopsy method.
+Information sources
+We conducted a systematic search of 4 major electronic databases comprising medical and social science studies (PubMed, Scopus, Science Direct, and PsycINFO) for titles and abstracts (January 1980-January 2015) relevant to our research question. We additionally hand-searched bibliographies from retrieved articles and from published reviews. We also contacted study authors for further details about the included studies.
+Search terms
+The following search query was used in Pubmed: adolescent (MeSH) AND Suicide (MeSH) AND (epidemiology OR rates OR trends OR incidence) AND [adverse life events (MeSH) OR adversities OR maltreatment (MeSH) OR abuse (TI) OR neglect (TI) OR parental death (TI)]. In Scopus, the search query was: TITLE (adolescent) AND TITLE-ABS-KEY (suicide) AND TITLE-ABS-KEY (adverse life events). Another search strategy was used
+about the same topic in Science Direct: (TITLE-ABS-KEY (adolescent) AND TITLE-ABS-KEY (suicide) AND TITLE-ABS-KEY (life events). In PsycInfo, the search query was “adolescent” AND “suicide” OR “ideation” AND “life events” OR “abuse” OR “parental death.”
+Selection of studies
+Articles were screened and selected in a two-step process to minimize bias. First, two independent researchers (C.M. and G.P.) conducted the literature search. Any discrepancies between the two reviewers who, blind to each other, examined the studies for possible inclusion were resolved by consultation with the senior reviewers (E.F. and M.A.). In the second phase, full-text articles that met our inclusion criteria were retrieved and independently reviewed by G.S. and M.P, who discussed the design and characteristics of the studies to test whether they could be included in the review. If doubts remained, the study was put on the list of those awaiting assessment, pending acquisition of more information, and then was carefully re-analyzed for possible inclusion. Any disagreements in this step were resolved by discussion between reviewers.
+Data collection process
+A data extraction document was developed [23]. C.M. and G.P. independently extracted the following data elements from the 28 studies included in this review (see ‘Study sample’ below): author/s and publication year, study design, sample size, follow-up, main findings, and main adversities (see Table 1). Reviewers acquired the full text of all 28 articles. The principal reviewers (G.S. and M.P.) analyzed independently all studies. Any disagreements were resolved by discussion with the senior reviewers (E.F., M.A.), who also independently read all articles.
+Summary measures
+We assessed the selected 28 studies for quality using the following criteria: (1) representativeness of the sample from the general population (0-2 points), (2) presence and representativeness of a control group (0-2 points), (3) presence of follow-up (0-2 points), (4) evidencebased measures of adverse life events/adversities (e.g., Child Trauma Questionnaire, Life Events Checklist, or other psychometric evaluation) (0-2 points), (5) presence of raters who identified independently the presence of adverse life events (0-2 points), (6) statistical evaluation of inter-rater reliability (0-2 points), and (7) evidencebased measures of suicidal ideation or suicide attempts
+(e.g., Suicide Risk Scale, Suicidal Ideation Questionnaire, Beck Hopelessness Scale, or other psychometric evaluation) (0-2 points). Quality scores ranged from 0 to 14. Studies were differentiated in quality as follows: (1) good quality (10-14 points) if most or all the criteria were fulfilled, or, where they were not met, the study conclusions were deemed very robust; (2) moderate quality (5-9 points) if some criteria were fulfilled, or, where they were not met, the study conclusions were deemed robust; (3) low quality (0-4 points) if few criteria were fulfilled or the conclusions of the study were not deemed robust. Caution was exercised in interpreting the findings from the low-quality studies (Tables 2, 3, 4).
+Results
+Study sample
+The searches in Pubmed, Scopus, Science Direct, and Psy-cInfo databases revealed, after the removal of duplicates (17 articles), a total of 235 potentially relevant articles. In particular, the search in Pubmed generated 149 articles, that in Scopus and Science Direct generated 20 and 45 additional articles, respectively, and the search in PsycInfo provided other 38 articles. Of these, 124 were excluded because they were without an abstract or had an abstract that did not explicitly mention suicidal behavior (or suicidal ideation, suicidal thoughts, or suicide attempts) and adverse life events. Four articles were excluded because they were not published in English, and 8 were studies published before 1980. Therefore, 111 full-text articles remained. Of these, 81 were excluded because they did not critically analyze the link between adverse life events and suicidal behavior in adolescence/early adulthood, and 2 were excluded because they were psychological autopsy studies. Thus, 28 articles met our inclusion criteria and were, therefore, used for the present review. Figure 1 summarizes the main results of the search strategy (identification, screening, eligibility, and inclusion process) used for selecting studies.
+Study types and sample characteristics
+We selected 11 cross-sectional studies including 31,833 individuals, 4 case-control studies including 72,979 subjects and 69,497 controls, 7 longitudinal follow-up studies including 6113 individuals, and 6 retrospective studies including 45,455 subjects and 423,670 controls. Clinical samples included mainly adolescents with major depression or borderline personality disorder, and adolescent inpatients at risk for suicide.
+Study quality assessment
+According to our quality score system, the mean score of the 28 studies that were included in this review was 5.8. Most of studies (N = 15) were of moderate quality, 3 were of good quality, and 10 of low quality. Below we discuss the main findings from these 28 studies, grouped by life event specification.
+Studies on the association between the number of adverse life events and suicidal behavior
+In general, it appears that adverse life events cause distress. McKeown et al. [26] showed in a 2-year follow-up longitudinal study that negative life events (such as financial problems, death of a parent or a close friend, parental divorce, and childhood abuse) were significant predictors of subsequent suicide plans (OR 1.10). King et al. [27] confirmed this relationship but also showed differential effects by type of suicidal behavior: suicide attempters were significantly more likely to have experienced stressful life events compared with suicide ideators. Similarly, Liu and Tein [28] showed, in a sample of 1362 Chinese adolescents, that negative life events occurred most frequently in suicide attempters, followed by suicide ideators and finally non-suicidal adolescents, suggesting a dose-response relationship between number of negative life events and suicidal behavior. Importantly, this relationship remained significant (although it was strongly attenuated) even after controlling for the presence of internalizing/externalizing problems. More recently, the Kaplow et al.’s [29] cross-sectional study confirmed the significant positive relationship between the number of adverse life events experienced and risk of suicidal ideation, but also partly explained (38 %), via emotional suppression, the effect of adverse events on suicide attempts. The positive dose-response relationship between the number of adverse life events (such as sexual abuse, drug or alcohol abuse by a family member, running away from home and homelessness) and risk to attempt suicide was also confirmed by Bhatta et al. [30], in another cross-sectional study on 3156 adolescents at a juvenile detention facility. Bhatta and colleagues also reported that the risk to attempt suicide was almost 8 times higher for those who had experienced all of these adversities compared to those with no such experiences.
+These relationships were also established with longitudinal data. For example, Thompson et al. [23] confirmed that the number of lifetime adversities was associated with adolescent suicidal behavior, but also showed that the impact of adversities early in life could vary depending on whether they occurred during childhood or adolescence. Psychological maltreatment and sexual abuse had a lower impact if they occurred in childhood, and a higher impact if they were experienced in adolescence.
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+Furthermore, childhood adversities moderated the effects of adolescent adversities on suicidal ideation; the effects of adolescent adversities on adolescent suicidal behavior were stronger at lower (compared to higher) levels of childhood adversities. Nrugham et al. [21] also showed moderation, this time by type rather than timing of adversity. Violent life events were strongly associated (OR 3.85) with suicide attempts but only if experienced, not witnessed.
+The association between number of adverse life events experienced and adolescent suicidal behavior was confirmed in clinical samples as well. Stone et al. [24] found that female inpatients with higher rates of dependent events at baseline were at higher risk (42 vs. 21 %) of suicidal behavior during the 34 weeks following their discharge from hospital. Horesh et al. [31] in a case-control study comparing the effect of stressful life events on suicidal behavior in three groups of adolescents (suicide attempters with Major Depressive Disorder, suicide attempters with Borderline Personality Disorder, and healthy controls) reported that suicidal patients experienced a significantly higher number of stressful life events in the year before their suicide attempt compared with healthy controls.
+Studies on the association between maltreatment and suicidal behavior
+The link between maltreatment—such as sexual, physical, or emotional abuse—and suicidal behavior in young people was investigated in twelve studies. Although maltreatment, in general, was related to suicidal behavior [32], effects appeared to differ by its type. Sexual abuse was the type most consistently and strongly associated with suicidal behavior [17, 30, 33]. For example, Bensley et al. [34] in a cross-sectional study on 4,790 students reported that the association between history of abuse and suicidal behavior (in five levels of severity: “none,” “thoughts,” “plans,” “non-injurious attempts,” and “injurious attempts”) was stronger for combined sexual abuse and molestation compared with non-sexual abuse or sexual molestation alone. In addition, the association was stronger for more severe forms of suicidal behavior, such as injurious suicide attempts (OR 47.1) compared to non-injurious suicide attempts (OR 12.0), suicide plans (OR 6.8) or suicidal thoughts (OR 4.4). Injurious suicide attempts, different from self-injurious behavior (‘selfharm’) [35, 36], may be described as attempts aimed to kill oneself by intentionally cutting, burning, bruising, or otherwise self-injuring.
+The role of physical abuse in suicidal behavior in young people is less clear. There are reports of null effects [37], although some studies suggest an independent
+association, even after accounting for sexual abuse. For example, Johnson and colleagues [18] found that, after controlling for covariates, sexual and physical abuses were significantly associated with risk of suicide attempts during late adolescence/early adulthood (ORs 7.22 and 5.10, respectively). A 6-year follow-up study [38] also suggested that a history of physical abuse increased the risk of suicidal ideation and suicide attempts (ORs 3.6 and 5.6, respectively), even after controlling for gender and other factors. Finally, Brezo et al. [20] showed that the prevalence of lifetime suicidal ideation was higher in their physically abused group (36.6 %) compared to the nonabused group (25.4 %), although those who were sexually abused had higher odds of repeated and late-onset suicide attempts and suicidal thoughts than those who were physically abused. Importantly, the prevalence of lifetime suicidal ideation was higher for young people who experienced both sexual and physical abuse (58.1 %). That study also showed that the impact of abuse frequency on suicide attempts depended on the identity of the abuser, with abuse by a member of the immediate family carrying the greatest risk (RR = 5.0).
+The role of emotional abuse or neglect in suicidal behavior was investigated in two studies. Lipschitz and colleagues [39], who conducted a cross-sectional study on 71 adolescent inpatients, found that emotional neglect was a significant predictor of both suicide attempts and self-mutilation. They also reported that emotional neglect was more strongly associated with suicidal ideation and self-mutilation than physical abuse or physical neglect. Tanaka et al. [22] in a 2-year follow-up study found that low self-compassion, which was associated with emotional abuse and neglect, was significantly related to psychological distress and suicidal behavior.
+Studies on the correlation between parental death, parental divorce, or family climate and suicidal behavior
+The correlation between parental death, parental divorce, or family climate (such as parenting and inter-parental relationship) and suicidal behavior among young people was investigated in six studies. In general, parental death appeared to raise the odds of youth suicidal behavior [40] particularly if the death was a suicide [41]. Parental divorce and also the overall family climate appeared to be associated with this risk, as well. For example, Johnson and colleagues [18], in a longitudinal study conducted on a community sample of 659 families, found that parental separation or divorce was associated with subsequent suicide attempts (OR 1.20). However, maladaptive parenting and harsh parental discipline also raised significantly the odds of suicidal behavior. The role of these and other, related, aspects of the family environment was explored
+in four studies. King et al. [27] reported significant associations between suicidal behavior and poor family environment (OR 3.6), low parental monitoring (OR 5.0), and parental history of psychiatric disorders (OR 2.0). Liu and Tein [28] found that inter-parental conflict was related to both suicidal ideation (OR 1.94) and suicide attempts (OR 2.67), and Xing et al. [42] confirmed the link between suicidal behavior, harsh parental discipline and maladaptive parenting. By contrast, a supportive and positive family climate appeared to be a protective factor for suicidal behavior in youth. McKeown et al. [26] found that family cohesion was a significant protective factor for suicide attempts although not for suicide plans or suicidal ideation.
+Studies on the association between school/interpersonal problems and suicidal behavior
+Three studies investigated the association between school/ interpersonal problems and suicidal behavior. Baldry et al. [43] found that both direct and, more strongly, relational victimization at school were positively associated with suicidal cognition in youth, and Johnson et al. [18] that a high level of school violence was significantly related to suicide attempts (OR 3.53). Liu and Tein [28] examined, in a sample of rural Chinese adolescents, the role of several school-related problems and adverse experiences in suicidal behavior. Of those, school dissatisfaction had the largest OR (2.34) for suicidal ideation, followed by very high parental expectations (OR 1.99), and change of (or suspension from) school (OR 1.98). The risk of suicide attempts was raised for those who failed in an examination (OR 2.93), felt pressure to enter a better school or college (OR 3.23), and changed or were suspended from school (OR 3.16). However, when all life events and school experiences and other covariates such as age, gender, and family socio-economic status were considered simultaneously, only school dissatisfaction and very high parental expectations remained significant predictors of suicidal ideation (ORs 1.87 and 1.51, respectively). None predicted, independently, the risk of suicide attempts.
+Conclusions and discussion
+Summary of main findings
+The main purpose of this systematic review was to investigate the association between experience of negative life events and suicidal behavior in adolescence and early adulthood. The adversities examined included (1) sexual abuse and molestation (sexual abuse without sexual contact); physical abuse and maltreatment; child abuse and
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+neglect not otherwise specified; (2) family dysfunction and exposure to domestic violence; (3) separation from or death of a biological parent, family member or close friend; parental divorce; (4) poor interpersonal relationships and breaking up with boyfriend/girlfriend; (5) vic-timization/distress at school. Based on the main findings from our selected studies, experience of adversities or negative life events was significantly related to youth suicidal behavior [17-24, 26-47]. Another important finding was that some adversities are very common, as is the distress associated with experiencing multiple adversities [18, 24]. The third important finding from this review was the strong, positive dose-response relationship between number of events experienced and risk of suicidal behavior [23, 28, 29]. However, it also appears that the relationship between life adversity and suicidal behavior may differ by type of suicidal behavior. For example, young people who had attempted suicide were significantly more likely than those with suicidal ideation to have experienced stressful life events [21, 27, 31, 36]. In turn, the correlation between suicide attempts and adverse life events seems to differ by type of life event. Young people were at higher risk of suicide attempts if they had experienced maltreatment (e.g., abuse or neglect) [22, 39], and, again, this association differed by type of maltreatment, in line with other studies [13]. Our review suggested that sexual abuse, rather than physical abuse or neglect, appears to be more strongly associated with suicidal behavior [20], with sexual abuse being a particularly powerful predictor of several types of suicidal behavior in young people [17, 30, 33, 36, 48, 49]. For example, in the study of Martin and colleagues [17], sexually abused boys had a 10-fold increased risk of making suicidal plans and threats and a 15-fold increased risk of attempting suicide compared to those who were not abused. (By contrast, the findings about the role of non-sexual physical abuse in suicidal behavior were equivocal [37, 38]). Furthermore, it appears that the impact of sexual abuse is particularly severe if the perpetrator was a family member or an intimate partner. For example, Brezo et al. [20] showed that sexual abuse by a member of the immediate family was associated with the highest suicide risk, perhaps because such abuse occurs more frequently in families with multiple difficulties that do not usually guarantee safe conditions after abuse. Also, sexual abuse by a family member can exert long-term consequences on the development of healthy attachment patterns that are needed for mental health [50]. Sexual abuse by an intimate partner also appears to carry significant risk, such as elevated levels of antisocial, violent, and suicidal behavior [19].
+As well as the type, the timing of maltreatment seems to matter for suicidal behavior in young people.
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+Earlier onset of maltreatment/abuse is associated with more adverse mental health outcomes, in general [51-53], but also with suicidal behavior. As shown by another recent study [54], those exposed to physical and sexual abuse in childhood or adolescence were more likely to experience high levels of depression and suicidal ideation in young adulthood, compared to those who were not exposed to any maltreatment. Interestingly, among those who experienced maltreatment, first exposure during the early childhood period was associated with the most negative outcome. In particular, those first exposed to physical or sexual abuse during early childhood reported a 77 and 146 % increase in the odds of depression and suicidal ideation, respectively, compared to those individuals first maltreated later in adolescence. Early, compared to later, exposure to maltreatment in childhood may be associated with the most negative consequences because it occurs in a biologically sensitive period. Abuse and neglect early in life impact significantly on brain development, resulting in emotional, social and cognitive impairments, in turn increasing the risk of psychiatric conditions [55-59], psychopathological and attachment disorders, emotional dysregulation, abnormal stress reactivity and executive dysfunction [60, 61], and, as suggested by the studies we reviewed, suicidal behavior.
+However, even less severe forms of childhood adversities can impact on suicidal behavior in young people. Our review showed that factors, both in the school and the home context, that were associated with poor mental health outcomes in young people [62, 63] were also related to youth suicidal behavior. Victimization at school [43], school dissatisfaction [28], and experience of school violence [18] were all related to suicidal behavior in young people. Risk factors in the family included poor family environment, low parental monitoring, low family support and cohesion, inter-parental conflict [26-28, 42], and loss of a family member [40, 41]. Early parent loss, especially by suicide, was particularly important. Young people who had lost a parent by suicide early in life were three times more likely to die by suicide themselves than their non-bereaved peers, and more likely than those who had lost a parent as young adults [41].
+Of course, not all children exposed to such adversities will show suicidal behavior later in life. It is, therefore, important to consider, albeit briefly given our study aim, the role of protective factors. In general, there has been a study on the role of protective factors in suicidal behavior [64], but few studies have explored their role in buffering the effects of adverse life events, especially in adolescence. A recent review has pointed to the importance of a positive attributional style, higher levels of agency, and greater social support [65], but more research is needed.
+Main limitations
+Our review should be considered in the light of several limitations. First, we could not carry out a meta-analysis because our studies included different life events and different outcomes. Also, although our review aimed to summarize systematically the most relevant studies in the field, their inclusion and exclusion may reflect our choice, on the basis of our expertise. Moreover, some studies had
+small sample sizes and small numbers of suicide ideators or attempters, and, as a result, reduced statistical power. In addition, studies did not always distinguish between suicidal ideation and suicide attempts. Also, most of our studies had adopted retrospective designs, and thus findings may have been hampered by recall bias. Finally, some of our studies recruited heterogeneous samples, included a relatively small number of events, or did not include control groups.
+Implications and future directions
+Most of the studies included in the present review reported a positive, statistically significant association between life adversities, and suicidality in young people. There seemed to be a strong, positive dose-response relationship between number of events experienced and risk of youth suicidal behavior. While the number of events was significant, their type and timing also mattered. Exposure to adversities (in particular sexual abuse/molestation) during vulnerable periods of life may be a critical risk factor for the emergence of suicidal behavior in adolescence and early adulthood. Future studies should elucidate the extent and type of the association between adverse experiences and risk of suicide in youth.
+Compliance with ethical standards
+Conflicts of interest The authors declare no conflicts of interest regarding this manuscript.
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+Over the past 20 years, participatory approaches to mental health services research have gained considerable momentum and growing representation within the pages of Psychiatric Services. However, as both reviews and national surveys suggest, participatory involvement efforts tend to be mostly surface level, often limited to a stakeholder advisory group or to “one-touch” consultation activities (1-3). Although coproduction, in which researchers and community members exercise equivalent leadership, are important additions to the family of meaningful involvement strategies, concerns have consistently been raised as to the extent to which such approaches actualize stated goals. Furthermore, significant structural barriers, such as the ineligibility of nonfaculty researchers for National Institutes of Health primary investigator roles, fundamentally limit and reproduce inequities in capacity to initiate and lead funded research.
+We therefore argue that in order to play a more meaningful role in research and, in turn, realize the potential for deeper and more transformative change, individuals with lived experience of the conditions, systems, and services we study must be central research decision makers (4-6). Consultation—understood as predominantly unidirectional activities designed to gather stakeholder input or feedback— is not a substitute for direct involvement and leadership of persons with lived experience in project decision making (4). In research contexts, this means major roles in developing research ideas, setting agendas, and obtaining funding for substantial research projects and in initiating and leading such projects. Reaching this level of involvement of individuals with
+lived experience will require a serious investment by the mental health services research community in developing and sustaining a pipeline of mental health services researchers with experience of significant disabilities.
+What “Lived Experience” Means Here
+Before we continue, a note about terminology. Whenever advocates make the argument for greater involvement of people with lived experience in the research process, a frequent counterargument is that people with mental illness are already amply represented within existing research efforts: among students, faculty, and clinicians. If our definition of lived experience is mild to moderate anxiety and depression, such as are treated in an outpatient or primary care setting, this is demonstrably true (7). In fact, the myriad social and academic pressures within research pathways have themselves been repeatedly associated with high stress and poor mental health.
+In this Open Forum, our purpose is not to define lived experience or its variants in any particular way but rather to pivot in order to emphasize diversification of the perspectives represented, with explicit attention to severity of impact and intersectionality. Clearly, there is a continuum from mental health to (functional) disability, and from widely accepted (normative) psychological and emotional states to those socially constructed as nonconsensual and unacceptable. In this Open Forum, we want to emphasize the need for greater inclusion of individuals at the farther end of these continua: those with the most (potentially) disabling and
+stigmatized diagnoses, such as schizophrenia, borderline personality disorder, and severe substance use disorders; with intersecting experiences of the public benefits system, homelessness, housing instability, incarceration, poverty, racism, and other forms of structural discrimination; and whose experiences or diagnoses, for one reason or another, have led to strongly negative societal responses, including social rejection and clinical force. Too often, debates about the terms we use (“lived experience,” “service user,” etc.) serve to obscure a continuing reluctance to commit to, and support, individuals who have faced significant and substantial barriers to their participation in higher education and research, thereby also excluding the insights and experiential knowledge that such histories help engender. Through the remainder of this Open Forum, we use the abbreviation PD/LE (for psychiatric disability/lived experience) to refer to significant psychiatric disabilities and lived experiences.
+Blueprint for a Transformed Workforce
+With this context in mind, the particular goal of this Open Forum is to advocate for intentional and formalized workforce development. Specifically, we call for efforts and initiatives that acknowledge and support people with PD/ LE across the academic training and funding continuum— including undergraduate students, research assistants and associates, and early- and midcareer researchers—and that do so on a meaningful scale. Rather than supporting or celebrating a small handful of researchers who have made it “against all the odds,” we ask for investment in building a sustainable pipeline of diverse PD/LE mental health services researchers and making systemic changes to help ensure that significant psychiatric disabilities are ultimately significantly better represented within the ranks of tenured faculty and extramurally supported primary investigators.
+Expanding on broader research and best practices in mentoring, workforce diversity, inclusion, and antidiscrimination (8), we propose a series of actionable steps. (A table detailing these steps is available as an online supplement to this Open Forum.) These steps are meant to be suggestive rather than comprehensive, and they exclude broader supports with relatively more established empirical and political backing (such as student and employee wellness programs).
+Proactive Recruitment, Hiring, and Sponsorship
+As has been the case with efforts to diversify the research workforce in terms of race and gender, recruitment and hiring of students, staff, and researchers with PD/LE must be proactive. Academic programs and research teams should, for example, reach out to peer or service user groups and organizations on campus and in the broader community. Recruitment advertisements must convey thoughtful, concrete support for PD/LE and explicitly encourage applications from individuals with experience relevant to the focus area of the lab or research center. For example, a center focused on homelessness and mental illness might communicate a strong
+interest in applicants with a history of homelessness or mental health challenges. We want to emphasize that with a newly funded project, there is almost always a choice between hiring one or more students or support staff identified with the community of interest and prioritizing efficiency, as the rationale sometimes goes. We strongly encourage investment in the former. Disability statements as part of the application process are a legally sanctioned way of discerning what a given applicant might bring to the table, especially when support for PD/LE has been successfully communicated.
+Combating Academic Ableism
+Work environments must be welcoming to newly recruited students and staff with PD/LE and offer them specific supports as needed. Critically, this must include a flexible approach to work and academic accommodations and an active commitment to challenging ableism—that is, the assumption that psychiatric disability, particularly when involving psychosis or cognitive challenges—is the antithesis of academic excellence (9). All too often, students and young people with a history of significant disability will already have internalized society’s judgments and lowered expectations of them. Patience, flexibility, and reassurance from senior faculty, mentors, and supervisors are essential. Additional direct and indirect actions (see the online supplement) include increasing the visibility and representation of researchers with disclosed PD/LE on journal and professional association boards and committees and as expert commissioners and invited speakers at conferences and colloquia. They should also include working to develop academic cultures that emphasize the value of the perspectives and insights that those with PD/LE bring. Rather than holding value as “token” representatives, these researchers’ perspectives should be embraced inasmuch as they inspire teams to ask different research questions or pursue different kinds of goals.
+Recognition of and Support for Multiple Roles and Identities
+Students, fellows, and research staff with PD/LE identities often face a unique set of emotional challenges navigating research spaces in which it is normative to speak of individuals with mental health or psychiatric diagnoses in oth-ering, medicalized ways. A dispassionate discussion of outcomes tied to involuntary hospitalization or restraint that is unremarkable to a student with no connection to such experiences, for example, can be deeply painful for students who have themselves been restrained in an inpatient ward. Typically, such pain is suppressed in order to appear as an objective scientist. Similarly, research trainees may be asked to adopt language (e.g., “mental illness” or “brain disorder”) that has been rejected by the advocacy community with which they identify. These situations can easily become a major source of personal stress for individuals, particularly early in a research career when it is difficult to speak up and request changes to collaborative work or feel sufficiently
+empowered to communicate the concerns of a particular community. Over time, internal struggles can further erode students’ confidence. Having a mentor who validates these struggles, and personally addresses them where possible, is critical.
+Breaking Glass Ceilings
+In the United States, individuals with disabilities of all kinds remain seriously underrepresented among the ranks of tenured faculty (9). As has been well documented with respect to women and members of underrepresented minority groups, mentoring and support cannot stop with the completion of a doctorate. Both tenure and “independence” in research funding are glass ceilings that can be exceptionally difficult to break through. To assist in doing so, mentors, department chairs, and others in leadership positions need to commit to actively supporting the retention, promotion, and successful grantsmanship of fellows and junior faculty with PD/LE. Many models to support advancement for other underrepresented groups have been developed, including targeted fellowships, mentoring programs, and summer training institutes (8). To the best of our knowledge, no such explicit structures exist for researchers with PD/LE in psychiatry and allied fields.
+Speaking Up and Speaking Out
+We are aware of at least a handful of researchers who have written “coming out” stories, some within the pages of Psychiatric Services. Important, if not exceptional, efforts to address discrimination in licensure have been led by senior clinicians with lived experiences (10), as have been efforts to document the disclosure and accommodation experiences of faculty with psychiatric disabilities (11). There are nevertheless innumerable times and places in which speaking out on issues of inclusion would be possible, many with existing analogs to efforts to address the lack of inclusion of women and racial-ethnic minority groups: for example, board members—whether of a journal or research association— could call attention to the lack of PD/LE representation, or faculty could actively question admissions practices in which disclosure of mental health experiences are flagged as a “kiss of death,” as has been reported in the literature (12). Speaking out is important both locally and in public venues such as academic journals. For example, the impact of senior thought leaders publicly pushing for greater support and inclusion of those with PD/LE in academic projects could be far reaching.
+Conclusions
+In this Open Forum, we have argued that the actualization of meaningful involvement of individuals with PD/LE in research requires not just inclusion but leadership. We call for greater, and more purposeful, investment in building a pipeline of researchers with personal experiences of
+significant psychiatric disabilities or with other frequently studied target experiences in mental health services research in the public sector. Investing in this pipeline will require commitment and action—commitment that remains achievable and fully aligned with the social justice aspirations of fields such as community psychiatry, community psychology, and social work. We encourage leaders in these fields to embrace this challenge and to act now.
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+1.	Introduction
+Loneliness, defined as the subjectiveperception ofa lack ofcontactwith other people (HM Government, 2018; Perlman and Peplau, 1981), is associated with premature mortality (Elovainio et al., 2017; Rico-Uribe et al., 2018), physical and mental ill-health, worse cognitive function (Cacioppo et al., 2014; Hakulinen et al., 2018; Hawkley and Cacioppo, 2010; Solmi et al., 2020) and increased use of health services (Dreyer et al., 2018). Loneliness affects people of all ages (Age UK, 2018) and has been made a ministerial responsibility by the UK government (HM Government, 2018). While living alone has been consistently linked with self-harm and suicide, it is currently not clear whether subjective loneliness per se is the primary reason why people living alone may be at increased risk of suicidal behaviour.
+Living alone and loneliness indicate relationships and social
+connections, nonetheless they are separate constructs with overlapping features (Smith and Victor, 2019). Living alone is distinct from coha-bitating relationships, as well as residency with non-partners such as parents, children or friends, who might be expected to be sources of emotional, financial and practical support (Amato, 2014; van Hedel et al., 2018). However, clearly people living alone may engage with others outside the household and potentially receive emotional support from other sources. In contrast, loneliness is the subjectiveperception of a lack of contact with other people (Hawkley and Cacioppo, 2010). Emotional support is a related concept. It involves the provision of caring, empathy, love and trust within a relationship (Langford et al., 1997) and indicates that somebody is taken care of, valued, not alone and has somebody to confide in (Shensa et al., 2020; Yao et al., 2015).
+Theories that would support a relationship between suicide and loneliness, living arrangements and lack of emotional support, trace
+back to the work of Durkheim (Stanley et al., 2016). In particular, egoistic suicide is described as a lack of social integration because of reasons such as an individual's lack of social bonds to family and friends. These ideas have been developed further in modern theories such as the Interpersonal Theory of Suicide. One aspect of the Interpersonal Theory of Suicide of particular relevance is the concept of ‘thwarted belongingness’ which suggests that loneliness along with the absence emotional support can lead to self-destructive behaviours (Stanley et al., 2016; Van Orden et al., 2010). However, there is little empirical research in this area (Van Orden et al., 2010). Identifying robust risk factors for suicidal behaviour is methodologically challenging for a number of reasons, not least because suicide is a rare event (Klonsky et al., 2016; Stickley and Koyanagi, 2016). Most studies of loneliness and suicidal behaviour have used self-reported measures of suicidality (Bennardi et al., 2019), which may be prone to reporting biases (Beutel et al., 2017; Stickley and Koyanagi, 2016), and only a few studies (mostly case-control studies) have investigated loneliness as a potential cause of deaths by suicide (Courtin and Knapp, 2017; Holt-Lunstad et al., 2015).
+The extensive data within the UK Biobank cohort represents a unique opportunity to overcome these methodological challenges. The cohort consists of over half a million people, and the baseline questionnaire included detailed questions on living arrangements, loneliness and emotional support, in addition to key sociodemographic and health data. These data have also been linked prospectively to hospital episode statistics and mortality records (Sudlow et al., 2015).
+Our primary hypothesis was that living alone may represent an independent risk factor for self-harm and suicide. We also set out to assess whether any observed association between living alone and suicidal behaviour might be explained by subjective loneliness or by perceived lack of emotional support.
+2.	Methods
+2.1.	Data
+All adults aged between 40 and 70 years who were registered with the UK National Health Service (NHS) and living within 25 miles of 22 assessment centres across England, Scotland and Wales were invited to participate in UK Biobank at baseline. The achieved sample of 502,536 people had a response rate of 5.5% (Sudlow et al., 2015) and an age range of between 37 and 73. Participants were recruited between March 2006 and October 2010 (Sudlow et al., 2015) and for each participant baseline assessments consisted of a single visit lasting approximately two to three hours, including a computerised self-completion touch screen questionnaire, nurse interviews, and physical measurements. For all participants, the date and cause of death were sought from death certificates held within the National Health Service Information Centre (England and Wales), and National Health Service Central Register Scotland. At the time of analysis, mortality data were available up to the middle of February 2018 for England and Wales and until June 2017 for Scotland. Data for Hospital admission records for self-harm were only available for participants in England and for the period up to March 2015, thus analyses for self-harm were restricted to those who attended a Biobank assessment centre in England (n = 448,811). This study is covered by the generic ethical approval for UK Biobank studies from the National Health Service National Research Ethics Service (June 17, 2011; Ref 11/NW/0382). Participants provided electronic informed consent for the baseline assessments and the register linkage.
+2.2.	Outcomes
+Death by suicide was defined as the act of intentionally ending one's own life (Nock et al., 2008) and was ascertained from death records using ICD 10 codes X60-X84 (intentional self-harm), Y10-34
+(undetermined cause), as used by the UK Office for National Statistics (2019). Participants dying from other causes of death were censored at time of death.
+Hospital admissions for self-harm were defined as any act of intentional self-poisoning or self-injury carried out by an individual, irrespective of the motivation or suicidal intent (National Collaborating Centre for Mental Health, 2011). This was assessed using the first admission for self-harm following attendance at the UK Biobank baseline assessment centre. Hospitalization for self-harm was assessed using ICD 10 codes X60 to-84 and Z91.5, for diagnosis and causes of admissions.
+2.3.	Main exposures of interest
+Living arrangements (alone; husband, wife or partner; other) were assessed using data from the baseline touch screen questionnaire collected when participants first attended a UK Biobank assessment centre. Participants were asked how many people lived in their household. If there was more than one person, the participant was then asked how people were related to them. If any member of the household was a spouse or partner, participants were classified as living with a husband, wife or partner. The other category included both relatives and unrelated people.
+Loneliness was assessed using a single question taken from the baseline touchscreen questionnaire: “Do you often feel lonely?” (Responses: yes; no; do not know; prefer not to answer). This item was taken from a longer scale and has previously been shown to be associated with health outcomes (McCormack et al., 2014).
+Emotional Support was assessed with the question “How often are you able to confide in someone close to you?” (Shensa et al., 2020). The potential responses were “Almost daily; 2-4 times a week; about once a week; about once a month or once every few months; never or almost never; do not know or prefer not answer” (the latter were coded as missing).
+2.4.	Covariates
+We included sociodemographic and health variables which, which were collected during participants’ attendance at baseline assessment centres, and might confound relationships between the key variables of interest and death by suicide and hospital admissions for self-harm.
+The socio-demographic variables included were: sex, age (continuous), and self-reported measures of ethnicity (derived here into White British, other), current employment status, (employed, retired, other), education (degree; professional; NVQ, HND or HNC; A level; O level; CSE; none) ever having a same sex partner (none, at least one), and area deprivation indicated using the Townsend Index (continuous) for the participant's postcode at recruitment.
+The health variables included were: a measure of multimorbidity, developed for a previous study (Nicholl et al., 2014) which was the number of physical morbidities participants reported to the interviewing nurse (zero, one, two, three or more); Body Mass Index (BMI) based on measurements made at the assessment centre (normal or underweight, overweight, obese); self-reported measure of depression based on of ever seeing a GP for nerves, anxiety tension or depression (yes, no); participants’ report during their baseline interview of taking psychotropic medication (yes/no); alcohol consumption (daily/almost daily, 3-4 days a week, 1-2 times a week /once a month, special oc-casions/never, former); and smoking status (never, previous, current).
+2.5.	Statistical analysis
+Prior research (Kyung-Sook et al., 2018), and based on statistical interactions we found in preliminary analyses, indicated that gender modifies the relationship between the main exposures of interest and suicidality. Consequently, we carried out the analyses stratified by gender. Cox proportional hazards regression was used to investigate
+deaths by suicide. The proportional hazards assumption was tested for using Schoenfeld residuals. For hospital admissions due to self-harm, however, the proportional hazards assumption was not met for loneliness, so data were reanalysed using a Royston Parmar model (Royston and Lambert, 2011), with Akaike information criterion from preliminary analyses indicating that two knots should be used to model the baseline hazard, and single time varying parameter, for loneliness.
+For each participant the start date for the follow-up period used in analyses was the date of their first attendance at a UK Biobank centre at baseline, which ranged from March 2006 to October 2010. Participants were censored upon death, and for the death by suicide analyses, the last date (February 2018 for England and Wales and until June 2017 for Scotland) that mortality records were available, and for the analyses of self-harm, the last date (March 2015) that hospital records were available.
+Six different models are presented for both deaths by suicide and hospital admissions for self-harm. The first three models are presented for each of living arrangements, loneliness, and emotional support separately. Models 1 are univarable regression models only including each of the main independent variables. Models 2 adjust for all sociodemographic variables, and Models 3 additionally adjust for the health variables. Models 4 adds loneliness and Models 5 adds emotional support to Models 3. In Models 6, all variables were included.
+We accounted for missing data using multiple imputation by chained equations, generating twenty imputed data sets. Imputation models were stratified by gender and included age, living arrangements, loneliness, emotional support, all variables used in the models, the Nelson-Aalen estimate of cumulative hazard, survival status (Cleves et al., 2016), and additional variables to improve model fit, including household income, participation in social groups, contact with friends and family, parental depression, limiting longstanding illness and self-rated health. These variables were not included in the main models because they either had comparatively high rates of missing data which limited their utility in preliminary complete case analysis, or, in the case of health variables, might mediate the relationship between our exposures of interest and outcomes. Our models were fitted to each imputed data set and combined in accordance with Rubin's rules. All analyses were carried out using Stata 16.0.
+3.	Results
+Sociodemographic characteristics of study participants by gender are shown in table 1. With respect to the main independent variables of interest, men were more likely to cohabit, whereas women were more likely to live alone or with non-partner(s). Women were somewhat more likely to report often feeling lonely, and men were much more likely to report that they never had any emotional support. Men were much more likely to have died by suicide (n = 181, 8.9 deaths per 100,000 participants per year) than women (n = 85, 3.5 deaths per 100,000 participants per year). amongst potential confounders, men were more likely than woman to be in both the most and least advantaged categories of the socioeconomic measures, and women generally had poorer health.
+3.1.	Death by suicide
+The results of Cox proportional hazard models for deaths by suicide are shown in table 2. For men there were initially strong relationships, relative to cohabitation, between living alone or with a non-partner and death by suicide (model 1). These associations were attenuated after adjusting for sociodemographic factors (sex, age, ethnicity, employment status, area deprivation education, and ever had a same sex relationship (model 2) and health measures (physical morbidities, BMI, ever seen GP for depression, psychotropic medication, alcohol consumption and smoking status) (model 3). Adjusting for loneliness (model 4) or emotional support (model 5) only led to a slight
+attenuation of associations, and in the final fully adjusted model (model 6) both living alone (Hazard Ratio (HR) 2.02, 95% CI 1.40 to 293) and living with a person who was not a partner (HR 1.72, 95% CI 1.03 to 2.88) were associated with death by suicide.
+The relationship between loneliness and low levels of emotional support and death by suicide, although relatively strong in unadjusted models (model 1), fell after adjustment for sociodemographic factors and health. Once living arrangements were accounted for, loneliness (model 4), and lower levels of emotional support (model 5) were only modestly associated with death by suicide. In contrast, for women, there was little evidence of any association between death by suicide and living arrangements, loneliness or emotional support.
+Finally, in fully adjusted models, we conducted interaction tests to assess whether the relationship between living arrangements and death by suicide was modified by loneliness or emotional support. For men, a Wald test indicated a significant interaction (p = 0.002) between living arrangements and loneliness, presented in Fig. 1a. Men who often experienced loneliness or those who were not lonely and living alone, or with a non-partner only, had three times the risk of dying by suicide compared to those who cohabit and are not lonely.
+3.2.	Hospital admissions for self-harm
+The results for the Royston Parmar models for associations between hospital admissions for self-harm and living arrangements, loneliness and perceived emotional support are shown in table 3.
+For men, all of living arrangements, loneliness and perceived emotional support were associated with hospital admissions for selfharm in unadjusted analyses. The strength of these associations was reduced after adjusting for sociodemographic characteristics (model 2) and health measures (model 3). Both loneliness (model 4) and lower levels of emotional support (model 5) explained part of the relationship between living alone and self-harm. In the final model, there was no evidence of an association between living alone and self-harm. In contrast, both lower levels of emotional support and loneliness were associated with increased risk of hospitalization for self-harm.
+Women differed from men in that the living arrangements categories had weaker relationships with self-harm within the unadjusted model (model 1). Furthermore, the associations for women were explained by health (model 3). For women, loneliness (model 4) and lower levels of perceived emotional support (model 5) were associated with increased risk of self-harm, independent of other factors (with the caveat that the relationship between lack of emotional supports and self-harm could be explained by loneliness).
+For men, adjusting for all confounders, we found a significant (p = 0.023) interaction indicating that loneliness modified the relationship between living arrangements and hospitalization for selfharm. Overall, loneliness removed any protective associations of cohabitation over living alone, such that men who were often lonely had similarly increased HRs of hospitalization of around 2, irrespective of their living arrangements. In contrast, amongst men who did not report loneliness, living alone was associated with a modest increase in risk of hospitalization for self-harm (HR 1.32, 95% CI 0.95 to 1.82) and a greater increase was found for those living only with non-partners (HR 1.88, 95% CI 1.24 to 2.83) (see Fig. 1b).
+4.	Discussion
+Our goal was to investigate the association between living arrangements, loneliness, perceived emotional support and subsequent risk of suicidal behaviours, within a large general population cohort in middle-age.
+For men, given that both living alone and living with a non-partner were both associated with an increased risk of death by suicide, it is possible that having a partner is protective against death by suicide. Subjective loneliness and perceived emotional support had modest
+relationships with death by suicide and these variables explained little of the relationship between living alone and death by suicide. For women, none of living arrangements, loneliness, nor emotional support were associated with death by suicide.
+For both men and women, loneliness and emotional support were associated with increased risk of hospitalization for self-harm. However, associations between living arrangements and self-harm were more limited, being explained by health for women, and health, loneliness and emotional support for men.
+Our study has some important novel findings. While loneliness has been linked to suicidal ideation and attempts (Stickley and Koyanagi, 2016), including within case-control studies (Sinclair et al.,
+2005), a recent systematic review (Solmi et al., 2020) suggests that our study is the first longitudinal study of its kind to investigate the relationship between loneliness and deaths by suicide. The absence of prior research is not a surprise given the methodological challenges of studying such rare outcomes as suicide (Van Orden et al., 2010).
+Our finding that deaths by suicide and hospital admissions for selfharm for men were associated with living arrangements, is consistent with the literature for marital status that finds that married or cohabiting people have lower risks of suicide compared to single people (Conejero et al., 2016; Frisch and Simonsen, 2013). Our study adds to this literature by showing that for men actually living with a partner (not just from not living alone) appears to be associated with reduced
+risk of death by suicide and hospital admissions for self-harm. Demographic factors, such as the older age of the UK Biobank sample, could explain why we did not find any associations between living arrangements and suicide and self-harm for women. Kyung-Sook et al. (2018) found associations between marital status and suicide only amongst younger women.
+An original contribution of our study with respect to living arrangements is that most studies of suicidal behaviour focus on the
+concept that it is living alone that is harmful (Turecki and Brent, 2016). However, our results indicate that both men who lived alone and with non-partners were at increased risk of dying by suicide and self-harm. Apart from Frisch and Simonsen's (2013) study, which indicated that people living in households with more than nine people had increased risk of suicide, the focus has generally been on living alone as a risk factor, rather than other relationships. However, this is to some extent consistent with a wider literature which has found that those living
+alone and those living with people other than a partner are more likely to have anxiety or depressive disorders (Joutsenniemi et al., 2006).
+Another original contribution with respect to living arrangements is that our results are the first to indicate that the associations between living alone and self-harm might be explained by loneliness and emotional support. However, this does not appear to be the case for deaths by suicide. Such findings, particularly if replicated elsewhere, may have implications for theories that try to explain the psychological and social antecedents of suicidal behaviour.
+4.1.	Implications
+As noted above, the rarity of death by suicide as an outcome has made it very difficult to investigate some theorised risks for death by suicide, as well as other relatively infrequent outcomes such as hospitalization for self-harm (Klonsky et al., 2016; Van Orden et al., 2010). Many of the theories proposed to explain the development of suicidal behaviour are based on studies investigating risk factors for other forms of suicidal behaviour, such as self-reported suicidal ideation or attempts. Clearly, the assumption that the risk factors for all types of suicidal thoughts and behaviours will be the same is problematic (DeJong et al., 2010; Klonsky et al., 2016). From a public health perspective, it is important to identify potentially causal and modifiable factors that may differ between death by suicide and other suicidal behaviours.
+This study clearly shows that, for men, living arrangements, loneliness and emotional support are important risk factors for both death by suicide and hospitalization for self-harm. For women, loneliness and lack of emotional support are important risk factors for hospitalization for self-harm. Our results are less certain with respect to the role that living arrangements, loneliness and emotional support might play in deaths by suicide for women. Given the large differences in rates of death by suicide between men and women (Scourfield and Evans, 2015) and systematic review findings that the relationship between marital
+status and suicide is moderated by gender (Kyung-Sook et al., 2018), a priori we decided to analyse the results separately by gender. The large gender difference in death by suicide rates indicates that either risk factors’ associations or their prevalence differ by gender. From our stratified analyses, we find much more modest associations (around half the strength of equivalent associations for men) between living arrangements and loneliness and death by suicide in unadjusted models for women, and these associations are largely explained by socioeconomic factors, health and mental health at baseline. The confidence intervals for the sample do not completely rule out the possibility of there being associations between death by suicide and living arrangements, loneliness and emotional support for women within the general population. To carry out improved analyses for women would require much larger samples, such as national-level UK census linked to hospital and mortality records. Even then, only some aspects of our analyses could be replicated as these administrative data lack many important variables, particularly loneliness.
+Our findings on the relationship between death by suicide and living arrangements, loneliness and emotional support for men suggest that these are not simply distal risk factors: the associations persist after adjusting for measures of physical and mental health. With respect to death by suicide, the protective associations of living with a partner are particularly important. Cohabiting with a partner appears to be associated with protection against death by suicide even after adjusting for socioeconomic and demographic factors, physical health, mental health, loneliness and emotional support. This could be the case for a number of reasons but operationalizing loneliness or emotional support using single item measures is unlikely to be the explanation. The loneliness and emotional support measures we used were strongly associated with self-harm. An alternative methodological explanation is that men who die by suicide may be less willing to seek treatment for poor mental health and that the risk of suicide is greater amongst those who never see a GP, compared to those who see a GP once a year (Windfuhr et al., 2016), hence there may be residual confounding due
+to poor health. However, that would not in itself explain why men who are not living with a partner have increased risk of poor mental health, that in turn leads to death by suicide.
+There are also theoretical reasons that could explain why having a partner is protective against death by suicide. One possibility is that the benefits of having a partner may be linked to men's sense of masculinity and self-image (Scourfield et al., 2012), rather than emotional support or companionship. Given that living with a non-partner does not appear to be associated with any protective effects, and the removal of the protective associations of having a partner amongst people who are lonely, some of the risk might be due to the concept of “perceived burdensomeness” from the Interpersonal Theory of Suicide (Van Orden et al., 2010). Components of perceived burdensomeness, which include self-hatred and feeling so flawed that one becomes a liability to others, could be theorized as being active drivers for death by suicide. Perceived burdensomeness could arise in situations in which living arrangements suggested a dysfunctional relationship (such as being lonely while cohabiting) or living in situations in which men are unable to fulfil traditional male roles that require having a partner (Scourfield and Evans, 2015). It is possible that perceived burdensomeness may not just be a driver of suicidal behaviour in general, but also a driver towards more lethal self-harm behaviours.
+For hospitalization for self-harm, in contrast to death by suicide, loneliness and emotional support appear to be more important than living arrangements. Associations exist after adjusting for sociodemographic and mental and physical health factors, and for men loneliness and emotional wellbeing explain most of the relationship between living alone and hospitalization for self-harm. This is consistent with the idea that loneliness and lack of emotional support are mechanisms through which living alone, or at least without a partner, could increase risk of suicide. This is also consistent with the concept of ‘thwarted belongingness’ from the Interpersonal Theory of Suicide, which suggests that loneliness alongside the absence of reciprocal caring relationships can lead to self-destructive behaviours (Stanley et al., 2016; Van Orden et al., 2010). However, given that in the presence of loneliness any protective associations of cohabitation are removed for men, it also raise questions about the extent to which a lonely person can also feel that they do or do not have a reciprocal caring relationship.
+Our findings are consistent with some differences in the risks for different types of suicidal behaviour (DeJong et al., 2010; Klonsky et al., 2016). One possibility is that while thwarted belongingness and perceived burdensomeness both drive suicidal behaviour, the latter more strongly drives individuals towards more lethal methods. However, our results may also be consistent with other theories for suicidality. An alternative is that loneliness could be considered a form of emotional dysregulation. Emotional dysregulation theory proposes that while emotional dysregulation is an important factor in self-harm, some aspects of emotional dysregulation may be protective against something that is as daunting and as fearful as lethal self-harm (Stanley et al., 2016). It should be noted that lack of emotional support was more strongly associated with self-harm than it was associated with death by suicide. Given that emotionally supportive relationships can improve emotional regulation (Overall and Simpson, 2013), this would also be consistent with emotional dysre-gulation theory.
+4.2.	Strengths and limitations
+The key strength of this study is that UK Biobank had a baseline sample of more than 500,000 people. This very large sample provided an opportunity to study death by suicide and hospitalization for selfharm, which are both rare outcomes. However, UK Biobank data do have some potential limitations. The recruitment of such a large sample is only justifiable if it collects data on a broad range of topics, many of which are necessarily operationalised using single item questions. The
+use of single item measures for loneliness, which was a simple dichotomous measure, and perceived emotional support might be considered a weakness of our study. They are items drawn from longer scales and have not been validated as single items. The extent of loneliness may be underreported as there are negative connotations to being lonely and people may not always admit that they feel lonely (de Jong Gierveld, 1998). However, single-item measures are considered appropriate (Stickley and Koyanagi, 2016) and have been recommended for the study of loneliness (HM Government, 2018). Another limitation is that UK Biobank had an invitation response rate of only 5.5%, and, compared to the general population UK Biobank is less economically deprived with some evidence of a healthy volunteer selection bias (Fry et al., 2017). A heathy volunteer selection bias may explain why the yearly death by suicide rates for both men (8.9 deaths per 100,000 per year) for women (3.5 deaths per 100,000 per year) in UK Biobank is lower than death rate for suicides in the UK (Office for National Statistics, 2019).
+Our study uses observational data with the inherent limitations for inferring causality. We have adjusted for a broad range of potential confounding variables at baseline and we had follow-up data on deaths by suicide and hospitalisation for self-harm. However, our baseline measures were only recorded at a single time point and potentially participants’ circumstances could have changed over time. In addition, with measurements recorded at only one time point, it is impossible to determine the causal direction between measures conclusively. It is likely that the relationship between the social connection measures and baseline health is bidirectional. However, given that the peak age of onset for major depressive disorder is early adulthood (Myrna M. Weissman et al., 2016), we decided to focus on a somewhat conservative approach, prioritising relationships presented in models that have adjusted for both mental health and sociodemographic measures. The mental health measures that are available for all UK Biobank participants at baseline were: self-report of ever seeing a GP for nerves, anxiety, and depression; and receipt of psychotropic medication. This may underestimate depression, which might confound the associations found in the study. In addition, the frequency of alcohol consumption measure will not fully capture substance abuse. There are other potential confounding measures for which data is unavailable in UK Biobank including personality disorders, conflict and stressful life events. We were also limited in our analyses by only having self-harm hospital admission data for England (these data were not available for Wales or Scotland). It is also the case that many who self-harm do not seek help from services or, when they do, are not admitted but are reviewed as out-patients (Gunnell et al., 2005).
+Finally, the target sample of this population was those aged 40 to 70 living in the United Kingdom, and the sample contained only a handful of people outside this age range. These results may not be generalizable to other age groups or to other cultures where attitudes to suicide, or other societal level risk factors for suicide, such as the availability of fire arms, may be very different.
+5.	Conclusions
+This study raises several questions for future exploration. Our results suggest that addressing loneliness in the general population may reduce the risk of self-harm but, for death by suicide, there is a much more complex (and likely sex-specific) relationship between loneliness, living arrangements and perceived emotional support. Overall, this work demonstrates that for men (but not for women) living alone or with a non-partner is associated with increased risk of suicide, a finding not explained by perceived loneliness. It appears likely that loneliness may be more important as a risk factor for self-harm than for suicide. These findings may reflect differences in the theoretical pathways for death by suicide and self-harm.
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+1.	Introduction
+Suicide is a global health concern with over 800,000 deaths by suicide worldwide every year (World Health Organization, 2017). In some countries one in nine young adults report making a suicide attempt (Wetherall et al., 2018). Progress in predicting suicidal behaviour has not improved markedly in the last 50 years (Franklin et al., 2017) and therefore identifying more specific risk factors for suicidal behaviour remains an urgent research priority.
+There are many theories which offer explanations for suicidal behaviour. One such approach is the Integrated Motivational-Volitional Model of suicidal behaviour (IMV; O'Connor and Kirtley, 2011, 2018) which allows for the exploration of biological, psychological and social
+factors contributing to self-injurious acts. Psychological factors could be considered more enmeshed when compared to biological or social factors. Relative to psychiatric illness, psychological factors are comparatively under-researched. For the purposes of this review we focused on the psychological factor of loneliness in relation to self-injurious behaviour.
+Loneliness is defined as ‘when a person's network of social relations is deficient in some important way, either quantitively or qualitatively’ (Perlman and Peplau, 1981, p. 31). The distinction between social isolation and loneliness is important to highlight. Social isolation is outwardly visible to an onlooker; inferred by the lack of social proximity and engagement with others, though the individual themselves may not feel alone. By contrast, loneliness is a subjective psychological
+state identified through introspection and thereby incorporates those who may feel lonely within a crowd (Bondevik and Skogstad, 1998).
+Loneliness has gained increasing attention from national governments and public health organisations (UK Government, 2018; Loneliness Taskforce, 2018), with the recognition that worldwide, approximately 11-17% of the general population experience loneliness at some time in their lives (Beutel et al., 2017; British Red Cross, 2016; Victor and Yang, 2012). Loneliness has consistently been found to be associated with both suicidal ideation and behaviour in research studies (Hedley et al., 2018; Stickley and Koyanagi, 2016; Stravynski and Boyer, 2001; Teo et al., 2018) as well as in more general systematic reviews (Calati et al., 2019; Mushtaq et al., 2014). Furthermore, some studies suggest that loneliness is more closely related to suicide risk than perceived social support (Chang et al., 2017).
+Cross-sectional research indicates that the frequency of loneliness is age-dependent (Batigun, 2005); being most prevalent in those <30 and >80 years of age (Yang and Victor, 2011); peaking in adolescence and old age (Qualter et al., 2015). These age ranges coincide with increased prevalence of suicidal behaviour (though not suicide death) in younger and older adults compared to other age groups (Nock and Prinstein, 2005; Turecki and Brent, 2016). This therefore suggests that demographic factors may influence the detection of loneliness predicting later suicidal ideation and/ or behaviour (SIB). However, the nature of the relationship between gender, loneliness and SIB is less clear. Although men are three times more likely to die by suicide than women (Office for National Statistics, 2019), women are more likely to experience suicidal ideation or engage in self-harm (O'Connor et al., 2018). In comparison, gender differences in loneliness have been less consistent. Some studies have found loneliness to be more prevalent in men while others have reported the reverse (De Jong Gierveld and Van Tilburg, 2010; Stokes and Levin, 1986), with a recent meta-analysis finding no gender differences in loneliness overall (Maes et al., 2019). Collectively, the evidence points to no gender difference in the association between loneliness and SIB cross-sectionally (Beutel et al., 2017). These findings therefore suggest that prospectively, age may be the only demographic factor to moderate the loneliness-SIB relationship. However, given that the concept of loneliness is likely to be culturally influenced, we also aimed to investigate whether the latter relationship is affected by geographical location.
+To date, prospective studies investigating the relationship between loneliness and SIB are scarce; reviews have typically focused on loneliness as a risk factor for mental health difficulties (e.g. affective disorder), specifically excluding SIB as outcome measures (Holt-Lunstad et al., 2015). These prospective reviews have found loneliness to be a stronger predictor of later depression, when compared to anxiety or substance abuse as outcome variables (Beutel et al., 2017; Van Orden et al., 2010; Vanhalst et al., 2012; Wang et al., 2018). Furthermore, as loneliness has been found to have a reciprocal relationship with depression (Cacioppo et al., 2006; Qualter, 2010), and depression is associated with SIB (Hawton et al., 2013), it could be argued that depression may mediate a prospective loneliness-SIB relationship. However, to date no review has systematically explored the role of depression in the loneliness-SIB relationship over time, and therefore we investigated its mediating role in the present review.
+To robustly explore whether loneliness is a prospective risk factor of SIB, a broad definition of suicidal behaviour was used to include selfharm, with the latter defined by the National Institute for Health and Care Excellence Guidelines (NICE, 2011) as “self-injury or self-poisoning irrespective of the apparent purpose of the act”. As a result, we included any studies of non-suicidal self-injury (NSSI), suicide attempts and suicide. In addition to acts of suicidal behaviour, given that approximately 12% of individuals who experience suicidal ideation or NSSI will attempt suicide within 5 years (Mars et al., 2019), we also investigated the relationship between loneliness and suicidal ideation or thoughts of self-harm.
+1.1.	Current aims
+This review had the following three aims:
+i)	to explore whether loneliness was a significant predictor of later SIB;
+ii)	to identify if the loneliness-SIB relationship varied as a function of socio-demographics (specifically age, gender) and/ or geographic location;
+iii)	to determine whether the loneliness-SIB relationship is mediated by depression.
+2.	Methods
+2.1.	Research Strategy
+Five major psychological and medical databases (CINHAL, MedLine, PsychArticles, PsychInfo and Web of Knowledge) were searched up to 18th of December 2019 using the following search terms; (i) lonel* OR "perceived social isolation" OR "perceived social exclusion" AND (ii) suicid* OR "self-injurious" or “self-injury” OR "self injurious" OR “self injury” OR "self-harm" OR "self harm". Data collection had finished before being registered with Prospero and therefore could not be listed on the website. PRISMA Guidelines (Moher et al., 2015) were followed (see Figure 1) where titles and abstracts were screened by the first author and an inter-rater check of 95% accuracy of 40 papers was conducted by a researcher external to the research team to ensure appropriate selection/exclusion of studies.
+2.2.	Inclusion and exclusion criteria
+The inclusion criteria required studies to be (i) an empirical paper, (ii) written in English, (iii) reporting a prospective design (i.e. where loneliness was measured as a predictor of later SIB at a future time point) and (iv) loneliness and SIB assessments were both measured directly. Studies reporting suicidal ideation and all forms of suicidal behaviours (including suicide death, non-suicidal self-harm and suicide attempt) were included. Papers were excluded if i) they were a review paper, ii) they explored assisted suicide, or iii) loneliness was inferred by using an indirect measure (e.g. living status). Any uncertainty regarding the inclusion or exclusion criteria was discussed between the study authors until agreement was reached.
+2.3.	Data Extraction
+Study sample demographics, key measures, findings, analyses, confounding variables and author interpretations were extracted by the first author and collated on a data extraction sheet.
+43% (n = 9) of included papers were checked by an external researcher (a psychology graduate) for inter-rater reliability with 100% concordance after discussion.
+2.4.	Quality assessment
+A quality assessment tool (see table 1) was designed specifically for this review based on the Quality Assessment Tool for Systematic Observational studies (QATSO; Wong et al., 2008). Quality assessments were based on the aims of this review and therefore any extensive analysis of measures used for other variables was not considered when evaluating each study against the quality assessment criteria. Quality assessments were completed by the first author and 20% of the papers were checked by another researcher external to the team for inter-rater reliability. Disagreements between the researchers were resolved via discussion with 100% post-discussion concordance. Quality assessment scores were calculated with higher totals reflecting higher quality studies (max score= 9).
+2.5.	Statistical analyses
+Comprehensive Meta-Analysis (version 3, Borenstein et al., 2013) was used to conduct all meta-analyses, weighted by sample size. Moderation analysis was used to explore whether findings varied as a function of gender, age and quality assessment score. Due to the small number of studies, it was not possible to examine moderating effects for studies of suicidal ideation and behaviour outcomes separately. In each moderation analysis, averages were calculated for studies where multiple effect sizes were reported (e.g. across multiple timepoints or suicidal ideation and behaviour). In all cases where gender ratio was reported, this was done so using a binary scale. Subgroup analyses of gender were dichotomised based on gender prevalence within the sample (i.e., sample demographics were >50% female vs <50% female) as well as investigated continuously (i.e., % female in the sample). Moderation analysis of age was based on all studies where the mean age of the participant sample was reported and this was treated as a continuous variable. Analysis of depression as a mediator between loneliness and SIB was conducted using calculated r-values.
+3.	Results
+As illustrated in Fig. 1, a total of 947 original studies were initially identified by database searches for potential inclusion in the systematic review, of which 20 met the review criteria. One further article was identified through a search of references of included studies, resulting in a total of 21 papers selected for the review. This included one manuscript that published two studies within the same paper (Kleiman et al., 2017), one study that reported only some of their outcome measures (Bennardi et al., 2019), three papers that measured loneliness at two timepoints (Gallagher et al, 2014; Hom et al., 2019; Schinka et al., 2013) and a final paper that, despite being an editorial (Pietrzak et al., 2017), it was agreed between the review authors that this study should be included as it was consistent with the inclusion and exclusion criteria of this review. See Appendix A for additional information regarding these studies and how they are referred to within this review. In all, 22 studies from 21 papers are discussed in this systematic review, with 28 results regarding loneliness as a predictor of later SIB. Summaries of each study's sample demographics, measures
+used, findings and quality assessment score are displayed in Table 2.
+Where relevant data were not available in the papers, authors of the studies included in this review were contacted for additional information for inclusion in the meta-analysis. In total, 17 studies (23 effect sizes) were included in the meta-analysis (see Appendix B for details of excluded studies). Effect sizes used were either reported by study authors or calculated by the authors of this review from information available in the paper. In order to effectively synthesise the findings from the papers included in this review, factors that influence the loneliness-SIB relationship were also critically examined in tandem with the aims outlined in the introduction. To investigate the extent to which loneliness predicts SIB, the results presented here are grouped by outcome variable (suicidal ideation vs. all suicidal behaviour including suicide death, suicide attempt and non-suicidal self-harm). The results of this review are separated by approach, with narrative summaries discussed in section 3.1 and meta-analytical findings discussed in 3.2.
+3.1.	Narrative Summary of Study Findings
+This section discusses all 22 studies included in the review. The results are presented as follows:
+i Identification of a loneliness-SIB relationship
+ii Methodological quality
+iii Evidence of a loneliness-SIB relationship in adjusted and unadjusted univariate analyses;
+iv Moderating effects of socio-demographic characteristics (age, gender, ethnicity) or geographical location on to the loneliness-SIB relationship;
+v The role of depression as a mediator of the loneliness-SIB relationship;
+vi Other confounding variables (e.g. psychometric measures used, follow-up duration, study sample size, recruiting sites) affecting loneliness-SIB relationship
+3.1.1.	Identification of a loneliness-SIB relationship
+17 studies (20 analyses) explored suicidal ideation as an outcome, while seven studies (eight results) measured suicidal behaviour, this includes two studies which measured both suicidal ideation and
+behaviour at two different timepoints (see Table 2). Of the 20 analyses that explored suicidal ideation 12 results indicated that loneliness was a significant predictor variable. Additionally, Stein et al. (2017) reported an indirect pathway from post-traumatic stress syndrome (PTSS) to loneliness at the same timepoint predicting later suicidal ideation. Gallagher et al. (2014; T2-T3) reported a significant association while Gallagher et al. (2014; T1-T3) did not.
+Three (Junker et al., 2017; Nickel, 2006; Wichstr0m, 2009) of the seven studies (8 analyses) which explored any form of suicidal behaviour found loneliness to be a significant predictor. Studies which reported a significant association were all those which explored self-harm as the outcome. Of the six studies to measure suicide attempt, the only study to report a significant association with loneliness and suicide attempt was Wichstr0m (2009), however for this study suicide attempt and self-harm was measured as a single outcome variable.
+3.1.2.	Methodological quality
+Individual quality assessment scores are reported in Table 2. The maximum score obtainable was nine. The mean score across the 22 studies was 5.18 ± 1.8 (range: 2 to 8). The lowest scoring domain was study design, where under a third of studies reported using representative samples.
+3.1.3.	Unadjusted Univariate Analysis
+Across the 22 studies in this review, 26 unadjusted and nine adjusted effect sizes were reported, including seven studies that reported both adjusted and unadjusted results. Of the 26 unadjusted effect sizes (n = 20 studies) identified within the systematic review, half reached the generally accepted level of statistical significance (p<0.05). In those studies where a significant loneliness-SIB association was found, they tended to be European-based studies, to have larger than average sample size, and to include participants that were predominantly female. Six studies (seven analyses) explored the unadjusted relationship between loneliness and suicidal behaviour with only two of these studies finding a significant loneliness- suicidal behaviour association; these studies were also the only two studies to include self-harm without suicidal intent as an outcome variable (Nickel et al. 2006; Wichstr0m, 2009). However, it should be noted that Wichstr0m's (2009) measure of suicidal behaviour included both self-
+harm and suicide attempt. By comparison, 11 of the 19 studies identified a significant unadjusted effect size between loneliness and suicidal ideation. This included all European studies which measured suicidal ideation, further trends were not identified.
+3.1.4.	Adjusted Univariate Analyses
+Nine studies reported adjusted effect sizes, descriptions of the controlled variables are summarised in Appendix C. Four of these studies reported that the loneliness-SIB relationship remained significant after controlling for various demographic factors (Ayalon and Shiovitz-Ezra, 2011; Bennardi et al., 2019; Junker et al., 2017; Stein et al., 2017). There was no discernible pattern of associations between control variables and the loneliness SIB relationship.
+3.1.5.	Age
+Across all 22 studies there was evidence that the association between loneliness and SIB was age dependent. Participants ranged in age (at baseline) from 9 to 102 years old across the included studies (see Table 2). Studies exploring either younger (16 to 20 years, n = 7; Groholt et al., 2006; Hom et al., 2009; Joiner and Rudd, 1996; Junker et al., 2017; Lasgaard et al., 2011; McGraw et a., 2008; Wichstr0m, 2009) or older adults (>58 years, n = 5; Ayalon and Shiovitz-Ezra, 2011; Bonner and Rich, 1988, Joling et al., 2018; Pietrzak et al., 2017; Stolz et al., 2016) were more likely to identify loneliness as a significant predictor of SIB than studies with an average participant age either less than 14 years (Gallagher et al., 2014 T1-T2; Salzinger et al., 2007; Schinka et al., 2013 T1-T3 and T2-T3) or between 23 to 54 years old on average (n= 3; Kleiman et al., 2017, Study 2; Stein et al., 2017; Trakhtenbrot et al., 2016). Only two of the studies in this review directly explored age differences as a study aim and both used suicidal ideation as the outcome variable. Ayalon and Shiovitz-Ezra (2011) found that loneliness did not predict later suicidal ideation in those over 75 years of age but did in those aged 55-65 and 66-75 years. Bennardi et al. (2019) found that loneliness only predicted suicidal ideation in the participant group aged >60 years old in comparison to those aged under 60 years of age.
+3.1.6.	Gender
+The collective distribution of men and women in the selected studies was slightly higher than that of the world population (The World Bank, 2019); mean (% female) 57.6 ± sd. 28.8. Only two studies focused on a single gender (Stein et al., 2017, male-only; Nickel et al., 2006 female-only).
+Ten (Ayalon and Shiovitz-Ezra, 2011; Bonner and Rich, 1988; Gallagher et al., 2014 T1-T3; Hom et al., 2019; Joling et al., 2018; Lasgaard et al., 2011; McGraw, 2008; Nickel et al., 2006; Stolz et al., 2016; Wichstr0m, 2009) of the 15 studies (20 analyses) that recruited predominantly female participants (>50% female participants) found loneliness to be a significant predictor of later SIB compared to three of the seven studies (eight analyses) that contained predominantly male participants.
+3.1.7.	Ethnicity
+Nine studies reported the ethnicity of the study sample; eight studies included primarily white participants (Fulginiti et al, 2018; Gallagher et al. 2014; Hom et al. 2019; Joiner and Rudd, 1996; Kleiman et al., 2017 Study 1; Kleiman et al., 2017, Study 2; Pietrzak et al., 2017; Schinka et al., 2013) while Salzinger (2007) recruited predominantly Hispanic participants (54%). Due to the variability of outcome measures and other participant demographics, no inferences could be made regarding the role of ethnicity in relation to the relationship between loneliness and SIB.
+3.1.8.	Geography
+All studies were conducted in high income, Western countries, most commonly either in the USA (n = 9; Bonner and Rich, 1988;
+Fulginiti et al., 2018; Gallagher et al., 2014; Hom et al. 2019; Joiner and Rudd, 1996; Kleiman et al., 2017, Study 2; Pietrzak et al., 2017; Salzinger, 2007; Schinka et al., 2013) or Europe (n = 9; Ayalon and Shiovitz-Ezra, 2011; Bennardi et al., 2019; Groholt et al., 2006; Joling et al., 2018; Junker et al., 2017; Lasgaard et al., 2011; Nickel et al., 2006; Stolz et al., 2016; Wichstr0m, 2009). Eight European studies identified a significant univariate relationship between loneliness and later suicidal ideation (Ayalon and Shiovitz-Ezra, 2011; Bennardi et al., 2019; Joling et al., 2018; Lasgaard et al., 2011; Stolz et al., 2016) and behaviour (Junker et al., 2017; Nickel, 2006; Wichstr0m, 2009). Groholt et al. (2006) did not identify a significant loneliness-SIB association however this study also had the smallest sample size. USA-based results were more equivocal, with five of the nine studies reporting a significant loneliness-SIB association including Gallagher et al. (2014) who reported a significant association in one analysis (between Time 2 and Time 3) but not in another (between Time 1 and Time 3).
+Of the remaining studies, those conducted in Israel (Stein et al., 2017; Trakhtenbrot et al., 2016) or worldwide (Kleiman et al., 2017 Study 1) found that loneliness was not a significant predictor of SIB, while a significant association between loneliness and later suicidal ideation was identified in the Australian study (McGraw et al., 2008).
+3.1.9.	Other factors associated with the loneliness-SIB relationship
+Other factors which were associated with the identification and detection of a loneliness-SIB relationship are summarised below. These include the measures employed in each study, as well as sample size, generalisability of the study sample to the target population, where participants were recruited from and duration of the follow-up.
+3.1.10.	Suicidal Ideation Measures
+As noted in section 3.1.1, 17 studies recorded suicidal ideation (see table 2). Seven studies employed a single-item measure taken from a larger psychometric assessment (Ayalon and Shiovitz-Ezra, 2011; Fulginiti et al., 2018; Joling et al., 2018; 2008; Pietrzak et al., 2017; Schinka et al., 2013; Stein et al., 2017; Stolz et al., 2016) of which four identified loneliness as a significant predictor of later suicidal ideation. Studies which used a subscale from a wider measure (Bennardi et al., 2019; Hom et al., 2019; Joiner and Rudd, 1996; Lasgaard et al., 2011) consistently found an unadjusted univariate association between loneliness and SIB. Salzinger et al. (2007) measured suicidal ideation based on four items from a larger measure and found no significant association. Two studies (three results) using a bespoke questionnaire (Bonner and Rich, 1988; Gallagher et al., 2014, T2-T3), found a significant association whereas Gallagher et al. (2014, T1-T3) did not. The remaining three studies employed either a one- (McGraw et al., 2008) or three-item (Kleiman et al., 2017, Study 1; Kleiman et al., 2017, Study 2) non-validated suicidal ideation measure. Of these studies, only
+McGraw et al. (2008) identified loneliness to be a significant predictor of SIB. Overall, 12 of the 17 studies that measured suicidal ideation found loneliness to be a significant predictor, however this reduced to ten studies once some studies controlled for other factors (see section 3.1.4).
+3.1.11.	Suicidal Behaviour Measures
+Suicidal behaviour was measured in seven studies in this review (see table 2) with a total of six different measures. Five studies measured attempts to die by suicide (Groholt et al., 2006; Salzinger et al., 2007; Schinka et al., 2013; Trakhtenbrot et al., 2016; Wichstr0m, 2009), Schinka et al. (2013) was the only study to measure both suicide attempt and self-harm using one question while Wichstr0m (2009) measured these separately with one question each. All studies used selfreport measures with the exception of Junker et al. (2017) and Trakhtenbrot et al. (2016) who used hospital records. No studies included suicide death as an independent outcome measure. Of the seven studies to measure suicidal behaviour, three identified a significant association; this included the three studies where self-harm was included as an outcome variable (Junker et al., 2017; Nickel et al., 2006; Wichstr0m, 2009). These three studies also had among the largest sample sizes and were based in Europe.
+3.1.12.	Loneliness Measures
+Ten measures of loneliness were utilised across the studies included in this review. Six studies employed a single-item loneliness assessment; either an unvalidated one-word ecological monetary assessment (EMA; Kleiman et al., 2017, Study 1; Kleiman et al., 2017, Study 2), an unvalidated single-item question (Junker et al., 2017; Stolz et al. 2016), or used a validated item from a wider psychometric measure (Ayalon and Shiovitz-Ezra., 2011; Nickel et al., 2006). Only studies which used EMA (Kleiman et al., 2017, Study 1; Kleiman et al., 2017, Study 2) did not identify loneliness to significantly predict later SIB.
+The four studies (9 results; Fulginiti et al., 2018; Gallagher et al., 2014, Salzinger et al., 2007; Schinka et al., 2013) which utilised the Loneliness and Social Dissatisfaction Questionnaire (LSDQ), all recruited participants aged <18 years in the USA. Only Gallagher et al. (2014, T1-T2) found a significant association between baseline loneliness and later SIB.
+Ten studies (11 results) used a form of the UCLA Loneliness scale of which eight results reported a significant association (Bennardi et al., 2019; Bonner and Rich, 1988; Hom et al., 2019, T1-T3; Hom et al., 2019, T2-T3; Joiner and Rudd, 1996; Lasgaard et al., 2011; McGraw, 2008; Wichstr0m, 2009). Neither of the studies from Israel (based on psychiatric inpatient or veteran ex-prisoner of war populations), or from a Norwegian hospital (Groholt et al., 2006) found a significant loneliness-SIB association, while all studies which recruited from the general population in other countries did. The remaining two
+studies used the De Jong Gierveld Loneliness Scale (Joling et al., 2018) or the Short Loneliness Scale (Pietrzak et al., 2017) and both identified loneliness as a significant predictor of suicidal ideation.
+3.1.13.	Sample Size
+Sample sizes in the selected studies ranged from 36 (Kleiman et al., 2017, Study 2) to 12,107 (Ayalon and Shiovitz-Ezra, 2011) with the median sample size across the studies being 291 participants. Sample sizes >186 participants had a tendency be more associated with a significant loneliness-SIB association.
+3.1.14.	Generalisability of Sample Population
+Six studies stated that their study sample was generalizable to the target population (Bennardi et al., 2019; Fulginiti et al., 2018; Joiner and Rudd, 1996; Junker et al., 2017; Lasgaard et al., 2011; McGraw et al., 2008). However, these studies also reported significant participant attrition (>40%). A further four studies (Bonner and Rich, 1988; Pietrzak et al., 2017; Salzinger et al., 2007; Schinka et al., 2013), either reported significant participant attrition (>40%) or did not comment on attrition in their study. Nickel et al. (2006) and Salzinger et al. (2007) reported that their samples did not reflect their target populations. As three quarters of the studies included in this review were not likely to be representative of their target populations, the findings from these papers may not be generalisable to their respective populations.
+3.1.15.	Recruitment site: Geography
+11 of the 14 studies which recruited exclusively from the general population identified loneliness as a significant predictor of later suicidal ideation (Ayalon and Shiovitz-Ezra, 2011; Bennardi et al., 2019; Bonner and Rich, 1988; Hom et al., 2019; Joiner and Rudd et al, 1996; Joling et al., 2018; Lasgaard et al., 2011; McGraw et al., 2008; Stolz et al., 2016) or behaviour (Junker et al., 2017; Wichstr0m, 2009). Of the three general population-based studies which did not identify loneliness as a significant predictor, two were from the United States (Salzinger et al., 2007; Schinka et al., 2013) and two contained sample sizes significantly below the median (Kleiman et al., 2017, Study 1; Salzinger et al., 2007).
+Of the three studies (4 results) which recruited exclusively from psychiatric inpatient populations, only Gallagher et al. (2014, T2-T3) found that loneliness was a significant predictor of later SIB. Additionally, Nickel (2006) recruited a combination of inpatient, outpatient and community-based participants with a larger sample size and identified loneliness as a significant predictor of later suicidal behaviour. Pietrzak et al. (2017) and Stein et al. (2017) both recruited from veteran populations with contrasting results, however the heterogeneity of those studies made it impossible to infer the reasons for the conflicting findings.
+3.1.16.	Follow-Up Duration
+Follow-up duration ranged from an average of seven days (Kleiman et al., 2017, Study 2) to 12 years (Stein et al., 2017). Loneliness was commonly found to be a significant predictor of SIB between one month to five years after baseline loneliness assessment (Ayalon and Shiovitz-Ezra, 2011; Bennardi et al., 2019; Bonner and Rich, 1988; Fulginiti et al., 2018; Gallagher et al., 2014; Hom et al., 2019; Joiner and Rudd, 1996; Joling et al., 2018; Lasgaard et al., 2011; McGraw, 2008; Nickel et al., 2006; Pietrzak et al., 2017; Stolz et al., 2016; Wichstr0m, 2009). Of the 18 results within this timespan, only four results were not significant (Fulginiti et al., 2018; Gallagher et al., 2014, T1-T3; Schinka et al., 2013, T2-T3 ideation; Schinka et al., 2013, T2-T3 behaviour). Commonalities between these non-significant results included the recruitment of some of the youngest participants within this review and all studies used the LSDQ measure for loneliness. Only two of the studies with follow-ups of less than a month (Kleiman et al., 2017, Study 1; Kleiman et al., 2017, Study 2) yielded non-significant
+results, while only one study (Junker at al., 2017) of the six which measured beyond five years found a significant result. A distinguishing feature of Junker et al. (2017) was that they recruited significantly more participants than the other studies where follow-up was out-with the 1 month-5-year timeframe.
+3.2.	Meta-analysis
+17 studies were included within the meta-analysis to explore the association between loneliness and later SIB. However as there were differences in data availability across the studies, the number of studies reported within each section of the meta-analysis varies.
+The meta-analytic findings are described as follows:
+i Identification of a loneliness-SIB relationship ii Methodological quality
+iii Moderating effects of socio-demographic characteristics (age, gender) on to the loneliness-SIB relationship;
+iv The role of depression as a mediator of the loneliness-SIB relationship;
+3.2.1.	Association between loneliness and SIB
+Effect sizes for the overall study samples were entered into the metaanalysis irrespective of whether the outcome was ideation, self-harm or suicide attempts. To prevent over-representation of study samples, overall effect sizes were calculated for studies where loneliness was measured at more than one timepoint. This resulted in 17 studies with one effect size calculated for each study. With the exception of both Bennardi et al. (2019) who controlled for multiple demographics and health factors, and Junker et al. (2017) who controlled for age, all effect sizes were unadjusted. A random effects model illustrated that loneliness was a significant predictor of later SIB (r= 0.21 95% CI; 0.14-0.28, z = 5.97, p<0.001). Although there was significant statistical heterogeneity across the studies (I2 = 97.5%, Cochrane Q: 647.501 p<0.001), there was no publication bias (Classic Fail-Safe N= 4473; z-value= 31.84998, p<0.00001) as illustrated by the funnel plot in Fig. 3. Two papers (Salzinger et al.,2007; Schinka et al.,2013) measured both suicidal ideation and behaviour as outcome variables. To avoid over-representation, these papers were excluded from the moderation analysis to explore any statistical difference between loneliness predicting suicidal ideation compared to behaviour. Moderation analysis revealed that the effect sizes for suicidal ideation and behaviour were significantly different (Q (1) = 181.566, p<0.001) with fixed effects models showing that that loneliness was a stronger predictor of suicidal behaviour (r = 0.28, 95% CI: 0.23-0.3, p<0.001, n = 6 studies) than suicidal ideation (r=0.16, 95% CI: 0.15-0.17, p<0.001, n=13 studies)
+3.2.2.	Methodological quality
+Moderation analysis indicated that the quality assessment score was not a statistically significant moderator of the loneliness-SIB relationship.
+3.2.3.	Moderating effect of age
+13 studies provided sufficient data to explore whether age moderated the association between loneliness and SIB. Moderation analysis indicated that age did not statistically affect the loneliness and later SIB relationship. However, there was a dearth of studies covering mid-life (25 to 55 years; see Fig. 4).
+3.2.4.	Moderating effect of gender
+All 17 studies were included in the moderation analysis to explore loneliness predicting SIB as a function of gender. Overall, fixed-effects moderation analysis indicated that in the majority female studies (n = 13 studies) loneliness accounted for 15.5% of the variance in later SIB (95% CI 0.144, 0.167, p<0.001) whereas in majority male studies (n=4) loneliness accounted for 34.4% of the SIB variance (95% CI
+0.327, 0.360, p<0.001). However, there was significant heterogeneity across both groups of studies (Q(15)= 314.884, p<0.001) and a mixed effects model showed there was no significant difference between the dichotomised groups (males vs females) or when gender was reported as a continuous variable (percentage of sample being female).
+3.5. Depression as a mediator of loneliness and later SIB
+16 studies were available to explore whether depression mediated the association between loneliness and later SIB (see Appendix C for a list of included studies). For studies with multiple results, a single correlation value was calculated between each combination pair of the three variables (loneliness, depression, SIB). Models were run from a correlation matrix and specified in M Plus 8.4 (Muthén and Muthén, 2017) using maximum likelihood estimation. Of the 16 papers that were included in the present analysis, the number of studies from which data were provided was as follows; associations between loneliness and depression (N = 6), depression and SIB (N = 11) and loneliness and SIB (N = 16). Based on this the following estimates were entered into the meta-analytic mediation model: (1) the average association between loneliness and depression (r = .3617), depression and SIB (r = .3227) and loneliness and SIB (r = .1713). The sample sizes ranged from 78 to 12,107, the median sample size was 387 and the average was 1862. Based on the average sample size the relationships between loneliness and depression (0 = 0.362, p<0.001), depression and SIB (0 = 0.300, p<0.001) and loneliness and SIB (0 = 0.063, p = .007) were all significant as was the indirect effect from loneliness to SIB via depression (0 = 0.109, p<0.0001). Based on the median sample size the relationship between loneliness and depression and depression and SIB remained significant but loneliness and suicide ideation/behaviour was now non-significant. However, there was still a significant indirect effect from loneliness to SIB via depression (0=0.109, p< .0001).
+4. Discussion
+This review aimed to synthesise findings from existing studies pertaining to whether loneliness predicted later SIB, and if so, whether socio-demographic factors were associated with this relationship or depression acted as a mediator. Of the 22 studies (28 results) that met review criteria, 14 studies (15 results) found that loneliness was a significant predictor of later SIB. There was also evidence that depression mediated the loneliness and later SIB relationship. Of all studies considered within the narrative component of the review, the loneliness-SIB association was more frequently observed in studies that were predominantly female in composition and age-dependent effects were evident.
+The finding that loneliness predicted later SIB fits with several theories of the emergence of SIB. For example, the IMV model (O'Connor and Kirtley, 2018) argues that loneliness may act similarly to social isolation which is included in the model. If so, loneliness may act as a motivational phase factor; increasing the likelihood that entrapment, a key precursor of suicidal ideation, develops. The Interpersonal Theory of Suicide (ITS; Van Orden et al., 2010) also suggests that loneliness in the form of thwarted belongingness is an important predictor of suicidal behaviour.
+Loneliness was more strongly associated with SIB in the longer term compared to in the short-term. This may relate to the stability of loneliness, if present over long time being more pernicious, although this requires more detailed investigation. The moderation analysis revealed that loneliness was a stronger predictor of suicidal behaviour than of suicidal ideation. It is important to note though, that although suicide attempts were assessed in many of the studies, no study measured suicide death. Additionally, the potential lethality or suicidal intent of the suicidal acts were not investigated in the review. The metaanalysis also found that depression mediated the relationship between loneliness and later SIB. Further research is required to determine the
+Age
+Fig. 4. Age as a continuous moderator between loneliness and later SIB.
+Combined effect sizes for all studies with multiple outputs (i.e. Gallagher et al., 2014, Hom et al., 2019; Salzinger et al., 2007; Schinka et al., 2013)
+potential mechanisms through which loneliness may lead to depression.
+Of the subsample of studies included in the moderation analysis exploring gender as a moderator of loneliness and SIB, no statistically significant difference was identified. However, when considering all studies included this review, a large majority of studies comprising of mainly female participants identified loneliness as a predictor of later SIB compared to male-dominant studies which remained at chancelevel. However, it is important to note that the male participants were particularly under-represented in this review. Despite this, any potential gender differences may be affected by social stigma which is associated with self-reporting loneliness in male populations (Borys and Perlman, 1985; Nicolaisen and Thorsen, 2014), with those of Western countries reportedly being less accepting of men disclosing loneliness. Nicolaisen and Thorsen (2014) suggested that the De Jong Gierveld measures may be the only studies to detect gender differences due to their assessment of social and emotional loneliness seperately, however only one study here used the scale and did not explore gender differences. Finally, all studies in the review reported gender on a binary scale, which may have affected the findings. Future research investigating the loneliness-SIB relationship may benefit from reporting the loneliness-SIB relationship in non-binary populations when capturing demographic information.
+With regard to age, observations made in this review supported existing research (Victor and Yang, 2012) in that the loneliness-SIB relationship was more likely to be identified in those aged 16-20 or >58 years at baseline, thereby suggestive of a U-shaped trend. It may be that these two age groups coincide with when loneliness peaks
+across the lifespan as major transitions in social status occur: school graduate (e.g. student to young adult/ labour market) and working adult to retiree. Nicolaisen and Thorsen (2014) argue that at these social transition timepoints, individuals spend more time focusing on their next role in society, thereby loosening ties with existing social supports (e.g. school friends, colleagues). As the transition progresses, new bonds are established and the maintenance of former social bonds become more difficult. If these new bonds are not formed, or social identity is not suitably adjusted, this may create an opportunity for loneliness to develop.
+Despite this age-related trend, two studies (Ayalon and Shiovitz-Ezra, 2011; Bennardi et al., 2019) noted a ‘drop-off’ in the loneliness-SIB relationship in adults aged approximately 65 years old. It could be argued that the transition from working adult to retiree had already happened for those aged >65 years old, where these populations had already adjusted to their new role in society, leading to this loss in the loneliness-SIB association. Both Ayalon and Shiovitz-Ezra (2011) and Bennardi et al. (2019) postulated this observation was perhaps due to loneliness being considered ‘an on-time event’ (Ayalon and Shiovitz-Ezra, 2011) due to the limitations associated with older age (e.g. diminishing social life, the death of older and frailer friends and family, one's own limited health and mobility) while trying to maintain a social life.
+Commonalities across studies were also observed in terms of geography. Most of studies in this review were from Europe or from the United States, however virtually all of the European studies found a significant relationship between loneliness and later SIB while USA
+based results were more variable. Research comparing the prevalence of loneliness across continents is limited, therefore there is little room for speculation regarding observed or hypothesised differences. Despite this, it is important to highlight that the European-based studies often had larger sample sizes than other countries in this review, as well as having more female-dominant sample populations. The findings here suggest the loneliness-SIB relationship is more detectable in studies with larger participant sample sizes (potential small effects). However, as females were over-represented in this review and the range of geographical locations of studies was limited, it is not yet possible to infer whether geography or gender moderate the relationship between loneliness and SIB. Lastly, while most studies used interviews or paper questionnaires to assess the key measures, two studies used EMA (Kleiman et al., 2017, Study 1; Kleiman et al., 2017, Study 2) and these were outliers in respect of trends observed (e.g. gender and follow-up duration). Thus, the mode of measurement may influence whether a loneliness -SIB relationship is detected. Therefore, future research is required to better understand whether EMA studies of loneliness are exploring something different from traditional study measurement scales.
+4.1. Limitations
+The considerable heterogeneity across the studies means that the aggregate findings discussed here should be interpreted with caution. Although this review finds evidence that loneliness may predict SIB, the definition of suicidal behaviour and its constituent terms (e.g. selfharm, suicide attempt) varied considerably between studies (as illustrated by Nickel et al., 2006 see Appendix A). Furthermore, no studies included suicide death as a distinct outcome measure. For example, although Groholt et al. (2006) excluded participants who were deceased at follow-up, their study did include two participants who died by suicide. Meanwhile Trakhtenbrot et al. (2016) included all participants who died by suicide within their suicide attempt group but did
+not make any comparisons between those who had died or survived. These limitations prevent this review from fully exploring the extent to which loneliness predicts SIB in relation to the full range of suicide attempt outcomes. However, this does illustrate that suicide death as an outcome variable is lacking in the extant literature.
+With regard to predictors of a loneliness-SIB association, femaledominant studies typically had larger participant sample sizes and were usually based in Europe. Observationally, these three features (gender, locality and sample size) were consistently associated with identifying a significant relationship between loneliness and later SIB so it is not possible to distinguish which of these elements is the most influential. Meta-analysis did not reveal any of these features to influence the loneliness-later SIB association, however certain factors must be considered when interpreting these results. For example, male populations were under-represented in this review. Furthermore, studies with a participant baseline age of less than 18 years old accounted for half of the results considered here, and no study with a mean participant age between 24 and 55 provided sufficient data to be included in a metaanalysis investigating age as a moderator.
+Finally, an exclusion criterion for this review was that studies must have been available in English, therefore not all published works on the topic of loneliness in relation to later SIB may have been included. This may be reflected by the absence of studies based in Asia or Africa, where papers on this topic may have been written in a non-English language. Additionally, all studies were from Western countries where self-reliance and independence (i.e. individualism) is the cultural norm. Research indicates that when compared to collectivism, individualism is a protective factor against loneliness (Lykes and Kemmelmeier, 2014), which would suggest that the loneliness-SIB relationship may be stronger in countries not addressed in this review. Due to the lack of collectivist countries included in this review, comparisons could not be made to identify whether these results were limited to individualistic populations or were internationally applicable.
+4.2. Conclusion
+In conclusion, loneliness was shown to predict future SIB in both the narrative review and meta-analysis. There was evidence of a loneliness and later SIB relationship among those aged 16 to 20 years, or over 58 years at baseline and in participant samples that were predominantly female. However, these differential relationships were not supported by moderation analyses in a subsample of the studies. Mediation analysis found that depression acted as a mediator of the loneliness to later SIB relationship. Finally, it was observed that loneliness was particularly predictive of later SIB in the short to medium term (up to five years). No prospective studies specifically measured suicide death as an outcome measure and future research would benefit from studying more collectivist cultures.
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+Introduction
+The prevalence of intimate partner abuse (IPA) is high (Bonomi et al., 2006; Morse, 1995) and is correlated with numerous negative effects for both women and men (Campbell, 2002; Coker et al., 2002), but the research documenting long-term outcomes is sparse. Understanding the negative consequences of IPA and the effects it has on both men and women is an important but difficult question to address in extant literature given the need for longitudinal data. Existing research focuses on risk factors for IPA (Capaldi, Knoble, Shortt, & Kim, 2012), using clinical or cross-sectional samples (Campbell, Kub, Belknap, & Templin, 1997; McCauley et al., 1995), and primarily studies outcomes of victimization on women (Zlotnick, Johnson, & Kohn, 2006). The overreliance on cross-sectional designs is problematic because with cross-sectional designs, there are issues with establishing temporal order and, as such, investigating cause and effect (Menard & Elliott, 1990). In addition, with cross-sectional data, there are issues with retrospective data with long recall periods (Menard & Elliott, 1990). Also, there is much debate as to gender differences in IPA and to what extent there is mutual violence (Archer, 2000; Morse, 1995).
+Given that extant literature in this area is lacking in nationally representative, longitudinal data analysis and focuses primarily on IPA outcomes for women, the current study aims to extend research in this area in a number of noteworthy ways. First, a national, longitudinal, and prospective sample of women and men will be analyzed to explore the long-term negative outcomes associated with IPA. Second, IPA victimization and perpetration measures will be separated into minor and violent categories, which are further defined in the “Methods” section of this article. This separation allows us to better understand the differences between minor and violent victimization and perpetration with regard to negative outcomes and gender differences. Third, the lagged dependent variable is controlled for. By controlling for prior involvement with the outcomes, important confounds are eliminated, such as involvement in the behavior influencing IPA experiences.
+Literature Review
+Physical Health
+A number of the negative physical health consequences relating to IPA have been documented in extant literature; however, the long-term physical effects resulting from IPA have not been as well documented (Coker, Smith, Bethea, King, & McKeown, 2000; Logan, Walker, Cole, & Leukefeld, 2002). Results
+from the National Intimate Partner and Sexual Violence Survey (NISVS) indicate that 4,741,000 women and 5,565,000 men experienced physical IPA in the 12 months preceding the survey (Black et al., 2011). Lifetime estimates of IPA victimization indicate that 1 in 4 women and 1 in 7 men will experience severe physical violence by an intimate partner (Black et al., 2011). Regarding injuries, approximately 14% of women and 3.5% of men are injured as a result of IPA (Breiding et al., 2014).
+There is not much specific research on the physical health outcomes of male victims of IPA. This could be because male IPA victims often do not report their victimization due to embarrassment or fear of ridicule or disbelief by law enforcement (Drijber, Reijnders, & Ceelen, 2012). However, what is available suggests that men experience similar forms of mental and physical abuse as women (Drijber et al., 2012; Du-Plat Jones, 2006; George & Yarwood, 2004). For women, those who are abused by intimate partners are more likely to be injured than those who are assaulted by nonintimates (Tjaden & Thoennes, 2000). The injuries sustained by abused woman can vary in severity, from scratches and bruises to burns and bullet wounds (Tjaden & Thoennes, 2000). There may also be an increased risk of traumatic brain injuries among abused women; the risk varies depending on length and severity of violence (Corrigan, Wolfe, Mysiw, Jackson, & Bogner, 2003; Jackson, Philp, Nuttall, & Diller, 2002; Monahan & O’Leary, 1999).
+The general health of individuals, both male and female, who report experiencing abuse, is significantly poorer than individuals who do not experience abuse (Coker et al., 2002; Follingstad, Wright, Lloyd, & Sebastian, 1991; Logan et al., 2002; Plichta, 2004). Indirectly, IPA victimization may contribute to gastrointestinal disorders or other, stress-related problems for women (Plichta, 2004). Overall, abused women note that they experience more chronic conditions, such as fibromyalgia and irritable bowel syndrome, surgeries, hospitalizations, and visits to doctors than non-abused women (Logan et al., 2002). In addition, abused women are more likely to experience sexually transmitted diseases (STDs), pelvic inflammatory disease, chronic pain, bladder, kidney, and urinary tract infections, broken bones, seizures, headaches, stomach ulcers, spastic colon, indigestion, and hypertension (Coker et al., 2000). Abused women may be involved in unhealthy weight control behaviors, including vomiting and the use of laxatives (Silverman, Raj, Mucci, & Hathaway, 2001) and may have overall worse diets than nonabused women (McNutt, Carlson, Persaud, & Postmus, 2002). Abused men report impotence and other sexual problems and loss of weight and appetite (George & Yarwood, 2004).
+Emotional Well-Being
+Depression is the most common health problem reported by abused populations (Campbell, 2002; Campbell et al., 1997; Fergusson, Horwood, & Ridder, 2005; Gleason, 1993). The length and severity of abuse are related to the extent of mental health issues experienced by abused women (Bonomi et al., 2006; Campbell et al., 1997; Dutton et al., 2006). Other mental health issues reported by abused men and women include suicidal thoughts and posttraumatic stress disorder (PTSD; Astin, Ogland-Hand, Coleman, & Foy, 1995; George & Yarwood, 2004; Golding, 1999). Research indicates that female victims are 3 to 5 times more likely than non-victims to experience depression, suicidal thoughts, PTSD, and substance use (Dutton et al., 2006; Golding, 1999). Zlotnick, Johnson, and Kohn (2006) found in their study of a community sample of married or cohabitating women that abused women were more likely than non-abused women to report symptoms of depression, functional impairment, lower self-esteem, and lower life satisfaction in a 5-year follow-up period. However, they only studied outcomes for women, whereas the current study uses a sample of both men and women. In the Male Domestic Violence Victims Survey, George and Yarwood (2004) reported that male victims of IPA noted significant loss of confidence and self-esteem, severe anxiety, mistrust of women, severe depression, suicidal thoughts, and suicide attempts in response to IPA victimization experiences. In addition, male victims report feeling deeply ashamed, frightened, guilty, and confused, and have a loss of self-worth (Du-Plat Jones, 2006; Leonard, 2003).
+Work dissatisfaction is another issue that is relatively common among abused men and women, and there are two related workplace issues: (a) IPA victimization affecting work outcomes and (b) IPA perpetrators going to victims’ workplaces and committing violence.1 The focus of the current study is the first workplace issue, IPA victimization affecting work outcomes for which there is limited research. Work dissatisfaction is included because understanding how IPA victimization and perpetration experiences can influence the work environment is important for a more complete understanding of the emotional well-being consequences of IPA. Available research on work dissatisfaction suggests that an increase in violence at home results in an increase in absenteeism, a decrease in work productivity, and an increased risk of job loss (Leone, Johnson, Cohan, & Lloyd, 2004; Riger, Raja, & Camacho, 2002; Shepard & Pence, 1988; Tolman & Rosen, 2001). The longterm consequences of IPA experiences on work-related outcomes for abused women include inconsistent work histories, underemployment, and a reduction in earnings (Brush, 2003; Tolman & Raphael, 2000). For men, IPA victimization is associated with neglecting work responsibilities and job loss (George & Yarwood, 2004).
+In addition to work dissatisfaction, relationship dissatisfaction and relationship instability are often found in abusive relationships, but it is difficult to establish whether or not relationship dissatisfaction and relationship instability cause IPA victimization/perpetration or whether IPA victimization/perpetration causes relationship dissatisfaction and relationship instability (Capaldi et al., 2012; Stith, Green, Smith, & Ward, 2008). Extant literature suggests that high levels of marital discord and low levels of marital satisfaction are risk markers for IPA (Aldarondo & Sugarman, 1996; Cano & Vivian, 2003; Hotaling & Sugarman, 1990; Stith et al., 2008; Stith, Smith, Penn, Ward, & Tritt, 2004). Lower levels of relationship satisfaction are not exclusive to married couples; dating violence victims report lower levels of relationship satisfaction, as well (Cramer, 2003; Dye & Eckhardt, 2000; Kaura & Lohman, 2007; Testa & Leonard, 2001; Weigel & Ballard-Reisch, 2002). Katz, Kuffel, and Coblentz (2002) found that the seriousness of the relationship moderated the effect that partner violence had on relationship satisfaction; women in serious dating relationships were overall less satisfied with their relationships than were women in non-serious relationships when partner violence was a factor. This is, however, not always the case as other researchers have found that partner violence may be unrelated to relationship satisfaction and may not work to alter an individual’s satisfaction (Capaldi & Crosby, 1997; Gray & Foshee, 1997).
+Problem Behaviors
+Substance use has a well-documented relationship with IPA. In fact, substance abuse is one of the most frequently reported health problems in abused women and it has also been noted in abused men (Campbell, 2002; Coker et al., 2002). Research indicates that abused women are 5 times more likely to abuse substances than are non-abused women (Dutton et al., 2006). It is often found that women may use substances as a coping mechanism for the abuse they are experiencing (Humphreys, Regan, River, & Thiara, 2005; Kaysen et al., 2008; Khantzian, 1997; Kilpatrick, Acierno, Resnick, Saunders, & Best, 1997; Kyriacou et al., 1999; Logan et al., 2002; Manhal-Baugus, 1998; Quigley & Leonard, 2000; Testa, Livingston, & Leonard, 2003; Wingood, DiClemente, & Raj, 2000). The substances often used by abused women include not only alcohol but also marijuana and illicit drugs (Logan et al., 2002). A reciprocal relationship between substance use and victimization has been suggested; women respond to abuse by increasing substance use, which in turn increases their risk of re-victimization (Kilpatrick et al., 1997).
+Offending behaviors are often seen as a precursor or predictor of IPA perpetration, as opposed to an outcome of IPA perpetration and victimization. As such, perpetration experiences are found to be associated with offending
+behaviors for adult women (Browne, Miller, & Maguin, 1999). For adults, researchers note that early problem behaviors are found to predict later IPA perpetration (Capaldi & Clark, 1998; Capaldi, Dishion, Stoolmiller, & Yoerger, 2001; Ehrensaft, Moffitt, & Caspi, 2004; Lussier, Farrington, & Moffitt, 2009; Magdol, Moffitt, Caspi, & Silva, 1998). Given that there is scant research that examines offending as an outcome of IPA perpetration and victimization, excluding research that examines IPA perpetration recidivism, this is a limitation in extant research that should be addressed. Not only do abused populations have substance use, arrest, and offending behavior problems related to abuse, but they may also engage in increased risky sexual behavior. Deviant sexual behavior and a high number of sexual partners are often found among women who have been victimized in relationships (Logan et al., 2002; Plichta, 2004). In addition, abused women are at an increased risk of STDs (Raj, Reed, Welles, Santana, & Silverman, 2008). Male perpetrators of IPA are found to have recent sexually transmitted infection (STI)/HIV diagnoses, to have more unprotected sex, to participate in the sex trade, to have a high number of sexual partners, and to be unfaithful to their partners (El-Bassel et al., 2001; Raj et al., 2008; Santana, Raj, Decker, La Marche, & Silverman, 2006).
+To summarize, the physical, emotional, and mental health costs of IPA appear to be high. Few studies, however, have been able to document the toll that IPA takes long term. Given this gap in the literature, the current study will explore long-term consequences of IPA victimization and IPA perpetration on negative outcomes. As such, the current study extends previous research by Simmons, Knight, and Menard (2015), in which a sample of women and men from the National Youth Survey Family Study (NYSFS) were analyzed regarding their IPA experiences and substance use and depression outcomes 3 years later. This study seeks to delve deeper into IPA experiences by broadening the outcomes examined to include physical health outcomes, emotional well-being outcomes, and other problem behavior outcomes across a 9-year period. The goals of the current study are to test the following six hypotheses.
+Long-Term Consequences of IPA for Women
+Female Hypothesis 1: For women, IPA victimization and perpetration (at Time 1) will be significantly and positively associated with negative physical health outcomes 9 years later (at Time 2 or Time 3).
+Female Hypothesis 2: For women, IPA victimization and perpetration (at Time 1) will be significantly and positively associated with negative emotional outcomes 9 years later (at Time 2 or Time 3).
+Female Hypothesis 3: For women, IPA victimization and perpetration (at Time 1) will be significantly and positively associated with problem behavior 9 years later (at Time 2 or Time 3).
+Long-Term Consequences of IPA for Men
+Male Hypothesis 1: For men, IPA victimization and perpetration (at Time 1) will be significantly and positively associated with negative physical health outcomes 9 years later (at Time 2 or Time 3).
+Male Hypothesis 2: For men, IPA victimization and perpetration (at Time 1) will be significantly and positively associated with negative emotional outcomes 9 years later (at Time 2 or Time 3).
+Male Hypothesis 3: For men, IPA victimization and perpetration (at Time 1) will be significantly and positively associated with problem behavior 9 years later (at Time 2 or Time 3).
+Method
+Data
+The NYSFS is a nationally representative, longitudinal, and prospective study that followed respondents over much of their life course using mostly household interviews (Elliott, Huizinga, & Menard, 1989). The NYSFS began in 1977 with 1,725 adolescent participants (ages 11-17) and their family members. To test the hypotheses in the current study, data are drawn from Waves 9, 10, and 11 of the NYSFS. Wave 9 was collected in 1993 when respondents were 26 to 34 years old, Wave 10 was collected in 2002 when respondents were 35 to 44 years old, and Wave 11 was collected in 2003 when respondents were 36 to 45 years old. Waves 9, 10, and 11 were selected given the 9- and 10-year difference between the waves, which is important for understanding the long-term consequences of IPA. Retention was 78% for Wave 9, 75% for Wave 10, and 70% for Wave 11. Given the 28-year time span of the study, these retention rates are quite reasonable as compared with other longitudinal studies (Menard, 2012; Menard, Morris, Gerber, & Covey, 2011). Wave 9, Wave 10, and Wave 11 will hereafter be referred to as Time 1, Time 2, and Time 3 for clarification purposes.
+Analytic sample. For the current study, the analytic sample was created in the following stages. First, inclusion was limited to those respondents who were in romantic relationships and who answered the Conflict Tactics Scales (CTS) about their relationships at Time 1 (n = 1,002). Second, from those respondents who answered the CTS at Time 1, only those who were still enrolled in the study at Time 2 or Time 3 were retained. Respondents did not need to be in a relationship at follow-up to be included in the analytic sample. Therefore, the final analytic sample size is n = 879. Given the construction of the analytic sample, missing data are minimal (i.e., between 2% and 9% depending on the measure) and are described along with the descriptive statistics in Table 1;
+case-wise deletion was used when data were missing (please see Table 2 for descriptive statistics by gender).
+Measures
+Independent variables. The primary independent variable for this research study is IPA collected at Time 1 using questions from the CTS (Straus, 1979). The NYSFS uses questions from the CTS to measure IPA victimization and perpetration (Straus, 1979). In total, 20 CTS items were included in the NYSFS survey. Of these, 10 items were used in the current study. Items were excluded to be consistent across waves. Intimate partner abuse was broken into four index measures: minor IPA victimization, violent IPA victimization, minor IPA perpetration, and violent IPA perpetration. IPA was separated into these four index measures to better ascertain the effects of each form of violence. Minor IPA victimization and minor IPA perpetration summed five prevalence measures, including questions about insulting/swearing, throwing things, pushing, grabbing, and shoving, threatening to hit or throw something, and slapping. Violent IPA victimization and violent IPA perpetration summed five prevalence measures, including questions about kicking, biting, and hitting, hitting with something, beating up, threatening to use a gun or knife, and using a knife or firing a gun.
+Although some of the questions included in the minor IPA indexes could, potentially, be considered violent forms of IPA depending on the individuals
+involved, these questions were categorized based on the assumption that there are certain acts that involve more injury than others, and existing literature consistently uses this minor/violent dichotomy (Mihalic & Elliott, 1997; Morse, 1995; Simmons et al., 2015; Testa et al., 2003). Therefore, those acts in which there is a greater risk of injury were classified as violent IPA. These independent variables were then transformed into annual prevalence scores, 0 = did not experience in the past 12 months, 1 = did experience in the past 12 months. Given that only a few people had either perpetrated or experienced the most severe forms of IPA (e.g., threatening to use a gun or knife and using a knife or firing a gun), while others were more likely to experience more frequently the less severe forms of IPA, dichotomous measures of IPA were thought to provide the best representation of the IPA experiences of this sample.
+Control variables. In an effort to limit spurious effects, a number of demographic control variables were included in the current study as previous research indicates the importance of these demographic characteristics in understanding IPA (Capaldi et al., 2012). The demographic control variables for this study were collected from Time 1 and include gender (47% male), race (16% non-White), education, in number of years including college (M = 13.50; SD = 2.16), age (M = 30.30; SD = 2.04), and receipt of public assistance in the prior year (3%). Other control variables include the lagged dependent variables measured at Time 1 (all using the same coding as described for the dependent variables measured at Time 2 and Time 3). Following Menard (2008), the lagged dependent variables were included as controls to account for previous participation in or experiences of an outcome, and by controlling for lagged dependent variables, time-stable traits are controlled for; thus, change in the outcome can be
+attributed to IPA experiences more precisely. The only models that did not include the lagged dependent variables were the deviant sexual behavior models because this measure was only collected at Time 1.
+Dependent variables. There were three broad categories of dependent variables for this study: physical health, emotional well-being, and problem behaviors. Physical health includes physical health restrictions and body mass index (BMI). Emotional well-being includes work satisfaction, relationship satisfaction, relationship stability, deviant beliefs, and depression. Problem behaviors include marijuana use, alcohol use, other drug use, arrest, offending behaviors, and deviant sexual behavior. Unless otherwise noted, for the dependent variables, the maximum score across Times 2 and 3 was retained to maximize the analytic sample size and to account for intermittency in behaviors that may occur as individuals get older.
+Physical health. Physical health restriction is a one-item, dichotomous measure asking respondents whether they have any physical problems that restrict their activities based on existing research that suggests individuals who experience IPA may have long-term physical health problems. Answers are coded 0 = no physical restrictions, 1 = physical restrictions. BMI (M = 25.19; SD = 4.79) was calculated using the equation, (weight / height2) x 703.0704 (National Center for Chronic Disease Prevention and Health Promotion, 2014).
+Emotional well-being. Work satisfaction (M = 3.54; SD = 0.69) is one item measured by a question asking respondents how satisfied they are with their job based on research that notes individuals who experience IPA may also experience less satisfaction in their work environments. Only those who indicated on a previous question that they had a job answered this question. Relationship satisfaction (M = 3.65; SD = 0.74) is one item measured by a question asking respondents how satisfied they are with their relationship with their intimate partners; as such, only those who indicated on a previous question that they were in a relationship answered this question. Answers for both work satisfaction and relationship satisfaction range from 0 (very dissatisfied) to 4 (very satisfied). Relationship stability (M = 32.04; SD = 4.49; a = .81) is a summed eight-item scale asking respondents about how much they agree with their partners about family finances, making major decisions, vacations or recreation, demonstrations of affection, household tasks, sexual relations, friends, and philosophy of life. Again, only those who indicated on a previous question that they were in a relationship answered this question. Answers range from 0 (always disagree) to 5 (always agree). Deviant beliefs
+(M = 25.20; SD = 6.57; a = .89) is a summed 17-item scale asking respondents about how wrong it is to cheat on income taxes, destroy property that is not theirs, use marijuana, steal something worth less than 5 dollars, hit or threaten to hit someone for no reason, use cocaine or crack cocaine, break into a vehicle, sell hard drugs, steal something worth more than 50 dollars, get high, use prescription drugs without medical need, give or sell alcohol to minors, attack someone, speed, use force to get something from someone, hit or injure spouse, and pressure or force someone sexually. These items were reverse coded to range from 1 (an action is very wrong) to 4 (an action is not wrong at all). Depression is a prevalence score from Time 3 based on multiple items from the Diagnostic Interview Schedule (DIS), which was based on the Diagnostic and Statistical Manual of Mental Disorders (3rd ed.; DSM-III; American Psychiatric Association, 1980), to determine whether a diagnosis of chronic depression, exclusive of depression resulting from prescription or nonprescription (including illicit) drugs and also exclusive of depression produced by acute conditions such as a death in the family, is appropriate (Robins, Helzer, Croughan, Williams, & Spitzer, 1981).
+Problem behavior. Marijuana use is a one-item prevalence score asking respondents whether they had 0 = not used marijuana in the past 12 months or 1 = used marijuana in the past 12 months. Other drug use is an index created by summing 10 drug prevalence measures, including inhalants, barbiturates, tranquilizers, amphetamines, crack cocaine, powder cocaine, angel dust, hallucinogens, codeine, and heroin. Other drug use was then transformed into an annual prevalence score, 0 = no drug use in the past 12 months, 1 = other drug use in the past 12 months. Alcohol use (M = 2.63; SD = 1.85) is a one-item measure asking respondents how many times they had used alcohol in the past 12 months. Given that alcohol use is a normative behavior and is highly skewed, a natural logarithmic transformation was conducted to reduce skew.2
+Arrest is a one-item prevalence measure asking respondents whether they had been arrested in the past year, with answers coded 0 = have not been arrested, 1 = have been arrested. The offending behavior index was created by summing 12 offending prevalence items, including destroyed or damaged other’s property, set or tried to set a building, car, or property on fire, stolen or attempted to steal a motor vehicle, used force to get something from someone, forced sexual relations, attacked someone, sold hard drugs, paid to have sexual relations with someone, been paid to have sexual relations with someone, and used checks illegally. Offending behavior was then transformed into an annual prevalence score, 0 = no offending behavior in the past 12 months, 1 = offending behavior in the past 12 months. Again, given that only a few respondents had committed some of the more serious offenses (e.g., forced sexual
+relations, paid to have sexual relations with someone), prevalence scores were used to better account for respondents’ participation in offending behaviors.
+Deviant sexual behavior (M = 2.23; SD = 1.49; a = .66) consists of 12 summed prevalence items from Time 1 asking respondents whether they had ever purposefully/secretly watched others as they undressed or engaged in sexual acts, made sexual advances to or engaged in sexual behavior with children, purposefully exposed sexual parts of body to strangers, looked at magazines featuring nudity but not sexual activity, looked at X-rated magazines that showed people engaging in sex, looked at pornographic books that described sexual activity, watched X-rated movies or videos, sent for/looked at mail-order photographs of people engaging in sex, watched live sex shows, read or seen any types of materials other than what was mentioned, had sexual relations where engaged in cruel behavior and inflicted pain on partner, or had sexual relations where sought cruel, dominating, or abusive behavior from partner.
+Results
+Analytic Strategy
+The statistical analyses for the current study were conducted using SPSS Version 20. First, descriptive statistics and bivariate correlations were analyzed for males and females separately. Second, predictors were tested in multivariate logistic and ordinary least squares (OLS) regression models, depending on the distribution of the dependent variable. The sample was split by gender so we could look at gender-specific effects. Models included the following control variables and the lagged dependent variables for each outcome: age, non-White, public assistance, and education level; exceptions are noted where applicable. Given the number of outcomes tested, only the main significant effects concerning separate IPA victimization and perpetration predictors are reported below.
+Descriptive Statistics and Correlations
+In this sample, 53% of men and 56% of women experienced minor IPA victimization, 15% of men and 6.6% of women experienced violent IPA victimization, 52% of men and 68% of women perpetrated minor IPA, and 5% of men and 12% of women perpetrated violent IPA. In total, 55% of respondents experienced minor IPA victimization, 11% of respondents experienced violent IPA victimization, 60% of respondents perpetrated minor IPA, and 8% of respondents perpetrated violent IPA (see Table 2). In addition, correlations between perpetration and victimization variables were examined. Correlations
+indicate that both abusive women and abusive men are also more likely to be victimized by intimate partners (see Table 3).
+Female Multivariate Results
+Physical health. Starting with Table 4, physical health restrictions and BMI are considered indicators of overall physical health. As can be seen in Table 5, female respondents’ history of IPA victimization and perpetration is not significantly predictive of their later physical health restrictions or BMI.
+Emotional well-being. Work satisfaction, relationship satisfaction, relationship stability, deviant beliefs, and depression are considered indicators of emotional well-being as seen in Table 5. First, in the analysis of work satisfaction, after adjusting for demographic factors and the lagged dependent variable, female respondents’ histories of minor IPA victimization (b = -0.23, SE = 0.11, p = .03) and violent IPA perpetration (b = 0.27, SE = 0.14, p = .05) are predictive of lower work satisfaction and higher work satisfaction, respectively. Second, in the analysis of relationship satisfaction, female respondents’ histories of minor IPA victimization (b = —0.20, SE = 0.11, p = .05) and violent IPA victimization (b = -0.73, SE = 0.20, p = .000) are predictive of lower relationship satisfaction at follow-up. Third, in the analysis of relationship stability, female respondents’ prior violent IPA victimization (b = -4.61, SE = 1.58, p = .004) is predictive of lower relationship stability at follow-up. Fourth, female respondents’ histories of IPA victimization and perpetration are not predictive of respondents’ deviant beliefs at follow-up. Fifth, for depression, female respondents’ histories of IPA victimization and perpetration are not significantly predictive of respondents’ depression symptoms at follow-up.
+Problem behaviors. Marijuana use, other drug use, alcohol use, offending behaviors, and deviant sexual behavior are considered indicators of problem behaviors as shown in Table 6. First, in analysis of marijuana use, after controlling for demographic factors and the lagged dependent variable, female respondents’ history of minor IPA perpetration (b = 1.44, SE = 0.74, p = .05) is predictive of increased marijuana use at follow-up. The odds ratio of minor IPA perpetration is 4.23. The odds ratio indicates that, compared with women who did not report minor IPA perpetration at Time 1, those who did report minor IPA perpetration had 4.23 times the odds of reporting marijuana use at follow-up.
+Second, in analysis of other drug use, female respondents’ history of IPA victimization and perpetration is not predictive of their other drug use at follow-up. Third, in analysis of alcohol use, female respondents’ history of violent IPA perpetration (b = —0.59, SE = 0.24, p = .01) is associated with lower alcohol use at follow-up. Fourth, in analysis of offending behaviors, female respondents’ history of IPA victimization and perpetration is not predictive of their offending behavior at follow-up. Fifth, female respondents’ history of minor IPA perpetration (b = 0.51, SE = 0.15, p = .001) is associated with respondents’ deviant sexual behavior. The effects for deviant sexual behavior are contemporaneous given that the questions were only asked at Time 1 which is the same wave that IPA was assessed.
+Male Respondents Multivariate Results
+Physical health. Starting with Table 7, physical health restrictions and BMI are considered indicators of overall physical health. As can be seen in Table 7, male respondents’ history of IPA victimization and perpetration is not significantly predictive of their later physical health restrictions or BMI.
+Emotional well-being. Work satisfaction, relationship satisfaction, relationship stability, deviant beliefs, and depression are considered indicators of emotional well-being as seen in Table 8. First, male respondents’ histories of IPA victimization and perpetration are not predictive of respondents’ work satisfaction at follow-up. Second, in the analysis of relationship satisfaction, after adjusting for demographic factors and the lagged dependent variable, male respondents’ history of violent IPA perpetration (b = —0.38, SE = 0.17, p = .02) is predictive of lower relationship satisfaction. Third, in the analysis of relationship stability, male respondents’ prior violent IPA perpetration (b = —4.32, SE = 1.79, p = .02) is predictive of lower relationship stability at follow-up. Fourth, male respondents’ histories of IPA victimization and perpetration are not predictive of respondents’ deviant beliefs at follow-up. Fifth, male respondents’ histories of IPA victimization and perpetration are not predictive of respondents’ depression symptoms at follow-up.
+Problem behaviors. Marijuana use, other drug use, alcohol use, offending behaviors, and deviant sexual behavior are considered indicators of problem behaviors as shown in Table 9. First, in analysis of marijuana use, after controlling for demographic factors and the lagged dependent variable, male
+respondents’ history of violent IPA victimization (b = 1.20, SE = 0.48, p = .01) is predictive of increased marijuana use at follow-up. The odds ratio of violent IPA victimization is 3.30. The odds ratio indicates that, compared with men who did not report violent IPA victimization at Time 1, those who did report violent IPA victimization had 3.30 times the odds of reporting marijuana use at follow-up.
+Second, male respondents’ histories of IPA victimization and perpetration are not predictive of respondents’ other drug use at follow-up. Third, male respondents’ histories of IPA victimization and perpetration are not predictive of respondents’ alcohol use at follow-up. Fourth, in analysis of offending behaviors, male respondents’ history of violent IPA perpetration (b = 1.74, SE = 0.64, p = .007) is predictive of increased offending behavior at followup. The odds ratio of violent IPA perpetration is 5.68. The odds ratio indicates that, compared with men who did not report violent IPA perpetration at Time 1, those who did report violent IPA perpetration had 5.68 times the odds of reporting offending behaviors at follow-up. Fifth, male respondents’ histories of violent IPA victimization (b = 0.41, SE = 0.25, p = .10) and minor IPA perpetration (b = 0.67, SE = 0.20, p = .001) are associated with respondents’ deviant sexual behavior.
+Discussion
+The current study seeks to extend research on the long-term negative outcomes associated with minor and violent IPA victimization and perpetration. Data from a national, longitudinal, and prospective sample collected by the NYSFS across a 9-year period were analyzed. Six hypotheses were tested while controlling for demographic factors and prior involvement in the outcome. We begin the discussion by summarizing the findings for each hypothesis.
+For female respondents, two of the three hypotheses found some support and for male respondents, two hypotheses found some support. For women, first, the hypothesis that involvement with IPA (at Time 1) will be significantly and positively associated with negative physical health outcomes at follow-up (9 years later) found no support. Second, for women, the hypothesis that involvement with IPA (at Time 1) will be significantly and positively associated with negative emotional outcomes at follow-up (9 years later) found some support in that minor IPA victimization is predictive of work dissatisfaction, minor IPA victimization and violent IPA victimization are predictive of relationship dissatisfaction, and violent IPA victimization is predictive of relationship instability. Contrary to what is hypothesized, violent IPA perpetration is positively associated with work satisfaction for
+female respondents. Third, for women, the hypothesis that involvement with IPA (at Time 1) will be significantly and positively associated with problem behavior at follow-up (9 years later) found some support in that minor IPA perpetration is predictive of increased marijuana use and is associated with deviant sexual behavior. Again, contrary to what is hypothesized for female respondents, violent IPA perpetration is negatively associated with alcohol use, which suggests that a history of violent IPA perpetration is predictive of less alcohol use at follow-up.
+For male respondents, first, the hypothesis that IPA victimization and perpetration experiences will have negative consequences for physical health restrictions and BMI found no support. Second, the hypothesis that IPA victimization and perpetration experiences will have negative consequences on emotional well-being found some support in that violent IPA perpetration is predictive of relationship dissatisfaction and relationship instability. Third, the hypothesis that IPA victimization and perpetration experiences will have negative consequences on problem behaviors found some support in that violent IPA victimization predicted increased marijuana use, violent IPA perpetration predicted increased offending behaviors, and violent IPA victimization and minor IPA perpetration were associated with deviant sexual behaviors. Overall, the findings of the current study suggest that both men and women are affected negatively by IPA experiences, but they may also experience effects specific to their gender. What is clear, however, is that both genders still bear negative outcomes as a result of IPA victimization and perpetration after a long time.
+The key finding in the current study is that minor and violent IPA victimization and perpetration show long-term consequences on a number of varied outcomes. This is important because it illustrates the need to understand the numerous consequences of IPA and solidifies IPA’s status as a public health issue. As existing research on IPA has shown, the prevalence of IPA is high (Bonomi et al., 2006; Morse, 1995), and, as the current study finds, there are negative consequences experienced by victims and perpetrators. Overall, the results highlight the importance of longitudinal analyses, the need for separation of IPA by severity (e.g., minor and violent), and the value of separating victimization and perpetration IPA measures. In addition to finding support for our specific hypotheses, other important findings that emerged from the analyses—related to relationships, work, alcohol use, offending, and stability of prior involvement in outcomes— are discussed below.
+Results from the current study indicate that violent IPA victimization experiences are particularly troubling for female victims with regard to relationship dissatisfaction and relationship stability, and violent
+IPA perpetration experiences predicted relationship dissatisfaction and relationship instability for male respondents. For female respondents, this finding is consistent with extant dating and marriage literature addressing relationship dissatisfaction and violent victimization (Kaura & Lohman, 2007). Stith, McCollum, Rosen, and Thomsen (2004) noted that marital discord is high in violent relationships and, as such, if marital discord is not addressed, physical violence is likely to recur given the issues within the relationship. IPA perpetration can also result in negative impacts on relationships, which can help explain why male respondents may report dissatisfaction and instability within their romantic relationships. Thus, the findings for male respondents may be attributed to a cyclical pattern of violence and relationship dissatisfaction, or it may be that male respondents perpetrate violence toward their partners and subsequently note dissatisfaction and instability in their relationships. Although relationship dissatisfaction and relationship instability may often be seen as precursors to IPA, they can also be results of IPA as found in the current study. The relationship dissatisfaction and relationship instability outcomes are not surprising given existing research that notes the negative impacts IPA experiences can have on relationships, especially regarding marital discord (Aldarondo & Sugarman, 1996; Cano & Vivian, 2003).
+With regard to work dissatisfaction, for female respondents, research indicates that when there is increased violence in the home, there are increased problems within the workplace, including increased absenteeism, decreased productivity, increased risk of job loss, inconsistent work histories, underemployment, and reduced earning (Brush, 2003; Leone et al., 2004; Riger et al., 2002; Shepard & Pence, 1988; Tolman & Raphael, 2000; Tolman & Rosen, 2001). For female respondents, the unexpected positive relationship of work satisfaction with violent IPA perpetration and the negative relationship of alcohol use with violent IPA perpetration are interesting. There could be a number of explanations for these findings. One such explanation for the increase in work satisfaction is whether women’s perpetration of IPA reduced their stress levels, leading to more satisfactory work environments.
+In addition, an explanation of the negative relationship of alcohol use with violent IPA perpetration is whether alcohol had played a role in past IPA and, as a result, female respondents reduced their alcohol use to try and prevent future IPA. For female respondents, minor IPA perpetration was predictive of increased marijuana use, and for male respondents, violent IPA victimization was predictive of increased marijuana use. It is no surprise that substance use, in this case, marijuana use, has a relationship with IPA, as the relationship between substance use and IPA experiences has
+been noted in abused men and women (Campbell, 2002; Coker et al., 2002). The results for women are interesting because substance use, including marijuana use, is often noted as a result of victimization by intimate partners (Campbell, 2002), and not much research is available documenting use of substances as a result of perpetration of IPA. The results for men, that violent IPA victimization predicted increased marijuana use, could be potentially explained by the same factors that have been used to explain female victims’ substance use after victimization—use of substances as a coping mechanism for abuse (Humphreys et al., 2005; Kaysen et al., 2008; Khantzian, 1997; Kilpatrick et al., 1997).
+For male respondents, violent IPA perpetration was predictive of increased offending behavior. As stated previously, offending behaviors are often found to predict IPA perpetration and are not often seen as outcomes of IPA perpetration and victimization (Capaldi & Clark, 1998; Capaldi et al., 2001; Ehrensaft et al., 2004; Lussier et al., 2009; Magdol et al., 1998). However, it is possible that male perpetrators of violent IPA may be more aggressive and, as such, more likely to offend in other ways, as well. Given the limited research that examines offending as an outcome of IPA perpetration and victimization, more research should be conducted on offending behaviors. For both female and male respondents, minor IPA perpetration was associated with deviant sexual behavior. This is an interesting finding considering women are usually thought to experience coercive sexual behavior at the hands of male intimate partners (Watts & Zimmerman, 2002). Research by Malamuth (1981) indicates that male participants who indicated a higher likelihood of raping were similar to convicted rapists with regard to rape myths and sexual arousal to rape depictions. In addition, increased likelihood of raping was associated with increased aggression toward women (Malamuth, 1981). However, the findings for deviant sexual behavior were contemporaneous; thus, future research in this area is warranted.
+Last, across all models, results indicate that there is continuity in physical health, emotional well-being, and problem behavior across a 9-year period, as indicated by the lagged dependent variable being significant in every model examined (but note that these results are not shown in the tables) for female respondents. For male respondents, across all models except the depression models, the lagged dependent variable is always significant, indicating continuity in physical health, emotional well-being, and problem behavior across a 9-year period. Overall, the consistency of these findings suggests that it is important to control for prior involvement in outcomes to accurately estimate the effects of IPA.
+The limitations of the current study bear mentioning. First, the NYSFS used the CTS to measure the extent to which intimate partners self-report
+abuse toward one another. Although this is beneficial in that all eligible respondents provided IPA data, as opposed to just those who reported IPA to law enforcement personnel, for example, the information gathered from the CTS does not reveal the context of violence, which is important for uncovering whether the violence is mutual (Morse, 1995). Generally, the CTS captures common couple’s violence, but important consequences of IPA are still seen. There are a number of strengths to the CTS that make it a valid tool to use in this type of research, especially when examining a national probability sample. The CTS allows researchers to quantitatively study IPA events from both the abuser and the victim. Also, the CTS makes a distinction between minor and severe forms of violence, which is important in distinguishing the outcomes of different levels of violence.
+Second, given the 9-year follow-up period, immediate changes in physical and sexual health, emotional well-being, and problem behavior could not be measured. However, because the purpose of this study was to examine the long-term outcomes of IPA victimization and perpetration, the 9-year followup period is also a strength to the study. Third, what mediates IPA and subsequent problematic outcomes is not tested; future research in this area is warranted. Fourth, out of 12 items, 1 item used in the deviant sex measure was pornography. Although pornography is presently considered an increasingly normative behavior, during the time this measure was collected in the 1993 wave of the NYSFS, the Internet was not yet the popular entity it is today. Fourth, IPA experiences and severity of IPA victimization and perpetration were coded based on respondents’ experiences in the 12 months prior to Time 1. As such, it is likely that any historical abuse prior to the 12 month recall period was not accounted for. Fifth, follow-up surveys are not able to capture all relevant life events that may influence the dependent variables at the time of the survey. Overall, the strengths of the CTS, the construction of the IPA scales (e.g., distinguishing between minor and violent victimization and perpetration), and the extended follow-up period of 9 years may outweigh the study’s limitations, especially because we were able to establish temporal order and document the effects of IPA while controlling for important demographic factors and prior involvement in outcomes. In conclusion, this study contributes to the body of literature on IPA victimization and perpetration by examining its long-term negative outcomes on men and women. Results find support for a number of negative outcomes that may have implications for both theory and practice, given that a number of these outcomes may not be addressed in intervention and counseling services. Differences in men and women’s IPA experiences need to be taken into account to effectively tailor intervention programs to both perpetrators and victims of minor and violent partner violence (Carney, Buttell, & Dutton, 2007).
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+Introduction
+Past review studies have provided strong evidence of the relationship between unemployment and suicide [1-4]. However, the magnitude of the relative and attributable risk associated with unemployment has been contested, as various authors have suggested that the effect of socio-economic factors on suicide tend to be overestimated when the contribution of psychiatric disorders is not taken into account [5]. This assumption is problematic as it assumes that mental illness and unemployment have separate and independent effects on suicide when, in fact, mental illness is a likely intermediatory factor between unemployment and suicide [6].
+A further problem is that many past studies view unemployment as a simple static and binary state (i.e. unemployed versus employed), thereby ignoring the dynamic nature of employment status. A person may only be without a job for a finite period
+before re-employment, or from unemployment they may exit the labour market. The importance of considering the potential risks associated with length of unemployment has recently been recognised in epidemiological studies, which have demonstrated that longer durations of unemployment affect cause-specific and all-cause mortality differentially over time [7,8]. Variation in the effect of unemployment duration has also been shown in studies of psychological wellbeing, which have demonstrated pronounced adverse mental health consequences the longer a person is unemployed [9]. At the same time, past meta-analyses on mental health outcomes and all-cause mortality [7,9] suggest a possible amelioration or “adjustment” to the adverse effects of unemployment on mental health and mortality over time.
+There has been no systematic review and meta-analysis on length of unemployment and suicide. However, given the results of the meta-analyses [7,9] discussed above, there is good reason to suspect that unemployment may have differential effects on suicide
+over time. This systematic review and meta-analysis seeks to summarise evidence to date on the effects of duration of unemployment on suicide attempts and deaths. The main hypotheses of the study were that: 1) long term unemployment would be a risk for suicide attempt and mortality, and; 2) the relationship between unemployment and suicide would vary over time.
+Methods
+Unemployment duration was measured as either a continuous (e.g., number of days since the loss of a job) or categorical (e.g., short versus long term unemployment) variable. The study protocol was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (http://www. prisma-statement.org/).
+Databases and search terms
+The search was conducted using four databases: Pub Med, Web of Knowledge, Scopus and Proquest. These databases were chosen to ensure that the literature search strategy comprehensively examined research from medicine, epidemiology, sociology and psychology. A secondary search of reference lists was undertaken from within retrieved articles. Search terms used for the search were: suicid* OR self injur* OR deliberate self harm AND job loss OR unemploy*. Authors were contacted to identify additional statistical details on retrieved studies. The first author conducted the initial searches and shortlisting. Subsequent searches and checking was undertaken by the other two authors, with mismatches in classification resolved by consensus.
+Inclusion and exclusion criteria
+Articles were considered if the search terms were included in the abstract or title of the article and were published in the last thirty years (i.e. 1980 or later), which was when the last review study on the unemployment and suicide was conducted (criterion A) [2]. After a review of the title and abstract, review articles, editorials and papers not in English were excluded. Only peer reviewed research was considered (criterion B). Duplicates were also removed. Following this, the abstract and text were reviewed to assess whether unemployment was a key independent variable of interest (criterion C) and suicide was a measured outcome variable (criterion D). Among the remaining articles, preference was given to those articles and scholarly pieces that measured the effect duration of unemployment over time (criterion E), or the risk associated with suicide following job loss (criterion F).
+Study design and variables of interest
+This review included studies conducted at the individual and the aggregate (ecological) level. Qualitative and case-series studies were excluded. Studies were classified according to whether they were: (1) population-based cohort or case-control designs; (2) population-based ecological designs, or; (3) hospital based clinical cohort or case-control designs. Suicidal behaviours could be measured as either non-fatal (suicide attempts) or fatal (suicide).
+Data extraction
+The data extracted from identified studies included suicide attempt or death, duration of unemployment or follow up from the point of unemployment, and results by sex (where available). The results of studies were described individually using summary measures such as risk or rate ratios.
+Meta- analysis
+Of those studies included in the overall systematic review, only a subset of longitudinal cohort studies on mortality were considered eligible for meta-analysis. The reason for this was that these were the only studies that provided comparable measurements of exposure (unemployment duration or follow up from the time of unemployment) and outcome (suicide). Pooled effect size and 95% confidence intervals were calculated using random effects metaanalysis using the inverse variance (Dersimonian and Laird) method. The effects assessed to be eligible in in the meta-analysis included hazard, odds or rate ratios. Heterogeneity between studies was assessed using the I2 statistic, which provided an estimate of the percentage of variability in the outcome that is due to differences in exposure-outcome association. Where possible, adjusted estimates (with 95% confidence intervals) were used and results were stratified by sex. The possibility that unemployment would have nonlinear effects over time was tested as an a priori hypothesis based on the findings of a previous meta-analysis [7], which demonstrated the most significant levels of relative risk of all cause-mortality occurred within either five years or five to ten years of unemployment, and was reduced thereafter. The metaanalysis was carried out in Stata Version 12 [10]. Meta-regression was conducted to assess the impact of duration of unemployment as a source of heterogeneity. Funnel plots were used to assess publication bias and small study effects [11].
+Results
+The process of inclusion and exclusion of articles can be seen in Figure 1. After reviewing titles and abstracts, articles were excluded if they did not specifically investigate the association between unemployment and suicide, were duplicate studies or published prior to 1980. Among the 874 remaining articles, 87 editorial, review and conceptual articles were removed. Articles were also excluded if the article was not in English. The abstract and text were reviewed to exclude those articles that were descriptive case-series or qualitative designs. Over 300 articles were ecological studies, conducted either cross-sectionally or longitudinally, in which unemployment duration and/or suicide were not the primary variables of interest. Restricting studies to those where a measure of duration of unemployment or time since job loss was included resulted in 16 articles relevant to this systematic review (Table S1).
+Study quality
+The quality of studies was assessed before inclusion in the systematic review and meta-analysis, based on published recommendations [12]. Seven studies of suicide used cohort designs [8,13-18], while three examined the relationship between unemployment duration and suicide using an ecological design [19-21]. Four other studies examined unemployment duration in relation to suicide attempts using data from hospitals [22-25], while two studies examined suicide attempts and ideation using a case-control design [26,27]. The source populations were more representative in large cohort studies than those in clinical cohorts based in smaller hospitals. This is likely related to differences in the ability to collect population-level data on suicide attempts versus mortality.
+There were considerable differences in the measurement of unemployment duration between studies (exposure) (Table S1), with some studies defining unemployment as a categorical variable and others measuring it continuously. The representativeness of exposure was higher in population representative cohort studies than clinical cohort studies. Clinical cohort studies relying on self-
+Figure 1. Search strategy following PRISMA guidelines. doi:10.1371/journal.pone.0051333.g001
+report of unemployment status may be subject to non-differential selection bias.
+Cohort studies included in the meta-analysis were not subject to this problem as unemployment data was usually ascertained from objective data sources (e.g., welfare payment or official employment figures). However, these sources may suffer from a lack of sensitivity and specificity in both the exposure and the outcome, and issue that was not generally addressed in the design of studies. This bias is likely to be non-differential, with an equal likelihood of affecting both cases and controls. Loss to follow-up is another issue that may affect longitudinal cohort studies. These limitations not withstanding, retrospective cohort designs capturing data at the population level were deemed to be the highest quality studies available.
+Attempted suicide
+Four clinical cohort studies examined employment status in those persons attending a hospital after engaging in suicidal behaviours [22-25]. Among the earliest published were hospitalbased studies by Platt and colleagues [22,23], which found consistently higher rates of suicide among the long-term unemployed than employed or short-term unemployed suicide attempt-ers (Table S1). There were two population-based cohort studies of attempted suicide in young people (under 25 years) in New Zealand [26,27]. In comparison to those persons who experienced no unemployment, the relative risk of attempted suicide in those who had experienced #6 months of unemployment was 1.31 (95% CI 0.94 to 1.82), and in those who had experienced over 6 months was 1.72 (95% CI 0.89 to 3.32) after adjusting for mental disorders [27].
+Suicide
+An ecological study on suicide over the period 1948-1978 in the USA [20], found that longer duration (measured as a continuous
+variable) was associated with higher male and female suicide rates. Another ecological study by Shah [19] found no relationship between long term unemployment (time period undefined) and suicide rates across 27 countries. The outcome variable in this study was suicide rates in those aged 65 years and over, a population not widely represented in the labour market.
+Recently, Classen and Dunnn [21] conducted an ecological study on the relationships between unemployment duration associated with mass lay-off events and suicide across 50 states of the USA. Results of this study indicated that a rise in the number of workers who were unemployed between 15 and 26 weeks was associated with an increase in suicides among males, but this relationship was no longer apparent after 26 weeks. For females, unemployment longer than 5 weeks was associated with an increase in suicides.
+Meta-analysis
+The remaining studies on suicide were retrospective cohort designs capturing data at the population level in Sweden, Finland, and Denmark [8,13-18]. As discussed above, these studies generally were assessed to have substantially higher quality than the papers reviewed above. The data sources used in these studies originated from longitudinal employment and mortality registers. Four studies obtained information on psychiatric history from hospital databases [8,14,17,18], while one other obtained psychiatric information from military conscription testing [15]. Following closer inspection of the data sources, one study [13] was excluded from the meta-analysis as its analysis of unemployment duration and suicide was conducted on a limited sample of cases (those who were previously admitted to a hospital for a psychiatric disorder).
+The majority of studies used hazard ratios as outcome measures, while one study used odds ratios. As suicide can be considered a rare outcome in a population, these measures of risk were seen as
+comparable [28]. As can be seen in Figure 2, the overall pooled relative risk of suicide associated with longer unemployment (average follow-up time 7.8 years) compared to those currently employed was 1.70 (95% CI 1.22 to 2.18). Inspection of the I2 indicated a high degree of heterogeneity between studies (93.6%). This variation between studies and initial inspection of the forest plot (Figure 2) was consistent with the a priori hypothesis of a nonlinear relationship between unemployment and suicide over time. Results were then stratified by studies with follow-up periods below and above five years after unemployment. The overall pooled relative risk in studies with follow up less than five years was 2.50 (95% CI 1.83 to 3.17) compared to those currently employed, while the risk in those studies with follow up periods between 12 and 16 years was 1.21 (95% CI 1.10 to 1.33) compared to those currently employed.
+Meta-regression was used to assess the impact of duration of unemployment as a source of heterogeneity and revealed significant differences based on time to follow up. Studies with a longer duration of follow up had significantly lower RR than those that had a shorter follow-up period. Meta-regression was also used to assess the possible influence of SES and other factors such as mental illness. The results of this test suggested that controlling for these factors had no significant influence on results.
+Possible publication bias and small study effects were assessed through inspection of a funnel plot (see Figure S1). This indicated an asymmetric plot, with smaller studies showing larger effect sizes, which may suggest publication bias. Following this, we investigated funnel plot asymmetry using Egger and colleagues’ 1997 test for small study effects in meta-analysis [11]. The estimated bias coefficient was —1.86 with a standard error of 2.2, giving a p-value
+of 0.414. This test provides no evidence of small study effects, although the large standard error indicates considerable heterogeneity in results. Further, it is important to note that results of this test may be limited by the small number of studies included in analysis.
+Discussion
+This study systematically reviewed the evidence on the relationship between unemployment duration and suicide. Unemployment duration was identified as the main study factor in 16 studies. Based on this relatively small number of studies (compared to the proportionally larger number of studies of general unemployment and suicide), it appears that the effect of unemployment duration is not frequently explored in suicide research. Even within those studies that examined differences in unemployment duration, this was often poorly assessed. For example, two ecological studies failed to explain how long a person was unemployed before they were classified as ‘‘long term’’, [19,20] and the majority of the cohort studies included in metaanalysis did not provide a clear indication of whether employment status was assessed continuously or periodically throughout the follow-up period.
+There is tension in epidemiological research about whether the primary objectives of meta-analytic studies should be either the estimation of an overall summary or average effect across studies, or the identification and estimation of differences between studies [28]. In this study, considerable efforts have been made to illustrate the extent of differences between studies and to emphasise sources of heterogeneity. Despite these differences,
+consistent findings were still apparent across identified studies. Long term unemployed persons (compared to short-term unemployed persons) had a greater number of suicide attempts and were at increased risk of re-attempting suicide in both the clinical context and the general population. Findings from the metaanalysis also provide some evidence that longer durations of unemployment were associated with a higher relative risk of suicide compared to those currently employed and that the association is likely to be nonlinear with stronger relative risk estimates for earlier follow-up periods [8,16] compared to later follow-up periods [14-17]. Further evidence is needed in order to understand when suicide risk peaks.
+Psychological studies on unemployment suggest that there is a critical time period of between three months and a year during which people may be most at-risk of mental disorder [29,30]. This time is thought to coincide with the growing sense of hopelessness that may accompany the perceived transition from short- to longterm unemployment [9,29]. Some findings suggest that symptoms of mental ill health may stabilise at an elevated level during the second year of unemployment, before being associated with a renewed increase in distress in the long-term unemployed [9]. A recent meta-analysis of all-cause mortality also suggests that the influence of duration changes over time, with a higher risk of mortality at 5 years follow-up (to 73% increased risk of mortality) but reducing at 10 years follow-up to a level above that observed at baseline (to 42% increased risk of mortality) [7]. These findings are similar to the studies reported in this review, which suggests that there may be a reduction in risk after a period of 5- years of follow up.
+The definition and measurement of unemployment duration was a problem in studies included in our meta-analysis, as most studies provided incomplete information on employment status during follow-up. We assumed that follow-up was synonymous with unemployment duration. In some studies this was clearly the case; in others, it was less so. This problem has been noted in another meta-analysis on unemployment duration in relation to all-cause mortality [7]. The high degree of heterogeneity between studies is another limitation in understanding the association between unemployment duration and suicide, which may be attributable to factors such as differences in how unemployment was measured (e.g., continuous [19-21]], ordinal [15,24], or interval measurement [8,14,16,17]), differences in study design and, differences in study populations. Variation may also reflect the notable dissimilarity in follow-up and the lack of information about the relationship between unemployment duration and suicide between five and 12 years. Geographical context, labour market opportunities (e.g., unemployment rates) and other contextual influences across the samples included in meta-analysis may also affect results.
+Another notable problem was that some studies reported only adjusted results while others only provided crude results. A sensitivity analysis using meta-regression indicated no significant differences depending on whether results adjusted for covariates such as mental illness, age and SES. However, this analysis was limited by small sample size and a heterogeneous sample. It is possible that unmeasured confounders such as age and SES could wholly explain the observed association if no effect of unemployment on suicide existed. Information on the relationship between SES and suicide from a recent published paper published (kindly provided by Lundin [32]), indicated that the relationship between
+References
+1.	Jin RL, Shah CP, Svoboda TJ (1995) The impact of unemployment on health: a review of the evidence. CMAJ 153: 529.
+unemployment and suicide reduced after controlling for lower SES. Similarly, after controlling for age, the relationship between unemployment and suicide reduced. However, neither of these confounders attenuated the relationship between suicide and unemployment toward the null. The data in this recent paper [32] was used in several other studies included in the meta-analysis and therefore the results of this paper are likely to be generalizable to the present meta-analytic study.
+Studies in the meta-analysis with over 12 years of follow-up also controlled for mental illness, which some researchers would argue explains a substantial amount of the relationship between unemployment and suicide [31]. This would suggest that cases of suicide among unemployed populations are a reflection of vulnerabilities that precede the loss of a job (i.e., the health selection hypothesis or the latent sickness hypothesis) [7,15]. The smaller relative risk estimates in those studies with over five years of follow-up may indicate survivor bias, in that those participants remaining at longer periods of follow-up are less likely to be experiencing an ongoing mental illness [14,15]. However, mental distress following unemployment has also been considered as a mediator of the relationship between unemployment and suicide, rather than a confounder [6]. Thus, controlling for mental illness is likely to lead to an underestimation of the relationship between unemployment and suicide. Similarly, adjusting for other likely intermediatories such as alcohol use and low emotional control would have the effect of biasing results towards the null. Other limitations include the possibility that a number of relevant articles were excluded in the review process. The review also excluded qualitative studies and case reports, articles not in English, and studies that were not published in the peer-reviewed literature.
+In conclusion, the general finding of this review is that the longterm unemployed have a greater risk of suicide and attempted suicide compared to those unemployed in the short term, or compared to the general employed population. In order to better characterise the relationship between unemployment duration and suicide, there is a need for future studies to indicate a continuous measurement of employment status throughout the duration of follow-up. Future studies also need to capture individual influences on the relationship between unemployment duration and suicide, such as whether the individual has previously been unemployed, their occupation, and their age and sex, [7,9]. Knowledge in this area would also benefit from an assessment of contextual factors affecting outcomes for long term unemployed such as the availability of jobs at a population level and social welfare programs for the unemployed [7,9].
+Supporting Information
+Table S1 Papers assessing the relationship between unemployment and suicide.
+(DOCX)
+Figure S1 Funnel plot to assess publishing bias and small study effects, meta-analysis of suicide risk following unemployment, follow up over time.
+(TIF)
+Author Contributions
+Interpreted results: AM AP AL. Conceived and designed the experiments: AM AP. Analyzed the data: AM. Wrote the paper: AM AP AL.
+2.	Platt S (1984) Unemployment and suicidal behaviour: a review of the literature. Soc Sci Med 19: 93-115.
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diff --git a/Makers of central inflamation.txt b/Makers of central inflamation.txt
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+1.	Introduction	depressive disorder (MDD) is increasingly acknowledged. The evidence
+derives from both epidemiological studies, which have linked increased The role of inflammation in the pathophysiology of major peripheral (e.g., blood) inflammation and MDD, and from animal
+models and human studies showing development of depressive symptoms following administration of immune challenges (Raison et al., 2010). Many studies have now investigated the presence of central inflammation in patients with MDD; however, these studies often focus on different markers of neuroinflammation, making it difficult to interpret inconsistent findings. The presence and the role of immune activation in the brain of patients with MDD remain, therefore, still partly unclear.
+In this review of central inflammation in MDD, we focus on different markers of immune dysregulation in the brain, including levels of
+cytokines in the cerebrospinal fluid (CSF) and measures of number and function of brain cells involved in immune regulation, such as microglia, astrocytes and oligodendrocytes. Microglia and astrocytes are the main key players in the immune response in the central nervous system (Miller and Raison, 2016). Microglia is the resident macrophages of the central nervous system and it adopts different phenotypes in response to inflammatory and injury stimuli (Mondelli et al., 2017). In an activated state, microglia secretes numerous pro- inflammatory cytokines and chemokines, including interleukin (IL) 1, IL-6, IL-12, and tumor necrosis alpha (TNF-a) (Mondelli et al., 2017). Astrocytes are the most
+numerous glial cells and can be activated by pro-inflammatory cytokines produced by microglia. Astrocytes have several active roles in the brain, including phagocytic properties and secretion of pro-inflammatory cytokines (IL-6, TNF-a) (Rajkowska and Stockmeier, 2013). Oligodendroglia is a glial cell which derives from oligodendrocytes progenitor cells and plays a key role in myelination (Mechawar and Savitz, 2016). It can produce cytokines (IL-1) and chemokines (monocytes chemoattractant protein 1) post injury (Mechawar and Savitz, 2016).
+Previous research has implemented different methodological approaches to address the question on the presence of neuroinflammation in MDD. Most studies investigating brain immune cells have focused on post-mortem brain tissue. However, more recently, in order to investigate microglia activation in humans in vivo, an increasing number of studies have used brain PET scans and measured expression of the translocator protein (TSPO). Increased TSPO expression was initially proposed as an indirect measure of microglia activation; however, this view has been challenged by a recent study suggesting that increased TSPO expression in humans may reflect local myeloid cell proliferation or an increased monocyte recruitment rather than activation of microglia (Owen, et al., 2017).
+A number of previous meta-analyses (Wang and Miller, 2018) and reviews (Czeh and Nagy, 2018; Mechawar and Savitz, 2016; Rajkowska and Stockmeier, 2013) have been published in the last few years in this field by focussing on specific aspects or areas of neuroinflammation. A recent meta-analysis focused specifically on CSF cytokines in patients with MDD, schizophrenia and bipolar disorder, and found increased CSF levels of IL-6 and IL-8 in these patients (Wang and Miller, 2018); however, this systematic review and meta-analysis did not include two previous studies, which focused exclusively on major depression (Hestad et al., 2016; Stubner et al., 1999). Another recent article reviewed evidence on glial markers from post-mortem studies, highlighting the role of microglial activation and reduction in astrocytes function in patients with MDD; however, this was not a systematic review and did not present the large amount of inconclusive post-mortem studies on glial markers in patients with MDD and bipolar disorder (Czeh and Nagy, 2018).
+The aim of this paper is to systematically review all studies focusing on markers of central inflammation in vivo (CSF and PET markers of inflammation) and in post-mortem in patients with MDD compared with controls and to discuss how the findings for each component (cytokines, microglia, astrocytes, oligodendrocytes) may integrate with each other. We additionally used a meta-analytical approach to summarise data when the quality of studies would allow for such approach. A secondary aim of the paper was to review data on central inflammation more specifically in relation to suicidality in patients with MDD.
+2.	Methods
+2.1.	Search strategy
+This study followed the PRISMA guidelines for conducting and reporting systematic reviews (Panic et al., 2013). The original publications were identified by searching Pub Med electronic database and by scanning reference lists until December 2018. One reviewer (ED) screened the titles and the abstracts for eligibility. Two reviewers (ED and MV) assessed all publications of potential relevance for inclusion.
+We searched the English literature after search words: [“inflammation” OR “cytokines” OR “cerebrospinal fluid cytokines” OR “TSPO” OR “PET microglia”] AND [“depression” OR “major depression”]. For post-mortem studies we searched after the words [“inflammation” OR “microglia” OR “astrocytes” OR “cytokines”] AND “major depressive disorder” OR “depression”] AND “post-mortem” (Fig. 1).
+2.2.	Study selection and data extraction
+Inclusion criteria were: (a) patients with unipolar major depressive disorder; (b) comparison with controls; either (c) PET imaging of microglia and/or CSF cytokines and chemokines in subjects with depression as compared with subjects without depression; or (d) cell type specific markers for astrocytes, microglia and cytokines in post-mortem brains of MDD.
+Exclusion criteria were: (a) depressive symptoms or MDD in other major psychiatric disorders as bipolar disorder, schizophrenia, eating disorders, mild and major neurocognitive disorders; (b) assessment of only plasma or serum cytokines and chemokines and no assessment of central inflammatory marker; (c) assessment of others inflammatory biomarkers in the CSF such as substance P or corticotrophin-releasing factor; (d) use of non-cell specific markers for astrocytes and microglia in post-mortem studies (see Table 1).
+We retrieved 8781 studies in PubMed and Medline databases assessing in-vivo and post-mortem markers of inflammation in patients with depression. After removing reviews articles, articles written in other languages than English and studies involving animals, we included 360 studies for revision. Sixty-nine original publications met all the inclusion criteria (Fig. 1). The main characteristics and findings of the studies are presented in Tables 2-5, and Supplementary Tables 1, 3 and 4.
+From the included studies we extracted information on: (a) the population (number of participants in the study, age, number of patients with MDD, severity of depression (mean (SD) of the score on depression scales); (b) type of the biomarker of inflammation (cytokines and chemokines, PET microglia); (c) type of the biomarker of inflammation in post-mortem studies (cell specific markers for microglia, astrocytes and oligodendrocytes, levels of cytokines and chemokines); (d) outcome levels of biomarkers in patients with MDD and controls (mean and standard deviations).
+2.3.	Statistical analysis
+A meta-analysis approach was used only for in vivo studies of CSF and PET markers of inflammation in patients with MDD, as postmortem studies were too varied to allow using this approach. From each article included in the meta-analysis we extracted data on sample size, mean and standard deviation, number of patients with MDD and controls. When needed we estimated mean ± standard deviation (S.D.) from median/range or interquartile range/standard error of the mean using formulas previously described (Hozo et al., 2005). We calculated standardised mean difference (SMD) as a measure of effect size and the 95% confidence intervals (95% CIs). P-values < 0.05 were considered statistically significant. The heterogeneity in the analysis was assessed with x2-test (the heterogeneity in effect size estimates) and I2 -index (estimated percentage of variation in effect size attributable to heterogeneity). The pooled analysis was considered significantly heterogeneous if the p-value in the x2-test was below 0.05 and I2 -index was more than 50%. We carried out a sensitivity analysis to examine the impact of individual studies on the heterogeneity by excluding one study at a time and repeated the meta-analysis procedure. The statistical analyses were performed with the software STATA 15 (StataCorp LP).
+3.	Results
+3.1.	CSF or PET biomarkers of inflammation in patients with depression
+3.1.1.	CSF cytokines and chemokines in patients with depression
+We included 12 studies which investigated cytokines, chemokines and complement C5 in CSF of MDD patients (Blasko et al., 2006; Boufidou et al., 2009; Carpenter et al., 2004; Hestad et al., 2016; Ishii et al., 2018; Kern et al., 2014; Levine et al., 1999; Lindqvist et al., 2009;
+Martinez et al., 2012; Palhagen et al., 2010; Sasayama et al., 2013; Stubner et al., 1999).
+CSF levels of IL-6 were measured in 9 studies. Three studies reported increased levels of IL-6 in patients with MDD compared with controls (Lindqvist et al., 2009; Martinez et al., 2012; Sasayama et al., 2013), while 2 studies reported decreased levels of CSF IL-6 in patients with MDD compared with controls (Levine et al., 1999; Stubner et al., 1999). Four studies found similar levels of CSF IL-6 between patients with MDD and controls (Carpenter et al., 2004; Hestad et al., 2016; Kern et al., 2014; Palhagen et al., 2010). Five studies measured CSF levels of TNF-a and all reported similar CSF levels of TNF-a between MDD and controls (Blasko et al., 2006; Hestad et al., 2016; Levine et al., 1999; Lindqvist et al., 2009; Martinez et al., 2012). Three studies measured CSF levels of IL-8 (Hestad et al., 2016; Kern et al., 2014; Lindqvist et al., 2009), one study reported increased CSF levels of IL-8 (Kern et al., 2014) and two studies reported similar levels between MDD and controls (Hestad et al., 2016; Lindqvist et al., 2009). Three studies measured CSF levels of IL-1p (Hestad et al., 2016; Levine et al., 1999; Lindqvist et al., 2009). One study found increased CSF levels of IL-1p in patients with MDD compared with controls (Levine et al., 1999), while two others reported similar levels between MDD and controls (Hestad et al., 2016; Lindqvist et al., 2009).
+Most of the studies included small samples and reported conflicting results on the CSF levels of IL-6, TNF-a, IL-8 and IL-1p in patients with MDD. We employed a meta-analysis method to estimate the difference between patients with MDD and controls. CSF levels of IL-6, TNF-a and IL-8 were significantly increased in patients with MDD compared with controls (CSF IL-6: SMD 0.37, 95%CI: 0.17-0.57, z = 3.57, p < 0.001, X2 = 68.66, p < 0.0001, I2 = 88.3%; CSF TNF-a: SMD 0.58, 95%CI: 0.30-0.45, z = 3.59, p < 0.0001, X2 = 61.20, p < 0.0001, I2 = 95.1%; CSF IL-8: SMD 0.82, 95%CI: 0.52-1.13, z = 5.28, p < 0.0001, X2 = 13.62, p = 0.001, I2 = 85.3%) (Fig. 2). Heterogeneity was very high across the studies. In a sensitivity analysis for CSF IL-6 heterogeneity was no longer significant after excluding Lindqvist et al(Lindqvist et al., 2009), Levine et al (Levine et al., 1999) and Stubner et al. (1999) (SMD 0.40, 95%CI: 0.17, 0.63, z = 3.4, p = 0.001, X2 = 4.3, p = 0.5, I2 = 0%). In a sensitivity analysis CSF levels of TNFa were similar between patients with MDD and controls after excluding Lindqvist et al (Lindqvist et al., 2009) (SMD -0.07, 95%CI: -0.43, 0.30, z = 0.4, p = 0.7, X2 = 0.80, p = 0.7, I2 = 0%). Table 1 illustrates the results of the sensitivity analysis performed for CSF levels of IL-8 and IL-1p.
+A longitudinal study of pregnant women by Boufidou and colleagues focussing on women at risk of developing postpartum depression was included for the purposes of the review but not for the meta-ana-lysis. The authors found that high CSF levels of IL-6 and TNF-a at the time of delivery were positively associated with the occurrence of depressive symptoms postpartum at 4 days and 6 weeks after delivery (Boufidou et al., 2009).
+Two studies reported on CSF levels of chemokines. Blasko and colleagues found similar CSF levels of monocyte chemoattactant protein 1 (MCP-1) and macrophage inflammatory protein 1 alpha (MIP-1a) in MDD compared with controls (Blasko et al., 2006). While Janelidze and colleagues found that patients with MDD and suicide attempt had lower CSF levels of chemokines monocyte chemotactic protein 4 and thymus and activation-regulated chemokine compared with controls (Janelidze et al., 2013).
+3.1.2.	PET translocator protein (TSPO) expression in patients with depression
+This is the first meta-analysis published on PET (translocator protein) TSPO studies in patients with MDD. We included 6 studies assessing the brain TSPO expression on 147 patients with MDD and 106 controls (Hannestad et al., 2013; Holmes et al., 2018; Li et al., 2018; Richards et al., 2018; Setiawan et al., 2018; Su et al., 2016). First (Holmes et al., 2018; Su et al., 2016) and second (Hannestad et al.,
+2013; Li et al., 2018; Richards et al., 2018; Setiawan et al., 2018) generation TSPO PET tracers were used. Out of the 6 studies, 5 studies found elevated TSPO in different brain regions of patients with MDD compared with controls (Holmes et al., 2018; Li et al., 2018; Richards et al., 2018; Setiawan et al., 2018; Su et al., 2016), while one study found similar TSPO levels between MDD and controls (Hannestad et al., 2013).
+We employed a meta-analysis to explore the brain regions where the TSPO expression is more pronounced. TSPO was elevated in patients with MDD compared with controls in the anterior cingulate cortex (SMD0.71, 95%CI 0.40-1.03) (Holmes et al., 2018; Richards et al., 2018; Setiawan et al., 2018; Su et al., 2016), temporal lobe, (SMD 0.51, 95%CI: 0.18-0.84) (Hannestad et al., 2013; Li et al., 2018; Setiawan et al., 2018), frontal lobe (Hannestad et al., 2013; Li et al., 2018), prefrontal cortex (Holmes et al., 2018; Setiawan et al., 2018), insula (Holmes et al., 2018; Setiawan et al., 2018), and hippocampus (Li et al., 2018; Setiawan et al., 2018). The levels of translocator protein were similar between patients with MDD and controls in the occipital cortex (Hannestad et al., 2013; Setiawan et al., 2018), parietal cortex (Hannestad et al., 2013; Setiawan et al., 2018) and thalamus (Hannestad et al., 2013; Setiawan et al., 2018) (See Fig. 3, Table 3, Supplementary Table 2).
+3.1.3.	Correlations between central andperipheral markers of inflammation
+Out of 22 studies on CSF and PET-TSPO, 9 studies conducted correlation analyses between markers of central inflammation and peripheral inflammation (Bay-Richter et al., 2015; Hannestad et al., 2013; Hestad et al., 2016; Holmes et al., 2018; Isung et al., 2012; Levine et al., 1999; Lindqvist et al., 2009; Sasayama et al., 2013; Setiawan et al., 2015). Seven studies found no correlation between markers of central inflammation (CSF cytokines or PET TSPO) and peripheral inflammation (Bay-Richter et al., 2015; Hannestad et al., 2013; Holmes et al., 2018; Isung et al., 2012; Lindqvist et al., 2009; Sasayama et al., 2013; Setiawan et al., 2015). Hestad and colleagues found a moderate correlation between serum and CSF levels of eotaxine, interferon-inducible protein 10 and macrophage inflammatory protein 1 beta in patients
+with MDD (Hestad et al., 2016). Levine and colleagues found a correlation between CSF levels of IL-1p and serum levels of TNF (Levine et al., 1999).
+3.2.	Markers of inflammation in post-mortem studies of patients with depression
+As mentioned above, after careful consideration a meta-analysis was not possible for the post-mortem studies as different cell-specific markers of glial cells are measured, often repeatedly in the same small samples of post-mortem brains cohorts. Moreover, different cellular and molecular inflammatory markers were measured in different brain regions, using a broad range of methods and techniques.
+3.2.1.	Cytokines, chemokines and other inflammatory markers in postmortem studies
+Ten studies measured cytokines (Clark et al., 2016; Dean et al., 2013, 2010; Hoyo-Becerra et al., 2013; Pandey et al., 2019, 2012; Pantazatos et al., 2017; Tonelli et al., 2008; Wang et al., 2018) and chemokines (Clark et al., 2016; Torres-Platas et al., 2014) expression in postmortem brains of MDD (Supplementary Table 3). The presence of cytokines and chemokines in postmortem MDD brains is debatable, with some studies reporting increased expression of TNFa and monocyte chemoattractant protein-1 (MCP-1) (Dean et al., 2010; Wang et al., 2018; Torres-Platas et al., 2014), other studies reporting reduced expression of TNFa, IL-8, MCP-1 and macrophage inflammatory protein 1 beta (MIP-1p) (Clark et al., 2016; Pantazatos et al., 2017), and other authors finding similar levels of TNFa, and IL-6 between subjects with MDD and controls (Dean et al., 2013; Tonelli et al., 2008). Although IL-6 mRNA was no different between depressed and controls subjects, IL-6 mRNA has been reported to be increased in suicide victims (Hoyo-Becerra et al., 2013; Pandey et al., 2012). When reviewing studies investigating other inflammation-related markers, Clark et al. found lower quinolinic levels and lower kynurenine: tryptophan ratio in brains of patients with MDD (Clark et al., 2016).
+3.2.2.	Microglia in post-mortem brains of patients with depression
+We included 8 studies which investigated microglial markers in post-mortem brains of patients with MDD (Brisch et al., 2017; Busse et al., 2015; Clark et al., 2016; Foster et al., 2006; Steiner et al., 2011, 2008; Torres-Platas et al., 2014; Wesseling et al., 2014) (Table 4).
+Of the 8 studies, 4 did not detect any MDD related changes (Brisch et al., 2017; Torres-Platas et al., 2014; Steiner et al., 2008; Foster et al., 2006). The other four reported conflicting findings. Clark et al reported an increase in the proportion of IBA1-positive amoeboid (active) microglia in the ventrolateral prefrontal cortex of patients with MDD compared with controls and similar density of hypertrophic (non-ac-tive) microglia (Clark et al., 2016). Wesseling et al reported lower levels of coronin1A, a marker of microglia (Wesseling et al., 2014) in MDD compared with controls. The other 2 studies focussed on quinolinic immunoreactive microglia, with Busse et al showing a reduction in the hippocampal cornu ammonis 1 (CA1) (Busse et al., 2015), while Steiner et al reporting an increase in the subgenual ACC and anterior mid-cingulate cortex, notably from post-mortem brains from the same biobank (Steiner et al., 2011).
+Of note only 4 out of 8 studies investigated microglia morphology between MDD and controls (Clark et al., 2016; Steiner et al., 2011; Torres-Platas et al., 2014; Brisch et al., 2017). Of these 4 studies, 3 reported increase in density of activated microglia according to morphology (Clark et al., 2016; Steiner et al., 2011; Torres-Platas et al., 2014), while one reported no differences between groups when looking at microglia morphology (Brisch et al., 2017).
+Cause of death was suicide for most of the subjects with MDD. Two studies included only MDD subjects who died through suicide (Busse et al., 2015). Increased density of microglia (microgliosis) was reported in suicide victims irrespective of psychiatric diagnosis (Steiner et al., 2011).
+3.2.3.	Astrocytes in post-mortem brains of patients with depression
+We included 24 studies which assessed astrocytes in postmortem brains of patients with MDD (Altshuler et al., 2010; Barley et al., 2009; Bernard et al., 2011; Chandley et al., 2013; Cobb et al., 2016; Damadzic et al., 2001; Davis et al., 2002; Fatemi et al., 2004; Gos et al., 2013; Miguel-Hidalgo et al., 2014, 2011, 2010, 2000, 2017; Rajkowska et al.,
+2018; Ramaker et al., 2017; Toro et al., 2006; Torres-Platas et al., 2016, 2011; Webster et al., 2005, 2001; Wesseling et al., 2014; Williams et al., 2014; Zhao et al., 2016) (Table 5). Cause of death was suicide for most of the subjects with MDD included. Two studies included only MDD suicide victims (Torres-Platas et al., 2016, 2011), while one study excluded MDD suicide victims (Davis et al., 2002). Of the 24 studies, 13 studies found a decrease in the astrocytes specific markers, 3 reported an increase, whereas 11 studies found similar levels of astrocytes specific markers between patients with MDD and controls. The markers used to investigate astrocytes varied across the studies, with 20 studies focusing on glial fibrillary acidic protein (GFAP) expression or immunoreactivity distribution and 9 studies investigating other markers including S100 calcium binding protein B (S100B), glutamate transporters genes or performing morphometric analyses. Reactive astrocytes have an increased expression of glial fibrillary acidic protein (GFAP), which in postmortem studies is used as astrocytes specific marker (Rajkowska and Stockmeier, 2013).
+When focusing only on the immunohistochemical studies investigating GFAP immunoreactivity between patients with MDD and controls, four studies found a reduction in GFAP immunoreactive astrocytes in MDD compared with controls in the basolateral nucleus of the amygdala (Altshuler et al., 2010), orbitofrontal cortex (Miguel-Hidalgo et al., 2010), locus coeruleus (Chandley et al., 2013) and white matter in ventral prefrontal cortex (Rajkowska et al., 2018); six studies found similar GFAP immunoreactive astrocytes between patients with MDD and controls, in the hippocampus (Cobb et al., 2016), dorsolateral prefrontal cortex (Miguel-Hidalgo et al., 2000), orbitofrontal cortex (Toro et al., 2006), entorhinal cortex (Damadzic et al., 2001), substantia nigra (Williams et al., 2014), and anterior cingulate cortex (Davis et al., 2002); and only one study showed higher GFAP immunoreactivity in dorsolateral prefrontal cortex in brains of elderly patients with MDD (Davis et al., 2002). In contrast, another study reported a decrease in phosphorylated GFAP-positive cells in contact with the blood vessels of the dorsolateral prefrontal cortex in MDD (Webster et al., 2001).
+Lower mRNA or protein levels of GFAP in MDD compared with controls were found in mediodorsal thalamus (Torres-Platas et al., 2016), caudate nucleus (Torres-Platas et al., 2016), locus coeruleus
+(Bernard et al., 2011; Chandley et al., 2013), orbitofrontal cortex (Miguel-Hidalgo et al., 2017), lateral cerebellum (Fatemi et al., 2004), and white matter in ventral prefrontal cortex (Rajkowska et al., 2018). Only one study reported higher GFAP mRNA in thalamus (anteroventral nucleus, mediodorsally nucleus), intern capsule and putamen in MDD (Barley et al., 2009). Two studies found similar mRNA or protein levels of GFAP between MDD and controls in cerebellar cortex, primary motor cortex (Brodmann’s area-BA- 4), primary visual cortex (BA 17) (Torres-Platas et al., 2016), and in anterior cingulate cortex (Webster et al., 2005). The results regarding other astrocytic markers are summarized in Table 5.
+3.2.4.	Oligodendrocytes in post-mortem brains of patients with depression
+We included 12 studies which measured oligodendrocytes in postmortem MDD brains (Aston et al., 2005; Barley et al., 2009; Gos et al., 2013; Hayashi et al., 2011; Honer et al., 1999; Lutz et al., 2017; Miguel-Hidalgo et al., 2017; Rajkowska et al., 2015; Tanti et al., 2018; Uranova et al., 2004; Vostrikov et al., 2007; Williams et al., 2014). Cause of death was suicide for most of the MDD subjects. Two studies included only MDD suicide victims (Lutz et al., 2017; Tanti et al., 2018) (Supplementary Table 4).
+Six studies measured markers of oligodendrocytes in the prefrontal cortex. Four out of six reported a decrease in oligodendrocytes in this brain region (Hayashi et al., 2011; Miguel-Hidalgo et al., 2017; Uranova et al., 2004; Vostrikov et al., 2007). Uranova et al, Vostrikov et al and Hayashi et al reported in the same cohort of MDD brains from Stanley Foundation Neuropathology Consortium a reduction in the whole population of oligodendrocytes in the gray matter of the prefrontal cortex (BA 9) layer VI (Uranova et al., 2004), and layer III (Vostrikov et al., 2007), and in the frontopolar prefrontal cortex (BA 10) (Hayashi et al., 2011).
+Using immunohistochemical analysis for the whole population of oligodendrocytes, 5 studies reported a similar density of oligodendrocytes in post-mortem brains of patients with MDD and those of controls in the white matter of ventromedial (Tanti et al., 2018), ventral prefrontal cortex (Rajkowska et al., 2015), in the gray matter of substantia nigra (Williams et al., 2014), hippocampus (Gos et al., 2013) and anterior cingulate cortex (Lutz et al., 2017). Rajkowska et al found a decreased soma size of oligodendrocytes in the gyral white matter of the ventral prefrontal cortex in MDD, but a similar soma size of oligodendrocytes in the deep white matter of the ventral prefrontal cortex between MDD and controls (Rajkowska et al., 2015). Using PCR, Aston et al found decreased transcription factors OLIG2 and SOX 10 mRNA in temporal lobe of MDD brains compared with controls (Aston et al., 2005), while Barley et al found similar expression of Sox10 and OLIG 2 mRNA in the thalamus (anteroventral nucleus, mediodorsal nucleus), internal capsule and putamen of MDD compared with control brains (Barley et al., 2009).
+Tanti el al and Lutz et al reported a reduction in whole population of oligodendrocytes in the white matter of the ventromedial prefrontal cortex and of the anterior cingulate cortex of MDD suicide victims with childhood abuse compared with MDD suicide victims without childhood abuse and normal controls (Lutz et al., 2017; Tanti et al., 2018).
+3.3.	Markers of central inflammation and suicidal behaviour in MDD
+3.3.1.	CSF cytokines in subjects with suicidal behaviour
+We included 5 studies which measured the CSF levels of cytokines and chemokines in patients with suicide attempts regardless of their diagnosis. Suicide attempters regardless of the neuropsychiatric disorder had higher CSF levels of IL-6 (Lindqvist et al., 2009) and quinolinic acid (Bay-Richter et al., 2015; Erhardt et al., 2013), but lower CSF levels of kynurenic acid, endotaxin1, macrophage inflammatory protein 1p, monocyte chemoattractant protein-1, monocyte chemotactic protein 4 and thymus and activation-regulated chemokine than controls (Bay-Richter et al., 2015; Janelidze et al., 2013). Erhardt et al (Erhardt
+et al., 2013) and Bay-Richter et al (Bay-Richter et al., 2015) reported in two studies from the same cohort that CSF levels of quinolinic acid decreased at 6 months follow up (Erhardt et al., 2013), but remain higher at almost 2 years after a suicide attempt (Bay-Richter et al., 2015). In another cohort of patients with suicide attempts Isung et al found lower CSF levels of IL-8 in patients with a suicide attempt compared with controls, but the authors reported similar CSF levels of IL-6 between suicide attempters and controls (Isung et al., 2012).
+3.3.2.	Markers of central inflammation between depressed subjects with suicidal behaviour and depressed subjects without suicidal behaviour
+In this review we also try to highlight CSF, PET and post-mortem studies, which compared different inflammatory markers between patients with MDD with suicidal behaviour and MDD without suicidal behaviour.
+Martinez et al reported a positive correlation between levels of CSF IL-6 and IL-1 and suicidal ideation (Martinez et al., 2012). We found one PET study, which reported microglia activation only among the 9 MDD patients with suicidal thoughts compared with 5 MDD patients without suicidal thoughts and controls (Holmes et al., 2018).
+In post-mortem studies suicide was main cause of death for individuals with MDD. In a post-mortem study Wang et al (Wang et al., 2018) found similarly increased levels of TNF-a mRNA, and Pantazatos et al (Pantazatos et al., 2017) found similarly lower IL-8 and chemokine macrophage inflammatory protein 1 beta in MDD suicide victims compared with MDD non-suicide victims.
+Pandey et al. (2019, 2014) found similar levels of toll like receptor 1, 2, 3, 4 mRNA in MDD suicide victims and MDD no suicide victims, but increased levels of protein toll like receptor 3, 4 and 6 in MDD suicide victims compared with MDD non-suicide victims (Pandey et al., 2019).
+Zhao et al found reduced levels of astrocytes specific glutamate reuptake transporter mRNA in MDD suicide victims compared with MDD non-suicide victims (Zhao et al., 2016). Miguel- Hidalgo et al found similarly low levels of astrocytes connexin 43 protein in MDD suicide victims and MDD non-suicide victims compared with controls (Miguel-Hidalgo et al., 2014). Tanti el al and Lutz et al reported a similar density of oligodendrocytes in MDD suicide victims compared with MDD non-suicide victims (Lutz et al., 2017; Tanti et al., 2018).
+4.	Discussion
+Our systematic review and meta-analysis support the presence of increased neuroinflammation in patients with MDD as shown by increased pro-inflammatory cytokines in CSF. At cellular level, our paper suggests the presence of microglia activation but not necessarily an increased density of microglia, as indicated by the post-mortem studies showing an increased in primed and activated microglia. Density of astrocytes and oligodendrocytes in brains of patients with MDD appears mainly reduced or similar to that of controls (Fig. 4).
+This paper summarizes the overall evidence of different markers of neuroinflammation in patients with MDD, rather than focussing on a single or a couple of markers. This is particularly important if we want to better understand the communication between immune and central nervous system, given the role of the cross-talk between microglia, astrocytes and oligodendrocytes plays in maintaining the brain homeostasis.
+Our review shows that patients with MDD have increased levels of CSF cytokines such as IL-6 and IL-8 and an increased expression of TNF-a mRNA (Wang et al., 2018), monocytes chemoattractant protein 1 mRNA (Torres-Platas et al., 2014) and toll like receptor 3 and 4 in postmortem brains (Pandey et al., 2014). Toll like receptor 3 and 4 mediate the activation of microglia and increase the production of proin-flammatory cytokines in the dorsolateral prefrontal cortex (Facci et al., 2014; Pandey et al., 2014). PET studies show an elevated TSPO in specific brain regions in patients with MDD, including the anterior
+cingulate cortex, frontal cortex, temporal cortex, hippocampus and insula. The increased expression of TSPO from brain PET studies in MDD is difficult to interpret given that the notion that TSPO expression is a marker of microglia activation has been recently challenged by an in vitro study (Owen, et al., 2017). The recent study suggests that increased TSPO expression in humans may indicate instead an increased monocyte recruitment. This appears possibly in agreement with postmortem studies. Indeed, Torres-Platas et al. (2014) reported a higher proportion of blood vessels surrounded by a high density of macrophages in MDD suicide victims than in controls, suggesting an increased recruitment of peripheral monocytes in suicidal patients (Torres-Platas et al., 2014). These findings support the hypothesis that cytokines and chemokines play a key role in recruiting peripheral monocytes and activating microglia without necessary the occurrence of microgliosis. Although post-mortem studies found similar density of microglia in patients with MDD and in controls, Torres-Platas et al found an increased ratio of primed over ramified (‘‘resting’’) microglia in the anterior cingulate cortex of MDD suicide victims compared with brains from controls (Torres-Platas et al., 2014). Activated microglia release pro-inflammatory cytokines (such as IL-6 (Wang and Miller, 2018) and TNF-a (Wang et al., 2018)). Cytokines administration has been shown to induce activation of the hypothalamic-pituitary-adrenal (HPA) axis and peripheral glucocorticoid secretion (Felger and Lotrich, 2013). In turn, glucocorticoids modulate the microglial activation and the activity of the HPA axis (Walker and Spencer, 2018). Most of the studies on CSF and PET could not find a correlation between peripheral and central cytokines. These findings may question the hypothesis that central inflammation derives from an increased peripheral inflammation in major depressive disorders (Miller and Raison, 2016). However, the lack of correlation between peripheral and central cytokines may also be due to other possible moderating factors such as the permeability of the blood brain barrier (Miller and Raison, 2016).
+The implication of astrocytes and oligodendrocytes in the immune response is more debatable. Astrocytes play a key role in maintaining the neurotransmitters homeostasis (glutamate and GABA), water transport homeostasis, ion homeostasis, metabolic support, synapto-genesis and synaptic plasticity, maintaining the integrity of the blood brain barrier (Verkhratsky and Nedergaard, 2018). The post-mortem studies show that several specific markers for astrocytes, such as GFAP, gap junction proteins (connexin 43, water channel aquaporin 4,
+calcium binding protein S100B, the glutamate transporters (EAAT1, EAAT2 or SLC1A3 and SLC1A2) and glutamine synthetase are reduced in patients with MDD) (Bernard et al., 2011; Chandley et al., 2013; Miguel-Hidalgo et al., 2011). The reduction in the astrocytes specific markers was found in brain areas that are well known to be involved in depressed mood and anhedonia such as the prefrontal cortex (Miguel-Hidalgo et al., 2017, 2011, 2010; Webster et al., 2001), anterior cingulate cortex (Torres-Platas et al., 2011; Wesseling et al., 2014), amygdala (Altshuler et al., 2010) and locus coeruleus (Bernard et al., 2011; Chandley et al., 2013). The reduction in the astrocytes markers may reflect impairment of their function, including possibly an effect on the integrity of the blood brain barrier, particularly the S100B (Gos et al., 2013). Some studies, which found a decrease in neurotrophic factors in patients with MDD, argue that in patients with MDD there is primarily an impairment in synaptic plasticity and neuroplasticity (Martinez et al., 2012; Rajkowska and Stockmeier, 2013).
+Post-mortem morphometry studies found no difference in the astrocytes soma between MDD and controls, which suggest that there is no atrophy or degeneration of the astrocytes in gray and white matter (Rajkowska et al., 2018; Torres-Platas et al., 2011). Post-mortem studies which measured the glutamate transporters in astrocytes found a reduction in the SCL1 gene expression and its protein the glutamate transporter EAAT1, suggesting an impairment of the glutamate reuptake from the synaptic cleft (Miller and Raison, 2016). Additionally, the GAP junction between astrocytes and astrocytes and between astrocytes and neurons may be affected and this would lead to an impairment in the ion homeostasis and synaptic circuit (Rajkowska and Stockmeier, 2013; Verkhratsky et al., 2014).
+The concomitant activation of microglia and reduced function of astrocytes could also have downstream effects on the kynurenine pathway (Dantzer et al., 2011). Cytokines may activate the indolamine 2,3-dioxygenase (IDO) an enzyme expressed in microglia and astrocytes. IDO is catabolising the amino acid tryptophan, essential precursor of serotonin neurotransmitter into different metabolic products. Two of the end products of the kynurenine pathway are the quinolinic (QUIN) and kynurenic (KYNA) acid (Dantzer et al., 2011). Microglia expresses the enzyme kynurenine-3-monooxygenase which is essential to produce QUIN, while astrocytes express the enzyme kynurenine aminotransferase essential to produce KYNA. QUIN is regarded as a neurotoxic end product of the kynurenic pathway, while KYNA is
+neuroprotective (Borsini et al., 2015; Miller and Raison, 2016). Therefore, an unbalance between microglia and astrocytes activation may influence production of QUIN and KYNA. These two metabolites also interact with glutamate neurotransmitter system. QUIN has been shown to activate the N-methyl-D-aspartate receptor (NMDA) and to increase the glutamate in the synaptic cleft through increase glutamate release and decrease the glutamate reuptake by the astrocytes (Dantzer et al., 2011). Some post-mortem studies found increased density of QUIN-positive microglia cells in the subgenual anterior cingulate cortex and the anterior midcingulate cortex of MDD patients, and a decreased re-uptake of glutamate from the synaptic cleft (Bernard et al., 2011; Chandley et al., 2013; Steiner et al., 2011). Furthermore, an increased level of cytokines and impairment in astrocytes function may lead to an impairment of the myelination and reduced oligodendrocytes density (Barnett and Linington, 2013; Rajkowska et al., 2018). In agreement with this possible effect on oligodendrocytes density, studies included in this review show a reduction in oligodendrocytes markers in the prefrontal cortex, but no MDD changes in other brain regions of MDD brains. This is in line with diffusion tensor imaging studies which reported reduced fronto-subcortical connectivity in patients with MDD, disruptions which occur in early stages of the disease (Liao et al., 2013; Ma et al., 2007).
+4.1.	Methodological considerations
+One limitation in this review is the increased heterogeneity across studies in study design, methodology used to measure inflammatory markers, and sample selection. For example, CSF and PET studies included patients with different degrees of severity of MDD (current or remitted depression). Some CSF and PET tried to address the role of antidepressants in inflammation including patients with MDD who were drug-free for several weeks and months before the assessment (Martinez et al., 2012; Richards et al., 2018; Setiawan et al., 2018). However, almost all post-mortem studies included patients with MDD who were exposed to several psychotropic medications (antidepressants, sedative hypnotics and antipsychotics) before death. There is also heterogeneity due to specific methodology and outcome measures. A previous meta-analysis found elevated TSPO binding in patient with schizophrenia when the outcome was TSPO- Binding Potential (BPND), but not when the outcome was TSPO- Volume of Distribution (VT) (Marques et al., 2018). In our meta-analysis four out of six studies used TSPO- VT, while two other studies used TSPO-BPND. Microglia activation appears mainly supported by post-mortem studies looking specifically at microglia morphology and further studies would need to be conducted to further confirm this finding.
+Suicidal behaviour is part of clinical assessment of MDD; therefore, it was difficult to include only studies assessing inflammatory markers in MDD without suicidal behaviour. Few in vivo CSF and PET studies reported on the degree of suicidal behaviour of the patients with MDD. Most of the post-mortem studies were conducted in a mixed population of MDD suicide and non-suicide victims, and the cohorts are not always well characterized. In addition, most of the post-mortem studies measured the glial cells and cytokines in different brain regions. Controls were recruited from different clinics, sometimes from neurological department; in post-mortem studies the main cause of death for controls was cardiovascular diseases which are also associated with altered inflammatory processes (Ruparelia et al., 2017).
+4.2.	Conclusions
+Our paper suggests the presence of neuroinflammation in patients with MDD which is mainly characterized by increased levels of pro-inflammatory cytokines in CSF and increased activation of microglia without microgliosis and decreased density of astrocytes and prefrontal cortex oligodendrocytes. This suggests that specific changes in the cross-talk of the different glial cells may contribute to a disruption in
+38
+the communication between the immune and central nervous system and downstream alterations of the kynurenine pathway and glutama-tergic function in patients with MDD.
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+++ b/Medical Journal of Australia - 2009 - Clarke - Depression anxiety and their relationship with chronic diseases a review.txt	
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+The co-occurrence of depression and physical illnesses is an important issue. The burden of disease for depression itself is similar to that for heart disease.1 In any year, nearly 18% of Australians have one of the common mental disorders (depression, anxiety or substance misuse), and 43% of these . people have a physical illness.2 Having a physical illness is one of the strongest risk factors for depression.3 Moreover, evidence now shows that depression is also a risk factor for physical illness and for early death.4 Thus, both the depression and the physical illness need to be considered if we are to understand the complexities of this association and the best ways to treat each.
+Our aim was to review and outline the evidence in relation to depression and anxiety and the common chronic diseases — those that are the subject of the National Health Priority Areas. These include cardiovascular disease (heart disease and stroke), diabetes mellitus, asthma, cancer, arthritis and osteoporosis. We included anxiety with depression because the two are often coexistent, and not always easily differentiated. We were interested in finding data on the prevalence of depression and anxiety in patients with these diseases, risk factors for depression and anxiety occurring in patients with these diseases, depression and anxiety as possible risk factors for physical illness, and evidence for effective management of comorbid depression and anxiety and physical illness. Because of the broad scope of the study, we limited the review to secondary sources. This review employs the same methods as, and thus extends, an earlier scoping study conducted on behalf of the Australian Government, commissioned by the National Health Priority Action Council in 2004.5
+METHODS
+Each of the six major disease groups was considered in three sections: epidemiol-ogy/p revalence; risk factors; and management. Computer searches of literature databases were conducted in each of these areas. The searches were limited to the best evidence in the form of systematic reviews,
+S54
+meta-analyses and evidence-based clinical practice guidelines (National Health and Medical Research Council [NHMRC] Level 1 evidence).6
+The same strategy was used to search each health area for thesaurus and freetext search terms for: depression, anxiety and panic; health area (heart disease, stroke, diabetes mellitus, asthma, cancer, arthritis and osteoporosis); and best evidence (randomised controlled trial.pt, meta-analy-sis.pt, etc). We will provide full search details on request. The following databases were first searched during April and May 2003: Evidence-Based Medicine Reviews, MEDLINE, Pre-MEDLINE, CINAHL, Psyc-INFO, Australasian Medical Index, PubMed, The Cochrane Library, National Guidelines Clearinghouse, and the Scottish Intercollegiate Guidelines Network. Results were limited to studies in humans and published in English from 1995 onwards. We repeated the search in May 2007 for items published between 2003 and 2007, inclusive. Each review was examined and
+MJA • Volume 190 Number 7 • 6 April 2009
+summarised by two people before compilation.
+Systematic reviews were included if they provided documented inclusion/exclusion criteria and a search strategy, and assessed the methods of included primary studies. Reviews that did not report on direct, specific measures of depression or anxiety (including panic) were excluded. To avoid redundancy, where there were recent (2003-2007) reviews we have reported just those; where not, we refer to earlier reviews. Where there was a lack of Level 1 evidence, but there was other significant literature, we have identified it, although it was beyond scope to appraise this evidence.
+RESULTS
+A total of 159 reviews were identified (32 on heart disease, 23 on stroke, 19 on diabetes mellitus, 12 on asthma, 36 on cancer, 24 on arthritis and osteoporosis, and 13 general reviews). We will provide a full list on request.
+Epidemiology
+The prevalence of depression was markedly and consistently higher in people with heart disease,7-12 stroke,13-15 diabetes mellitus,16,17 cancer,18-20 rheumatoid arthritis,21,22 and osteoporosis23 than in the general population.2 A summary of the prevalence of comor-bid depression, anxiety and panic disorder and other epidemiological factors is shown in Box 1.
+The association between heart disease and depression is complex. Rates were similar for myocardial infarction (MI), coronary artery disease, and heart failure,10,12 although about 33%-50% of people with heart disease have pre-existing depression.11 Where depression was diagnosed in hospitalised patients with MI, 60%-70% of patients were still depressed at 1-4 months.7
+Post-stroke depression rates are significantly high (up to 40%), and also persist beyond 6 months.13-15
+No review was identified for asthma, but data from an Australian survey indicate that, among patients with asthma, the prevalence of depression is more than twice that of populations without asthma.28
+In patients with cancer, the prevalence of depression has been estimated to be up to four times that in the general population.18,36 It varies by time from receiving a diagnosis19 and through stages of disease progression.30 Prevalence appears to be higher in cancers with poorer prognoses, such as pancreatic, oropharyngeal and breast cancer,18,19 and colorectal cancer.29
+Study estimates of the prevalence of major depression in patients with rheumatoid arthritis vary widely, from 13%-17%21,22 up to 80%,31 although some of these studies use more general terms, such as “psychiatric comorbidity”. Young people with chronic arthritis also have an increased risk of depression, anxiety and social withdrawal.32
+MJA • Volume 190 Number 7 • 6 April 2009
+One systematic review found a strong and consistent association between osteoporosis and depression.23
+Few systematic reviews were found that examined the prevalence of anxiety disorders. A high prevalence of panic disorder is found in patients with cardiac disorders (10%-50%).24 Evidence-based clinical practice guidelines report anxiety to be high in patients with cancer,19,29 with estimates ranging up to 69% as disease progressed.30 A systematic review of post-traumatic stress disorder in survivors of childhood cancer reported a point prevalence of 4.7%-21% and a lifetime prevalence of 20.5%-35%.20
+Women with heart disease tend to report more symptoms of depression and anxiety than men,25 although some authors have suggested that this may be due to reporting bias. Among patients with diabetes, the prevalence of both
+S55
+depression and anxiety in women is significantly higher than in men.26,27
+Patients with rheumatoid arthritis who experience depression tend to be younger than those who do not.35
+The wide variation in overall prevalence rates of depression and anxiety has been attributed to methodological issues, and differences in rating tools and diagnostic criteria.
+Risk
+Risk factors for depression in National Health Priority Area diseases include at least some or all of the following: worsening condition,37 unrelieved pain,37-40 dysphasia,41 functional impairment,41 social isolation,41,42 past history of psychological disturbance,41 and diagnostic and treatment regimens.18,36
+Comorbid depression is a risk factor for increased disease severity10 because of noncompliance with treatment and greater com
+S56
+plications,18,43 and is associated with longer hospital stays, increased morbidity44 and increased mortality.13
+Depression may be a risk factor for developing heart disease,7,9,24,25 stroke,45 diabetes mellitus18,46 and osteoporosis.47 However, some reviewers noted significant heterogeneity between, and lack of power within, the reviewed studies, and therefore concluded that depression is not yet firmly established as an independent risk factor, at least for heart disease.8,48 A summary of risk factors among depression and anxiety and National Health Priority Area diseases is shown in Box 2.
+Management
+Treatment modalities are considered under pharmacological interventions and psychological, behavioural and educational interventions (see Box 3).
+No systematic reviews of pharmacological therapies for treatment of depression were
+MJA • Volume 190 Number 7 • 6 April 2009
+identified for asthma or arthritis and osteoporosis.
+There is a consistent body of evidence for the effectiveness of selective serotonin reuptake inhibitors (SSRIs) for treating depression in patients with cancer.18,86,87
+In heart disease, SSRIs were safe and had modest efficacy in patients with MI or unstable angina with recurrent or severe depression,67 but did not significantly reduce cardiac adverse events.50
+In stroke, there was evidence of effectiveness of antidepressants for treating depression;69,70 however, for prevention of depression, there was inconsistent evidence for the efficacy of antidepressants.72,73
+In diabetes mellitus, antidepressants (nortriptyline) ameliorated depression but decreased glycaemic control,75 whereas monoamine oxidase inhibitors increased hypoglycaemia and increased food cravings.76
+Cognitive behaviour therapy (CBT) was a modestly effective treatment for depression and anxiety in patients with heart disease,50 adult patients with diabetes,77 children with asthma,82 and people with a range of cancers.37,88 CBT has also been shown to be effective in reducing depression in patients with rheumatoid arthritis and osteoarthritis.98 There was some evidence of effectiveness for other interventions, such as relaxation therapies in patients with mild to moderate heart failure45,68 and cancer,19 and possibly for patients with rheumatoid arthritis when combined with education and biofeedback.99,101 Exercise and exercise-based rehabilitation was effective in people with ischaemic heart disease,45,68 and patients with rheumatoid arthritis and depression and anxiety.100 A summary of the range of interventions is shown in Box 3.
+DISCUSSION
+This review of Level 1 evidence of the association between depression and anxiety and physical illness provides an overview of the current research and knowledge in the area. We have not examined the details of individual studies, but have reported the conclusions of the original reviewers. Our review shows that there is a strong association between physical illness and depression and anxiety in all the National Health Priority Area disease groups — that is, that having a physical illness is a risk factor for depression and/or anxiety. Depression, in particular, is also associated with worse functional outcomes for people with physical diseases. Furthermore, the evidence is growing, and supports considering depression as an important risk factor for disease and disease-specific outcomes in heart disease, stroke and diabetes. The actual nature of the association is more uncertain. There are developed biological theories linking depression and heart disease, stroke and diabetes, although research in the area is hampered by the heterogeneity of the clinical conditions. This would be a worthwhile field for future research. Large prospective studies will be required to establish the links with greater certainty.
+There is preliminary evidence for modest effectiveness of antidepressant medications in treating depression, and this has been shown for, in particular, heart disease, stroke, cancer and arthritis. Psychological therapies also appear to be effective in reducing depression and anxiety in patients with heart disease, cancer and asthma. Behavioural treatments (eg, psycho-educa
+S58
+tion, relaxation) have been shown to be effective in reducing depression and anxiety in patients with cancer and arthritis. Although these and other treatments may be expected to lead to improvements in mood, functioning and wellbeing, in general, the number of studies that have been completed in each disease group is small, and much more research is needed to provide certainty.
+In light of the weight of evidence for increased morbidity and mortality associated with depression and anxiety in these physical illnesses, it becomes apparent that the research task of finding effective solutions is lagging a long way behind. The problem of depression in patients who are physically ill needs to be tackled for these reasons, as well as simply to relieve the suffering that depression brings. Potentially useful targets for research include examining the effectiveness and safety of antidepressant medications, and the effectiveness of psychological and behavioural interventions in the physically ill. The “management” of depression and anxiety is complicated, as is the “management” of chronic illness. Because of the interaction of these two components, solutions will need to be integrated. It is therefore timely to develop and test the clinical and cost-effectiveness of integrated disease management systems for depression in physically ill patients.
+There are models to guide this, although systems of chronic disease management have been slow to be taken up by the health system, and evidence for their usefulness is still limited.103 Furthermore, although effective models for incorporating care for depression in chronic disease management do exist,104 they are very few in number. At present, health care is linear — we treat the physical disease first, and then refer the patient for mental health care, or vice versa.105 This is not effective, efficient or cost-effective. Models of integrated care need to be implemented and evaluated. Based on the principles of chronic disease management and the findings of this review, an integrated disease management system could include screening and monitoring, good disease information and self-management advice, as well as a range of cognitive and behavioural strategies applied in a stepped or tiered model.
+Our review has drawn together the evidence around the question of depression and anxiety occurring with the common chronic diseases that are the subject of the National Health Priority Areas. The results highlight a substantial body of evidence
+MJA • Volume 190 Number 7 • 6 April 2009
+supporting the interactive effect of depression and anxiety and these physical illnesses. Policy and practice is lagging behind the known evidence. Our review of research shows that there are promising interventions and systems which ought to be developed and tested. Attention needs to be given to matters of research, policy and practice to achieve the necessary improvements in patient outcomes.
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diff --git a/Mental health stigma and attitudes to psychiatry among Bangladeshi medical students.txt b/Mental health stigma and attitudes to psychiatry among Bangladeshi medical students.txt
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@@ -0,0 +1,36 @@
+Introduction
+Worldwide, 12-month prevalence rates of mental disorders are relatively high, ranging from 8.4% to 29.1% (World Health Organization (WHO) International Consortium in Psychiatric Epidemiology, 2006). Equally high is the treatment gap, defined by the difference between the true and the untreated prevalence. The gap ranges from 32.2% to 78.1% according to the type of disorder, methods and region of the world (Kohn, Saxena, Levav, & Saraceno, 2004). Bangladesh, a lower-middle income country, according to the World Bank classification, shares this world condition. A recent nationwide study showed that about 16.1% of the adult people were diagnosed with mental disorders (Firoz, Karim, Alam, Rahman, & Zaman, 2006), and of them, a small rate (approximately 32 per 100,000 people) sought help from psychiatric facilities (WHO and Ministry of Health & Family Welfare, 2006). For schizophrenic disorders, Lora et al. (2012) estimated a 97% treatment gap.
+Also, the treatment lag, the time elapsed from disorder onset to reaching specialized care, was found wide. The median delay from disease onset to reaching any caregiver was 4.0, 24.5 and 14.5 weeks, for depressive, anxiety and psychotic symptoms, respectively (Giasuddin, Chowdhury, Hashimoto, Fujisawa, & Waheed, 2012). Importantly, this study (Giasuddin et al., 2012) also showed that 84% of the
+service users in Bangladesh consulted other caregivers before seeking help from a mental health professional (MHP).
+Bangladesh is not up to facing the above-noted mental health challenges. For example, the country has one of the lowest rates of psychiatrists and other MHPs in the world, about 0.5 MHPs (0.07 psychiatrists) per 100,000 population (WHO and Ministry of Health & Family Welfare, 2006). While the need to increase the number of psychiatrists is obvious, it is unlikely that this will occur in the immediate future. As a result, the general health sector is called in to fill the vacuum. The implementation of such strategy requires that the primary care practitioner receive appropriate training during and after medical school. Following training, medical students, our study population, should be expected to be better informed and more able to overcome the widely spread public mental health stigma. This stigma does not skip health professionals (Hussain, Mohit, Alam, Ahmed, & Rabbani, 2008; Qusar, 2010; Sartorius, 2002; Tasman & Mohr, 2011). Of note, medical students’ attitudes to psychiatry influence their future professional practice, such as the response to their patients’ psychological problems as well as their recruitment into the specialty (Feifel, Montier, & Swerdlow, 1999; Samimi, Noroozi, & Mottaghipour, 2006). (Parenthetically, Bangladeshi doctors, who are also working abroad, may be carrying along the stigmatized attitudes and practices from their homeland.)
+Public mental health stigma is widespread in Asia (Lauber & Rossler, 2007). Despite geographical distance, the beliefs about the causes of mental disorders and the attitudes toward persons with mental disorders (PMDs) in Asian countries have more commonalities with Western countries than there are differences (Lauber & Rossler, 2007). Ironically, the public acceptance of modern views in different places of the world about mental disorders is coupled with a stubborn persistence of negative opinions, attitudes and behaviors (Pescosolido, Medina, Martin, & Long, 2013). As noted above, the health professionals are not free from stigma toward PMDs (Lawrie et al., 1998; Ücok et al., 2006) and may behave negatively toward a service user once a psychiatric diagnosis is suspected (Magliano et al., 2011; Sartorius, 2002). Indeed, focus group studies found that PMDs receive less attention and care from family physicians and other health care providers because of stigma (Pinfold, Byrne, & Toumlin, 2005; Schulze & Angermeyer, 2003).
+Stigma toward PMDs and attitudes to psychiatry have been found associated with many variables, such as socioeconomic conditions (Braunholtz, Davidson, Myant, & O’Connor, 2007), quality of living conditions (Economou, Richardson, Gramandani, Stalikas, & Stefanis, 2009; Tabuchi, Fukuhara, & Iso, 2012), knowledge about mental
+illness (Corrigan & O’Shaughnessy, 2007), familiarity with PMDs (Corrigan, Green, Lundin, Kubiak, & Penn, 2001), personal or family experience with mental disorder and contact with PMD (Corrigan et al., 2001; Corrigan & O’Shaughnessy, 2007; Holmes, Corrigan, Williams, Canar, & Kubiak, 1999), parental attitudes (Scheff, 1966) and the culture of affiliation (Korszun, Dinos, Ahmed, & Bhui, 2012; Pescosolido et al., 2013). With regard to medical students, for example, age (Al-Ansari & Alsadadi, 2002), gender (Al-Ansari & Alsadadi, 2002; Alexander & Eagles, 1986; Korszun et al., 2012), contact with PMD (Korszun et al., 2012), academic year (Ay, Save, & Fidanoglu, 2006) and rotation through psychiatry (Ay et al., 2006; Holm-Petersen, Vinge, Hansen, & Gyrd-Hansen, 2007; McParland, Livingston, & McManus, 2003) also were found to influence stigma and attitudes formation. As a result, factors that precede medical training, if they enhance stigma, may hinder potential advances to be achieved by medical training and, conversely, if the latter is not well conceived and conducted, it may further increase pre-medical school negative attitudes.
+Objectives
+To examine stigma toward PMDs and attitudes toward psychiatry among medical students in Bangladesh at their pre- (first year) and post-rotation (fifth year) in psychiatry, and selected variables associated with those outcomes.
+Methods
+Sample
+This study was conducted at the state-run Faridpur Medical College, one of the 70 medical schools in the country. This 5-year school has a total of 635 students. It is situated at about 145 km from Dhaka, the capital city. The students are selected through a nationwide common admission process. In this study, we included first and fifth year students. The latter were recruited after completion of the set of lectures and 5-week psychiatry ward teaching.
+All the students of the respective medical school years of Faridpur were included. The acceptance to the invitation to participate was almost complete, 100 of the 107 students in first year, and 100 of the 101 enrolled in fifth year (N = 200). The few students who were not available during the survey or dropped out from the academic year were excluded. The first year students made up the preexposure group, and the fifth year students made up the post-exposure intervention group. Anonymous information was obtained from all participants. A brief, nonbiasing description of the survey was made prior to handing over the self-administered questionnaires. The study was approved by the College Ethical Review Committee.
+Measures
+It comprised three instruments: (1) On sociodemographic variables (age, gender, permanent address, marital status, parents’ education, family’s socioeconomic status (SES), academic year, previous consultation for mental/neuro-logical complaints (by the respondent, family members or friends) and media exposure to psychiatric issues). (2) The 26-item ‘Modified Corrigan Attribution Questionnaire’ (MCAQ), that was constructed after the ‘Attribution Questionnaire-27’ (AQ-27) (Corrigan, Markowitz, Watson, Rowan, & Kubiak, 2003), was used to assess stigma toward psychiatric illness. The MCAQ provides a brief vignette about Hasib, a 30-year-old single man with schizophrenia who lives alone and works as a clerk at a large private firm. He had been hospitalized six times because of his illness. We selected a vignette depicting a service user with schizophrenia because in Bangladesh, it is the general doctor who is entrusted with the care as a result of the lack of dedicated psychiatric services. One question from the original questionnaire was deleted: ‘If I were in charge of the treatment of Hasib, I would force him to live in a group home’, since this service option is unavailable in the country. The responses were given on a 0-9 Likert scale, where 0 meant full rejection and 9 meant full approval. The responses of each subject were summed into a total score (range: 0-234), where higher score denoted higher level of stigma. The scale had good internal reliability (Cronbach’s alpha = .71). (3) The 31-item ‘Modified Balon Attitudes Questionnaire’ (MBAQ), which was used to assess attitudes toward psychiatry. Our version included three questions that differed from the original questionnaire (Balon et al., 1999): ‘Because my fellow students or other friends will laugh at me if I select psychiatry, I would not go into it even if I am well paid’; ‘I think the psychiatric hospital is not a place to treat patients’; and ‘To me, all psychiatrists are strange people’. One question, ‘With few exceptions, clinical psychologists and social workers are just as qualified as psychiatrists to diagnose and treat emotionally disturbed persons’ was omitted, due to their unavailability in the services. The responses were given on a 1 (strongly disagree) to 4 (strongly agree) Likert scale. The responses of each subject were summed into a total score (range: 31124), where higher score denoted more favorable attitudes toward psychiatric issues. A few items were scored in the reverse direction. The scale showed good reliability (Cronbach’s alpha = .79).
+Both the MCAQ and MBAQ were translated from the original English version into Bengali by expert translators, and back-translated by another group. Subsequently, they were compared with the original versions, and necessary corrections were made. Whenever there was doubt regarding the most appropriate Bengali term, expert opinions from senior psychiatrists were obtained.
+Data analysis
+Associations between medical school year, MCAQ and MBAQ were tested using t -tests and Pearson correlations. Following the bivariate association tests, we examined background and exposure variables as possible confound-ers of the associations between school year and MCAQ and MBAQ scores. Background variables were gender, permanent address (urban, rural), marital status (married, unmarried), father’s and mother’s educational level (lower, higher) and family’s SES (upper, middle, lower). Exposure variables were consultation for mental or neurological complaints by self, family or friends, and media exposure. Since the variables did not show an association with both school year and MCAQ or MBAQ scores, we tested the effects of the variables showing associations with questionnaires’ scores in separate analysis of variance (ANOVA) models, each including the background/expo-sure variable and medical school year. We used SPSS (Statistical Package for Social Sciences, 20th version) for Windows (IBM Corporation, New York) to analyze the data.
+Results
+The sociodemographic characteristics of the students, exposure to psychiatry-related issues through texts and visual media and previous contact with a caregiver for mental or neurological complaints are presented in Table 1. Associations with medical school year were noted with regard to age only (t = 36, df = 198, p < .001).
+Higher level of stigma toward PMDs as indicated by MCAQ scores was shown by fifth year (119.8 ± 22.8) compared to first year (113 ± 2.1) students (t = 2.21, df = 198, p = .028). In addition, slightly more favorable attitudes to psychiatry as indicated by MBAQ scores were shown by fifth year (98.8 ± 11.5) compared to first year (96.2 ± 8.7) medical students (t = 1.81, df = 198, p = .073).
+A positive correlation was found between students’ age and MCAQ score (r = .20, p = .005). No association was found between age and MBAQ scores (r = -.11, p = .11). Significant associations with MCAQ were found for mother’s education and SES. Students who reported lower mothers’ education showed higher MCAQ scores (stigma) compared to those reporting higher education (Table 2). The 2 x 2 ANOVA indicated a main effect of mother’s education (F = 4.13, df = 1,196, p = .043), but not mother’s education by school year interaction (p = .7). In addition, higher MCAQ scores were observed among students of both lower and upper compared to middle SES. The 2 x 3 ANOVA indicated a main effect of SES (F = 4.90, df = 2,194, p < .01), but not SES by school year interaction (p = .7).
+Significant associations with MBAQ scores (attitudes) were found for gender and SES. Women showed higher MBAQ scores compared to men (Table 2). The 2 x 2
+With respect to media exposure, no significant difference were observed with MCAQ (F = 0.01, df = 1,191, p = .9) or MBAQ scores (F = -0.26, df = 1,191, p = .6) between students who were and were not exposed to text or visual media regarding psychiatry-related issues.
+ANOVA showed a main effect of gender (F = 5.69, df = 1,196, p = .018), but no gender by school year interaction (p = .17). While at fifth year middle SES was associated with higher MBAQ scores compared to both lower and upper status, no such difference was seen in first year medical students. The 2 x 3 ANOVA showed a main effect of SES (F = 4.44, df = 2,194, p = .013), as well as a SES by school year interaction that approached significance (F = 2.96, df = 2,194, p = .054).
+MCAQ scores (stigma) were significantly higher among students that consulted a care provider for mental or neurological complaints (F = 4.66, df = 1,196, p = .032) (Table 2). In contrast, no association was found between MBAQ scores (attitudes) and self-consultation (F = -1.09, df = 1,196, p = .3). No significant associations were found between MCAQ scores and consultation history by family (F = 0.01, df = 1,183, p = .9) or friends (F = 0.62, df = 1,145 p = .4). Similarly, MBAQ scores regarding family and friend consultation were not significant (F = 1.02, df = 1,183, p = .3; F = 2.96, df = 1,145 p = .088, respectively).
+Discussion
+This study measured two important constructs with regard to mental health care, stigma and attitudes to psychiatry, both pre- and post-exposure to the respective rotation of medical school students. Both constructs influence the choice of psychiatry as a field of practice and, more generally, mental health-related practice among the primary care doctors and other specialists. Several factors were associated with stigma (age, mother’s educational level, SES, self-consultation and, importantly, year of study) and the attitudes to psychiatry (gender, SES and year of study). Students whose mothers have lower academic level and those from lower and upper socioeconomic levels showed increased level of stigma. Stigma toward PMDs was significantly higher in those students who consulted for themselves for mental or neurological complaints, a possible result of self-stigma and/or negative treatment experience.
+With regard to attitudes to psychiatry, female students and students from middle socioeconomic level showed
+more favorable attitudes. More specifically to the effect of the medical studies, we found that the exposure to training increased stigma, but improved attitudes to psychiatric issues slightly. It seems that the first year medical students carry into their cycle of studies the stigmatic attitudes toward PMDs originated in their home environments (Firoz et al., 2006; Qusar, 2010). Ours does not constitute a single finding in the literature: increase in stigma toward PMDs was seen after completion of training in psychiatry in Greece (Economou, Peppou, Louki, & Stefanis, 2012) and Turkey (Ay et al., 2006). Increased level of stigma in higher academic year was observed also in Italy (Magliano et al., 2011). Conceivably, the increase in stigma may result from the quality/nature of the psychiatry curriculum, the attitudes of the teaching staff and the site and scope of training. The identification of these and other possible factors calls for further studies. Improved attitudes toward psychiatry following rotation or training in psychiatry was observed in Malawi (Platt, Beaglehole, Baig, Leuvennink, & Eagles, 2010), Spain (Bulbena, Pailhez, & Coll, 2005), the United Kingdom (Baxter, Singh, Standen, & Duggan, 2001) and Portugal (Xavier & Almeida, 2010). As Pescosolido et al. (2013) stated, a stubborn persistence of negative opinions, attitudes and behaviors seems to intermingle with the considerable acceptance of the modern medical views of mental disorders.
+As found elsewhere (Lyons, 2013), psychiatry showed limited capacity to attract students - too few of them expressed interest to specialize in psychiatry. To compound the problem, a large number of them work outside the country; the number of psychiatrists enrolled in the United Kingdom and the United States currently totals (n = 149) almost equal to the number working in native Bangladesh (Jenkins et al., 2010). Thus, the national cadre of specialists (WHO and Ministry of Health & Family Welfare, 2006) may remain small. In parallel, the reliance on non-specialized medical staff to care for PMDs, as promoted by WHO (2008), may face attitudinal obstacles that need to be carefully addressed. Hopefully, by doing so, the mhGAP (Mental Health Gap Action Program) efforts of the WHO (2008) to upgrade the quality of care of mental health problems provided by general physicians will be facilitated. At the same time, we need to involve and empower the PMDs and caregivers in the treatment process to deliver acceptable, affordable and quality services that are evidence-based, as rightly noted by the anonymous reviewer.
+The study has a few limitations. The sample was taken from only one medical school. However, the participant students came from a common intake process established for all medical schools, and the institution where the study was conducted does not differ much from all other schools in both academic settings and facilities. Another limitation may derive from the translation of the MCAQ and the MBAQ. However, the validation of the back-translated
+version reduced the amount of disparity with the original versions to a minimum. The case vignette described in MCAQ referred to a specific mental health scenario only, schizophrenia, limiting its ability to generalize to all cases of mental disorders. Also, the respondents faced a hypothetical situation in the case vignette which may not anticipate their actual behavior in real-life situation. Important strength of the study is the high response rate achieved through the assurance of total anonymity, an assurance that was fully honored.
+Conclusion
+As elsewhere, we found that the stigma toward the PMDs, often found among all medical students, increased following the psychiatric rotation. The recommendations for change are simple to formulate but, perhaps, less easy to attain them. In addition to seminars on the etiology, course and patterns of recovery from psychiatric disorders, well-tailored activities to decrease stigma are required. For example, joint rounds with other medical specialties are advisable to highlight the interaction between mental and physical disorders. It is necessary to encourage and give proper remuneration to the psychiatrists to attract them to this profession and to prevent the draining of specialists to high-income countries like the United Kingdom or Australia. Contact with persons who have recovered from psychiatric disorders or are living with the illness may improve students’ attitudes toward psychiatry and PMDs. At all times, students need to be made aware of the destructive impact of stigma on the lives of the PMDs, and on the doctor-user relationship. Up to now, the undergraduate medical curriculum in Bangladesh does not have any specific anti-stigma training component. This deficit has to be met, and its changes monitored and evaluated.
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+By April 10, there were 253 suspected and probable cases in Canada. Two hundred and six of these were in Ontario, the majority in the Toronto area. Ten deaths had occurred, all in Ontario.1 After March 17, most new cases occurred in health care workers.1 Public health efforts to contain the spread of SARS resulted in infection control measures coming to dominate hospital procedures and policy throughout the Toronto area and selected surrounding regions. Provincial public health recommendations, including restricted access to hospitals, screening of employees entering hospitals, isolation precautions and restrictions on transfers of patients between institutions, changed as frequently as daily early in the outbreak.
+How should a hospital respond to the occupational and psychological challenges that such an outbreak poses to its staff and patients? Despite a large epidemiological literature regarding disease outbreaks, there is little information available to guide interventions to support staff and patients. A report of a 1999 community outbreak of meningococcal disease emphasizes that effective management requires coordination, communication and collaboration.5 A study of a hospital outbreak of vancomycin-resistant enterococci (VRE) describes a severe burden on nursing staff. Nurses assigned to deal with a VRE outbreak were significantly stressed by the sense that they had to function as gatekeepers to the patients for staff and visitors and often felt inadequately supported, blamed for the outbreak and resentful of the increased workload.6
+Although several recent disease outbreaks have required an extraordinary public health response, including the Escherichia coli outbreak in Walkerton, Ont.,7 bovine encephalopathy8 and Norwalk virus,9 the SARS outbreak is unique in recent history in its rapidity of transmission, its concentration in health care settings and the large number of health care workers who have been infected. In this paper, we describe the early experience of a university teaching hospital in responding to the psychological and occupational impact of the SARS outbreak upon hospital inpatients with SARS, inpatients without SARS and health care workers. As cases of probable and suspected SARS are identified in other provinces and countries, our experience
+CMAJ • MAY 13, 2003; 168 (10)	1245
+The first patient with a probable case of severe acute respiratory syndrome (SARS) in Canada was admitted to hospital on Mar. 7, 2003,1 in Toronto. SARS is believed to be caused by a coronavirus.2,3 In the absence of serologic tests, clinicians use a standard case definition.4
+may benefit others who are developing a comprehensive psychosocial response to SARS. Furthermore, this experience may be useful in planning a response to other infectious outbreaks, such as pandemic influenza.10
+Methods
+Descriptions of the experience of patients with SARS and those without, the experience of health care workers and the institutional response were collected through unstructured interviews by the 2 principal authors (R.M., J.H.) with core team members and mental health care providers at Mount Sinai Hospital, Toronto. The core team members included the Vice-President of Nursing (L.V.), the Program Director of Nursing (J.B.), the Chair of the Medical Advisory Council (J.S.), the Director of Community Health Programs (R.S.), mental health professionals attending patients with SARS and patients without SARS (2 consultation-liaison psychiatrists [J.H., R.M.], a psychiatric clinician nurse specialist [N.P.] and a social worker [L.M.V.]) and a psychiatrist (M.L.) who met with health care workers individually to provide support at their request.
+Our observations were made for the period from Mar. 13, 2003, to Apr. 10, 2003, based on clinical observations of 19 patients with SARS and those without SARS who were also receiving care from the same mental health professionals (N.P., L.M.V., J.H., R.M.), observations by leaders and managers (L.V., J.B., R.S.) of staff, observations by mental health professionals (N.P., L.M.V., J.H., R.M., J.S., M.L.) of staff members who sought psychological support formally and informally, and first-hand observations by leaders (L.V., J.B., J.S.) of the administrative response. A microbiologist (T.M.) provided information about the cases, the disease and its treatment. We met in small groups and communicated by email to review and analyze these observations in order to determine the face validity of our observations by consensus in an iterative process between Apr. 3 and Apr. 13, 2003.
+The unexpected onset and rapid expansion of the SARS outbreak over its first 4 weeks, and the clinical responsibilities of the team, precluded systematic data collection. Psychological responses are condensed into narrative descriptions rather than being quantified.
+Results
+Description of cases
+The first patient with a case of SARS at Mount Sinai Hospital, Toronto, was admitted on Mar. 13, 2003. By April 10, 19 patients with probable and suspected SARS had been treated (Fig. 1). Of these, 11 were health care workers (5 nurses, 4 physicians, 2 others). Of the 11 health care workers, 9 worked at our hospital.
+One case of SARS was diagnosed at autopsy about 1 week after that patient’s transfer from our intensive care unit (ICU) to another hospital. This patient had been in our ICU for about 36 hours (Mar. 22-23). After about 14 hours in the ICU, clinical suspicion of SARS resulted in the use of isolation precautions. Unprotected exposure before the isolation precautions were instituted was the likely source of infection for 6 cases. Likely sources of infection for other cases were exposure to infected patients or staff in other health care settings in 6 cases, domestic exposure to a probable SARS case in 6 and exposure during transport of an infected patient in 1 case. Episodes of unprotected exposure of health care workers are documented in Table 1. As of April 10, there were no cases of secondary transmission to health care workers who were following isolation precautions.
+Fourteen patients were treated in the SARS isolation unit that the hospital set up on March 28, 2 were treated initially in the ICU and 8 were treated in isolation rooms on other units. (The number of patients is greater than 19, because of patient transfers between units.)
+The hospital's response
+The timeline of the responsive measures instituted by Mount Sinai Hospital is outlined in Table 2 and the screening questions asked of staff at the hospital entrance are listed in Appendix 1.
+Impact of SARS
+The hospital established a command centre, headed by the Vice-President of Nursing and the Chief Information Officer, who led a team composed of key senior administrators and the Chair of the Medical Advisory Committee. The composition of the team was fluid as experts and department heads (e.g., microbiologists, Chief of Medicine, human resources and occupational health personnel) were included as required for a changing list of operational tasks. Administrators received frequent public health directives from the Ontario Ministry of Health and Long-Term Care and participated in daily conference calls with other hospitals in the same network to discuss implementation of the directives, which were increasingly inflexible (e.g., no transfers of critically ill patients between hospitals without prior approval from the province).
+The hospital’s CEO, the Vice-President of Nursing and the Chief Information Officer sent a daily joint email message to all staff updating SARS information, outlining procedural changes, and providing information about the numbers of patients with SARS, the number of staff in quarantine and the number of staff admitted to hospital for treatment. The email message was used to express praise and gratitude to all staff for their contributions. The hospital’s Intranet was also used extensively, for example, to provide instructions on the proper use of face masks.
+Administrators faced the emotional challenge of balancing their responsibilities to ensure optimal care for patients with SARS while ensuring the safety and well-being of health care workers. These efforts were complicated by incomplete knowledge about the actual risks, especially about modes of transmission of the infectious agent(s).
+On occasion, staff were observed to be not fully complying with infection control procedures. Under the circumstances it could not be determined whether this was due to inadequate communication (especially because of frequently changing guidelines), technical difficulty, or because of psychological responses such as denying risk or simple rebelliousness. Leaders responded with clear, authoritative instructions to inform staff of new directives and maximize compliance. There was minimal resistance to this approach.
+Substantial logistical problems were posed by, for example, the need to screen over 1800 people entering the building daily, or to procure the daily allotment of 5000 masks and 3000-4000 gowns. Staff were redeployed to overtaxed areas to meet these needs. Management staff volunteered to coordinate and supervise screening functions without direct instruction from the command team.
+Psychological effects of the SARS outbreak
+Patients with SARS
+Hospital inpatients with suspected and probable SARS presented a range of symptom severity from acute respiratory distress syndrome to relatively mild symptoms such as fever, headache and myalgia. Generally, more psychosocial
+support was required by patients with mild-to-moderate symptoms, and these patients were in the majority.
+Shortly after admission, recent contacts who would require quarantine were identified, resulting in feelings of guilt, anger and fear for the welfare of friends and family. Identifying contacts also raised fear that the patient would be resented. Patients worried that their contacts would be stigmatized and would lose income due to quarantine.
+Patients with SARS often had to spend several hours alone between brief contacts with staff. Outside communication was available by telephone and, in some cases, by email. As a result, patients with mild symptoms complained of boredom and loneliness.
+The most prominent symptoms were fever, myalgia, cough and fatigue. Patients were treated with ribavirin, often in combination with corticosteroids. Doses of ribavirin varied between 1.5 g and 4 g daily. A typical course was a 2-g intravenous loading dose, followed by 1 g intravenously every 6 hours for 3 days, followed by a lower dose (e.g., 500 mg every 8 h) for 4 more days. Patients were switched to oral dosing when tolerated. Ribavirin caused uncomfortable side effects, especially nausea. Insomnia was common as a result of treatment with corticosteroids, anxiety, physical discomfort and hospital routines.
+In the absence of specific laboratory tests to indicate disease progression, each patient’s temperature was monitored carefully by staff and patients. Several patients who experienced waxing and waning anxiety throughout their stay in hospital reported that peaks of anxiety coincided with feeling feverish or learning of an elevated temperature. One patient with a pre-existing panic disorder experienced episodes of panic during spikes of fever. Other patients reported feeling discouraged and frightened by the return of fever after an afebrile period.
+Most expressed sadness about missing their loved ones. Concern was expressed by health care worker patients about the infectious risk to staff caring for them. Fear of the potential lethality of the illness and anger because their risk of infectious exposure had not been recognized earlier were voiced less often than other concerns.
+Family members at home found it difficult that they could not provide direct support to their sick relative by visiting. Child care issues for single parents with SARS who had children in quarantine and management of pre-existing marital tensions were recurrent difficulties.
+Patients without SARS
+Hospital inpatients without SARS were concerned about becoming infected. Restrictions on transfer to other institutions, cancelled procedures, the need for quarantine upon discharge or delayed discharge were common frustrations. Patients deprived of family visits experienced insomnia, anxiety and interpersonal friction with staff.
+Limited access to external resources resulted in difficulty obtaining items that would usually provide comfort, such as books, music and toiletries. Asian patients reported stigmatization and racist reactions in the community, because the outbreak was thought to have originated in China.
+Health care workers
+The SARS outbreak and the public health response to it substantially changed working conditions. The perception of personal danger was exacerbated by uncertainty. Modification of infection control procedures and public health recommendations day by day, and sometimes hour by hour, increased uncertainty. The perception of personal danger was heightened by the known lethality of the syndrome and intense media coverage of the outbreak and its effects (e.g., “Hospital masks are in short supply” — Toronto Star, Mar. 29, 2003).
+Staff members were discouraged from interacting outside the hospital with colleagues and staff meetings were discouraged, at a time at which people wished to seek each other out for support. Eating and drinking, which require removing a mask, were done alone or outside the hospital. As face-to-face communication became more difficult, email was used extensively.
+Staff were prevented by provincial directives from working in multiple institutions, which imposed a financial burden on staff whose income depends on working in several institutions and on doctors who divide duties between settings. Doctors with clinical offices in the hospital were required to cancel their outpatient practices until further notice.
+Hospital employees with a potential contact with SARS entered voluntary 10-day quarantine. Quarantined staff had concerns about their personal safety, about transmitting disease to family members, about stigmatization and about interpersonal isolation. Working staff members were concerned about understaffing due to quarantines and about overwork caused by colleagues’ calling in sick.
+Staff members who were not directly involved in patient care (about 40%-50% of staff) were deemed nonessential and were asked to stay at home indefinitely. Nonessential staff reported feeling isolated and ineffective in contributing meaningfully to the crisis. The term “nonessential” may have contributed to this sense. Some were called back to work in redeployed roles and indicated that it was psychologically more satisfying to work than to stay home.
+Public health guidelines indicated that staff did not need to take special precautions such as using masks at home. This left many worried about transmitting illness to loved ones. Instructions to avoid meeting other hospital staff outside the hospital and not to work in multiple institutions left staff members uncertain as to whether or not they were considered potential vectors of disease. Some felt stigmatized within their communities and avoided identifying themselves as hospital workers.
+Prominent among the varied responses of individual staff members were themes of fear, anxiety, anger and frustration. Many expressed conflict between their roles as health care provider and parent, feeling on one hand altruism and professional responsibility and, on the other hand, fear and guilt about potentially exposing their families to infection. Some nurses on units that had no patients with SARS felt that their needs became secondary. Collaboration and collegiality were observed in units that volunteered to send staff to other units to assist with care.
+Supervisors and leaders expressed difficulty in remaining at home or leaving work because of their sense of responsibility to be present with their staff. Throughout the hospital it was found that many staff required “permission” from peers or supervisors to refrain from doing too much. When returning to work after days off, people felt disconnected from the current state of the organization. Staff reflected on the stark contrast of the seemingly “normal” external environment and a highly stressed work environment. There were wide discrepancies in workload between those subjected by circumstances or personal attitudes to overwork and those prevented from working by quarantine or a “nonessential” designation.
+On medical wards that treated patients with SARS, some staff reported anxiety about infection and resentment about being chosen for the task. Nurses who were assigned to pa
+tients with SARS were not allowed to refuse the assignment (except for accommodations that could be made for pregnant nurses to avoid potential exposure to the teratogenic effects of ribavirin). Although there were incidents of professional and nonprofessional staff refusing to care for patients with SARS in respiratory isolation on general medical floors, there was no refusal of work assignments by nurses on the SARS unit. Staff attributed this to feeling confident about being well-equipped, maximally protected by isolation precautions and well supported in the hospital.
+On the SARS isolation unit, spikes of anxiety occurred in association with several events: when isolation precaution procedures changed, when infectious disease staff entered quarantine or treatment, when health care workers were admitted with an unclear source of infection, when one of the SARS-unit nurses developed a fever (not due to SARS) and when a discharged patient with SARS was readmitted with fever. Staff reported fatigue, insomnia, irritability and decreased appetite.
+SARS staff had the emotionally complex task of caring for patients who were themselves health care workers. The clear line between patients and staff became blurred as staff experienced a strong emotional identification with colleagues who were now patients. Caring for colleagues increased the anxiety of some staff regarding their competence and skills.
+Pyschological support
+Patients with SARS
+Patients with SARS received an initial visit from the psychiatric clinician nurse specialist, the consultation-liaison psychiatrist and/or a social worker familiar with the intensive care setting. In these screening assessment interviews, it was emphasized that a wide range of emotional responses is to be expected in the face of such an extraordinary situation. Concerns and feelings expressed were interpreted as expected, normal responses. Immediate concerns were reviewed, especially the patient’s family situation, relationship with people on his or her “contact list,” expectations and fears about their own medical condition, and current symptoms.
+When indicated and desired, subsequent supportive psychotherapy aimed to balance a permissive approach to expression of feelings with pragmatic attention to the particulars of the patients’ external reality. For the patients who were both parents and health care providers, particular attention was given to issues of powerlessness and the conflicting responsibilities of these 2 roles. In some cases, the simple presence of a person with the time and willingness to visit was identified as most valuable, especially for patients with SARS who were “doing well” and thus receiving relatively less nursing contact.
+Some useful interventions were straightforwardly pragmatic, such as arranging for pizza to be delivered to a house
+under quarantine, or making a trip to the drug store for hygiene supplies for a patient in isolation. In response to the social isolation and boredom experienced by patients with SARS, an attempt was made to provide access to the Internet, telephone, newspapers, television and books.
+Identifying families’ needs, offering an opportunity to express feelings, and supporting effective coping strategies helped to enhance the families’ sense of competence and control.
+Pharmacological and behavioural interventions to treat insomnia were used extensively.
+Support of staff
+On units that received patients with SARS, the initial reactions of uncertainty and fear of the unknown among the staff were met with immediate clear information in repeated, succinct messages, staff meetings, and provision of appropriate equipment and supplies. Occupational therapists developed a pamphlet identifying signs of anxiety and stress and information about support resources, which was distributed to every nursing unit and program area.
+Psychiatric staff who were on the units to see patients lingered to chat with staff. Informal individual contacts occurred between psychiatric staff and colleagues in medicine, surgery and administration in which simple gestures of support and advice, for example, about sleep, were offered. When it became apparent that some staff were reluctant to talk about personal concerns with psychiatrists with whom they had working relationships, another psychiatrist offered time to any staff at the request of the nursing unit administrator. This resource was used briefly during a period in which nurses’ anxieties were high after several staff had become ill over a short period.
+A drop-in support centre in the now-vacant medical ambulatory care clinic was provided immediately and then modified because it was not being used. It was replaced with a drop-in lounge in an open setting with soothing music, comfortable chairs and snacks. Senior staff acted as role models by making use of this support service and bringing others with them.
+A confidential telephone support line staffed by inpatient psychiatric nurses was set up for all hospital staff and was used particularly effectively by those in quarantine. An informal network of mutual telephone contact and support was arranged by quarantined staff of the intensive care unit. Staff on home isolation who had email access were able to receive all communications from the hospital.
+Interpretation
+The psychosocial response to an infectious event of this magnitude is complex. In the SARS outbreak, as in previous outbreaks of disease,5 the hospital’s response emphasized clear communication of directives and disease information and a high degree of collaboration between
+disciplines. In addition, our experience highlights the importance of leadership during times of crisis, consistent with group psychology theory, which emphasizes the effect of leadership on maintenance of team cohesion.11
+Although systematic methods of study and longer follow-up are required to determine the burden of the SARS outbreak on nursing staff, our immediate experience suggests that SARS-unit nurses may have experienced less distress than nurses on other medical wards caring for patients with SARS. This may be because, in contrast to the burden reported previously upon nurses acting as VRE gatekeepers,6 the “gate” was at the hospital entrance, education was institutewide, the SARS-unit nurses had a greater sense of competency and multiple support measures were quickly put into place.
+The most prominent emotional effects upon patients with SARS were feelings of fear, loneliness, boredom and anger. Patients with SARS worried about the effects of quarantine and contagion on loved ones. They also experienced the psychological effects of physical symptoms, especially anxiety about fever, dysphoria due to nausea and the effects of insomnia on mood and coping. Staff were adversely affected by fear of contagion and of infecting loved ones. Caring for health care workers as patients increased discomfort for many. Uncertainty and stigma were prominent themes for both staff and patients.
+The validity of these observations is limited by the non-systematic methods of data gathering and interpretation of data by expert opinion and consensus. Information was collected quickly over a period of 4 weeks in which there was much uncertainty about the nature of the disease being observed. The virtue of rapid communication of our experience is accompanied by the expectation that further experience over the coming weeks will bring greater clarity about the phenomena reported here. Furthermore, the observations were made in one large teaching hospital and may not be generalizable to other settings. An outbreak in a smaller community, for example, would probably present different challenges.
+In intervening with staff and patients, we found the stress-adaptation model particularly relevant. According to this model,12,13 the experience of stress is taken to be understandable as a universally experienced response to extraordinary life circumstances. Stressors must be identified, articulated and normalized as much as possible. The range of normal reactions, including anxiety and preoccupation, is not viewed as pathological but is, rather, encompassed, supported and realigned where appropriate, in order to facilitate adaptation. In our experience, support services for staff needed to be flexible, collegial and unintrusive. The presence of psychiatrists at nursing stations and at staff meetings helped to foster communication. Just the knowledge that support is available may suffice for many resilient staff members. There is an opportunity for leadership by example, when leaders advocate and use peer support.
+In this outbreak, the Department of Psychiatry was in
+eluded in the command team not by design but by circumstance, because the Chair of the Medical Advisory Committee was the Chief of Psychiatry. Our experience suggests that psychiatry may have a special role to play in supporting institutional leadership during an outbreak, especially through the assessment of special staff and patient needs and the organization of a supportive institutional response. At our centre, strong pre-existing relationships among psychiatrists, administrators, nurses and social workers were very helpful in crafting flexible and responsive solutions to changing demands and stresses on staff, patients and families.
+Our experience in the early days of this outbreak taught us the paramount importance of a few frequently recurrent clinical themes. First, restorative sleep may be the first casualty of such an outbreak for all concerned and merits aggressive attempts to educate staff and patients about the impairment that results from sleep deprivation and to treat insomnia. Second, most people cope very well in their own way and benefit a great deal from a relatively small quotient of shared concern, good information and support. Third, when facing such a crisis it is crucial to feel that one is not alone. All efforts to overcome interpersonal isolation, from sharing jokes on the nursing station to conference calls, serve an important role in times of intense strain and stress.
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+Researchers have consistently reported that people with mental disorders have elevated mortality compared with the general population. In 1937, Malzberg1 reported that psychiatric inpatients had a mortality rate that was 6 times greater than the rate in the general population of New York. Since then, numerous studies and reviews have beencon-ducted on the mortality risks of people with a variety of mental disorders2-6 and specific diagnoses (eg, schizophrenia,7 depression,8,9 and bipolar disorder10).
+The studies11,12 on the global burden of disease illustrate the growing burden of mental disorders, although this burden has largely been reflected in disability rather than mortality. The link between mental disorders and mortality is complicated because most people with mental disorders do not die of their condition; rather, they die of heart disease and other chronic diseases, infections, suicide, and other causes.11,12 Another complicating factor is that mental disorders are associated with risk factors for mortality. People with mental disorders have high rates of adverse health behaviors, including tobacco smoking, substance use, physical inactivity, and poor diet. In turn, these behaviors contribute to the high rates of chronic medical conditions among people with mental disorders.13,14
+Quantifying and understanding the excess mortality among people with mental disorders can inform approaches for addressing this persistent issue and widen discussion of the effect of mental disorders on mortality. The purpose of this study was to systematically review the literature to examine the excess mortality rate of people with mental disorders, extending existing reviews of individual disorders. We sought to provide comprehensive estimates of individual- and population-level mortality rates related to mental disorders.
+Methods
+Data Sources and Searches
+We followed the Meta-analysis of Observational Studies in Epidemiological guidelines for this meta-analysis.15 We searched EMBASE, MEDLINE, PsychINFO, and Web of Science from inception through May 7, 2014. Searching was conducted by the investigators after consulting with a librarian about the search strategy. Our search strategy included terms for mental disorders (eg, mental disorders, serious mental illness , and severe mental illness), specific diagnoses (eg, schizophrenia , depression, anxiety, and bipolar disorder), and mortality. We also limited the search or included terms to identify English-language cohort (eg, cohort or follow-up) studies depending on the database (eTable 1 in the Supplement includes the full search strings). We also searched references of eligible articles and used Google Scholar to identify articles that cited eligible articles.
+Study Selection
+Studies were included if they (1) were original research papers that used a cohort study design; (2) included diagnosed mental disorders (eg, not symptoms); (3) reported mortality as an outcome by comparing people with mental disorders with
+jamapsychiatry.com
+a general population or controls from the same study setting without mental illness; and (4) were written in the English language. Studies were excluded if the study population was restricted to people with specific medical conditions (eg, heart disease) or subgroups of the population (eg, homeless) or the study reported duplicate data. Titles, abstracts, and articles were reviewed by2 independent reviewers (E.R.W. and R.E.M.), and disagreements were settled by consensus.
+Data Extraction
+From all eligible articles, we abstracted the first author’s name, year of publication, country, setting, year of baseline, years of follow-up, sample source, sample size, mental disorders included, population of people with mental disorders (eg, inpatient, outpatient, and community), method of diagnosing mental disorders, diagnostic system, control or comparison group, and assessment of mortality. We also abstracted the observed number of deaths and/or rate of death among people with mental disorders (when possible, we did not include substance use or dementia), expected number of deaths and/or rate of death among people with mental disorders, risk of mortality (eg, standardized mortality ratio [SMR], relative risk [RR], odds ratio, hazard ratio, and years of potential life lost [YPLL]), and adjustment variables. All-cause mortality and mortality due to natural causes (eg, acute and chronic illnesses) and unnatural causes (eg, suicide and unintentional injury) were abstracted separately. One reviewer conducted a full abstraction of all data, and 2 reviewers (E.R.W. and R.E.M.) verified accuracy. We addressed study quality in 2 ways: by incorporating quality components, such as study design, into the study selection criteria and by including quality criteria as independent variables in the meta-regression. The authors of 7 studies were contacted for information that was not included in their articles. Two emails were undeliverable. Of the remaining 5 researchers, 4 were able to provide information about numbers of deaths or YPLL.
+Statistical Analysis
+We compared information on study time frame, data source, and geographic location to identify potential sample overlap. In the case of overlap, we chose the article with the longest follow-up period. As a result, 54 of the studies were excluded from our analyses. An estimate from one study16 was excluded from the overall all-cause analyses because of overlap with another study,17 but it was included in the diagnosis-specific analyses. We included the following measures of risk: SMR, RR, odds ratio, and hazard ratio.18,19 We aimed to use comparably adjusted estimates across the different ratio types. The SMRs are typically adjusted for age and sex; therefore, if several estimates were reported, we chose the most basic adjusted model. For articles that reported multiple estimates by subgroup, we combined the ratios using the methods described by van Doo-ren and colleagues20 to derive one estimate per study for each analysis. For studies that reported multiple estimates by mental disorder diagnoses whose diagnoses were not mutually exclusive, we chose the estimate of the largest diagnostic group. For one study,21 2 ratios were included in the analyses because the researchers examined mortality in 2 nonoverlap-
+JAMA Psychiatry April 2015 Volume 72, Number 4	335
+Downloaded From: https://jamanetwork.com/ on 12/23/2022
+tal disorders, we used the PAR formula b[(r - 1)/r], where b is the worldwide prevalence of mental disorders and r is the pooled RR.30 For the prevalence of all mental disorders and specific diagnoses, we used estimates from the World Health Organization World Mental Health Surveys.31,32 We calculated the approximate number of deaths attributable to mental disorders by multiplying the PAR by the number of deaths worldwide in 2012.33 The PAR and number of deaths were estimated for all-cause mortality and for all mental disorders and specific diagnoses (eg, mood disorders, anxiety disorders, and psychoses).
+ping populations. One study22 was excluded from the analyses because the SMR was an outlier.
+For the meta-analysis, mortality ratios were pooled using DerSimonian-Laird random-effects models to allow for heterogeneity across studies.23,24 Heterogeneity was assessed with theCochranQandthe 12tests.25,26 We ran random-effects metaregression models to determine which study characteristics could explain heterogeneity.26,27 We first ran each variable in a separate model and then ran a model with all the variables together. We also conducted sensitivity analyses by study quality criteria and significant variables in the meta-regression. Potential bias was examined using the funnel plot and Egger test.28,29 We conducted allmeta-analytic analyses on the natural log scale using STATA statistical software, version 12.1 (Stata Corp).
+For YPLL analyses, when studies reported estimates by subgroups, we calculated a mean. When numbers of deaths by subgroup were available, we calculated weighted estimates of YPLL for people with mental disorders. Median and range of YPLL by mortality due to all causes, natural causes, and unnatural causes were determined.
+We identified studies that included a measure of population attributable risk (PAR). We also calculated the number and percentage of deaths worldwide associated with mental disorders based on the pooled RRs and global prevalence of mental disorders. For the percentage of deaths attributable to men
+Results
+We identified 2481 articles through the literature search (Figure). Of the 1923 unique articles, the full text of 230 articles was reviewed, along with an additional 71 articles identified through hand searching and Google Scholar. Most excluded studies did not include a sample of people with a diagnosed mental disorder or did not report mortality ratios of people with mental disorders compared with an appropriate comparison group. Fifty-four studies were excluded because they included data that overlapped with other studies. A total of 203 studies met the criteria for this systematic review and were included in the meta-analysis.
+The 203 studies were heterogeneous in terms of sample source, type of sample included, and comparison group. Overall descriptive data of the included studies are presented in Table 1, and descriptive information for each study is included in eTable 2 in the Supplement. The articles represented 29 countries on 6 continents. Most studies were conducted in Europe (n = 125), primarily Sweden (n = 30) and the United Kingdom (n = 18). The remaining studies were conducted in North America (United States [n = 42] and Canada [n = 9]), Asia (n = 16), Australia (n = 8), Africa (n = 2), and South America (n = 1). Included studies mainly used linked registers (42.9%) to identify samples, although community-or population-based samples were used in 21.2% of studies. Most studies included samples of inpatients (37.4%) or inpatients and outpatients (26.1%). For most studies (66.5%), mental disorder diagnoses were identified from medical records, registers, or administrative data; diagnostic interviews were conducted in 24.6% of studies. Follow-up time ranged from 1 to 52 years, with a median of 10 years. The studies used in the following analyses are cited in eTable 3 in the Supplement.
+Mortality
+For all-cause mortality, 148 studies provided 149 RRs onthe mortality of people with mental disorders (Table 2). Of these studies, 135 revealed that mortality among people with mental disorders was significantly higher than the comparison population. Fourteen studies (9.4%) reported no significant difference in mortality risks between the 2 groups, and no studies reported lower mortality risks for people with mental disorders. No patterns were identifiable among these 14 studies that distinguished them from the rest of the studies.
+The total number of deaths, reported in 133 studies, was 338 381. The overall pooled RR for mortality among people with mental disorders was 2.22 (95% CI, 2.12-2.33). eFigure 1 in the Supplement displays the forest plot of estimates of all-cause mortality. The Cochran Q and I2 tests indicated a high level of heterogeneity across studies (P < .001). No significant bias was observedbased on the funnelplot and Egger test (P = .23) (eFig
+ure2 in the Supplement). For specific diagnoses, all-cause mortality was significantly elevated for psychoses, mood disorders, and anxiety (Table 2). The mortality risk for psychoses was significantly higher than those for depression (P < .001), bipolar disorder (P = .01), and anxiety (P < .001).
+Results from the meta-regression models with each variable entered separately provided evidence of differential
+mortality risks by population of people with mental disorders, method of identifying mental disorders, decade of first year of baseline, and length of follow-up. No differences were found in mortality risk based on sample source, diagnostic system, or geographic location (Asia, Europe, North America, and Africa, Australia, and South America grouped together). In the full meta-regression model, populations of people with mental disorders, length of follow-up, and first year of baseline remained significant. Compared with studies that included inpatients, studies that included outpatients (P = .03) and studies that included community- or population-based samples (P = .04) were associated with smaller effects. Studies with a follow-up of more than 10 years were associated with lower mortality risks compared with studies with shorter follow-up lengths (P = .02). Studies with a first year of baseline during the 1990s were associated with stronger effects compared with studies with a first year of baseline before 1970 (P = .009). Table 2 lists the pooled RRs from the stratified analyses for each of these variables separately.
+The analysis of natural causes of death included 100 studies and resulted in a pooled RR of 1.80 (95% CI, 1.71-1.88). For unnatural causes, the pooled RR from 106 studies was 7.22 (95% CI, 6.43-8.12). Across the 86 studies that reported number of deaths, 213 773 deaths occurred from natural causes. A total of46 051 deaths occurred from unnatural causes across the 91
+studies that reported number of deaths by unnatural causes. Fifty-seven studies reported the number of deaths for all, natural, and unnatural causes. From these studies, we estimate that 67.3% of deaths were due to natural causes and 17.5% were due to unnatural causes, with the remainder being unknown or unidentified.
+Years of Potential Life Lost
+Twenty-four studies included estimates of life expectancy or YPLL for people with mental disorders (eTable 4 in the Supplement). Results from all these studies indicated that people with mental disorders had more YPLL compared with people in the general population. For all-cause mortality, the reduction in life expectancy ranged from 1.4 to 32 years, with a median of 10.1 years (n = 22 studies). The YPLL ranged from 3 to 26.3 years for natural causes (n = 8 studies; median, 9.6 years) and 8.4 to 41.2 years for unnatural causes (n = 4 studies; median, 21.6 years).
+Population Attributable Risk
+Four studies34-37 in the systematic review included a PAR estimate. For all-cause deaths, the PAR was estimated at 1.3% for schizophrenia34 and 12.7% for depression.37 Among suicides, reported PAR estimates were 8.9% for schizophrenia,3411.2% for depression,35 4.8% for manic-depressive disorders,35 and 7.7% for severe mental illness.36
+The overall PAR estimate and number of deaths by diagnosis based on the meta-analysis results are given in Table 3. According to the World Health Organization World Mental Health Surveys, the median lifetime prevalence of any mental disorder across 17 countries is 26.1%. On the basis of this prevalence and the pooled RR from the meta-analysis, approximately 8 million deaths worldwide are attributable to mental disorders each year. Mood disorders and anxiety disorders have similar PARs, which are much higher than the PARs for psychoses.
+Discussion
+We conducted a meta-analysis to examine mortality among people with mental disorders across a range of diagnoses. People with mental disorders have a mortality rate that is 2.22 times higher than the general population or people without mental disorders, with a decade of YPLL. Our results align with other reviews, including a meta-review by Chesney and colleagues6 and a meta-analysis of depression by Cuijpers and Smit.8 In contrast to earlier work, our study provides individual- and population-based estimates of mortality due to mental disorders. The PAR due to mental disorders is estimated at 14.3%, which indicates that annually 8 million deaths worldwide can be attributable to mental disorders.
+This is the most comprehensive meta-analysis of mortality related to mental disorders of which we are aware. Results included 203 studies that were conducted in 29 countries. A wide range of samples, diagnoses, and populations of people with mental disorders were examined, which contributed to the substantial heterogeneity across studies. The full metaregression model included several factors that shed light on variability across study characteristics and subpopulations.
+Length of follow-up was associated with differential risks of mortality; studies with longer follow-up tended to report lower mortality ratios compared with studies with a follow-up of 10or fewer years. One explanation may be that people with mental illness die earlier and that, during a long followup, the background rate of mortality among people without mental illness starts to catch up with people with mental illness as the whole sample ages. The other study characteristics-sample source, methods of assessing mental disorders, and diagnostic system-were not significantly associated with
+mortality. However, we also found differential mortality rates by setting and first year of baseline.
+Inpatients had significantly higher mortality rates compared with samples with outpatients and with communitybased samples. The elevated mortality in inpatients is not surprising because inpatients tend to have more advanced psychiatric and general medical conditions than outpatients.38 Although inpatient samples may be most useful when looking to target the population with the greatest mortality burden, community-basedsamples are essential for examining the overall burden of mental disorders. Examining both types of studies in conjunction can help provide these perspectives on the mortality burden associated with mental disorders.
+Higher mortality rates were found among more recent studies (Table 2), particularly those with a first year of baseline in the 1990s compared with before 1970. These results extend the findings by Saha and colleagues,7 who found that the mortality gap between people with schizophrenia and the general population has been increasing over time. Our results indicate that the mortality gap may apply to people with a variety of mental disorders and not only schizophrenia. It appears that people with mental disorders are not experiencing the increased life expectancy of the general population.
+Examining individual-level measures, such as RR, and population-based measures, such as YPLL and estimated PAR, provides important and distinct perspectives on the excess mortality associated with mental disorders. The PAR reveals the high global burden of mortality associated with mental disorders, thus pointing to the importance of addressing mental illness along with more proximal causes of death. For specific diagnoses, whereas pooled RRs of mortality due to psychoses are significantly higher compared with depression and anxiety, depression and anxiety contribute to more deaths overall compared with psychoses because of their high base prevalence. Successfully reducing the mortality burden of mental disorders will require attention to less common but more severe illnesses and more prevalent but milder conditions.
+Similarly, although RRs for unnatural causes of death were higher compared with those of natural causes of death, natural causes accounted for more than two-thirds of deaths among people with mental disorders. These findings suggest that a variety of approaches are necessary to address different causes of death among people with mental disorders. Efforts to re
+Research Originalinvestigation
+Mental Disorder Mortality
+duce unnatural causes of death, such as suicide, need to focus on high-risk populations with mental disorders. However, efforts to reduce the excess burden of mortality among people with mental disorders need to also address the problem of natural causes, including cardiovascular disease, particularly as populations age. Differential mortality in people with mental disorders most likely stems from a number of causes, including behavioral and lifestyle factors, access to and quality of health care, and social determinants of health, such as poverty and social connectedness.13,14
+Milstein and colleagues39 laid out a 3-pronged strategy for reducing avoidable deaths in the general population that involves expanding access to care, providing better preventive and chronic care, and enabling healthier behaviors and safer environments. These approaches are also applicable for people with mental disorders. Prevention aimed at reducing mental disorders and chronic medical conditions is crucial.40 In addition, evidence-based strategies for suicide prevention for people with mental disorders include physician education for diagnosing mental disorders and provision of quality mental health treatment.41,42 Prevention and care of chronic medical conditions among people with mental disorders require promotion of healthy behaviors, early diagnosis and coordinated management, and integrated care between the mental health and medical systems. People with mental disorders often do not receive preventive services, such as immunizations, cancer screenings, and tobacco counseling,43 and often receive a lower quality of care for medical conditions.14,44
+Our results must be considered in light of several limitations. First, we searched for published English-language studies; therefore, some studies may have been missed. However, given the number of studies included in our analysis, it is unlikely that the results would be substantially affected. Second, the broad range of included studies resulted in a large amount of heterogeneity that could not be fully explained by the variables we assessed. Third, we were unable to specifically examine excess mortality due to substance use disorders; future work should examine the excess mortality associated with primary or comorbid substance use conditions. Fourth, the PAR and number of deaths attributable to mental disorders are estimates based on the best epidemiologic studies available on global mental health. The use of lifetime prevalence of mental disorders in the PAR estimate may be susceptible to recall bias but provides a comprehensive estimate.
+Conclusions
+People with mental disorders experience a high burden of mortality at the individual and population levels. Reduction of this burden will require a focus on less prevalent but more severe diagnoses and more common mental disorders. Likewise, efforts must be made to prevent and manage comorbid medical conditions and reduce the occurrence of unnatural deaths in this vulnerable population.
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+a result, specific recommendations will be made on clinical practice and training. Inquiry questionnaires have been re-designed with these aims in mind.
+CASE IDENTIFICATION
+The National Confidential Inquiry into Suicide and Homicide by People with Mental Illness has been re-established at the University of Manchester with a new Director and research team, and a number of changes to its aims and methods have taken place. This article details these changes so that they can become familiar to psychiatrists, on whose cooperation the Inquiry depends. On average, a consultant psychiatrist can expect to be contacted by the Inquiry once per year.
+The Confidential Inquiry into Homicides and Suicides by Mentally Ill People was first established in 1992 by the Department of Health following consultation with the Royal College of Psychiatrists. Its main objective was to enquire into, and record the details of, homicides and suicides committed by people under the care of, or recently discharged by, mental health services, in order to recommend measures by which services might reduce the number of such incidents. A preliminary report on homicide (Steering Committee, 1994) was followed by a full report on both homicide and suicide (Steering Committee, 1996). The latter report made a number of key recommendations: that the risk assessment skills of clinical staff should be strengthened; that there should be an increase in face-to-face contact with patients; that genuinely multidisciplinary teams should be developed; that communication between professionals within multidisciplinary teams and between professionals and families should be improved; that professionals should receive training in the use of legal powers under mental health legislation; and that treatment environments should be more acceptable to patients. These recommendations mirrored recommendations made by several inquiries after individual homicides (for example Ritchie et al, 1994).
+Although the Inquiry was successful in achieving a high professional and public profile, its findings on homicide were criticised as simplistic (Bowden, 1995) and
+those on suicide were undermined by problems of case ascertainment (House, 1996). The suicide inquiry, in particular, suffered from substantial and probably biased under-reporting because it relied on clinicians to alert it to suitable cases. The homicide inquiry received information from the Home Office on all homicide cases in which previous psychiatric contact had been noted, and on individuals committing homicide who subsequently became the responsibility of mental health services under the powers of the 1983 Mental Health Act -largely those convicted of manslaughter on grounds of diminished responsibility. Detailed data collection, however, was restricted to the few cases who had been in contact with mental health services in the 12 months preceding the homicide (39 cases in 25 months). This source of data also omitted homicides followed by suicide, and some homicides by people with mental illness that led to convictions by other verdicts (e.g. murder or manslaughter on grounds other than diminished responsibility). In addition, the Inquiry’s general aims led to mainly general conclusions, and its data collection was largely limited to England.
+The new (re-named) Inquiry aims to achieve a comprehensive sample that is truly national. It will report findings on its entire sample but will focus on suicides and homicides by six priority groups for whom service recommendations are most required. These are current in-patients, patients discharged from in-patient care less than three months earlier, patients subject to multidisiplinary review under the care programme approach, patients who have recently failed to attend or been non-compliant with drug treatment, and patients from ethnic minorities. Among several specific aims, examination of risk assessment prior to suicide or homicide is crucial - who does it, what conclusions are reached and on what basis, whether training has previously been received and how risk is communicated to other staff. As
+Comprehensive reporting is essential to the success of the Inquiry, and new systems of case identification have been introduced for both suicides and homicides.
+For suicides in England and Wales, the system is an adaptation of one that has proved successful in research in Manchester (Dennehy et al, 1996) and that makes use of the official process of death notification and reporting. Following a coroner’s inquest, the director of public health (DPH) in the deceased’s district of residence is notified of the death by the local registrar. Over one-third of DPHs receive equivalent information from the Office for National Statistics. The Inquiry has arranged for DPHs to forward information on all suicides and probable suicides (open verdicts/deaths from undetermined cause). In an average district of 450 000 people, there should be around four cases per month, so the amount of work for any one district is small.
+By checking information on each case against records held by local mental health services and some specialist supra-district services, the Inquiry will identify those with a history of service contact in the year before death (this remains the main inclusion criterion for suicides) and the consultant psychiatrist whose team was involved. The latter will be asked to hold a multidisciplinary review of the case (many mental health teams already do so) and to complete a standard questionnaire.
+The main disadvantages of the Inquiry’s new methods are the delay of three to six months before most inquests, and the reliance on multiple sources of data (there are 105 health districts and nearly 200 trusts treating mental illness in England and Wales). The delay, however, is common to most suicide research and its problems are outweighed by the advantage of a uniform definition of suicide. In time, it may prove possible to receive initial details of all cases direct from a single central source (i.e. the Office for National Statistics), although this is not an immediate option. For Scotland and Northern Ireland, submission of cases from an equivalent central source, the General Register Office, is currently under discussion.
+For homicides in England and Wales the Inquiry will receive information on all cases of murder, manslaughter and infanticide from the Home Office Homicide Index. From these details the court where the case was tried will be identified. Inquiry staff will then collect further information from court records, in particular from the psychiatric reports which are prepared on all those charged with homicide. A similar system is being planned in Scotland and Northern Ireland.
+These records will show whether there has been any previous contact with mental health services, and a questionnaire will be sent to the responsible consultant as in the suicide inquiry. The Inquiry will therefore collect psychiatric data on all homicides, and detailed clinical information on all those with a history of mental health service contact, whenever this occurred. Cases in which contact occurred less than a year before the homicide will remain identifiable within the total sample. Homicides followed by suicide will be detected through the suicide inquiry.
+INQUIRY DATA
+The new Inquiry questionnaires consist of 10 sections, covering the following: priority groups (as above); demographic information; psychiatric history; details of suicide/ homicide; in-patient suicides/homicides; suicides/homicides by out-patients, community patients and discharged patients; details of last contact; antecedents of suicide/ homicide; respondent’s view on prevention; and a final section for comments and additional information. Similar questions are asked about suicides and homicides, although in addition the homicide questionnaire enquires about previous violence. The questionnaire takes about 20 minutes to complete.
+The potential size of the suicide inquiry sample is over 1000 cases per year and it should be possible to gather information on important groups of patients that appear in
+only small numbers in most studies. The questionnaires have been constructed to identify these groups so that they can be studied separately and/or compared with the total sample. Examples include prisoners, diagnostic subgroups, people with a secondary diagnosis of substance misuse or personality disorder, people aged over 65, and people referred to services urgently under Section 136 of the Mental Health Act. Similarly, in the homicide inquiry it will be possible to study many of these groups and to examine cases of infanticide separately, in addition to comparing patients with and without a history of violence. One section of both the suicide and homicide questionnaires places in chronological order events that may have indicated increasing risk, such as non-compliance, increased alcohol misuse or failed service contact. The information collected will be used to construct models of suicide or homicide, each with different opportunities for preventive intervention.
+DISSEMINATION OF FINDINGS
+The overall findings of the Inquiry are expected to be published in major reports, the first after two years of data collection, and a series of detailed papers is also planned. Most importantly, results will inform the development of training packages in the assessment and management of risk for front-line mental health staff from different disciplines, and provide the basis for recommendations on clinical practice. For example, we expect to advise on how the care programme approach can be used to
+prevent suicide more effectively, and on the development of guidelines on responding to non-attendance and non-compliance by high-risk patients.
+The Confidential Inquiry represents a unique opportunity to collect detailed information about suicides and homicides by people with mental illness but its goals can be attained only if clinicians feel able to provide information frankly. We shall ensure the confidentiality of data submitted to the Inquiry. All data will be aggregated for analysis and no individual cases will be reported. We hope that, because we are seeking information after an inquest or a trial, the anxieties about disclosure that the legal process inevitably arouses will have subsided. Suicide and homicide by people with mental illness are sensitive subjects, but the aim of the Inquiry is to improve services, not to blame them.
+REFERENCES
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+Up to 80% of individuals being treated with antipsychotics suffer from medication-induced weight gain.1 The magnitude of this weight gain may be substantially higher than usually reported.2 Young people experiencing a first episode of psychosis are particularly susceptible to rapid and pronounced weight gain.3 Weight gain has become a major concern in the treatment of psychosis because it may adversely affect treatment adherence and clinical outcomes and is associated with reduced quality of life, social stigma, and greater morbidity and mortality.4
+As a result, there has been a growing interest in developing treatment alternatives to control or attenuate weight gain. A recent review of interventions to reduce weight gain in schizophrenia concluded that there was insufficient evidence to support the general use of adjunctive pharmacological interventions.5 Therefore, the present study aimed to undertake a systematic review and meta-analysis of all relevant randomised controlled trials (RCTs) of non-pharmacological interventions to control antipsychotic-induced weight gain in patients with first-episode or chronic schizophrenia.
+Method
+Search strategy
+Systematic bibliographic searches were performed to find relevant English and non-English language trials from the following databases: the Cochrane Central Register of Controlled Trials (CENTRAL), Medline, EMBASE, PsycINFO, CINAHL, UMI Proquest Digital Dissertations, Information Science Citation Index Expanded (SCI-EXPANDED), Information Social Sciences Citation Index (SSCI), Information Arts and Humanities Citation Index (A&HCI) and registers of ongoing clinical trials, with each
+database being searched from inception to May 2007. We additionally searched conference abstracts from ISI Science and Technology proceedings, and ISI Information Social Science and Humanities proceedings. The abstracts, titles and index terms of studies were searched using the following keywords: ‘weight gain’, ‘weight loss’, ‘weight change’ and ‘body weight’ in conjunction with ‘exercise’, ‘psychoeducation’, ‘intervention’, ‘diet’, ‘behavioural therapy’, ‘cognitive therapy’, ‘physical therapy’, ‘group intervention’, ‘management’, and ‘schizophrenia’ or ‘psychosis’. Further papers were found by hand-searching the references of all retrieved articles and previous reviews. We also screened hand-searched copies of the following journals (from January 2000): British Journal of Psychiatry, Journal of Clinical Psychiatry, American Journal of Psychiatry, Schizophrenia Bulletin, Schizophrenia Research and Journal of Clinical Psychopharmacology.
+Study selection
+Considered for inclusion were RCTs of a specific non-pharmacological adjunctive intervention aimed at preventing or controlling antipsychotic-induced weight gain, with at least 75% of participants diagnosed with schizophrenia-spectrum disorders using either DSM or ICD criteria. Comparison interventions could include either standard care or an active comparator intervention. Participants could be both young adults with recent-onset psychosis and adults with chronic schizophrenia, hospitalised or out-patients, during treatment with first- or second-generation antipsychotics. The primary outcome was considered to be mean change in body weight and body mass index (BMI) by the end of intervention, with secondary outcome measures including mean change in both body weight and BMI by follow-up. Additional
+secondary outcome measures comprised mean change on ratings of quality of life, medication adherence and relapse rates.
+Two reviewers (M.A.-J. and C.G.-B.) independently assessed all potentially relevant articles for inclusion. Any disagreements were resolved through discussion.
+Data extraction
+Two reviewers (S.H. and M.A.-J.) independently extracted relevant data from included trials, including treatment approach (prevention of weight gain v. weight loss), the nature of the intervention (cognitive-behavioural therapy, CBT) v. nutritional counselling (psychoeducation, diet and exercise), treatment format (group v. individual), intervention provider, length of intervention, participants’ characteristics, comparison intervention, antipsychotic type and dosage. Additional extracted information included measures of quality of life, medication adherence and relapse rates. Any discrepancies were resolved by consensus. Authors were contacted for the provision of missing data if necessary for the meta-analysis and to determine the eligibility of several studies.
+Assessment of methodological quality
+Trials were assessed against the following quality criteria: random sequence generation, allocation concealment, masked assessment of outcomes, number of withdrawals, intention-to-treat analysis and manual-based intervention. A maximum credit of five points was given if random allocation and allocation concealment were adequate, outcome was assessed by masked raters, data were assessed according to the intention-to-treat principle and the intervention was manualised.
+Statistical analyses
+Outcomes were pooled using MetaView, meta-analytic standard software used by the Cochrane Collaboration (RevMan 4.2.9 (PC version), Cochrane Collaboration, Oxford, England). Given that weight and BMI are continuous outcome measures, the weighted mean difference (WMD) was estimated using a fixed-effect meta-analysis with 95% confidence intervals for both end-of-treatment and follow-up time points. We conducted one primary comparison (non-pharmacological interventions v. treatment as usual) and three subgroup comparisons (preventive v. weight loss interventions; individual v. group therapy; CBT v. nutritional counselling). We further examined treatment effects according to sample characteristics (recent-onset psychosis v. chronic schizophrenia). To investigate treatment effects in different subgroups the overlap of the confidence intervals of the summary estimates was considered. In addition, the significant differences between subgroups were explored following the method of Deeks et al.6 This method is based on the chi-squared statistic test for heterogeneity. The statistic estimated is compared with a chi-squared distribution to test the significant difference between subgroups.
+We assessed heterogeneity of intervention estimates by visually inspecting the overlap of confidence intervals on the forest plots and by the I-squared statistic. The I -test of heterogeneity describes the proportion of total variation in study estimates that is due to heterogeneity.7 If there was evidence of inconsistency of estimates across trials, a random-effects meta-analysis was fitted.8 Random effects are, in general, more conservative than fixed-effects models because they take heterogeneity among studies into account. With decreasing heterogeneity the randomeffects approach moves asymptotically towards a fixed-effects model. Additionally, data from included trials were entered into a funnel graph (trial effect v. trial size) in order to investigate
+the likelihood of overt publication bias.9 In the absence of bias, the plot should resemble a symmetrical inverted funnel.10 If publication bias exists it is expected that, of published studies, the largest ones will report the smallest effects.11
+Sensitivity analyses were performed to further assess the robustness of the findings to the choice of statistical method (fixed- or random-effects model), the exclusion of the lowest-quality trials (trials with a quality score lower than 1) and the exclusion of the smallest trials (trials with a sample size of less than 40 participants).
+Results
+Of 28 studies retrieved, 10 were eligible for inclusion. We excluded 5 studies that did not include comparison groups;1 -6 6 studies that were non-randomised;17-22 2 RCTs that did not fully describe the sample characteristics and further information could not be obtained;2 ’ 4 1 RCT after the authors confirmed that less than 75% of the sample had a diagnosis of schizophrenia-spectrum disorders;25 1 RCT that reported 90% withdrawal rates and did not provide comparison group data;26 1 RCT that only measured eating habits and did not provide body weight or BMI changes;27 and 1 which did not provide data in a usable format and we were unable to obtain further information.28
+Six of the included trials investigated cognitive-behavioural intervention strategies;29-34 three nutritional counselling interventions;3 - 7 and one combined nutritional and exercise interventions.38 Five trials tested group intervention formats30,31,33,34,36 and five examined individual interventions.29,32,35,37,38 Four studies aimed to prevent antipsychotic-induced weight gain29,35-37 and six aimed to reduce body weight in those who had already experienced weight increase.30-34,38 Data could be extracted and pooled in meta-analyses from seven of the ten eligible studies. In three studies we were able to pool relevant data with the help of the authors.31,33,37
+Interventions lasted between 8 weeks and 6 months with efficacy measures taken at the completion of the trial intervention. Three studies reported follow-up periods ranging from 2 to 3 months after the end of the intervention.3 ’ ’ With one exception,38 all trials were carried out in out-patient settings. Only one trial utilised a sample of patients with recent-onset psychosis.29 Trials were conducted in Europe, Asia, the USA and Australia. Study medications included a broad range of firstand second-generation antipsychotics. Other characteristics of the included trials are outlined in the online Table DS1.
+Results for all non-pharmacological interventions
+Ten trials involving 482 patients compared non-pharmacological interventions with treatment as usual. There was a statistically significant reduction in mean body weight for those in the non-pharmacological intervention groups compared with those on treatment as usual (WMD= — 2.56 kg, 95% CI —3.20 to — 1.92 kg, P< 0.001) (Fig. 1). There was no evidence of statistical heterogeneity (12=28.9%).
+Pooling treatment effects of mean BMI change across all interventions yielded similar significant results in favour of the non-pharmacological interventions (WMD= — 0.91 kg/m2, 95% CI —1.13 to —0.68kg/m2, P<0.001), with no evidence of statistical heterogeneity (12=13.8%).
+Follow-up outcomes
+Three trials incorporated follow-up measures ranging from 2 months36 to 3 months.31,35 Pooling treatment effects of mean
+change in body weight and in BMI demonstrated that the statistically significant advantages of non-pharmacological interventions were maintained at follow-up (WMD=- 4.14 kg, 95% CI -5.80 to -2.49 kg, P< 0.001). Although one trial35 with high discontinuation rates at follow-up (n=31; 61%) reported results only for those who completed follow-up assessment, exclusion of this trial resulted in equivalent treatment effects.
+Subgroup analyses
+Prevention of antipsychotic-induced weight gain v weight loss
+Trials were analysed according to whether they aimed to prevent antipsychotic-induced weight gain or whether they were designed to reduce weight in patients who were already overweight or obese (Fig. 1). Although there was evidence of some statistical heterogeneity among trials that intended to reduce weight gain (12=51.0% v. 12=0.0% among those aimed to prevent weight gain) treatment effects were similar. Furthermore, when a random-effects model was fitted there was little change on the subgroup overall estimates (WMD= -2.32kg, 95% CI -3.10 to -1.54kg, P<0.001 v. WMD=- 2.37 kg, 95% CI -3.54 to -1.21kg, P< 0.001 using a random-effects model). Trials that aimed to prevent weight gain appeared to show a slightly larger effect on mean body weight change than those designed to reduce weight (Fig. 1). However, the confidence intervals of the summary treatment estimates overlapped to an important degree. Subsequently, the approach described by Deeks et al6 showed that there was no statistically significant difference between both subgroups (%2=1.10, P=0.29).
+Group v individual therapy
+The effect of intervention format was examined by analysing separately trials of group interventions and trials of individual approaches (Fig. 2). Although there was some evidence of inconsistency among group intervention trials (12=56.8% v. 12=0.0% among individual intervention trials), estimates were similar. In addition, when a random-effects meta-analysis was fitted there was little effect on the subgroup overall estimates (WMD= -2.09 kg, 95% CI -3.05 to -1.13kg, P<0.001 v. WMD=-2.30kg, 95% CI -3.82 to -0.78kg, P<0.001 fitting
+the random-effects model). Studies evaluating individual interventions seemed to show more benefit than the group intervention studies (Fig. 2). Again, visual examination of the confidence interval of the summary estimates indicated some degree of overlapping which was further confirmed by the lack of significant difference between subgroups (w2=1.67, P=0.20).
+Cognitive-behavioural therapy v nutritional counselling interventions Trials were analysed by type of non-pharmacological intervention: CBT v. nutritional counselling (Fig. 3). Although CBT trials appeared to show a smaller effect compared with nutritional counselling intervention trials (WMD=-2.14 kg, 95% CI -2.98 to -1.30kg, P<0.001 v. WMD=-3.12kg, 95% CI -4.10 to -2.14 kg, P<0.001 respectively), the confidence interval of the summary effects overlapped and there was no statistically significant difference between the subgroups (w2=2.22, P=0.14).
+Recent-onset psychosis v chronic schizophrenia
+Finally, trials were examined according to the characteristics of the sample: recent-onset psychosis v. chronic schizophrenia (Fig. 4). The only trial that evaluated an early intervention in young patients with recent-onset psychosis found that weight gain could be significantly attenuated (WMD=-2.80 kg, 95% CI -4.93 to -0.67 kg, P<0.01). Similar treatment effects were obtained in trials with participants with chronic schizophrenia (WMD= -2.54kg, 95% CI -3.20 to -1.87kg, P<0.001).
+Additional outcome measures
+Only two trials provided data regarding the impact on quality of life of these interventions. Know et al32 did not find differences between the groups in terms of quality of life (only a trend towards statistical difference in the physical score changes), but Evans et al35 reported significant differences in favour of the treatment group in subjective improvement in quality of life.
+Finally, no trials reported data regarding the influence of weight-management interventions on medication adherence.
+Assessment of risk of bias
+A description of the conduct of the trials included in the metaanalysis and assessment of the risk of bias is presented in the online Table DS2. Few trials gave explicit reports of trial conduct; one described the generation of random sequences,29 only one fully disclosed allocation concealment,29 and a few provided explicit description of who was masked. The attrition rate for the 10 trials varied between 0 and 50% in the control groups, and 0 and 20.7% in the intervention groups. Only two trials29,36 appeared to include all randomised patients in their analysis. Four trials were conducted using manual-based interventions.
+To determine the influence of study quality on the overall estimates, we performed stratified analysis according to methodological quality. The four low-quality trials (0 points)32,35,37,38 showed more benefit than the higher-quality trials (WMD= - 2.96 kg, 95% CI -3.90 to -2.03 kg). Exclusion of these studies, however, affected the overall effect and the
+confidence intervals only marginally (WMD=-2.21 kg, 95% CI -3.08 to -1.33 kg).
+Publication bias
+The funnel plot showed evidence of mild asymmetry (Fig. 5). The smallest studies (fewer than 40 participants included in the analysis)34,35,37 showed slightly larger effects (WMD=- 3.00 kg, 95% CI -4.53 to -1.46kg). However, exclusion of the smallest studies had little effect on the overall estimate WMD=-2.47 kg, 95% CI -3.17 to -1.77kg).
+Discussion
+Adjunctive non-pharmacological interventions are effective in reducing or attenuating antipsychotic-induced weight gain when
+compared with treatment as usual in patients with schizophreniaspectrum disorders. These findings with regard to reduction in mean body weight were confirmed by similar reductions in BMI, which is considered to be a better indicator of obesity and being overweight. Furthermore, treatment effects may be maintained at follow-up.
+gains.21 Similarly, preventive, multicomponent and flexible approaches that included exercise, diet and behavioural interventions have shown to be highly acceptable for young people with recent-onset psychosis.29 Thus, the tailored combination of weight-management techniques in a flexible and innovative manner which addresses individual needs and promotes therapeutic alliance is likely to produce best outcomes.
+Effects of intervention modality
+Results from this study showed no statistically significant or practically important differences between therapeutic approaches, either individual compared with group interventions, or CBT compared with nutritional counselling. Conversely, there is evidence that suggests that adherence to weight-management programmes is positively correlated with further weight loss.3 The choice of therapeutic approach will depend, then, on those factors that are likely to engage patients in a therapeutic alliance in order to produce associated losses. It is plausible, however, that particular patient age groups have different needs (e.g. young people may have different developmental needs to those who develop psychosis later in life) with regard to engagement in psychological treatments.40 Adventure- and recreation-based interventions, for instance, have been shown to be acceptable for individuals with chronic schizophrenia and may increase treatment adherence and promote further occupational and social
+Weight gain induced by antipsychotics and first-episode psychosis
+To date, only one RCT has shown the effectiveness of preventive strategies in attenuating antipsychotic-induced weight gain in a young cohort with recent-onset psychosis.29 Although there are few studies, it seems apparent that there is great potential for interventions aimed at early stages, before weight gain takes place. Weight gain is arguably a greater problem for young people experiencing a first episode of psychosis. This group is considered to be especially susceptible to substantial weight gain,2 which could interfere with the early recovery process. First, younger populations are already less disposed to adhering to medication regimes41 and potential weight gain may exacerbate nonadherence. Second, the physical changes produced by weight gain may result in social discrimination and stigma as young patients are more sensitive to issues of body image and self-esteem than their older counterparts.42 Early interventions could prevent or attenuate this medication side-effect as well as the adverse consequences derived from weight gain.
+This is consistent with a clinical staging model where treatment effects are thought to be the greatest when delivered as early as possible.43 Two fundamental assumptions underlie this model. First, patients in the earliest stages of schizophrenia have a better response to treatment and a better prognosis than those in later stages. Second, treatments offered in the early stages should be more benign as well as more effective. Given this background, preventive weight-management interventions have the potential to be more effective, acceptable, cost-efficient and beneficial.
+Clinical implications
+How clinically meaningful is a weight loss of 2.6 kg? Several authoritative bodies, such as the Institute of Medicine,44 have
+implied that weight losses of as little as 5% in individuals at risk of metabolic syndromes can result in clinically meaningful reductions in morbidity and risk of early mortality. The majority of individuals with schizophrenia experience clinically significant weight gain, which is associated with greater risk of developing several diseases, including diabetes, hypertension and coronary heart disease. As a result, people with schizophrenia have a 20% shorter life expectancy than the population at large.45 In this review, the average baseline weight was approximately 80 kg (ranging from 66.5 to 101.3 kg). Therefore, even a weight loss of 1.9-3.2kg represents a reduction of 2.5-4.0% of initial body weight in a significant number of patients. It may be plausible, then, to expect that these reductions in body weight could result in corresponding reductions in morbidity and early mortality.
+Limitations of the study
+This study has some limitations. First, most of the trials included short-term follow-up periods. As a result we could not draw conclusions on the long-term effectiveness of these interventions. Second, reporting on generation of random sequence, allocation concealment, intention-to-treat analyses and masking was poor, making assessment of the potential for biased estimates of treatment effect difficult.7 Given the relationship between poor reporting and larger treatment effects,46 findings reported by these trials may have overestimated summary treatment effects. Third, it must be noted that subgroup analyses are observational in their nature and are not based on randomised comparisons. Moreover, some of these comparisons were limited by the sample size. Therefore, differences between treatment modalities need to be explored in adequately designed RCTs. Furthermore, there was evidence of skew in the data provided by several trials included in the present review. Meta-analytic techniques frequently face the problem of managing non-parametric data. Although there is not a clear consensus regarding the resolution of this statistical issue, we note the limitations of our analysis in accounting for skewed data. Another limitation relates to the generalisability of the findings to clinical practice. Therapists in clinical trials are highly motivated and skilled in the implementation of the intervention being tested, which may affect the generalisability of the results to the population of therapists. As a result, these findings need to be evaluated in pragmatic trials of intervention effectiveness in a range of clinical settings. Finally, as with all systematic reviews, publication bias is a potential source of error. Although there was some evidence of such bias, exclusion of the smallest studies only marginally affected the overall effect.
+Strengths of the study
+Although it is plausible that some studies assessing non-pharmacological interventions to manage antipsychotic-induced weight gain were not discovered by our literature search, our procedures kept this to a minimum. We conducted a thorough search of the electronic literature, including databases that contain unpublished literature, undertook hand-searches and made efforts to access grey literature. Another common problem in meta-analysis is incomplete reporting of consistent outcome data in primary articles. We minimised the impact of such incomplete reporting by contacting authors when feasible.
+This review includes several trials not included in previous meta-analysis of weight-management interventions,5 a focus on non-pharmacological approaches with careful evaluation of different treatment strategies and an assessment of trial conduct and potential risk of bias. Although previous systematic reviews have also suggested the effectiveness of healthy living interventions in patients with schizophrenia,47 they included a limited number
+of RCTs as well as quasi-experimental studies and did not perform meta-analytic techniques. Furthermore, we found a notable consistency across all study estimates, which was reflected in the robustness of the findings across analytic methods and when the smallest and lowest-quality studies were excluded.
+Implications for future research
+Although the results from this study suggest that non-pharmacological interventions may be effective in reducing antipsychotic-induced weight gain, further research needs to address several salient issues. Given the adverse impact of weight gain on medication adherence and relapse rates,48 quality of life,49 social stigma and discrimination50 as well as self-esteem,51 interventions to prevent weight gain have the potential to reduce these negative effects. Even though these outcomes were not consistently reported or measured, there is some evidence that nutritional counselling improves quality of life, overall health and body im-age.35 Further, CBT may promote client satisfaction30 and physical well-being.32 Moreover, we are aware of no data that would allow precise quantification of the impact of weight-management interventions on adherence to medication regimens, subsequent relapse rates and other salient aspects such as perception of social stigma and social isolation. Further research should investigate these issues in order to fully elucidate all the potential benefits of these interventions.
+Well-designed trials are required, including further comparison studies of one type of treatment against another. These trials should also address fundamental questions such as the effects of longer interventions and booster sessions, long-term maintenance of outcomes, intervention effects on clinical morbidity and physical health, as well as their cost-effectiveness. In addition, the development and evolution of preventive treatment strategies is critical. Future interventions should be innovative and encourage engagement with therapy by promoting well-being and global recovery.
\ No newline at end of file
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@@ -0,0 +1,69 @@
+Up to 80% of individuals being treated with antipsychotics suffer from medication-induced weight gain.1 The magnitude of this weight gain may be substantially higher than usually reported.2 Young people experiencing a first episode of psychosis are particularly susceptible to rapid and pronounced weight gain.3 Weight gain has become a major concern in the treatment of psychosis because it may adversely affect treatment adherence and clinical outcomes and is associated with reduced quality of life, social stigma, and greater morbidity and mortality.4
+As a result, there has been a growing interest in developing treatment alternatives to control or attenuate weight gain. A recent review of interventions to reduce weight gain in schizophrenia concluded that there was insufficient evidence to support the general use of adjunctive pharmacological interventions.5 Therefore, the present study aimed to undertake a systematic review and meta-analysis of all relevant randomised controlled trials (RCTs) of non-pharmacological interventions to control antipsychotic-induced weight gain in patients with first-episode or chronic schizophrenia.
+Method
+Search strategy
+Systematic bibliographic searches were performed to find relevant English and non-English language trials from the following databases: the Cochrane Central Register of Controlled Trials (CENTRAL), Medline, EMBASE, PsycINFO, CINAHL, UMI Proquest Digital Dissertations, Information Science Citation Index Expanded (SCI-EXPANDED), Information Social Sciences Citation Index (SSCI), Information Arts and Humanities Citation Index (A&HCI) and registers of ongoing clinical trials, with each
+database being searched from inception to May 2007. We additionally searched conference abstracts from ISI Science and Technology proceedings, and ISI Information Social Science and Humanities proceedings. The abstracts, titles and index terms of studies were searched using the following keywords: ‘weight gain’, ‘weight loss’, ‘weight change’ and ‘body weight’ in conjunction with ‘exercise’, ‘psychoeducation’, ‘intervention’, ‘diet’, ‘behavioural therapy’, ‘cognitive therapy’, ‘physical therapy’, ‘group intervention’, ‘management’, and ‘schizophrenia’ or ‘psychosis’. Further papers were found by hand-searching the references of all retrieved articles and previous reviews. We also screened hand-searched copies of the following journals (from January 2000): British Journal of Psychiatry, Journal of Clinical Psychiatry, American Journal of Psychiatry, Schizophrenia Bulletin, Schizophrenia Research and Journal of Clinical Psychopharmacology.
+Study selection
+Considered for inclusion were RCTs of a specific non-pharmacological adjunctive intervention aimed at preventing or controlling antipsychotic-induced weight gain, with at least 75% of participants diagnosed with schizophrenia-spectrum disorders using either DSM or ICD criteria. Comparison interventions could include either standard care or an active comparator intervention. Participants could be both young adults with recent-onset psychosis and adults with chronic schizophrenia, hospitalised or out-patients, during treatment with first- or second-generation antipsychotics. The primary outcome was considered to be mean change in body weight and body mass index (BMI) by the end of intervention, with secondary outcome measures including mean change in both body weight and BMI by follow-up. Additional
+https://doi.org/10.1192/bjp.bp.107.042853 Published online by Cambridge University Press
+secondary outcome measures comprised mean change on ratings of quality of life, medication adherence and relapse rates.
+Two reviewers (M.A.-J. and C.G.-B.) independently assessed all potentially relevant articles for inclusion. Any disagreements were resolved through discussion.
+Data extraction
+Two reviewers (S.H. and M.A.-J.) independently extracted relevant data from included trials, including treatment approach (prevention of weight gain v. weight loss), the nature of the intervention (cognitive-behavioural therapy, CBT) v. nutritional counselling (psychoeducation, diet and exercise), treatment format (group v. individual), intervention provider, length of intervention, participants’ characteristics, comparison intervention, antipsychotic type and dosage. Additional extracted information included measures of quality of life, medication adherence and relapse rates. Any discrepancies were resolved by consensus. Authors were contacted for the provision of missing data if necessary for the meta-analysis and to determine the eligibility of several studies.
+Assessment of methodological quality
+Trials were assessed against the following quality criteria: random sequence generation, allocation concealment, masked assessment of outcomes, number of withdrawals, intention-to-treat analysis and manual-based intervention. A maximum credit of five points was given if random allocation and allocation concealment were adequate, outcome was assessed by masked raters, data were assessed according to the intention-to-treat principle and the intervention was manualised.
+Statistical analyses
+Outcomes were pooled using MetaView, meta-analytic standard software used by the Cochrane Collaboration (RevMan 4.2.9 (PC version), Cochrane Collaboration, Oxford, England). Given that weight and BMI are continuous outcome measures, the weighted mean difference (WMD) was estimated using a fixed-effect meta-analysis with 95% confidence intervals for both end-of-treatment and follow-up time points. We conducted one primary comparison (non-pharmacological interventions v. treatment as usual) and three subgroup comparisons (preventive v. weight loss interventions; individual v. group therapy; CBT v. nutritional counselling). We further examined treatment effects according to sample characteristics (recent-onset psychosis v. chronic schizophrenia). To investigate treatment effects in different subgroups the overlap of the confidence intervals of the summary estimates was considered. In addition, the significant differences between subgroups were explored following the method of Deeks et al.6 This method is based on the chi-squared statistic test for heterogeneity. The statistic estimated is compared with a chi-squared distribution to test the significant difference between subgroups.
+We assessed heterogeneity of intervention estimates by visually inspecting the overlap of confidence intervals on the forest plots and by the I-squared statistic. The I -test of heterogeneity describes the proportion of total variation in study estimates that is due to heterogeneity.7 If there was evidence of inconsistency of estimates across trials, a random-effects meta-analysis was fitted.8 Random effects are, in general, more conservative than fixed-effects models because they take heterogeneity among studies into account. With decreasing heterogeneity the randomeffects approach moves asymptotically towards a fixed-effects model. Additionally, data from included trials were entered into a funnel graph (trial effect v. trial size) in order to investigate
+the likelihood of overt publication bias.9 In the absence of bias, the plot should resemble a symmetrical inverted funnel.10 If publication bias exists it is expected that, of published studies, the largest ones will report the smallest effects.11
+Sensitivity analyses were performed to further assess the robustness of the findings to the choice of statistical method (fixed- or random-effects model), the exclusion of the lowest-quality trials (trials with a quality score lower than 1) and the exclusion of the smallest trials (trials with a sample size of less than 40 participants).
+Results
+Of 28 studies retrieved, 10 were eligible for inclusion. We excluded 5 studies that did not include comparison groups;1 -6 6 studies that were non-randomised;17-22 2 RCTs that did not fully describe the sample characteristics and further information could not be obtained;2 ’ 4 1 RCT after the authors confirmed that less than 75% of the sample had a diagnosis of schizophrenia-spectrum disorders;25 1 RCT that reported 90% withdrawal rates and did not provide comparison group data;26 1 RCT that only measured eating habits and did not provide body weight or BMI changes;27 and 1 which did not provide data in a usable format and we were unable to obtain further information.28
+Six of the included trials investigated cognitive-behavioural intervention strategies;29-34 three nutritional counselling interventions;3 - 7 and one combined nutritional and exercise interventions.38 Five trials tested group intervention formats30,31,33,34,36 and five examined individual interventions.29,32,35,37,38 Four studies aimed to prevent antipsychotic-induced weight gain29,35-37 and six aimed to reduce body weight in those who had already experienced weight increase.30-34,38 Data could be extracted and pooled in meta-analyses from seven of the ten eligible studies. In three studies we were able to pool relevant data with the help of the authors.31,33,37
+Interventions lasted between 8 weeks and 6 months with efficacy measures taken at the completion of the trial intervention. Three studies reported follow-up periods ranging from 2 to 3 months after the end of the intervention.3 ’ ’ With one exception,38 all trials were carried out in out-patient settings. Only one trial utilised a sample of patients with recent-onset psychosis.29 Trials were conducted in Europe, Asia, the USA and Australia. Study medications included a broad range of firstand second-generation antipsychotics. Other characteristics of the included trials are outlined in the online Table DS1.
+Results for all non-pharmacological interventions
+Ten trials involving 482 patients compared non-pharmacological interventions with treatment as usual. There was a statistically significant reduction in mean body weight for those in the non-pharmacological intervention groups compared with those on treatment as usual (WMD= — 2.56 kg, 95% CI —3.20 to — 1.92 kg, P< 0.001) (Fig. 1). There was no evidence of statistical heterogeneity (12=28.9%).
+Pooling treatment effects of mean BMI change across all interventions yielded similar significant results in favour of the non-pharmacological interventions (WMD= — 0.91 kg/m2, 95% CI —1.13 to —0.68kg/m2, P<0.001), with no evidence of statistical heterogeneity (12=13.8%).
+Follow-up outcomes
+Three trials incorporated follow-up measures ranging from 2 months36 to 3 months.31,35 Pooling treatment effects of mean
+change in body weight and in BMI demonstrated that the statistically significant advantages of non-pharmacological interventions were maintained at follow-up (WMD=- 4.14 kg, 95% CI -5.80 to -2.49 kg, P< 0.001). Although one trial35 with high discontinuation rates at follow-up (n=31; 61%) reported results only for those who completed follow-up assessment, exclusion of this trial resulted in equivalent treatment effects.
+Subgroup analyses
+Prevention of antipsychotic-induced weight gain v weight loss
+Trials were analysed according to whether they aimed to prevent antipsychotic-induced weight gain or whether they were designed to reduce weight in patients who were already overweight or obese (Fig. 1). Although there was evidence of some statistical heterogeneity among trials that intended to reduce weight gain (12=51.0% v. 12=0.0% among those aimed to prevent weight gain) treatment effects were similar. Furthermore, when a random-effects model was fitted there was little change on the subgroup overall estimates (WMD= -2.32kg, 95% CI -3.10 to -1.54kg, P<0.001 v. WMD=- 2.37 kg, 95% CI -3.54 to -1.21kg, P< 0.001 using a random-effects model). Trials that aimed to prevent weight gain appeared to show a slightly larger effect on mean body weight change than those designed to reduce weight (Fig. 1). However, the confidence intervals of the summary treatment estimates overlapped to an important degree. Subsequently, the approach described by Deeks et al6 showed that there was no statistically significant difference between both subgroups (%2=1.10, P=0.29).
+Group v individual therapy
+The effect of intervention format was examined by analysing separately trials of group interventions and trials of individual approaches (Fig. 2). Although there was some evidence of inconsistency among group intervention trials (12=56.8% v. 12=0.0% among individual intervention trials), estimates were similar. In addition, when a random-effects meta-analysis was fitted there was little effect on the subgroup overall estimates (WMD= -2.09 kg, 95% CI -3.05 to -1.13kg, P<0.001 v. WMD=-2.30kg, 95% CI -3.82 to -0.78kg, P<0.001 fitting
+the random-effects model). Studies evaluating individual interventions seemed to show more benefit than the group intervention studies (Fig. 2). Again, visual examination of the confidence interval of the summary estimates indicated some degree of overlapping which was further confirmed by the lack of significant difference between subgroups (w2=1.67, P=0.20).
+Cognitive-behavioural therapy v nutritional counselling interventions Trials were analysed by type of non-pharmacological intervention: CBT v. nutritional counselling (Fig. 3). Although CBT trials appeared to show a smaller effect compared with nutritional counselling intervention trials (WMD= -2.14kg, 95% CI -2.98 to -1.30kg, P<0.001 v. WMD=-3.12kg, 95% CI -4.10 to -2.14kg, P<0.001 respectively), the confidence interval of the summary effects overlapped and there was no statistically significant difference between the subgroups (w2=2.22, P=0.14).
+Recent-onset psychosis v chronic schizophrenia
+Finally, trials were examined according to the characteristics of the sample: recent-onset psychosis v. chronic schizophrenia (Fig. 4). The only trial that evaluated an early intervention in young patients with recent-onset psychosis found that weight gain could be significantly attenuated (WMD= -2.80 kg, 95% CI -4.93 to -0.67 kg, P<0.01). Similar treatment effects were obtained in trials with participants with chronic schizophrenia (WMD=-2.54kg, 95% CI -3.20 to -1.87kg, P<0.001).
+Additional outcome measures
+Only two trials provided data regarding the impact on quality of life of these interventions. Know et al32 did not find differences between the groups in terms of quality of life (only a trend towards statistical difference in the physical score changes), but Evans et al35 reported significant differences in favour of the treatment group in subjective improvement in quality of life.
+Finally, no trials reported data regarding the influence of weight-management interventions on medication adherence.
+Assessment of risk of bias
+A description of the conduct of the trials included in the metaanalysis and assessment of the risk of bias is presented in the online Table DS2. Few trials gave explicit reports of trial conduct; one described the generation of random sequences,29 only one fully disclosed allocation concealment,29 and a few provided explicit description of who was masked. The attrition rate for the 10 trials varied between 0 and 50% in the control groups, and 0 and 20.7% in the intervention groups. Only two trials29,36 appeared to include all randomised patients in their analysis. Four trials were conducted using manual-based interventions.
+To determine the influence of study quality on the overall estimates, we performed stratified analysis according to methodological quality. The four low-quality trials (0 points)32,35,37,38 showed more benefit than the higher-quality trials (WMD= - 2.96 kg, 95% CI -3.90 to -2.03 kg). Exclusion of these studies, however, affected the overall effect and the
+confidence intervals only marginally (WMD=-2.21 kg, 95% CI -3.08 to -1.33 kg).
+Publication bias
+The funnel plot showed evidence of mild asymmetry (Fig. 5). The smallest studies (fewer than 40 participants included in the analysis)34,35,37 showed slightly larger effects (WMD=- 3.00 kg, 95% CI -4.53 to -1.46kg). However, exclusion of the smallest studies had little effect on the overall estimate WMD=-2.47 kg, 95% CI -3.17 to -1.77kg).
+Discussion
+Adjunctive non-pharmacological interventions are effective in reducing or attenuating antipsychotic-induced weight gain when
+compared with treatment as usual in patients with schizophreniaspectrum disorders. These findings with regard to reduction in mean body weight were confirmed by similar reductions in BMI, which is considered to be a better indicator of obesity and being overweight. Furthermore, treatment effects may be maintained at follow-up.
+gains.21 Similarly, preventive, multicomponent and flexible approaches that included exercise, diet and behavioural interventions have shown to be highly acceptable for young people with recent-onset psychosis.29 Thus, the tailored combination of weight-management techniques in a flexible and innovative manner which addresses individual needs and promotes therapeutic alliance is likely to produce best outcomes.
+Effects of intervention modality
+Results from this study showed no statistically significant or practically important differences between therapeutic approaches, either individual compared with group interventions, or CBT compared with nutritional counselling. Conversely, there is evidence that suggests that adherence to weight-management programmes is positively correlated with further weight loss.3 The choice of therapeutic approach will depend, then, on those factors that are likely to engage patients in a therapeutic alliance in order to produce associated losses. It is plausible, however, that particular patient age groups have different needs (e.g. young people may have different developmental needs to those who develop psychosis later in life) with regard to engagement in psychological treatments.40 Adventure- and recreation-based interventions, for instance, have been shown to be acceptable for individuals with chronic schizophrenia and may increase treatment adherence and promote further occupational and social
+Weight gain induced by antipsychotics and first-episode psychosis
+To date, only one RCT has shown the effectiveness of preventive strategies in attenuating antipsychotic-induced weight gain in a young cohort with recent-onset psychosis.29 Although there are few studies, it seems apparent that there is great potential for interventions aimed at early stages, before weight gain takes place. Weight gain is arguably a greater problem for young people experiencing a first episode of psychosis. This group is considered to be especially susceptible to substantial weight gain,2 which could interfere with the early recovery process. First, younger populations are already less disposed to adhering to medication regimes41 and potential weight gain may exacerbate nonadherence. Second, the physical changes produced by weight gain may result in social discrimination and stigma as young patients are more sensitive to issues of body image and self-esteem than their older counterparts.42 Early interventions could prevent or attenuate this medication side-effect as well as the adverse consequences derived from weight gain.
+This is consistent with a clinical staging model where treatment effects are thought to be the greatest when delivered as early as possible.43 Two fundamental assumptions underlie this model. First, patients in the earliest stages of schizophrenia have a better response to treatment and a better prognosis than those in later stages. Second, treatments offered in the early stages should be more benign as well as more effective. Given this background, preventive weight-management interventions have the potential to be more effective, acceptable, cost-efficient and beneficial.
+Clinical implications
+How clinically meaningful is a weight loss of 2.6 kg? Several authoritative bodies, such as the Institute of Medicine,44 have
+implied that weight losses of as little as 5% in individuals at risk of metabolic syndromes can result in clinically meaningful reductions in morbidity and risk of early mortality. The majority of individuals with schizophrenia experience clinically significant weight gain, which is associated with greater risk of developing several diseases, including diabetes, hypertension and coronary heart disease. As a result, people with schizophrenia have a 20% shorter life expectancy than the population at large.45 In this review, the average baseline weight was approximately 80 kg (ranging from 66.5 to 101.3 kg). Therefore, even a weight loss of 1.9-3.2kg represents a reduction of 2.5-4.0% of initial body weight in a significant number of patients. It may be plausible, then, to expect that these reductions in body weight could result in corresponding reductions in morbidity and early mortality.
+Limitations of the study
+This study has some limitations. First, most of the trials included short-term follow-up periods. As a result we could not draw conclusions on the long-term effectiveness of these interventions. Second, reporting on generation of random sequence, allocation concealment, intention-to-treat analyses and masking was poor, making assessment of the potential for biased estimates of treatment effect difficult.7 Given the relationship between poor reporting and larger treatment effects,46 findings reported by these trials may have overestimated summary treatment effects. Third, it must be noted that subgroup analyses are observational in their nature and are not based on randomised comparisons. Moreover, some of these comparisons were limited by the sample size. Therefore, differences between treatment modalities need to be explored in adequately designed RCTs. Furthermore, there was evidence of skew in the data provided by several trials included in the present review. Meta-analytic techniques frequently face the problem of managing non-parametric data. Although there is not a clear consensus regarding the resolution of this statistical issue, we note the limitations of our analysis in accounting for skewed data. Another limitation relates to the generalisability of the findings to clinical practice. Therapists in clinical trials are highly motivated and skilled in the implementation of the intervention being tested, which may affect the generalisability of the results to the population of therapists. As a result, these findings need to be evaluated in pragmatic trials of intervention effectiveness in a range of clinical settings. Finally, as with all systematic reviews, publication bias is a potential source of error. Although there was some evidence of such bias, exclusion of the smallest studies only marginally affected the overall effect.
+Strengths of the study
+Although it is plausible that some studies assessing non-pharmacological interventions to manage antipsychotic-induced weight gain were not discovered by our literature search, our procedures kept this to a minimum. We conducted a thorough search of the electronic literature, including databases that contain unpublished literature, undertook hand-searches and made efforts to access grey literature. Another common problem in meta-analysis is incomplete reporting of consistent outcome data in primary articles. We minimised the impact of such incomplete reporting by contacting authors when feasible.
+This review includes several trials not included in previous meta-analysis of weight-management interventions,5 a focus on non-pharmacological approaches with careful evaluation of different treatment strategies and an assessment of trial conduct and potential risk of bias. Although previous systematic reviews have also suggested the effectiveness of healthy living interventions in patients with schizophrenia,47 they included a limited number
+of RCTs as well as quasi-experimental studies and did not perform meta-analytic techniques. Furthermore, we found a notable consistency across all study estimates, which was reflected in the robustness of the findings across analytic methods and when the smallest and lowest-quality studies were excluded.
+Implications for future research
+Although the results from this study suggest that non-pharmacological interventions may be effective in reducing antipsychotic-induced weight gain, further research needs to address several salient issues. Given the adverse impact of weight gain on medication adherence and relapse rates,48 quality of life,49 social stigma and discrimination50 as well as self-esteem,51 interventions to prevent weight gain have the potential to reduce these negative effects. Even though these outcomes were not consistently reported or measured, there is some evidence that nutritional counselling improves quality of life, overall health and body im-age.35 Further, CBT may promote client satisfaction30 and physical well-being.32 Moreover, we are aware of no data that would allow precise quantification of the impact of weight-management interventions on adherence to medication regimens, subsequent relapse rates and other salient aspects such as perception of social stigma and social isolation. Further research should investigate these issues in order to fully elucidate all the potential benefits of these interventions.
+Well-designed trials are required, including further comparison studies of one type of treatment against another. These trials should also address fundamental questions such as the effects of longer interventions and booster sessions, long-term maintenance of outcomes, intervention effects on clinical morbidity and physical health, as well as their cost-effectiveness. In addition, the development and evolution of preventive treatment strategies is critical. Future interventions should be innovative and encourage engagement with therapy by promoting well-being and global recovery.
\ No newline at end of file
diff --git a/OConnor_Gartland_OConnor_in press.txt b/OConnor_Gartland_OConnor_in press.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b835c683d7f1cc63401f99abef5eb805c125aaf9
--- /dev/null
+++ b/OConnor_Gartland_OConnor_in press.txt	
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+Introduction
+Every 40 seconds a person dies by suicide somewhere in the world (WHO, 2014). Suicide is a leading cause of mortality and is a major global health issue. It is estimated that 800,000 people die by suicide each year and there are 25 million nonfatal suicide attempts annually. As a result, for many decades, researchers have been exploring the causes of suicidal behavior with an aim to identify targets for suicide prevention. Numerous models have been proposed that differ in their emphasis on the role of psychological, social, psychiatric and neurobiological factors in predicting risk of suicide. (Mann et al., 1999; O’Connor, 2011; O’Connor & Kirtley, 2018; O’Connor & Nock, 2014; van Heeringen and Mann, 2014; van Orden et al., 2010). However, central to many models is a stressdiathesis component which states that suicidal behavior is a result of an interaction between acutely stressful events and a susceptibility to suicidal behavior (a diathesis). Research findings are accruing from post-mortem, neuroimaging and in-vivo studies that a trait diathesis is manifested in dysregulation of hypothalamic-pituitary-adrenal (HPA) axis stress response activity as well as in impairments of the serotonergic and noradrenergic neurotransmitter systems, in structural brain abnormalities and via epigenetic pathways (Mann, 2013; Turecki et al., 2012; van Heeringen et al., 2011; van Heeringen and Mann, 2014). Indeed, evidence is emerging to suggest that biomarkers of a trait diathesis following serious stressful and traumatic psychosocial events, independent of psychiatric co-morbidities, may be useful predictors of suicide risk (van Heeringen and Mann, 2014). However, surprisingly, a relatively small body of work has explored the role of stress and its concomitant biomarker, cortisol, in the context of suicide and suicide vulnerability. The aim of the current chapter is to provide an overview of studies that have investigated the role of stress and cortisol in the context of suicide risk together with studies that have examined other putative stress-related risk factors including childhood trauma, impaired executive function, impulsivity, disrupted sleep and perinatal and epigenetic influences on suicide risk.
+The study of stress has a long history. Scientific interest dates back to the First World War, when soldiers were found to exhibit “shellshock”, an extreme reaction to the trauma of battle that was subsequently acknowledged to be a manifestation of post-traumatic stress disorder (Lazarus, 1999). Since this time, stress has become part of everyday vernacular, and there has been a marked increase
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+in media coverage of stress, and as a result, this has led to increased research and public awareness. In terms of research, over many decades we have learned that when we experience stress, the HPA axis is activated and releases cortisol from the adrenal glands. Once released, cortisol has several important functions such as increasing access to energy stores, increasing protein and fat mobilisation, as well as regulating the magnitude and duration of inflammatory responses (Sapolsky et al., 2000). As such, cortisol is the primary effector hormone of the HPA axis stress response system. The HPA axis is regulated by a negative feedback system, whereby the hypothalamus and the pituitary gland have receptors that detect changes in cortisol levels. For example, cortisol secretion will be inhibited when circulating levels rise or it will be stimulated when levels fall. However, if the HPA axis is repeatedly activated, this will trigger increased cortisol output, thereby exposing bodily tissues to excessive concentrations of the hormone (McEwen, 1998; McEwen, 2000; Miller et al., 2007). Over time, such repetitive activation may contribute to tissue damage and future ill health by placing excessive pressure on various bodily systems including the HPA axis (known as allostatic load; McEwen, 1998). In addition, in the longer-term repeated activation may lead to dysregulation of the HPA axis as evidenced by flattened patterns of cortisol secretion across the day (including in the morning as well as in response to stressors). Indeed, in the context of suicide, evidence is accumulating to suggest a link between dysregulation of the HPA axis following chronic exposure to stress and vulnerability to suicide. An important issue this chapter will return to soon, but first a brief overview of the role of stress in the leading models of suicide.
+Stress-Diathesis Models of Suicidal Behavior
+Stress-diathesis models have a long history in the field of suicide research (see O’Connor et al., 2016). More than thirty years ago, Schotte and Clum (1987) put forward their distress-stress-hopelessness model of suicidal behavior. Therein, they posited and found evidence that impaired social problem-solving, a specific cognitive vulnerability factor acted as a diathesis; it was associated with suicide risk in the presence of stress. Since then, there has been an exponential growth in studies
+which have investigated how a range of different diatheses are associated with suicide risk under
+particular circumstances. Some of these diatheses are biological, others are cognitive in nature, and
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+others still are personality factors. For example, there is a considerable body of research illustrating how distinct components of perfectionism increase one’s risk of suicidal thinking and behaviour in the presence of stress (O’Connor, 2007).
+Another diathesis-stress model, developed by Mann and colleagues, was the clinical model of suicidal behaviour (Mann et al., 1999) where risk is posited to vary as a function of the interaction between psychiatric disorder (stressor) and a trait-like diathesis (e.g., impulsivity). This clinical model has been especially influential within psychiatry and clinical medicine. Whereas psychiatric disorder was a key element within the Mann et al. model, in 2008 Wenzel and Beck (2008) put forward a cognitive model of suicidal behavior which focuses on psychological treatment for suicidal behavior. Similar to the clinical model, it has adopted a distress-stress framework; however on this occasion, the model is psychological in orientation and is characterised by three main constructs: (i) dispositional vulnerability factors, (ii) cognitive processes associated with psychiatric problems and (iii) cognitive processes associated with suicidal behavior. When it was published, this latter model was noteworthy as it systematically identified theoretical components which could be targeted in the delivery of cognitive therapy for suicidal patients. More recently still there has been greater recognition of the heterogeneity of suicide risk and the identification of suicidal sub-types (Bernanke, Stanley, & Oquendo, 2017) in the context of the relationship between stress and suicide risk. To this end, Bernanke et al. (2017) have proposed two distinct phenotypes of suicidal behavior, with one being stress-responsive (governed by the cortisol system) and the other being non-stress responsive (associated with the serotonin system). As these two phenotypes have been suggested as only two of potentially numerous suicidal subtypes, more research is required to better describe the complexity of suicide risk in terms of diathesis-stress responses.
+The Integrated Motivational-Volitional Model of Suicidal Behavior
+Building upon the work of Mann et al. (1999) and Williams (1997), O’Connor (2011) published the integrated motivational-volitional (IMV) model of suicidal behavior in 2011 and refined in 2018 (O’Connor & Kirtley, 2018). The aim of this model was to bring together the disparate constructs from existing models of suicide and integrate them into a single overarching theoretical framework.
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+At its core, the IMV model is a diathesis-stress model which tracks the development of suicide risk across three phases (See Figure 1). The first phase, the pre-motivational phase, outlines the context in which suicidal thinking and suicidal behavior emerge. In this phase, it is posited that vulnerabilities interact with life stress and environmental influences to increase the likelihood that suicidal thinking may occur. However, the presence of vulnerabilities and stress are not sufficient to explain the increase in suicide risk on their own. According to the model, in phase 2 (the motivational phase) suicidal thinking is more likely to emerge if an individual is trapped by feelings of defeat, humiliation and loss. Needless to say, a stressful life event is often a key driver to feelings of defeat or humiliation from which the individual is endeavoring to escape. Defeat and entrapment are part of the final common pathway to the emergence of suicidal thinking. The final phase of the IMV model, the volitional phase, is concerned with the transition from thinking about suicide to acting upon one’s thoughts of suicide, i.e., attempting suicide/dying by suicide. In this behavioural enaction phase, an individual is more likely to attempt suicide if volitional phase factors are also present. These volitional phase factors include having access to the means of suicide, being impulsive, being exposed to the suicidal behavior of others and having higher levels of fearlessness about death (O’Connor & Kirtley, 2018). In addition, although stress is not a key driver to the emergence of suicidal thoughts (beyond defeat and entrapment), it may be important in behavioural enaction (O’Connor et al., 2012). Across a series of studies, as predicted by the IMV model, we have shown that the presence of such factors differentiate individuals who think about suicide or self-harm from those who engage in suicidal behaviour or self-harm (Branley-Bell et al., 2019; Mars et al., 2018; Wetherall et al., 2018). In short, the IMV model is a useful model to consider the role of stress in the context of suicide risk.
+[ Insert Figure 1 about here ]
+Cortisol and suicide risk
+Broadly speaking previous research on HPA axis, cortisol and suicidal behavior has focused in three main areas: 1) assessing HPA axis functioning through pharmacological manipulation of the stress system (Mann and Currier, 2007; Pompili et al., 2010) using the Dexamethasone Suppression Test (DST; Carroll et al., 1968), 2) exploring naturally fluctuating cortisol levels and suicidal behavior
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+and, 3) investigating HPA axis functioning following acute laboratory stressors in vulnerable and non-vulnerable groups.
+The Dexamethasone Suppression Test (DST) and suicide risk
+For many decades researchers have been concentrating scientific effort in identifying clinical and biological predictors of suicide. In particular, during the early 1980s, studies were emerging to suggest that death by suicide may be associated with HPA axis hyperactivity and that a useful clinical tool to detect HPA axis hyperactivity was the dexamethasone suppression test (DST). The DST usually involves participants receiving oral administration of the synthetic glucocorticoid dexamethasone (e.g., 1 mg) on one morning (say at 11am) and then plasma cortisol levels being assessed the following day in the morning (8am) and afternoon (4pm). Failure to suppress cortisol is evidence for HPA axis hyperactivity (due to glucocorticoid receptor insensivity) and has been found, in a number of studies, to predict completed suicide in different groups vulnerable to suicide (Coryell and Schlesser, 1981; Coryell et al., 2006; Jokinen and Nordstrom, 2008; Jokinen and Nordstrom, 2009; Norman et al., 1990). An early example comes from a study by Coryell and Schlesser (1981) in patients with major depressive disorder. These authors showed that the risk estimate of suicide was around 27% in a group of patients who failed to suppress cortisol levels following DST compared to only 3% in patients who exhibited cortisol suppression. Similarly, Jokinen and Nordstrom (2008), in a 17 year follow-up study of elderly hospitalised mood disorder patients, found that DST nonsuppression doubled the suicide risk and that patients who had completed suicide had higher postdexamethasone serum cortisol levels compared to survivors.
+Other existing evidence from prospective DST studies suggests that HPA hyperactivity is more consistently associated with completed suicide compared to suicide attempt (Mann & Currier, 2007). For example, in a sample of patients who met the criteria for major depressive disorder, mania, or schizoaffective disorder, Coryell and Schlesser (2001) reported that there was a 14 fold higher risk of suicide in individuals who failed to exhibit suppression of their cortisol levels in the DST compared to individuals who did exhibit suppression. However, the evidence for a clear relationship between
+HPA hyperactivity, as assessed using the DST, and suicide attempt is mixed. Some studies have shown that DST suppression status is unable to distinguish between individuals who will attempt
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+suicide and those who will not. Yet, as outlined above, other research findings have demonstrated DST non-suppression is associated with a higher rate of suicide attempts (see Mann & Currier, 2007 for a review). In their review of biological predictors of suicidal behaviour in individuals with mood disorders, Mann and Currier (2007) suggest that an important reason why non-suppression on the DST is predictive of completed suicide may be because it is also associated with “a failure to respond to antidepressant treatment or a tendency for early relapse such as shortly after discharge” (p. 10).
+Nevertheless, a recent meta-analysis of biological risk factors for suicidal behaviours was inconclusive with regards to the prediction of future suicide behaviours (Chang et al., 2016). Of the small number of tests included (4 for suicide attempt, 8 for completed suicide), the results showed that DST suppression significantly predicted completed suicide (Odds Ratio = 1.75 [1.05-2.90]), but did not significantly predict suicide attempt (Odds Ratio = 1.49 [0.58-3.82]). Moreover, there was also some evidence of publication bias suggesting that if three missing cases (below the mean) were included, the weighted mean odds ratio would have been non-significant.
+Therefore, taken together, whilst DST research has contributed enormously to knowledge regarding HPA axis dysregulation and suicide vulnerability, findings remain inconsistent and contradictory (McGirr et al., 2011; Chang et al., 2016). Pharmacological manipulation has also been criticised as it may not adequately mimic the size of the endogenous HPA response to naturally occurring stressors (Burke et al., 2005). In addition, more recent studies have begun to explore other aspects of the cortisol response, such as the diurnal cortisol rhythm (including morning and afternoon/ evening cortisol levels; e.g., O’Connor et al., 2018) and cortisol reactivity to stressors (e.g., McGirr et al., 2010) in order to improve understanding of the role of the stress response system and the HPA axis in the context of suicide behaviours.
+Naturally fluctuating cortisol and suicidal behaviour
+The second broad area of research investigating the HPA axis and suicidal behaviour has focussed on exploring the relationship between naturally fluctuating (or baseline) cortisol levels and suicide behaviours. However, before outlining this research, it is important to note that cortisol has a distinct pattern over any 24 hour period. The diurnal pattern of cortisol production is characterised by
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+two distinct components: the peak levels after awakening (i.e., the cortisol awakening response, CAR) and the diminishing levels throughout the rest of the day (i.e., the diurnal slope). As will be shown later, evidence is beginning to converge to suggest that lower (or blunted) CAR and a flatter cortisol slope across the day are associated with more negative health outcomes (e.g., O’Connor et al., 2009; 2020; Adam et al., 2017).
+Similar to the findings from the DST studies, research that has explored the associations between naturally fluctuating cortisol and different aspects of suicide behaviours have yielded inconsistent findings. For example, Westrin et al. (1999) found elevated cortisol levels in patients who had recently attempted suicide compared to healthy controls, and Chatzittofis et al. (2013) found higher cortisol levels in (medication free) individuals who had attempted suicide compared to healthy volunteers. In contrast, Lindqvist and colleagues (2008) found that cortisol levels were significantly lower in individuals who had attempted suicide compared to controls and more recently McGirr et al. (2011) also showed patients with depressive disorders exhibited lower levels of cortisol. A number of methodological factors may account for these mixed findings including the timing of the cortisol sampling (morning vs afternoon/evening), study quality, absence of a control comparison group and age of the sample. Given these disparate findings, O’Connor and colleagues (2016) conducted a metaanalysis of all existing studies that has compared participants with at least one prior suicide attempt with a comparison group with no suicide attempt history in order: i) to estimate the strength and variability of the association between naturally fluctuating cortisol levels and suicidal behaviour and ii) to identify moderators of this relationship. The systematic literature identified 27 studies (N = 2226; 779 suicide attempters & 1447 non-attempters) that met the inclusion criteria. Overall, there was no significant effect of suicide group on cortisol. However, significant associations between cortisol and suicide attempts were observed as a function of age (see Figure 2). In studies where the mean age of the sample was below 40 years the association was positive (i.e., higher cortisol was associated with suicide attempts; r = .234, p < .001), and where the mean age was 40 or above the association was negative (i.e., lower cortisol was associated with suicide attempts; r = - .129, p < .001).
+[ Insert Figure 2 about here ]
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+The authors concluded that these meta-analytic findings confirm that HPA axis activity, as indicated by age-dependent variations in naturally occurring cortisol levels, are associated with suicide attempt. Moreover, these findings suggest that a reversal in the association between cortisol and suicide attempt occurs when the average age of the sample is around 40 years (or older). This is not to imply that for any individual the shift would happen at 40 years, this is on average (and was the mean age for the sample of studies that were included in this meta-analysis). Nonetheless, what these analyses do show is that for older people the association is negative and for younger people it is positive and that the relationship between cortisol and suicide attempts is more nuanced and complicated than past research has recognised. Furthermore, these results may have implications for research studies (reviewed earlier) that have assessed HPA axis functioning using pharmacological manipulation of the stress system such as the DST and raises the possibility that age may also moderate cortisol suppression following DST manipulation.
+An important question remains unanswered by the findings of this meta-analysis. How might the reversal in the association be explained? It is likely that some of the variability will be accounted for by differences in study design, participants, suicide attempt measures and cortisol measurement. However, the findings are broadly consistent with McEwen’s notion of allostatic load, whereby if the HPA axis is repeatedly activated (by stress) the immune, cardiovascular and the endocrine systems are potentially exposed to excessive demands that over time can lead to dysregulation of these systems (McEwen, 1998; 2000). Moreover, in the context of suicide vulnerability, naturally fluctuating cortisol levels may provide an index (or proxy) for the amount of stress exposure that vulnerable individuals have encountered (O’Connor et al., 2009). This view is also consistent with Fries and colleagues (2005) account of the development of hypocortisolism, which suggests that the latter phenomenon occurs after a prolonged period of hyperactivity of the HPA axis due to chronic stress. Therefore, it would follow that in individuals who are older (40 years or older) and who have likely been exposed to stressful and traumatic events over a more sustained period, their HPA axis is more likely to have become dysregulated leading to lower secretion of cortisol levels. Such patterns have
+been observed in older Holocaust survivors with PTSD compared to those without PTSD (Y ehuda et al., 1995). In contrast, younger individuals (less than 40 years), who have been exposed to serious
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+stressful and psychosocial events, are likely to continue to exhibit an adaptive HPA axis stress response in the short to medium term (by releasing high levels of naturally fluctuating cortisol in response to their adverse and stressful environment).
+The findings from this meta-analysis are also important because they demonstrate that both types of observations (hypereactivity and hyporeactivity) may be valid and true in terms of the relationship between cortisol levels and suicide attempt, but may be accounted for by age-dependent exposure to stress over time. However, much more work is required to understand how naturally fluctuating cortisol-suicide vulnerability relations change prospectively. Future research ought to improve the quality of their studies in this area by utilising longitudinal designs (over many years) that incorporate assessments of suicidal behavior using clinical interviews or validated scales and ensure cortisol is measured at numerous time points across the day (morning, afternoon, evening) over multiple days (cf., Gartland et al., 2014) to capture the full profile of cortisol, in in doing so, use appropriate measurement (e.g. accuracy of sampling, accounting for variables known to influence cortisol etc).
+Cortisol reactivity to laboratory stress and suicide behaviour
+The third broad area investigating the HPA axis and suicidal behaviour involves studies that have examined whether cortisol reactivity to a laboratory stress task can differentiate individuals who have a history of suicide attempt or ideation compared to individuals who have no such history (e.g., Giletta et al., 2015; McGirr et al., 2010; O’Connor et al., 2017). A leading study in this area was conducted by McGirr and colleagues (2010). These authors investigated whether dysregulation of the HPA axis to a laboratory stressor was a heritable risk factor for suicidal behavior. A sample of first-degree relatives of individuals who had died by suicide and matched controls were compared on their cortisol reactivity to a well-established psychosocial stressor known as the Trier Social Stress Test, a public speaking task and mental arithmetic task in front of a judgmental/negative audience (TSST; Kirschbaum et al., 1993). The results showed that the first-degree relatives exhibited a blunted (i.e. lower) cortisol response to stress. The authors have argued that their findings indicate that blunted cortisol reactivity to stress may represent a trait marker (or phenotype) of suicide risk.
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+More recently, two studies have used acute laboratory stressors to examine HPA axis responses to stress in vulnerable, at risk groups (Melhem et al., 2016; O’Connor et al., 2017). Melhem et al. (2016) examined cortisol responses to stress (i.e., the TSST) in a large sample of adult offspring of parents with mood disorder. This study found that an offspring suicide attempter group exhibited the lowest levels of total cortisol output during the stressor compared to an offspring with suicide-related behavior but never attempted suicide group, a non-suicidal offspring group and a healthy control group. Moreover, the suicide attempter group also showed the lowest baseline cortisol levels pre-TSST, but, contrary to expectations, there were no significant differences between groups on their measure of cortisol reactivity to stress.
+A second study, conducted by O’Connor et al (2017), aimed to investigate whether cortisol reactivity to the Maastricht Acute Stress Test (MAST, Smeets et al., 2012) differentiated individuals who had previously made a suicide attempt from those who had thought about suicide (a suicide ideation group) and control participants. The MAST stress protocol was designed to be both physiologically and psychologically challenging by combining an uncontrollable physical stressor (i.e., a cold pressor challenge) with a social-evaluative (i.e., mental arithmetic) component (Smeets et al., 2012). The results showed that participants who had made a previous suicide attempt exhibited significantly lower cortisol response to the MAST compared to participants in the ideator and control groups (see Figure 3). Furthermore, participants who made an attempt within the past year exhibited a blunted cortisol response compared to participants with a more distant history of attempt. In addition, lower levels of cortisol in response to the MAST were associated with higher levels of suicidal ideation at 1-month follow-up in the suicide attempters group.
+[ insert Figure 3 about here ]
+In the O’Connor et al. (2017) study, the finding that participants who attempted suicide within the last 12 months appear to exhibit a blunted cortisol response to the laboratory stressor, compared to those with a lifetime history of suicide attempt, is a noteworthy observation. It is important because it suggests, in this study at least, that the cortisol response to stress may have returned to close to normal in the lifetime history group, although, these levels remain lower than in the control and ideator groups. This latter finding is promising as it is consistent with the notion that psychological and
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+pharmacological intervention may yield benefits over time and help facilitate (partial) recovery of the HPA axis stress response system reflecting the higher cortisol levels in the lifetime history group. Therefore, an obvious next step would be for researchers to utilise longitudinal designs to explore whether dysregulation of cortisol reactivity to stress is restored over time and to investigate if the HPA axis has the potential to return to normal following psychological (e.g., stress management interventions) and/or pharmacological intervention.
+Taken together, the results from recent laboratory based cortisol reactivity studies suggest that blunted or lower HPA axis activity may increase risk for suicide attempt among vulnerable individuals. The findings also indicate that the HPA axis stress response system may have become dysregulated in individuals who have tried to take their own lives and as such may increase future suicide risk by impairing their ability to cope and adapt to acute and non-acute stressors.
+Childhood trauma - cortisol - suicide risk
+Childhood trauma has been identified as an important variable in the aetiology of suicide risk. For example, Marshall et al. (2013) found high levels of moderate and severe childhood trauma being associated with suicide attempt in a prospective cohort study of illicit drug users. In particular, they showed that severe sexual, physical and emotional childhood abuse conferred a substantial increased repeated suicide risk in adulthood. In another study, Sachiapone et al. (2007) found that high levels of childhood trauma were associated with suicide attempt in patients with unipolar depression. Similarly, a large longitudinal population-based study in the Netherlands (Enns et al., 2006) found that childhood neglect, psychological abuse and physical abuse were strongly associated with new onset suicide ideation and suicide attempt over a 3-year follow-up. More recently, O’Connor et al. (2018) found that 78.7% of participants with a history of suicide attempt reported exposure to at least one type of childhood trauma that was classified as moderate or severe compared to 37.7% and 17.8% in an ideation and control group, respectively.
+Research has begun to focus on the links between childhood trauma and altered dynamics of the HPA axis. In the context of depression, Heim and colleagues (2000; 2008) have shown associations between childhood trauma and dysregulated HPA axis and to persistent sensitization of
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+the stress response system. Childhood trauma effects on depression have also been explained by changes in glucocorticoid resistance, increased central corticotropin-releasing factor (CRF) activity, immune activation, and reduced hippocampal volume. In contrast, the results are less clear relating childhood trauma to cortisol activity (e.g., cortisol reactivity to stress). A study by Carpenter et al. (2007) showed decreased cortisol levels in response to a laboratory stressor in childhood maltreated men who were never depressed. In a later study, the same team also found that women who reported childhood physical abuse displayed a blunted cortisol response to the TSST compared to women without physical abuse (Carpenter et al., 2011). These findings contradict earlier work by Heim et al. (2000) who showed that women who had a history of childhood abuse, with and without major depression, exhibited increased cortisol to an acute laboratory stressor. However, more broadly, there is also converging evidence to suggest that early life adversity is associated with blunted cortisol reactivity to stress (e.g., Lovallo et al., 2012). For example, Lovallo et al. (2012) using data from the Oklahoma Family Health Patterns Project showed that experience of adversity predicted reduced cortisol response to an acute laboratory stress challenge.
+Similarly, two recent studies by O’Connor and colleagues (2018; 2020) provide further support linking childhood trauma with blunted, or lower cortisol levels in response to stress and in naturalistic settings. In a laboratory-based study investigating the effects of childhood trauma on cortisol reactivity to an acute stressor and on resting cortisol levels, O’Connor et al. (2018) found that higher levels of trauma were associated with blunted cortisol reactivity to stress and lower resting cortisol levels. In particular, individuals who reported more than one moderate or severe type of childhood trauma exhibited the lowest cortisol levels in response to stress (see Figure 4) and at rest. In a second study, O’Connor et al. (2020), investigated for the first time, whether childhood trauma and daily stressors and emotions were associated with diurnal cortisol levels (i.e., cortisol levels following waking and the decline in cortisol levels across the rest of the day) over a 7-day study in individuals vulnerable to suicide. The results showed that participants with a history of suicide attempt (a suicide
+attempt group) or previously had thoughts of ending their life (an ideation group) released
+significantly lower cortisol upon awakening (CAR) and had a tendency towards flatter wake-peak to
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+12 hour (WP-12) cortisol slopes compared to individuals with no history of attempt or ideation. Moreover, childhood trauma was found to be associated with significantly lower CAR and a tendency towards flatter WP-12 cortisol slope and it had an indirect effect on suicide vulnerability group membership via lower daily CAR levels. The latter finding is particularly important as it shows, for the first time, that the effects of childhood trauma has indirect, as well as, direct effects on suicide vulnerability through lower levels of daily CAR.
+[ insert Figure 4 about here ]
+Taken together, the studies reviewed are important as they suggest that the experience of childhood trauma may predispose individuals to vulnerability to suicide in adulthood by leading to diminished HPA axis activity during awakening (and possibly a tendency towards a flatter diurnal profile across the day) as well as during stress. These findings are in keeping with a recent large scale meta-analysis by Adam and colleagues (2017) that showed flatter cortisol cycles were common to a wide range of mental and physical health outcomes. Moreover, these results are also consistent with the development of hypocortisolism posited by Fries et al (2005), as outlined earlier, which suggests that hypocortisolism occurs after a prolonged period of hyperactivity of the HPA axis due to chronic stress. Moreover, we have previously suggested that Lovallo’s (2013) conceptual model of addiction linking adverse life experiences in childhood and adolescence to adverse health outcomes in adulthood should be extended to suicide risk (O’Connor et al., 2018). Lovallo (2013) has argued that adverse life experiences cause modifications in frontolimbic brain function which may then lead directly to: 1) reduced stress reactivity, 2) altered cognition (characterised by a shift in focus to more short-term goals and impulsive response selection) and 3) unstable affect regulation. Lovallo (2013) has also suggested that these three negative consequences influence the development of a more impulsive behavioral style that may increase risk of addiction and the engagement in poor health behaviours. We believe that exhibiting a low or blunted CAR may be another negative consequence of the modification of brain function (Boehringer et al., 2015).
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+Possible mechanisms linking stress and suicide risk
+The association between stress and suicide has been described in the models of suicide risk, and the role of the HPA axis in this relationship has been outlined above. However, there are likely to be multiple interrelated mechanisms that link stress and suicide. We will look at a few more possible pathways here that may help to answer the question: How does the experience of stress influence subsequent suicidal behaviour, sometimes decades later? Executive Function and Impulsivity
+One possible mechanism tying stress to suicide behaviour is executive function. Executive function is a broad term for a range of cognitive processes which manage and control thoughts, emotions and actions. These functions are required whenever we must pay attention to a task or are effortfully pursuing a goal; they help us to concentrate, consider possible courses of action, and make informed decisions. There are three core executive functions (Diamond, 2013): inhibition (this includes both the self-control of behaviour, as well was stopping interferences to thought necessary for selective attention), working memory (keeping information temporarily available for processing), and cognitive flexibility (the ability to switch from thinking about one concept to another, and also the ability to adapt thoughts or behaviours based on changes in the environment). The personality trait ‘impulsivity’ is related to executive function as it is characterised by behaviours which reflect impaired self-regulation. At the behavioural level, this might include poor planning, premature responding without considering the consequences of one’s actions, taking risks and an inability to delay gratification. These behaviours are suggested to originate from deficits in working memory, self-regulation of affect-motivation-arousal, internalisation of speech and behavioural analysis that affords hindsight, forethought, and goal-directed action (Barkley, 1997; Gvion & Apter, 2011). Impulsivity tends to lead to the underestimation of potential consequences of actions, has been shown to be positively associated with suicide risk (Brezo et al., 2006; Gvion & Apter, 2011; McGirr et al., 2009). Dysfunctional executive decision-making, such as cognitive rigidity, has also been suggested to result in suicidal mental states (Marzuk et al., 2005; for review see Bredemeier & Miller, 2015).
+There is evidence to support the suggestion that both distal and proximal stress can have an effect on executive function. Greater levels of adverse life experience (such as physical and sexual
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+abuse, separation from parents, and a family history of substance abuse) has been shown to predict lower working memory function, greater impulsive decision-making, and lower mental age (Lovallo, 2013; Lovallo et al., 2013). As rates of early adversity are high in individuals who have attempted suicide (O’Connor et al., 2018), a mediated pathway is plausible where stressful experiences early in life alter cognitive function and that these altered thought processes can increase the risk of suicidal behaviours throughout the lifecourse.
+However, a moderation pathway is also possible. Some aspects of executive control are considered heritable (Swan & Carmelli, 2002). McGirr et al. (2010) compared relatives of suicide completers with matched controls, and found no difference in baseline measures of executive function. However, performance on the Word-Colour Inhibition Test and Trail Making Test were differentially affected by a controlled laboratory stressor (the Trier Social Stress Test). Relatives of suicide completers failed to improve on executive function tests after the TSST, specifically on switching and inhibition conditions. This indicates a level of cognitive inflexibility in these individuals, but only after stress induction. McGirr and colleagues argue that cognitive inflexibility and a decreased ability to inhibit inappropriate action in response to real-life stressors could be potential factors that increase the risk of suicidal behaviour. These findings provide a possible moderation mechanism for the stress diathesis hypothesis, where ‘at risk’ individuals respond to stress with cognitions which could increase their risk of suicidal behaviour.
+Family history
+Suicidal behaviour aggregates in families (Brent et al., 1996, 2002; Kim et al., 2005; McGirr et al., 2009, 2011). In a register-based case control study, Mittendorfer-Rutz et al. (2008) demonstrated that individuals whose sibling had attempted suicide were nearly 3.5 times more likely to attempt suicide themselves; a maternal suicide attempt carried a 2.7 times greater risk and paternal suicide attempt carried a 1.9 times greater risk. Genetic transmission of personality traits such as impulsivity has been one factor suggested to account for familial aggregation of suicidal behaviour in families (Mittendorfer-Rutz et al., 2008). However, it is difficult to tease out the relationships between stress, family history of suicide and suicidal behaviour because a family history of suicide can be a
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+substantial source of stress in itself, as well as providing a potentially direct genetic/hereditary pathway to suicidal behaviour.
+There is evidence that in a sample of depressed outpatients, a family history of suicide was associated with lower plasma cortisol levels (McGirr et al., 2011). This effect was independent of psychopathology and the individual’s previous suicide attempts, and suggests an overall downregulation of HPA-axis activity in this group. However, from this we cannot determine where in the diurnal rhythm of cortisol the levels are reduced. Different points of this rhythm are implicated in different aspects of HPA functionality and reactivity; research focussing on HPA stress reactivity has used cortisol saliva samples alongside acute laboratory stressors to determine whether differences in cortisol stress reactivity exist between these two groups.
+Evidence from first-degree family members of suicide completers shows low (or blunted) salivary cortisol responses to an acute laboratory stressor, compared to controls (McGirr et al., 2010). O’Connor et al. (2017) also found that having a family history of suicide was associated with the lowest cortisol response to an acute laboratory stressor. Melhem and colleagues (2016) found that offspring of parents who had attempted suicide exhibited lower levels of total cortisol output during an acute laboratory stressor compared to offspring of parents with mood disorder but had not attempted suicide, but they did not find significant differences in cortisol reactivity to the task between these groups. These studies tentatively suggest that having a family history of suicide is associated with blunted HPA axis stress reactivity, and are consistent with the idea that dysregulation of the stress response system may be a heritable risk factor for suicidal behaviour. Again, this dysregulation of the stress system would act as a moderation pathway, where the relationship between stress and suicidal behaviour is strengthened due to the dysregulated HPA axis response to stress in these individuals. Indeed, in a sample of suicide attempters, lower levels of cortisol in response to acute stress have been shown to predict higher levels of suicidal ideation one month later (O’Connor et al., 2017). Therefore, HPA axis dysregulation may exacerbate the stress-suicide relationship such that the link between stress and suicide is stronger for those with a family history of suicide.
+However, it cannot be determined whether this dysregulation occurs through a genetic commonality between family members or whether it is an effect of the stress of losing a close family member to
+18
+suicide. Do members of families with a history of suicide have a shared genetic vulnerability or diathesis, which increases the risk of suicide in the face of stress? Or has the stressful experience of losing a close relative to suicide caused the dysregulation of the HPA axis in these individuals? Further research is warranted, but will require carefully controlled groups to compare those highly affected by family history of suicide and those less affected, for example where the parental suicide attempt was before the child was born. Adoption studies could also provide valuable insights.
+Perinatal Influences and Epigenetics
+Familial influences on suicide risk may also work through adverse in-utero and perinatal conditions. In a recent systematic review, Orri and colleagues (2019) assessed family and parental characteristics during pregnancy and around the time of birth in relation to suicide, suicide attempt and suicide ideation throughout the lifespan. Factors associated with higher suicide risk included high birth order, teenage mothers, single mothers, low maternal and paternal education level, fetal growth and small for gestational age. Only one study in this review directly measured maternal stress, in the form of bereavement (Class et al., 2014). This Swedish population-based study of over 2,000,000 offspring found that the death of a first degree relative of the mother during the first postnatal year increased the risk of suicide attempt and completed suicide in offspring. Orri and colleagues also argue that factors such as teenage mothers, single mothers, and low socioeconomic position at birth may reflect a wider adverse psychosocial environment which would be associated with greater levels of maternal stress both during pregnancy and the perinatal period. While other psychosocial mechanisms are likely to be at work, there is some evidence that maternal stress may influence foetal brain development through epigenetic (non-genetic influences on gene expression) or gene-by-environment interaction mechanisms.
+There is evidence that prenatal anxiety is associated with higher levels of waking cortisol in children at age 10, suggesting that prenatal experiences can influence HPA activity in offspring (O’Connor et al., 2005). Turecki and colleagues (2012) outline a model to explain increased risk of suicide in individuals exposed to early-life adversity through HPA axis dysregulation (Figure 5). Early life stress is proposed to increase methylation (addition of a methyl group to a DNA nucleotide)
+19
+of hippocampal glucocorticoid receptor (GR) genes, which disrupts the GR gene expression. One of the studies that support this suggestion demonstrated increased methylation of the GR promoter gene
+in adolescent children in cases where their mothers were exposed to intimate partner violence during pregnancy (Radtke et al., 2011). The methylation status of the GR gene in the mothers was not affected by intimate partner violence, but the prenatal stress experienced appears to have had a long-lasting impact on the gene expression of their children. This may provide a mechanism to explain findings that prenatal stress alters HPA axis activity later in life (O’Connor et al., 2005). These hippocampal receptors play a crucial role in the negative feedback loop controlling cortisol levels, and thus alterations in the number and sensitivity of these receptors influences the body’s ability to regulate the amount of circulating cortisol. This, in turn, is proposed to lead to the development of emotional, behavioural and cognitive phenotypes (e.g. chronic anxiety, impulsivity) and cognitive alterations (e.g. executive function, as discussed above) which are associated with increased suicide risk. For comprehensive reviews of the animal and human research into the epigenetics of stress in the early years of life, see Roy and Dwivedi (2017), Turecki et al. (2012), and Turecki and Meaney (2016). For a review of the evidence linking epigenetic changes with suicidal behaviour, see Labonte and Turecki (2010).
+[ insert Figure 5 about here ] Epigenetic research has also suggested that the neuropeptide oxytocin, which is sensitive to environmental stress, may be implicated in the transmission of maternal stress during pre- and postnatal periods (Toepfer et al., 2018). Further investigation for the role of epigenetic mechanisms in suicidal behaviour comes from research assessing GR gene expression in post-mortem hippocampi obtained from suicide completers with a history of childhood abuse, suicide completers with no history of childhood abuse, and controls (McGowan et al., 2009; Labonte et al., 2012). These studies demonstrate reduced hippocampal GR gene expression in suicide victims with a history of childhood abuse in comparison to suicide victims with no history of abuse and controls, while there was no difference between the non-abused and control groups. This suggests that while childhood abuse is associated with epigenetic changes in gene expression which influences HPA function, but does not provide evidence for a link between these changes and risk of suicidal behaviour.
+20
+Using a whole genome-wide approach to investigate DNA methylation in the hippocampi of suicide completers, Labonte and colleagues (2013) confirmed their previous findings that promoter DNA methylation levels are greater in suicide completers compared to controls. Interestingly, they report increased levels of methylation in promoters of four specific genes known to be involved in cognitive processes related to executive function (e.g. learning, working memory, behaviour). Therefore, taken with previous findings that childhood abuse is related to DNA methylation, this provides initial evidence for an epigenetic mechanism where stress in childhood leads to changes in hippocampal gene expression which could cause impairments in executive function that increase the risk of suicide.
+Sleep
+Another mechanism by which stress could potentially affect suicide behaviours is through disruption to sleep. There is strong evidence that insomnia and nightmares are associated with increased suicide risk (Bernert et al., 2015; Nadorff et al., 2011; 2013; Pigeon et al., 2012). However, causality and third variable effects are hard to establish. Research to date has mainly focussed on psychological mediators of this effect, identifying defeat and entrapment, as well as emotional regulation, social isolation and negative appraisals as mediators of this effect (Russell et al., 2018; for review see Littlewood et al., 2017). However, it has also been suggested that disturbances in sleep may lead to cognitive impairments and impulsive decision making (Porras-Segovia et al., 2019). Interestingly, evidence suggests that the duration of sleep disturbance (e.g. for how long an individual has been experiencing nightmares) is a significant factor in risk of suicide, where longer durations are associated with increased risk (Golding et al., 2015; Nadorff et al., 2013).
+The main issue with this body of literature is that it consists predominantly of cross-sectional studies which cannot determine the direction of the relationships between sleep and suicidal behaviours. A recently ecological momentary assessment study by Littlewood and colleagues (2019) addressed this issue and demonstrated a unidirectional relationship between sleep disturbance and suicidal thoughts. Objectively and subjectively determined short sleep duration, and poor sleep quality predicted more severe next-day suicidal thoughts; there was no relationship between suicidal ideation
+21
+and sleep duration or quality the following night. This study establishes the causal direction of this day-to-day relationship, which is a valuable step in our understanding. However, reciprocal and bidirectional relationships are still possible along longer time scales and merit investigation.
+High levels of stress are associated with both chronic insomnia symptoms and recurrent short sleep duration (Abell et al., 2016). Stress effects sleep not only in terms of sleep quality, but it also disrupts the EEG spectral profile of sleep in both healthy participants and patients with chronic insomnia (Ackermann et al., 2019; Hall et al., 2000). In insomnia patients, the tendency to experience stress-related intrusive thoughts is associated with poorer subjective sleep quality, and higher levels of subjective stress burden are associated with decreases in delta activity which is an indication of hyperarousal during sleep (Hall et al., 2000). Therefore, different aspects of stress may influence distinct characteristics of sleep. In another study with healthy participants, an acute laboratory-based stressor was used to investigate the immediate effects of psychosocial stress on napping; this form of acute stress increased sleep latency, but also reduced slow wave activity and enhanced alpha activity (Ackermann et al., 2019).
+Sleep disruption also influences HPA axis activity. While some older research did not find associations between measures of sleep quality or insomnia with salivary cortisol, more recent research with improved methodologies have confirmed an effect of shorter sleep and poor sleep quality on diurnal cortisol (Castro-Diehl et al., 2015). Cross-sectional research has also found that those who report frequent nightmares show a blunted CAR on a working day, but not on a leisure day (Nagy et al., 2015). In an impressive 10-year follow up in the Whitehall II study, Abell and colleagues (2016) provide evidence that recurrent short sleep (measured at 3 time points during the 10-year period) was associated with a flatter diurnal cortisol pattern, characterised by higher levels of cortisol later in the day. A steeper CAR was also observed in those who reported insomnia symptoms at all three timepoints and those reporting short sleep twice, compared to those who did not report sleep problems at any time point. These findings have yet to be related to suicide risk, but given the accumulating evidence for different aspects of HPA axis dysregulation in suicide risk and the potential pathways between stress, sleep, and cortisol, this seems a promising avenue for future research.
+22
+However, the inter-relations of these variables are complex and potentially reciprocal and therefore are not easy to disentangle. While it is possible that there is a direct pathway from stress to suicidal behaviour via sleep disturbance, it is likely that any such mechanism will also interact with the other variables we have mentioned here. For example, disruption to sleep patterns could influence executive function, and there is evidence that familial risk for insomnia can be measured through HPA axis dysregulation in response to stress (Drake et al., 2017). Disruption to sleep also has been hypothesised as a stressor in itself, contributing to allostatic load (McEwen, 2006). This idea is consistent with the findings suggesting that duration of insomnia or nightmares is predictive of suicidal risk, as the cumulative effects of ongoing sleep disturbance leads to wear and tear on bodily systems. Research into the interconnections of these mechanisms could provide vital and effective insights for the development of interventions, through the identification of vulnerable populations and provision of targeted tools to reduce the risk of suicide in vulnerable populations.
+General conclusion
+This article has presented an overview of studies that demonstrate that stress and dysregulated hypothalamic-pituitary-adrenal (HPA) axis activity, as measured by cortisol levels, are important additional risk factors for suicide. It has also highlighted the IMV model of suicide as a useful framework to understand suicide risk. Evidence for other stress-related putative suicide risk factors including childhood trauma, impaired executive function, impulsivity and disrupted sleep have been shown to play an important role together with family history of suicide, perinatal and epigenetic influences on suicide risk. In order to further improve our understanding of the precise pathways through which stress and HPA axis dysregulation contribute to suicide, there is a need for future research to investigate simultaneously the impact of distal and proximal determinants of suicidal behavior.
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+Background
+Many health conditions perceived to be contagious, dangerous or incurable, to result in clearly visible signs, or to be caused by breaking taboos or immoral behavior share a common attribute - an association with stigma and discrimination. These health conditions are diverse in nature and include infectious diseases like HIV, tuberculosis (TB), leprosy and lymphatic filariasis,
+non-infectious chronic conditions such as epilepsy and cancers, and mental health conditions such as schizophrenia, depression, and substance abuse. Jones et al. [1] proposed six features, namely, (1) esthetics, (2) concealability, (3) course, (4) disruptiveness, (5) origin, and (6) peril, that help in recognizing and understanding why particular conditions are more vulnerable to health-related stigma, what factors would worsen or reduce a given stigma, and why some stigmas may be easier to address than others.
+People often have co-morbidities and live with one or more of these health conditions and experience simultaneously different types of health-related stigma. Stigma is problematic because it affects people psychologically and restricts their social participation, and it can also create barriers to accessing healthcare, including retention in care for people living with HIV (PLHIV), relationships, education, and housing, thereby further marginalizing already vulnerable populations [2-4]. While the etiology of stigma may differ between conditions and, sometimes, cultural settings, the manifestations and psychosocial consequences of stigma and discrimination are remarkably similar [3, 5, 6]. Regardless of the condition, stigma is a dynamic process enacted through structures and individuals, mediated by relationships of power and control that are constantly being produced and reproduced [7]. Similarities across conditions are most likely due to the fact that the core of stigma is social in nature and therefore a common problem based on common human interpersonal responses to differentness and the mechanisms by which these responses might be expressed [8, 9]. Nevertheless, responses to persons with the same condition may also differ in different locations, based on local differences in social determinants of stigma (e.g., religious beliefs). They may vary between conditions, depending on perceived cause and danger (e.g., in HIV or leprosy, people might avoid sharing a meal to avoid infection).
+The cross-cutting nature of stigma is evidenced by the measurement methods used and the interventions that have been shown to be effective to reduce stigma or mitigate its impact across conditions [3, 10-13]. In many of the disciplines dealing with stigmatized conditions, the problem has been recognized and is addressed to some extent, but often only in a condition-specific manner. One challenge is that the funding, research, assessment tools, and interventions often address stigma related to only one particular condition. If measurement tools and interventions that assess and address common dimensions of stigma were possible, the scarce resources to address stigma could be used more efficiently and healthcare providers could use the same tools and approaches, across conditions. Several theoretical models describing common elements of stigma have been proposed, including those by Scambler [14, 15], Link and Phelan [16], Pescosolido et al. [17], and Weiss [5].
+Health-related stigma
+Stigma has been extensively studied in leprosy, mental health, HIV, epilepsy, and physical disability [3]. Lung cancer can also conjure a similar attribution of blame as that found with HIV and/or AIDS due to its frequent association with smoking cigarettes (tobacco) [18]. Yet, most of these have been studied only within their own
+field, often with development of condition-specific measurement instruments and interventions. From a health systems perspective, the application of generic tools for stigma assessment and of the same or similar interventions to address multiple stigmas would be highly beneficial. This benefit becomes even more evident in the light of an increasing frequency of co-morbidities and of the compounding impact of multiple intersecting stigmas.
+To address this ‘siloed approach’ to stigma, the concept of ‘health-related stigma’ has been advocated [19, 20]. It should be noted that discrimination, also known as enacted or experienced stigma, is part of the construct of stigma. Health-related stigma is a personal experience related to a health condition [21], characterized by the perception of exclusion, rejection, and blame [22], and contributes to psychological, physical, and social morbidity [23]. The judgment inherent in any health-related stigma is medically unwarranted and may adversely affect health status and health outcomes [22]. Health-related stigma is associated with depression and limited social support and acts as a barrier to healthcare access, treatment uptake, retention, and adherence [3, 24-31]. It thus contributes to increased severity of morbidity and disability [32, 33], prolonged treatment duration and, through poor adherence, to development of drug resistance [34]. For example, stigma among individuals with mental illness can lead to adverse coping behaviors, including secrecy and withdrawal from others who do not share the stigmatizing status [35, 36], and has shown negative impact on treatment seeking (showing consistent small-to-moderate negative effects in a meta-synthesis [37]). In the field of HIV, stigma hinders access to and engagement in the HIV care continuum as a barrier to HIV testing, linkage to care, retention, and treatment adherence, and detrimentally impacts mental and physical wellbeing [30, 38, 39]. However, with the exception of several literature reviews on stigma measurement and interventions [3, 10-12, 40], there is a gap in evidence in the published literature demonstrating the case for a cross-cutting approach to reduction and mitigation of the intrapersonal and interpersonal aspects of stigma. This paper seeks to address this gap using research data of studies on stigma and discrimination related to a number of diverse conditions.
+Conceptual model
+For this paper, we will use a conceptual model (see Fig. 1), which is both a simplification and an expansion of the model proposed by Weiss [5], which in turn was an extension of Scambler’s Hidden Distress Model [14]. This model differentiates two main perspectives on health-related stigma, that of persons who are being stigmatized, and that of ‘those who stigmatize’. We have called the latter ‘sources of stigma’ to allow inclusion of structural forms of stigma. It is important to realize that
+people may belong to both categories. For example, persons affected by one condition may stigmatize those with another. Also, health workers in leprosy, HIV, or mental health services may be stigmatized for working in such programs or for having the same condition; yet, they themselves may stigmatize the beneficiaries of the program. The model further distinguishes different types of stigma that can be recognized across conditions and cultures [3, 5, 6, 10]. Both the two perspectives and the different types of stigma have a bearing on the assessment of stigma and on selecting relevant interventions. A comprehensive definition of health-related stigma encompassing differences in perspectives and types is offered by Weiss and Ramakrishna [22], “A social process or related personal experience characterized by exclusion, rejection, blame, or devaluation that results from experience or reasonable anticipation of an adverse social judgment about a person or group identified with a particular health problem”.
+We will demonstrate the cross-cutting nature of health-related stigma using data from studies of leprosy, HIV, TB, mental illness, inflammatory bowel disease, disability, obesity, and cancer. We will briefly present the instruments and interventions used, discuss the way they have been used across conditions, and then draw together the findings and lessons learnt regarding common aspects of stigma, proposing that ‘generic health-related
+stigma’ is a concept that can be used across stigmatized health conditions.
+Stigma measurement
+In an attempt to ‘capture’ stigma, as well as in assessing its severity and monitoring and evaluating the impact of interventions to reduce stigma, a large number of instruments have been developed, often within specific fields such as mental health [41] and HIV [28]. In addition, tools have been developed for many of the different domains of stigma such as perceived or anticipated stigma, internalized stigma, public stigma, stigma by association, and healthcare provider-based stigma [3, 9]. For an extensive review of the types of stigma assessments as well as their use in evaluating changes in mental health-related stigma interventions, please see the recent report from the U.S. National Academy of Sciences/Institute of Medicine [42]. Unfortunately, most instruments are both condition specific and limited to a particular domain of stigma (e.g., internalized or public stigma). Despite these silos of tools, a detailed analysis of stigma assessments showed that many similarities exist in the approaches used across conditions and in the issues addressed in the items used in questionnaires and scales [3]. It is informative to pay particular attention to the instruments that have been used across several conditions, including the Social Distance Scale (SDS) [41, 43], the
+Berger Stigma scale [24], the Internalized Stigma of Mental Illness (ISMI) scale [44], and the Explanatory Model Interview Catalogue (EMIC) [45]. Some of these have also been used across domains to assess internalized stigma, public stigma, and healthcare provider-based stigma. Having shown applicability across different conditions, we might consider the aspects of stigma contained in these instruments to be ‘common’ elements of stigma across illnesses.
+Instruments to measure public stigma
+Social Distance Scale (SDS)
+The SDS was designed by Bogardus [46] to measure the level of acceptability of various types of social relationships between Americans and members of common ethnic groups [41, 47]. The first use of the SDS in the context of mental health was by Cumming and Cumming in 1957 [41]. The modified SDS has been widely used to measure mental health-related stigma and to understand the importance of labels attached to people with former mental illnesses [41, 48]. The modified version consists of seven questions that represent social contact with different degrees of distance, such as renting a room to someone with a condition under study, working in the same place, marrying one’s child to a person with the condition(s), or engaging someone in child care. The SDS measures the acceptability of different degrees of social distance and thus, by inference, the attitude of the respondent to the person with the condition [43]. The SDS uses gender-specific, condition-adjusted vignettes that describe a man or a woman with typical features of the condition. Seven statements with a four-option ‘degree of willingness’ scale assess the willingness of the respondent to interact with the person described in the vignette (‘Definitely willing’ (0), ‘Probably willing’ (1), ‘Probably not willing’ (2), ‘Definitely not willing’ (3)). The SDS sum score represents the attitude of the respondent towards the condition.
+EMIC Community Stigma Scale (EMIC-CSS)
+The EMIC is available in different versions. The EMIC was designed by Weiss et al. [45] to examine the nature of the illness experience, including impact of stigma, on leprosy patients in India, with special reference to their mental health. The original EMIC combined quantitative questions that were scored and qualitative, open questions that provided explanations and more depth to the quantitative scores. The instrument was designed to be usable across conditions and has since been used in a variety of conditions. The more recent studies have often only used the quantitative EMIC stigma scale, rather than the ‘mixed-methods instrument’. Later on, the instrument was adapted to assess the perception of stigmatizing attitudes and behavior among community
+members (lay persons), patients (affected persons) and healthcare workers [49]. The EMIC measures perceived attitude and behavior of the target group towards persons affected by the stigmatized condition. In various studies over the years, the length of the scale has varied from 8 to 25 items. The response scales contain four options, as follows: ‘Yes’ (2), ‘Possibly’ (1), ‘No’ (0), and ‘Don’t know’ (0). In the 15-item version, the sum score therefore ranges from 0 to 30. In contrast to the SDS, the EMIC-CSS asks about the views and behavior of ‘other people’ in the community, rather than that of the respondent directly. This may help to minimize social desirability bias in responses.
+Instruments to measure stigma experienced by those with the condition
+Berger Stigma Scale
+The Berger Stigma Scale was designed to measure stigma as perceived by PLHIV organized along four underlying factors, including personalized stigma (18 items); disclosure concerns (12 items); negative self-image (9 items); and concern with public attitudes about people with HIV (12 items) [24]. To develop the scale, Berger et al. [24] first developed a model of perceived stigma in PLHIV organized around precursors (perception of societal attitudes towards PLHIV and knowledge of personal sero-status), perceived stigma of having HIV (actual or potential experiences of social disqualification, limited opportunities, negative change in social identity), and possible responses to perceived stigma (change in self-image, emotional response to stigma, strategies to avoid or mitigate stigma, and redefined worldview and priorities). The actual scale items were selected and developed from a review of literature and expert consultation, field tested in the USA, and subjected to factor analysis. Responses to items are measures with a 4-point Likert scale ranging from ‘strongly agree’ to ‘strongly disagree’. While quite lengthy, the scale has since been widely used and adapted both in a range of settings and for conditions other than HIV [50-53].
+EMIC affected persons
+The aim of the EMIC developed by Weiss et al. [45] was to “elicit illness-related perceptions, beliefs and practices in a cultural study of leprosy and mental health”. The current ‘EMIC affected persons’ version is used to assess perceived and experienced stigma among those with the stigmatized condition. Its content is very similar to the EMIC-CSS.
+Internalized Stigma of Mental Illness (ISMI) scale
+The ISMI scale was developed to measure the subjective experience of stigma, especially the internalization of stigma [44]. ISMI subscales measure Alienation, Stereotype Endorsement, Perceived Discrimination, Social
+Withdrawal, and Stigma Resistance. The ISMI was developed together with people with mental illnesses. The instrument comprises 29 Likert items. Each statement is rated on a 4-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. The ISMI was originally validated among mental health outpatients. Results showed that the ISMI had high internal consistency and test-retest reliability. Construct validity was supported by positive correlations with measures of stigma beliefs and depressive symptoms, and negative correlations with measures of self-esteem, empowerment, and recovery orientation. More recently, a brief version of the ISMI was developed and validated [54].
+Stigma interventions
+Information-based interventions are very likely the most common approach to addressing public stigma against any condition. However, they differ in content across conditions because they often address condition-specific knowledge gaps, stereotypes, fears, and other drivers of stigma; not infrequently, these are the only strategies used. However, while knowledge or education is often an essential part of stigma reduction, it is insufficient on its own [55-57].
+Many authors have reviewed stigma reduction strategies and interventions from either a disease-specific or generic perspective [11, 57-63]. Evidence of effectiveness from well-designed studies using larger samples, particularly of longer-term impact, is scarce [58, 62]. However, available evidence suggests that stigma should be tackled at multiple levels, by using multiple strategies and the interventions must be context specific and continued or repeated to achieve a lasting impact [6, 8, 11, 64-66].
+Cross-condition methods to address public stigma Information-based interventions
+Information-based strategies are often used to reduce negative attitudes and perceived stigma in the community (public stigma). The assumption is that negative attitudes are likely to be based on a lack of knowledge, incorrect knowledge, myths, beliefs, and/or stereotypes about a given condition that can be ‘corrected’ with the right information [67]. Information-based interventions try to fill gaps in knowledge about the condition and dispel myths and demonstrate that stereotypes are often not true. An example is information about the availability of medical treatment for a given infectious disease; such information is assumed to contribute to reduction of stigma against that disease [68]. The second example is educating people with scientific facts, e.g., ‘leprosy is an infectious disease’ or ‘leprosy is caused by a bacterium’. Health promotion media campaigns have been widely used, involving printed materials, such as posters in health facilities, and/or radio and television and
+internet messages [69]. It is crucial that education messages and campaigns take the local worldview, culture, language, and specific fears and beliefs into account [65, 66, 70].
+Contact between persons with the condition and the community, health professionals, or others
+Facilitating contact between persons affected by a particular condition and members of the general public or healthcare workers has been shown to be effective in improving attitudes and in changing negative stereotypes [71]. This is based on the principle that attitudes can only be changed or replaced by positive attitudes when they have been shown to be dysfunctional [72]. Similarly, contact with individuals who ‘moderately disconfirm’ stereotypes is also important, i.e., with individuals who are symptomatic and are in treatment, but who also work, socialize, and have meaningful relationships [73]. The contact intervention has been used in different forms, either by facilitating direct, live contact or through electronic media. Examples are testimonies from persons affected in the community or from well-known ‘champions’, (participatory) videos and comics used during community events and meetings [74], screening on television, etc. Opportunities for discussion are also an important element.
+Change agents/Popular opinion leaders (POLs)
+Rooted in the Diffusion of Innovations Theory - a theory which focuses on how a new practice or idea can be dispersed through a social network to the point that it becomes a social norm [75] - a promising strategy to address stigmatization is the use of ‘change agents’ or POLs [76]. The hypothesis is that, when such POLs display positive attitudes, spread a non-stigmatizing message, or even fight enacted stigma in a social group, they model a new behavior and thus alter the perception and eventually even the social norm. POL interventions have been profusely and successfully applied, across different (stigmatizing) populations and across different continents, in HIV and sexually transmitted infection interventions [77-79], and more recently also in the context of the TB/HIV co-epidemic [80]. The latter on-going trial is the first attempt to apply the POL strategy to implement a cross-cutting, and thus not disease specific, stigma-reduction intervention (Rau et al., submitted for publication). Crucial to the success of such POL interventions is the selection and training of these POLs. When community members identify themselves as the members who are influential in a stratified manner, for example, by asking randomly selected respondents to nominate influential community members or by asking gatekeepers (village or organization heads) to recommend popular individuals [78], and when these potential
+POLs are then adequately trained, increasing knowledge as well as adapting behavior, this approach has the potential to be a suitable cross-cutting strategy applicable to a wide range of stigmatized conditions [76].
+Cross-condition methods to address stigma experienced by persons affected
+(Peer) counselling
+Peer counselling is an intervention in which suitable persons with the same condition are selected and offered training in counselling [81]; this focuses on listening and problem-solving skills, as well as increasing knowledge about the condition and, as in the case of a study in Indonesia [82], about human rights. In the case of peer counsellors, the counsellor can also serve as a role model to the counselee. Peer counselling and similar approaches have also been used in the fields of mental health and HIV, although terms like ‘peer educator’, ‘expert client, or ‘community-linkage facilitator’ are more commonly used. However, these do not necessarily engage HIV-positive peers as educators, but rather a variety of other peers such as students in schools (e.g., Denison et al. [83]). Counselling, as part of ‘voluntary counselling and testing’ has been extensively used in HIV, but not primarily as a stigma-reduction strategy.
+Skills building and empowerment
+Interventions for socioeconomic development or improvement of the livelihoods of persons affected can be seen as economic empowerment [84, 85]. By enabling persons who are stigmatized to find a job or improve their income, self-esteem and the feeling of self-worth are improved [86]. Importantly, people get hope that there is a way out of their predicament. In low- and middle-income countries, such socioeconomic interventions are often linked to people organizing themselves in self-help groups (SHGs) [87], which may then start a saving scheme and/or be linked to a micro-finance institution (Dadun et al., submitted). Collateral-free individual or group micro-credit loans are then given from the collective savings or by the bank or institution [88]. People may start a small business or invest the loan in agricultural activities. Being able to contribute to the family income or to the community in this way often helps greatly in regaining identity and respect, either reducing public stigma or offering additional resilience to cope with it [72, 89].
+Evidence of how measurement instruments are used across conditions
+Table 1 shows examples of stigma instruments that have been used across several conditions to measure attitudes and perceived and enacted stigma among the public or community. The SDS has a long history and was
+originally designed to assess willingness to associate with persons of different ethnic backgrounds [46]. Link et al. [90] used a version adapted for mental health to assess attitudes towards persons with mental health conditions. Lee et al. [91] assessed ‘victim blaming’ of persons with HIV or AIDS among US college students using the SDS. Peters et al. [43] used social distance as a proxy for respondent attitudes towards persons affected by leprosy in Indonesia, and a study in Germany assessed stigma against persons with obesity using the SDS [92]. The EMIC-CSS has been used across conditions most often, including in a study assessing attitudes and perceived behavior against persons with onchocerciasis [93], mental health conditions [49], Buruli ulcer [94], tuberculosis [95], and leprosy [43, 96, 97]. Additionally, the cultures were very diverse, including four countries in Africa and four in Asia.
+In the same way, instruments used to assess stigma experienced by persons affected across a range of conditions are shown in Table 2. The Berger Stigma Scale, originally designed to measure perceived and experienced stigma among PLHIV [24], was successfully adapted for use in leprosy [98] and meticillin-resistant Staphylococcus aureus [53]. The ISMI was used most frequently, with no less than 81 papers covering 42 completed translations [13]. Most studies used the instrument in mental health, but other studies demonstrated the usefulness of the ISMI among persons with substance abuse, leprosy, HIV, and inflammatory bowel disease [96, 99-101]. The EMIC Affected Persons scale has been used most widely in terms of range of conditions. Originally designed to measure the impact of leprosy on the mental health of persons affected [45], it has since been used to measure experienced stigma related to mental health conditions, including depression, schizophrenia and bi-polar disorder [102-104], onchocerciasis [105], Buruli ulcer [94], HIV [101], TB [106], and leprosy [96].
+Evidence of how stigma interventions are used across conditions
+Interventions to reduce public stigma were also very similar across diverse conditions.
+Table 3 shows examples of information-based interventions being used to address attitudes of college students towards persons with mental health conditions in the USA [107], general public attitudes towards HIV in Ghana [108], and community attitudes to leprosy in Indonesia [109]. Another very commonly used stigma intervention is the contact intervention, which was used with success to improve attitudes to mental illness among college students in the USA [110], attitudes towards PLHIV among nurses in Hong Kong [111], and
+attitudes of community members towards persons affected by leprosy in Indonesia [74, 109]. Education about the condition and related beliefs and fears, and contact between persons with the concerned conditions and members of the community or other target group are often used together; this combination of interventions has been shown to work across conditions and cultures [11, 60, 62, 109, 111, 112]. Training and engagement of POLs or change agents was successful in different conditions (leprosy, HIV, and TB) and very different cultural settings (Nepal, USA, Peru, China, and South Africa) [77, 78, 113, 114].
+Interventions to mitigate the impact of stigma have addressed the mental wellbeing of the persons affected, their resilience, self-efficacy and sense of self-worth, and ability to speak up for themselves through empowerment, skills building, and participation in the actual interventions. Nuwaha et al. [115] and Jurgensen et al.
+[116] found home-based counselling to be successful in reducing different aspects of HIV-related stigma in Uganda and Zambia. Conner et al. [117] found peer education was effective to reduce internalized stigma in a small study with older adults with mental health conditions in the USA. Across the globe, Lusli et al. [82] trained lay and peer counsellors among persons affected by leprosy in Cirebon, Indonesia; they, in turn, counselled others. Their approach, which included building resilience, restoring dignity, and awareness of human rights, was shown to be effective in reducing stigma, improving social participation, and improving quality of life among the counselees [118].
+Skills building and empowerment of persons who are stigmatized is another strategy shown to be effective across conditions and cultures. The Stigma Elimination Project in south Nepal trained a small group of persons with visible signs of leprosy who showed leadership
+potential [76], who became leaders of a rapidly growing number of SHGs. After 3 years, the level of social participation of SHG members was at the level or better than that of a community control group. Bellamy and Mowbray [119] found a ‘supported education program’ to be successful in empowering adults with mental health conditions in the USA and strengthening their self-efficacy to (re-)enter post-secondary education. Dalal [72] reported empowerment of persons with disabilities in north India to be very successful in overcoming shame, increasing social participation, and improving health outcomes as well as in changing community attitudes towards disability. Uys et al. [71] used skills building and empowerment among both nurses and PLHIV to reduce stigma and improve quality of care in healthcare settings in five African countries. This was successful in reducing stigma and increasing self-esteem among PLHIV, but did not affect stigma among the nurses. However, the HIV testing behavior of the latter improved significantly.
+The concept of health-related stigma
+The current paper demonstrates that ‘health-related stigma’ is a viable concept with clearly identifiable characteristics that are similar across a variety of stigmatized health conditions in very diverse cultures. The etiology of stigma differs between conditions and sometimes between cultural settings. For example, persons with schizophrenia are stigmatized because people perceive them to be unpredictable or dangerous, while PLHIV may be stigmatized and discriminated against because, in certain cultures, HIV is associated with homosexuality and promiscuity, and because it is perceived to be a
+highly infectious, as well as fatal and incurable disease. Leprosy is often stigmatized because of the notion that the person affected has committed a sin or broken a taboo, either in this or a previous life; it may also be due to fear of the associated disfigurements. Even regarding the etiology and origins of stigma and discrimination, ‘shared dimensional features’ can be readily recognized. Pachankis et al. [120] used the six features identified by Jones et al. [1] (aesthetics, concealability, course, disruptiveness, origin, and peril) as a taxonomy for characterizing and investigating the perceived burden of stigma on health and wellbeing across no less than 93 health and other conditions.
+As noted in the Background section, the expressions or manifestations and psychosocial consequences of stigma and discrimination are often remarkably similar, even across very different cultures and levels of socioeconomic development [3, 5, 6, 8]. Stigma starts when salient differences between people are recognized, labelled, and connected to stereotypes or social identities [16]. This process leads on to a separation between ‘us’ and ‘them’, resulting in status loss and discrimination. Depending on the culture and time, these differences may include a large variety of characteristics, including ethnicity, sexual orientation, skin color, body weight, religious beliefs, and a wide range of health conditions. In this paper, we limited ourselves to health conditions, though we are well aware of the intersectionality of stigma where health-related and other stigmas interacted and may compound each other [121-123]. A substantial body of literature addresses the intersectionality of stigma related to particular conditions. For example, Lowie et al. [121] examined how gender, race, sexual
+orientation, and sex work intersect with HIV-related stigma. Very few studies have investigated types of stigma, stigma assessment, or stigma interventions across multiple stigmatized conditions. A notable exception are the studies that have looked jointly at HIV- and TB-related stigma [124, 125]. Mak et al. [126] compared SARS-related stigma with that of HIV and TB. However, the great majority of studies of stigma related to health
+conditions occurred within the specific field dealing with a specific condition or range of conditions (e.g., mental health conditions). Within these fields, authors have demonstrated the similarities and differences across cultures and languages, e.g., in leprosy [127], HIV [8], TB [106], and mental health [6]. However, very few studies have attempted in-depth analyses across different health conditions. Van Brakel [3] included mental health,
+epilepsy, HIV, leprosy, TB, Buruli ulcer, onchocerciasis, and physical disability in his review of measurement of health-related stigma, noting many commonalities in the approaches and tools used to measure different stigmas. A more recent review investigated stigma across 10 neglected tropical diseases and noted many similarities in the types of stigma reported, the manifestations, and the approaches used to mitigate stigma [10]. Although not limited to health-related stigma, the study of Pachankis et al. [120] included 44 health conditions. They examined similarities and differences regarding each of the six characteristics proposed by Jones et al. [1] and investigated their association with a range of different stigma-related measures, including the SDS. One of the findings was that “Visibility and course were not associated with social distance. In contrast, participants indicated a desire for greater social distance with respect to stigmatized statuses that were perceived as disruptive, aesthetically unappealing, onset controllable, and perilous” [120]; these features are shared by many stigmatized health conditions.
+The above findings show that there is a scientific rationale for the concept of health-related stigma, as proposed by Weiss et al. [19] and Scambler [20, 128]. A more generic approach to the study of health-related stigma opens up important practical opportunities. This paper illustrated this with two aspects of work - stigma measurement and interventions to reduce or mitigate stigma.
+Towards common stigma measurement approaches for health-related stigma
+If it were possible to measure stigma and discrimination using generic instruments, this would have clear advantages, especially for use in public health programs and social services. Use of measurement tools requires training. With a different tool for each condition, staff in health and social services have to learn and keep up with many different instruments, some of which they may only use infrequently, thus never acquiring a ‘feel’ for the instrument and the results it produces. In the current age of mobile data collection, one could envisage that adaptation of a given instrument to a particular condition would be done by just indicating on the opening screen which condition one wants to test; the software would automatically adapt the instrument to that condition. Tools for which this would be very easy are those indicated in Table 1 and Table 2. Instruments like the SDS, EMIC, and ISMI were shown to be highly suitable for use across conditions since the content includes manifestations and impact common to many stigmatized health conditions.
+Researchers in the health-related stigma field can clearly also benefit from the use of instruments that can be adapted very easily for use across conditions;
+the study of Pachankis et al. [120] illustrates this point very nicely.
+A disadvantage of using generic instruments is a potential lack of sensitivity and/or specificity. Where this would be required, one could envisage using an add-on module comprising a few condition-specific items. This would retain the advantage of a common core of items that can be used and compared across conditions. A very similar approach that is widely accepted is the measurement of health-related quality of life. Generic tools like the WHO Quality of Life scale, abbreviated version (WHOQOL--BREF), and the Short Form 36 items are used across a myriad of disabling and stigmatized conditions and in very culturally diverse circumstances. In certain situations, add-on modules are used, such as the WHOQOL-DIS for disability, or the WHOQOL-SRPB for the effects of spirituality, religion and personal beliefs.
+Towards common stigma intervention approaches for health-related stigma
+Many of the same advantages that apply to cross-condition measurement tools also apply to interventions.
+Table 3 and Table 4 illustrate the several interventions that have already been used successfully with multiple conditions; this is hardly surprising because of the common social and psychological processes underlying health-related stigma [5, 16, 19]. Manifestations, such as difficulties in finding and maintaining employment, broken relationships, and impacts on socioeconomic status and mental wellbeing, including shame and reduced self-esteem, are common across conditions, thus offering entry points for cross-cutting interventions. It should be noted that, although the studies included have been classified under one, or at the most two, intervention types, almost all studies used multiple interventions. Sometimes, these addressed different levels and sometimes they addressed both the sources of stigma and the persons affected by stigma. Even when used on a single level, there is evidence that using multiple interventions is more effective than using a single intervention [111].
+In contrast to the use of instruments, certain interventions can even be used across multiple conditions simultaneously. This is the case for counselling services, skills-building, and economic empowerment programs and SHGs, for example.
+One major problem is that funders of stigma reduction programs usually only fund condition-specific studies, measures, and interventions. Surveillance for stigma and stigma-mitigating interventions can be integrated in regular health and social services using generic tools and interventions. For example, in China, a stigma-reduction intervention focused on infection control through education and providing adequate supplies for practicing universal precautions [78, 129]. Similarly, in Vietnam, a
+stigma-reduction intervention allowed healthcare facility staff to develop practical skills around infection prevention and a code of practice, tailored for their own hospital’s needs, on implementing stigma-free practices and universal precautions [130]. In the field of leprosy, counselling to mitigate the effects of stigma has been integrated in a range of hospitals that offer leprosy services in Nepal and India [131, 132].
+Using generic tools and interventions within the health services would help overcome the siloed approach by demonstrating the advantages of integration, while simultaneously contributing to health systems strengthening. Dr Gottfried Hirnschall, WHO HIV Director, said, “We need to ensure that frontline health workers have the information and skills required to effectively identify, address and avoid stigma and discrimination of all types, including those related to HIV’.1 Developing generic health-related stigma assessment and monitoring tools as well as generic stigma interventions would provide essential building blocks for making this possible.
+Limitations
+A limitation of this paper is that it is not based on a systematic literature review. We can therefore make no claim to completeness of the evidence to support the concept of health-related stigma. However, we believe that the cross-condition use of each instrument and intervention has been adequately demonstrated through our use of these selective, illustrative examples.
+Conclusions
+•	Researchers, research funders, public health and social services managers, and health and social
+services practitioners should adopt cross-cutting, more cost-effective approaches to health-related stigma, seeking to use generic instruments and interventions where possible.
+•	Stigma studies should demonstrate how stigma theory and frameworks apply across conditions and delineate commonalities, as well as conditionspecific exceptions that might be important for understanding, measurement, or interventions.
+•	Researchers studying stigma should approach the issues more generically, adapting (potentially) generic stigma instruments to containing an optimal common core of items, identifying, where necessary, condition-specific add-on items or modules.
+•	Stigma studies should be commissioned to demonstrate the advantages and effectiveness of cross-condition approaches to measurement and interventions.
+Endnotes
+1	http://www.who.int/mediacentre/commentaries/zero-discrimination-day/en/; Accessed 13 June 2018
+Abbreviations
+CSS: Community Stigma Scale; EMIC: Explanatory ModelInterview Catalogue; ISMI: Internalized Stigma of Mental Illness; PLHIV: People living with HIV; POLs: Popular opinion leaders; SDS: SocialDistance Scale; SHG: Self-help group; TB: Tuberculosis; WHOQOL-BREF: WHO Quality of Life scale
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+Strong primary care is widely accepted to be a prerequisite for effective, efficient, equitable health systems and to lead to better population health.1 Introduced in 2004, the UK Quality and Outcomes Framework (QOF) is arguably the most comprehensive national primary care pay-for-performance (P4P) scheme in the world.2 The QOF is more than a payment scheme; it is a complex intervention comprising a number of elements (Table 1), including financial incentives and information technology (computerized prompts and decision support), designed to promote structured and team-based care with the aim of achieving evidencebased quality targets.3
+It was one component in the reorganization of primary care resulting from a new General Medical Services contract for general practitioners
+ANNALS OF FAMILY MEDICINE ♦ WWW.ANNFAMMED.ORG ♦ VOL. 10, NO. 5 ♦ SEPTEMBER/OCTOBER 2012
+IMPACT OF PAY-FOR-PERFORMANCE IN THE UK
+that led to a practice-based, rather than practitionerbased, contract and investment to reward quality of care through both fixed and performance-related funding streams. The financial incentives are substantial, with a maximum of 1,000 points available to practices, and an average payment per practice in 2011-2012 of £130 (US $204) for each point achieved.4 More than one-half of these points are allocated to clinical indicators, which currently cover 20 chronic conditions.5
+In 2009-2010, practices in England achieved an average of 937 points, with a range in each of the 152 primary care trusts from 878 to 972 points.6
+Since its introduction, the effects of the QOF on quality of care have been the subject of considerable debate, which is now being informed by an accumulating body of research. As national governments seek to improve the quality of health systems in the face of financial stringency, searching analysis of this evidence is timely. The successes and failures of the QOF as a national centralized system may be predictive of the effects of P4P in the United States.7
+P4P schemes have been extensively reviewed.7-14 A recent Cochrane review found that, whereas they improved patients’ well-being, the effects of financial incentive schemes on the quality of primary health care were “modest and variable.”15 Previous reviews have focused on a single dimension of care or have had strict inclusion criteria with few articles retrieved or have not been conducted systematically. The huge variation in P4P schemes in different countries has made it difficult for reviewers to draw generalizable
+conclusions, whereas the uniform design of the QOF lends itself to close scrutiny. The research evidence about the QOF has grown rapidly and merits systematic review. We sought to examine the impact of the QOF on the quality of UK primary medical care, using broad inclusion criteria.
+METHODS
+We searched MEDLINE, EMBASE, and PsycINFO databases to identify all publications that sought to evaluate the QOF. The following search terms were used as free text in the title, abstract, or key words: (quality outcomes framework) OR (QOF) OR (pay for performance) OR (P4P) OR (pay-for-performance) AND (England) OR (Scotland) OR (Wales) OR (UK) OR (United Kingdom) OR (Great Britain). We limited the search to publications from January 1, 2004, to July 31, 2011, and limited the search to publications in the English language. We retrieved 575 references from MEDLINE (n = 348), EMBASE (n = 294), and PsycINFO (n = 55). We further searched by hand the reference lists of these articles for additional relevant studies. After eliminating duplicates and screening abstracts for relevance, 305 articles were excluded. The remaining 124 were read and rated by 2 authors (S.G. and N.S.) using a modified Downs and Black rating scale for observational studies (n = 110) and a Critical Appraisal Skills Programme rating scale for qualitative studies (n = 14). Thirty articles were excluded because they did not meet quality scores (less than 5 of 7 for observational or less than 7 of 10 for qualitative studies), did not evaluate the effect of the QOF, were a repeat publication, or were a review of previous research. Discrepancies in coding and areas of disagreement were resolved through discussion and adjudication by the third author (A.N.S.). The flow of information through the review is presented as a PRISMA flowchart (Figure 1).16 Several typologies have been used to define quality in primary care,17-20 and the Institute of Medicine’s 6 dimensions of quality have been widely used: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity.20 We used these dimensions to categorize the included studies.
+RESULTS
+Our search and review retained 94 articles, which we have categorized into 4 areas: effectiveness, efficiency, equity, and patient
+tive scheme (2004-2005) for 22 of the 23 incentivized indicators. These reached a plateau after 2004-2005, but quality of care in 2006-2007 remained higher than predicted by preincentive trends for 14 incentivized indicators. There was no overall effect on the rate of improvement for nonincentivized indicators in the first year of the scheme, but by 2006-2007 achievement rates were significantly below those predicted by preincentive trends.22 These improvements have been accompanied by rising prescription rates in associated drug categories.23 Performance improvements for those conditions that were not included in the QOF were significantly lower than for incentivized indicators, and these differences increased over time.22,24
+Overall, modest population mortality reductions have been estimated.25 Fleetcroft et al modeled a potential saving of 11 lives per 100,000 people per year aggregated across all clinical indicators and domains in the first year of the contract, with no further gain in the second year as performance for a typical practice already exceeded the target payment levels.26
+experience. These correspond to dimensions of the Institute of Medicine’s definition,20 with the exception of safety and timeliness, for which we did not find any relevant studies. Several studies have been considered in more than one of these dimensions. Most of the qualitative studies examined professionals’ experience and team working and were considered separately as a fifth area.
+Effectiveness
+Supplemental Table 1 (47 studies) displays descriptions of the studies on the impact of the QOF on effective care (available at http://annfammed.org/ content/10/5/461/suppl/DC1). The QOF has helped consolidate evidence-based methods for improving care by increasing the use of computers, decision support, clinician prompts, patient reminders, and recalls.21 It has resulted in better recorded care, enhanced processes, and improved intermediate outcomes for most conditions, notably diabetes. These improvements decreased after the first year of the QOF, however, and subsequent increases have followed secular trends. For example, Doran et al showed that achievement rates improved for most indicators in the preincentive period. There were significant increases in the rate of improvement in the first year of the incen
+Efficiency
+Supplemental Table 2 (5 studies) displays descriptions of the studies on the impact of the QOF on efficiency and costs (available at http://annfammed.org/ content/10/5/461/suppl/DC1). There is limited evidence that increasing the quality of ambulatory care may reduce admission rates and hence costs for some conditions.25 For example, epilepsy care as incentivized by the QOF was associated with fewer epilepsy-related emergency admissions.27
+There have been few attempts to model the cost effectiveness of QOF attainments.28,29 Walker et al could assess only a minority of indicators, and concluded that QOF incentive payments were costeffective, even with only modest improvements in care, although they took no account of the costs of administering the QOF scheme. They found average indicator payments ranged from £0.63 to £40.61 per patient, and the percentage of eligible patients treated ranged from 63% to 90%. The improvements in performance required for QOF payments to be cost-effective varied by indicator from less than 1% to 20%. There was no relationship between the size of payments in a clinical domain and the likely resulting health gain.30,31
+Equity
+Supplemental Table 3 (25 studies) displays descriptions of the studies on the impact of the QOF on equity (available at http://annfammed.org/ content/10/5/461/suppl/DC1). The QOF was not specifically designed to reduce health inequalities resulting from socioeconomic disadvantage. Never
+theless, inequalities in processes of care comparing the most and least deprived areas have narrowed. For example, Doran et found that the gap in median achievement comparing practices from the most deprived and least deprived quintiles narrowed from 4% to 0.8% between 2004 and 2007.32 In contrast, there have been variable effects on inequalities in care for long-term conditions based on age, sex, and ethnicity. Differences in care according to age for cardiovascular disease and diabetes narrowed after the introduction of the QOF as a result of greater improvement in those patients (usually older groups) receiving worse care. Disparities between men (who more often received better care) and women for cardiovascular disease and diabetes persisted or increased. Similarly, ethnic variations in quality of care have been reduced. For example, Millet et al found that improvements in blood pressure control were greater in the black group than the white, with disparities evident at baseline being attenuated (black 54.8% vs white 58.3% reaching target in 2005). Lower recording of blood pressure in the south Asian group, evident in 2003, was attenuated in 2005.33 The QOF has encouraged greater consistency of care irrespective of deprivation, but the practitioners’ option to exclude (exception report) hard-to-reach patients from the population used to determine payment may limit its impact on health inequalities.34
+Patient Experience
+Supplemental Table 4 (7 studies) displays descriptions of the studies on the impact of the QOF on patient experience (available at http://annfammed.org/
+*1 content/10/5/461/suppl/DC1). There were no significant changes in quality of care reported by patients between 2003 and 2007 for communication, nursing care, coordination, or overall satisfaction.35
+Continuity of care worsened for patients with chronic disease, and only access to urgent appointments improved significantly but modestly for these patients but not for adult patients more generally; overall, patients reported seeing their usual physician less often and gave lower satisfaction ratings for continuity of care.36 The few detailed ethnographic studies suggest that some practice teams have changed their consultations and clinical care in ways that may result in patients receiving a more biomedical type of care.37 There are also health professionals who acknowledge that an emphasis on protocol-driven care (“box-ticking”) may have distracted them from patient-led consultations and listening to patients’ concerns.38
+Professionals and Team Working
+Supplemental Table 5 (6 studies) displays descriptions of the studies on the impact of the QOF on profes-
+sionals and team working (available at http://	'■’■j
+annfammed.org/content/10/5/461/suppl/DC1). f Interviews with doctors and nurses suggest that the QOF has had positive effects on practice organization. For example, on team working and the diversification of nursing roles, both groups acknowledge that an enhanced role for nurses in managing long-term conditions could result in potential deskilling of doctors. They regret a decline in personal continuity of care between doctors and patients.39,40 The QOF appears to have introduced new hierarchies within practice teams and led to greater stratification of medical roles.41 Some team members resent not benefiting financially from payments, and there are concerns about an ongoing culture of performance monitoring in the United Kingdom.42 Quality of care may have become too narrowly focused on QOF domains and targets, with less regard to other areas for practice development, innovation, and quality improvement.43
+DISCUSSION
+There are inevitably conflicting findings from this large and diverse body of research, but some consistent themes have emerged. The implementation of the QOF has helped consolidate evidence-based meth-ods.44 It has been associated with an increased rate of improvement of quality of care during the first year of implementation, returning to preintervention rates of improvement in subsequent years. There have been modest reductions in mortality and hospital admissions in some areas, and where they have been assessed, these modest improvements appear cost-effective. The QOF has led to narrowing of differences in performance in deprived areas compared with areas not deprived.45 It has strengthened team working.
+The effect of the QOF in unincentivized areas has been disappointing. Prescription rates for antidepressants, statins, and other drugs have increased, but this increase is not clearly attributable to the QOF.46-48 The costs of administering the scheme are substantial, and some staff are concerned that primary care has become more biomedical in focus and less patient centered.
+The QOF has strengthened team working and promoted a diversity of new roles, especially for nurses. Indeed, the QOF may have diminished the workload of general practitioners, enabled them to concentrate on more complex care, and led to teams in which work and knowledge is more distributed among its members.
+The QOF has been described as scientific bureaucratic medicine, where indicators and guidelines are perceived as threatening professionalism in various ways.49 For better or worse, the QOF can be seen to have reduced clinical autonomy and provided per
+formance data that can be used to compare clinicians nationally.
+Remarkably little is known of what patients make of these changes, although anecdotal reports point to unintended consequences detracting from patientcentered care.50 The fear expressed by some that adherence to single disease-based guidelines might override respect for patient autonomy, lead clinicians to ignore comorbidities, promote a mechanistic approach to chronic disease management, or reduce clinical practice to a series of dichotomized decisions at the expense of personal aspects of care,51 has not been borne out by the research to date.
+Strengths and Limitations
+The great majority of studies used statistical analyses of trends or before and after comparisons, there being no possibility of controlled trials. The influence of many other regulatory, workforce-related, and educational changes on the quality of general practice is hard to disentangle. The development of evidencebased medicine, guidelines, and the introduction of other contractual incentives predated the QOF.
+Some of the most intriguing findings, particularly concerning the culture of care and professional and patient experience, derived from the small number of qualitative studies. The incorporation of qualitative research into the conventional processes of systematic review presents epistemological and methodological challenges that are unresolved.52 Strengths of this review are that it has been conducted systematically, it includes more studies on the QOF than any previous review, and it considers a broad range of outcomes.
+To what extent the apparent improvements in quality of care are the result of improved data entry remains unclear, but some of the documented improvement is likely to be due to recording of care previously delivered.53 Several factors impair the QOF’s impact at the population level. Setting targets below 100% and the process of exception reporting reduces the public health effectiveness of population targets by shifting the focus of the practice away from patients who are harder to reach.53 More fundamentally, payment for adhering to guidelines cannot be assumed to improve health status, regardless of whether it improves performance: improved processes (eg, treating hypertension) may not always translate to improved outcomes (eg, stroke prevention); and other powerful confounding influences affect outcomes, such as differential access to care, nonmodifiable risk factors (genetics, familial), or patterns of comorbidity. Process measures are often preferred for incentive schemes, as they are under the control of the health system and can be more efficient.7,54 The QOF’s evidence base will only ever be
+partial because its indicators by their very nature will focus on measuring the measurable.
+Implications for Policy
+The lessons that policy makers draw must, of course, take account of the different historical and organizational contexts in which their health system operates. A sensible verdict regarding the QOF’s effectiveness must balance a nuanced assessment of health and other gains against its costs, many of which are hard to describe, let alone quantify.
+The system of P4P and how it is designed will affect how it professionals feel and behave. Family doctors in the United Kingdom, despite feeling that the QOF has changed the nature of the consultation, appear less negative toward P4P than doctors working in California, where lack of new funding, rewards directed at organizations rather than individual physicians, lack of identification with or ownership of measures, and thresholds rather than incremental levels of improvement have led to resentment and greater evidence of dysfunctional or coercive behavior toward patients regarded as noncompliant.55 It may be that the nature of medical practice is too complex to be improved by simple financial incentives.56
+The limited evidence for cost-effectiveness and opportunity cost of the scheme is a central critique for the QOF’s detractors. If £1 billion a year of additional funding to general practice has yielded only modest improvements in measured quality of care, might greater benefits have been achieved if this investment had funded an alternative approach to quality improvement? The opportunity costs of the QOF are to a great extent unknown and unknowable, but the imperfect evidence available suggests that the same benefits could be maintained at reduced cost, particularly if systems are designed to involve clinicians and align with their values.57,58 More sophisticated modeling is required.
+Developing the QOF
+Although some have argued for discarding the QOF, it seems wiser to concentrate on addressing weaknesses rather than throwing away the gains. There is no reason why both technical aspects of quality and personal care cannot improve together.59 The involvement of the National Institute for Health and Clinical Excellence has greatly strengthened the QOF’s scientific underpinnings.60 There will always be a fine judgment about timing, level of evidence required, and whether to accept a consensus rather than evidence-based indicator. An argument for greater consistency of care should not prevail where evidence is lacking. The evidence base for existing indicators
+465
+needs to be under constant review. Some indicators for which performance has reached a ceiling may need to be retired,61 although performance may not be maintained,62 and new indicators should be introduced after piloting.63
+Gaming is known to occur in many systems that are driven by P4P; however, there has been little evidence of gaming in the QOF despite, or perhaps because of, a rigorous system of checks at various levels.64 On the contrary, practices are exceeding the upper payment thresholds and levels of exception reporting continue to fall year on year.65 Nevertheless, vigilant monitoring systems are needed. The balance of fixed vs performance-related funding should be reviewed. There is merit in linking the size of financial rewards to the public health impact of attaining individual indicators.66
+In view of our findings that observed improvements in care from a very large payment-for-perfor-mance scheme in the United Kingdom were modest, with uncertainty about possible adverse effects, we recommend that policy makers continue to exercise caution about implementing similar schemes. Consideration should be given to improving different dimensions of quality, including user experience and equity. Costs should be monitored and balanced against benefits. Wherever possible, schemes should be designed in collaboration with health service researchers to evaluate the benefits of minor differences in system design. Payment for performance is still an imperfect approach to improving primary care, and should be considered as only one option alongside alternative quality improvement methods.
+Future Research
+The conclusions of this review are based on the available observational evidence, with all its limitations. They raise many questions about the design and implementation of payment for performance in health care. There is a clear need for more experimental research in many areas, and we suggest 5 high-priority areas. First, does the size of incentive payment affect achievement? Psychological research has surprisingly found that large incentives for tasks requiring greater levels of cognition may lead to lower levels of achievement, yet no research has been done on this finding in health care.56
+Second, how can the patient-user experience be better assessed and more directly linked to the payment of financial incentives? Third, do incentives lead to a trade-off between technical and patient-centered dimensions of quality, or can they produce improvements across different dimensions of quality? Fourth, what effect do incentives have on such harder-to-mea-
+sure outcomes as the interpersonal aspects of care and care for underserved populations? Fifth, what is the optimum time for a quality indicator to be included in a payment scheme before being reviewed or replaced by a different incentive?
+To read or post commentaries in response to this article, see it online at http://www.annfammed.org/content/10/5Z461.
+Key words: primary care; general practice; pay for performance; reimbursement, incentive; quality of health care; quality improvement; review, systematic
+Submitted December 17, 2010; submitted, revised, December 16, 2011; accepted January 6, 2012.
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+Introduction
+There is growing international evidence that mental ill health and poverty interact in a negative cycle.1 This cycle increases the risk of mental illness among people who live in poverty and increases the likelihood that those living with mental illness will drift into or remain in poverty. Although the evidence for this pattern in high-income countries is fairly robust,1-3 only in the past two decades have emerging epidemiological data confirmed the trend in low-income and middle-income countries.4,5 Longitudinal data remain sparse and precise causal
+Key messages
+•	Mental ill health and poverty interact in a negative cycle in low-income and middle-income countries.
+•	To break this cycle, interventions are needed that address both the social causes of mental illness and the disabilities and economic deprivation that are a consequence of mental illness.
+•	On the basis of data from two systematic reviews, we found that the mental health effect of poverty alleviation interventions was inconclusive, although some conditional cash transfer and asset promotion programmes showed mental health benefits.
+•	By contrast, mental health interventions were associated with improved economic outcomes in all studies, although the difference was not statistically significant in every study. Improvements in economic status go hand in hand with improvements in clinical symptoms, creating a virtuous cycle of increasing returns.
+•	The findings support the call to scale up mental health care and include mental health on international development agendas.
+mechanisms are difficult to identify. Nevertheless, two principal causal pathways have been postulated. According to the social causation hypothesis, conditions of poverty increase the risk of mental illness through heightened stress, social exclusion, decreased social capital, malnutrition, and increased obstetric risks, violence, and trauma.4-6 Conversely, according to the social selection or social drift hypothesis, people with mental illness are at increased risk of drifting into or remaining in poverty through increased health expenditure, reduced productivity, stigma, and loss of employment and associated earnings.3 The social causation pathway might apply more readily to common mental disorders such as depression, whereas the social selection hypothesis might be more applicable to disorders such as schizophrenia and intellectual disabilities.3 However, these pathways are complex and evidence suggests that they move in both directions for most mental, neurological, and substance misuse disorders.
+The WHO Mental Health and Development report7 emphasised the importance of mental health as a development issue in countries with low and middle incomes, providing compelling evidence that people with mental disorders constitute a vulnerable group who need to be targeted in development assistance. A UN General Assembly Declaration on global health and foreign policy welcomed this report, and recognised that mental health problems have “huge social and economic costs”.8 This challenge begs the question: what interventions are needed to break the cycle of poverty and mental ill health in these countries? More specifically, should such interventions target the economic circumstances of people who live in poverty, and through increasing access to financial resources attempt to improve mental health outcomes of populations (intervening in the social
+causation pathway); or should they target the symptoms and disabilities associated with mental ill health, thus improving the “capabilities”9 of people living with mental illness to participate in economic activity (intervening in the social drift pathway)?
+Little is known about the strength of the evidence for these interventions. Yet, such questions are important in the context of the Millennium Development Goals (MDGs) and calls to include mental health in the MDGs and subsequent international development targets.10,11 If mental health is to be included in future development targets beyond 2015, assessment of the evidence base and feasibility of interventions that attempt to break the cycle of poverty and mental ill health is important.
+We undertook two systematic reviews to address these questions. The objective of Review 1 was to assess the effect of poverty alleviation interventions on mental, neurological, and substance misuse disorder outcomes in countries with low and middle incomes. The objective of Review 2 was to assess the effect of mental health interventions on individual and family or carer economic status in these countries. Panel 1 presents the methods used in both systematic reviews. For both reviews, heterogeneity of methods, instrumentation, study settings, interventions, outcomes, populations, and analyses precluded an attempt to draw summary estimates of effect size. Instead, we present a qualitative summary of findings.
+Combating social causation: poverty alleviation interventions and their mental health effect
+Description of studies
+Figure 1 shows the literature search process for Review 1. Five reports were included in the review. These reports related to four studies undertaken in four countries: one study of a conditional cash transfer programme in Mexico,17,18 one of unconditional cash transfers in Ecuador,19 one of small loans in South Africa,20 and one of an asset promotion intervention in Uganda.21 Reports were grouped into one study if the interventions were defined in the same way at multiple follow-up times and if findings referred to the same study population. All studies were randomised controlled trials, in which the intervention was randomly assigned either at a cluster level (eg, household or family) or at the individual level. In one case, the study was based on a subset of a randomised trial.21 No non-randomised longitudinal intervention studies met the inclusion criteria.
+Table 1 shows study characteristics and main findings. Of the nine mental health outcomes assessed, two were perceived stress in adults, two adult depression, two childhood cognitive development, two childhood behaviour problems, and one adolescent self-esteem. Mental health outcome tools included a range of developmental, behavioural, and mood assessment measures. Cash transfer studies assessed both child and adult outcomes. Follow-up for the studies varied between
+6 months and 10 years. No studies were identified on the effects of poverty alleviation interventions on substance misuse. Although no time limitations were placed on the search, all the studies were published from 2007 onwards.
+Effect on mental health status
+The mental health effect of these poverty alleviation interventions was varied. In children, conditional cash transfer evaluations after 10 years comparing early recipients and later recipients of the Oportunidades programme in Mexico showed a significant effect on reduction of behavioural problem indices but a nonsignificant effect on cognitive scores.18 When the same intervention was assessed as a continuous outcome (total amount of cash received) after 5 years, a significant improvement in all cognitive assessments was associated with the intervention.17 The small loans intervention in South Africa was associated with an increase in stress levels among programme participants 6 months after the end of the intervention; results for depressive symptoms were non-significant.20 The evaluation ofthe unconditional cash transfer programme in Ecuador did not note any significant effects of the programme on children’s cognitive and behavioural outcomes or caregivers’ depression indices after 2 years.19 Finally, the asset promotion programme in Uganda reported positive effects on schoolchildren’s self-esteem after 10 months.21
+Discussion
+The scarcity of data makes it difficult to draw clear conclusions. There are some indications that conditional cash transfers and asset promotion are more clearly associated with mental health benefits than are other poverty alleviation interventions. The unconditional cash transfer programme had no significant mental health effect for children or adults, and the microcredit intervention had negative consequences, increasing stress levels among recipients. The negative findings in South Africa are consistent with other recent findings that microcredit programmes can entrench poverty for some groups in sub-Saharan Africa22 and increase risk of common mental disorders among poor mothers in Andhra Pradesh, India.23 Some microcredit programmes have had mixed mental health effects; for example, the Bangladesh Rural Assistance Committee (BRAC) showed no effect on women’s emotional stress24 and a significant improvement in mental health items of the 36-item short-form health survey among poor BRAC members compared with poor non-members (p=0-038).25 The findings suggest that intervention effects are greatly dependent on the precise nature of the intervention (eg, whether the intervention is a loan, a conditional cash transfer, or an unconditional cash transfer; the level of input, for example amount of cash; and the level of active involvement required from participants), the mental health outcome being assessed, and the context. With respect to causal mechanisms, the scarce evidence for
+Panel 1: Methods for Reviews 1 and 2
+Inclusion criteria for Review 1, social causation: do poverty alleviation interventions improve mental, neurological, and substance disorder outcomes in low-income and middle-income countries?
+•	Individual and cluster randomised controlled trials and non-randomised intervention studies undertaken in low-income and middle-income countries were included if they reported a quantitative estimate of the effect of a financial poverty alleviation intervention on priority mental, neurological, and substance misuse disorder outcomes as identified by mental health Gap Action Programme (mhGAP),12 including mental and substance misuse disorders and epilepsy, as well as psychological measures that have been shown to predict some mental, neurological, and substance misuse disorder outcomes such as psychological distress13 and self-esteem.14 Studies were excluded if the condition of interest was not a mental health problem, substance misuse, or epilepsy (eg, stroke, multiple sclerosis, or other neurological condition), and if the study used a case-control or cross-sectional method.
+•	Interventions were included if they aimed to improve an individual’s poverty status, and included: cash transfers, microfinance, loans, social insurance, debt management, and financial services. In-kind interventions, such as food relief or nutrition supplementation, as well as employment and educational interventions, were excluded for two reasons: first, because we wished to focus on financial interventions, and including these would introduce a wide range of interventions, with varying causal mechanisms from which it would be difficult to draw clear conclusions; and second, because these would relate to a range of policy recommendations in a range of different sectors.
+Inclusion criteria for Review 2, social drift: do mental health interventions improve individual and family or carer economic status in low-income and middle-income countries?
+•	Individual and cluster randomised controlled trials and non-randomised intervention studies undertaken in low-income and middle-income countries were included if they reported a quantitative estimate of the effect of an intervention to improve mental health on the economic status of either the individual receiving the intervention (of any age) or their family or carers. All priority mental, neurological, and substance disorder outcomes as identified by mhGAP consisting of mental and substance misuse disorders and epilepsy were included.12
+•	Interventions of any type (pharmacological, psychological, and psychosocial) were included if their aim was to improve the lives of people with mental, neurological, and substance misuse disorders and their families. This definition includes but is not limited to pharmacological interventions, psychological therapies, social skills training, supported employment, psychoeducation, and other educational measures to improve social (as opposed to purely health)
+outcomes. Studies were included if they compared the intervention with a placebo or treatment as usual control group, or for non-randomised intervention studies without a comparison group, if they provided before and after estimates of the outcome. Comparisons of two active treatments (such as two drug treatments or two psychological therapies) were excluded to estimate the effect of removal of the treatment gap on economic status.
+•	Economic status outcomes consisted of direct measures of the economic status of individuals and families, as opposed to indirect measures, which could be used to infer economic status such as depression-free days and disability scores. Examples of economic status outcomes include: employment status (eg, occupation, including homemaking for women, unemployment, number of lost work days), finances (eg, earnings from employment, household income), and costs of health care (eg, out of pocket expenses for treatment).
+Search strategy
+•	The search was not restricted by date, language, or publication status. We searched the following electronic databases: Medline, PsycInfo, Cochrane Central, Econlit, and ISI Web of Science using Medical Subject Heading (MeSH) terms (or equivalent terms) for published peer-reviewed journal articles. Terms used to capture studies relating to mental illness were “mental disorders” and all terms included in MeSH as subheadings of mental disorders. Terms for capturing economic status were “income”, “poverty”, “employment”, “rehabilitation, vocational”, “education”, and “educational status”. Those for capturing studies undertaken in low-income and middle-income countries were “developing countries”, and the names of all the individual countries classified as low-income or middle-income countries by the World Bank. Those for capturing the methodological criteria included the search terms and MeSH headings for “clinical trials”, “randomized controlled trial”, “prospective studies”, “follow-up studies”, “comparative study”, “randomized”, “cohort studies”, and “evaluation studies”. The last search was done in October, 2010. We also screened the reference lists of all selected papers and contacted authors of relevant studies.
+•	For Review 1, 28 scholars in the specialty were contacted and asked whether they had personally undertaken any research in this area and whether they knew of other studies that might be relevant. Of the 20 scholars who replied, seven provided potential papers with a total of 16 papers provided. Three of these papers were unpublished, two were already included in the review, and 11 did not meet the inclusion criteria.
+•	For Review 2, 12 key scholars were contacted, as well as all authors (n=41) of the randomised controlled studies that were documented in the 2007 Lancet Series on global mental health, which assessed the effectiveness of interventions for the treatment and prevention of selected
+(Continues on next page)
+(Continued from previous page)
+mental disorders in low-income and middle-income countries.15 The authors of the randomised trials were asked whether they had done any further trials measuring the economic effect of the mental health intervention since the 2007 Lancet publication. Of the 25 Lancet authors who responded, 21 indicated that they had not done any further trials and four said that they were currently undertaking trials, the results of which had not yet been published. All 12 of the additional scholars in the specialty who were contacted responded; however, only one provided a potential paper. This paper was, however, still unpublished and thus was not applicable.
+Data collection and analysis
+•	Initial screening of irrelevant abstracts involved one author searching through the database of search results for papers that had nothing to do with mental health (eg, searching for “cancer” or “heart disease” in the title, scanning the title, and then excluding). The authors also did keyword searches for study design—eg, “qualitative” and “prevalence” to exclude non-intervention studies. This method was the most efficient way of ensuring that two people could double screen the relevant results, since our initial search had more than 13 000 results. After the initial screening of search results for irrelevant studies, two authors (CL and SP for Review 1 and MDS and SC for Review 2) independently screened the titles and abstracts of the search results. Full-text copies of all potentially relevant studies were obtained and independently assessed by CL and SP for Review 1 and MDS and SC for Review 2 to establish whether they met the inclusion criteria.
+Non-English language papers were translated before being tested for inclusion. Data were extracted from included studies using a standard data extraction form by one author (SP and SC for Review 1 and 2, respectively), and data extraction checked by a second author (CL and MDS, respectively). The quality of included studies was assessed with the Cochrane Risk of Bias Tool for randomised controlled trials and the Effective Public Health Practice Project tool for all other study designs. Quality assessment was undertaken by one author (SP and SC for Review 1 and 2, respectively), and checked by a second author (CL and MDS, respectively). Using the Cochrane Risk of Bias Tool,16 we found some studies to have high risk of bias in some domains, but these risks did not substantially compromise the validity of the findings of these studies. In Review 2, the quality of the non-randomised intervention study was strong, but two of the three before-and-after cohort studies were judged to be weak, largely because of selection bias resulting from the selection of the cohort or large losses to follow-up.
+Limitations of review
+•	Although we included studies published in any language, only search terms in English were used and the databases predominantly reported English language studies, so we are likely to have missed some studies that were not published in English. Additionally, the mental health outcomes of poverty alleviation programme evaluations are not always reported in the peer-reviewed literature. Although concerted attempts were made to uncover available evidence, some studies might therefore have been missed.
+poverty alleviation interventions with a financial component do not allow strong conclusions, particularly in view of the complexity of some of the interventions. Evaluations that include an analysis of separate components of the interventions might contribute to a clearer picture—eg, whether the regularity of payments or inputs, their conditionality, or their cumulative amounts are key factors determining mental health outcomes. In our review, only the Oportunidades programme evaluated the effect of a specific component, namely the cash component, which did show a benefit for children’s cognitive development after 5 years.17
+The interventions in Review 1 suffer from a problem common to many prevention interventions, namely that they target all people identified as poor within a population, and only intervene with one facet of poverty, primarily finance. In the context of multifaceted poverty and the complex relationship between poverty and mental ill health, such interventions are unlikely to have an effect on mental health unless they address more specific mechanisms of the association between poverty and mental health and target a specific vulnerable subgroup of the population. This idea is supported by findings from observational reports in low-income and middle-income
+countries suggesting that the strength of the association between poverty and mental health varies for different dimensions of poverty (eg, income versus education deprivation).4 This variation reinforces the need to monitor mental health outcomes ofpoverty alleviation programmes (where possible broken down into their multiple components) to identify which aspects can help to prevent mental illness or promote mental health, and which subpopulations might benefit from such interventions.
+Of note, four intervention studies that did not meet our inclusion criteria by virtue ofonly reporting cross-sectional data nevertheless produced interesting findings that corroborate and expand on the findings of the included studies. For example, children who had been in the Oportunidades conditional cash transfer programme in Mexico had lower salivary cortisol concentrations (as a proxy for stress levels) than did those who had not participated in the programme, while controlling for a wide range of individual, household, and community-level variables.26 The effect was stronger among children of mothers with high depressive symptoms (p<0-001). Similarly, the Oportunidades programme was associated with a 10% decrement in aggressive or oppositional symptoms among children, although there was no
+significant difference in anxiety or depressive symptoms in children, or in total problem behaviours, while controlling for covariates.27 In Malawi, unconditional cash transfers led to a 38% reduction in psychological distress among schoolgirls, assessed with the General Health Questionnaire (GHQ-12).28 In South Africa, the depression index of household members in the Langeberg rural area was lower the greater the number of pensioners in the household, while controlling for the presence ofhousehold members who were eligible for pensions, suggesting an independent effect of pension income on depression.29
+The findings are consistent with some high-income country findings; for example, the evaluation of a natural experiment in the USA found that income supplementation had an effect on older children’s and young adolescents’ aggressive or oppositional, but not anxiety or depressive symptoms.30 However, the small number of studies, wide range of populations and ages, varied interventions, and range of mental health outcomes make synthesis difficult and limit the conclusions that can be drawn.
+Preventing social drift: mental health interventions and their economic effects
+Description of studies
+Figure 2 shows the literature search process for Review 2. Nine reports were included in the review. The included studies varied substantially in terms of study design, population, intervention assessed, and outcomes. The studies were undertaken in six countries, with three studies from China, one from Thailand, two from India, one from Uganda, one from Nigeria, and
+one from Iran. There were five randomised controlled trials, one non-randomised intervention study, and three before-and-after cohort studies. Of the 11 interventions evaluated by the nine studies, three were psychiatric drugs, two were community-based rehabilitation programmes including psychotherapy and psychiatric drugs, two were individual or group psychotherapy, two were residential drug-treatment programmes, one was family psychoeducation (provided once per month for 9 months), and one was epilepsy surgery. Family psychoeducation involved providing the family and the patient with basic information about mental illness, treatment, and rehabilitation, and was tailored to the specific condition of the patient, their symptoms, prognosis, treatment recommendations, and long-term management. Of the 18 economic status outcomes assessed, 13 assessed the effect on the individual patient and included measures of employment status (such as unemployment, employment duration, or type of employment) or culturally validated measures of ability to undertake locally relevant economic activities (such as farming or growing food). Five measured the effect on the family including the effect on family finances, the effect on the working patterns of non-ill family members and the health-care costs of the intervention to the patient and family.
+Effect on economic status
+Table 2 summarises the characteristics and main findings of all studies included in the review. Of the 19 associations tested, ten showed the intervention to have a significant positive effect on economic status and nine a non-significant positive effect (or no tests of significance were provided). No study showed a mental health intervention to have a significant negative effect on economic status.
+The three studies on interventions for depression were all randomised controlled trials. Group interpersonal psychotherapy for depression was associated with significant improvements in women’s but not men’s daily economic tasks in Uganda.31 Family-based community rehabilitation including drug treatment and psychoeducation significantly decreased family economic burden, increased family employment, and increased the working ability of the patient in China.32 Antidepressant treatment showed a non-significant reduction, and individual psychological therapy a nonsignificant increase, in family out-of-pocket payments for treatment in India.33
+Two of the three studies on interventions for psychosis were randomised controlled trials, one of which showed a significant positive effect of the intervention on economic status. Community-based rehabilitation in China including drug treatment and family psychoeducation had a significant positive effect on duration of employment and the burden on family finances, but no effect on non-ill family members’ working patterns35 or
+the patients’ ability to work.34 A non-randomised intervention study in India showed a reduction in work-related disability among participants who were prescribed antipsychotic drugs.36
+The two cohort studies that evaluated the effect of residential treatment programmes for substance misuse in Iran37 and Nigeria38 showed improvements in
+employment status as a result of the intervention, but no tests of significance were provided, and both studies had biases that might have affected their results. The cohort study evaluating the effect of successful epilepsy surgery on multiple dimensions of employment status identified very large significant increases in productive work, average income, and job status.39
+Discussion
+The findings of this review show a clear trend in which mental health interventions are associated with improved economic outcomes in low-income and middle-income countries. All studies showed an economic benefit, although the difference was not statistically significant in every study. Whether some of the interventions included in the review, although effective in improving economic and clinical outcomes, would be suitable for scaling up in resource-poor settings is questionable. Five of the interventions are complex and involved both drug treatment and psychological therapy delivered over a period ofmonths as either an outpatient32,34,35 or inpatient,37,38 and one involved surgery for epilepsy delivered in a tertiary health-care setting.39 The drug treatment and psychosocial interventions have low compliance rates, which could affect the ability of the intervention to improve economic status outcomes. For example, only 53% ofthe intervention group were defined as actively compliant in a Chinese randomised trial of community rehabilitation, and outcomes were better for compliant than for non-compliant patients.35 Three studies evaluate fairly simple and brief interventions that either were or could be delivered by non-specialist health workers.31,33,36 Two of these studies showed significant improvements in economic status for small investments,31,36 and the third showed a significant cost-effectiveness benefit to the
+health-care provider of antidepressant treatment in improving clinical symptoms.33
+Improvements in economic status go hand in hand with improvements in clinical symptoms, creating a virtuous cycle of increasing returns. All of the studies that showed a significant effect on economic status also showed a significant improvement in clinical status. These clinical improvements could also account for improvements in family economic status. Both randomised trials that explored the effect on family burden showed that patients in the intervention group had significantly fewer readmissions to hospital, shorter duration of hospital stay, and longer time in gainful employment compared with the control group, accounting for the reduced effect on the family finances in the intervention group.32,35
+Priorities for future research
+Tackling the cycle of mental ill health and poverty is urgent for several reasons. First, the link between income and ill health is stronger for mental health than for general health, as shown in high-income countries such as the UK40 and South Korea.41 In the UK, the extent of inequality increased with the severity of mental health problems, with the greatest inequality recorded for psychosis.40 Second, in response to the present global economic recession, mental health inequalities in populations are likely to worsen. In an analysis of data from South Korea over a 10-year period, Hong and colleagues41 showed a widening of mental health inequalities after South Korea’s major recession in the late 1990s. Worsening macroeconomic circumstances over coming years could exacerbate the already difficult relation between poverty and mental ill health if active policy steps are not taken. In view of the substantial gaps in the discipline identified by this review, establishment of a research agenda for policy interventions that aim to break the cycle of poverty and mental illness in countries with low and middle incomes is important.
+The first priority is to undertake an increased number of high-quality intervention studies in countries with low and middle incomes. Despite screening of more than 13 000 titles and abstracts, only five studies were eligible for inclusion in Review 1, and nine in Review 2. Of the 1521 randomised trials of mental health interventions identified in the 2007 Lancet Series on global mental health,15 only four measured economic status outcomes and thus were included in this review, with only one new randomised trial published since 2007 that measured economic status outcomes and therefore could be included in this review.32 This paucity of studies mirrors findings from other recent systematic reviews of mental health research in low-income and middle-income countries.15,42 Furthermore, only two of the 14 included studies were set in a low-income country (Uganda), with the remainder from countries with lower-middle and upper-middle incomes. Thus, the effect of mental health and poverty
+Poverty alleviation intervention studies should:
+•	Include locally valid mental health outcome measures, preferably pertaining to so-called hard assessment of mental, neurological, and substance misuse disorders such as screening tools for specific disorders or groups of disorders, rather than soft measures such as stress and self-esteem. Although stress and self-esteem are predictive of mental, neurological, and substance misuse disorder, they are likely to provide less robust assessments of disability and distress. Where relevant, suicide outcomes should also be assessed—a measure that might have assisted, for example, in more rigorous evaluation of microfinance interventions in Andhra Pradesh, India.43
+•	Use precise measures of the causal mechanisms to be tested; for example, the conditionality of cash transfers, the volume of the intervention, and local contextually relevant factors.
+•	Target specific vulnerable populations who might yield the greatest mental health gains from a particular intervention; for example, cash transfers for adolescent girls in some settings such as Malawi might reduce their reliance on engaging in transactional sex to generate income, and hence improve their mental health status.28
+Mental health intervention studies should:
+•	Include robust, locally relevant, and multidimensional outcome measures of economic status. Outcome measures in Review 2 were often one-dimensional (eg, employed vs unemployed) and did not capture the nuances of an individual’s or family’s economic status such as type ofjob or hours worked. Such distinctions are crucial in the context of evidence for improved outcomes for people with psychosis in low-resource settings.44 Detailed measures of employment status show that crude measures such as employed or unemployed mask a change in working patterns towards low paid, unskilled work,45-47 compounded by social pressure for men to be the primary wage earner in settings where there is an absence of social security.46 Furthermore, one-dimensional measures of employment status are problematic in low-income and middle-income settings, which commonly have high unemployment rates in the general population and a proliferation of so-called informal economies with complex resource-sharing networks and living conditions.
+These factors make the development of local culturally valid functional assessment tools, such as those developed by Bolton and colleagues,31 particularly pertinent.
+•	Incorporate outcome measures of family or household economic status and burden. Both studies that assessed family economic status showed a positive effect.32-35 A reduction in family and caregiver burden is an important outcome in settings in which most people with mental disorders are cared for at home by their families and when a reduction in family burden is associated with improved social functioning and clinical outcome for the patient, creating a virtuous circle.44
+All studies should evaluate a broad range of interventions. Both reviews identified only a narrow range of interventions. In Review 1, although we searched for interventions related to debt relief and social insurance, we only found cash transfers, loans, and asset promotion interventions. In relation to Review 2, the individual placement and support model of supported employment has been shown in systematic reviews from high-income countries to be one of the most robust interventions for improvement of economic outcomes for people with severe mental illness,48 and this is supported by studies in high-income non-western settings.49 However, we found no studies from low-income and middle-income settings that evaluated the effect of such interventions.
+There is a need not only for an increased number of randomised controlled trials with robust analyses, but also for studies with follow-up that is long enough to gain an understanding of long-term effects. Only two of the five randomised trials in Review 1 and one of the five randomised trials in Review 2 followed up participants after the initial postintervention assessment.33 In Review 2, in particular, economic effects such as changes in employment status and earnings and getting out of debt, for the person with mental ill health or their family members, might take longer to manifest themselves. Although some studies followed up patients for up to 2 years,32-35 this design was used because the intervention was complex and of long duration, and patients were assessed at the end of the intervention. Long-term postintervention follow-up of all treated patients is essential to establish whether effects on economic status are sustained.
+alleviation interventions in very low resource settings remains largely unknown. There is therefore a pressing need for high-quality experimental studies from low-income and middle-income countries assessing the effect of poverty alleviation interventions on mental health status and the effect of mental health interventions on individual and family economic status. These studies should include several features, which are listed in panel 2.
+The second priority is to assess the macroeconomic consequences of scaling up of mental health care in
+countries with low and middle incomes. The finding that mental health interventions can offer clear economic benefits at the microeconomic level of households strengthens the economic case for investment in mental health care. This outcome also raises a broader question: if provision of mental health treatment or rehabilitation programmes has economic benefits, what might be the costs and economic benefits of implementing such programmes at the macroeconomic or national level? The costs of scaling up a core package of mental health
+services have been set out in the previous Lancet Series on global mental health,50 so there is already a basis for calculation of the direct health-care investments needed. However, estimation of the macroeconomic benefits or payoff associated with this investment needs further development, owing to well established deficiencies with prevailing approaches to the estimation of productivity gains or losses (such as the assumption that economies operate at full employment).51
+One feasible alternative to the cost-of-illness method is an economic growth accounting approach or model,52 which relates the contribution of labour, capital, and other factors to aggregate production levels in a country—ie, its gross domestic product (GDP). Ill health enters the model as a check on labour supply, and uses up resources (for health care) that could otherwise be saved or put to an alternative use. On the basis of this approach, the projected GDP that a country will achieve in the absence ofa particular disease (ie, 0% prevalence) can be compared with the GDP that results from prevailing or target levels of prevalence reduction or disease control. Ultimately, the scope of such an assessment should go beyond GDP effects alone and also incorporate the intrinsic value of improved mental health status or psychological wellbeing. However, there are several methodological challenges to first address, including estimation ofthe effect of reducing health-related disability on labour supply or productivity, and the economic value to be accorded in different settings to a year spent in full health.
+The third priority is to assess the effects ofredistribution and market failures. State involvement in financing or providing mental health services is typically justified either by a desire to distribute or redistribute resources more fairly or to address so-called market failures that prevent achievement of socially efficient outcomes. Each is potentially a fruitful area for research in low-income and middle-income countries.
+A policy maker with redistributive goals would compare the marginal benefits across the income range of interventions for mental health relative to interventions for other illnesses. Although infectious diseases such as tuberculosis and malaria might have higher relative incidence in the poor population, interventions for these illnesses might already be high enough that the health or welfare benefits of additional investment in treating these illnesses is lower than investment in mental health (for which prevalence is also unequally distributed across the income range, but existing levels of treatment are very low). Research that examines such comparative benefits in the poor population could yield evidence to support investment in mental health.
+Few studies in low-income and middle-income countries specifically assess market failures in the mental health domain, but here too there are good reasons to invest in mental health research. First, many people with mental health problems lack insight into their condition, or fear stigma associated with careseeking; these characteristics lower demand for care below what is optimum for them, their families, and society, and lead to under-supply of services. The contribution of these characteristics to suboptimum demand for, access to, or uptake of mental health services has rarely been studied. Second, mental health problems are associated with substantial uncertainty and variability concerning symptom duration and severity, and hence uncertainty about personal economic effects, particularly for chronic conditions such as schizophrenia. Treatment effectiveness is also uncertain. These factors complicate the establishment of adequate insurance arrangements.53 In countries with low and middle incomes, mental health is typically not covered under standard health insurance products, leading to substantial welfare losses when such illnesses strike. These welfare losses have been documented in the case of other illnesses54 in these countries, but not for mental disorders.
+Third, market failure can also stem from so-called externalities—the effect of poor mental health beyond the person with the illness. Unlike infectious diseases, in which contagion risks are well understood and studied, research into the effects of poor mental health in low-income and middle-income countries has typically been restricted to individuals with mental illness. Nevertheless, studies show32,33,55 that individuals living with people with poor mental health are more likely to report worse mental health themselves. Poor mental health could have spillover effects, not only on the rest of the family, but also on society.53 Documentation of the extent of such spillover effects would improve understanding of the wider benefits of mental health interventions.
+Priorities for policy
+These reviews have identified several interventions that can address the cycle of poverty and mental ill health in countries with low and middle incomes (figure 3). The preliminary findings from Review 2 suggest that although
+the discipline is in its infancy, there is reasonably strong evidence that mental health interventions have economic benefits for individuals and families in low-income and middle-income countries, and have the potential to interrupt the cycle of poverty and mental ill health. The findings are important for strengthening of the economic case for investment in evidence-based mental health care. Our first recommendation therefore supports the call to scale up mental health services,50 not only as a public health and human rights priority, but also, on the basis of evidence from this review, as a development priority.
+By contrast with the findings for Review 2, the findings for the mental health benefits of poverty alleviation programmes in Review 1 are more equivocal. However, this outcome should not be interpreted as an indication that such programmes do not convey mental health benefits. There are individual studies that show that they do, particularly for conditional cash transfers, and the findings of this review point to the need for more precise assessments of the effect of particular components of such programmes. The second recommendation is therefore that mental health should become integrated as a central element of monitoring of the outcomes of poverty alleviation programmes. When combined with longitudinal data, evidence from household surveys (rather than individual patients alone) could yield valuable insights both into the ability of households to insure against mental disorders and the wider effects of such disorders on the family. Integration of such household surveys with randomised controlled trials that intervene either in the mental health or in the poverty domain can provide causal evidence for the broader temporal and spatial links between mental health and poverty. Available evidence suggests that poverty alleviation programmes can have mixed effects on mental health, and further research is needed to provide a more conclusive picture.
+Conclusion
+In the same manner that the first Lancet Series on global mental health in 2007 drew attention to the need to address global mental health as a neglected public health priority, this study draws attention to the need to address mental health as a neglected priority in international development economics. The findings of the systematic reviews that we have undertaken suggest that breaking of the cycle of poverty and mental ill health in countries with low and middle incomes is possible in specific settings and for specific interventions. Currently, the evidence for interventions that address the social selection or social drift pathway, by providing treatment and rehabilitation interventions for people with mental illness, seems to be the most robust. This finding does not preclude the possibility that poverty alleviation interventions convey mental health benefits for populations by addressing the social causation pathway. However, the evidence for poverty alleviation interventions is less strong, and there is an urgent need for further research, particularly to
+include methodologically sound mental health outcomes in evaluations of poverty alleviation interventions.
+Some of the differences in the findings between Review 1 and Review 2 can be accounted for by the more targeted nature of the interventions in Review 2, which largely focused on a specific disorder or group of disorders, within an identified age range. In this context, the evidence for interventions that address the social selection or social drift pathway by providing treatment and rehabilitation interventions for people with mental illness supports the call to scale up mental health services.1150 This is not only a public health and human rights priority, it is also a development priority.
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+Doctors have often played leading roles in social movements to improve the public’s health. These range from the early days of John Snow isolating the role of contaminated water supplies in spreading cholera, through to advocating harm reduction, challenging HIV stigma and, more recently, highlighting the public health catastrophe of mass incarceration in the USA.1 Almost all examples are rooted in poverty. There is now increasing recognition that mental health problems form the greatest public health challenge of our time, and that the poor bear the greatest burden of mental illness.2
+Our article draws on data from Scotland, and especially Glasgow, which contains some of the areas of greatest need and widest health inequalities in Western Europe. However, the relationship between poverty, social stress and mental health problems is not a new phenomenon and was reported by social psychiatrists half a century ago in Langner & Michael’s 1963 New York study3 and consistently since then. Poverty is both a cause of mental health problems and a consequence. Poverty in childhood and among adults can cause poor mental health through social stresses, stigma and trauma. Equally, mental health problems can lead to impoverishment through loss of employment or underemployment, or fragmentation of social relationships. This vicious cycle is in reality even more complex, as many people with mental health problems move in and out of poverty, living precarious lives.
+Poverty and mental health
+The mental health of individuals is shaped by the social, environmental and economic conditions in which they are
+born, grow, work and age.4-7 Poverty and deprivation are key determinants of children’s social and behavioural devel-opment8,9 and adult mental health.10 In Scotland, individuals living in the most deprived areas report higher levels of mental ill health and lower levels of well-being than those living in the most affluent areas. In 2018 for example, 23% of men and 26% of women living in the most deprived areas of Scotland reported levels of mental distress indicative of a possible psychiatric disorder, compared with 12 and 16% of men and women living in the least deprived areas.11 There is also a clear relationship between area deprivation and suicide in Scotland, with suicides three times more likely in the least than in the most deprived areas.12
+Inequalities in mental health emerge early in life and become more pronounced throughout childhood. In one cohort study, 7.3% of 4-year-olds in the most deprived areas of Glasgow were rated by their teacher as displaying ‘abnormal’ social, behavioural and emotional difficulties, compared with only 4.1% in the least deprived areas. By age 7, the gap between these groups had widened substantially: 14.7% of children in the most deprived areas were rated as having ‘abnormal’ difficulties, compared with 3.6% of children in the least deprived.13 National data from parental ratings of children’s behaviour show a similar pattern: at around 4 years of age, 20% of children living in the most deprived areas of Scotland are rated as having ‘borderline’ or ‘abnormal’ levels of difficulties, compared with only 7% living in the least deprived areas.14
+These findings reflect a broader pattern of socioeconomic inequalities in health that is observed internationally.15 The
+primary causes of these inequalities are structural differences in socioeconomic groups’ access to economic, social and political resources, which in turn affect health through a range of more immediate environmental, psychological and behavioural processes.16,17 A wide range of risk factors are more prevalent among low income groups for example, including low levels of perceived control18 and unhealthy behaviours such as smoking and low levels of physical activity,11 although these are best understood as mechanisms that link the structural causes of inequality to health outcomes.17
+Excess mortality and mental health in Glasgow
+Glasgow has some of the highest Scottish rates of income deprivation, working-age adults claiming out of work benefits, and children living in low-income families.19 Moreover, the city also reports poor mental health, relative to the Scottish average, on a host of indicators, including lower mental well-being and life satisfaction, and higher rates of common mental health problems, prescriptions for anxiety, depression or psychosis, and greater numbers of patients with hospital admissions for psychiatric conditions.19
+These statistics are consistent with Glasgow’s overall health profile and high rates of mortality. Life expectancy in Glasgow is the lowest in Scotland. For example, men and women born in Glasgow in 2016-2018 can expect to live 3.6 and 2.7 fewer years respectively than the Scottish average.20 Within Glasgow, men and women living in the most deprived areas of the city can expect to live 13.5 and 10.7 fewer years respectively than those living in the least deprived areas.21
+The high level of mortality in Glasgow can largely be attributed to the effects of deprivation and poverty in the city, although high levels of excess mortality have also been recorded in Glasgow, meaning a significant level of mortality in excess of that which can be explained by deprivation. For example, premature mortality (deaths under 65 years of age) is 30% higher in Glasgow compared with Liverpool and Manchester, despite the similar levels of deprivation between these cities.22 Crucially, this excess premature mortality is in large part driven by higher rates of ‘deaths of despair’23 in Glasgow, namely deaths from suicide and alcohol- and drug-related causes.22
+It has been proposed that excess mortality in Glasgow can be explained by a number of historical processes that have rendered the city especially vulnerable to the hazardous effects of deprivation and poverty. These include the lagged effects of historically high levels of deprivation and overcrowding; regional policies that saw industry and sections of the population moved out of Glasgow; the nature of urban change in Glasgow during the post-war period and its effects on living conditions and social connections; and local government responses to UK policies during the 1980s.24 On the last point, Walsh and colleagues24 describe how the UK government introduced a host of neoliberal policies during this period - including rapid deindustrialisation -that had particularly adverse effects in cities such as Glasgow, Manchester and Liverpool. While Manchester and Liverpool were able to mitigate the negative effects of these national
+policies to some extent by pursuing urban regeneration and mobilising the political participation of citizens, there were fewer such efforts made in Glasgow, which contributed to the diverging health profiles of the cities.
+These researchers have also suggested that this excess mortality may partly reflect an inadequate measurement of deprivation.24 However, that does not capture the reality of living in poverty. One aspect of this lived experience that may be important is the experience of poverty-based stigma and discrimination.25 Stigma is a fundamental cause of health inequalities,26 and international evidence has demonstrated that poverty stigma is associated with poor mental health among low-income groups.27 Individuals living in socioeconomically deprived areas may also experience ‘spatial’ stigma, which similarly has a range of adverse health effects for residents28 and, crucially, may be unintentionally exacerbated by media and public health professionals’ reports of regional health inequalities.29 Given the continued focus on Glasgow’s relatively poor health it is possible that the city is more vulnerable to such stigmatising processes. However, we stress that additional research will be required to test whether stigma is an important aspect of the lived reality of poverty, particularly as several psychosocial explanations have already been offered for the excess mortality, with varying levels of supporting evidence.24 The notion of intersectional stigma is also gaining traction and requires further research.
+Understanding the life-course impact of poverty on mental health is also important. Childhood adversity is one mechanism through which poverty and deprivation have an impact on mental health. Adverse childhood experiences, such as exposure to abuse or household dysfunction, are relatively common in the population. Marryat & Frank examined the prevalence of seven adverse childhood experiences among children born in 2004-2005 in Scotland, and found that approximately two-thirds had experienced at least one adverse experience by age 8.30 Moreover, the prevalence was greatest in low-income households: only 1% of children in the highest-income households had four or more adverse childhood experiences, compared with 10.8% in the lowest-income households. Adverse childhood experiences are also strong predictors of mental health in adulthood: individuals who have experienced at least four are at a considerably greater risk of mental ill health, problematic alcohol use and drug misuse.31 It has also been suggested that experiences of childhood adversity and complex trauma may contribute to Glasgow’s - and Scotland’s - excess mortality, particularly that which is attributable to violence, suicide and alcohol and drug-related deaths.32 The implications are significant for psychiatry. Not only does it offer a broader explanation of causation; it also highlights the importance of supporting early interventions for young people’s mental health and supporting the families - including children -of those experiencing mental health problems.
+Implications
+When faced with the scale of the challenge the response can be daunting. This is especially so at a time when we see increasing poverty and socioeconomic inequalities within
+our society and challenging political conditions. The complexity and enduring nature of the problems necessitate a multilevel response from psychiatry across practice, policy, advocacy and research, which we explore in this section. We argue that this response should address three broad areas.
+law.36 These principles are already being put into action. For example across Scotland, including Glasgow, several general practices working in the most deprived areas (referred to as Deep End practices) have recently trialled the integration of money advice workers within primary care, which has generated considerable financial gains for patients.37
+Reinvigorate social psychiatry and influence public policy
+The demise of social psychiatry in the UK and USA in recent decades has deflected focus away from the social causes and consequences of mental health problems at the very time that social inequalities have been increasing. Now is the time to renew social psychiatry at professional and academic levels. There is considerable scope to form alliances with other areas - especially public mental health agencies and charities. Psychiatry as a profession should support those advocating for progressive public policies to reduce poverty and its impact. If we do not, then, as Phelan and colleagues outline, we will focus only on the intermediate causes of health inequalities, rather than the fundamental causes, and this will ensure that these inequalities persist and are reproduced over time.33 Activism with those who have consistently highlighted the links between poverty and mental health problems, such as The Equality Trust, may effect change among policy makers.
+Tackle intersectional stigma and disadvantage
+We must understand, research and tackle stigma in a much more sophisticated way by recognising that mental health stigma does not sit in isolation. We need to understand and address what Turan and colleagues define as intersectional stigma.34 Intersectional stigma explains the convergence of multiple stigmatised identities that can include ethnicity, gender, sexuality, poverty and health status. This can then magnify the impact on the person’s life. In this context, the reality is that you have a much greater chance of getting a mental health problem if you experience poverty. And if you do, then you will likely experience more stigma and discrimination. Its impact on your life will be greater, for example on precarious employment, housing, education and finances. It is harder to recover and the impact on family members may be magnified. Intersectional stigma remains poorly researched and understood,35 although the health impact of poverty stigma is now emerging as an important issue in studies in Glasgow and elsewhere.25
+Embed poverty-aware practice and commissioning
+We conclude with our third idea, to ensure that poverty-aware practice is embedded in services through commissioning, training and teaching. This means that recognising and responding to poverty is part of assessments and care. Income maximisation schemes should be available as an important dimension of healthcare: how to access benefits, manage debt, access local childcare and access support for employment at the earliest stages. This needs to be matched by a major investment in mental health services focused on low-income areas, to address the inverse care
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diff --git a/Predictors of future suicide attempt among adolescents.txt b/Predictors of future suicide attempt among adolescents.txt
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+Introduction
+Suicidal behaviour is a major public health concern in adolescents. Although suicidal thoughts and non-suicidal self-harm are strong predictors of suicide attempts, little is known about the factors that predict attempts in these high-risk groups. A better understanding of these factors is crucial for improved suicide prediction and prevention.
+Only a third of adolescents who have suicidal thoughts are estimated to go on to make a suicide attempt.1 Theoretical models of suicide, including the interpersonal theory,2 the integrated motivational-volitional model,3 and the three-step theory,4 are consistent with an ideation-to-action framework. This framework proposes that the factors involved in the development of suicidal thoughts are distinct from those involved in the transition from thoughts to attempts. Several large epidemiological and meta-analytical studies provide empirical support for this framework and have found that many well established
+risk factors for suicide (such as depression, impulsivity, and hopelessness) do not meaningfully differentiate individuals with suicidal thoughts from those who have made an attempt.15-7 According to a recent review,8 the factors that most consistently predict suicide attempts among people with ideation relate to suicide capability (ie, the degree to which an individual feels able to make a suicide attempt). In a previous study of more than 4500 adolescents,9 we explored a wide range of risk factors and found that exposure to self-harm in others, psychiatric disorders, and substance use most strongly distinguished between adolescents with suicidal thoughts and those who acted on those thoughts. However, like most previous studies exploring this issue,1,10-14 the analyses were crosssectional, and the extent to which these factors would predict future suicide attempts is currently unknown.
+Much of the scientific literature and theory exploring transitions to suicide attempts has focused on suicidal
+Research in context
+Evidence before this study
+Suicidal thoughts and non-suicidal self-harm are strongly associated with suicide attempts. However, the majority of adolescents who think about suicide or engage in non-suicidal self-harm will not make an attempt on their life. We searched PubMed for studies published in English before Dec 13, 2018, investigating risk factors for suicide attempts among these high-risk groups. We did two separate searches of the scientific literature. One search was for suicidal thoughts using the query (“suicidal thoughts” OR “suicidal ideation”) AND (“suicide attempt” OR “suicidal behaviour” OR “ideation to action”). The other search was for non-suicidal self-harm using the query (“non-suicidal self-harm” OR “non-suicidal self-injury” OR “NSSI”) AND (“suicide attempt” OR “suicidal behaviour”). We also checked citations of relevant publications and searched the reference lists of selected articles. Existing research suggests that many well established risk factors for suicide (such as depression, hopelessness, and impulsivity) do not predict suicide attempts among adolescents who have suicidal thoughts or engage in non-suicidal self-harm. Longitudinal studies investigating predictors of future suicide attempts in these high-risk groups are extremely scarce.
+Added value of this study
+This is the first population-based birth cohort study to explore predictors of future suicide attempts among adolescents who have suicidal thoughts or engage in non-suicidal self-harm. We were able to explore associations with a wide range of prospectively recorded risk factors from different domains. Previous studies have used either cross-sectional study designs (thereby limiting causal inference because they rely on recall of both risk factors and suicidal behaviour) or clinical (or atypical) cohorts with small sample sizes and few risk factor data. Among participants with suicidal thoughts, we found that the strongest predictors of transition to attempts were non-suicidal self-harm, cannabis use, other illicit drug use, exposure to self-harm, and higher levels of the personality type intellect/ openness. Among participants with non-suicidal self-harm at baseline, the strongest predictors were cannabis use, other illicit drug use, sleep problems, and lower levels of the personality type extraversion.
+Implications of all the available evidence
+Our findings could help practitioners to identify which adolescents are at greatest risk of attempting suicide in the future, which could lead to improved targeting of prevention and intervention strategies.
+thoughts. However, investigation of predictors of attempts among people who engage in non-suicidal selfharm is also important, because this factor is strongly associated with suicide attempt history and predicts future attempts in longitudinal studies.15-19 A metaanalysis of 52 studies (all using retrospective self-report) found that the strongest correlates of suicide attempts among adolescents who engaged in non-suicidal selfharm were suicidal ideation, hopelessness, and non-suicidal self-harm characteristics (frequency and number of methods).20 As found for suicidal thoughts, many often-cited risk factors for suicide were generally poor at distinguishing between adolescents with suicidal and non-suicidal self-harm. The only previous longitudinal study21 also found self-harm frequency to be an important predictor of suicidal behaviour among adolescents who engage in non-suicidal self-harm. Other factors identified were reduced social connectedness and sense of meaning in life, and increased levels of mental health treatment.
+An important limitation of previous research is a reliance on cross-sectional studies and the retrospective reporting of both risk factors and suicide-related outcomes. Such studies can be subject to recall bias, and the temporal direction of associations is often unclear. Longitudinal studies adopting an ideation-to-action framework are extremely scarce,8,22 and the few existing studies have been done in clinical or atypical samples (university students).21,23,24 We aimed to extend previous work by using longitudinal data to explore associations between a comprehensive range of prospectively recorded
+risk factors and first-time suicide attempts among adolescents with suicidal thoughts and non-suicidal selfharm. Associations were explored in a community-based sample that was more than twice as large as those used in previous longitudinal investigations.
+Methods
+Participants
+The Avon Longitudinal Study of Parents And Children (ALSPAC) is an ongoing population-based birth cohort study examining influences on health and development across the life course. The ALSPAC core enrolled sample consists of 14 541 pregnant women resident in the former county ofAvon in southwest England (UK), with expected delivery dates between April 1, 1991, and Dec 31, 1992.25,26 Of the 14062 livebirths, 13 798 were singletons or firstborn of twins and were alive at 1 year of age. Participants have been followed up regularly since recruitment through questionnaires and research clinics. The study website contains details of all the data that are available through a fully searchable data dictionary. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees.
+This investigation is based on the subsample of participants who completed a detailed self-report questionnaire on suicidal thoughts and self-harm at 16 and 21 years of age. Two samples were used for analysis. The first sample included adolescents who reported suicidal thoughts at baseline (n=456), assessed
+Articles
+with the question, “Have you ever thought of killing yourself, even if you would not really do it?”. The second sample included adolescents who reported non-suicidal self-harm at baseline (n=569), assessed with the question, “Have you ever hurt yourself on purpose in any way (eg, by taking an overdose of pills, or by cutting yourself)?”. Participants who reported having attempted suicide at the age of 16 years (n=325) were excluded to focus on predictors of first-time suicide attempts.
+Measures
+Participants were classified according to whether they reported having ever attempted suicide at 21 years of age. Individuals who indicated having self-harmed, which was assessed by answering “yes” to the question “have you ever hurt yourself on purpose in any way (eg, by
+taking an overdose of pills or by cutting yourself)?”, were then asked a series of follow-up questions to establish suicidal intent. Participants were classified as having selfharmed with suicidal intent if they either gave the answer “I wanted to die” when asked to give reasons for selfharming or answered “yes” to: “On any of the occasions when you have hurt yourself on purpose, have you ever seriously wanted to kill yourself?”. Suicide attempts were assessed in the same way at 16 years of age.
+A description of the risk factors examined in this study is provided in table 1. These risk factors are all known to be associated with self-harm, and their selection was informed by psychological models of suicide and by previous scientific literature. The risk factors included sex, intelligence quotient, executive function, impulsivity, sensation seeking, personality traits, exposure to self-harm in others,
+life events, early adversity, body dissatisfaction, sleep problems, psychiatric disorders, hopelessness, symptoms of depression, substance use, suicidal plans, and non-suicidal self-harm characteristics. All risk factors were assessed at or before the assessment at 16 years of age.
+Additional analyses controlled for the possible confounding effects of child sex and socioeconomic position. Socioeconomic position was assessed by a maternal questionnaire and included average weekly household disposable income recorded at the ages of 3 and 4 years; highest maternal or paternal social class, assessed during pregnancy (professional or managerial, or other); and highest maternal educational attainment, assessed during pregnancy (less than O level, O level, A level, or university degree).
+Statistical analysis
+We used logistic regression analyses to examine associations between prospectively recorded risk factors and suicide attempts reported at the age of 21 years. We adjusted for potentially confounding effects of sex and socioeconomic position, but we did not adjust for additional confounders because our aim was to identify potential risk factors for the transition to suicide attempts,
+rather than to build the most parsimonious prediction model. Continuous risk factors were standardised before analysis to create Z scores with a mean of0 and an SD of 1.
+Our analyses were done on an imputed dataset based on participants who reported suicidal thoughts (n=456) and non-suicidal self-harm (n=569) at baseline. We used multiple imputation by chained equations27,28 to generate 50 imputed datasets for each exposure of interest. This method assumes that data are missing at random, whereby any systematic differences between the missing and the observed values can be explained by differences in observed data. Comparison of the estimates from the complete case and imputed data analysis are presented in the appendix. For the non-suicidal group, we did a sensitivity analysis excluding individuals who reported having self-poisoned on the most recent self-harm occasion (appendix). We did all analyses using Stata, version 15.
+Role of the funding source
+The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
+Results
+Complete outcome data at 21 years of age were available for 310 participants with suicidal thoughts and 380 participants who had engaged in non-suicidal selfharm (figure). However, by use of the wealth of auxiliary data available in ALSPAC, we were able to impute up to the sample of adolescents with complete data on suicidal thoughts or non-suicidal self-harm at baseline. Participants with and without missing outcome data were found to be similar across a range of demographic variables (appendix); however, several differences were found between responders and non-responders to the self-harm questionnaire completed at 16 years of age (appendix). Participants who responded were more likely to be female and from more highly educated, affluent backgrounds. Findings were broadly consistent in the imputed and complete case analysis. 38 (12%) of 310 participants with suicidal thoughts and 46 (12%) of 380 participants who had engaged in non-suicidal self-harm reported having attempted suicide for the first time by the follow-up at 21 years of age. 107 participants reported both suicidal thoughts and non-suicidal self-harm at 16 years of age. Of these, 22 (21%) reported having attempted suicide by the follow-up at 21 years of age, compared with 32 (1%) of 2283 participants in the subsample who did not report either suicidal thoughts or non-suicidal self-harm at baseline (see the appendix for the prevalence of risk factors in this subgroup). Demographic information for the samples is shown in table 2.
+Table 3 shows associations between each risk factor and future suicide attempts among the subsample with suicidal thoughts at baseline. In both unadjusted and adjusted analyses, the strongest evidence for an association was found for cannabis use (adjusted odds ratio [OR] 2-61, 95% CI 1-11-6.14; p=0-029), other illicit drug use (2-47, 1-02-5-96; p=0-045), non-suicidal selfharm (2-78, 1-35-5-74; p=0-0059), and higher levels of the personality type intellect/openness (1-62, 1-06-2-46; p=0-025). There was also weak evidence of an association with exposure to self-harm in others (family member self-harm adjusted OR 2-03, 95% CI 0-93-4-44, p=0-076; friend self-harm 1-85, 0-93-3-69, p=0-081).
+Table 4 shows associations between each risk factor and future suicide attempts among the subsample with non-suicidal self-harm at baseline. In both unadjusted and adjusted analyses, the strongest evidence predicting the transition to suicide attempts was found for cannabis use (adjusted OR 2-14, 95% CI 1-04-4-41; p=0-038), other illicit drug use (2-17, 1-10-4-27; p=0-025), and insufficient sleep (1-97, 1-02-3-81; p=0-043). There was also weak evidence of an association with waking in the night (adjusted OR 1-91, 95% CI 0-93-4-44; p=0-069) and lower levels ofthe personality type extraversion (0-71, 0-49-1-03; p=0-068).
+A small proportion (15 [4%] of 380) of adolescents in the non-suicidal self-harm group reported having selfpoisoned on the most recent self-harm occasion; however,
+sensitivity analysis excluding these individuals did not change the pattern of results (appendix).
+Discussion
+To our knowledge, this is the largest longitudinal study to explore the transition to suicide attempts among adolescents with suicidal thoughts or non-suicidal selfharm. We identified several risk factors that predicted future suicide attempts in these high-risk groups. Among participants with suicidal thoughts at 16 years of age, future risk of suicide attempt was associated with non-suicidal self-harm history, cannabis use, other illicit drug use, higher intellect/openness score, and exposure to self-harm in others. This finding is consistent with a cross-sectional analysis of this cohort,9 which found substance use and exposure to self-harm differentiated between adolescents with suicidal thoughts and those who had attempted suicide at age 16 years. Both cannabis and other illicit drug use also predicted the transition to attempts among participants with non-suicidal self-harm at baseline, along with a lower extraversion score and sleep difficulties.
+Although some differences were found in the predictors of transition for participants with suicidal thoughts and those with non-suicidal self-harm at baseline, other illicit drug use and cannabis use were identified in both samples, suggesting that these factors might be particularly robust predictors of future suicide attempt risk. Consistent with our findings, a previous meta-analysis5 found drug use moderately distinguished between adolescents with suicidal thoughts and attempts. However, a separate meta-analysis20 did not find an association with attempts among adolescents with non-suicidal self-harm. It is possible that substances such as cannabis and other illicit drugs increase suicide capability by lowering inhibitions and impairing decision making. It is also possible that drug use leads to mental illness over time, and this mental illness leads to suicide attempts. Alternatively, substance use might be a proxy for particular types of coping in response to stress that are maladaptive. There is also evidence to suggest that there might be a bidirectional relationship; several longitudinal studies29-33 have reported an association between adolescent self-harm and substance use problems in adulthood. Notably, we did not find evidence for an association with alcohol use or smoking in either sample, which highlights the importance of exploring relationships with different substances independently. Future research should explore whether associations differ for different forms of illicit drug use (eg, injection drug use).
+Previous research suggests that non-suicidal self-harm is a robust predictor of future suicide attempts;15-19 however, non-suicidal self-harm has rarely been considered within an ideation-to-action framework. Our study extends previous work by demonstrating that non-suicidal self-harm is specifically associated with the transition from suicidal thinking to action. Several
+explanations for this association are possible, including shared neurobiological vulnerability to self-harm, an increased risk of social exclusion or mental illness as a result of non-suicidal self-harm,34 or a direct effect on reducing the inhibition to attempt suicide in the face of suicidal thoughts.2 Our findings are in line with those of a previous prospective community study of adolescents35 and indicate that those individuals who report both suicidal thoughts and non-suicidal self-harm might be an especially high-risk group. We found that approximately 1 in 5 (21%) of the adolescents who reported both suicidal thoughts and non-suicidal self-harm at baseline went on to make a suicide attempt, which compares with only 1% of those who did not report either of these behaviours. Despite the low prevalence, it is notable that this group accounted for approximately a quarter of participants who attempted suicide over the follow-up. In contrast to some previous studies,20,21 we did not find characteristics of non-suicidal self-harm (such as method and frequency) to be strong predictors of future suicide attempts. This difference could be due to methodological differences in sample or definition of non-suicidal self-harm: for example, the timeframe of assessment (past year vs lifetime) or method choice (lifetime vs most recent). Alternatively, we might have been underpowered to detect effects; however, our sample size is more than twice as large as the only other longitudinal study21 exploring predictors of concurrent and future suicide attempts among adolescents with non-suicidal self-harm.
+Other factors that were associated with future suicide attempts among participants with non-suicidal self-harm included sleep problems and a lower extraversion score. Both of these factors have been associated with suicidal behaviour in previous research;36-39 however, our study is the first to explore prospectively the role of sleep difficulties and personality traits in the transition to suicide attempts over time. It might be that individuals who are less extraverted are more socially disconnected, which has been shown to predict future suicide attempts in a sample of university students with non-suicidal selfharm.21 Sleep problems could affect feelings of social connection by impairing an individual’s ability and motivation to interact with others.40,41 They might also have a more direct effect on suicide risk, leading to increased distress at a time when fewer social supports are available.
+A growing number of cross-sectional studies have found that exposure to self-harm in others differentiates between adolescents with suicidal ideation and attempts.9,10,12,13,42 In this study, we found weak evidence to suggest that exposure to self-harm also predicts future suicide attempts in adolescents who have thought about suicide, but not among those who have been engaged in non-suicidal self-harm. One explanation is that self-harm exposure might increase the capability of suicide among adolescents with suicidal thoughts by increasing the
+salience and acceptability of self-harm (eg, increased awareness of self-harm as an option, its functional utility, and knowledge of methods),43 whereas those who have engaged in non-suicidal self-harm are already aware of self-harm methods. Further research will need to investigate the mechanisms by which exposure to selfharm in others increases the risk of suicide attempts. Potential candidate mechanisms include genetic influences, social transmission, imitation, and assortative relating among people at high risk.
+It might appear surprising that we did not find evidence of an association for several well established suicide risk factors, including depression symptoms, psychiatric disorder, suicidal plans, and impulsivity. However, our results are consistent with previous research5,20 that has suggested that these factors appear to be associated with suicide attempts because they are associated with the development of suicidal thoughts or non-suicidal selfharm, but are not involved in the transition. An alternative methodological explanation for this negative finding could be that we (in common with other large epidemiological studies) did not measure symptoms immediately before the suicide attempt, when there might have been a stronger association than at 16 years of age. The CIs for some predictors are also wide, and it is possible there is an association that we were underpowered to detect.
+This study has many strengths, including the large population-based sample, longitudinal design, and ability to explore a wide range of prospectively recorded risk factors. The vast majority of research in this area has been cross-sectional, and therefore limited by retrospective reporting of both risk factors and outcomes. We also excluded people with a previous suicide attempt at baseline, which enabled us to establish the direction of effects between our measures and ensure that we were not modelling risk for repeat suicide attempts.
+There are also several limitations to consider. First, it cannot be assumed that the associations identified in this study are causal. We adjusted only for two confounding variables (sex and socioeconomic position); however, it was not our aim to identify independent predictors, and to examine this adequately would require a separate theory-driven analytical model for each exposure. This analysis was beyond the scope of the current paper, but is an important area for future research. Second, information was not available on the date of the first suicide attempt. We therefore focused on risk factors that occurred at or before the age 16 years’ assessment to ensure that they preceded the outcome. 5 years is a relatively long follow-up period, and risk factors that predict the transition to suicide attempts over the short term might differ from those that predict over the long term. Newly emerging methods of data collection, such as Ecological Momentary Assessment, could be used in future studies to explore predictors of transitions over a shorter timeframe (ie, hours, days, or weeks). Third, we
+excluded individuals who had attempted suicide before the age of 16 years so that we could examine predictors of incident suicide attempts. Although we consider this approach to be a strength of the study, it is possible that it weakened associations with some of our risk factors. For example, if a particular risk factor is strongly associated with suicide attempts (eg, suicidal plans), then it is more likely that individuals with that risk factor would have already attempted suicide, and therefore been excluded from the analyses. This means that our findings might not be applicable to individuals who have already attempted suicide by the age of 16 years. However, identifying individuals who will make a first attempt in late adolescence or young adulthood is important, because this is the age at which hospital presentations for self-harm are at their highest.44 Further longitudinal research is needed to explore whether there are differences in the risk factors for incident and repeat suicide attempts among individuals with current suicidal ideation. Fourth, we did not correct for multiple testing as analyses were exploratory. Our results are therefore in need of replication, given the large number of tests done. Finally, as with all longitudinal studies, there was some attrition over time that might have biased our complete case analyses. However, findings were similar using imputed data, suggesting that the effects of this potential bias were not substantial. Although we cannot say with certainty that our data are missing at random, ALSPAC contains a wealth of auxiliary data, which increases the plausibility of this assumption. There were also some differences between those individuals who did and did not respond to the age 16 years’ self-harm questionnaire and this non-random response might limit the generalisability of our results.
+Identification of factors that predict the transition from suicidal thoughts or non-suicidal self-harm to suicide attempts is crucial for improved suicide prediction and prevention. Although results of existing cross-sectional research have provided important information about the factors that differentiate between individuals with suicidal thoughts or non-suicidal self-harm and those with attempts, longitudinal studies such as this are required to investigate whether the identified factors predict the transition to attempts over time. Our findings suggest that asking about factors such as substance use, non-suicidal self-harm, sleep, personality traits, and exposure to self-harm might help clinicians to identify which adolescents are at greatest risk of attempting suicide in the future.
+Contributors
+BM, JH, EDK, PM, RCO’C, PW, KT, and DG contributed to conception and design of the study. BM, JH, and DG contributed to the organisation of the conduct of the study. BM carried out the study (including acquisition of data), analysed data, and drafted the output. BM, JH, EDK, PM, RCO’C, PW, KT, and DG contributed to interpretation of data.
+JH, EDK, PM, RCO’C, PW, KT, and DG critiqued the output for important intellectual content. All authors have read and approved the final version of the manuscript. BM serves as guarantor for the contents of this paper.
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diff --git a/Prevalence of Suicide in ImmigrantsRefugees  A Systematic Review and Meta-Analysis.txt b/Prevalence of Suicide in ImmigrantsRefugees  A Systematic Review and Meta-Analysis.txt
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+Routledge
+Taylor & Francis Group
+INTRODUCTION
+Suicide is one of the major health problems in the world, and it leads to one million deaths annually (World Health Organization, n.d.a, n.d.b). It has been noted that 1.3% of all deaths worldwide are attributed to suicide, and it is also the second leading cause of death at ages 15 to 29 (Saxena, Krug, Chestnov, & World Health Organization, 2014). The other two dimensions of suicide behaviors are suicide attempts and suicide ideation, which are closely related to suicide death (Klonsky, May, & Saffer, 2016). In some studies, the lifetime prevalence of suicidal ideation was 18.49% (Lee et al., 2010).
+In a study of 17 countries, the prevalence of suicide ideation was 9.2% and prevalence of suicide attempt was approximately one-third, 2.7% (Nock et al., 2008).
+Many personal and social factors have been addressed in the study of the causes of suicide behaviors (Franklin et al., 2017), such as depression and hopelessness (Ribeiro, Huang, Fox, & Franklin, 2018), mental disorder (Gili et al., 2019; Nock et al., 2009), interpersonal violence (Castellvi, Miranda-Mendizabal, & Pares-Badell, 2017), biological factors (Chang et al., 2016), and substance use disorder (Poorolajal, Haghtalab, Farhadi, & Darvishi, 2016). In addition to individual factors, other demographic, social, and political factors are closely related to the dimensions of suicide; these risk factors include sex, marital status, education, age, race, social capital, cultural continuity, religion, social ties, and political and economic changes (Chandler & Lalonde, 1998; Conwell, Duberstein, & Caine, 2002; Kessler, Borges, & Walters, 1999; Kolves, Milner, & Varnik, 2013; Lawrence, Brent, & Mann, 2016; Mignone & O’Neil, 2005; Petronis, Samuels, Moscicki, & Anthony, 1990; Sorenson & Golding, 1988). Suicide is one of the health problems that is increasing in minority and non-indigenous groups (Dickson, Cruise, McCall, & Taylor, 2019). With increasing numbers of immigrant populations around the word, attention to their mental health has increased (Abbott, 2016; Derr, 2016; Forte et al., 2018; Straiton et al., 2014).
+There has been a significant increase in migration over the last few decades, and it was estimated that 260 million migrants were in the world in 2017 including refugees (United Nations Department of Economic and Social Affairs (DESA) PD, 2017). According to a 2003 report, there are about 13 million refugees in the world (US Committee for Refugees, 2004). Refugees are at higher risk for psychiatric problems (Hollifield et al., 2002), traumas, and mental health problems (Allden et al., 1996; Eisenman, Gelberg, Liu, & Shapiro, 2003; Jaranson et al., 2004). For example, a study of refugee populations in western countries found that the prevalence of post-traumatic stress disorder in this population is 10 times higher than in the general population (Fazel, Wheeler, & Danesh, 2005). In recent years, studies have tended to examine the dimension of mental health in immigrants. Accordingly, studies showed the prevalence of mental health issues in immigrants (Anderson, Hatch, Comacchio, & Howard, 2017; Butler, Warfa, Khatib, & Bhui, 2015; Jurado et al., 2017). Review studies have also examined mental health in immigrants including postnatal depression (Wittkowski, Patel, & Fox, 2017), mental health service use (Derr, 2016), prevalence of mental disorders (Mills, Singh, Roach, & Chong, 2008), depression and anxiety (Falah-Hassani, Shiri, Vigod, & Dennis, 2015; Foo et al., 2018; Lindert, Ehrenstein, Priebe, Mielck, & Brahler, 2009), mental disorders in refugees (Kien et al., 2019), psychotic disorders (Bourque, van der Ven, & Malla, 2011), and serious mental disorder (Fazel et al., 2005).
+Some studies have examined the dimensions and prevalence of suicide in immigrants (Bursztein Lipsicas et al., 2013; Forte et al., 2018; Voracek & Loibl, 2008). Although a number of studies have examined the dimensions of suicide in immigrants and refugees, critical review has revealed several important points and so it became necessary to investigate the prevalence of suicide dimensions in immigrants. First, those studies that examined suicide in immigrants are merely reviewing the literature and did not provided an accurate account of the global prevalence of suicide among immigrants. Second, these studies were more limited to one continent or one country and did not
+provide a global prevalence of suicide in immigrants. Third, studies have not examined all types of suicide behaviors such as suicide ideation, suicide mortality, suicide attempts, and plan of suicide. Last, it is necessary to distinguish between men and women, first- and second-generation immigrants, as well as immigrants and refugees.
+Therefore, this study aims to present the global prevalence of suicide between immigrants and refugees as well as to report the prevalence of suicide ideation, suicide mortality, suicide attempts, and plan of suicide and ultimately to identify differences between men and women, different generations, and types of immigrations. Finally, it reports an indicator of the risk of suicide among immigrants compared to the native population.
+METHOD
+Protocol and Search
+Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA’s) rules were used as a guide in the current research path (Moher, Liberati, Tetzlaff, Altman, & The PRISMA Group, 2009). A suite of keywords related to suicide and immigration are summarized and two online databases, PubMed and EMBASE, were targeted for the study until December 2019.
+Eligibility Criteria
+(1) Any original study that has documented the prevalence of suicide or types of suicide among external immigrants, refugees, or asylum seekers is eligible. (2) Studies have reported one of these indictors: the prevalence of suicide or odds ratio and the risk ratio of suicide in immigrants or refugees relative to the native population. (3) Studies with these conditions were excluded: the sample size was fewer than 50 participants; studies did not have a reference group (native population); several studies that published from a single database; one study remained and others were excluded; studies on minority groups were not eligible; and also review studies.
+Collected Data
+The information required for the current study, which is included in Table 1, is as follows: In the first step, information about the study authors, the country and continent under study, the design used in the study, the sex composition of participants, the size of the population, suicide types, and the methods of suicide assessment were provided. In the second step, the results of prevalence or indictors of the association between immigrations and suicide were extracted along with the variables that were controlled.
+Qualitative Measurement
+Effective Public Health Practice Project Criteria (Armijo-Olivo, Stiles, Hagen, Biondo, & Cummings, 2012) has several components to evaluate the quality of studies, which is a
+comprehensive tool. In this meta-analysis, three adjusted components were used and the other components were not required for this study.
+STATISTICAL ANALYSIS
+The event of suicide was recorded in eligible studies, including deaths from suicide, suicide ideation, plan of suicide, and suicide attempts; sample size was also recorded. The “Metaprop” command in STATA was used to determine the prevalence of suicide. The same path was applied to subgroups at several levels. The pooled odds ratio and confidence interval (CI) was used to investigate the suicide ratio among immigrants and refugees compared to the native population by random effects. In examination of heterogeneity, two statistical tests (%2 and I2) calculated which represent the size of the heterogeneity, which also examined in the subgroups to show changes in heterogeneity (Higgins & Thompson, 2002; Ioannidis, Patsopoulos, & Evangelou, 2007). In the measurement of publication bias, the presence or absence of symmetry in the funnel plot is a sign that reflects the publication bias; its significance can be assessed through the Begg test as well as Egger test; in the following one can see how many studies there must be in order to reach a symmetry, and this route was performed by trim-and-fill (Begg & Mazumdar, 1994; Duval & Tweedie, 2000a, 2000b; Egger, Davey Smith, Schneider, & Minder, 1997).
+RESULTS
+Study Selection
+In the first step, 788 articles were collected from PubMed and 1,475 articles from EMBASE. In screening studies showed in Figure 1, the PRISMA guide has been used as follows: In the second step, namely the elimination of duplicate studies, there were 134 articles declined (2,109 records remained). In the third step, articles were excluded by title and abstracts; the results were that 1,308 articles were deleted (801 records remained). In the fourth step, qualitative synthesis was started on the full text of the articles; the result is that 89 articles were excluded because they did not have the necessary criteria. Thus, 51 studies reached meta-analysis (Al-Maskari et al., 2011; Bauwelinck, Deboosere, Willaert, & Vandenheede, 2017; Bayard-Burfield, Sundquist, Johansson, & Traskman-Bendz, 1999; Betancourt, Newnham, Birman, Lee, Ellis, & Layne, 2017; Beutel et al., 2016; Bhui et al., 2006; Borges, Orozco, Rafful, Miller, & Breslau, 2012; Borges et al., 2011; Chau, Kabuth, & Chau, 2016; Cho & Haslam, 2010; Cochran et al., 2013; Deckert, Winkler, Meisinger, Heier, & Becher, 2015; Di Thiene, Alexanderson, Tinghog, La Torre, & Mittendorfer-Rutz, 2015; Donath, Bergmann, Kliem, Hillemacher, & Baier, 2019; Falb, McCormick, Hemenway, Anfinson, & Silverman, 2013; Ferrada-Noli, Asberg, Ormstad, & Nordstrom, 1995; Fortuna et al., 2016; Goosen et al., 2011; Hahm et al., 2013; Haukka, Suvisaari, Sarvimaki, & Martikainen, 2017; Hettige, Bani-Fatemi, Kennedy, & De Luca, 2017; Johansson, Sundquist, Johansson, & Bergman, 1997; Kennedy, Parhar, Samra, & Gorzalka, 2005; Kliewer & Ward, 1988; Kõlves & De Leo, 2015; Kosidou et al., 2012; Kposowa, McElvain, & Breault, 2008; Kwan & Ip, 2007; Lane & Miranda, 2018; Lipsicas et al., 2014; Lurie, Barnea, Caspi, Olmer, & Baruch, 2020; McMahon et al., 2017; Mirsky, Kohn, Dolberg, & Levav, 2011; Nasseri & Moulton, 2011; Norredam, Olsbjerg, Petersen, Laursen, & Krasnik, 2013; Pan & Carpiano, 2013; Park, Rim, & Jun, 2018; Peõa et al.,
+2008; Pignon et al., 2018; Puzo, Mehlum, & Qin, 2017; Radeloff et al., 2017; Rahman & Hafeez, 2003; Saunders et al., 2017; Shah, Lindesay, & Dennis, 2009; Singh & Siahpush, 2001; Sohn et al., 2019; Sorenson & Golding, 1988; Stirbu, Kunst, Bos, & Mackenbach, 2006; Termorshuizen, Braam, & van Ameijden, 2015; van Bergen, Eikelenboom, & van de Looij-Jansen, 2018; Vazsonyi, Mikuska, & Gassova, 2017).
+Quality Assessment
+Three dimensions of quality assessment are reported in Table 1. In all three dimensions, evaluation results have shown that most studies are of high quality.
+Prevalence of Suicide
+Figure 2 shows the overall suicide prevalence in immigrations/refugees. The prevalence of suicide was 2% (CI: 0.02-0.03, I2 = 99.2%).
+The prevalence of suicide mortality was very low, 0.002% (CI: 0.00-0.002, I2 = 97.0%). The prevalence of suicidal ideation was higher than all suicide types and was
+16% (CI: 0.12-0.20, I2 = 99.4%). The prevalence of attempted suicide was 6% (CI: 0.05-0.08, I2 = 98.0%). The prevalence of suicide plan was 4% (CI: 0.00-0.08, I2 = 96.8%) (Figure 3).
+The prevalence of suicide mortality was very low in both sexes. The prevalence of suicidal ideation was 10% (CI: 0.04-0.17, I2 = 0.0%) in men and 17% (CI: 0.10-0.24, I2 = 96.8%) in women. The prevalence of attempted suicide was 1% (CI: 0.01-0.02, I2 = 0.0%) in men and 7% (CI: 0.03-0.10, I2 = 94.4%) in women (Figure 4).
+Figure 5 depicts the prevalence of suicide based on first- and second-generation status. The prevalence of suicide in the first generation was equal to 7% (CI: 0.05-0.08, I2 = 98.8%). The prevalence of suicide in the second generation was equal to 7% (CI: 0.05-0.10, I2 = 98.9%).
+The prevalence of suicide in immigrants was equal to 2% (CI: 0.02-0.02, I2 = 99.3%). The prevalence of suicide in refugees was equal to 10% (CI: 0.05-0.15, I2 = 95.2%) (Figure 6).
+Figure 7 shows the prevalence of suicide in immigrants/refugees by continent. In Europe, the prevalence of suicide among immigrants was 1% (CI: 0.01-0.01; I2 =
+99.4%). In America, the prevalence among immigrants was higher and was 6% (CI: 0.05-0.08; I2 = 98.5%). Finally, in Asia the prevalence of suicide in immigrants was 13% (CI: 0.07-0.19; I2 = 96.4%).
+ASSOCIATION BETWEEN IMMIGRATION AND SUICIDE
+Figure 8 shows the odds of suicide among immigrants/refugees compared to the native population. The odds ratio of suicide mortality among immigrants was lower than
+among the native population and was 0.91 (CI: 0.90-0.93, p < 0.001; I2 = 97.6%). The odds ratio of suicide ideation among immigrants was 1.04 (CI: 0.99-1.09, p = 0.136; I2 = 93.0%). The odds ratio of attempted suicide among immigrants was higher than among the native population and was 1.15 (CI: 1.10-1.20, p < 0.001; I2 = 92.0%). The odds ratio of suicide plan among immigrants was higher than in the native population and was 1.36 (CI: 1.20-1.54, p < 0.001; I2 = 68.1%).
+The odds ratio of suicide mortality among women immigrants was lower than in the women native population and was 0.84 (CI: 0.79-0.99, p < 0.041; I2 = 91.4%), and the odds ratio of suicide ideation result was 1.16 (CI: 1.03-1.31, p < 0.018; I2 = 32.2%). In the men, the result was non-significant (Figure 9).
+Figure 10 shows that first- and second-generation immigrants are not significantly different from the native population in suicide.
+SENSITIVITY ANALYSIS
+After excluding two studies with a sample size well above the rest of the studies, the overall prevalence of suicide reached 10% (CI: 0.09-0.11, I2 = 99.1%).
+HETEROGENEITY AND PUBLICATION BIAS
+The amount of heterogeneity in studies that reported suicide prevalence was 99.2%. This degree of heterogeneity is high (Higgins, Thompson, Deeks, & Altman, 2003). v2 result was equal with 3,607.72 (df = 28) with p < 0.001. In the publication bias, 39 studies that reported odds ratio for association between immigration and suicide were examined; Begg’s test (p = 0.933) (Egger test; p = 0.936) rejected publication bias (funnel plot in Figure 11). In trim-and-fill (Duval & Tweedie, 2000b), no missing studies were found (odds ratio = 0.99, CI: 0.89-1.09).
+DISCUSSION
+The global prevalence of suicide in immigrations/refugees was 2%. But the prevalence of suicide ideation was highest, followed by suicide attempts and suicide planning. That suicide ideation and suicide attempts are more common than suicide mortality may be
+because suicide ideation and attempts are the most important predictors of suicide mortality (McKenzie, Bhui, Nanchahal, & Blizard, 2008). People who eventually die by suicide have had these ideations and attempts before, but some of them who have these ideations and attempts ultimately die by suicide, not all of them. In this regard, the report of the World Health Organization (WHO) states that previous suicide attempts are one of the most important factors of suicide deaths (World Health Organization, 2017), and another study has shown that the suicide ideation is the prelude to suicide attempts (Nock et al., 2013). Therefore, one of the reasons for the higher prevalence of suicidal ideation and suicide attempts compared to suicide deaths is that not all people who have suicidal ideation achieve suicide-related deaths.
+The odds of suicide were lower in immigrants/refugees than in the native population, while immigrants were more likely to attempt suicide and plan of suicide. Overall, it seems to be that immigrants/refugees are more likely to experience some suicide dimensions than native populations. This view is in line with the WHO statement that has listed suicide risk factors that put immigrants and refugees at risk of suicide
+(World Health Organization, 2014). Stress caused by displacement and relocation of the migrants may be a mechanism that increases the risk of suicide, as studies have provided evidence to this issue (Berry & Kim, 1988; Hovey & King, 1997). Another mechanism that could lead to a higher prevalence of suicide in immigrants and increase risk in this population is related to the experience of depression and its symptoms in this group, as prevalence studies have shown that 15.6% of migrants have depression (Foo et al., 2018).
+The prevalence of suicide ideation and suicide attempts by sex showed that women have a higher prevalence of suicide ideation and suicide attempts than men. As to why suicide is more prevalent in women, one factor could be that women are almost twice as likely to have major depression than men, and this disorder is the root cause of nearly half of all suicides (Alonso et al., 2004; Qin, Agerbo, Westergard-Nielsen, Eriksson, & Mortensen, 2000). However, other studies indicate serious suicide attempts in men against women (Freeman et al., 2017), but one important point to note is that
+suicide risk factors in men and women can be very different in situations (Qin et al., 2000), and this may play a role in the differences between the two sexes.
+Another finding of the study showed that the prevalence of suicide in refugees was 10% and was five times higher than in the immigrant group. This finding is consistent with some studies that have indicated a higher prevalence of psychiatric disorders and
+higher exposure to traumatic life events in refugees (Close et al., 2016; Steel et al., 2009). It should be borne in mind that immigrants may have migrated at their own discretion but refugees have to migrate, and this compulsion itself leads to a negative psychological burden. Post-immigrant conditions are a variable for refugees that affect their mental health (Li, Liddell, & Nickerson, 2016). Refugees are displaced by factors such as
+war, conflict, and violence and these have adverse effects on the mental health of these groups (Fazel et al., 2005). Therefore, a higher prevalence of suicide dimensions in refugee populations seems to be possible compared to immigrants. Because the immigrant population after migration may be better placed in terms of prosperity, on the other hand, their migration was more voluntary than refugees who were forced to emigrate. Therefore, as the study of the refugees population has shown, the mental health status of this population is affected by a set of factors. Therefore, in comparing the population of refugees and immigrants (for work, education, etc.), it is necessary to have a complete view of the effective factors.
+The prevalence of suicide in immigrants in Asia was 13%, in America 6%, and in Europe 1%. What can be seen is that suicide prevalence among immigrants who migrated to Asian countries was much higher than in Europe and almost twice as high as in America. One factor that can explain the higher prevalence of suicide is the existence of economic and social disparities; for example, low income levels and also unemployment are contributing to suicide (Andres & Halicioglu, 2010; Zammit et al., 2014), and immigrants in Asian countries may have more economic problems, which exposes them to suicide behaviors. On the other hand, according to the WHO, more than 75% of suicides in worldwide are in low- and middle-income countries (Saxena et al., 2014). In addition to the economic factors influencing the prevalence of suicide in immigrants and refugees, there are also important social and political factors. For example, the perception of racism is an influential factor in suicidal ideation (Walker, Salami, Carter, & Flowers, 2014; Wang et al., 2018); also discrimination is associated with suicidal ideation, regardless of gender or ethnicity (Assari, Lankarani, & Caldwell, 2017; Li et al., 2018). What these findings suggest is that a comprehensive view consisting of a set of factors in the study of the prevalence of suicide in immigrants and refugees should be considered. Domestic violence against women is widespread in the immigrant population (Erez, 2000; O’Donnell, Smith, & Madison, 2002), and this violence is a factor in suicidal behavior (Colucci & Montesinos, 2013).
+STRENGTHS AND LIMITATIONS
+The current study examined the prevalence of suicide and its variants in immigrants and refugees, a comprehensive study of its kind. It also provided details results on sex differences and prevalence across continents and generations. An important strength in the current study is a large population of meta-analysis. The results of this study can be useful in defining clinical and health policies for immigrant and refugee populations, as this study has a wide range of dimensions and the results can be highly generalizable. One important limitation was methodological, due to the high heterogeneity at the study level. One limitation is the lack of control over some variables in some studies in the meta-analysis. Another limitation is that the review focuses on prevalence/epidemi-ology, while a similar review but on risk and protective factors in these populations much also be made, accompanied by qualitative research, to help understand the differences found in this epidemiological review. Finally, it should be noted that only one study in the present meta-analysis was reported on refugees, and studies on refugees
+were limited. Therefore, it is necessary to pay more attention to these groups in the dimensions of epidemiology and etiology in future research.
+CONCLUSION
+The current study comprehensively examined the dimensions of suicide in the immigrant and refugee population in order to obtain the dimensions of the prevalence of suicide in this population. Therefore, due to the prevalence of various types of suicide behaviors in the immigrant and refugee population and the odds of suicide in these, it is necessary to continuously examine the health prospects of this population in the host countries. In addition, the findings of this study, along with other studies, could be used to develop health policies.
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diff --git a/Process-evaluation-of-a-district-mental-healthcare-plan-in-Nepal-A-mixedmethods-case-studyBJPsych-Open.txt b/Process-evaluation-of-a-district-mental-healthcare-plan-in-Nepal-A-mixedmethods-case-studyBJPsych-Open.txt
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+BJPsych Open (2020)
+6, e77, 1-11. doi: 10.1192/bjO.2020.60
+Background
+Globally, mental, neurological and substance-use disorders are among the leading causes of disability, contributing to 10.4% global disability-adjusted life-years.1 The burden of these disorders has consistently risen in the context of major demographic and socio-political transitions.2 Although there is an increasing evidence base for mental health interventions there is a significant gap between the number of people in need of mental healthcare and those actually receiving treatment. A recent World Health Organization (WHO) World Mental Health Survey reported that 86.3% of people with anxiety, mood or substance disorders in low- and middle-income countries (LMICs) have not received any treatment in the 12 months preceding the survey.3 Among those who receive treatment, only a few get adequate treatment.4 A recent study conducted in 21 countries reported that 1 out of 27 people living with depressive disorder in LMICs receives minimally adequate treatment.5
+In Nepal, there is no nationally representative data on prevalence of mental disorders, however, studies conducted with specific populations or populations affected by conflict or humanitarian emergency reported high prevalence rates of mental disorders (i.e. depression, 14.0-80%; anxiety, 22.9-81.0%; and alcohol use disorder (AUD) 1.5-25%)6,7 and access to mental health services is extremely low (i.e. only 8.1% people with depression and 5.1% people with AUD received treatment from any providers in the past 12 months).8
+Mental Health Gap Action Programme
+Evidence shows that mental health services can be delivered effectively by trained non-specialists healthcare providers through community
+based programmes.9,10 The integration of mental health services in community and primary healthcare (PHC) settings has also been advocated as a strategy to reduce the treatment gap, particularly in LMICs, where mental health specialists are limited. The WHO launched the Mental Health Gap Action Programme (mhGAP) in 2008 and the mhGAP Intervention Guide in 2010,11 with the aim of providing evidence-based clinical guidance to PHC workers for detection, diagnosis and treatment of mental disorders in primary care. As part of PRogramme for Improving Mental Health CarE (PRIME),12 we developed a mhGAP-based district mental healthcare plan (MHCP) by involving a wide range of stakeholders. The MHCP comprised intervention packages to be implemented at community, health facilities and health service organisation levels.13
+The community-level intervention packages included a community sensitisation programme, case detection in the community by using the Community Informant Detection Tool (CIDT),14 treatment adherence support through home-based care and community counselling. The health-facility-level packages included training and supervision of healthcare providers to detect, diagnose and treat mental disorders based on the WHO mhGAP Intervention Guide.11 The health-organisation-level packages included human resource mobilisation, procurement and supply of psychotropic medicines and referrals for specialised care. Details of the MHCP components are published elsewhere.13
+Aims
+Our prior studies demonstrated a significant impact of the district MHCP on treatment coverage, detection of mental disorders in
+primary care and initiation of minimally adequate treatment after diagnosis and small-to-moderate effect sizes on individual-level treatment outcomes after introduction of the district MHCP.15 These research findings and the available literature on mental healthcare describe what ‘works and what did not work’, but there is a lack of knowledge on how a particular intervention was implemented taking into account (possible) barriers and facilitating factors. This paper aims to describe the implementation process, particularly the barriers and facilitators of the district MHCP, and evaluate the measures related to the implementation process defined by the theory of change (ToC).
+Method
+Setting
+Nepal, one of the poorest countries in South Asia, has a total population of approximately 26.4 million and life expectancy at birth of 69.1 years. Nepal’s gross national income per capita at purchasing power parity was $2500 in 2017, ranking 193 out of 226 countries. The district MHCP was implemented in Chitwan, a district in southern Nepal. The total population of Chitwan is 579 984 with a literacy rate of 77%, which is higher than the national average of 57%. Although a variety of caste/ethnic groups reside in Chitwan, Brahmin (28.6%), Chhetri (11.4%) and Tharu (10.9%) are the dominant groups. Chitwan district was chosen as mental health specialists are available in the district hospital and private hospitals. The MHCP was implemented in three overlapping phases: pilot testing (2 health facilities), implementation and evaluation (10 health facilities) and scaling up (34 health facilities).
+Study design
+We used the ToC approach16 to develop the district MHCP and an evaluation framework.17 A ToC describes how a programme or an intervention brings desired long-term outcomes through a logical sequence of short-term and intermediate outcomes.18 In recent years, ToC has increasingly been used for designing and refining interventions, and as a framework for evaluation.19 We conducted four ToC workshops with national and district-level stakeholders including mental health specialists and primary care workers in order to develop the MHCP, and the related evaluation frame-work.20 In the first two ToC workshops (district-level stakeholders, n =14 and policymakers, n =10), we determined short-term and intermediate outcomes, interventions and assumptions to achieve the overall impact of the district plan. Stakeholders in the last two ToC workshops (district-level stakeholders, n =11 and policymakers, n = 8) defined indicators to measure each of the MHCP intermediate outcomes. These indicators were used to assess whether key stages in the causal pathways of MHCP are achieved. Details of the ToC can be found in Breuer et al.20
+The MHCP was evaluated using multiple methods, including pre- and post-community- and health-facility-based surveys, cohort studies and process evaluations of implementation of the care plans.1 Pre- and post-cross-sectional community surveys were conducted to assess changes in treatment contact coverage, pre- and post-facility-based surveys were conducted to measure changes in detection and initiation of minimally adequate treatment by trained PHC workers. The cohort studies were done to assess the impact of mental health services on patients’ clinical, social and economic outcomes. The results of these studies are reported else-where.15 The implementation process, particularly the barriers and facilitators of the MHCP, and indicators related to the implementation process, was evaluated using a case study method.17
+The case study evaluated the input and process indicators defined by the ToC,21 which are not otherwise captured by the community, facility detection surveys and cohort studies described in the paragraph above.15 The case study assessed; (a) the social, political, economic and cultural context that may affect the implementation of the MHCP; (b) the availability of physical, human and financial resources required for the implementation of the MHCP; (c) the implementation process of the MHCP including reach and coverage of the services; (d) the training and supervision of the service providers implementing the MHCP; (e) the perspectives of service providers, patients and caregivers on the acceptability and feasibility of the services; and (f) the barriers and facilitating factors for the implementation of the care plan. A range of qualitative and quantitative methods were used in data collection including; (a) district and community profiles, (b) health facility profiles, (c) monthly implementation logs, (d) training and supervision evaluation, and (e) in-depth qualitative interviews. Details of the different methods are presented in Table 1.
+Data analysis
+The data were analysed using the following methods. Descriptive statistics such as percentages and proportions were used to analyse the quantitative process and input data from the facility, community and district profiles and the implementation logs. For the training evaluation data, we compared calculated percentages of correct response for knowledge and mean scores for attitude and efficacy, and individual items scores were calculated for ENhancing Assessment of Common Therapeutic factors.24 Pearson correlation and paired t-test were used to test changes between pre- and post-training evaluation. The qualitative interviews were audio-recorded first and transcribed in the original language (Nepali) by the interviewers. The transcriptions were then translated into English by professional translators. Qualitative data were analysed thematic content analysis methods using NVivo.
+Ethics
+This study received ethical approval from the Nepal Health Research Council (Ref. No. 162/2015), the national ethical body of the government of Nepal; the ethical review board of WHO Geneva, and the University of Cape Town (HREC Ref: 412/2011). Written and oral information was provided to each of the study participants about the objectives and process of the study. Consent was also obtained from facility managers to use health-facility-level data. Participants provided written consent to confirm their participation. Only those people who voluntarily agreed to participate were included in the study. As interviews were planned to be audio-recorded, this was explicitly included in the informed consent procedure.
+Results
+What was achieved?
+Mental health case-load in PHC
+Figure 1 presents the proportion of the total patients attending primary care that received mental healthcare. The proportion was very low (0.15%) before implementation of the MHCP and increased to 3.24% 3 years after implementation. The trained health workers also reported in the qualitative interviews that before introducing the MHCP, no one was aware about mental illness and its treatment process. However, after conducting awareness programmes with a range of key stakeholders in the community, people slowly started coming to health facilities for treatment.
+‘In the older days, mental health was not seen as a problem, people were thinking that it doesn’t need any sort of treatment. But later, the TPO [Transcultural Psychosocial Organisation] visited different healing places like traditional healers, and community leaders, volunteers, and then people started to learn about it.’ (PHC worker-13)
+Figure 2 shows the longitudinal trend of service utilisation for different disorders. Overall, the number of patients receiving mental healthcare increased substantially after the mhGAP-based training was initiated in early 2014. The number of patients receiving treatment for psychosis remained highest in most of the quarters except in January 2015. The number of patients with AUD decreased dramatically after 2015. This trend is also supported by the experience of trained health workers. Health workers reported that initially many people with AUD visited health facilities for treatment, and many of them stopped drinking after the treatment. However, after a few months, many people who were treated successfully started drinking again and did not come for follow-up. Therefore, health workers stopped initiating diagnosis and treatment of AUD when someone comes without family members and does not show commitment to quit alcohol forever. This narrative could explain why only a few health workers initiated treatment for patients with AUD after 2015.
+‘In my experience, among the regular cases in this facility, the hardest to manage are the AUD cases because many patients go home and start drinking again, they relapse often. Then, we feel bad as service providers because the service users start again. The service users will not come back for treatment because they will feel guilty of relapsing after having regular treatment and they will be scared to face eyes with us, they will feel guilty and ashamed.’ (PHC worker-11)
+Continued care
+Table 2 presents details of the follow-up visits of patients receiving mental health services from PHC. On average, patients visited
+4
+https://doi.org/10.1192/bjo.2020.60 Published online by Cambridge University Press
+health facilities 7.1 times for follow-up, and there was a large variation in the number of follow-up visits by disorder. For example, people with epilepsy made an average of 14 visits, whereas it was only 5.0 visits for depression, 12.2 visits for psychosis and 3.0 visits for AUD. To motivate patients to come for follow-up, female community health volunteers (FCHVs) were trained in home-based care, where they discussed with both the patients and the family members about the importance of follow-up care.13
+In the qualitative interviews, both patients and caregivers highlighted that the availability of mental health services (both psychological and pharmacological) free of charge was the most important facilitating factor for follow-up care in their community. One of the caregivers expressed that he was ‘extremely happy’ that he was able to get such quality service in his own place.
+Progress towards other indicators
+Table 3 presents the indicators for other MHCP components, intended outcome indicators and supporting evidence. It shows that the programme was successful in achieving most of the indicators defined by the ToCs. Out of six health-organisation-level indicators, four indicators (67%) were fully achieved. Six new psychotropic medicines that were used in PRIME are now included in the Ministry of Health (MoH) essential medication list, and the MoH has allocated a separate budget for scaling of mental health services. Out of 15 health-facility-level indicators, 9 indicators (60%) were fully achieved, 5 (33%) partially achieved and 1 (7%) was not achieved at all. All health workers (both prescriber and non-prescriber) from the implementation area were trained and supervised regularly. Out of six psychotropic medicines, five medicines were available most of the time in all health facilities. At the community level, we were able to fully achieve 8 (73%) out of 11 indicators. We trained and mobilised all FCHVs (n = 103) and 14 psychosocial counsellors. Psychosocial counsellors provided services to all patients referred by trained health workers and FCHVs. FCHVs made more than 1800 home visits. However,
+home visits did not achieve the intended outcome relating to dropout rate (Table 3).
+Evaluation of training
+Table 4 presents pre- and post-training evaluation results among prescriber-level health workers. The results demonstrated significant improvement in mental-health-related knowledge, attitudes and clinical competencies after the 10 days of mhGAP-based training. However, at the post-training evaluation, only 71% mean knowledge and 81% mean competency was achieved, suggesting that there remains a need for improvement in knowledge and competency.
+The improvements in knowledge, attitudes and competencies among health workers have also been supported by the experience of the patients and caregivers. Many patients reported that the health workers were knowledgeable and skilful. The caregivers held the perception that if the health workers were not competent, there would not have been improvement or positive changes in the patients’ condition. The patients and their caregivers perceived the health workers to be competent because of the positive change in the patients’ health.
+What was implemented?
+Table 5 presents the overview of the district MHCP, implementation processes for each of the intervention packages, the role of PRIME, and the barriers and facilitating factors for successful implementation. The MHCP was implemented within the existing community and PHC system. Medical officers, health assistants
+6
+https://doi.org/10.1192/bjo.2020.60 Published online by Cambridge University Press
+and auxiliary health workers were responsible for detection and management of mental disorders by using the mhGAP Intervention Guide in PHC facilities. Staff nurses and auxiliary nurse midwives provided psychosocial support in the health facilities. FCHVs and community counsellors implemented the treatment packages in the community (see Table 5).
+The PRIME team provided support for implementation of the packages, including organising training and workshops, managing logistics for training and supervision, and encouraging trained health workers in mental health services delivery. As there was no provision of psychosocial counsellors in the governmental PHC and community healthcare system, PRIME recruited and trained a separate cadre of psychosocial workers to provide psychological interventions in the community as well as in the health facilities where a confidential place was available for psychological intervention. Considering the current lack of mental health supervision in the existing healthcare system, PRIME also took a leading role in the development and implementation of a new supervision system for the trained healthcare workers. Supervision was conducted through monthly/quarterly case conferences led by psychiatrists, face to face and by telephone as needed.
+What were barriers and facilitating factors?
+Health organisation level
+Table 5 presents the barriers and facilitating factors for implementation of each MHCP component. The major barriers for effective implementation of the health-organisation-level intervention packages included mental health not being a government priority,
+no mental health focal unit/person in the MoH, and lack of basic psychotropic medicines in the free drug list. A memorandum of understanding between PRIME and MoH facilitated the appointment of a senior-level MoH officer to coordinate PRIME activities and procurement of new psychotropic medicines through PRIME. Another key facilitating factor for engagement of senior-level MoH officials was the use of evidence-based intervention packages, particularly the WHO recommended mhGAP intervention guidelines. The supportive role of the district public health office (DPHO) and psychiatrists in the district hospital were other key facilitating factors at this level.
+Health facility level
+The major challenges for implementation of health-facility-level intervention packages included low mental health literacy among PHC workers, heavy workload among PHC workers, frequent transfer of the trained health workers, mental health stigma among service providers, lack of adequate physical facilities, particularly lack of private rooms for consultation, lengthy and complicated drug procurement and distribution process, and lack of a mental health supervision system in primary care. The facilitating factors and strategies adapted to overcome these barriers included: the supportive role of the MoH and DPHO, motivation of PHC workers to learn about mental healthcare, procurement of new psychotropic medicines through PRIME, and initiation of case conferences by psychiatrist for mentoring and clinical supervision of the trained health workers. The feasibility of delivering psychological interventions was another major barrier encountered at this level. This was a barrier for three reasons in particular: first, most of the PHC workers remained busy in out-patient clinics and community outreach activities. Second, PHC workers lack skills to deliver focused psychological interventions. Third, the lack of a private room for providing psychological interventions in the health facilities. To address these barriers, we trained a new cadre of psychosocial counsellors to provide focused psychological support in PHC facilities or in the community setting in case there was no confidential place in the health facilities.
+Community level
+The major barriers for implementation of the community-level intervention packages included limited mental health awareness, low perceived needs for mental healthcare and high level of stigma. The facilitating factors for successful implementation at this level included: involvement of FCHVs, use of CIDT as a strategy to increase demand for services, and mobilisation of a new cadre of psychosocial counsellors to deliver psychological interventions in the community.
+Discussion
+The uniqueness of this case study is that it evaluated the impact of a comprehensive district-level MHCP and assessed the barriers and facilitating factors for successful implementation of a MHCP in a real-world setting. The MHCP included four priority disorders, namely psychosis, depression, epilepsy and AUD, recommended by the expert panel,27 which was first pilot tested in 2 health facilities, evaluated in 10 facilities, and subsequently scaled up in the entire district (i.e. 34 clinics). The systematic process that we used for development, pilot testing and evaluation of the MHCP was instrumental in getting political buy-in for scaling up of the programme to other districts (n = 7).
+The area-based approach, which we followed in our study, has also been used in other countries such as Nigeria, Mozambique and Afghanistan, for development and evaluation of mental health services in primary and community care settings. In Nigeria28 and Afghanistan29 the intervention was tested with priority mental disorders similar to our study, whereas in Mozambique, the pilot programme included epilepsy only.30 The results indicate that despite the various contextual, cultural and programmatic challenges, the programme was successful in achieving the intended outcome indicators outlined in the ToC map.21 The combination of psychological and mhGAP-based training delivered by mental health specialists was found to be effective for improving mental health knowledge, attitude and the clinical competencies of PHC workers. The barriers and facilitating factors demonstrated by our study are consistent with those reported in Afghanistan, Nigeria and Mozambique.28-30
+The results show that the number of people receiving primarycare-based mental health services increased significantly after the introduction of the MHCP. On average, patients visited facilities 7.1 times for follow-up care despite a large variation in this number by disorder. About one-third of the patients initiating primary-care-based mental health treatment dropped out after their first visit. These results are consistent with the drop-out rates from mental health services in general medical care reported in Madrid, Spain;31 Israel;32 and USA.33 The possible reasons for a high drop-out rate in our sample could be the availability of a single medicine for each disorder and the frequent transfer of trained health workers. A big drop-out rate for patients with AUD could be explained by health workers losing their faith in treating patients with AUD when many people relapse.16 In a nested randomised controlled trial conducted in the PRIME implementation area we demonstrated no added value of community counsellors-delivered psychosocial treatment (i.e. counselling for alcohol problem) over primary health worker-delivered mental healthcare in the treatment of AUD.25
+Psychotropic medicines were available most of the time in the health facilities, which contrasts with most of the previous studies34-36 where supply of psychotropic medicines was one of the major barriers for integration of mental health services in primary care. In Nepal, procurement and distribution of medicines require a lengthy administrative process. However, during the PRIME implementation period, psychotropic medicines were available regularly in most of the health facilities because the PRIME team took a lead role in procurement and distribution of the psychotropic medicines. Now the MoH, provincial government and respective municipalities are sustaining the procurement and distribution of psychotropic medicines. PRIME results support previous evidence that mental health services can be delivered effectively in primary and community healthcare systems in low-resource settings through a task-shifting approach. In our experience, this approach can work effectively only if multiple stakeholders such as mental health specialists, PHC workers and community volunteers are involved in the programme.
+Impact on policy and legislation
+The PRIME results and best practices have been used in policies, treatment protocols and guidelines, and training materials by the MoH. First, the PRIME results and best practices have been used in the community mental health care package, which was developed by the Primary Health Care Revitalization Division to facilitate implementation of the National Mental Health Policy (1996).37 Second, PRIME results informed the standard treatment protocol that was developed to help PHC workers in detection and treatment of mental health problems.38 Third, the essential drugs list has been revised, and six new psychotropic medicines initiated by PRIME have been included. These medicines are now being procured and distributed by the local government (municipalities and village municipalities) and DPHOs. Fourth, the WHO mhGAP Intervention Guide (v2) has been translated and adapted for use in Nepal. The Nepali version of the mhGAP Intervention Guide has added two modules for anxiety disorder and conversion disorders. The MoH has not yet endorsed the adapted Nepali version of the mhGAP Intervention Guide.
+Finally, the National Health Training Center, with technical support from Transcultural Psychosocial Organisation Nepal, has developed training manuals and facilitator guides for both PHC and community healthcare workers. These included a training manual for non-prescribers on psychosocial support, a training manual and facilitators’ guides for prescribers, a training manual on advanced psychological interventions (healthy activity program; HAP) and a training manual for FCHVs on the CIDT.
+Limitations
+This study has some limitations. First, the evaluation of the MHCP was conducted in ten health facilities in Chitwan. The selection of the district and the area within the district may limit the generalisability of the findings. Second, because of the lack of a baseline on organizational readiness to change, we could not determine whether this affected the MHCP implementation. Finally, although several indicators have helped to explain the success and failure of the MHCP in Nepal, several aspects, which may have contributed to the results, could not be controlled for and tested in the study.
+Policy and practice implications
+Community level
+First, despite the efforts made at the community level to sensitise community members on mental health issues and available services through community awareness and sensitisation programmes, our
+analysis of the outcomes of the programme published elsewhere showed no significant changes in the treatment coverage and barriers to mental healthcare after implementation of the district MHCP.6 A possible explanation could be that the sensitisation and awareness programme primarily aimed to increase mental health literacy and to make people aware about the services available in their community. Previous studies have demonstrated that mental health literacy can change attitudes, but there is no evidence that literacy programmes improve help-seeking intention and behaviour.39 There is evidence that help-seeking attitudes and intension can predict behaviour.40,41 Therefore, future community interventions should target improving knowledge about mental illness and available services, as well as reducing stigma or negative attitudes towards mental health service utilisation rather than only providing information about mental illness and available services.
+Second, it was found that most people receiving mental health treatment from PHCs had a low socioeconomic status. Evidence suggests mental illness and poverty create a vicious cycle that affects the life of people living in poverty and with mental illness throughout the lifespan. Therefore, the programme would have been much more effective for improving the lives of people with mental illness if vocational training for income generation had been included in the in the community-level care package.
+Third, only FCHVs were trained on the CIDT, but this approach can be used with other community stakeholders such as mothers’ groups, traditional healers and teachers in the impact in future programmes. This is supported by a study on the accuracy of the CIDT that demonstrated CIDT as an effective tool for detection of people with mental illness in the community.14
+Fourth, considering the low mental health literacy of non-prescribers and their busy schedule, there is a need for community counsellors to look after psychological intervention in the community. A randomised control trial embedded within the PRIME cohort study demonstrated that a psychological intervention (i.e. HAP) delivered by community-based psychosocial counsellors increased treatment effects for depression compared to those who only received mhGAP-based services in primary care.16 In addition, it was also found that because of stigma associated with mental illness and lack of a confidential place in the health facilities for consultation, many patients with mental illness were found to be hesitant to disclose their problems in front of other people. In many health facilities there is no private place for psychological interventions. Dedicated community-based psychosocial counsellors could be a helpful strategy to provide evidence-based psychological interventions in the community, which may also help to minimise the current work burden of PHC workers.
+Health facility level
+First, the 10-day training for prescriber-level health workers was divided into two parts: psychosocial support (5 days) and mhGAP-disorder-specific training (5 days). The psychosocial part of the training was facilitated by a psychologist or an experienced psychosocial counsellor, whereas the mhGAP part was delivered by a psychiatrist. Based on the findings of this study, it would have been more effective if the training had been delivered together by a psychologist and a psychiatrist.
+Second, it was not always possible to involve the same psychiatrist in both training and supervision of a trained PHC workers. However, health workers were more comfortable contacting psychiatrists through mobile phone or other means of communication to get support if the same psychiatrist both trained and supervised them. Therefore, we recommend involving the same psychiatrist in both the training and supervision of PHC workers in the impact in future programmes.
+Third, the training participants were taken to the district hospital for interaction with actual patients in the initial phase of the mhGAP training. In the later phase, patients were invited to the training venue. Inviting patients to the training venue was much more effective in clarifying various aspects of mental health problems, and the participants also liked this approach better. This approach is recommended for future mhGAP training. In addition, we embedded a study within PRIME in which we trained mental health patients to provide photographic narratives of recovery. Based on a mixed qualitative-quantitative analysis of this proof concept, this approach has potential to improve knowledge, attitudes and clinical competence of PHC workers in mhGAP training.42
+Fourth, in most of the health facilities, there was no private place for clinical consultation. Because of stigma, patients with mental illness were hesitant to disclose their problems in front of other people; therefore, a separate room should be made available in each health facility for clinical consultation and psychological interventions.
+Fifth, despite a very high prevalence of mental health problems among pregnant and postnatal women in Nepal, the data shows that only a small number of them received mental health services from trained health workers. A possible reason could be that pregnant or postnatal women generally consult with non-prescriber-level health workers for pregnancy check-ups, whereas non-prescribers were not trained in diagnosis and management of mental health problems. The non-prescribers should be trained on detection of maternal depression and initiate appropriate psychological interventions. A small pilot study conducted in a few health facilities within PRIME showed that routine screening of perinatal depression and initiation of evidence-based psychological treatment is feasible and effective (results will be published separately). These results are also supported by previous studies where nurses and other lay community health workers delivered psychosocial interventions effectively.10,43
+Finally, despite the tremendous efforts made by FCHVs to minimise the drop-out rate, about a quarter of the patients initiating primary-care-based mental health services did not come for follow-up. According to FCHVs, patients felt uncomfortable when they made multiple home visits to remind patients about their follow-up care. This could be because of stigma associated with mental illness; therefore, an alternative approach should be developed to minimise the high drop-out rate. One possible strategy is a phone call follow-up by health workers, which could be less stigmatised than FCHVs visiting patients’ house.
+Health organisation level
+First, the PRIME results are based on a model of training all prescribing health workers in a facility, including medical officers (doctors), health assistants and auxiliary health workers. However, the recent treatment protocol endorsed by MoH does not include training auxiliary health workers. Except for a few auxiliary health workers, who were upgraded from other positions, PRIME data shows that auxiliary health workers were the healthcare provider for approximately 60% of all patients in primary care. Despite the benefit of the government taking on more mental health service delivery, the model designed does not match the evidence generated by PRIME. This risks leaving many people without care even in settings where the government mental health model is implemented. This has also raised questions about how to prevent the disconnect between evidence generation and policy making in future.
+Second, one of the reasons reported for the high drop-out rate was availability of limited psychotropic medicines (i.e. one medicine for each disorder) in primary care and irregular supply of the medicines. Similarly, frequent transfer of trained health workers was also reported to be another important reason for drop-out. Therefore,
+regular provision of minimally adequate psychotropic medicines in PHC facilities and regulation of frequent transfer of the trained health workers could help to minimise the high drop-out rate in the impact in future programmes.
+Finally, the psychiatrists’ case conference, which was initiated by PRIME for supervision of trained health workers, was found to be effective in building the clinical capacity of the trained healthcare workers and providing specialists care for patients with severe mental health problems. Currently there is no mental health supervision structure for PHC workers in the existing healthcare system; therefore, the ‘psychiatrists’ case conference’ could be an appropriate strategy to fill the current gap in mental health supervision for trained PHC workers.
+In conclusion, despite the various contextual challenges, the MHCP resulted in achievement of most of the outcome indicators. The key lessons learned from this study for future integration of mental health services within primary care include the provision of targeted interventions to increase demand for services, and to ensure clinical supervision for health workers, private space for consultations, a separate cadre of psychosocial workers and a regular supply of psychotropic medicines.
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+Background
+Comorbidity conditions have been studied from the perspectives of different outcomes, one of them being readmission after hospital discharge [1-3] and could be an important risk factors associated with readmission for people with psychiatric disorders. However, this issue remains poorly understood.
+It is estimated that almost one in seven persons hospitalised for psychiatric reasons are readmitted within 1 month of discharge [4]. Since readmission rates in psychiatric patients are high, it is of great interest to determine potential predictors of such recidivism. Psychiatric patients have been widely reported to be at an increased risk of morbidity and mortality due to physical disorders [5-7]. A serious and persistent mental disorder can result in patient’s losing up to four years of life, compared to individuals without mental disorder. Suicide, cancer, accidents, liver disease, and septicaemia increase premature mortality among persons with serious and persistent mental disorder [8].
+The results of conducted research on comorbidity influenced as well the classification systems of mental disorders by pointing out, that current psychiatric diagnoses are not discrete entities and most patients with one diagnosis also fulfil the diagnostic criteria for another diagnosis, implying that comorbidity of related disorders is rather a rule than exception [9]. Heterogeneous category of diagnoses / diseases by using exclusion criteria show hierarchy between diagnoses, and related clinical entities lead to frequent co-occurrence of diagnoses of mental disorders [10].
+In the 2001-2003 US National Comorbidity Survey Replication (NCS -R), a representative epidemiological survey revealed that comorbidity between medical and mental disorders is the rule rather than the exception [11, 12]. More than 68% of adults with a mental disorder (diagnosed with a structured clinical interview) reported having at least one general medical disorder, and 29% of those with a medical disorder had a comorbid mental health condition. Elderly patients and those with diagnoses of organic brain syndromes reportedly having the highest risk for comorbid medical illness [13]. Thus, there is an indication that having a mental disorder is a risk factor for physical disorder and vice versa. For example, having a physical illness is one of the strongest risk factors for depression; and depression is also a risk factor for physical illness [14, 15]. Among respondents in the 1999 epidemiological National Health Interview Survey (NHIS; an ongoing national household survey of non-military and noninstitutionalized persons in the United States) the likelihood of having major depression diagnosed (via a screening instrument) increased with each additional comorbid chronic medical condition [16]. In other studies, depression is reported to be comorbid
+with 26 disease categories and is most prevalent in combination with gastrointestinal diseases, stroke, musculoskeletal diseases, Parkinson’s disease, respiratory diseases, and obesity [17]. A study by Andres et al. [18] revealed that in addition to survival risks associated with postmyocardial depression in patients with recurrence of acute myocardial infarction (AMI), psychiatric disorders influenced the consecutive readmission for AMI with the same severity as did tobacco, diabetes, and obesity.
+A growing body of evidence demonstrates that certain physical conditions are observed with increased frequency in patients with severe mental illness [1, 19-21]. As summarized by de Hert et al. [5], there is very good or good evidence for increased risk for various physical diseases in patients with mental disorders, for example, human immunodeficiency virus (HIV), impaired lung function, obstetric complications, stroke, myocardial infarction (MI), hypertension, obesity, diabetes mellitus to name a few.
+Unfortunately, several authors reported that clinicians fail to recognize these comorbid medical illnesses in nearly half of all cases [22, 23]. In a number of patients, physical illness could then lead to psychiatric conditions themselves, or worsening of existing symptoms. As well as the mental disorder itself, adverse effects of medications or other treatments can result in serious medical pathology [24]. It seems that the physical health of people with a severe mental illness has been neglected for decades, and still is today [5, 6].
+In the literature we can notice a diverse use of terminology for mental and physical health conditions: mental disorder, mental illness, mental impairment, psychiatric disorder, psychological disorder, somatic condition, medical condition, physical illnesses, etc. In our study, we mainly used terms: mental and physical disorders, unless when referring to studies where authors or the context required different terminology. Mental disorders comprise a broad range of problems, with different symptoms (reflecting in various categories of diagnoses/diseases). However, they are generally characterized by some combination of abnormal thoughts, emotions, behaviour and relationships with others. For the purposes of our literature review it was most suitable to use the term mental disorders allowing us to include different characteristics of psychiatric patients described in reviewed studies (e.g., diagnoses, symptoms, diseases, etc.).
+The concept of comorbidity
+The term “comorbidity” is well-recognised in research and clinical settings, but the concept remains rather complex and methodological approaches differ. Approaches to study the impact of comorbidity become challenging also due to the lack of consensus about how to define and measure the concept of comorbidity [27].
+The concept of comorbidity was established by Feinstein in 1970 [25] to denote cases in which a “distinct additional clinical entity” occurs during the clinical course of a patients’ index disease. Later on, more complex concepts of comorbidity were developed intended for use in clinical setting, research and health care management and planning [26]. There is currently no consensus around the definition of comorbidity, which can be defined in several different ways. Consequently, clinicians, researchers and managers are using different comorbidity concepts when faced with co-occurring chronic diseases, disorders, health conditions, illnesses or health problems. Overall, the term comorbidity has three meanings [19]: a) Indicating a medical condition in a patient existing simultaneously but independently with another condition; b) Indicating a medical condition in a patient that causes, is caused by, or is otherwise related to another condition in the same patient; c) Indicating two or more medical conditions in a patient that exist simultaneously, regardless of their causal relationship.
+An increasing interest in the subject as well as methodological obstacles in analysing data on comorbidity has resulted in the first comprehensive trial of integrating different aspects of comorbidity definitions [27]. Authors combined different constructs and measures associated with the core concept of comorbidity, the coexistence of two or more conditions in a patient. In this respect, four major distinctions were made according to the nature of the health condition, the relative importance of the cooccurring conditions, and the chronology of the conditions: comorbidity, multimorbidity, morbidity burden and patient’s complexity.
+The Charlson and Elixhauser comorbidity measures are of the most frequently used methods in the comparative research on comorbidity, reflecting the morbidity burden [28-32]. The Charlson Comorbidity Index predicts the ten-year mortality for a patient in relation to a range of comorbid conditions.
+The Elixhauser comorbidity measure developed a list of 30 comorbidities relying on the ICD-9-CM coding manual. The comorbidities were not simplified as an index as each comorbid condition may affect several outcomes (length of hospital stay, hospital changes, and mortality) differently among diverse groups of patients [33]. Both, the Charlson and the Elixhauser indices were originally used to predict mortality for inpatient populations, but t have also been applied to outpatient populations to measure other health outcomes in the clinical research (prediction of service use, readmission risk, health costs, etc.) [31, 33-35].
+Since each construct of comorbidity illuminates different aspects of morbidity it is important to distinguish between them, mostly because of their use in research, clinical practice, and management of services [27]. For
+instance in clinical research, the construct of choice will be determined by its ability to inform patient management. Although the perception of patient complexity is relevant to all aspects of care, the construct of comorbidity, with its emphasis on an index disease, may be predominantly useful in specialist care, whereas multimorbidity and morbidity burden may prove better constructs for primary care. From an epidemiological and public health perspective, the constructs of comorbidity and multimorbidity are of greatest interest, while morbidity burden and patient complexity seems to be more suitable from the health services research and policy perspective [27, 31].
+Outcome research and comorbidity
+The comorbidity between mental and somatic disorders is an important field in everyday medical practice, and is becoming widely recognised also in psychiatry [5, 36]. There is growing interest among practitioners and researchers in the impact of comorbidity on a range of outcomes, such as mortality, health-related quality of life, patient's functioning, and health care utilization [37]. Readmission after psychiatric hospitalization is commonly used as a quality of care indicator by government funding agencies, policy-makers, and hospitals deciding on clinical priorities [38].
+Comorbidity issues are also linked with higher economic burden since the increased direct health costs (usually represent the costs associated with medical resource utilization, including the consumption of inpatient, outpatient, and pharmaceutical services within the health care delivery system) and indirect health costs (defined as the expenses incurred from the cessation or reduction of work productivity as a result of the morbidity and mortality associated with a given disease, typically consist of work loss, worker replacement, and reduced productivity from illness and disease), are also associated with treatment of patients with more chronic condition [39]. For example, about 80% of Medicare spending is devoted to patients with four or more chronic conditions, with costs increasing exponentially as the number of chronic conditions increase [40, 41].
+Since physical comorbidity could be an important risk factor for readmission, much effort has been put into developing reliable risk prediction models for hospital readmission whereas physical comorbidity have been integrated as well [42]. Authors emphasized that the majority of the 26 readmission risk prediction models, studied within the systematic review, have poor predictive ability [42]. Physical comorbidities, basic demographic data, and clinical variables have proved to much better predict mortality than readmission risk. Namely, hospital and health system-level factors, social, environmental, and medical factors (e.g., the timeliness of post-discharge
+follow-up, coordination of care with the primary care physician, the supply of hospital beds, access to care, social support, substance abuse, and functional status) can also contribute to readmission risk; however the utility of such factors has not been widely studied. Authors concluded that the inclusion of such factors could conceivably improve the predictive ability of prediction models for readmission risk [42]. Recently a new risk tool was introduced: READMIT - A clinical risk index to predict 30-day readmission after discharge from acute psychiatric units by Vigod et al. [43]. A comprehensive risk tool consists of several variables, independently associated with one month readmission: repeat admissions, emergent admissions, diagnoses, unplanned discharge, medical comorbidity (including Charlson Comorbidity Index), prior service use intensity and time in hospital. Their study confirmed the medical comorbidity as a significant risk factor in predicting of 30-day readmission [43].
+In patients with comorbidities besides higher risk of dying, a poorer functional status or quality of life also a greater use of health services has been reported [44, 45]. These findings led to the conclusion that among patients with comorbidity, the focus of health care should not only be on one specific disease, but also on the pathology in other organs and on indicators for quality of care such as complications of treatment, readmissions, treatment strategies and compliance to generally accepted clinical guidelines. In order to improve outcomes and reduce medical costs, a better understanding of the associations between physical comorbidities and psychiatric readmissions is needed. Namely, from a clinical or a policy decision-making point it would be very useful to be able to identify those patients with high risk of readmission in order to ensure a better follow-up of mental and somatic disorders after discharge, or to be able to calculate standardized readmission rates as indicators of quality of health care.
+This systematic review belongs to a series of reviews from the Comparative Effectiveness Research on Psychiatric Hospitalisation (CEPHOS-LINK) project on determinants of readmission after discharge from psychiatric hospital care. The main objective of this study was to review and describe the effect of physical comorbidity variables on readmission after discharge from psychiatric or general health inpatient care with a psychiatric diagnosis.
+Method
+Search methods for identification of studies
+Comprehensive literature searches were conducted in the electronic bibliographic databases Ovid Medline, Psy-cINFO, ProQuest Health Management and OpenGrey. In addition, Google Scholar was utilized. Relevant publications published between January 1990 and June 2014 were included.
+Studies on the association between mental health and readmission were searched using combinations of keywords (used as MeSH terms or free text, depending on the database) describing mental health services and readmission. For more detailed description of the search terms please see Additional files 1 and 2 (Detailed search strategies and Detailed search strategy for articles on physical comorbidity). In addition, the reference lists of all included articles were manually checked for additional studies.
+Criteria for considering studies for review on physical comorbidity
+Studies on readmission (to a psychiatric or non-psychiatric bed) after discharge from psychiatric, general or specialised inpatient care were included in this review. The original discharge had to be one with a main psychiatric diagnosis and additional medical diagnoses (both diagnosed using for example the ICD-10 system [49]) or medical conditions relevant for physical comorbidity. Admissions to day hospitals or community programmes were not considered as readmissions.
+Quantitative longitudinal studies were selected for this systematic review, including both observational and intervention studies. Qualitative studies, case reports, papers not including original data, such as editorials, letters to the Editor and commentaries were excluded. The same applies to the studies that were not published as full reports. Three review papers were retrieved from initial search. They were excluded because physical comorbidity was not included among reviewed characteristics of psychiatric readmission.
+Several medical conditions relevant for physical comorbidity (physical comorbidity variables) were considered at admission, at discharge and at readmission. They can be grouped into three core categories:
+a)	Medical diagnoses (according to codes from International Classification of Diseases - ICD codes, DSM IV / Axis III (medical condition) classification) [47]
+b)	Physical conditions (specified medical illnesses without classification codes e.g., cardiovascular disease, cardiac problems, diabetes, trauma, nutritional and metabolic diseases, etc.)
+c)	Variables describing the burden of medical illness indicated as “Number of medical diagnoses”, “Physical health problems”, “Charlson Comorbidity Index”, “Number of somatic complaints”.
+Only studies examining adult populations (age > 18 years) were included in the review. In the case of studies examining also adolescents we included these studies in the review if the reported mean age in the cohort was at least 18 years.
+A primary outcome of interest was related to the existence or not of a link between physical comorbidities and readmission to inpatient hospital care (psychiatric or
+non-psychiatric/general), and the studies that did not report results on readmission were thus excluded.
+In addition we included in the review also studies that addressed physical comorbidity only at admission / discharge. This aspect of the review was carried out due to the fact that we were attentive also in identifying which variables of physical comorbidities were observed in association with psychiatric conditions in order to identify those possible specific physical conditions that may be related to certain mental disorder.
+No restrictions regarding language or publication status were used in the original searches. However, a few studies had to be excluded from the final examination because translation was not available into any of the language mastered by the multi-lingual research team (e.g., from Chinese). In the end, all but one of the included studies were written in English. The only non-English study was in Spanish. The flow of studies through the selection process is detailed in Fig. 1 the PRISMA flow-chart; [48].
+Data collection and analysis
+Two pairs of researchers [LS, RS and VD, EL] independently screened all abstracts. Full-texts were
+screened, if necessary to establish the eligibility of articles. In a subsequent step full text of all candidate papers were retrieved and independently screened by two researchers [LS, MZD]. Discrepancies were resolved by discussion by these two researchers, until agreement on inclusion or exclusion of the study was reached.
+Available structured data on physical comorbidity variables associated with readmission were extracted from the included studies and entered into an evidence evaluation table independently by two researchers [LS, MZD]. The evidence evaluation table included the following information: study period, study design, type of study (observational/ interventional), characteristics of study population, time to follow-up, inclusion/exclusion criteria, main outcomes, number of participants, age and gender distribution in the data, included diagnostic groups/ diagnostic distribution, physical comorbidity variables included in the study, readmission rate, used readmission time/time since discharge, key factors affecting readmission, mortality rate, description of performed statistical analyses, and countries in which the included studies were carried out.
+An integrative research review was conducted since meta-analysis was not feasible due to the heterogeneity of the studies and low number of data observations associated with physical comorbidity variables.
+Results
+The selection process of the included studies
+Although, psychiatric readmission was studied in different clinical settings and diagnostic groups of mental disorders, several studies included the presence of physical comorbidities within exclusion criteria, considering them as cofounders. Of the 734 unique articles identified in the initial search only 52 were included in the review. After additional screening and selection, a further 31 full-text studies were excluded due to following reasons:
+-	not discharged with a primary psychiatric diagnosis, n = 11
+-	not including physical comorbidity, only psychiatric comorbidities (F diagnoses), n = 9
+-	without specified data on physical comorbidity variables, n = 7
+-	reviews, not eligible for physical comorbidity, n = 3 - not corresponding to the study criteria on physical comorbidity, n = 1
+Through subsequently hand searching of the reference list in included papers, identified through initial database, two additional eligible articles on physical comorbidity have been retrieved. Finally, 23 full-text articles (all with observational type of studies) were included for full text assessment of eligibility and into integrative review (Fig. 1).
+Overall description of reviewed studies
+Key characteristics of the studies selected for the systematic review on physical comorbidity are presented in the Additional file 3: Table S1, Additional file 4: Table S2 and Table 1.
+In general, included studies (n = 23) documented physical comorbidity variables at time of hospitalisation (admission, discharge, readmission). But physical comorbidity was not analysed in all studies from the perspective of psychiatric readmission. 17 studies reported on physical comorbidity at readmission (Additional file 3: Table S1, Additional file 4: Table S2; studies listed from No 1. to 17.). In view of this, we included in the review also those studies (n = 6) that addressed physical comorbidity (regardless the type of the physical comorbidity variable - diagnoses, numbers the physical disorders, Charlson Comorbidity Index, etc.) only at admission / discharge. Above studies didn’t report the potential associations between physical comorbidities and psychiatric readmission, since physical comorbidities were
+only recorded at the time of the initial admission with a descriptive objective (Tables Additional file 3: Table S1, Additional file 4: Table S2; studies listed from No 18. to 23.). Besides reviewing studies according to physical comorbidity issue, they were further analysed from the perspective of constructs covering different aspects of comorbidity (described in the Introduction chapter) [27]. Namely, comorbidity, multimorbidity, morbidity burden, patient’s complexity, implies a different understanding of the concept of comorbidity (Additional file 3: Table S1 and Table 1).
+General characteristics of the reviewed studies
+Out of 23 reviewed studies 17 were published after year 2000, the oldest published in the year 1991 and most recent, published in the year 2013 (Table 1). The largest number (n = 4) of included studies originated from year 2011. According to the geographical scope of conducted studies, 61% of reviewed studies were carried out in USA, two in Canada, Denmark, Australia, and in United Kingdom, Spain, and Japan (Additional file 3: Table S1 and Table 1).
+The majority of included studies (44%) obtained data from hospital medical records only, 31% from big administrative database (national registries) and 26% of studies combined data from hospital medical records and interviews and/or self- assessment questionnaires and clinical assessment instruments (Table 1). According to the applied statistical method, 83% of included studies used bivariate / multivariate statistical analyses (Additional file 3: Table S1).
+In one third of reviewed researches (n = 7) a study population consisted of patients with affective disorders (predominantly with depression, followed by bipolar disorder). Another seven studies included patients with substance use disorders (SUD), six studies included all psychiatric diagnoses and three studies focused on patients diagnosed with serious mental illnesses (SMI; schizophrenia, schizoaffective disorder, bipolar disorder, personality disorders).
+Most of studies (92%) included both genders. One study was restricted to female s only [69], and one study only included a male study population [68]. The study restricted to female population included a group of female veterans discharged from Veterans Affairs Hospital and the male study involved male veterans of either World War II or the Korean War, treated at the Houston Veterans Affairs Medical Center. Age of study populations in reviewed publications ranged from 18 -80+. Five studies were focused only on elderly population [50, 55, 59, 63, 68] (Table 1).
+The periods of follow-up varied from less than 1 month (n = 1) to more than seven years (n = 4). Most frequently reported follow-up periods were 12 months (n = 5) and
+one month (n = 3). More than 80% of reviewed studies (n = 19) did not document mortality rates during the follow- up period. Among studies that monitored mortality, rates depended considerably on the length of follow-up period, age range of the study population and burden of co-morbid psychiatric and physical conditions [52, 55, 57, 59].
+Physical comorbidity variables, identified in the 23 reviewed studies, are summarized in Additional file 3: Table S1 and Table 1. Variables were classified according to the physical conditions relevant for physical comorbidity and reflect medical illnesses (medical diagnoses according to ICD codes and listed medical problems without codes) and the burden of medical illness (indicated as number of medical diagnoses, somatic complaints, Charlson Comorbidity Index) co-occurring with psychiatric condition. Six studies documented physical comorbidity variables only at admission /discharge, and 17 studies as well at readmission.
+A supplementary evaluation of applied constructs covering different aspects of comorbidity was conducted in order to ascertain which aspects of comorbidity have been addressed. Evaluation revealed that all studies did not follow to the same concept of comorbidity. The majority of studies (48%) were based on multimorbidity concept (presence of multiple diseases in one individual). Patient’s complexity (overall impact of the different diseases in an individual taking into account their severity and other health-related attributes) was the next most frequent applied concept (31% of studies). Morbidity burden concept (overall impact of the different diseases in an individual taking into account their severity) was applied in 17% of studies. The least frequently used concept was comorbidity (presence of additional diseases in relation to an index disease in one individual), applied only in one study (in 4% of all included studies). Concepts of comorbidity construct differed also according to categories of psychiatric diagnoses. Morbidity burden prevailed in category of affective disorders, whereas multimorbidity construct in SUD. More detailed description of applied comorbidity constructs can be seen in Additional file 3: Table S1 and Table 1.
+Physical comorbidity variables in patients with mental disorders
+An analysis of co-occurring physical and mental disorders was carried out in order to identify those physical variables that most commonly co-occur with certain mental disorders, as well to identify which of specified physical comorbidity variables might have a potential impact on hospital readmission (Additional file 3: Table S1, Additional file 4: Table S2 and Table 1).
+Comorbidity physical variables were widely documented in a form of classification codes (6 studies) and specified medical illnesses without classification codes (6 studies), followed by Charlson Comorbidity Index (5 studies), not
+specified health problems (5 studies) and number of medical diagnoses/somatic complains (3 studies). Overall, several studies reported that patients with mental disorders had more physical comorbidities compared to those without mental disorders conditions [52, 63, 65, 69] (Additional file 3: Table S1 and Table 1).
+Multimorbidity concept was used in almost half of studies and frequently applied in retrospective cohort studies based on medical records from large administrative databases or national patient registries [2, 34, 35, 57, 59, 65]. Patient’s complexity concept was applied in one third of reviewed studies, acknowledging that morbidity burden is influenced not only by health-related characteristics, but also by socioeconomic, cultural, environmental, and patient behaviour features. For instance, the study of Mark et al. [2] revealed that social factors have been found to contribute to 39% of admissions in patients with SMI, followed by factors related to mental and physical disorders (31%) and dangerousness to self or others (20%). Aggressive behaviour, self-injurious behaviour and sexually inappropriate behaviour co-occurring with physical health deterioration in patients with learning disabilities have been reported as risk factors for hospital readmission [58]. Also the following patient related factors were found as significant predictors of readmission : residential instability, alcohol as a primary drug of choice, single marital status, unemployment, multiple drug use, an older age, ethnicity, treatment incompletion, care distress, maladaptive family functioning, poorer psychosocial functioning [50, 54, 55, 67].
+Several physical disorders have been described in admitted patients with main psychiatric diagnosis (Additional file 4: Table S2 and Table 1). The following most common physical conditions (medical diagnoses/illness) were found in some categories of mental disorders at hospital admission / discharge:
+1)	All psychiatric diagnoses: cellulitis, chronic obstructive pulmonary disease, liver disease, diabetes, hypertension, circulatory heart conditions, epilepsy, hypothyroidism [2, 51, 65, 68];
+2)	Affective disorders: diabetes, hyperthyroidism, obesity, cardiovascular disease, hypertension, high cholesterol [56, 57, 66];
+3)	Substances use disorders: chronic lung conditions, asthma, hepatitis C virus (HCV) infection, hepatitis B, HIV(+), epilepsy, hypothyroidism, hypertension, skin and subcutaneous tissue diseases, infectious parasitic diseases, digestive diseases, cardiac problems/angina, cirrhosis, gastritis, diabetes, pregnancy, accidental poisonings, adverse drug reactions, accidental falls [52, 53, 59, 61, 67, 69].
+Physical comorbidity variables associated with burden of medical illness were documented in all categories of
+mental disorders in form of: Charlson Comorbidity Index, number of medical diagnoses, physical health problems and somatic complaints (Additional file 3: Table S1 and Table 1).
+The influence of physical comorbidity on readmission of patients with mental disorders
+Out of 17 studies which documented physical comorbidity variables at readmission, 12 demonstrated that physical comorbidity may be associated with hospital readmission while four studies did not show that medical comorbidity is linked with a higher risk for readmission [51, 54, 55, 57]. Summarised results on the effects of most frequent reported physical comorbidity variables on readmission in patients with main psychiatric diagnosis are presented in Table 1. More detailed report on results from the reviewed studies is presented in Additional file 4: Table S2. Below are the key findings:
+Physical disorders were more common among readmitted patients than single admission patients, nevertheless their impact on readmission varied according to the nature of mental disorders, characteristics of study population and study protocol (e.g., the duration of follow up period, index population, inclusion/exclusion criteria, etc.). In general, the main body of study outcomes support the hypothesis that patients with mental disorders were at increased risk of readmission if they had co-occurring medical conditions [3, 33, 61, 63]. Mercer et al. [68] reported that psychiatric patients were found to have approximately four times more psychiatric hospitalizations than medical hospitalizations despite the existence of multiple physical disorders in this population. Physical health problems contributed to the decision to readmit (readmission time: 36 months) in 16.5% of admissions of patients with SMI [62].
+Physical comorbidity was not associated with psychiatric readmission in two studies [54, 55]. The negative associations between physical comorbidities and the probability of psychiatric readmission were identified in two studies, revealing that comorbidity with medical condition did reduce the readmission risk by 41% of psychiatric patients [51], and that less medical diagnoses increased the risk of mental disorder readmissions [59].
+In almost all categories of psychiatric diagnoses (Affective disorders, SUD, SMI) the following physical comorbidity variables indicated a higher probability for readmission: no specified medical illness, more physical health problems, more somatic complaints, more medical diagnoses and higher Charlson Comorbidity Index score [35, 51, 62].
+Several medical diagnosis/ physical disorders were reported to be associated with hospital readmissions in patients with main psychiatric diagnosis (Additional file 4: Table S2 and Table 1). Some of the physical comorbid
+conditions were found to increase the probability of readmission, like chronic lung conditions and hepatitis C virus infection in patients with SUD diagnosis [52, 60] and hypertension in patients with mental and/or SUD [2]. The study from Mai et al. [65] stated that patients with mental health disorders were about twice as likely as non-mental health patients to experience potentially preventable hospitalisations that accounted for more than 10% of all hospital admissions/discharges in this study population. Diabetes and its complications, adverse drug events, COPD, convulsions and epilepsy, and congestive heart failure have been the most common causes. For almost all comorbid conditions evaluated in the study of Mark et al. [2], a larger percentage of patients who were readmitted with mental and/or SUD diagnosis (readmission time: 8-30 days) had a comorbid condition compared with those who were not readmitted. The largest percentage difference has been reported for cellulitis, COPD, liver disease, diabetes, hypertension, and circulatory heart conditions.
+Some studies indicated that the presence of mental disorder could worsen patient’s physical health or course of illness, consequently leading to hospital readmission due to non-psychiatric reasons. Thomsen & Kessing [56] reported that patients with bipolar disorder were found at greater risk of subsequent hospitalization (readmission time: 58 months, 70 months, 79 months) with hyperthyroidism in comparison with patients with depressive disorder. Also age was shown as important factor associated with poorer patient’s physical health. Kessing et al. [57] revealed that patients in age groups between 45 and 80 years of age discharged with a diagnosis of mania/ bipolar disorder had a slightly increased rate (not significant) of getting a diagnosis of diabetes at readmission (readmission time: 240 months) whereas younger and older patients with mania/bipolar illness had a slightly decreased rate of diabetes.
+Discussion
+This systematic review was conducted in order to synthesize the available research data on medical and physical comorbidity as risk factors that could be linked with hospital readmission of patients with comorbid psychiatric and medical conditions. Accordingly the relationships between psychiatric diagnoses and specific physical comorbidities that have been identified through this review only refer to hospitalized patients. Our literature review, irrespective of very diverse applied approaches in reviewed studies and limited generalizability, revealed also some recognizable trends in mental and physical disorder conditions.
+Among 734 records identified through database searching only 23 studies documented physical comorbidity as a variable which was analysed at admission/
+discharge of patients with the main psychiatric diagnosis. Of these, 17 studies documented physical comorbidity also at readmission. Thus, several studies on psychiatric readmission included data on physical comorbidity within exclusion criteria. Some studies did check the Charlson Comorbidity Index at admission/discharge, predominantly to ensure that studied groups of patients did not significantly differ in medical comorbidity as authors considered it as a confounding variable [64-66]. Since our interest was also to examine if there are any specific physical conditions that may be related to particular mental disorders, we included 6 studies in our review where medical problems were recorded only at admission / discharge without being analysed from the perspective of readmission risk. In 23 of the reviewed studies we found a variety of applied aspects regarding comorbidity construct, selection of index population, source of data, outcome measures and research questions, study design, duration of follow up period, patient’s sociodemographic characteristics, etc. The majority of papers were not representative of the general psychiatric population discharged from an inpatient service. Generalizability is limited since reported results from several papers can be considered as biased according to: a) included categories of psychiatric diagnoses (only particular diagnoses were included from the whole psychiatric admitted population); b) gender inclusion (some studies were performed with only or predominantly in male or female groups of patients; c) age range (some studies included only a specific age-group e.g., the elderly); d) inclusion of different follow-up periods after discharge (from less than one month to several years); e) association of readmission risk with implemented study design (e.g., different inclusion / exclusion criteria, applied statistical models and source of data); f) scarce data on medical condition of included populations; g) geographical scope of included studies (uneven inclusion of studies from different countries, e.g., 61% of included studies in the review were performed in USA); h) applied concept of comorbidity (different models have been used with different types of variables, e.g., number of medical diagnoses, Charlson Comorbidity Index, specified medical diagnoses with or without ICD codes, etc.).
+Complex pathways of comorbid mental and physical disorder conditions
+Studies included in this systematic review reported a broad spectre of co-occurring physical and mental disorder ‘conditions. Physical conditions consisted mainly of chronic noncommunicable disorders: cardiovascular disease, hypertension, diabetes, hyperthyroidism, hypothyroidism, high cholesterol, obesity, cellulitis, chronic lung conditions, chronic obstructive pulmonary disease, asthma, hepatitis C virus (HCV) infection, hepatitis B, HIV(+), epilepsy, skin and subcutaneous tissue diseases, infectious parasitic
+diseases, digestive diseases, liver disease, gastritis. The examined mental disorder conditions fell predominantly into the category of chronic, disabling and prevalent mental disorders: SUD, mood disorders (major depression, bipolar mood disorder), SMI (schizophrenia, bipolar mood disorder, schizoaffective disorder and personality disorders).
+The pathways leading to comorbidity of mental and physical disorders are in several aspects interrelated. A broader insight into the dynamic of mental and physical comorbid conditions and its consequences can be reached when also taking into account outcomes from studies which examined readmission risk in patients with medical index disease and comorbid mental disorder. Two main characteristics can be noticed in the literature in this respect:
+Firstly, the pathways leading to comorbidity of mental and physical disorders are complex and often bidirectional [70]. Epidemiological studies have been important in examining these pathways. For instance, physical conditions with a high symptom burden, such as migraine or back pain, might lead to depression [71] while major depression could represent a risk factor for developing a physical condition, such as cardiovascular disease [72].
+Secondly, the course of comorbid mental disorder and physical conditions could be influenced by each other, leading to a worsening of either mental disorder and/or physical condition, consequently leading to hospital readmission due to non-psychiatric reasons. That could be demonstrated through proxy: longer hospital stay, frequent hospital readmission and increased mortality. For example, persons with bipolar mood disorder had a more severe course of disease, a higher total number of in-hospital deaths and a substantial higher burden of comorbidities [73]. Wells et al. [74] reported that depressive symptoms had an independent additive effect on the physical and social functioning of patients with chronic medical illness. Bipolar disorder was found at greater risk of subsequent hospitalization with hyperthyroidism [55]. Increased hospital mortality and readmission risk in patients with comorbid heart condition and depression were described in some other studies [18, 46, 75].
+The influence of comorbidity physical variables on readmission
+Patients with mental disorders have been recognized in several studies as a vulnerable population for increased risk of readmission if they had co-occurring medical conditions [33, 35, 50, 60, 61, 63]. However, some studies in our review did not show that trend. In the study of Jaramillo et al. [51] it was demonstrated that having comorbidity with any medical condition reduces the readmission risk. Authors associated the protective effect of the medical comorbidity presence with two possible causes: a) most patients had comorbid epilepsy or
+thyroid problems, conditions which, if not properly controlled increase the risk of decompensation of psychotic or affective; b) having a medical condition may be related to better adherence to treatment, taking into account the possibility that the patient does not have the stigma of psychiatric diagnosis. In the study of Brennan et al. [59] a similar trend was observed, indicating that the burden of medical disease not necessarily increases the psychiatric readmission, since less medical diagnoses did increase the risk of mental disorder readmissions in elderly with SUD diagnosis in both genders.
+Co-occurring psychiatric and physical conditions are described as a common condition also in studies with medical inpatients as index populations [36]. A range of studies revealed that pre-existing or co-occurring mental disorder may worsen the course of medical illness and can be seen as risk factor for readmission. For example in the recent study of Ahmedani et al. [76], the rate of readmission in patients with heart failure, acute MI, and pneumonia was 5% greater for individuals with a psychiatric comorbidity. Some studies reported that the risk of rehospitalisation among patients with COPD was increased in subjects with anxiety [77] and that patients hospitalized with a primary medical diagnosis and any co-occurring SMI were more likely to experience a subsequent medical hospitalization [78].
+Regardless of the 52% of studies included in a systematic literature review showing that physical comorbidity may be associated with hospital readmission, it should be noted that the most common physical comorbidity variables with higher probability for readmission were mostly associated with specific categories of psychiatric diagnoses (Table 1). Thus non specified medical illness, somatic complaints, number of medical diagnoses and hyperthyroidism were associated with higher readmission risk in patients with main psychiatric diagnosis of depression or bipolar disorder. Discharged patients with SMI diagnoses and a higher Charlson Comorbidity Index score, somatic complaints and physical health problems have been reported at increased risk of subsequent hospital admission. Chronic lung conditions, hepatitis C virus (HCV) infection, hypertension and number of medical diagnoses were associated with readmission risk in patents with SUD.
+Methodological issues in studies with comorbid conditions
+The comorbidity between mental and physical disorders is an important field in everyday medical practice and it is recognised as important topic in psychiatry. Notably in psychiatric practise the term comorbidity can also be used to indicate the coexistence of two or more psychiatric diagnoses which is arguably inappropriate. Because in most cases it is unclear whether the coexisting
+diagnoses actually reflect the presence of distinct clinical entities or refer to multiple manifestations of a single clinical entity. In psychiatric classification, comorbidity does not necessarily indicate the presence of multiple diseases, but instead can reflect current inability of psychiatrists to supply a single diagnosis that accounts for all symptoms [79].
+Studies included in the present review addressed cooccurring psychiatric and physical conditions within a constructs which are related to different aspects of comorbidity [27]: comorbidity, multimorbidity, morbidity burden and patient’s complexity, implying a diverse understanding of comorbidity variables that might affect the readmission. This fact requires some caution in generalizing and understanding of the nature of the cooccurring mental and physical disorder conditions and their potential impact on hospital readmissions. The review revealed that different constructs of comorbidity were applied which limits a comparison of results on the possible impact of physical comorbidity regarding the psychiatric readmissions. In addition, authors did not describe why they selected particular comorbidity construct. Possibly that also the availability of data source influenced their choice.
+Studies on comorbidity may be hampered by the so-called Berksons bias [80]. Patients who have been diagnosed with a disorder (e.g., depression) have greater chances of being diagnosed with a second disorder (e.g., diabetes) compared to subjects for whom no diagnosis has been made, as a doctor sees patients more often. Only one study [56] applied this criterion in the research protocol where patients with osteoarthritis were chosen as a control group due to its chronic and progressive nature, and because the disease and the treatment do not, as far as known, cause any biological affection on the brain and mood.
+Study limitations
+Prospective studies on readmission in patients with cooccurring physical and mental disorders are not rare, but only a few examined the association between physical conditions and psychiatric readmissions. In the reviewed studies outcomes varied considerably, possibly because of differences in applied methods, data collection, definition of comorbidity and the number of chronic conditions included in analysis. In this regard, more high quality research is needed in the future to understand the associations between physical comorbidities and psychiatric readmissions.
+Two main limitations of the present literature review need to be acknowledged. Firstly, although the methods for searching the literature were valid, we cannot be certain that all relevant studies on co-occurring psychiatric and medical conditions associated with readmission have
+been identified. Secondly, in the review included studies addressing co-occurring psychiatric and physical conditions within different comorbidity constructs. This circumstance requires some caution in terms of generalizing of results since small number of studies has been retrieved (n = 23), with diverse study protocols, different concept of comorbidity, index population, and follow up periods.
+Since, to our knowledge, there are no previous systematic reviews in this area, this is the first systematic attempt taking into account all literature addressing the impact physical comorbidities on hospital readmission of patients with psychiatric diagnoses. The presented review covers publications over a more than 20 year period and provides a broad and systemised reporting of different aspects of co-occurring psychiatric and medical conditions in association with hospital readmission of patients with psychiatric diagnosis. In addition, the present systematic review addresses also different concepts on comorbidity. This provides an additional explanation on diversity of research results we are facing with, when co-existing physical and mental disorder conditions are studied in respect to hospital readmissions.
+Conclusions
+Co-occurrence of mental and physical disorder conditions is very common in a clinical setting. However, the exact nature of the relationship between them is very complex and so far still not well understood. This vagueness is also reflected in the understanding of the influence that some physical comorbidities may have on psychiatric readmission. In this respect it is important to apply an adequate model of comorbidity, since various factors such as unhealthy lifestyle habits, psychotropic medication, and inadequate medical treatment or provision may have an important influence on readmission rates in psychiatric study population.
+So far, very little work has been done on physical co-morbid conditions among readmitted patients with mental disorders since comorbidity was seldom the main objective of studies, making it difficult to draw a solid conclusion about actual impact of physical comorbidity on readmission in psychiatric populations. Nevertheless, physical comorbid conditions seem to be more common among readmitted psychiatric patients than single admission patients, their association with readmission can vary according to the nature of mental disorders, characteristics of study population and study protocol.
+The main body of reviewed studies supported the hypothesis that patients with mental disorders are at increased risk of readmission if they had a co-occurring medical condition, higher Charlson Comorbidity Index score, in and more medical diagnoses. Additionally, comorbidity is generally associated with mortality, quality of life, and health care but the consequences of specific
+disease combinations depend on many issues. The scarcity of eligible studies on psychiatric readmission and its association with physical conditions became apparent during performance of this review. It may be related also to the fact that several studies in this field did include the presence of physical comorbidities within the exclusion criteria. Namely, at admission/discharge have been documented several different types of physical comorbidity variables mainly in order to describe the study population, or to ensure that included samples matched in main medical conditions, or to describe a basic medical characteristics of index population. Due to the importance of the physical comorbidity issue in patients with mental disorders it would be advisable to include more variables on physical comorbidity in the future outcome research of mental disorders in naturalistic setting.
+The impact of physical comorbidity on psychiatric readmission is still insufficiently investigated problem. But there is a growing interest among practitioners and researchers in the impact of physical comorbidity on a variety of outcomes in mental disorders, such as mortality, health-related quality of life and health care spending, which is substantially higher for patients with comorbid conditions [39]. The comorbidity of mental and physical disorders is on the increase and as pointed out by Sartorius [81] this issue is becoming a main challenge to medicine in the 21st Century.
+Future research should address these topics with more in-depth studies since new insights in this field could lead to better prevention strategies to reduce psychiatric readmissions. From a clinical perspective it would be very useful to be able to recognise high risks for readmission in order to ensure a better monitoring and treating psychiatric patients with co-occurring physical disorders.
+Additional files
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+1	| INTRODUCTION
+Worldwide, the prevalence of psychiatric disorder regards 1 out of 4 people with about 450 million having severe mental illness (SMI), of whom 300 million are living with depression, 21 million with schizophrenia and 46 million with bipolar disorders,1 with similarities between North America2 Europe3 and other parts of the world,
+including Asian-Pacific areas.4 In fact, a survey in Europe3 involving 28 European Union countries plus Norway and Switzerland, also confirms that about 38% of residents of the EU, or around 165 million people, are affected by a mental illness at some point in any given year and that it is the challenge of the 21st century. COVID-19 certainly has added to the detection and service delivery but the problems with the SMI and cancer care have been long standing. In fact,
+1446 - Wl LEY-------------------------------------------------------
+only about one-quarter of those with a mental illness in Europe received any treatment, and only about 10% had “nationally adequate” care and integrated medical, social, employment and psychological provision which is the key for recovery.3
+Besides the several implications of SMI in terms of poor quality of life and level of functioning, problems at work, social integration and stigma, the last two decades have seen much of the research focused on physical health, not on the integration of SMI and cancer care.5,6 One of the most important and recognized problems is that people with SMI, especially schizophrenia, bipolar disorders and severe depressive disorders, have both an increase rate of morbidity because of somatic disorders and higher mortality rates, aside from cancer.7-9 The provisional conclusions of these studies is that there is sufficient evidence that people with SMI are less likely to receive standard levels of care for most diseases, such as diabetes and heart disease associated with lifestyle factors. They are less likely to be considered for screening such as for mammography or prostate surveillance; less likely to receive baseline testing of numerous important physical parameters such as obesity, high blood pressure; so that access to and quality of health care remains to be improved for individuals with SMI.10 This is responsible for a shorter life expectancy of 15-20 years compared to the general population. This has brought clinical scientists to underline that the contravention of the international conventions for the right to health for the poorer physical healthcare and premature mortality is significantly worsened in people with SMI, and is a worldwide problem deserving attention and quick solutions.11
+Whereas most studies have pointed attention to cardiovascular disease and related disorders, including hypertension, diabetes and metabolic syndrome,12,13 cancer has been also considered an important topic to be taken into account in research and clinical practice.14-16
+cancer screening practices in a large sample of 715 705 women with mental illness. Their pooled meta-analysis demonstrated significantly reduced rates of mammography screening in women with mental illness, especially mood disorders and schizophrenia.
+Similar results were found as far as screening for cervical cancer with studies indicating that women with schizophrenia were less likely to have a Pap test (58.8% vs 67.8%) compared to all other women.21 A further review confirmed substantial evidence in the literature for disparities in breast and cervical cancer screening rates among women with SMI.22
+For lung cancer, it has been shown that patients with SMI have less access to smoking cessation and cancer screening programs because of lack of clinician expertise tailoring communication about cancer care and tobacco cessation, and the fragmentation of mental health and cancer care.23 Also treatment is poorer when lung cancer has been diagnosed.24 Likewise, a study in Hong Kong indicated a low cancer screening utilization for several conditions, including mammography; clinical breast examination; pap-smear test; prostate examination; and colorectal cancer screening.25
+A very large and recent metanalysis involving almost 5 million people (501 559 patients with mental illness, and 4 216 280 controls) from 47 publications and involving many possible mental illnesses (ie, schizophrenia or schizoaffective or psychosis, depression or bipolar disorder or mania, eating disorder or anorexia nervosa or bulimia nervosa or binge eating disorder, obsessive-compulsive disorders, post-trauamtic stress disorder, anxiety disorder or panic disorder) provided a global overview of the problem. Data included studies relative to screening for cancer with results showing that screening was significantly less frequent in people with any mental disease compared with the general population for breast cancer, cervical cancer, and prostate cancer, but not for colorectal cancer.26
+2	| PREVENTION ISSUES AND SCREENING FOR CANCER IN PEOPLE WITH SMI
+Disparities in health services provided to people with SMI have been documented for preventive services in general. This is true also as far as prevention and screening for cancer.
+Regarding mammography for example, preliminary data on women with mental illness and/or substance abuse, regardless of severity, showed that there was a risk for underscreening.17 Other research on patients with SMI indicate that women with mental illness are 32% less likely to undergo at least one screening mammography and among those who received at least one screening mammography, fewer women with mental illness received screening mammography on an annual basis.18 These data are in line with a further study showing that women with a mental illness are at risk for not adhering to recommended routine breast cancer screening and may require more intensive efforts to achieve optimal rates of recommended breast cancer screening.19 More recently, by examining different types of mental disorders (ie, mood disorders, depression, SMI, distress and anxiety conditions) Mitchell et al,20 identified 24 studies reporting breast
+3	| INCIDENCE OF CANCER AND CANCER MORTALITY IN PEOPLE WITH SMI
+The problem of the incidence of cancer and cancer mortality among patients with SMI is still controversial, with some studies reporting a higher prevalence while others a similar or a lower prevalence. A series of reviews of data and meta-analyses which are available have tried to shed some light in this area. By analyzing the data from over 6000 female patients with schizophrenia from 13 studies in comparison to age matched general populations from the relevant country from 1986 to 2008, Bushe et al27 reported widely discrepant results, ranging from 52% increase in risk to 40% decrease, with six of 13 studies showing an increased or marginally increased incidence of breast cancer. In a further meta-analysis of 12 cohort studies that included 125 760 women and in which conventional methods of meta-analysis had been used, schizophrenia in women was associated with an increased breast cancer incidence compared with the general popula-tion.28 A further recent meta-analysis of studies29 showed that compared with general populations, the prevalence rates of prostate and colorectal cancer in male patients with schizophrenia are lower,
+and lung cancer prevalence is higher in female patients, while no difference was found in a different meta-analysis for lung cancer.30 There are also data indicating that the incidence of colon cancer was higher among people with schizophrenia in comparison with people with bipolar disorders or the general population,31 while a different Northern European population-based study (time span 1990-2013 for 1 424 829 person-years of follow-up) showed a higher risk for female breast cancer, lung cancer, esophageal cancer and pancreatic cancer and a lower risk of prostate cancer,32 as already reported in a previous study.33
+It is possible that there might be protective factors or risk factors among patients with schizophrenia that relate to specific cancers. With respect to this, changes in endocrine hormones caused by antipsychotic medications, familial or genetic factors (eg, tumor suppressor gene p53, enhanced natural killer cell activity, angiogenesis, or DNA repair mechanisms), reduced parity and hyperprolactinaemia may be factors contributing to determine such contradictory results.34-36
+Regarding bipolar disorders, elderly people are reported to have a delay in receiving specific cancer treatment, although no studies have investigated treatment outcomes.37 Also, a study carried out in Israel showed an enhanced cancer risk for bipolar disorder both in men (Standardized Incidence Ratio, SIR 1.59) and women, (SIR 1.75)38 As stated by Howard et al,39 when interpreting these results, it is useful to consider the effect of missing cancer diagnoses, short-ended life expectancy, the characteristics of health-service contexts, behavioral risk factors, and the possibility that genetic or drug effects can influence the results of the studies. It is mandatory for example, to control for age and sex in incidence studies since while SMI, including schizophrenia and bipolar disorders, are more common in young adults and are associated with a shortened life, most cancers are diagnosed in patients older than 60 years and the cancers affecting men and women differ. Also, patients with SMI have higher overall mortality rates, particularly from suicide and unnatural causes at younger ages.
+With these caveats, mortality for cancer is demonstrated to be higher among people with SMI. For example, in a prospective study Chou et al,40 showed that among 1145 patients with schizophrenia and 5294 controls, the incidence of breast cancer was lower (1.93% vs 2.97%), although the mortality rate among patients with schizophrenia was higher than that of the control group. Similar results were found among patients with schizophrenia and lung cancer,41 who are reported not having received stage-appropriate treatment, resulting in 24 poorer outcomes.24
+With respect to prostate cancer, a study of 49 985 patients with locoregional high-grade (nonmetastatic) cancer showed that having a SMI (bipolar disorder, schizophrenia, and other psychotic disorders) was associated with reduced odds of receiving surgery or radiation concurrent with hormone therapy as initial treatments in the year after diagnosis. Additionally, SMI was associated with higher hazard of 5-year cancer-specific death after accounting for competing risks of non-cancer death.42 Again, in an Australian study, although the incidence of cancer was not higher than in the general population, psychiatric patients were more likely to have metastases at diagnosis and
+-----------------------------------Wiley1447
+less likely to receive specialized interventions, with a greater case fatality.43 Similar results were found in a study of 16 636 elderly women in which patients with comorbid anxiety and depression had an increased risk for diagnosis delay of >90 days from symptom recognition, and those with severe mental illness had an increased risk for initial treatment delay of >60 days from diagnosis.44 More recently, in a large Danish study on 56 152 women with early-stage breast cancer diagnosed in 1995-2011, patients with schizophrenia or related disorders had a higher likelihood to not be provided proper cancer guideline treatment and to have a survival after breast cancer significantly worse than that of women without SMI.45
+4	| PALLIATIVE CARE IN PEOPLE WITH SMI
+The existence of disparities in health and health care between patients with schizophrenia and/or SMI and patients without a diagnosis of mental illness is extremely important also in end-of-life care. In concert with data underlining the problems of the stigma, poverty, lack of family support and social isolation, patient-level factors including cognitive impairment, psychiatric disabilities and chronicity,46 end-of-life care has been shown to be lacking for patients with schizophrenia and/or SMI.47
+In one of the first studies conducted in a palliative care setting, Chochinov et al48 found that compared to their matched cohort, Canadian patients with schizophrenia were less likely to see specialists other than psychiatrists, less likely to be prescribed analgesics, and less likely to receive palliative care. They also were much more likely to die in nursing homes where optimal physical, psychological and spiritual care is possibly less optimal than in palliative care units.49
+In a further Australian study,50 people with schizophrenia in the last year of life were less likely to be admitted to hospital and access community-based specialty palliative care, but more likely to attend emergency departments if male. Community-based specialist palliative care was associated with increased rates of hospital admissions. In general, what emerges is that stigma affects quality of care and access to care; that there are problems related to consent and capacity for the patients to make end-of-life care decisions or to appoint substitute decision makers; thta there is an urgent need for better practices for psychosocial interventions, pharmacology, family and health-care collaborations, including setting, communication, provider education, and access to care.51 These data were more recently confirmed by other studies in Taiwan52 and France.53 The latter was carried out on 2481 patients with schizophrenia and 9896 matched controls. The authors found that patients with schizophrenia were more likely to receive palliative care in the last 31 days of life and less likely to receive high-intensity end-of-life care (eg, chemotherapy and surgery), were more likely to die younger and had a shorter duration between cancer diagnosis and death than controls.
+Looking at the qualitative issues related to patients with SMI, clinicians have indicated several problems to be addressed. A first regard information processing and communication in part determined by
+““
+cognitive impairment and lack of insight secondary to SMI. A second issue has to do with the problem of identify carers in the home environment because of previous family disruption or living in sharehouses, alone or being homeless. Also it has to be underlined the lack of experience, lack of adequate educational resources and services, lack of training, policies or guidelines, or fear and ignorance about SMI among health carers. Last, there are common preoccupations related to the assumption that the patient will be unmanageable, because identifiable institutional or community care staff to provide adequate care are considered insufficient.54 A recent review55 indicated that an increased awareness of potential healthcare disparities in this population, creative approaches in multidisciplinary care, and provision of adequate palliative services and resources can enhance end-of-life care in schizophrenia.
+5	| POSSIBLE MECHANISMS INVOLVED IN THE PROBLEM
+One of the most significant debates regarding poor health care in people with SMI is related to the problem of stigma leading to the problem of discrimination and reduced expression of human rights.56-58 Available research in psychiatric settings indicates that stigma consists of both the auto perception of one-self as stigmatized and different from others (self-stigma, as the internalization of a negative stereotype that the person applies to oneself)59 and the stigma imposed by society (social stigma, as the series of stereotyped beliefs, prejudices and discriminatory attitudes). Elsewhere60 we have considered the dimension of stigma as the expression of the other side of dignity, where also it is possible to distinguish a self-related sense of personal value and dignity (intrinsic dignity) and a reciprocal and interpersonal experience of dignity related to what others provide us in terms of value and dignity (extrinsic dignity). Being seen as an equal human being, with the potential to experience self-worth, meaning and purpose are key factors to maintaining dignity, despite suffering the consequences of mental illness and having to fight for one's rights.61,62 Therefore, throughout the continuum of cancer care, the stigma of mental illness and poor dignity are factors influencing the management of the disease.
+In the context of cancer, Irwin et al63 have suggested that interrelated patient-based, provider-based, and systems-based factors, influenced by mental health stigma, may impact cancer prevention, diagnosis, treatment, and end of life care. More specifically the authors consider that disparities in cancer care for patients with schizophrenia (but it can be extended to others with SMI) is derived by the inter-relationship between these factors. From one side, patient's inappropriate affect, positive or negative psychotic symptoms, cognitive symptoms (eg, impaired attention and executive function), disorganized behavior, dysfunctional coping (eg, pathological denial), and poor health behavior (eg, smoking, substance use and/or abuse, poor adherence) intervene to create part of the problem. It is demonstrated that the tendency to avoid the health care system, to minimize or not understand physical symptoms because of poor
+insight to not cooperate with caregivers, cause a delay in diagnosis and presentation at cancer centers with late-stage cancer.64,65 Also functional impairment of people with SMI is one pf the most significant predictor of lower screening rate.66 On the other side, the difficulty of health-care providers (eg, GPs, oncologists, nurses) in relating to patients with poor functioning, and showing psychiatric symptoms that are unfamiliar and incomprehensible to the team, the fear of violent behavior or suicide risk, and the several prejudices on SMI and its un-treatability can determine a second part of the problem. Finally, the fragmentation of health care services, the difficulty in creating a whole-person centered approach, the tendency of psychiatry and somatic medicine to work in a separate or non-integrated way are a third cause of the problem. All the three aspects are subsumed under the concept of stigma. This in turn negatively influences cancer care, from prevention and screening, to early diagnosis, treatment and symptom management, as well as end-of-life care, with an impact in reducing both quality of life and survival. Therefore, it is important to improve the area of education and training, including communication skills and assessment and management of emotions and psychiatric symptoms when dealing with people with SMI in order to increase the quality of their cancer care, and physical health in general.67
+6	| DISCUSSION
+There are several considerations to be mentioned in the analysis we have done about the problem of cancer care among people with SMI.
+One of the most important findings emerging from the psychooncology literature concerning people with SMI is that there are now significant data indicating major disparities in screening and treatment for cancer for this vulnerable population compared with that of the general population. A second issue is that people with SMI are less likely to receive optimal treatment after diagnosis with poorer cancerspecific prognosis and lower survival time.
+These data are in line with what has been shown in the literature regarding poorer physical health in patients with SMI and the need, as indicated by Tosh et al,68 that physical health can be the target of intervention in both psychiatry and medicine in general, in order to favor the access to health services which, in turn, facilitates longer-term benefits, such as reduced mortality or morbidity. On the other hand, the WHO Comprehensive Mental Health Action Plan, endorsed by the World Health Assembly in 2013, has repeatedly outlined the need that Member States and organizations (eg, Refs.69,70) develop and implement effective policies, strategies and plans to improve the health, both physical and mental, of people living with SMI. The WHO itself indicates in fact actions that can be taken such as: creating protocols for physical and mental health needs of patients in the areas of prevention, identification, assessment and treatment; improving access to general health services through the integration of physical and mental health services; working to overcome the stigma associated mental illness and discrimination.
+With specific reference to cancer, in order to decrease the early mortality of patients with SMI, especially schizophrenia, Chou et al71
+have proposed a series of steps to be rapidly taken by the health care systems, namely:
+1.	enhance early detection and early treatment, such as increasing the cancer screening rate for patients with schizophrenia;
+2.	provide effective, timely treatment and rehabilitation;
+3.	improve patients' psychiatric symptoms and cognitive impairment;
+4.	promote healthy behavior in the general population and emphasize healthy lifestyles in vulnerable populations;
+5.	act on reducing the stigma of schizophrenia.
+Regarding palliative care several recommendations have been stressed by Woods et al72 who underlined that palliative care needs of people with SMI are similar to the general population (eg, pain and symptom control, maintenance of function, enhancement of quality of life, support for relationships, and the possibility of dying well); that palliative care must be centered on the needs of the individual person with SMI basing a therapeutic relationship created by respect, dignity, hope, and non-abandonment, integrating principles of hospice palliative care in end-of-life care for people with SMI; that policies and guidelines to address the needs of this population should be developed and revised by integrating the systems of care (mental health care, palliative care, family medicine, social services) for better intervention for people with SMI and their families.
+6.1	| Clinical implications
+Psycho-oncology has a very specific role in the area of SMI, with a strong commitment to better understand health inequalities in cancer care for people with psychiatric disorders and to plan and develop effective interventions. The time has come to bridge the gap between stigma and mental illness, and improve the links between oncology and psychiatry for more specific psycho-oncology programs addressed to this vulnerable segment of the population. Some interesting experiences have been reported in the area with data demonstrating that specific tailored screening and management intervention are possible for patients with severe mental disorders, such as schizophrenia, if organized in an integrated multidisciplinary way.73,74 With respect to this, it is therefore important to stress the fact that the introduction of psycho-oncologists in teams and the establishment of psychooncology departments / units should consider not only a link with specialties in oncology (eg, medical oncology, hematology, radiation oncology, surgery, palliative care), or biological sciences (eg, epidemiology, immunology, biology, pathology, genetics), but a more structured liaison with mental health department.75
+6.2	| Study limitations
+As a limitation of this study, we conducted a narrative review that had the aim to report the main data and results from significant studies in the area of the relationship between SMI and cancer. Therefore, more
+complete searches of usual databases (e.g. PubMed, CINAHL, Embase, and PsycInfo) on the different topics of this area (screening for cancer, risk of developing the disease, treatment, outcome and mortality) are necessary. Also the recommendations for systematic reviews should have been followed.
+7 | CONCLUSION
+With all this as background, it is mandatory to draw attention to the problem of cancer among people with SMI, both in terms of screening, incidence and mortality as well as palliative care. The time has come for psycho-oncology to take into consideration these issues and to launch a campaign, through a special issue of the Journal, to analyze in detail the various and complex aspects regarding this area of urgent clinical need.
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+2406 |	COHEN et al.
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+considered attempting suicide, 11% made a plan, and 7% attempted suicide (Ivey-Stephenson et al., 2020). These data should be understood in the context of the widely held notion that youth suicide is largely underreported (Ayer et al., 2020). Therefore, the actual impact of the public health crisis of youth suicide is likely much greater than available data suggests.
+Although White youth have typically demonstrated a higher suicide death rate compared to Black children and adolescents, an analysis of recent data from the Web-based Injury Statistics Query and Reporting System indicates that Black youth under the age of 13 have twice the suicide death rate as their White counterparts (Bridge et al., 2018). In addition, self-reported suicide attempts and suicide-related injuries that did not result in death among Black youth have increased since 1991 despite concurrent decreases among all other racial/ethnic groups (Lindsey et al., 2019).
+Research on the causal factors associated with newly emerged racial disparities in suicide risk is limited, but there is some indication that racial discrimination (Assari et al., 2017) and disparities in service access and utilization are important factors (Price & Khubchandani, 2019). One study found that after inpatient psychiatric care for suicidality, Black participants were less likely to have received follow-up care than individuals from other racial groups (Fontanella et al., 2020). Additionally, empirical work has linked suicide with neighborhood factors such as violence exposure (Lambert et al., 2008). High levels of neighborhood violence may be an important factor associated with growing suicide risk among Black children and adolescents (O'Donnell et al., 2019). In response to this ongoing crisis, the Congressional Black Caucus convened an emergency taskforce in 2019 to analyze data and develop solutions to the increasing risk of suicide among Black children and adolescents. In 2020, the National Institutes of Mental Health (NIMH) and Minority Health and Health Disparities (NIMHD) also issued a joint notice of special interest to encourage research on suicide in this critical area.
+2	| SUICIDE PREVENTION IN SCHOOL CONTEXTS
+Schools are an exceptionally strategic context to address youth suicide risk because most children and adolescents spend a vast portion of their time in these settings. Although there is ample research on the epidemiology of youth suicide, much of it is associated with clinic-based samples, thus presenting a barrier to translation of extant research to school settings (Miller & Mazza, 2018). School-based programs typically encompass various combinations of four general approaches: screening, gatekeeper training, universal psychoeducational programs for youth delivered at the classroom level, and postvention activities. Screening approaches utilize school-wide assessment procedures to identify students at risk for suicide and in need of follow-up supports. Columbia Teen Screen (Scott et al., 2010) is one example of a well-defined psychometrically validated screening method. Gatekeeper programs support staff in developing the knowledge and skills needed to identify suicidal youth and refer them for appropriate care. One example of a gatekeeper training program is question, persuade, refer (QPR; Wyman et al., 2010) which involves teaching school staff about warning signs for suicide risk and best practices for providing supports and referral. The third approach includes educating youth about mental health problems and suicide, and supporting skill development around coping and help-seeking. Youth Aware of Mental Health (YAM; Wasserman et al., 2015) is an example of an evidence-based psychoeducational program and was studied in a large-scale trial implemented with European students. When YAM, screening, and gatekeeper training were compared, YAM was the only school-based intervention with demonstrated reductions in suicidal ideation and attempts (Wasserman et al., 2015). Postvention activities include standardized procedures applied to address the aftermath of completed suicide. Postvention approaches are believed to be preventive in nature because they address mental health risks of survivors (Andriessen, 2009), and when a student dies by suicide, the rest of the school body may require significant psychosocial support. In addition, school-based postvention programs are recognized as an essential aspect of prevention programing because they seek to address the very real threat of suicide contagion, although there is limited empirical data supporting their effectiveness (O'Neill et al., 2020).
+COHEN et al.	| 2407
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+3	| CULTURALLY RELEVANT SCHOOL - BASED APPROACHES FOR BLACK YOUTH
+There are virtually no examples of empirically evaluated school-based suicide prevention programs for Black youth, aside from two published studies. Brown and Grumet (2009) described a school-based screening and care linkage program inclusive of 229 Black middle and high school students. Through their approach, they demonstrated successful identification and connection to services, but there were no notable culturally relevant aspects. In contrast, Robinson et al. (2016) adapted the Adolescent Coping with Stress Course (Clarke et al., 1995) to include culturally relevant language, values, coping strategies, and settings and provided it to 758 Black high school students. Results indicate that the culturally adapted small group intervention was associated with a reduction in suicide risk compared to standard care at the four participating high schools. The overall absence of culturally relevant approaches suggests that additional work is needed to address the public health crisis associated with Black youth suicide. Such efforts should not be limited to direct interventions, but rather they should address risk at individual and system levels. Outside of school-based interventions, programs with evidence of effectiveness in reducing suicidal behavior based on a meta-analysis conducted by Joe et al. (2018) include multisystemic therapy (Huey et al., 2004) and attachment-based family therapy (Diamond et al., 2010). Strategies that involve embedding mental health supports within faith-based settings to prevent suicidal behavior have enormous promise (Molock et al., 2008), but have not been studied extensively. Faith-based strategies are uniquely advantageous because they utilize trusted social connections that already exist among a large proportion of Black youth to deliver mental health promotive messaging and supports (Molock et al., 2008).
+4	| APPLYING THE ECOSOCIAL MODEL TO MEET THE UNIQUE NEEDS OF BLACK YOUTH
+Emergent disparities in suicide risk signal the importance of a strategic research and practice framework that is suited to address the specific needs of Black youth. Frameworks that focus on either individual or structural approaches are insufficient, while an integrative approach that tackles both will offer the greatest hope for progress on suicide outcomes in Black youth. The Ecosocial Theory of Disease Distribution (Krieger, 2012) posits that racial inequalities in health develop through the embodiment of population-specific social and ecological factors and that actions to address disparities must be simultaneously applied at individual and structural levels.
+In describing the Ecosocial Model of disease distribution in detail, Krieger (2012) notes that this approach necessitates:
+Explicit consideration of pathways of embodiment in relation to types and levels of exposure, the period and spatial expanse involved (i.e., spatiotemporal scale), and historical context, along with phenomena that affect susceptibility and resistance to exposure, ranging from micro... to macro (e.g., social organizing to challenge health inequities). Also germane are issues of accountability (causal responsibility for) and agency (the power and ability to act) at every level, because they pertain to not only the magnitude of health inequities but also how they are monitored, analyzed, and addressed. (p. 936).
+The term embodiment describes the way the social and environmental context becomes incorporated within an individual's health status (Krieger, 2011). In relation to suicidal thoughts and behaviors, embodiment manifests as cognitive and affective states associated with suicidal ideation, as well as morbidity and mortality associated with suicidal behavior. Krieger (2012) describes pathways of embodiment as exposure to risk related to a set of overarching factors including (1) economic and social deprivation, (2) toxic substances, pathogens, and hazardous
+2408 |	COHEN et al.
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+conditions, (3) discrimination and other forms of socially inflicted trauma, (4) targeted marketing of harmful commodities, (5) inadequate or degrading healthcare, and (6) degradation of ecosystems. While most of Krieger's pathways of embodiment apply to disparities in suicidality, we argue that only discrimination and trauma, and inadequate or degrading healthcare are significantly applicable to Black youth within educational contexts. This is because we identify these pathways as being central to the core functions of educational institutions. Discrimination against Black students is woven into the history of education in the United States, and schools represent the de facto primary service provider for all youth. Racial discrimination and unhelpful or harmful services are essential forms of risk that are uniquely relevant to schools.
+In relation to Krieger's (2012) notions of accountability and agency, an Ecosocial Model of Black youth suicide posits that our social contexts and the structures that dictate them should be held accountable for these negative outcomes and that efforts protect Black youth should provide access to power and opportunities for action that protect and build strength and resistance from harm. Consistent Krieger's conceptualization of pathways to embodiment and notions of accountability and agency, suicide prevention efforts for minoritized youth must recognize and leverage culturally distinct risk and protective factors.
+Figure 1 provides a visual guide to the theoretical framework described in this paper. Overall, the figure depicts relations between identified pathways to embodiment and sources of accountability and agency, with mental health and suicidality. Pathways to embodiment are illustrated to show that they degrade mental health and increase risk for suicidal thoughts and behavior. Sources of accountability and agency are depicted to indicate that they improve mental health, decrease suicide risk, and diminish pathways to embodiment. A link is presented between mental health and suicidality, illustrating how suicide risk can emerge from a state of worsening mental health (Al-Mateen & Rogers, 2018). Subsequent sections provide an overview of how elements presented in the ecosocial framework provided in Figure 1 relate to practical suicide prevention strategies for Black youth in schools.
+5	| ECOSOCIAL PATHWAYS FOR SUICIDALITY IN BLACK YOUTH WITHIN SCHOOL CONTEXTS
+The school setting represents an important source of suicide risk for Black children and adolescents. School professionals hold unfavorable stereotypes about Black youth (Priest et al., 2018), which can negatively influence teacher-student interactions, such as increasing their use of harmful discipline practices (Okonofua & Eberhardt,
+COHEN et al.	| 2409
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+2015). Black students also have among the highest levels of exposure to school violence (Wang et al., 2020). The presence of these factors in schools is concerning because discrimination and racial trauma are associated with suicide and risk factors for suicide (Williams et al., 2019). In addition, when Black students experience mental health problems such as depression and suicidality, they are less likely to receive treatment at school compared to all other students (Merikangas et al., 2011; Nestor et al., 2016). Taken together, it appears that multiple aspects of the school context converge to provide a distinctive set of pathways for suicide risk among Black students. Figure 1 describes the hypothesized links between discrimination and harmful services with mental health and suicide risk. Inadequate or degrading healthcare negatively impacts mental health, which is associated with higher suicide risk (Al-Mateen & Rogers, 2018). Discrimination and trauma are directly associated with poorer mental health and suicidality (Williams et al., 2019).
+5.1	| Discrimination and trauma
+A large research base connects racial discrimination with a wide array of negative health outcomes (Paradies, 2006), including a number of studies indicating links with suicidality in Black populations. Within schools, potential sources of discrimination are extensive and arise from interactions with staff members and peers (Benner & Graham, 2013). In addition, contextual manifestations of discrimination and sources of trauma including the pressure to relinquish cultural identity, the presence of violence, and other factors within the social environment increase suicide risk (Al-Mateen & Rogers, 2018), and are highly relevant to educational contexts.
+5.1.1 | Interpersonal discrimination
+Assari et al. (2017) evaluated the link between perceived discrimination and suicidality in a nationally representative sample of over 1100 Black adolescents of American and Caribbean descent. To assess experiences with interpersonal racism, they used an adapted version of the Everyday Discrimination Scale with three additional items to encompass discrimination by teachers and found that more discrimination was associated with a higher risk for suicidal ideation. Another study used a sample of 2490 adolescents that was comprised of over 50% Black youth and found that those experiencing at least occasional discrimination were significantly more likely to report having suicidal ideation in the past year (Tobler et al., 2013).
+Several studies also show links between discrimination and suicidality in Black adults. One notable study with Black adults also found links between perceived discrimination and suicidal ideation (Walker et al., 2014) and another study with an adult sample uncovered a specific relation between perceived discrimination and capability to engage in suicidal behavior (Brookes, 2020). In a predominantly Black and Latinx sample of young adults, Wang et al. (2018) found associations between perceived discrimination related to interpersonal racism, and subsequent ideation and attempts.
+There is also a preponderance of evidence that experience with discrimination is linked to developmental trajectories of increasing depression that present notable risk for future suicidality. A longitudinal study of over 700 Black adolescents found that over time perceived discrimination was associated with greater conduct problems and depressive symptoms (Brody et al., 2006). Using another large longitudinal sample of Black adolescents, Estrada-Martinez et al. (2012) found that racial discrimination in childhood was associated with increased risk of depression in adulthood. Coker et al. (2009) evaluated the association between discrimination and mental health in over 5000 5th graders across three public schools in large metropolitan areas and found that Black students experiencing interpersonal racism had a 2.6 greater odds of having symptoms of depression within the last year.
+A small number of studies have also demonstrated relations between suicidality and microaggressions: a more subtle form of explicit interpersonal racism. One study found that in Black adolescents, microaggressions were
+2410 |	COHEN et al.
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+concurrently and prospectively associated with suicidal ideation (Madubata etal., 2019). Hollingsworth etal. (2017) found that the relationship between some types of microaggressions and suicidal ideation is mediated by the extent to which participants perceived themselves as a burden to others.
+5.1.2 | Acculturation
+Beyond individual experiences associated with everyday discrimination, Black youth face several broad social and environmental conditions that represent structural and systemic forms of discrimination and racial trauma. Acculturation is the ongoing process of adapting to a different culture through interaction, conflict, and the negotiation of trade-offs associated with maintaining one's own cultural values and attributes, versus taking on the cultural characteristics of the dominant group (Smokowski et al., 2017). Berry (2006) delineates several outcomes of the acculturation process including integration, where useful aspects of dominant culture are absorbed while individual cultural identity is maintained, and assimilation, which occurs when individual cultural identity is abandoned and dominant culture is fully adopted in its place. In spite of the complexity and variety of outcomes associated with the acculturation process, which can have both positive and negative health consequences (Abraido-Lanza et al., 2006), suicide prevention literature generally conceptualizes the term acculturation as a process that results in assimilation. For example, Walker (2007) in describing how acculturation is related to Black populations, notes that:
+Because acculturation necessitates that individuals and groups embrace the values, beliefs, and practices of the majority culture, his or her own customs may be neglected as new ideologies are embraced. Traditional protective factors that have been ingrained in the community... are no longer sufficient preventative measures for psychological threats. (p. 387-388).
+In a social context where anti-blackness is a prominent feature (Dumas, 2016), Black youth are often positioned to relinquish their cultural identity to adapt, but there can be significant consequences. One study of Black young adults found that greater perceived acculturation was associated with a higher risk for suicidal ideation (Castle et al., 2011). Black participants experiencing familial acculturative stress in a study of racially/ethnically diverse young adults had four times greater odds of having a past suicide attempt than those without this type of stress (Gomez et al., 2011). It appears that acculturative processes that result in assimilation pose significant risk for suicidality for Black youth, but greater study in this area is needed given that other forms of acculturation may result in positive outcomes (Berry, 2006). Such forms may be protective with respect to suicidality if there is no associated degradation of culturally bound coping resources that are specific to Black youth.
+5.1.3 | Violence exposure
+Violence is an important source of trauma that disproportionately affects Black youth across a variety of settings including schools. Data collected from a nationally representative study of over 20,000 US elementary school students found that in 2016, 36% of Black youth attended a school where physical violence occurred at least one time per month, a figure that is eleven percentage points higher than any other group (Wang et al., 2020). While no studies have looked at the association between violence specifically occurring at school and suicidality, some empirical work has looked at the impacts of community violence. Lambert et al. (2008) conducted analyses with of predominantly Black sample of adolescents and found that exposure to community violence, which is inclusive of violence occurring at school, was indirectly linked to suicide risk at grade 8 through the development of depression and aggression during grade 7. This highlights the potential importance of internalizing and externalizing problems
+COHEN et al.	| 2411
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+as risk factors for suicidality within this population. Looking at a multiracial sample inclusive of over 2000 Black students, Flannery et al. (2004) evaluated associations between school violence exposure and mental health outcomes and violence. They found that students with high levels of violence exposure had higher levels of depression, posttraumatic stress, and violent behavior. Bullying is an important subtype of interpersonal violence. One study looked at associations between bully/victim status and psychological well-being in a sample of Black and Latinx youth and found that both bullies and bully-victims have higher levels of internalizing symptoms (Peskin et al., 2007).
+5.2	| Inadequate or degrading healthcare
+5.2.1 | Treatment access and quality
+Although approximately 1 in 6 children has a mental disorder, less than half receive treatment (Whitney & Peterson, 2019). Black youth are even less likely than other youth to receive mental health treatment, including those with symptoms that are relevant to suicidality. For example, Black youth with suicidal thoughts and behavior are much less likely than others to receive outpatient treatment (Nestor et al., 2016). No other studies to our knowledge look at treatment disparities for suicidality in Black youth, but several studies look disparate access for treatment for conditions linked to suicide risk.
+Merikangas et al. (2011) looked at service use in Black adolescents within a nationally representative sample and found that they were considerably less likely to receive treatment for mood disorders, even when they have severe symptoms. Another study found that Black youth with depression had a much lower odds of receiving treatment compared to other youth in the sample (Wu et al., 2001). Alexandre et al. (2009) created a metric for treatment adequacy based on having an appropriate number of treatment sessions and access to medication, as assessed differences in access to care with a large national data set of adolescents. They found nonwhite individuals were less likely to receive adequate treatment for major depressive disorder in the past year.
+Empirical work has specifically highlighted racial disparities in mental health treatment within school settings. A study looking at a sample of over 1500 at-risk youth in Southern California found that compared to their White peers, Black students displaying risk for mental health problems were less likely to receive treatment for mental health problems, and if they did receive treatment, it usually occurred at an older age (Wood et al., 2005). In addition, Gudino et al. (2009) found that within school contexts, Black students with internalizing problems were less likely to receive mental health services relative to White peers. Additional research is needed to account for service access and quality in schools as it specifically relates to suicidality in Black children and adolescents.
+Another important area of concern with respect to service quality is the relevance of intervention content to the unique needs of Black children and adolescents living in the USA. As mentioned previously, culturally relevant interventions targeting Black youth suicide are virtually nonexistent and greatly needed given the distinctive pathways related to racial trauma that are present within this population (Robinson et al., 2016). Overall, efforts to develop and rigorously evaluate preventive interventions for youth mental health conditions have historically centered on meeting the needs of White youth and neglected to include content that is specifically relevant to the unique strengths and challenges associated with the lived experience of minoritized children and adolescents (Coard et al., 2013). As such, there is a significant need to advance the development of culturally relevant treatment approaches for suicide and other mental health problems (Jones et al., 2020), and ensure that they can be successfully integrated into school contexts.
+5.2.2 | Treatment that causes harm
+Given that violence exposure is indirectly associated with suicidality through aggressive behavior (Lambert et al., 2008), and the presence of shared risk factors between violence and suicidal behavior (Decker et al., 2018), it is
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+critical that Black youth displaying externalizing problems receive effective evidence-based treatment. Unfortunately, exclusionary discipline, inclusive of suspension and expulsion, represents one of the most commonly used strategies applied in response to problem behavior on the part of Black students (Fenning & Rose, 2007). In addition to empirical work demonstrating that it fails to serve as an effective treatment for externalizing problems (Lamont et al., 2013), there is a large body of evidence that exclusionary discipline causes harm (Welsh & Little, 2018). For example, Cohen et al. (2021) found that with a predominantly Black sample of middle school students, the frequency of in-school and out-of-school suspension was associated with more problem behaviors and lower levels of emotion regulation at the end of the school year, controlling for measures of these outcomes at the beginning of the school year. The provision of adequate mental health services for Black youth includes protecting them from harmful treatments. Given the established links between aggression and suicidality (Decker et al., 2018), suicide prevention for Black youth includes taking steps to eliminate the use of exclusionary discipline as a means to treat problem behavior.
+5.3	| Applying accountability and agency to suicide prevention
+A key component of the Ecosocial Theory is the recognition of the role of accountability and agency in understanding and addressing health disparities. Krieger (2011) notes that this “directs attention to issues of power at every level, and hence to institutions' and individual people's capacity to act (agency) and their responsibility (accountability) for both actions taken and avoided” (p. 225). The presence of interpersonal and institutional racism (Williams et al., 2019), the demonstrated links between discrimination and suicide (Assari et al., 2017), and the recent acceleration of suicide deaths and attempts in Black children and adolescents (Bridge et al., 2018; Lindsey et al., 2019), suggests that applied efforts to prevent suicide must place a greater focus on accountability for distinct pathways to and indicators of suicide risk, and agency in relation to cultural identity development and access to culturally relevant coping resources. Figure 1 describes hypothesized relations between sources of accountability and agency with mental health and suicidality. Practices that hold systems accountable, and facilitate agency and power have the potential to promote positive mental health outcomes, minimize pathways to embodiment, and decrease suicide risk and disparities.
+5.3.1 | Accountability
+Given the primary nature of schools in children's lives, accountability for racism within educational contexts is a key avenue for suicide prevention activities, especially those that seek to address racial disparities. Educational professionals hold negative stereotypes about Black youth (Priest etal., 2018), and these stereotypes are connected to the biased application of harmful disciplinary approaches (Okonofua & Eberhardt, 2015). School policies are often centered around dominant culture and facilitate disproportionate harm to minoritized students (Fenning & Rose, 2007). Accountability for racism should be applied at all levels within school settings ranging from policy selection and implementation at the administrator level to culturally responsive professional development for staff, and actions that facilitate cultural acceptance and appreciation for students and families.
+5.3.2 | Agency
+In addition to accountability, agency is an important area of attention in addressing racial disparities in suicide, because it represents avenues for self-directed action based on culturally embedded strengths and resources. The development of a positive cultural identity and the application of culturally relevant coping strategies are critical
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+manifestations of agency. One study with a sample of ethnically diverse adolescents found high levels of acculturation was associated with suicidal ideation, except when study participants had a strong ethnic identity (Polanco-Roman & Miranda, 2013). School connectedness, involving the extent to which students feel like their educators care about their well-being (Blum, 2005), is another important contextual factor related to suicide prevention. School connectedness appears to be an important protective factor for all youth (McNeely et al., 2002) and can be especially important for Black youth. Tomek et al. (2018) examined over 2000 Black adolescents living in high poverty communities and found that school connectedness was associated with reduced risk for suicidal ideation over time.
+There is good reason to believe that a school's capacity to support positive cultural interactions and positive identity development is related to school connectedness among Black students. In describing the relevance of connectedness to campus racial climate, Byrd (2015) argues that “the degree to which racial interactions are positive will influence how connected students are to different- and same-race others in the setting” (p. 13). When cultural identity becomes eroded, which may be a result of socio-contextual factors including anti-blackness (Dumas, 2016), there is greater suicide risk.
+5.4	| Practical school-based approaches rooted in accountability and agency
+Given the importance of school context across multiple levels, there are a number of core domains of school functioning that can be addressed to support accountability and agency for health disparities in suicide in young people, including (1) treatment engagement and utilization, (2) inclusion of culturally relevant coping practices in prevention and intervention programs, (3) universal antiracist practices, and (4) culturally relevant risk assessment. Figure 1 illustrates relations between the four practices and pathways to embodiment, mental health, and suicidality. Treatment engagement/utilization strategies have the capacity to diminish sources of inadequate or degrading healthcare. Universal antiracist practices are depicted to be associated with decreases in discrimination and trauma. Culturally relevant practices may positively influence healthcare quality, and directly affect mental health and suicide by leveraging culturally specific protective factors. Culturally relevant risk assessment has the potential to directly decrease suicide risk by improving risk identification.
+5.4.1 | Treatment engagement and utilization
+Lindsey et al. (2013) conducted focus groups with Black adolescents and their caregivers and identified two key barriers to mental health treatment: negative expectations of the treatment experience and unfavorable social norms about seeking formal treatment, with a preference for informal means of addressing mental health problems. Another study found that Black adolescents had substantial distrust for mental health professionals, often linked to familial norms about trustworthiness of formal treatment providers (Lindsey et al., 2010). Rose et al. (2011) found that depression severity in sample of Black adolescents was associated with greater levels of stigma.
+A number of methods have been suggested to promote the use of mental health services in underserved populations, but it is important to recognize that lack of access to services and resources represents an important form of institutional racism. Youth and caregiver interviews reveal perceptions of a lack of adequate school staff (Lindsey et al., 2013) and insufficient school-based social and emotional resources as important barriers to service use (Planey et al., 2019). Successful suicide prevention efforts require the elimination of disparities in access to mental healthcare, which necessitates adequate levels of training and staffing to provide equal access to all students. Schools have the capacity to circumvent many common logistical and financial barriers associated with accessing mental healthcare (Langer et al., 2015), but only in the presence of adequate resources to provide such services. As schools work to provide greater access to mental health services, disaggregated data should be
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+collected to track student service use to see if efforts actually lead to greater utilization among Black students. Such data should be collected on a continuous basis to assess the service reach.
+Core barriers outside of the availability of services include stigma, negative expectations about treatment, and distrust of providers. For negative social norms associated with formal treatment, viable approaches include motivational interviewing and outreach/informational programs that build an accurate and informed knowledge base about mental health problems and treatments (Lindsey et al., 2013). It is also important to recognize that lack of trust is rooted in intergenerational experiences of discrimination. Across educational, medical, and social service contexts, there is a clear history of intentional and de facto discrimination and inequity directed toward Black Americans (Breland-Noble, 2004). The use of effective treatment engagement strategies and a focus on the development of cultural competency in staff are important approaches (Congressional Black Caucus, 2019). Strategies that promote school engagement by students, such as facilitating strong relationships with staff and creating opportunities for students to connect with staff about subjects that they find to be personally relevant (Bundick et al., 2014), could also be used to build trust and facilitate the use of necessary services. Approaches associated with family engagement such methods that facilitate home-school partnerships that provide opportunities for meaningful collaboration and participation in school activities (Vazquez-Nuttall et al., 2006) may also be helpful.
+5.5	| Inclusion of culturally relevant practices in prevention and intervention programs
+The integration of culturally relevant practices into universal and targeted school-based prevention and intervention programs is an important means of promoting agency for suicide prevention in Black students. These can be applied within existing programs and intervention frameworks.
+The connection between suicide risk and acculturation processes that lead to assimilation (Castle et al., 2011) demonstrates the importance of culture-bound coping assets for suicide prevention. Jones et al. (2020) synthesized extant scholarship regarding protective factors related to the psychological well-being of Black youth and emphasized four key protective factors: (1) racial identity, (2) racial socialization, (3) spirituality, and (4) familial psychosocial support. Content related to racial identity, socialization, and spirituality can be directly integrated into universal and targeted school-based programs for Black youth. Coping resources related to family support may also be accessed through the inclusion of family members in program sessions. While a clear body of evidence supports these coping resources and indicates that their inclusion in school-based programs would reduce suicide risk, applied research is needed to evaluate the effectiveness of approaches that target these cultural assets (Jones et al., 2020). In addition, it is essential that intervention development is informed by expert cultural knowledge provided by the people receiving the interventions and their families (Wong et al., 2014). For example, the Zuni Life Skills Development Program, a school-based intervention that facilitates social and emotional competence through the use of culturally adapted content, demonstrated reductions in suicidality and hopelessness (LaFromboise & Howard-Pitney, 1995). An essential aspect of the program is that all adaptations were extensively informed by a wide array of community members to align program content with the Zuni Tribe cultural norms. Such an approach should be applied to develop universal and targeted interventions for Black youth, especially in predominantly Black communities with high levels of suicide risk.
+Prevention and intervention programs can also be adapted to address culturally specific barriers. For example, given research documenting negative expectancies about the treatment process (Lindsey et al., 2013), universal programs can provide guidance on the benefits of seeking formal treatment and how to navigate the process of seeking help, emphasizing sources of support and assistance. In addition, intervention components that influence social norms have great promise in addressing cultural barriers related to stigma and reducing suicide risk. Wyman et al. (2010) used a peer leader intervention to impact social norms about suicide in nine high schools. Peer leaders were selected on the basis of being established opinion leaders in their school and students in intervention schools ultimately had more favorable views about seeking help for suicidality. This is notable in light of findings by Molock
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+et al. (2007), that Black adolescents expressed preference for informal helpers in school and community settings. A study of social networks in 38 high schools revealed that schools had less suicide risk when students and their friends had positive relationships with overlapping trusted adults (Wyman et al., 2019) demonstrating the impact of social norms and networks on suicidality and opportunities associated with diffusion-based interventions to address stigma and reluctance to seek formal help. Knowledgeable informal helpers who are well connected with a school's social ecology hold great potential to create care linkages and support greater use of formal mental healthcare.
+5.5.1 | Universal antiracist practices
+The application of antiracist strategies in schools represents a critical universal suicide prevention strategy. Kendi (2019) describes an antiracist idea as “any idea that suggests the racial groups are equals in all their apparent differences-that there is nothing right or wrong with any racial group. Antiracist ideas argue that racist policies are the cause of racial inequalities” (p. 20). Kendi (2019) highlights the choice between racist and antiracist ideas, actions, and policies, arguing that inaction in the face of racial injustice is a racist action. Antiracist actions should be applied across multiple levels in schools using an array of existing strategies that actively address sources of racial inequalities. Policies should be constructed and implemented to create accountability for the continuous application of activities that explicitly uphold and promote racial justice at all levels. These include several strategies such as district discipline policy reform to incorporate proactive evidence-based strategies (Green et al., 2020); discipline data disaggregation and consultation procedures for school staff that reduce racial disparities in discipline and positive reinforcement (Gion et al., 2020); effective prejudice reduction programs for students (Grapin et al., 2019) and staff (Whitford & Emerson, 2019); and culturally responsive teaching (Larson et al., 2018). Given the apparent impact of microaggressions on suicidality (Madubata et al., 2019), empirically validated methods for addressing microaggressions in educational contexts are greatly needed. The overarching theme is to engage in ongoing efforts to actively address all major sources of racial discrimination in the school environment and cultivate the development of social norms that support cultural diversity.
+5.5.2 | Culturally relevant risk assessment
+There is some evidence that suicide risk in Black children and adolescents presents in a manner that is notably distinctive from other groups. For example, some studies have found that suicide deaths in Black adolescents and adults were not related to depression (e.g., Abe et al., 2008). There is also an indication that some suicide at-tempters do not engage in prior ideation. Morrison and Downey (2000) found that Black college students were largely unaware of their own suicidality until it was subsequently identified through clinical assessment and Lindsey et al. (2019) found that while suicide attempts increased in the last ten years for Black adolescents in a nationally representative sample, suicide ideation and planning decreased suggesting that behaviors may occur in the absence of explicit suicidal thoughts. In addition, Lee and Wong (2020) looked at antecedents of completed suicides among youth in different racial/ethnic groups and found that compared to other groups, Black youth were less likely to attempt before dying by suicide. Lee and Wong (2020) hypothesized that existing racial trauma and exposure to violence enhances the capacity to complete a suicidal act because it can desensitize Black youth to the negative emotional reactions typically associated with self-directed violence.
+Further study of developmental trajectories of Black youth with suicidal thoughts and behaviors is greatly needed and emerging scholarship (e.g., Xiao & Lindsey, 2021) has begun to apply person-centered analyses to uncover distinct profiles to support clinical decision-making and inform the development of culturally responsive social and emotional screening methods. However, given that Black youth disproportionately encounter violence (Wang et al., 2020), which may confer greater capability to carry out suicidal behavior (Lee & Wong, 2020),
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+exposure to experiences of this nature should be viewed as especially relevant for Black students in the context of suicide risk.
+6	| INTERSECTIONALITY AS A KEY CONSIDERATION FOR SUICIDE PREVENTION
+This paper focuses on conceptualizing key considerations for suicide prevention for Black youth in school environments, but it should not be viewed a definitive approach for any one student given the variation of needs within cultural groups. In particular, it is critical to recognize that people are made up of intersecting identities that simultaneously confer layers of privilege and risk, and distinctive sets of corresponding needs (Crenshaw, 1990). Therefore, the recognition of intersectionality must be at the forefront of suicide prevention efforts (Wong et al., 2014). In the case of suicide risk in Black children and adolescents, sexual orientation (Mueller et al., 2015), and sex (Lindsay et al., 2019) represent important intersectional identities that appear to confer unique contributions to suicide risk. While there is evidence that gender and LGBTQ+ status represent important risk pathways for all youth, extant research has not specifically looked at how these characteristics confer additional risk in Black youth populations (Opara et al., 2020). Given the importance of considering intersectionality in supporting youth mental health, further study in this area is critical.
+7	| USING AN ECOSOCIAL FRAMEWORK IN SCHOOLS TO PREVENT SUICIDE
+A number of important factors associated with suicide risk are apparent in schools (Singer et al., 2019). The combination of these factors represent varying developmental pathways to suicidal thoughts and/or behavior. In the case of racial disparities in youth suicide, efforts to promote health equity must apply frameworks that account for the ways minoritized youth embody their social context “through diverse, concurrent, and interacting pathways” (Krieger, 2012, p. 937), and they must attend to power, accountability, and agency. Racism is woven into the lived experience of Black Americans and there are important and interrelated culturally specific barriers to receiving mental health treatment. As we face the ongoing crisis of suicide in Black youth, school systems must be responsive and accountable to these unique multilevel needs and problems.
+It is essential that school professionals take a proactive approach to youth suicide. Interventions that address racial discrimination and healthcare inequities represent key school-based suicide prevention strategies.
+8	| IMPLICATIONS FOR EMBEDDING EQUITY INTO SCHOOL MENTAL HEALTH
+Content within the accountability and agency section of this paper provides a number of practical recommendations to facilitate equity in school mental health and address the risk for suicide among Black students. These include:
+•	Promoting mental health treatment use and engagement by allocating sufficient resources and tracking use, providing motivational interviewing and informational campaigns to encourage service use, and applying established strategies to facilitate school engagement.
+•	Integrating culturally relevant components into prevention and treatment programs by including content related to culturally distinct protective factors and barriers, and informing program development with input from Black youth and family members in the community.
+•	Integrating concrete strategies that specifically recognize and counteract sources of racism in schools at multiple levels such as educational programs that reduce prejudice, discipline data disaggregation and associated staff coaching, discipline policy reform, and culturally responsive teaching.
+•	Engaging in culturally relevant suicide risk assessment methods that recognize exposure to racial trauma and violence as particularly important risk factors for Black students.
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+Flannery, D., Wester, K. L., & Singer, M. (2004). Impact of exposure to violence in school on child and adolescent mental health and behavior. Journal of Community Psychology, 32(5), 559-573.
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+15206807, 2022, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pits.22588 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+2420 |	COHEN et al.
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+15206807, 2022, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pits.22588 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+COHEN et al.	| 2421
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+How to cite this article: Cohen, D. R., Lindsey, M. A., & Lochman, J. E. (2022). Applying an Ecosocial Framework to Address Racial Disparities in Suicide Risk among Black Youth. Psychology in the Schools, 59, 2405-2421. https://doi.org/10.1002/pits.22588
+15206807, 2022, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pits.22588 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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+1.	Introduction
+1.1.	Stigma against mental illness among healthcare professionals
+As a social species, human beings react to others' suffering with empathy and behavioral drives to alleviate distress (de Waal and Preston, 2017). Across societies, certain individuals are recognized for their role helping others in distress. For healthcare professionals, in particular, the alleviation of suffering is a vocational calling. Given these sociobiological drives and professional roles, why do healthcare professionals often ostracize, discriminate against, and fail to provide adequate palliation of suffering for some groups of patients (Henderson et al., 2014; Nyblade et al., 2019)? Persons with infectious diseases are often stigmatized, and among noncommunicable diseases, persons with psychiatric disorders bear the burden of healthcare providers’ prejudice and discrimination. This stigmatization is associated with poor healthcare delivery including inadequate screening, diagnosis, and treatment leading to early mortality, especially in low- and middle-income countries (LMIC) (Kane et al., 2019).
+Understanding how to reduce stigma among healthcare providers is now of particular relevance because of the global push to integrate mental health services into primary care in both high-income countries and LMIC. Primary care providers are increasingly involved because of shortages of mental health specialists and the benefits of collaborative care models for both mental and physical health (Kroenke and Unutzer, 2017; World Health Organization, 2016). For settings where primary care providers lack prior mental health training, the World Health Organization (WHO) has developed the mental health Gap Action Programme (mhGAP) intervention guide and training materials (www. who.int/mental_health/mhgap/). However, failure to promote positive attitudes and reduce stigma already has been observed as a barrier to success of mhGAP and similar initiatives: the presence of stigma has been associated with low rates of detecting mental illness and poor clinical competency (Fekadu et al., 2017; Jenkins et al., 2013; Kauye et al., 2014; Kohrt et al., 2018b; Muga and Jenkins, 2008).
+1.2.	What matters most: an anthropological theory to inform of anti-stigma interventions
+Stigma is a heterogeneous concept including a range of behaviors and experiences. Taxonomies of stigma have expanded rapidly in the past decade (Pescosolido and Martin, 2015): “experiential variants” include perceived, endorsed, anticipated, received, and enacted stigma; “action-oriented variants” include self, courtesy, public, providerbased, and structural stigma. Healthcare provider stigma is an example of provider-based stigma, defined as “prejudice and discrimination voiced or exercised, consciously or unconsciously, by occupational groups designated to provide assistance to stigmatized groups,” (Pescosolido and Martin, 2015, p. 92). Traditionally, most interventions for provider stigma are developed from knowledge-attitude-practice (KAP) frameworks, which assume that if a provider has a more accurate biomedical understanding of mental illness, this will result in improved attitudes and behaviors. However, there have been numerous examples of knowledge-based approaches that do not result in attitudinal and behavioral changes, and some have negative impacts (Stuart et al., 2012). Although healthcare providers typically have greater biomedical knowledge of mental illness, this is not protective against stigmatizing attitudes and behaviors (Henderson et al., 2014; Thornicroft et al., 2016; Ungar et al., 2016). Therefore, other approaches are needed to reduce stigma among healthcare providers.
+An alternative to the KAP framework is ‘what matters most’, an anthropological theory grounded in the concept of moral experience within one's local world (Kleinman, 1999). ‘What matter most’ conceptualizes stigma as a moral phenomenon in which threats to personal and group identity within a particular local world lead to stigmatizing behaviors (Keusch et al., 2006; Kleinman, 2006; Yang et al., 2007,
+2014a). Healthcare providers' moral experience is shaped by the structure, symbols, and rituals in a specific healthcare setting (Kleinman and Hall-Clifford, 2009). Ethnographies have been written about medical students conducting psychiatry rotations, psychiatry residents in training, and emergency room staff caring for psychiatric patients, and each of these captures the complex moral experience of mental healthcare within particular local worlds (Konner, 1988; Luhrmann, 2000; Rhodes, 1991). A common narrative is that the social status associated with being a healer and the self-image of being able to alleviate suffering are threatened when facing a patient with mental illness—especially when that health professional does not have the psychiatric training or tools to provide effective care.
+Local worlds of healthcare providers also are shaped by broader societal norms, values, and practices (Baer et al., 2003). One cannot consider the local world of a healthcare provider without considering the range of societal attributes that shape other life experience, such as language, religion, caste, gender, age, and economic status. Those values held closely by one's culture (e.g., religious identity, national identity) will lead to stigmatization when jeopardized (Yang et al., 2014b). For example, in capitalist cultures, economic productivity is an aspect of identity, which can lead to stigmatization of those perceived as burdens on society (Scheff, 2017; Yang et al., 2014a).
+1.3.	Designing “what matters most’ interventions using social contact
+Social psychology theory emphasizes social contact as an active ingredient of successful stigma-reduction strategies. Social contact is hypothesized to breakdown in-group versus out-group differences (Pettigrew et al., 2011). During the Civil Rights era in the United States, social psychologists focused on social contact to reduce racial barriers, using activities such as racially-mixed student groups working together toward common goals, e.g., jigsaw classrooms (Allport, 1954). There have been more than 500 experimental and observational studies of social contact, with the majority reporting reduction in prejudice (Pettigrew et al., 2011). Interventions facilitating social contact—outside of a clinical care relationship—between healthcare providers and service users with a stigmatized condition have shown benefit for reducing stigma and discrimination (Corrigan et al., 2012; Henderson et al., 2014; Nyblade et al., 2019). A qualitative synthesis of effective anti-stigma interventions identified multiple forms of social contact as one of the key ingredients (Knaak et al., 2014).
+Although social contact interventions hold promise, there are some studies with neutral or negative results, and there are limited data on behavioral change and long-term sustainability of attitudinal change (Thornicroft et al., 2016). This leaves open an opportunity to explore what can be achieved when using social contact to target moral experience as a driver of stigma. A moral experience perspective suggests that interventions would work best when social contact addresses ‘what matters most’ to the stigmatizing group. This will vary based on local cultural and professional beliefs related to perceived threats such as violence, contagion, violation of religious norms, economic dependency, and failure to fulfill expected social and professional roles.
+1.4.	Objectives of study
+We employed the ‘what matters most’ conceptual framework and findings from social psychology to design an intervention for reducing primary care providers stigma against patients with mental illness. We conducted a proof-of-concept testing in rural Nepal including qualitative and quantitative evaluations.
+2.	Methods
+2.1.	Setting and context
+Recent development of primary care-based mental health services in
+Nepal provided a platform for developing and testing an anti-stigma intervention (Jordans et al., 2016). Nepal exemplifies settings with a high burden of negative social determinants of health and lack of mental health specialist services outside of urban centers (Luitel et al., 2015). Beginning in 2012, Nepal was one of five LMIC participating in the PRogramme for Improving Mental health carE (PRIME), in which mental health services were introduced into primary care and community settings (Lund et al., 2012). PRIME in Nepal was implemented in Chitwan district by Transcultural Psychosocial Organization (TPO) Nepal, a Nepali non-governmental mental health organization. Within PRIME, a district mental health plan included training for all primary care providers in the government health system. The plan addressed four mental, neurological and substance use (MNS) disorders: depression, psychosis, alcohol use disorder, and epilepsy (Jordans et al., 2016). These four conditions were selected based on a local priority setting exercise that also highlighted the need for stigma reduction for these conditions (Jordans et al., 2013). Primary care providers in government facilities include health assistants, community medical assistants, and auxiliary nurse midwives, all of whom are non-specia-lists with training ranging from 10 to 30 months. Some health facilities also include medical doctors with bachelor of medicine/bachelor of surgery (MBBS) credentials.
+In the Nepal PRIME district plan, primary care providers who had prescribing rights (herein referred to as ‘prescribers’) were trained using mhGAP and a psychosocial curriculum. The training was 10-days for 6.5 h each day (see Supplemental Table S1). The first 4.5 days included psychosocial concepts, verbal and non-verbal communication skills, psychoeducation, emotional support, and case management. The remaining time was focused on mhGAP material for the four priority MNS disorders. Primary care providers who did not have prescribing rights (referred to as ‘non-prescribers’) were trained for 5 days on the same basic psychosocial support skills, and a subset was trained for an additional 5 days in psychological interventions. Both prescribers and non-prescribers were obliged to attend the PRIME trainings by the local government administration. The health workers did not have the option of electing out of the trainings, and all received government rate perdiem for attendance.
+2.2.	Development of the REducing stigma among HealthcAre ProvidErs (RESHAPE) intervention
+We developed an anti-stigma intervention entitled REducing Stigma among HealthcAre ProvidErs (RESHAPE) based on ‘what matters most’ and social contact theories. The development followed three steps: (1) identification of what matters most to primary care providers; (2) selection of components for the intervention; and (3) recruitment and training of service users and other facilitators for the anti-stigma activities.
+2.2.1.	Identification of ‘what matters most’ to healthcare providers
+The first step in the RESHAPE process was to identify potential threats to ‘what matters most’. We began with general domains: survival, social, and professional threats (Griffith and Kohrt, 2016; Stangl et al., 2019). We contextualized these threats from ethnographic and other qualitative research on mental illness stigma in Nepal.
+Survival threats - Patients, such as persons with HIV or Ebola, are stigmatized, in part, because of the perceived threat to survival of a healthcare provider. The dominant survival threat when caring for patients with mental illness is typically violence with a potential to injure or kill someone, including healthcare providers. This is a ‘universal tangible threat’ because it can be observed across local worlds of experience (Yang et al., 2013). This is observable in Nepal. In Chitwan, Nepal, more than a third of healthcare providers thought patients with mental illness were too violent to receive treatment in primary care settings (Gartoulla et al., 2015). The association with violence and mental illness is supported by ethnographic studies documenting that
+mental illness is seen as a problem of the ‘brain-mind’ (Nepali: dimaag), which, when damaged or impaired, leads to a loss of inhibition; both psychosis and substance abuse disorders are associated with this lost inhibition and risk of violence (Kohrt and Harper, 2008).
+Social threats - Social threats jeopardize the status and prestige of a healthcare provider. This is manifest as anticipated stigma that a healthcare provider will be ostracized by co-workers, community members, and their families because of associating with persons with mental illness. There is a concern among health workers that those who treat patients with mental illness are also mentally ill: “paagal ko daktar pani paagal ho” (the doctor of mad patients is also mad). Primary care providers caring for mental health patients fear that they will become stigmatized just as psychiatrists are shunned in society: ‘‘For families, it is a bigger shame to have a child who is a psychiatrist than to have a child who is not a doctor at all,’’ (Kohrt and Harper, 2008, p. 480). Instead of ‘glory and support’ received in some health fields, engaging with persons with mental illness is associated with being stigmatized and discriminated against (Gurung et al., 2017). Because persons with mental illness in rural areas are often excluded from community groups, festivals, and social activities, there is a fear among healthcare providers that they will similarly be excluded (Angdembe et al., 2017).
+Professional threats - Healthcare providers also avoid mental health care because they see it as burdensome and ineffective (Kohrt and Harper, 2008), thus threatening their self-image as a competent professional. In the local world of healthcare providers, self-efficacy in performing one's clinical duties is at the heart of moral experience. Among health workers in Chitwan, 90% reported that it was not worthwhile to provide care because patients with mental illness will discontinue their medication and not follow-up, and 72% of the health workers thought that patients with mental illness do not have supportive families to assist in their care (Gartoulla et al., 2015). Another attitude in Nepal was that mental health patients cannot understand treatment and do not follow healthcare providers instructions (Kisa et al., 2016). Primary care workers and many of their supervisors also felt that only specialist care or traditional healers would be effective for treatment (Angdembe et al., 2017; Kisa et al., 2016). Fewer than 10% of healthcare providers considered counseling to be effective (Gartoulla et al., 2015). Suicide, in particular, carries a high stigma. This is partly because health professionals inaccurately think suicide is illegal and therefore should be dealt with only by police (Hagaman et al., 2016). In addition, healthcare providers do not ask about suicidality because it is a “hopeless situation” and attribute it to things that cannot be changed such as fate or personality characteristics (Hagaman et al., 2018).
+Also regarding professional threats, primary care providers consider mental health patients to be a burden forced upon them by the government and non-governmental organizations (NGOs) (Kisa et al., 2016). Some healthcare providers only attend for the daily payment but do not deliver care because of the perceived burden: “if NGOs add tasks to [primary care providers], then they need incentives to do it. They think NGOs are consuming their time. They may do it initially but, if they are not provided facilities, they may stop doing it,” (Angdembe et al., 2017, p. 11). This is relevant in the context of PRIME in Nepal because the local government obligated all primary care workers to attend the mental health trainings. Additionally, caring for mental health patients in primary care was not seen as valuable because they are not economically productive members of society (Angdembe et al., 2017). Three quarters of health workers in Chitwan thought that patients with mental illnesses—including depression and anxiety—should be barred from work (Gartoulla et al., 2015); therefore, even if one recovers, she/he should not return to work.
+These findings were used to frame the content of what matters most for primary care providers in the RESHAPE intervention.
+2.2.2.	Design of RESHAPE intervention components
+Five components (service user recovery stories and social contact; aspirational figures; myth busting; stigma didactics; and collaboration)
+were selected for the RESHAPE intervention based on evidence-supported elements of anti-stigma interventions (see Fig. 1). The ‘what matters most’ themes were incorporated into each of these components. The anti-stigma components were designed to be embedded within the 10-days training for prescribers (see Fig. 2) and the 5-days training for non-prescribers. The total number of days of prescriber and non-prescriber training were not increased with the addition of RESHAPE elements. Trainers were Nepali psychiatrists and psychosocial counselors employed by TPO Nepal. The NGO had established a memorandum of understanding with the Ministry of Health to provide the PRIME trainings. The psychiatrists and psychosocial counselors had been involved in the Nepali adaptation of mhGAP. Psychosocial counselors serving as trainers had been trained in a 6-month course and had been practicing for at least 5 years prior to serving as trainers in the program.
+Component 1. Service User Recovery Stories and Social Contact: The term ‘service user’ refers to persons living with mental illness who engage health services. The first component of RESHAPE was delivery of recovery stories by service users and their caregivers. Service users' participation in social contact interventions has evidence for stigma reduction (Corrigan et al., 2012; Knaak et al., 2014; Nyblade et al., 2019; Pescosolido and Manago, 2018; Pettigrew et al., 2011; Thornicroft et al., 2016; Ungar et al., 2016). Recovery stories were
+approximately 10 min in duration and were followed by 15 min of questions and answers. Service users stayed throughout the day of their presentation so they could interact with healthcare providers during tea breaks, meals, and energizer activities. In the prescriber training, two service user recovery stories were presented on Day 3 to introduce the experience of service users and caregivers to the health workers. Additional service user recovery stories were included for each of the mhGAP modules: depression, psychosis, alcohol use disorder, and epilepsy (Days 6-9). Eight service users took part in the prescriber training and were present for the full day for five of the ten days of training. For non-prescribers, six service users took part and they were present for three full days of the training.
+Recovery stories were developed based on the PhotoVoice method with each story accompanied by photographs taken by the service user (Kaiser et al., 2019; Rai et al., 2018). Recovery narratives were structured in three acts: life before treatment, experience of treatment, and life in recovery, see example in Supplemental Textbox S1. Regarding ‘what matters most’, the recovery narratives highlighted that mental illnesses were treatable and that primary care providers play an important role in this treatment, through both medication and psychosocial services. Another topic was that service users and caregivers were interested in understanding and adhering to the treatment. Consistent
+across service users' recovery narratives was also their ability to engage in economically productive activities after sustained participation in treatment. Although not explicitly prompted to include this, most service users included photos of farming, raising goats, and small business opportunities. Younger service users described going back to school. Images also depicted family functioning such as caring for children, helping with homework, and playing with grandchildren. One service user included photos of three healthcare providers and said ‘these are my three goddesses’. PhotoVoice recovery narratives were delivered in person by the service users while she/he displayed a series of photographs through a PowerPoint slide presentation.
+Component2. Aspirational figure recovery stories: Aspirational figures were local primary care providers who had previously been trained on mhGAP. The aspirational figures were selected based on their work during the PRIME program that preceded RESHAPE. They stood out among other primary care providers in terms of the positive attitudes they expressed toward mental health service users, the number of mental health patients they diagnosed, and the quality and fidelity of the care they provided in their clinics. The aspirational figures were selected to serve as role models for the new batches of primary care providers. We chose to include aspirational figures because the presence of an enthusiastic facilitator is an evidence-supported component (Knaak et al., 2014), and social network theory supports the presence of linking personnel who can bridge health workers with service users (Pescosolido and Manago, 2018). These aspirational figures represented someone “just like me” in relation to the primary care trainees.
+Aspirational figures were trained on giving presentations about their experiences within a three-act story structure. However, they did not receive PhotoVoice training and did not use their own photographs during their presentations. The first act described how they treated patients with mental illness before undergoing mhGAP training, the second act described what they learned in the mhGAP training, and the third act described how providing mental healthcare has benefited them and their patients. From the ‘what matters most’ perspective, they demonstrated that involvement in mental healthcare did not result in negative survival, social, or professional consequences. On the contrary, they described how it could be beneficial. We included two to four aspirational figures per training.
+Component 3. Myth-busting: In health messaging and health education, myth-busting refers to describing beliefs about an illness that are
+common in a particular context and culture. Myth-busting includes an explanation for why these beliefs are incorrect by providing factual information. Example mental illness-related myths are “mental illnesses are contagious” or “asking about suicide makes a person want to kill her/himself.” A review of anti-stigma programs found that mythbusting was a component of effective interventions (Knaak et al., 2014). Based on our ‘what matters most’ themes in Nepal, we developed eight statements for myth busting:
+1.	Mental illness cannot be treated.
+2.	Mental illness can only be treated with shots and pills.
+3.	Psychological counseling is no more helpful than just giving generic advice.
+4.	If you ask people about suicide, that increases the risk they will kill themselves.
+5.	All people with mental illness are violent.
+6.	Mental illnesses are contagious.
+7.	Only some people can get mental illness; most people can't become mentally ill.
+8.	Caring for people with mental illness makes health workers mentally ill.
+The first four statements address professional threats related to if and how mental illness can be treated, as well as risks of triggering suicide. The next two are survival threats. The last two address social threats related to what type of people do or do not become mental ill, with specific attention to the belief that health workers are ‘crazy’ if they treat mental health patients. The aspirational figures connected the myths and facts to their own clinical experiences.
+Component 4. Stigma didactics and discussion: There is an evidence base for understanding what to do and say in relation to stigma (Knaak et al., 2014). Therefore, one of the program staff members (a TPO employee) provided an hour-long didactic and discussion session to define stigma and discrimination, to discuss why language matters including avoiding stigmatizing mental health terms, and to reflect upon how mental illness stigma is just one type of stigma in society. The goal was for all participants to recognize when they also may have been stigmatized, and thus enhance empathy for service users. A common theme raised (without prompting) by the primary care workers was how some groups in health facilities got special treatment (e.g., local
+teachers, political party affiliates, and relatives of the health facility management committee) whereas other types of people got lower quality of care. The United Nations Convention on Rights of Persons with Disabilities was also introduced to draw attention to global guidance on social inclusion, e.g., the right for all persons to have opportunities for meaningful civic and occupational engagement.
+Component 5. Collaborative activity: Collaborative problem solving is based on social contact theory where two groups work together toward a common goal (Pettigrew et al., 2011). We focused on the common objective that health workers want to be seen as good providers in their community and that service users want good quality care provided. Modelled after a jigsaw classroom (Allport, 1954), health workers, service users, and aspirational figures worked together on the second to last day of training to address potential barriers when delivering mental health care. The groups brainstormed anticipated problems and came up with joint solutions. For example, primary care providers raised concerns about loss to follow-up, non-adherence, and lack of support from patients' families. Aspirational figures and service users provided suggestions based on their experience; in addition, service users offered to provide support when needed for patients and families who need help understanding recovery and treatment processes.
+2.2.3.	Recruitment and training of service users and aspirational figures for RESHAPE facilitation
+Working with previously-trained primary care and psychosocial workers in the region, we recruited persons living with mental illness who had received treatment and were currently in some state of recovery. After service users were selected, they participated in 5 sessions (7 days total) of PhotoVoice workshops to develop the three-act photographic recovery narrative and prepare for participation in the primary care worker training (Rai et al., 2018), see online Table S2 for additional details on the PhotoVoice training. The selection criteria for the aspirational figures was that they needed to be health workers at the same career level and with the same professional responsibilities as the anti-stigma program beneficiaries.
+2.3.	Proof-of-concept evaluation of RESHAPE embedded in PRIME trainings
+To evaluate embedding RESHAPE into PRIME trainings of primary care providers, we used proof-of-concept testing using qualitative and quantitative methods. This follows the UK Medical Research Council key elements of design and evaluation for new interventions: a development phase, feasibility/piloting phase, evaluation phase, and implementation phase (Fletcher et al., 2016). The current study was conducted from February 2016 through June 2017. We conducted two proof-of-concept trainings incorporating the RESHAPE intervention: one 10-day training with prescribers and one 5-day training with nonprescribers.
+2.3.1.	Quantitative data and analysis
+All healthcare providers completed a battery of measures at pretraining (T0), post-training (T1), and follow-ups of 4 months (T2) and 16 months (T3). Assessment domains included stigma, knowledge, and clinical competence:
+•	Social Distance Scale (SDS): The primary outcome is the SDS, previously used in Nepal (Kohrt et al., 2018b) and based on select sections of the Stigma in Global Context—Mental Health Study (Olafsdottir and Pescosolido, 2011; Pescosolido et al., 2013). The SDS is a widely used measure to assess willingness to interact with persons from a specific stigmatized group (Bogardus, 1925; van Brakel et al., 2019). We used a 12-item SDS with each item scored on a 6-point scale (1-6) for a total range of 12-72; usage in Nepal shows strong internal consistency (a = 0.80).
+•	mhGAP knowledge assessment. True-false and multiple-choice
+questions were adapted from mhGAP version 1.0 content for PRIME (Hanlon et al., 2018). These questions address psychosis, depression, alcohol use disorder and epilepsy. The prescriber battery includes 26 questions. Non-prescribers completed 19 questions; medication-related questions were removed.
+•	mhGAP attitudes assessment. Based on mhGAP Intervention Guide version 1.0 questions, PRIME also adapted a series of attitudinal questions about mental illness.
+•	ENhancing Assessment of Common Therapeutic factors (ENACT). The ENACT tool is used by raters observing standardized role plays of trainees (Kohrt et al., 2015). The ENACT was developed in Nepal; it includes 18 items plus assessing diagnosis and treatment. Competency on a single item is based on a score of 2 or 3 on a 3-point scale (Kohrt et al., 2018b).
+Changes in summary scores of outcome measures were each compared between pre-training (T0) and follow-up for each of the three post-training time-points (T1, T2 and T3) using Wilcoxon signed-rank tests. Descriptive summaries are also provided for single items drawn from these tools that relate to specific domains of what matters most.
+2.3.2.	Qualitative data and analysis
+We qualitatively evaluated the trainings by conducting four focus group discussions. one before and after each training. In addition, we conducted 25 key informant interviews. six primary care workers (four prescribers and two non-prescribers) were interviewed six-months post training; six trainers (three psychiatrists and three psychosocial trainers) were interviewed after the trainings; nine service users and eight of their caregivers were interviewed over multiple months of the project. Materials were coded in NVivo (QSR International, 2012). See Supplemental Table S3 for Consolidated criteria for reporting qualitative research (COREQ) reporting on qualitative methodology (Tong et al., 2007). In the current analysis, we focus on responses from the primary care providers. Qualitative findings from service users and caregivers have been discussed previously (Rai et al., 2018).
+2.4.	Ethical approval
+The study has been granted ethical approval by Duke University (Pro00055042), the Nepal Health Research Council (110/2014 and 133/2016), and George Washington University (051725). All participants completed a signed consent form in Nepali. Before the start of PhotoVoice training, service users were evaluated by psychiatrists to appraise ability to safely participate in the program. The psychiatrist was available if service users had symptom relapse during the weeks of the PhotoVoice trainings. A psychosocial counselor was present to support service users and caregivers for all PhotoVoice sessions and the healthcare provider trainings.
+3.	Results
+3.1.	Participants
+Forty-one primary care workers (19 non-prescribers and 22 prescribers, Table 1) participated in the PRIME mental health trainings that included the RESHAPE anti-stigma intervention. Non-prescribers were predominantly female (95%), whereas prescribers were predominantly male (81%).
+3.2.	What matters most themes
+The qualitative and quantitative findings are presented below grouped by ‘what matters most’ themes.
+3.2.1.	Survival threats
+Healthcare workers attributed changes in beliefs regarding violence
+to their interactions with service users in the training. For the majority of health workers, their only formal exposure prior to the PRIME training had been touring an inpatient psychiatric ward.
+“In the inpatient department, most of the patients were aggressive and the psychiatric ward was completely like a prison as the doors would be all closed, and the rooms would be locked. So, we used to feel scared. When we had to take histories of psychiatric patients, we used to feel scared even after they were stabilized and normal. We used to feel frightened
+that they would do something to us. But now, after coming here, I have been able to realize that the patients with psychosis are not that violent, and that we can interact with them normally. We can take their history. I have gained this confidence after coming here ... [A]fter seeing them here in person, I have gained the confidence that I can provide treatment to people with such problems.” (Prescriber #4).
+“We used to call people mad (Nepali: paagal) or psycho. While sitting and talking to those people, we would say that talking to them was of no use because they were not in the right mind. Our society says the same. We did not know that talking to him/her would help solve the problem. We used to be scared and run away, especially if it was a man. We were scared they might throw stones, shout at us after drinking so much, tease us, speak whatever they want, make noise and such... When we're alone, we would be scared that they might do us harm.” (Non-prescriber #27)
+The willingness to interact with persons with mental illness was supported by the quantitative measures. We evaluated within-group change for prescribers and non-prescribers, which demonstrated significant change on the majority of outcome measures (see Table 2). On the SDS at baseline (pre-training), 54% of combined sample (prescribers and non-prescribers) stated that they were definitely willing or probably willing to interact with persons with mental illness on all items of the scale. At 16 months follow-up, 81% were at least probably willing to interact with persons with mental illness (Prescribers: T0 median (interquartile range, IQR) = 32.5(19.2, 47.0), T3 = 15.0(12.0, 21.0), z = -3.1, p = 0.002; Non-prescribers: T0 = 28.8(21.6, 40.8); T3 = 17.0(13.0, 29.0), z = -2.3, p = 0.02). Similarly, there was improvement in a number of single-item scores related to survival threats (see Fig. 3).
+3.2.2.	Social threats
+In qualitative interviews conducted immediately after the training, participants remained concerned about social threats. They raised the issue that co-workers at the primary care facility who had not attended the training may be likely to stigmatize those who started providing mental health services.
+“But if I do work in a specific mental health center, then I could very well be a subject of humiliation. I personally have heard and listened to people cursing another person like me who works in a mental health center.” (Prescriber #26)
+There were comments about wishing that community health volunteers and other health facility staff also had attended similar trainings:
+“You have provided us training, but it would have been good if training could be provided to other health workers too ... We might tell our coworkers about the situation of the patient, but they might not care about it because they haven't received training.” (Non-prescriber #4)
+In the survey measures, 37% of non-prescribers reported that colleagues would “look down on me if they knew I interacted with persons with MNS disorders.” However, 16-months after the training, 0% of non-prescribers endorsed this statement (see Fig. 3).
+One strategy reported to prevent discrimination from colleagues was to share learnings about mental health and help colleagues with family members experiencing MNS disorders:
+“There are also cases of epilepsy. One sister who assists us in our health post, her daughter has such a problem. Her husband is alcoholic. She hasn't been able to provide treatment to her daughter. So, I was thinking that after receiving training, I would be able to provide treatment to her, and I was very happy about it.” (Non-prescriber #3)
+3.2.3.	Professional threats
+There were numerous responses from participants about changes in their beliefs regarding the benefit of providing mental healthcare.
+“When such people would come to the clinic, I did not think that they could be cured using medicines. I questioned if they were mad people. But, after the training I came to know that it is possible for them to receive help whatever the reason behind their illness and go back to living normally in the society.” (Prescriber #15)
+“I think we became more optimistic. Before we used to have psychiatry posting while doing MBBS. I used to doubt if the patients will really get well, if their condition would improve. So, when seeing those people who have recovered, we got the proof that their condition can improve if they get timely treatment and timely counseling ... We got to know how the patients feel and what drives them to do certain things, what triggers depression. We got to interact with patients who previously had postpartum depression and postpartum psychosis ... I felt really bad to know about the challenges they face in society. I could empathize with them and realize how they might have felt. So, I felt happy to be able to provide service to people with such problems.” (Prescriber #1)
+“The training yesterday was very nice because we got to see the real patients. When we used to see such awareness programs on television, we used to think that it’s fake, we used to think that they were taught to say those things. Butwhile seeing thepatients in the training, Ifeltvery happy to know that there can be such improvement ...” (Non-prescriber #4)
+“The real patients came and shared about the improvement in their condition, one sister shared that she had to drop out of her school, [but after treatment] later she joined again and it was good after that. So, I felt really happy to see the real patients. I could gain this understanding that there can be improvement in their condition. ” (Non-prescriber #4).
+They also recognized their role in suicide prevention
+“While I was working in a government health post, I wasn't able to do exact diagnosis and that patient committed suicide. That patient had depression, but I was not able to take a concrete history because I didn't have knowledge about the skill that's required. That patient came to me a couple of times and she wanted to share about her issues, but I wasn't able to create that kind of environment. So later, she committed suicide. I wasn't able to connect all of these points. When I came here, I realized that if I were able to do the correct diagnosis, then I could have saved that person.” (Prescriber #6)
+“Regarding suicide, I used to think that we shouldn't ask about such things but it has been repeatedly emphasized here that we have to ask about it. So, [after the training], we asked patients about it and then the patients told us about suicidal feelings. Some said that they were about to jump in a well, some were about to hang themselves. From this, I realized that some patients may want us to explore on those issues as well.” (Prescriber #4)
+3.3.	Behavioral outcome: clinical competence
+For the ENACT role play measure of clinical competence, 49% of the participants had minimal competence at baseline. At 16-months, 93% of the sample had minimal competence (Prescribers: T0 median (IQR) = 30.0(26.0, 32.0), T3 = 47.0(35.0, 50.0), z = 3.5, p < 0.001; Non-prescribers: T0 = 26.0(26.0, 27.0); T3 = 45.0(38.0, 49.0), z = 3.4, p < 0.001). Many of the skills improved to minimal competency (level 2 or 3) by four months post-training. Other competencies continued to improve from four to six months, such as “involvement of family”, “rapport building,” “harm to self and others,” and “explaining confidentiality,” (see Supplemental Figure S1).
+4.	Discussion
+4.1.	Role of what matters most in stigma reduction
+This proof of concept study explores how a ‘what matters most’
+framework can inform anti-stigma initiatives for healthcare providers. ‘What matter most’ takes the perspective that anti-stigma interventions should begin with local values and moral experience. Our qualitative findings suggest that addressing threats within the local world facilitated attitudinal changes and improved clinical behavior. Healthcare workers described their prior assumptions about violence among persons with mental illness, and how the interaction with service users changed this assumption. Similarly, the health workers described their prior beliefs that mental illness could not be treated—especially in primary care, but after the training they felt confident to provide services. The growing emphasis in social psychology on affective mediators in social contact (Pescosolido et al., 2008; Pettigrew et al., 2011) further supports a ‘what matters most’ framing, which predicts that addressing moral experience will be tied to affective engagement.
+However, we did not observe changes in all domains. Social threats
+remained a concern. Participants worried about reactions from other healthcare providers. They emphasized that other providers should receive this training, which suggests that in order for social threats to be reduced, all healthcare providers could need sensitization. This highlights the need to transform local worlds of moral experience, rather than only changing individual member's attitudes and behaviors. This is consistent with the recommendation that all personnel in health delivery systems should participate in stigma reduction programs (Nyblade et al., 2019).
+This study also offers an opportunity to consider how ‘what matters most’ contributes to differences in anti-stigma intervention design compared to what is typically prescribed from a social psychology perspective. Social psychology theories focus on intergroup contact. However, these frameworks are relatively agnostic to content. ‘What matters most’ calls for specific attention to the content of the messaging
+in relation to moral experience of healthcare providers. The social contact promotes components of moral experience (professional and social identities) within the local world of healthcare providers. For example, the messaging from service users and aspirational figures emphasized the clinical efficacy of healthcare providers for mental health services. This raises the question of whether other perspectives (e.g., a human rights perspective) may have limited impact if that is not core to the professional identity of the target group. Social contact interventions, although more effective than other strategies, still have inconsistent outcomes across some trials, and this may be because of the contact has not targeted 'what matters most' for healthcare providers.
+In addition, our work supports conclusions (Pettigrew et al., 2011) that not all of Allport's original intergroup contact criteria are required (Allport, 1954). Specifically, service users and healthcare providers did not share equal status. Some aspects of the PhotoVoice process (e.g., service users presenting their photographs in PowerPoint presentations and working alongside the mhGAP trainers) may have worked to reduce power gaps, but because of the highly hierarchical society in Nepal and the authority of healthcare providers, we cannot assume that we established a level playing field.
+One of Allport's other preconditions was that participation in intergroup contact cannot be forced. However, in the case of government-supported trainings, health workers cannot opt out. This raises the question of whether the ‘what matters most’ framework could have benefited from attending more to autonomy as an important aspect of local worlds of healthcare providers. Governments forcing mental health trainings reduces autonomy. Future stigma reduction could acknowledge the role of autonomy even within these required trainings. For example, aspirational figures' testimonials could emphasize that when healthcare providers leave the training it is their hands deciding whether or not to put mental health services into practice.
+One of the surprising findings was that reductions in social distance were maintained after the training, and there appeared to be continued reduction in social distance through the 16-month follow-up. One of the limitations of anti-stigma research among health workers has been that measurement of positive attitudinal changes appear to be short-term (Henderson et al., 2014). The continued reduction in social distance in our study may be because anti-stigma efforts were embedded in a program introducing new clinical skills, which creates a positive feedback loop. In this positively reinforcing cycle, lower social distance leads to more provision of quality mental health care that leads to more experience with positive outcomes and building of clinical self-efficacy, which could further decrease social distance and encourage more provision of quality care. The possibility of a positively-reinforcing cycle of clinical skills and positive attitudes is supported by continued improvement in clinical competency at each of the follow-up time points as measured by the ENACT.
+One consideration for ‘what matters most’ is whether any of the identity threats contradict one another. For example, does helping people who are suicidal go against religious norms? The relationship of religion and suicide is complex in Nepal. Although suicide has negative implications for reincarnation in both Hinduism and Buddhism, there are examples of suicide, especially for women, in traditional practice and religious text (Bennett, 1983). However, our qualitative findings did not suggest that preventing suicide conflicted with ‘what matters most’ regarding religious identity. In contrast, healthcare providers reported that they wished they had mental health training earlier to intervene for their patients who died by suicide.
+Our findings are relevant for structural stigma. In the US, structural stigma has been identified as increasing costs and other barriers to accessing care (Yang et al., 2014a). In our study, the collaborative activity demonstrated that service users and healthcare providers were interested in joining forces to address structural barriers such as lack of medication provided by the government, lack of physical space for confidential treatment, and lack of psychosocial counselors in the government workforce. This alliance of service users and healthcare
+providers could be a key step to change policy, funding, and clinical practice. Service users could also build upon the relationship formed through the experience to create local advocacy organizations and potential sustainable approaches to train others in PhotoVoice. Moreover, continued engagement of service users with health workers has the potential to further strengthen the positive feedback cycle of stigma reduction contributing to improved clinical self-efficacy and quality of care. Ultimately, elevating the status and visibility of actors from a discriminated group changes cultural constructions and contributes to de-stigmatization and improved health (Clair et al., 2016).
+Going forward, comprehensively addressing interpersonal, self, and structural stigma requires multi-level approaches (Rao et al., 2019) because stigma in one domain is associated with other domains (Moses, 2010). Only addressing stigma in one domain may lead to that stigma resurfacing out of the other domains. These lessons learned from RESHAPE are also relevant to high-income countries. In high-resource settings, there is still relative ‘low resource’ status of mental health services when compared to higher resources afforded to physical health care. Given the 'what-matters-most' framework, there is potential for anti-stigma efforts to improve both personal and structural stigma in these settings as long as the local professional, social, and survival threats underlying stigma are clearly identified and addressed.
+4.2.	Limitations
+Based on the current results, we are unable to make claims about improving outcomes in comparison to standard mhGAP or PRIME trainings, nor are we able to distinguish the relative importance of different components (service users vs. aspirational figures). A controlled trial comparing with standardized training against the training plus RESHAPE has been completed and results will be described in a subsequent publication (Kohrt et al., 2018a).
+We also need to evaluate the cost effectiveness to consider sustainability of the program given the training needed by service users and the costs for in-person participation of service users to facilitate social contact.
+One potential limitation is the lost-to-follow-up rates. Five prescribers (23% of the original sample) and 4 non-prescribers (21%) were not included in the 16-month follow-up assessment. All participants lost to follow-up were because of either retiring from the government health system or because of re-assignment to a different district. As retirement and re-assignment are not performance-based outcomes, we do not anticipate that these lost-to-follow-up participants biased the 16-month follow-up outcome.
+5.	Conclusion
+Drawing upon medical anthropology and social psychology, we found that facilitated engagement of primary care providers with mental health service users and aspirational figures has the potential to address survival and professional threats, and possibly social threats. According to the UK Medical Research Council, the next step is to evaluate the feasibility and acceptability of the intervention through a pilot trial (Fletcher et al., 2016). The ‘what matters most’ model holds promise to guide what elements are needed and may be applicable to diverse stakeholder groups including health workers, law enforcement, teachers, and social service workers for stigma reduction.
+B.A. Kohrt, et al
+Social Science & Medicine 250 (2020) 112852
+Data curation, Formal analysis, Writing - review & editing. Kathleen J. Sikkema: Conceptualization, Methodology, Supervision, Writing - review & editing. Adesewa Adelekun: Data curation, Formal analysis, Investigation. Manoj Dhakal: Data curation, Project administration, Investigation. Nagendra P. Luitel: Project administration, Supervision. Crick Lund: Conceptualization, Funding acquisition, Methodology, Supervision, Writing - review & editing. Vikram Patel: Conceptualization, Funding acquisition, Methodology, Supervision, Writing - review & editing. Mark J.D. Jordans: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing - review & editing.
+Acknowledgements
+This document is an output from the PRIME Research Programme Consortium, funded by the UK Department of International Development (DFID) for the benefit of developing countries. RESHAPE is supported by the National Institute of Mental Health (K01MH104310, Principal Investigator: B. Kohrt). The trial sponsors had no role in the study design; study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication. We thank James Griffith, Janice Cooper, and Bernice Pescosolido for initial conceptual contributions. We thank Anup Adhikari, Dristy Gurung, Ashley Hagaman, Bonnie Kaiser, Megan Ramaiya, and Cori Tergesen for their contributions to the RESHAPE project. We acknowledge the Nepal Health Research Council.
+Appendix A. Supplementary data
+Supplementary data to this article can be found online at https:// doi.org/10.1016/j.socscimed.2020.112852.
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+Rao, D., Elshafei, A., Nguyen, M., Hatzenbuehler, M.L., Frey, S., Go, V.F., 2019. A systematic review of multi-level stigma interventions: state of the science and future directions. BMC Med. 17, 41.
+Rhodes, L.A., 1991. Emptying Beds : the Work of an Emergency Psychiatric Unit. University of California Press, Berkeley.
+Scheff, T.J., 2017. Being Mentally Ill: A Sociological Study. Routledge.
+Stangl, A.L., Earnshaw, V.A., Logie, C.H., van Brakel, W., Simbayi, C., L, Barré, I., et al., 2019. The Health Stigma and Discrimination Framework: a global, crosscutting framework to inform research, intervention development, and policy on health-related stigmas. BMC Med. 17, 31.
+Stuart, H., Arboleda-Florez, J., Sartorius, N., 2012. Paradigms Lost: Fighting Stigma and the Lessons Learned. Oxford University Press, New York.
+Thornicroft, G., Mehta, N., Clement, S., Evans-Lacko, S., Doherty, M., Rose, D., et al., 2016. Evidence for effective interventions to reduce mental-health-related stigma and discrimination. Lancet 387, 1123-1132.
+Tong, A., Sainsbury, P., Craig, J., 2007. Consolidated criteria for reporting qualitative
+research (COREQ): a 32-item checklist for interviews and focus groups. Int. J. Qual. Health Care 19, 349-357.
+Ungar, T., Knaak, S., Szeto, A.C.H., 2016. Theoretical and practical considerations for combating mental illness stigma in health care. Community Ment. Health J. 52, 262-271.
+van Brakel, W.H., Cataldo, J., Grover, S., Kohrt, B.A., Nyblade, L., Stockton, M., et al., 2019. Out of the silos: identifying cross-cutting features of health-related stigma to advance measurement and intervention. BMC Med. 17, 13.
+World Health Organization, 2016. mhGAP Intervention Guide for Mental, Neurological and Substance Use Disorders in Non-specialized Health Settings: Mental Health Gap Action Programme (mhGAP) - Version 2.0. World Health Organization, Geneva, Switzerland.
+Y	ang, L.H., Kleinman, A., Link, B.G., Phelan, J.C., Lee, S., Good, B., 2007. Culture and stigma: adding moral experience to stigma theory. Soc. Sci. Med. 64, 1524-1535.
+Y	ang, L.H., Purdie-Vaughns, V., Kotabe, H., Link, B.G., Saw, A., Wong, G., et al., 2013. Culture, threat, and mental illness stigma: identifying culture-specific threat among Chinese-American groups. Soc. Sci. Med. 88, 56-67 1982.
+Y	ang, L.H., Chen, F.-p., Sia, K.J., Lam, J., Lam, K., Ngo, H., et al., 2014a. “What matters most:” A cultural mechanism moderating structural vulnerability and moral experience of mental illness stigma. Soc. Sci. Med. 103, 84-93.
+Y	ang, L.H., Thornicroft, G., Alvarado, R., Vega, E., Link, B.G., 2014b. Recent advances in cross-cultural measurement in psychiatric epidemiology: utilizing ‘what matters most’to identify culture-specific aspects of stigma. Int. J. Epidemiol. dyu039.
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+People with severe mental illness (SMI), including those with schizophrenia, schizophrenia-like disorders, bipolar disorder and severe affective disorders, on average die at a younger age compared with the general population.1-4 In the UK men with SMI die 8-15 years and women 7-18 years earlier than those without mental disorders.1 This life expectancy gap is increasing.2 Although suicide is an important cause of death in those with SMI, the majority of preventable deaths are due to chronic disease, primarily cardiovascular, cerebrovascular and respiratory diseases.2,5 In England and Wales people with schizophrenia have a three-fold risk of premature mortality compared with the general population; Brown et al found that risk of unnatural death (violent deaths and suicide) declined significantly between 1982 and 2006, whereas the risk of premature death due to cardiovascular disease more than doubled in comparison with the general population.5 A complex web of factors contributes to this life expectancy gap. The side-effects of psychotropic medications, particularly weight gain and impaired glucose tolerance, increase the risk of excess mortality in people with SMI directly through obesity and diabetes.6 Unhealthy lifestyles include inactivity and diets that are high in fat and low in fruit and vegetables; these lifestyle factors may be consequences of negative symptoms of mental illness as well as poor emotional regulation.7 In addition, there is a growing body of evidence that unequal healthcare provision contributes to the life expectancy gap.8 Mental disorders are associated with poorer clinical management of disease. People with SMI are less likely to receive timely and precise diagnosis because of ‘diagnostic overshadowing’ - that is, physical complaints are overlooked and partially or totally attributed to psychological and psychiatric factors.9 Differential access to effective care leads to poorer outcomes including preventable deaths,10 and incurs high costs in healthcare provision.11 Although evidence-based interventions for improving chronic disease
+outcomes are available there is little evidence of committed implementation for people with SMI. This may be driven by a lack of awareness of gaps in healthcare by the service providers, and poor knowledge about the strength of the evidence for specific interventions.9 Despite extensive research showing reduced life expectancy for people with severe mental illness, a comprehensive synthesis of existing evidence on interventions that might reduce mortality has not been attempted. This is necessary to guide practitioners and commissioners.
+This meta-review aimed to assess the evidence for the impact of health interventions in reducing excess mortality in people with severe mental illness. Reviews were sought that examined trials reporting mortality or physical health outcomes in people with SMI compared with those receiving ‘usual care’. As the major causes of excess deaths in these people are chronic diseases, we focused on interventions that might have an impact on physiological health indicators for these conditions. There is no single definition of chronic disease,12 so for consistency we decided to focus on physiological markers for common cardiometabolic diseases.
+Method
+The review was conducted in 2014 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.1 We focused on existing syntheses of the literature in which authors had looked at the effects of health interventions in people with SMI, generally defined in the mental and physical health literature as psychotic disorders (schizophrenia, schizoaffective and schizophrenia-like illnesses), bipolar disorder and severe major depression,14 and where mortality or physiological health parameters were reported as a primary outcome. We used a broad search string (e.g. [‘mental
+disorders’ OR schizo* OR depress* OR bipolar*] AND [interventions OR treatment] AND [mortality OR survival]) to search the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects (DARE), the Campbell Collaboration database of systematic reviews and the Database of Promoting Health Effectiveness Reviews (DoPHER). We conducted additional searches of citation lists from research papers and reports to identify additional reviews. Specific databases searched and the search strings used are reported in the online data supplement.
+Inclusion and exclusion criteria
+Systematic reviews were accepted if they included studies that compared an intervention with a control group for people with SMI. Because reviews that included mortality or survival outcomes were scarce, secondary physiological outcomes associated with chronic disease were also included: metabolic factors such as glycaemic control, dyslipidaemia and weight gain. Reviews of studies that reported only measures of behavioural change were not captured as the aim was to draw a stronger link between interventions and their potential to reduce premature mortality. We accepted any review that reported results of randomised controlled trials (RCTs), quasi-experimental studies and observational studies. Only reviews that employed systematic search methods and reported effect sizes were included. No limitation was placed on language, date of publication or publication status. Reviews that considered only studies of individuals with pre-existing physical conditions such as cardiac disease were excluded. Although this is an important area of research, causal direction is distinct in these patients compared with people with SMI who develop chronic disease.
+Synthesis method
+Titles and abstracts of all reviews were screened; relevant reviews were subjected to full text examination and were evaluated against the study criteria. Where multiple iterations of a review were found (e.g. a 2014 update was found for a 2010 Cochrane review),15,16 only the most up-to-date review was included. All reviews entering the study were downloaded into an EndNote database and duplicates were removed. Information describing study design, sample and comparison groups, interventions and outcome data were extracted from the full text. Two researchers (A.J.B. and Y.K.) independently extracted the data, and where discrepancies were found a third reviewer (M.G.H.) adjudicated differences. Types of interventions were identified and grouped into broad categories. Given that meta-analyses were not possible owing to heterogeneity in study design and focus, study findings were synthesised narratively to explore the impact of different types of interventions.
+Quality assessment
+Review quality was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR) measurement tool, developed specifically to assess the quality of systematic reviews with reference to the methodological and systematic rigour and synthesis of the evidence.17 This enables the quality of a systematic review to be scored on the following 11 items (scored as yes, 1; no, can’t answer or not applicable, 0):
+(a)	Was an a priori design provided?
+(b)	Were there duplicate study selection and data extraction?
+(c)	Was a comprehensive literature search performed?
+(d)	Was the status of publication (i.e. grey literature) used as an inclusion criterion?
+(e)	Was a list of studies (included and excluded) provided?
+(f)	Were the characteristics of the included studies provided?
+(g)	Was the scientific quality of the included studies assessed and documented?
+(h)	Was the scientific quality of the included studies used appropriately in formulating conclusions?
+(i)	Were the methods used to combine the findings of the studies appropriate?
+(j)	Was the likelihood of publication bias assessed?
+(k)	Was the conflict of interest stated?
+The authors of AMSTAR provide guidance notes as to how each item is scored. This guidance was followed with the exception of the final item, where clear acknowledgement of potential sources of support in the systematic review - rather than ‘the review and the included studies’ - sufficed to be scored as ‘yes’. The validity and reliability of AMSTAR have been established.17,18
+Results
+The database and citation searches yielded a total of 134 unique reviews. In total, 16 systematic reviews met the study criteria and entered the review (Fig. 1). The reviews showed substantial heterogeneity in terms of target groups, implementation strategy and outcome measures for the different categories of intervention. Of the 16 reviews included, eight identified mortality as an outcome of interest, including four that looked at both mortality and physiological health parameters, and another eight identified only physiological health parameters as outcomes. The quality of the included studies varied: out of a total score of 11 on the AMSTAR, six reviews achieved scores of 9 or above, six achieved scores between 6 and 8, and four achieved scores of 5 or lower. As there is no recommended cut-off for the AMSTAR, we refer
+to reviews scoring 9 or above as ‘high quality’, those scoring 6-8 as ‘medium quality’ and those scoring 5 or lower as ‘low quality’.
+Overview of interventions
+To facilitate comparison we grouped interventions into four categories: mental health interventions, integrative community care, interventions for lifestyle factors, and screening and monitoring of health parameters. Mental health interventions included psychiatric medications and psychological interventions including psychoeducational and behavioural therapies such as cognitive-behavioural therapy (CBT). Psychotherapies such as CBT are becoming increasingly available for people with SMI. These treatments can help link the person’s distress and problem behaviours to underlying patterns of thinking with the aim of enhancing coping strategies and general problem-solving skills.19 We defined integrative community care as multiprofessional team-based approaches to patient care, which aimed to improve mental and physical health outcomes in people with SMI. Components of care include scheduled patient follow-up and interprofessional communication between team members. Interventions aimed at improving lifestyle-related risk factors in people with SMI can take a variety of forms. We broadly categorised these reviews as having a primary outcome of reducing risky lifestyle factors. Interventions included pharmaceutical treatments and/or psychoeducational or behavioural approaches. These latter incorporate techniques such as problem-solving, goal-setting and self-monitoring, sometimes with a practical component in terms of an exercise regimen or dietary counselling. These are generally informed and adapted from existing lifestyle programmes developed for use in the general population. Findings of the reviews are summarised below by intervention category. Study characteristics and findings are summarised in online Table DS1 and AMSTAR quality ratings for each of the reviews included are given in online Table DS2.
+Mental health interventions
+We found two systematic reviews that focused on mortality-related outcomes associated with use of psychiatric medications, specifically antipsychotics and antidepressants.2 ’ Our search found one additional review that reported health outcomes of groups receiving psychological therapies.22 The studies included in the two reviews looking at excess mortality and use of psychiatric medications varied enormously in terms of study design (including RCTs, data linkage studies, observational secondary analyses and cohort studies), follow-up periods, control groups and consideration of comorbidities and other risk factors.2 ,1 In a medium-quality review Weinmann et al examined outcomes from 12 studies that looked at the risk of excess death in patients with schizophrenia prescribed antipsychotic medication. The majority of the studies included in their review showed that patients using antipsychotics were more likely to die prematurely compared with the general population.20 In a comparison of patients using antipsychotics with those not using antipsychotics, however, there was increased mortality only where several antipsychotic drugs were prescribed (polypharmacy), with deaths increasing in relation to the number of medications (incremental relative risk (RR) per additional antipsychotic 2.50, 95% CI 1.46-3.40) or where patients had discontinued medication;2 ’ cited by Weinmann et al.20 Two retrospective cohort studies reported a protective effect of antipsychotic use and these found a two-fold to ten-fold reduction in mortality for patients who used antipsychotics compared with those who did not;24,25 cited by Weinmann et al.20 However, observational studies may not
+adequately control for confounding variables such as disease severity and preferential prescribing.20
+A lower-quality review by Von Ruden et al identified three studies that looked at the association between selective serotonin reuptake inhibitors (SSRIs) and mortality, all of which defined SSRI use as ‘exposure’ without providing information on period since treatment, mental health status or presence of other risk factors.21 Findings were heterogeneous, with the first study reporting a positive association between SSRI exposure and hospital admission or death (hazard ratio 1.47, 95% CI 1.26-1.70), the second reporting an inverse association with cardiovascular disease mortality (relative risk 0.37, 95% CI 0.17-0.78) and the third finding no relationship with subsequent cardiac mortality or morbidity;26-28 cited by Von Ruden et al.21
+A third high-quality review looking at mental health interventions and their effect on mortality reduction examined the long-term effects of CBT in people with schizophrenia.22 The authors identified two trials reporting mortality as an outcome, neither of which found a significant association between CBT and risk of excess mortality. We were unable to find any review that looked at the effect of psychotherapies (aimed at improving symptoms of mental disorders) on physiological health outcomes in people with depression or bipolar disorder.
+Integrative community care
+Two Cochrane reviews evaluated health outcomes in people with SMI allocated to integrative community care management or intensive case management.29,30 In total the reviews identified 20 non-overlapping RCTs where all-cause and/or suicide deaths were reported as primary outcomes. In a high-quality review Dieterich et al pooled results from 14 RCTs comparing all-cause death for intensive case management v. standard care and results from 7 RCTs that evaluated mortality in intensive case management groups v. non-intensive management, and found no significant difference in risk of death.30 Malone et al found three RCTs that compared collaborative community health interventions v. standard care, producing a pooled RR of 0.47 (95% CI 0.17-1.34) for all-cause mortality.29 Although no result in either review was statistically significant, the authors reported a general trend across the studies suggesting fewer deaths due to suicide or suspicious circumstances and few deaths overall in treatment groups. Neither study reported physical health markers as outcomes of interest.
+Interventions for lifestyle factors
+We found ten systematic reviews that measured health outcomes associated with lifestyle and behavioural interventions in people with SMI. Three reviews reported mortality as an outcome of interest, all of which scored highly on the AMSTAR scale.3 -3 A Cochrane review by Tosh et al looked at interventions providing general health advice for people with SMI, reporting a broad range of outcomes including social and psychological health; physical health, awareness and behaviours; and adverse events such as death.31 Of the seven studies identified in the review two measured all-cause mortality,34,35 cited by Tosh et al,31 and found no significant difference for reduced mortality in those receiving the intervention compared with treatment as usual (RR=0.98, 95% CI 0.27-3.56).31 A third study looking at fatal cardiovascular disease as an outcome found no significant reduction in cardiac fatalities;36 cited by Tosh et al.31
+Hunt et al identified 32 studies in a systematic review and meta-analysis of RCTs that looked at psychosocial interventions for treatment of substance use, seven of which considered mortality. 3 Analyses found no change in mortality risk associated with either intensive case management, integrated models of care
+or motivational interviewing alone. However, follow-up periods were short (the longest being 3 years) and there were few deaths in either case or control groups.33 A third review, by Gierisch et al, found 32 RCTs that evaluated the effect of behavioural and/ or pharmaceutical interventions aimed at reducing cardiovascular disease risk, including metabolic factors (weight, glycaemic control or dyslipidaemia).32 This review also intended to examine mortality, but no death was reported in the included studies.
+Weight loss or obesity management were the most commonly measured health indicators (nine studies), , ,-3 followed by metabolic risk factors such as glucose levels, lipid profiles and/or blood pressure (five studies),31,32,37,40,41 and harmful substance use (two studies).33,40,44 Studies identified in these reviews varied in terms of intervention types and outcome measures, and this hampered the ability to synthesise results; overall only four of the 11 reviews were able to report pooled effect sizes.3 - ’ Overall, the reviews were consistent that interventions aimed at improving lifestyle factors can achieve modest but significant improvements in physical activity and eating habits.39,40 They showed that interventions could effectively reduce antipsychotic-induced weight gain,32,42,43 and achieve weight loss or body mass index (BMI) reduction in those already overweight.32, - Where reviews looked at both pharmacological treatment and behavioural interventions, both approaches were associated with a similar magnitude of improvement in weight and/or BMI control.32, ’ Few studies evaluated interventions specifically designed to address outcomes such as metabolic syndrome, glycaemic control, dyslipidaemia, blood pressure or other physiological markers of disease; these were considered as secondary measures to other outcomes and so analyses were often underpowered. The review by Tosh et al cited one study that specifically evaluated a lifestyle programme and its effect on physical health.31,36 Although no effect was found for mediation of metabolic syndrome in people with SMI, there was a trend for fewer metabolic risks, from 13 to 10, after 1 year of follow-up.31
+A review by Gierisch et al found two out of the seven studies with glycaemic control as an outcome showed a significant improvement in the invention group over the control group; in both cases metformin was prescribed as treatment.32 More positively, of the 15 trials that measured blood lipid levels, six found significant improvement in treatment groups (in each case treatment was pharmacological).32 Caemmerer et al conducted a meta-analysis focusing on effects of non-pharmaceutical interventions.37 This medium-quality study showed treatment was associated with a significant improvement in insulin levels (three RCTs; weighted mean difference (WMD) — 4.93 pIU/mL, P<0.001) and fasting glucose levels (six RCTs; WMD = — 5.79mg/dL, P<0.001).37 Interventions were also significantly associated with improved profiles for total cholesterol, low-density lipoprotein (LDL) cholesterol and triglyceride levels, but the same was not found for high-density lipoprotein (HDL) cholesterol or systolic blood pressure.37 A brief medium-quality review by Cabassa found 13 studies reporting metabolic risk measures, of which seven found statistically significant improvements in at least one physiological measure.41
+Screening and monitoring of health parameters
+We identified only one review meeting our criteria that looked at screening and/or monitoring of physical health parameters in people with SMI. Tosh et al found no trial reporting the effects of healthcare monitoring (either self-monitoring or by a healthcare professional).16 The low AMSTAR score for this review reflects the lack of data available on the impact on health outcomes of physical health screening in people with SMI.
+Discussion
+We sought to synthesise the current scientific evidence on interventions that may reduce excess mortality or improve physical health indicators of chronic disease in people with SMI. We evaluated the evidence relating to four broad intervention categories: mental health interventions, collaborative care interventions, interventions for risky lifestyle factors, and screening and monitoring of physical health parameters. Reviews suggest that psychiatric medications (antipsychotics and antidepressants) have some protective effect against excess mortality, but this is dependent on treatment adherence. Integrative community care programmes may reduce physical morbidity and excess mortality associated with SMI, but the effective ingredients of the interventions need to be identified. Interventions to improve risky lifestyle behaviours can reduce the profile of risk factors, but studies with long-term outcomes are lacking. Screening and preventive interventions and improved care in those with comorbid chronic disease are expected to reduce excess mortality, but there are currently no data available to support this. These findings highlight areas for policy, practice and research development. Below we explore the implications of our meta-review within the context of other research findings for each intervention category, taking into account a small number of studies post-dating the included systematic reviews.
+Mental health interventions
+We found the evidence on the effects of medication on mortality was equivocal. However, a number of trials published subsequent to these reviews suggest that antipsychotics and antidepressants may be effective in reducing excess mortality, but this is mediated by treatment adherence.45-47 In Finland, Tiihonen et al found that long-term use (7-11 years) of any antipsychotic treatment was associated with lower mortality compared with no drug use (adjusted HR=0.81, 95% CI 0.77-0.84).46 It has been noted, however, that methodological aspects of this study such as the exclusion of deaths occurring in hospital and the possibility of survivor bias mean these findings should be interpreted with caution.48 In apparent support of the findings from Finland, a recent study from North America by Cullen et al showed that those with most consistent adherence to antipsychotic medications had a 25% lower risk of excess mortality compared with those with the poorest adherence, after controlling for medical comorbidities.45 A study of antidepressant use found that depressed patients receiving 12 or more weeks of antidepressant treatment had decreased risk of all-cause mortality across all drug classes compared with those taking antidepressants for 0-11 weeks.47 Effect sizes ranged from HR=0.51 (95% CI 0.48-0.54) for serotonin-noradrenaline reuptake inhibitors (SNRIs) to HR = 0.66 (95% CI 0.62-0.71) for tricyclic antidepressants.47
+There are several pathways by which psychiatric medications may reduce risk of chronic disease and excess mortality. Medications can affect physical health directly through biochemical mechanisms and indirectly by reducing the duration and severity of symptoms. Although there are known cardiotoxic effects associated with some older antidepressants there is evidence that newer classes of antidepressants - specifically SSRIs and SNRIs - may normalise platelet activity,49 improve cardiac risk markers,50,51 and reduce the risk of cardiac events.47,52-54 Antidepressant medication may have a direct positive impact on biological factors shared by depression and cardiovascular disease, including overexpression of pro-inflammatory cytokines, platelet activation and vasoconstriction.55 For both antipsychotic and antidepressant
+medications mitigation of psychiatric symptoms may be important, because reduced severity of mental disorders leads to better health behaviours, such as reduced smoking and alcohol intake,56 and more proactive physical healthcare seeking.45 Comparison of long-term health outcomes at this stage, however, is obscured by heterogeneity in the drug class,21 and by underlying comorbidities and risk factors for chronic disease.57
+Integrative community care
+Existing summaries of the literature on integrative community care programmes were unable to show a significant impact on excess mortality in treatment groups, although a general trend for reduced deaths was noted across studies. Study authors reported few deaths in either treatment or sample groups in the short periods of follow-up (median 18 months). The recently published Prevention of Suicide in Primary Care Elderly: Collaborative Trial (PROSPECT) provides support for the efficacy of integrative care management strategies when longer intervention and follow-up periods apply.58 During 9 years of follow-up people with major depression, when allocated a mental-health specialist case manager to work with their regular general practitioner, were 24% less likely to have died, particularly from chronic disease, compared with those receiving usual care.58 The use of integrative care models to improve physical health in people with SMI is burgeoning in countries such as Canada and the USA, and this has become even more important in light of the Affordable Care Act. Although preventing excess mortality has been used as a key rationale for these models, there is little information as yet on survival as an outcome. However, trials published subsequent to these reviews have demonstrated a range of other positive, short-term outcomes including improved cardiovascular risk profiles.35 A number of RCTs are currently under way to address physical comorbidity outcomes in SMI, including the serious mental illness Health Improvement Profile (HIP) study, the Improving Physical Health and Reducing Substance Use in Psychosis (IMPACT) therapy trial and the Health Outcomes Management and Evaluation (HOME) study.59-61
+Taking into account our findings, together with other research, we recommend further work to identify the specific aspects of this approach that positively influence physical health. Researchers propose that the benefits of care management are probably multifactorial: clinicians may become more sensitive to changes in physical health outside the filter of the mental disorder diagnosis and patients may be more aware of health issues, have more frequent contact with health services and be primed to seek treatment;58 however, evidence is lacking. Furthermore, as shortterm research funding tends to preclude robust investigation of mortality as an outcome, researchers and funding bodies need to invest in longer periods of intervention and follow-up of health outcomes.
+Interventions for lifestyle factors
+Overall, our meta-review showed that interventions to improve risky lifestyle behaviours can reduce an individual’s health risk profile. One major limitation of studies in this intervention category was the shortness of the follow-up periods. This is important for two reasons: first, it prevented us from drawing firm conclusions on the effectiveness of such programmes in reducing mortality, and second, the positive effects of lifestyle interventions tend to deteriorate over time for people with SMI as well as for the general population.44,62 Given the motivational difficulties associated with medication effects and psychopathology, the SMI group faces additional challenges in instituting lifestyle
+changes. Papers identified in these reviews suggest that more tailored approaches to treatment with continued proactive follow-up by usual mental health clinicians are likely to contribute to more prolonged long-term changes in healthy behaviours.
+The 2011 British mental health outcomes strategy No Health without Mental Health sets out six objectives shared at all levels of community and government; it states one of its primary objectives as ensuring that ‘more people with mental health problems will have good physical health’.63 However, the commitments made to achieve this reflect a passive approach, for instance improving nutritional standards in catering services, improving access to fitness facilities and developing alcohol and tobacco control plans. Given that the literature on lifestyle factors and collaborative care models suggests that proactive approaches tend to be more successful, these policies could be improved by integrating a component that ensured active follow-up by community service providers.
+Screening and monitoring of health parameters
+Screening, preventive interventions and improved physical healthcare for people with SMI and comorbid chronic disease are expected to reduce excess mortality, but there are currently no data available to support this. Nonetheless, available studies suggest there are inequities in terms of diagnostic timeliness, use of monitoring and provision of physical healthcare interventions; each is considered below along with possible explanations. Despite their increased exposure to chronic disease risk factors, many people with SMI have limited access to general healthcare with less opportunity for metabolic risk factor screening and prevention,64 and lower rates of medical interventions than their counterparts in the general population.65-68 Crump et al found that, despite their increased risk of mortality due to ischaemic heart disease (IHD) and cancer, people with schizophrenia were less likely to receive diagnoses of IHD, hypertension, abnormal lipid levels, cancer or liver disease compared with those without schizophrenia.64 After restricting the analysis to those previously diagnosed with chronic disease, schizophrenia was only modestly associated with IHD mortality and was no longer associated with cancer mortality.64 This in turn suggests that reducing the life expectancy gap for people with SMI also requires improvements in the coverage, timeliness and quality of physical healthcare for this group.
+Although there is still insufficient information to determine a causal link, indirect evidence supports the hypothesis that mortality in people with SMI may be averted, to some degree, through ensuring better monitoring of physical health by mental health clinicians for chronic disease risk factors. Owing to the known relationship between atypical antipsychotics and metabolic disturbances, guidelines for screening and monitoring patients receiving these drugs have emerged over recent years, particularly in the case of clozapine.69-71 However, uptake and compliance with these guidelines remains poor.72,73 There is no similar guideline for monitoring metabolic risk factors in patients not currently receiving medication or prescribed other drugs. The Service Framework for Mental Health and Wellbeing states that people with SMI should have an annual physical health check, preferably in primary care.74 However, adherence to guidelines on screening and treatment of chronic disease is poor, and there is little information on why these barriers exist.75 Furthermore, the thresholds at which risk factor intervention is considered to be warranted are often determined by structured tools such as the QRISK2, which underpins the UK National Health Service health checks programme, and the Framingham coronary heart disease risk score, used in the USA.76,77 There is growing evidence, however, that these tools can underestimate chronic disease risk in
+those with SMI.78 Trials are urgently required to identify why guidelines are not routinely applied in the case of people with SMI, the extent to which adherence to guidelines can affect health indices and outcomes, and the most appropriate screening tools and guidelines for patients with SMI.
+Patients with SMI are also less likely to undergo standard surgical procedures or be prescribed medication for chronic disease compared with patients without mental disorders,65-68 and this in turn supports a link between deficits in usual care and higher rates of mortality.68 It is difficult to tell the degree to which this is explained by patient or doctor behaviour, or both. Given the apparent efficacy of case management programmes in reducing IHD mortality, as reported above, it is likely that patient behaviours such as follow-up with healthcare professionals can improve treatment access and increase the likelihood of success of such interventions. However, it is also possible that doctors are reluctant to offer surgical intervention because of concerns about patient capacity or cooperation, comorbid conditions or risk of developing complications post-operatively. This is a valid concern, with higher documented rates of bleeding and septicaemia and 30-day mortality in patients with schizophrenia following surgery.10
+Why might these patterns occur? Chronic diseases are underdiagnosed in people with SMI, and environmental factors such as lower socioeconomic status alone cannot account for this.64 Therefore an element of the mental disorder itself (i.e. behaviour of the patient) or barriers to provision of care are responsible for the underdiagnosis and treatment of physical disorders. Although mental health clinicians reported that primary care services should take responsibility for risk factor screening and management, people with SMI favour physical health screening by their mental healthcare providers.79 Diffusion of responsibility for mental and physical health care is a major barrier to ensuring adequate care for people with SMI and this has implications for the collaborative management and delivery of healthcare services. Additional training of physical healthcare providers to reduce stigma and improve understanding of mental disorders, and of mental health clinicians on the importance of and delivery of care for physical healthcare conditions, and the communication and collaboration of all healthcare providers should be an important goal. The excessive specialisation of healthcare providers and lack of consensus over who should take responsibility for the general healthcare needs of patients with mental illness has resulted in a continuing failure to provide appropriate services.80,81 In 2008 the World Health Organization pointed out that, despite the potential of primary prevention and health promotion to prevent as much as 70% of disease burden, resources continued to be targeted at the treatment of physical illnesses once they have already developed.81 Given that individuals with SMI are more likely to develop chronic disease and experience poorer outcomes, this highly vulnerable group would reap substantial benefit from preventive actions such as screening, monitoring and prompt treatment for chronic disease risk factors. A first step in implementing this approach would be improving adherence to guidelines for monitoring physical health factors in those prescribed antipsychotic medications.
+Strengths and weaknesses
+Some limitations should be acknowledged when considering these findings. This is a synthesis of reviews, based on systematic reviews published between 2007 and 2014. It therefore did not include some published trials conducted since these reviews were published; however, we considered these when interpreting our findings and they did not alter our conclusions. A degree of
+judgement was required to classify some interventions into categories. This was because some reviews focused on an intervention strategy (e.g. general medical advice), even though it formed part of a broader programme, whereas others brought together studies where care was delivered through a specific platform (e.g. collaborative community care). The quality of the included reviews varied substantially. Apart from the screening and monitoring of health parameters, however, all categories contained at least two reviews of good to high quality (scores of 8 or higher out of a maximum of 11) and scored on average between 7 and 9. Because of the variability of intervention strategies, outcome parameters, follow-up periods and statistical analyses it was not feasible to conduct statistical analyses necessary for meta-analysis.
+A number of reviews could not be included because studies reported behavioural change outcomes but not physiological outcomes, for instance those looking at cancer screening and smoking cessation programmes.44,82-85 It may be that physiological outcomes are not measured in many cases owing to the length of time it would take for physical health changes to become apparent (for instance, in the case of smoking-related disease). However, to develop an evidence base around programmes that lead to improved health outcomes, physiological markers of change are required to enable us to draw a more direct link between interventions and reduced premature mortality. This gap in our evidence synthesis highlights the importance of longitudinal follow-up of intervention outcomes to permit collection of physiological outcome measures.
+Study implications
+The findings of this meta-review are important not only for practitioners but also for commissioners and policy-makers who set priorities and allocate resources. The health and financial implications of not intervening to reduce important causes of preventable deaths in people with SMI need recognition and remedy. The excess mortality rate is an important marker of general health among these people. The growing inequity in life expectancy, particularly due to heart disease mortality, underlines the need for better physical healthcare programmes for this group. Two areas that warrant immediate action are improving adherence to psychiatric pharmacological guidelines, and improving adherence to guidelines for monitoring metabolic health. There is an urgent need to improve the inequitable screening, monitoring and treatment of chronic disease in people with SMI. Research efforts should focus on filling major evidence gaps regarding the barriers to provision of physical health monitoring and elucidating the aspects of integrative community care programmes that have a positive impact on long-term health outcomes.
+Amanda J. Baxter, PhD, Meredith G. Harris, MPH, MPASR, University of
+Queensl and, School of Publ ic Hea Ith, Herston, and Pol icy and Epidemiology Group, Queensl and Centre for Menta l Hea lth, Wacol, Austral ia; Yasmin Khatib, PhD, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, UK; Traolach S. Brugha, MD, PhD, Department of Health Sciences, University of Leicester, Leicester General Hospital, Leicester, UK; Heidrun Bien, PhD, Kamaldeep Bhui, MD, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, UK
+Correspondence: Amanda Baxter, Queensland Centre for Mental Health Research, Locked Bag 500, Sumner Park BC, Queensland 4074, Australia. Email: amanda_baxter@qcmhr.uq.edu.au
+First received 15 Jan 2015, final revision 5 Jun 2015, accepted 21 Sep 2015
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+Coronary heart disease is the leading cause of death worldwide.1,2 Acute coronary syndrome refers to acute myocardial ischaemia caused by atherosclerotic coronary disease and includes myocardial infarction and unstable angina. Patients with ST elevation myocardial infarction are recommended for immediate reperfusion therapy using thrombolytic agents or percutaneous coronary intervention (PCI).3,4 From 1980 to 1990 there was a 34% decline in coronary heart disease mortality in the USA.5 Over the same period there was a substantial growth in the number of cardiovascular procedures performed, particularly PCI. Improvements in the quality of care are thought to be a major factor underlying declining mortality.5 Yet despite these improvements evidence of sociodemographic inequalities in procedural rates has been accumulating.6-11 Inequalities have also been documented with regard to the medical care of those with known mental health diagnoses. Medical care includes medical treatment and processes of care such as investigations and monitoring. Mitchell et al evaluated 27 studies that examined receipt of medical care in those with and without mental illness (including 11 studies of severe mental illness or schizophrenia and 10 of dual diagnosis or substance use disorder).12 The majority of studies demonstrated significant inequalities in receipt of quality of care including 7 of 10 studies that examined the quality of cardiovascular care. Lord et al recently reviewed 25
+434
+https://doi.org/10.1192/bjp.bp.109.076950 Published online by Cambridge University Press
+studies that examined preventive care in individuals with or without psychiatric illness; for individuals with schizophrenia, 8 of9 analyses suggested inferior receipt of preventive care in several areas including blood pressure monitoring, vaccinations, mammography and cholesterol monitoring.13 From these narrative reviews it is clear that the magnitude of the deficits in quality of care varies considerably depending on the setting and method of data collection.
+There is substantial concern about the cardiovascular health of those with known mental health diagnoses, especially severe mental illness and schizophrenia.14,15 National guidelines from several countries are agreed that the medical care of patients with mental disorders is of paramount importance.16-19 However, serious concerns have been raised about the quality of medical care offered to patients with severe mental illness. People with schizophrenia have higher rates of several important conditions including the metabolic syndrome.20,21 Most studies,15,22-28 but not all,29,30 suggest that incidence of cardiovascular disease is higher in people with schizophrenia. Such individuals often have higher than expected rates of contributing background risk factors including smoking,31 obesity,32 dyslipidaemia,33 lack of exercise,34 and possibly essential hypertension.3 A meta-analysis suggested a relative risk of 1.87 (95% CI 1.68-2.09) for diabetes in schizophrenia, but with no significant elevation of hypertension
+or cholesterol.36 Of these factors, smoking and obesity may be most critical to future cardiovascular health.3 An estimated 42% of patients with schizophrenia have a body mass index above 27 kg/m2 compared with 27% among the general US population;1 three-quarters are regular cigarette smokers compared with a quarter of the general population.15 It is therefore probably not surprising that people with schizophrenia have higher than expected non-suicide-related mortality; in fact, mortality from comorbid physical illness outnumbers the excess mortality from suicide.38 A systematic review of 37 studies found that that those with schizophrenia have a 2.6 times greater rate of mortality compared with the general population, including 1.8 times the mortality rate from cardiovascular disease.3
+Given these numerous concerns regarding cardiovascular health in severe mental illness and in particular in schizophrenia, we aimed to examine and quantify, first, the receipt of medical procedures following acute coronary syndrome, and second, the mortality rate following acute coronary syndrome using metaanalysis of published data. We were interested in comparative studies that examined the adequacy of appropriate procedures and subsequent mortality for people with cardiac disorders stratified into those with and without severe mental illness/schizophrenia.
+Method
+Search
+We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a checklist of 27 items that ensure the quality of systematic review or meta-analysis.40 Inclusion criteria were studies of patients with acute coronary syndromes (with data on rates of subsequent invasive coronary procedures or mortality) which reported data for those with defined mental illness as well as those without mental ill health. We only included those with severe mental illness provided there was a subgroup with schizophrenia. We excluded noncomparative studies, and also those concerning depression or anxiety only. We searched Medline/PubMed and EMBASE abstract databases from inception to 20 July 2010. In these databases the keywords/MeSH terms (‘ACS or REVASCULARISATION or REVASCULARIZATION or PROCEDURES or GRAFT or ANGIOPLASTY or PERCUTANEOUS or CATHETERIZATION or CARDIA* or HEART’) and (‘PSYCHI* or MENTAL or PSYCHOSIS or PSYCHOTIC or SCHIZOPHR* or SEVERE MENTAL ILLNESS or SMI’) were used. In addition, four full text collections were searched: ScienceDirect, Ingenta Connect, SpringerLink and Wiley Online Library. In these online databases the same search terms were used as a full text search and as a citation search. The abstract databases Web of Knowledge and Scopus were searched, using the above terms as a text word search, and using key papers in a reverse citation search. Finally, a number of journals were hand-searched and several experts contacted. We excluded studies where the event rate was measured in the general population rather than in those with cardiovascular disease.41 Where 30-day and 365-day mortality rates were cited, we examined only 365-day rates. Data were extracted using a standard form (available from the authors on request) by A.J.M. and checked by D.L.
+Meta-analysis
+We used summary meta-analysis, pooling hazard ratios where reported. All hazard ratios were entered adjusted rather than unadjusted (where reported and except where indicated). Odds ratios were converted into relative risks (hazard ratios) using the reported control event rate.42 Confidence intervals were obtained from all studies or calculated from the data provided.
+Heterogeneity was reduced by stratifying using type of mental illness and procedure type; despite this, heterogeneity (defined by I2) remained high and so random effects meta-analysis was preferred. We required a minimum of three independent studies to justify pooling by procedure type. Any potential sources of bias were reported. Publication bias was assessed using the Begg-Mazumdar statistic.43
+Results
+The initial PubMed search generated 572 hits. Of these, only 16 discussed cardiac procedures and 58 discussed mortality (online data supplement). Searches in the four full-text collections generated 1572 hits (Fig. 1). Using these strategies we identified 241 references of interest but only 74 were primary data studies. After excluding studies with no relevant outcome, no comparison group or other methodological issues, we identified 9 papers relating to cardiac procedures following acute coronary syndrome and 6 papers relating to mortality following acute coronary syndrome.
+Studies examining procedure rate after acute coronary syndrome
+Our search identified 9 publications relating to cardiac procedures following acute coronary syndrome and contained in these reports were 22 analyses using broadly defined mental disorder or severe mental illness and 10 using an adequate definition of schizophrenia or related psychosis (online Table DS1). There was no evidence of publication bias using the Begg-Mazumdar statistic (Fig. DS1). The total sample size was 825 754 (mean 91 750, s.d. = 120 158). Druss et al examined cardiovascular care following an acute myocardial infarction.44 After adjusting for demographic, clinical, hospital and regional factors, those with mental disorders were only 41% (for schizophrenia) to 78% (for substance use) as likely to undergo cardiac catheterisation as those without mental disorder. In a further study, Druss et al found patients hospitalised for myocardial infarction with mental health diagnoses were less likely to have reperfusion conducted.45 Young & Foster identified people with mental illness who had experienced a myocardial infarction: this group had significantly lower levels of all three revascularisation procedures - cardiac catheterisation, percutaneous transluminal coronary angioplasty (PTCA) and coronary artery bypass graft (CABG) - compared with those without mental illness, with the lowest rates seen in those over 64 years old.46 Petersen et al examined the records of 4340 male veterans discharged after a clinically confirmed myocardial infarction: those with mental illness were less likely to have undergone in-patient diagnostic angiography (age-adjusted RR = 0.90, 95% CI 0.83-0.98) but there was no difference in CABG.47 Kisely et al carried out a population-based record-linkage analysis of related data from 1995 through 2001 compared with the general public for each outcome (n = 215 889): in psychiatric in-patients the adjusted rate ratios for cardiac catheterisation, PTCA and CABG were 0.41, 0.22 and 0.34 respectively.48 However, Plomondon et al found no difference in cardiac procedure rates after acute coronary syndromes presenting to Veterans Health Administration (VHA) hospitals.49 Similarly, Jones & Carney found no difference in the rates of revascularisation.50 Laursen et al followed 605 649 patients admitted with heart disease in Denmark between 1994 and 2007: people with admissions for severe mental disorder had higher mortality rates from heart disease following cardiac procedures but received lower rates of cardiac revascularisation.51 Abrams et al examined the rate of PCI or CABG within 30 days of
+admission.52 They examined mixed mental disorders, defined by ICD-9 codes, in 21745 patients admitted following acute myocardial infarction. They reported results in two related samples: patients with psychiatric comorbidity had lower receipt of coronary revascularisation (hazard ratio, HR = 0.92, 95% CI 0.85-0.99) in the out-patient sample but equal rates (HR= 1.00, 95% CI 0.91-1.1) in the in-patient sample.52
+Quantitative differences in procedure rates
+For those with mental illness or severe mental illness there was significant heterogeneity (I =98.1%). The meta-analytic random effects size was 0.86 (95% CI 0.80-0.92, P< 0.0001), suggesting that those with any mental illness received a 14% lower rate of cardiac procedures (Fig. 2). Looking at each procedure individually, there was significantly lower receipt of CABG (RR = 0.85,	95% CI 0.72-1.00), cardiac catheterisation
+(RR = 0.85, 95% CI 0.76-0.95) and PTCA/PCI (RR = 0.87, 95% CI 0.72-1.05). For those with schizophrenia there was moderate heterogeneity (I = 77.6%). The meta-analytic relative risk was 0.53 (95% CI 0.44-0.64, P<0.0001), suggesting that those with schizophrenia received about half the comparable rate of cardiac procedures (Fig. 3). Looking at each procedure individually, there was significantly lower receipt of CABG (RR = 0.69, 95% CI 0.55-0.85) and PTCA/PCI (RR = 0.50, 95% CI 0.34-0.75).
+Studies examining mortality after acute coronary syndrome
+We identified ten studies relating to mortality following cardiac events but two reported mortality as the general population rate,
+one had a significant methodological issue and one had insufficient data for analysis. The total sample size from six valid studies was 596 368 (mean 99 394, s.d. = 132 344). In Druss et al’s 2000 study, patients with mental disorders had a small but statistically significantly lower risk of mortality at baseline, and in unadjusted analysis 12.8% of those with schizophrenia died within 30 days compared with 10.8% in the comparator population; however, this was not significant after adjustments.44 Yet in their replication study, Druss et al found that mental disorder of all types was associated with a 19% increase in mortality at 1 year.45 Importantly, when the five quality indicators were added to the model the association was no longer significant, suggesting that elevated mortality is related to poor quality of care. Petersen et al examined the records of 4340 male veterans discharged after a clinically confirmed myocardial infarction and found a trend towards higher rate of death at 1 year in those with mental illness; the risk of death within 1 year was 1.25 (95% CI 1.00-1.53).47 Plomondon et al studied 14 194 patients (including 18% with severe mental illness) with acute coronary syndromes presenting to VHA hospitals between October 2003 and September 2005.49 One-year mortality was lower for patients with severe mental illness (15.8% v. 19.1%, P<0.001). However, in multivariable analysis there was no significant difference in mortality (HR = 0.91, 95% CI 0.81-1.02) between patients with and without severe mental illness.49
+Four additional studies were noteworthy but could not be entered into the meta-analysis. Young & Foster found that in the older cohort (5 65 years old) with mental illness there was a 21% lower risk-adjusted likelihood of death (P< 0.001) compared
+with those without mental illness.46 In the younger cohort those with schizophrenia and substance misuse had higher in-patient mortality rates (both P<0.001). Unfortunately, data were inadequately reported for extraction. Kisely et al examined mortality and revascularisation in a general population sample.48 They conducted a population-based record-linkage analysis of related data from 1995 through 2001 compared with the general population for each outcome (n = 215 889): the age-standardised mortality rate ratio for psychiatric patients was 1.31 (95% CI
+1.25-1.36). In the study from Lawrence et al, ischaemic heart disease was the major cause of excess mortality in psychiatric patients.41 The standardised mortality rate from ischaemic heart disease in mental health users was almost twice that in the overall population (1.91 in total ischaemic heart disease, 1.74 in acute myocardial infarction). However, in the latter two studies the risk was measured at the population level, not specifically in those with acute coronary syndrome. Recently, Blecker et al examined mortality in 341 individuals with heart failure and severe mental
+question whether people with severe mental illness follow through with advice they are given. These questions can only be answered by good-quality studies examining physician responses to unmet medical needs in those with mental ill health as well as follow-up of patient behaviour.
+illness compared with 1460 with heart failure and no severe mental illness.5 Mortality was 29.9% in the severe mental illness group compared with 31.7% in those without severe mental illness. However, we excluded this study from the meta-analysis because it was not clear whether the follow-up period was identical in the two groups.
+Quantitative differences in mortality following acute coronary syndrome
+For those with severe mental illness there was significant heterogeneity (I =91.6%), therefore random effects meta-analysis was preferred. The pooled relative risk for mortality was 1.15 (95% CI 1.02-1.29). Excluding the studies by Kisely et al and Lawrence et al which did not focus on acute coronary syndrome, the pooled relative risk for mortality was 1.11 (95% CI 1.00-1.24; P = 0.05), suggesting an 11% increase in mortality rates (Fig. 4).
+Discussion
+It is already known that people with depression have higher than expected mortality after myocardial infarction.54,55 We also know that background cardiovascular mortality in those with schizophrenia or severe mental illness is at least double the expected rate.27,39,56-60 Here, we extend these findings to a population with established acute coronary syndrome, largely myocardial infarction. Pooled results suggest an 11% increase in comparator mortality rates in those with severe mental illness. This is lower than previously documented in depression but nevertheless statistically significant. We also extend previous narrative reviews that highlighted inferior quality of medical care.12,16 From 22 analyses of coronary interventions following serious cardiac events, we found that those with defined mental disorder received 86% of comparable procedures with significantly lower receipt of CABG, cardiac catheterisation and PTCA/PCI. From 10 analyses, people with a diagnosis of schizophrenia or related psychosis received only 0.53 of the usual procedure rate, with significantly lower receipt of CABG and PTCA/PCI. One possible explanation is that physicians do not offer procedures to those with mental illness because they believe that such individuals are likely to have poorer uptake of care. However, findings regarding uptake of medical care are conflicting.12 Another possibility is that the needs of those with mental illness are crowded out by the focus on mental concerns or possibly other medical factors, which may lead physicians to think that procedures are not a priority in this group. There is also a
+Possible mechanisms underlying elevated mortality rates
+In the general population prolonged QTc interval and low heart rate variability have been associated with increased cardiovascular mortality and sudden death, particularly in people with prior cardiovascular disease and diabetes.61,62 Schizophrenia appears to be associated with similar QT abnormalities, possibly even in the absence of antipsychotic medication, and it is possible that these factors are influential following acute coronary syndrome.63-65 It is also well known that metabolic syndrome and diabetes increase the risk of mortality,66,67 and these conditions are often more common in those with severe mental illness. There is also a higher rate of sudden cardiac death in those taking antipsychotics;68 that said, sudden cardiac death accounts for only a small proportion of excess mortality in schizophrenia.69 Unfortunately none of the studies cited here examined use of psychotropic drugs. A second mechanism might be the influence of severe mental illness on the effectiveness of cardiac treatment, for example through low engagement in rehabilitation. Preliminary evidence suggests similar uptake but lower completion of physical exercise programmes in those with known mental ill health.70 Confounding factors such as alcohol and drug use and medical comorbidities may also be influential. Most concerning is whether deficits in quality of care influence high mortality in this population. Li et al analysed New York’s publicly released Cardiac Surgery Report of surgeons’ risk-adjusted mortality rates.71 After adjustments, patients with both substance use and psychiatric disorders were more likely to receive care from surgeons in the high-mortality group (OR=1.76, P =0.024). Druss et al found that the excess mortality following myocardial infarction was negated when five quality measures were added to the model, suggesting that poor quality of care may be an important explanatory variable.45 More recently, Copeland et al analysed whether patients’ reduced primary care use over time was a significant predictor of mortality over a 4-year period among VHA patients; those with schizophrenia were likely to have low primary care use decreasing with time, and this was linked with inferior survival.72
+Despite the large sample size we acknowledge several limitations in this meta-analysis. Our results may not be representative of all healthcare systems as all studies following acute coronary syndrome originated in the USA. It is not clear that inequalities would exist in other healthcare systems where there are fewer barriers to care for those who are most socially disadvantaged. One limitation is that the definition of severe mental illness varied in some studies, and although most used ICD-8 or ICD-9 coding the definition employed by one group was not entirely clear.44 A second limitation is that all studies were retrospective and none distinguished current from historical mental ill health and thus it is uncertain if risk applies to those with prior as well as current diagnoses. A third limitation in relation to mortality was that the studies by Lawrence et al and Kisely et al reported risk in population samples not specifically following acute coronary syndrome and were therefore excluded from the analysis.41,48 Additionally, Druss et al and Young & Foster did not report 1-year mortality rates,44,46 adding to the heterogeneity in mortality analysis. Finally, there was poor data
+Improving inequalities in cardiac care
+The disparities in cardiac care noted here are consistent with the wider literature documenting disparities in treatment of other medical domains including diabetes, general medicine and cancer care.12,73,74 Individuals with schizophrenia and severe mental illness receive as little as half of the monitoring offered to people without mental ill health.7 , For example, in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study, patients with schizophrenia had limited access to or received suboptimal medical care.77 In a retrospective analysis of 19982003 Medicaid claims, fewer than 20% of people starting treatment with antipsychotic medication received baseline glucose testing and fewer than 10% received baseline lipid testing.78 Unfortunately, professional responsibility for comorbid medical disorders is often unclear. Poor mental health status is linked with poor general practitioner accessibility and perceived barriers to medical treatment.79 Hence medical comorbidity often is overlooked in those with severe mental illness, with up to half of all chronic conditions remaining unrecognised.80-84 Yet there is great interest in effective interventions that might reduce cardiovascular mortality in schizophrenia as well as in mental illness in general. Several groups have developed screening and monitoring guidelines.85-88 However, implementation of these has been inconsistent.89-93 In the general population lifestyle interventions can significantly influence mortality.94 In diabetes glycaemic control has similar benefits. The magnitude of the effect of intensive glycaemic control in diabetes is of the same order as the excess risk documented here.67 Interventions specifically targeting weight control and eating habits in people with chronic mental illness have shown some promising results.95-99 However, it is uncertain if benefits are maintained and whether there is any measurable effect on mortality. Ultimately it has been suggested that a reorganisation of mental health services would help redefine responsibility for physical health.100 There is some support for a collaborative model of care, co-locating psychiatric and primary care.101 Yet to improve cardiac care in severe mental illness, interventions must be effective at the secondary-care level for hospital specialists with limited interest in mental illness.
+In conclusion, following cardiac events individuals with mental illness appear to receive about 14% less frequent therapeutic cardiac procedures (47% in the case of schizophrenia), and they have about an 11% increased mortality rate. Further work is required to explore whether these factors are causally linked and whether improvements in medical care might improve survival in those with mental ill health.
+quality regarding hospitalisation and patient-provider factors underlying poor outcomes.
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+Schizophrenia is a severe mental disorder affecting approximately 0.5-1% of the general population.1 People with schizophrenia have markedly increased risk of premature mortality, with 15-20 years of reduction in life expectancy,2,3 and the excess death is mainly attributable to physical diseases.2 It is well recognised that diabetes mellitus and its complications have contributed substantially to the worldwide burden of mortality and disability.4 Diabetes is a major risk factor for cardiovascular disease,5 which is the leading cause of death in schizophrenia.2 In fact, substantial evidence has shown that prevalence of diabetes is two to three times higher in people with schizophrenia than in the general popu-lation.6 A multitude of factors may underlie the association between schizophrenia and increased risk for diabetes, including unhealthy lifestyle such as smoking, poor diet and physical inactivity; antipsychotic-induced metabolic side-effects; altered immune-inflammatory responses and shared genetic vulnerability.
+There is limited research examining mortality risk in patients with schizophrenia with co-occurring diabetes. Accumulating data have revealed increased mortality rates among patients with schizophrenia and diabetes compared with those with diabetes only.7-12 However, many previous studies are hampered by several important methodological limitations, including small sample size;7-9,13 recruitment of prevalent diabetes cohorts without controlling for illness duration;7, , non-adjustment for confounding effect of baseline medical comorbidity; , , , 3 focus on broadly defined
+category of severe mental disorders without diagnostic breakdown into schizophrenia for analysis;10,13 and inclusion of patients with schizophrenia with history of psychiatric in-patient treatment only, which may result in potential bias towards those with greater illness severity.8,11 Even fewer studies have assessed occurrence of diabetes complications among patients with schizophrenia with diabetes. Mixed findings were observed, with patients having schizophrenia and diabetes being found to display higher,11,13 lower12 or comparable9,1 , 2 complication rates relative to those with diabetes alone. Until now, only one study has evaluated mortality after diabetes complications in a schizophrenia sample.12 Whether schizophrenia is associated with elevated risks for excess mortality and complications in people with diabetes remains to be clarified.
+Aims of study
+To this end, we conducted a population-based cohort study with an aim to examine all-cause mortality in patients with pre-existing schizophrenia and newly diagnosed diabetes, compared with patients with incident diabetes only, with up to 16 years of followup. In addition, we specifically investigated occurrence of diabetes complications within the first year of diagnosis of diabetes as an indicator of disease severity shortly after presentation, and allcause mortality after diabetes complications. We used clinical data retrieved from a territory-wide medical record database of public healthcare services in Hong Kong. Propensity score matching was
+applied to match patients with co-occurring schizophrenia and diabetes with those having diabetes only, to optimise control for potential confounding of baseline covariates, taking into consideration the effect of pre-existing medical comorbidity.
+Method
+Study design and data source
+This was a population-based cohort study comparing the risks of all-cause mortality and diabetes complications in patients with incident diabetes with versus without pre-existing schizophrenia in Hong Kong. We obtained study data from the Clinical Data Analysis and Reporting System (CDARS),14 a territory-wide electronic health record database developed by the Hospital Authority. The Hospital Authority is a statutory body that manages all public hospitals, specialist and general out-patient clinics in Hong Kong, and provides government-subsidised universal health coverage to all Hong Kong residents (approximately 7.5 million), including at least 90% of patients with diabetes in Hong Kong.15 The CDARS has been described in detail elsewhere.14 Briefly, CDARS is an integrated electronic record system capturing patients’ longitudinal clinical data across all Hospital Authority facilities since 1995, via its access to the computerised clinical management system, which contains clinical information about demographics, diagnoses, prescriptions, hospital admissions, outpatient attendances and visits to emergency departments. These clinical data are entered into the system by treating clinicians and other healthcare staff. Patients’ mortality data can be retrieved from CDARS through its internal linkage to regional deaths registry from the Immigration Department. Each patient is assigned a unique, anonymised identifier by CDARS, to facilitate linking to all medical records and to protect patient privacy. Clinical database extracted from CDARS has previously been used to generate high-quality population-based studies on various physical and psychiatric conditions, including diabetes and schizophrenia.16,17
+Study population
+All individuals aged >18 years, who were diagnosed with an incident diabetes managed by public healthcare services in Hong Kong between 1 January 2001 and 31 December 2016, were identified as a study cohort. Ascertainment of diabetes was defined by fulfilling any one of the following criteria: (a) first-recorded diagnosis of diabetes (type 1 or type 2) by the ICD-9-CM18 (codes 250, 357.2, 366.41 and 362.01-362.07) for in-patient admission or specialist out-patient attendance, or by the International Classification of Primary Care, Second Edition19 (code T90) for general out-patient attendance; and (b) dispensation of antihyperglycaemic medication (including sulfonylureas, insulins, metformin, thiazolidinediones, a-glucosidase inhibitors, meglitinides, incretin mimetics/glucagon-like peptide-1 analogues and dipeptidyl peptidase inhibitors). Onset of diagnosis (i.e. date of ascertainment of incident diabetes) was assigned as the earliest date that a patient met the defining criteria. We then derived a group of patients who were diagnosed with schizophrenia or schizoaffective disorder (henceforth termed as schizophrenia) by the ICD-10 (codes F20 and F25) for psychiatric in-patient admission or out-patient attendance preceding ascertainment of first diagnosis of diabetes from the incident diabetes cohort.20 Patients with past record of diabetes diagnosis or prescription of any antihyperglycaemic medications before diagnostic assignment of schizophrenia were excluded. To evaluate the influence of schizophrenia on mortality and occurrence of diabetes complications, the remaining patients in the diabetes cohort served as a comparison group for analyses. Patients with other non-affective psychoses, mania or bipolar disorder (ICD-10 codes
+F22, F23, F28, F29, F30 and F31) recorded as the principal psychiatric diagnosis were excluded from the comparison group. The study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 16470). The study data were anonymised and individual patient records were completely unidentifiable during the analysis. Because our study was based on a medical record database, the requirement for informed consent was waived.
+Propensity score matching and covariates
+Propensity score matching was performed to minimise differences in baseline characteristics between patients with diabetes in schizophrenia and comparison groups, so as to generate a matched cohort with well-balanced covariates. Propensity score was the conditional probability of being assigned to schizophrenia group as opposed to the comparison group, estimated using logistic regression model with the given covariates.21 Taking into consideration the availability of clinical information that were adequately recorded in, and could be reliably retrieved from, the database, an array of candidate covariates were selected a priori and included in the propensity model: demographics (age at incident diabetes, gender); calendar-year period (5-year interval) of ascertainment for incident diabetes; catchment area where patients received medical services for diabetes (for geographic and hospital-based variation); and physical comorbidities as measured by Charlson comorbidity index,22 hypertension and dyslipidaemia, and alcohol and substance use disorders. We employed a nearest-neighbour matching algorithm, and matched patients with schizophrenia to patients in the comparison group in a 1:10 ratio without replacement, with a calliper of 0.15 of the s.d. of the logit of propensity score. Our 1:10 matched cohort design enabled the original sample of the comparison group to be more fully utilised for data analyses, given its large sample size (n = 589 655). Unmatched patients were excluded from the analysis. We assessed between-group balance of covariates by computing standardised differences (<10% indicating good balance of covariates) for all covariates included in the propensity score model, and examining reduction in pseudo R2 (value close to zero indicating good balance of covariates) in the logistic regression model23 for the schizophrenia group, before and after matching. Standardised differences were <10% for all covariates (Supplementary Fig. 1 available at https://doi.org/10.1192/bjp.2020.248) and pseudo R2 decreased from 0.054 to 0.003 after matching, indicating that a balance of covariates was achieved. Physical diseases and alcohol/ substance use disorders were identified by ICD-9-CM and ICD-10 codes, respectively (Supplementary Table 1).
+Outcomes
+The primary outcome was all-cause mortality after ascertainment of incident diabetes. Two sets of secondary outcomes were also investigated. First, we assessed occurrence of diabetes complications within 1 year of incident diabetes, as a proxy measure for severity of diabetes, with the presence of complications indicative of more advanced disease stage shortly after presentation. Complications were classified and quantified by the adapted Diabetes Complications Severity Index (DCSI), which is a validated measure in predicting mortality, hospital admissions and healthcare utilisation among patients with diabetes.24 Briefly, the DCSI is a scoring scheme comprising seven categories of complications, including cardiovascular disease, cerebrovascular disease, peripheral vascular disease, nephropathy, neuropathy, retinopathy and metabolic complications. In addition to specific DCSI-derived categories, diabetes complications were classified as macrovascular (including cardiovascular, cerebrovascular and peripheral vascular diseases) or microvascular (including retinopathy, nephropathy
+and neuropathy), for analyses. Second, we examined all-cause mortality rates among patients diagnosed with macrovascular and microvascular complications within 1 year of incident diabetes. Diabetes complications were identified by ICD-9-CM codes (Supplementary Table 1) from both in-patient and out-patient records. Study cohort was followed from the date of ascertainment of incident diabetes until the date of death or until 31 December 2016, whichever came first.
+Statistical analyses
+The analyses of mortality and diabetes complications were based on the propensity score-matched sample. Demographic and baseline characteristics between matched schizophrenia and comparison groups were compared with chi-square and independent-samples t-tests for categorical and continuous variables, respectively. Incidence rates for all-cause mortality of the overall sample, as well as among subsamples of patients diagnosed with macrovascular and microvascular complications within 1 year after incident diabetes (i.e. post-macrovascular and post-microvascular complication all-cause mortality rates), were estimated by an exact 95% confidence interval based on a Poisson distribution. Survival rates were estimated with the Kaplan-Meier method, and compared between two groups by log-rank test. Cox proportional hazards regression models were applied to examine the effect of schizophrenia on mortality rates (with hazards ratios and 95% confidence intervals calculated), adjusting for covariates that were significantly different between two groups. The proportional hazards assumption was assessed according to scaled Schoenfeld residuals, and was fulfilled for each of the models. A series of multivariate logistic regression analyses (with odds ratios and 95% confidence intervals calculated), adjusting for covariates that were significantly different between groups, were performed to investigate the associations between schizophrenia and occurrence of diabetes complications (relative to the comparison group). Furthermore, two sets of additional analyses
+were conducted. First, we stratified the analyses on mortality and complications by age (<50 years, 50-69 years and >70 years). Second, the analyses were repeated for men and women separately, to assess gender specificity of the associations between schizophrenia and study outcomes. All statistical analyses were performed with SPSS version 25 for Windows, and P < 0.05 was considered statistically significant.
+Results
+Characteristics of the study sample
+A total of 596 656 individuals with incident diabetes, including 7001 patients with schizophrenia, were identified as the original cohort. After matching, the schizophrenia group comprised 6991 patients (3096 men and 3895 women; mean age 54.4 ± 12.9 years), and the comparison group comprised 68 682 patients (30 178 men and 38 504 women; mean age 54.0 ± 12.7 years). The mean duration of follow-up for the matched sample was 6.2 years (s.d. 4.3). Demographic and baseline characteristics of the schizophrenia and comparison groups before and after matching are summarised in Table 1. There were significant differences between two groups of the matched sample in age, as well as the prevalence of alcohol and substance use disorders (higher rates in schizophrenia group), which were adjusted as covariates in subsequent analyses for mortality and complication outcomes.
+Mortality of the overall diabetes sample
+As shown in Table 2, the incidence rates per 1000 person-years were 30.4 (95% CI 28.7-32.1) and 18.0 (95% CI 17.6-18.4) for all-cause mortality in the schizophrenia and comparison groups, respectively. The Kaplan-Meier survival curves showed increased mortality rate in patients with schizophrenia relative to those in the comparison group (P = 0.002 by log-rank test) (Fig. 1(a)). Cox proportional hazards regression analysis also indicated significant association
+between schizophrenia and heightened mortality risk, with an adjusted hazard ratio of 1.11 (95% CI 1.05-1.18). Stratified analyses further revealed that elevated mortality rates in the schizophrenia group were particularly noted among men in the older age groups (men overall: adjusted hazard ratio 1.17, 95% CI 1.07-1.27; men aged 50-69 years: adjusted hazard ratio 1.13, 95% CI 1.00-1.27; men aged >70 years: adjusted hazard ratio 1.35, 95% CI 1.161.57). For women with schizophrenia, increased mortality rate was only observed among those aged >70 years (women overall: adjusted hazard ratio 1.01, 95% CI 0.93-1.11; women aged >70 years: adjusted hazard ratio 1.16, 95% CI 1.01-1.33) (Table 2).
+Diabetes complications
+Table 3 presents diabetes complication rates within 1 year after diagnosis of incident diabetes. Patients with schizophrenia had significantly lower likelihood of experiencing any microvascular complications (adjusted odds ratio 0.75, 95% CI 0.65-0.86) and
+378
+https://doi.org/10.1192/bjp.2020.248 Published online by Cambridge University Press
+several specific complications, including cardiovascular disease (adjusted odds ratio 0.79, 95% CI 0.68-0.90), retinopathy (adjusted odds ratio 0.48,95% CI 0.37-0.64) and nephropathy (adjusted odds ratio 0.76, 95% CI 0.61-0.94), but higher likelihood of increased metabolic complication rate (adjusted odds ratio 1.99, 95% CI 1.63-2.42), compared with patients in the comparison group. There were no significant group differences regarding the number of diabetes complications. Similar patterns of diabetes complication rates as those observed in the overall sample were found in both men and women, except that there was a significantly higher cerebrovascular complication rate among female patients in the schizophrenia group compared with the comparison group (Supplementary Table 2). In contrast, stratified analyses revealed differential associations between schizophrenia and complication risks by age, with lower rates of specific macrovascular and microvascular complications being observed among patients with schizophrenia in <50 years and 50-69 years (but not >70 years) age groups, relative to those in the comparison group (Supplementary Table 3).
+Mortality after diabetes complication diagnoses
+The incidence rates per 1000 person-years were 120.7 (95% CI 107.1-136.1) and 123.0 (95% CI 102.1-148.3) forall-cause mortality in the schizophrenia group, after occurrence of macrovascular and microvascular complications, respectively (Table 2). In the comparison group, the incidence rates were 74.4 (95% CI 71.2-77.8) and 64.0 (95% CI 60.0-68.4) per 1000 person-years for post-macrovascular and post-microvascular complication allcause mortality, respectively. The Kaplan-Meier survival analyses demonstrated significantly higher mortality risks after both
+macrovascular (P = 0.006 by log-rank test) and microvascular (P = 0.003 by log-rank test) complications in the schizophrenia group compared with the comparison group (Fig. 1(b) and 1(c)). Adjusted hazard ratios of patients with schizophrenia were 1.19 (95% CI 1.04-1.37) for post-macrovascular complication mortality and 1.33 (95% CI 1.08-1.64) for post-microvascular complication mortality, indicating significant associations between schizophrenia and elevated mortality risks subsequent to diabetes complications. Gender-stratified analyses further showed that men, but not women with schizophrenia had increased post-complication
+mortality. In particular, men with schizophrenia exhibited heightened mortality rate after macrovascular complications (adjusted hazard ratio 1.25,95%CI 1.04-1.50), and higherpost-microvascular complication mortality, albeit approaching significance (P = 0.051) (Table 2).
+Discussion
+In this territory-wide population-based study of patients with newly diagnosed diabetes, we observed a significant, albeit small, association between schizophrenia and elevated all-cause mortality rate. Additional analyses further found that the effect of schizophrenia on increased mortality was more prominent in men and older age groups. Our results generally concur with the literature, which indicates increased mortality risk conferred by schizophrenia among patients with diabetes,7-12 but is contrary to one prior report showing comparable mortality rates between patients with diabetes with and without severe mental disorders (including schizophrenia) over a 7-year follow-up period.1 Of note, most previous studies demonstrated relatively higher mortality risk, with approximately two- to three-fold increase in excess death among patients with diabetes and schizophrenia compared with those with diabetes alone.8,10-12 Such discrepancy might partly be attributable to methodological differences across studies. In particular, the majority of earlier studies did not control for confounding effect of pre-existing physical comorbidity and alcohol or substance use disorder (more frequently co-occurring with schizophrenia) on mortality risk,7, , , 3 and some investigated prevalent (rather than incident) diabetes cohort,7, , 3 or included patients with schizophrenia with history of psychiatric hospital admissions only (excluding patients treated only in out-patient settings).8,11 These methodological constraints might lead to potential bias in overestimating the mortality risk associated with schizophrenia among patients with diabetes.
+Few studies have investigated occurrence of diabetes complications among patients with coexisting schizophrenia, particularly based on incident diabetes cohorts,8,11,12 and inconsistent findings were noted. Wu et al found that schizophrenia was associated with an increased risk for macrovascular (but not microvascular) complications among patients with incident diabetes,11 whereas a recent Danish nationwide study showed that individuals with schizophrenia and incident diabetes had lower (microvascular) or similar (macrovascular) complication rates compared with those
+with diabetes only.12 In the current study, we specifically examined the first-year, rather than long-term, occurrence of complications after diagnosis of incident diabetes. Our findings that schizophrenia was associated with increased risk of acute metabolic complications largely agree with a previous study reporting significantly a higher rate of hospital visits for hyper- or hypoglycaemia in patients with schizophrenia and newly diagnosed diabetes than those with diabetes alone.8 Conversely, our results revealed that overall, patients with schizophrenia exhibited lower microvascular and comparable macrovascular complication rates relative to patients with diabetes without schizophrenia. Importantly, the association between reduced complication rates and schizophrenia was most marked in the youngest age group (and was no longer observed in patients >70 years). As development of macrovascular and microvascular complications takes place gradually over time, occurrence of these complications within the first year of incident diabetes primarily indicates more advanced disease stage upon (or shortly after) presentation. In fact, it might be possible that the complication rates in the schizophrenia group was underestimated, as there is some evidence suggesting that the prevalence of undiagnosed diabetes is higher among patients with schizophrenia, particularly in the younger age group, than in the general population.25 Given that our diabetes cohort was identified with recorded diagnosis and/or prescription of antihyperglycaemic medications, we were not able to include patients without recognised diabetes diagnosis, thereby precluding us from estimating the degree of underdiagnosis of diabetes among patients with schizophrenia. Alternatively, it could be that patients with pre-existing schizophrenia diagnosis were receiving medical surveillance via specialist psychiatric services, which offered regular monitoring of glycaemic and metabolic para-meters.12 This may therefore increase the likelihood of earlier diagnosis of diabetes, with consequent decreased occurrence of complications upon presentation for diabetes treatment, especially among younger patients. As existing data is limited in this respect, further research is warranted to clarify the association between schizophrenia and occurrence of complications in the early course of treatment for diabetes.
+Thus far, there is only one published report (the Danish nationwide study) examining post-complication mortality among patients with schizophrenia and co-occurring diabetes, and this report demonstrated that schizophrenia was associated with elevated mortality rates subsequent to diabetes complications.12 Similarly, we found that patients with schizophrenia and diabetes had significantly higher all-cause mortality rates after both macrovascular
+and microvascular complications diagnosed during the first year of incident diabetes, compared with those with diabetes alone. Our additional analyses further suggested differential effect of schizophrenia on post-complication mortality between men and women, with increased mortality risk being observed only in men. This is contrary to the findings of the Danish study, which revealed significant association between schizophrenia and raised post-complication mortality rate in both men and women.12 It should, however, be noted that our results may not be directly comparable with those of the Danish study in this respect, because of the difference in the time period used to capture complication occurrence after diabetes diagnosis.
+It is acknowledged that the association between schizophrenia and excess mortality among patients with coexisting diabetes is likely multifactorial, encompassing patient, physician and system factors. Many, although not all,11 studies have shown that patients with schizophrenia are less likely to receive equitable diabetes care, including education about diabetes; guideline-recommended evaluations such as measurement of haemoglobin A1c and lipid profile, screening for nephropathy and eye and foot examination; and optimal treatment with antihyperglycaemic medications.26,27 Evidence also indicates lower receipt of cardioprotective medications among patients with schizophrenia for reduction of diabetes-related cardiovascular morbidity.28 Symptoms of schizophrenia, such as cognitive dysfunction and diminished motivation, may impair patients’ ability for proper diabetes self-management, resulting in poorer treatment outcomes. Alternatively, raised mortality in patients with diabetes with schizophrenia could be attributable to lifestyle behaviours, including physical inactivity, unhealthy diet and smoking. Future investigation is required to systematically delineate the impact of these potentially modifiable lifestyle factors on diabetes-related outcomes in patients with schizophrenia.
+Several limitations of this study should be noted. First, our retrieved data did not contain information to distinguish between type 1 and type 2 diabetes, which are associated with differential outcomes in terms of premature mortality and complication rates. Nonetheless, as our analysis only included participants aged >18 years with newly diagnosed diabetes during the study period (with >85% of patients in schizophrenia and comparison groups diagnosed with incident diabetes at >40 years old), and evidence indicates that type 1 diabetes is mainly diagnosed during childhood and adolescence, our cohort should comprise mostly patients with type 2 diabetes. Second, lifestyle variables, such as physical activity levels, dietary patterns and smoking, were insufficiently recorded in the database, and were therefore not included in the analysis. Third, we did not have prescription data on antipsychotic medications, which are associated with increased risk of diabetes. Fourth, patients’ adherence to prescribed antihyperglycaemic medications could not be assessed in this study. Fifth, we did not have information about the specific causes of death and were not able to examine diabetes-related mortality. Sixth, data regarding the rates of receipt for guideline-concordant assessments (such as haemoglobin A1c and lipid profile tests, retinal examination etc.) were not available, hence the relationship between quality of diabetes care and schizophrenia could not be evaluated. Seventh, although propensity score matching was used to balance baseline covariates between the schizophrenia and comparison groups, our findings might still be influenced by residual confounding because of differences in unmeasured variables. Lastly, CDARS-derived diagnosis of schizophrenia has not been systematically validated. Although evidence has shown that clinical diagnosis of schizophrenia routinely collected in a health record database is generally reliable for research (yielding high concordance rate with research diagnosis), future studies evaluating the validity of CDARS-derived diagnoses for schizophrenia and other psychoses are needed, to facilitate
+estimation of diagnostic accuracy and the potential effect of misdiagnosis bias on outcome analyses.
+In conclusion, in a large population-based cohort of patients with incident diabetes, our study indicates that schizophrenia is associated with elevated mortality risk. Patients with pre-existing schizophrenia generally present lower or similar rates of macrovas-cular and microvascular complications in the first year of diabetes diagnosis compared with those with diabetes alone, but exhibit increased risk of post-complication mortality. Our findings thus confirm the presence of physical health disparities, and highlight an unmet treatment need for diabetes in patients with schizophrenia. More research is needed to better understand the adverse effect of, and interactions among, various factors contributing to excess mortality among this vulnerable group of patients with schizophrenia with co-occurring diabetes. Future studies should also focus on effective interventions, in particular lifestyle modification, to improve long-term diabetes-related outcomes.
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+INTRODUCTION
+Death by suicide is the second leading cause of mortality among the 15-29 age group worldwide (WHO, 2012). It is estimated that 40 000 people die of suicide every year in the United States, and the global death toll is estimated to be at ~ 800 000 (WHO, 2014). The World Health Organization predicts close to one million deaths by the year 2030, contributing to a projected 1.4% of all deaths worldwide (World Health Organization, 2011). The actual numbers may be far higher, as suicidal deaths tend to be typically under reported because of societal taboos and criminalization of suicide in certain societies.
+Suicide attempts may, likewise, be grossly underestimated because of poor reporting and insufficient data collection. Twenty or more suicide attempts occur for every one death by suicide, suggesting that suicide attempts are more frequent than suicide itself (Bertolote et al, 2010). Suicide attempts are currently the best predictor of completed suicide, which indicates that proper reporting and close monitoring of the individuals who attempt suicide may serve to prevent future suicides. To this effect, Da Cruz et al determined that over 40% of individuals who died by suicide visited the emergency department at least once in the
+‘Correspondence: Dr K Thirtamara Rajamani, Department of Behavioral Medicine, Laboratory of Behavioral Medicine, Center for Neurodegenerative Science, Van Andel Research Institute, Grand Rapids, MI 49503, USA, Tel: +1 616 234 5321, Fax: +1 616 234 5180, E-mail: Keerthi. rajamani@vai.org
+Received 16 March 2016; revised 31 May 2016; accepted 28 June 2016; accepted article preview online 5 July 2016
+12 months before death; with 28% of them visiting on more than three occasions. Death by suicide followed soon after the last emergency department visit in the group of frequent attempters compared with other attenders (Da Cruz et al, 2011). This critically underscores the insufficiency of existing methods of suicide risk assessment, demonstrated by failure to identify high-risk individuals despite repeated visits to the emergency department before death.
+There are also some consistent gender differences in suicidal behavior that are important to note. In many countries, including the United States, male-completed suicides outnumber females-completed ones with a ratio ~ 3: 1, although women are more likely to experience suicidal ideation to a higher degree than men (SAMHSA, 2013). Men are also likely to employ more violent methods of suicide than women (Canetto and Sakinofsky, 1998). Both suicide deaths and attempts are acts of extreme psychological despair, and constitute a profound emotional burden to both the family and relatives of the victims. In essence, there is a great need to develop diagnostic methods, which include behavioral and biological indicators for reliable and timely identification of risk factors that may lead to suicidal behavior.
+CLINICAL ASPECTS OF SUICIDALITY
+Suicide is defined as an intentional act of taking one’s life by engaging in self-directed injurious behavior. Suicidal ideation and suicide attempts are suicidal behavior that may manifest as standalone events or precipitate a completed suicide (Crosby et al, 2011). Several theories have been
+proposed to model suicide risk, each taking into account etiological and phenotypic variability that is associated with suicide. They are categorized into risk factors that predispose (distal factors) and precipitate suicidal event (proximal factors). Proximal factors are typically associated with neuropsychiatric pathology and may be precipitated by stressful life events (Mann, 2003). Psychiatric disorders and the risk for suicidal behavior has been a topic of intense study given that ~ 90% of suicide completers are diagnosed with some form of psychiatric illness including major depressive disorder (MDD) and substance abuse disorders (Arsenault-Lapierre et al, 2004). Behavioral traits such as impulsivity and aggressive behavior may also contribute to the risk of suicidal behavior, specifically in adolescents or young adults. These traits are often co-morbid with different psychiatric disorders such as bipolar disorders and substance abuse disorders (Brent et al, 1994; Cuomo et al, 2008; McGirr et al, 2008).
+Familial transmission and early life adversity (ELA) are distal events associated with suicide risk (Mann, 2003; Moscicki, 1994). Physical or sexual abuse and parental neglect are significantly associated with suicidal risk (Brezo et al, 2008). The association between ELA and suicide risk in these individuals is strongly supported by the changes in several downstream factors, including epigenetic changes, hypothalamic-pituitary-adrenal (HPA) axis activation and neuronal plasticity, informed to a large extent by animal studies and post-mortem studies (Labonte et al, 2013; Labonte et al, 2012; McGowan et al, 2009; Roth et al, 2011).
+Family history of suicidal behavior has also been identified as a risk factor. In a registry-based study, Tidemalm et al (2011) assessed suicides in probands of individuals who died by suicide, and determined that the risk does run in families and is influenced by both genetic and shared environmental factors. Given that suicidal behavior is strongly associated with psychiatric disorders, Brent et al examined suicidal behavior and psychopathology among probands of adolescent suicide victims. They observed that the rates of suicide attempts were higher in relatives of adolescent suicide victims even after controlling for familial rates of psychopathology, suggesting that familial transmission of suicide is discrete from psychopathology (Brent et al, 1996).
+Substance abuse is also considered a risk factor for suicidal behavior. The risk for suicide is much higher in individuals with substance abuse disorder compared with general population (Wilcox et al, 2004). However, the majority of individuals with substance abuse issues are often diagnosed with other psychiatric conditions, a confounding factor which must be kept in perspective (Treatment. CfSA, 2009). Conditions, such as early childhood trauma and post-traumatic stress disorders (PTSDs), often co-occur with substance abuse and their ability to confer suicide risk has also been explored. Roy (2003) assessed the association between childhood trauma and suicide attempts in individuals with substance abuse and determined that in these individuals, suicide attempts were associated with childhood trauma. Furthermore, Price et al (2004) found that drug dependence was associated with PTSD and suicidal behavior
+in a cohort of Vietnam veterans assessed over a period of 25 years. Overall, suicidal behavior may manifest as a consequence of interaction between early life events, traits such as aggression and impulsivity, and psychiatric illness.
+INFLAMMATION AND SYMPTOMS OF SUICIDALITY
+Aberrations in inflammatory cytokines have been reported in several neuropsychiatric conditions, including MDD, schizophrenia, and bipolar disorders (Dowlati et al, 2010; Miller et al, 2011; Munkholm et al, 2013). Mechanistically, it is known that inflammation can trigger depressive symptoms and is associated with suicidality based on studies involving patients who receive interferon (IFN)-based or interleukin-2 (IL-2) immuno-therapy (Buter et al, 1993; Capuron et al, 2004; Dieperink et al, 2004; Janssen et al, 1994; Renault et al, 1987). It is well established that ~ 30-45% of patients receiving IFN treatment develop depressive-like symptoms during the course of therapy, with a proportion of them experiencing these symptoms long after therapy has ceased (Meyers et al, 1991; Miyaoka et al, 1999). Moreover, healthy volunteers who receive injections of lipopolysaccharide (LPS), a bacterial endotoxin, which induces a strong inflammatory response in the periphery as well as in the central nervous system, experience depressive symptoms (Yirmiya et al, 2000). However, questions arise as to whether inflammation contributes to symptoms of suicidality or merely exists as an epiphenomenon in patients who are considered ‘primary psychiatric patients’. Moreover, are there individuals in whom inflammation is particularly pronounced and is associated with a specific symptom profile? Several studies indicate that inflammation may be particularly pronounced in patients who experience suicidality (Figure 1; Table 1). An early study found that patients with a history of suicide attempts have increased blood levels of the soluble IL-2 receptors compared with healthy controls (Nassberger and Traskman-Bendz, 1993). This was followed by two post-mortem studies, which independently provided evidence of increased inflammation in the brains of suicide victims. First, Tonelli et al (2008) reported elevated mRNA transcripts of IL-4 and IL-13 in the orbitofrontal cortex of suicide victims. Subsequently, Steiner et al (2008) demonstrated increased microgliosis, indicative of an enhanced inflammation, in suicide victims with a diagnosis of depression and schizophrenia. These initial findings were supported by a study from our group, showing elevated levels of IL-6 in the cerebrospinal fluid (CSF) of recent suicide attempters. In addition, we observed that IL-6 levels in these patients correlated with the severity of depression as assessed by Montgomery-Asberg Depression Rating Scale (Lindqvist et al, 2009). A previous study had found lower CSF levels of neuroprotective IL-8 levels in suicide attempters compared with healthy controls, confirming dysregulation of the immune system in suicidal patients (Isung et al, 2012). Since then, we found that decreased IL-8 levels were specific
+to patients with anxiety and identified the presence of a single-nucleotide polymorphism in the promoter region of the IL-8 gene, which predicted more severe anxiety in the suicide attempters (Janelidze et al, 2015). Interestingly, O’Donovan et al (2013) demonstrated that increased inflammation correlated with the degree of suicidal ideation in patients with depression, even after controlling for active suicide attempts and degree of depressive symptoms. Limited post-mortem data on teenage suicide victims also points to the association between inflammation and suicide. Pandey et al (2012) reported that post-mortem brain tissue from teenage suicide victims had increased mRNA and protein levels of IL-1^, IL-6, and tumor necrosis factor alpha (TNF-a) in certain cortical regions (Brodmann area 10). More studies are clearly needed to support the relationship between inflammation and suicidality in youth, as suggested by a recent meta-analysis (Kim et al, 2014). Together, these studies suggest that suicidal individuals may have an inflammatory signature irrespective of their primary diagnoses and other underlying conditions. However, there are studies that also report a decrease in cytokine levels in suicidal individuals (Clark et al, 2016; Gabbay et al, 2009). Although some of these discrepancies may be attributed to differences in sample size, study design, and relevant controls, it is critical to acknowledge the complexity of drawing definitive conclusions, while controlling for diverse confounding factors such as medication history, post-mortem tissue integrity, past
+psychiatric and substance abuse history, and previous suicide attempts. In this context, meta-analyses provide a formal, qualitative assessment of previously published studies, which can then be used to derive conclusions. In one such metaanalysis study that assessed changes in inflammatory cytokines in blood, CSF, and post-mortem tissue of suicidal individuals, Black et al found a robust association between increased suicidality and plasma levels of IL-1^ and IL-6. These changes were significant to distinguish psychiatric patients with suicidality from psychiatric patients without suicidality and healthy controls. They also found increased IL-1^ and IL-6 in post-mortem brain tissue from suicide completers and reported that reduced CSF levels of IL-8 were associated with suicidal behavior (Black and Miller, 2015).
+In an interesting study, Torres-Platas et al (2014) showed that depressed individuals who committed suicide have a greater proportion of activated microglia in the anterior cingulate cortex white matter compared with subjects without psychiatric disorders who died from other causes. The same group also had specific microglial phenotypes that were associated with concurrent increases in vascular density and increased expression of perivascular macrophage markers. Schnieder et al (2014) reported similar findings, where they observed increased density of perivascular cells in prefrontal cortex white matter of suicide victims. It is known that peripheral cytokines can be trafficked into the CNS either through regions with limited blood-brain barrier (BBB)
+permeability, such as the circumventricular organs and the choroid plexus, or through a compromised BBB (Dantzer et al, 2008), which has been reported to be leaky in suicidal individuals (Bayard-Burfield et al, 1996; Falcone et al, 2010; Ventorp et al, 2016). In line with these findings, Wohleb et al demonstrated increased peripheral myeloid cell trafficking into perivascular spaces and specific brain regions using a repeated social defeat stress model. These changes were further accompanied by microglial activation and induction of anxiety-like behavior. This suggests an active role for peripheral myeloid cells in altering neuroimmune response and behavior (Wohleb et al, 2013).
+Certain behavioral traits, such as impulsivity and aggression, have been found to confer a greater degree of suicide risk (Brent et al, 1994; McGirr et al, 2008). Clinical studies also indicate a relationship between inflammation and traits of aggression and impulsivity (Coccaro et al, 2014; Isung et al, 2014; Mommersteeg et al, 2008; Suarez et al, 2002). For example, in individuals with personality disorders, elevated TNF-a and C-reactive protein (CRP) are associated with aggressive traits (Coccaro, 2006). Recently, the same group demonstrated that plasma CRP and IL-6 levels are associated with aggressive behavior in individuals diagnosed with intermittent explosive disorders (Coccaro et al, 2014). A study on suicide attempters by Isung et al (2014) found that plasma IL-6 levels were positively correlated with impulsivity trait and with violent suicide attempt methods.
+The above-mentioned studies provide evidence that suicidal behavior is associated with the changes in cytokine profiles in peripheral blood as well as in the brain. In addition, it is important to determine whether such changes are specific for suicidal behavior, independent of underlying psychiatric diagnosis. It is also of interest to assess whether similar or different degrees of inflammatory changes are found among individuals with suicidal ideation as well as among suicide attempters and completers. Addressing this, Janelidze et al (2011) observed plasma IL-6 and TNF-a levels to be elevated in suicidal depressed individuals compared with non-suicidal depressed individuals and healthy controls, suggesting that suicidal individuals may have a unique cytokine profile among depressive patients. Supporting this, O’Donovan et al (2013) found that a higher degree of suicidal ideation was associated with an increased inflammatory index in patients independent of the degree of depressive symptoms. These findings are in line with an early hallmark study, showing increased microglial density in schizophrenic and depressed suicide completers, although not in patients from the same diagnostic groups that died from other causes, stressing that the inflammatory changes might be specific to suicidality across diagnostic boundaries (Steiner et al, 2008).
+NEUROIMMUNOLOGICAL MECHANISMS INVOLVED IN SUICIDAL BEHAVIOR
+Inflammatory cytokines can be synthesized in the central nervous system or enter the brain from the periphery via
+275
+different mechanisms, including compromised BBB. We have previously shown that suicide attempters have an increased BBB permeability, which is associated with increased CSF levels of glycosaminoglycan hyaluronic acid, which is a ligand for CD44 and is indicative of increased neuroinflammation (Ventorp et al, 2016).
+One of the mechanisms, which could be responsible for the observed increase in inflammatory cytokine levels, is activation of the Toll-like receptors (TLRs). TLRs have a critical role in regulating innate immune response and facilitating immune function in the event of infection (Hanke and Kielian, 2011). They are widely expressed in different cell types of the CNS and are identified to have diverse functions, from cognition to memory (Okun et al, 2010; Okun et al, 2011). In the only study to date on suicidal subjects, Pandey et al (2014), concluded that, irrespective of psychiatric diagnoses, mRNA and protein levels of TLR3 and TLR4 were consistently dysregulated in suicide victims (depressed and non-depressed). Although preliminary, this study, for the first time, demonstrated the role of TLRs in suicide.
+Inflammatory cytokines may promote suicidal behavior by several mechanisms. Additional mechanisms by which cytokines may contribute to the pathophysiology of suicidal behavior include activation of the kynurenine pathway of tryptophan catabolism, dysregulation of the HPA axis and alterations in monoamine metabolism, as described below.
+The kynurenine pathway consists of a series of enzymes involved in the metabolism of the essential amino acid, tryptophan. The pathway is active in the periphery as well as in the central nervous system. The initial step in metabolism of tryptophan into kynurenine is catalyzed by indoleamine 2,3-dioxygenase (IDO) or tryptophan 2,3-dioxygenase (TDO). Kynurenine is further broken down into highly neuroactive compounds such as quinolinic acid (QUIN) and kynurenic acid (KYNA). Inflammatory cytokines such as IFN-y, IL-6, IL-1^, and TNF-a are potent activators of IDO and/or TDO (Kim et al, 2012; Mandi and Vecsei, 2012; Schwieler et al, 2015; Taylor and Feng, 1991; Urata et al, 2014). QUIN is one of the neuroactive metabolite that is synthesized in microglia. QUIN is a selective N-methyl-D-aspartic acid (NMDA) receptor agonist and acts via activation of the NR1+NR2A and NR1+NR2B NMDA receptor subunits (de Carvalho et al, 1996). In addition to NMDA receptor activation, QUIN also inhibits astrocytic uptake of glutamate by inhibiting glutamine synthetase and increases neuronal glutamate release (Tavares et al, 2002). KYNA, on the other hand, blocks the cholinergic a7 nicotinic receptor and antagonizes the glycine site of the NMDA receptor (Hilmas et al, 2001; Stone, 1993). KYNA also antagonizes a-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptor, which has been implicated in the pathophysiology of depression and in the mechanism of action of several antidepressant drugs (Freudenberg et al, 2015; Schwarcz et al, 2012). We found QUIN levels in CSF of suicide attempters to be two to three times higher than in healthy controls with no change in KYNA levels. Interestingly, in the same study, QUIN levels positively correlated
+Inflammation in suicidality
+L Brundin et al
+276
+with IL-6 in CSF, indicating that the activation of the kynurenine pathway in these patients was associated with an active inflammatory state. The increase in QUIN was also correlated with the suicidal intent in this cohort of patients (Erhardt et al, 2013). Furthermore, to determine whether QUIN levels remain elevated over time, we measured CSF QUIN for two continuous years after an initial suicide attempt. Not only did we find QUIN to be elevated over 2 years, we also observed that high levels of IL-6 and lower KYNA in the same patient group correlated with the severity of suicidal ideation and depressive symptoms (Bay-Richter et al, 2015). Interestingly, a post-mortem study found increased QUIN immunoreactivity in suicidal depressed patients seemingly in agreement with our findings (Steiner et al, 2011). In the same study, certain regions of the anterior cingulate cortex in suicide victims reportedly contained higher density of QUIN-positive microglia. However, a follow-up study found a decrease in the number of QUIN reactive microglia in the hippocampi from the same suicide victims (Busse et al, 2015). These findings suggest that certain brain regions may be more sensitive to inflammatory insult, which might have contributed to specific differences in the localization of QUIN reactive microglia in the brains of suicide victims. On the other hand, plasma KYNA has been previously shown to be reduced in depressed patients, although neither QUIN nor cytokine levels were profiled in these patients (Myint et al, 2007). Interestingly, we found a twofold increase in CSF QUIN/KYNA ratio in suicide attempters compared with healthy controls. KYNA, being an NMDA receptor antagonist, may induce a net positive effect on NMDA receptor signaling leading to aberrant glutamate signaling (Erhardt et al, 2013).
+Evidence suggests that the dysregulation of the kynurenine pathway might be more pronounced in depressed individuals with suicidality than depressed patients without suicidal behavior. Sublette et al (2011) were the first to demonstrate that blood kynurenine levels are elevated in suicide attempters compared with a depressed-only control group. Interestingly, Dahl et al (2015) also reported that depressive episodes in MDD patients are not associated with an increase in plasma kynurenine metabolite levels, despite the presence of elevated cytokine levels in these individuals. In addition, it should be noted that certain metabolites do not freely cross the BBB, and it might, therefore, be of importance to study the CSF levels of metabolites when determining their correlation to neuropsychiatric symptoms (Schwarcz et al, 2012).
+So far, only a few studies have compared suicidal depressed individuals with non-suicidal depressive individuals, trying to assess whether the biological changes of these two groups are different or, perhaps, similar but differing in magnitude. Indeed, a multitude of papers have shown that patients with depression display inflammation (Dowlati et al, 2010; Howren et al, 2009; Liu et al, 2012; Valkanova et al, 2013) and the exact same mechanisms (inflammation and activation of the kynurenine pathway) are proposed as the mechanism of depression (particularly of inflammation-induced depression) (Dantzer and Walker, 2014; Maes et al,
+2011; Myint et al, 2012; Reus et al, 2015). Evidence at this point is scarce, but indicates that the inflammatory changes and generation of kynurenine neurotoxic metabolites are, at least, more pronounced in suicidal individuals (Janelidze et al, 2011; O’Donovan et al, 2013; Sublette et al, 2011). It is difficult to determine from the available studies whether these changes are actually unique to patients with suicidal ideation or behavior. The reason is that studies on depressive patients frequently do not attempt to distinguish between suicidal and non-suicidal patients. Patients with a certain degree of suicidal ideation are often included among the non-suicidal depressive individuals. Additional studies on this topic are highly warranted in the future.
+In addition to the kynurenine pathway activation, inflammatory cytokines can also induce the changes in monoamine metabolism as well as in the HPA axis (Oquendo et al, 2014). Cytokine administration in both animals and humans is known to activate the HPA axis (Dunn, 2000). IFN-a used in the treatment of hepatitis C is a potent activator of the HPA axis as it causes increases in cortisol and adrenocorticotropic hormone levels within several hours of the treatment. HPA axis activation also correlates with the onset of depressive symptoms in patients who undergo interferon therapy compared with those who do not (Capuron et al, 2003). The serotonin system is one of the most widely studied neurotransmitter systems in depressive disorders (Vaswani et al, 2003) and its dysregula-tion by inflammatory cytokines could be one of the mechanisms underlying suicidal ideation and behavior (Oquendo et al, 2014). IL-1^ and TNF-a have been shown to enhance the expression and activation of serotonin transporters in cell lines via a p38 mitogen-activated protein kinase pathway (Mossner et al, 1998; Ramamoorthy et al, 1995; Zhu et al, 2006; Zhu et al, 2005). Acute IL-6 administration is also known to increase serotonin release in rat striatum (Zhang et al, 2001). Alterations in cytokine-induced serotonin metabolism have also been reported. 5-hydroxyl indole acetic acid (5-HIAA), a metabolic product of serotonin, has been shown to be elevated following LPS injections in rodent models of depression (O’Connor et al, 2009). Thus, inflammatory cytokines can influence a variety of neurotransmitter functions, ranging from synthesis to inducing changes directly at the receptor level.
+UPSTREAM TRIGGERS OF SUICIDALITY
+The rates of suicidal behavior in individuals affected by conditions that involve the immune system are generally higher than in individuals with somatic conditions that do not increase inflammation. For example, ~ 1% of adolescents with hemophilia, a genetic condition that does not involve inflammation, have previously attempted suicide compared with 7% of adolescents with thalassemia major, a condition that is often characterized by the presence of chronic vascular inflammatory state (Ghanizadeh and Baligh-Jahromi, 2009; Ghanizadeh et al, 2006). Furthermore, causal
+effects of inflammation contributing to depression and suicidal behavior are highlighted by studies showing increased incidence following IFN-based treatment in cancer and hepatitis patients (Capuron et al, 2000; Capuron et al, 2004; Dieperink et al, 2004; Janssen et al, 1994; Miyaoka et al, 1999). It is also known that MDD is more prevalent in patients afflicted with conditions characterized by chronic inflammation (such as cardiovascular diseases, type 2 diabetes, and rheumatoid arthritis) than in the general population (Steptoe, 2007).
+AUTOIMMUNE DISORDERS
+Multiple sclerosis (MS) is a chronic inflammatory neurological disorder characterized by sensory and motor loss, cognitive impairment, blindness, and fatigue. Patients with MS report high rates of depression reaching ~ 40% in these patients (Chwastiak et al, 2002). In a subset of MS patients with no history of psychiatric illness, treatment with IFN-y worsens depressive symptoms and induces suicidal ideation or attempts (Benros et al, 2011; Chwastiak et al, 2002; Fragoso et al, 2010). Death by suicide may account for at least 15% of mortality in MS populations (Sadovnick et al, 1991). Another study determined an increased risk for suicide completion in a Swedish cohort of MS patients (Fredrikson et al, 2003). A similar study in a smaller cohort of Danish MS patients identified a twofold increase in risk of death by suicide compared with the general population, with the greatest risk in the first year following diagnosis (Bronnum-Hansen et al, 2005). Systemic lupus erythematosus (SLE) is another autoimmune disorder predominantly affecting women of reproductive age. About 17-71% of SLE patients present with psychiatric symptoms (Wekking, 1993). Prevalence of depression is estimated to be fourfold higher in SLE patients compared with non-SLE patients with at least 75% of SLE patients being diagnosed with depression during their lifetime (Palagini et al, 2013). Interestingly, the presence of anti-NR1 antibodies has been consistently reported in the CSF of SLE patients and correlates positively with their neuropsychiatric symptoms (Arinuma et al, 2008; Fragoso-Loyo et al, 2008; Gono et al, 2011; Lapteva et al, 2006; Yoshio et al, 2006). As mentioned above, NR1 is a subunit of the ionotropic glutamate NMDA receptor (Perez-Otano et al, 2001). Examining a Chinese population of SLE patients, Pan et al reported a 34% prevalence of suicidal ideation, with lower estimates ranging from 8 to 12% reported in other SLE populations across the world (Ishikura et al, 2001; Xie et al, 2012; Zakeri et al, 2012). It is unclear if suicidal thoughts in these patients manifest as a consequence of the disease or from an underlying psychiatric condition or both.
+INFECTIONS
+Infections induce inflammation, and, as such, may activate the above-mentioned neuroinflammatory mechanisms
+277
+leading to depression and suicidal symptoms. Certain infections may be particularly strong triggers of neuroinflammation, as they invade the central nervous system, and such neurotrophic pathogens include herpes viruses, human HIV, and possibly hepatitis C virus. The prevalence of MDD, suicidal ideation, and attempted suicides in HIV patients is reported to be up to 27.2%, 31%, and 32.7%, respectively (Serafini et al, 2015). Chronic hepatitis C patients also display a higher prevalence of depression and suicide risk compared with the general population (Lucaciu and Dumitrascu, 2015). Alavi et al (2012) found that, before treatment, 36% of chronic hepatitis C patients suffer from major depression and 18% endure a moderate-to-severe suicide risk. A study by Okusaga et al (2011) found higher seropositivity for influenza B virus in patients with a history of suicide attempt, whereas seropositivity for all three types of viruses (influenza A, B, and coronaviruses) was associated with the history of mood disorders. Recently, the onset of de novo depression has been associated with the higher levels of cytomegalovirus IgG antibodies with the odds of incident depression being three times greater in individuals with antibody levels in the highest quartile (Simanek et al, 2014). Certain neurotrophic pathogens are not only capable of inducing inflammation but might manipulate the host neurons in very specific ways to trigger a behavioral response. Toxoplasma gondii is a common protozoan parasite that establishes latency in the muscle and brain (Henriquez et al, 2009). Infection in humans is frequently asymptomatic and can occur, for example, after consumption of undercooked meat infected with parasitic oocysts. The host immune response CD4+ and CD8+ T cells are responsible for keeping the parasite under control and relatively quiescent in the central nervous system (Gazzinelli et al, 1992). Interestingly, latent T. gondii infection has been identified as a risk factor for suicide and suicide attempts (Arling et al, 2009; Ling et al, 2011). Reactivation of infection has also been associated with traits such as aggression and impulsivity, two behavioral endophenotypes associated with suicidal behavior (Cook et al, 2015).
+Proposed mechanisms underlying T. gondii-associated behavioral changes include alterations in immune response as well as dysfunction associated with dopaminergic neurotransmission. In rodents, for example, infection with T. gondii elicits release of IFN-y, IL-12, IL-8, and TNF-a primarily by cells of the innate immune system (Miller et al, 2009). Recently, using a rodent model of T. gondii infection, Notarangelo et al (2014) showed that brain kynurenine and QUIN levels are persistently elevated in infected animals. Moreover, the T. gondii genome encodes for tyrosine hydroxylase, a rate-limiting enzyme in the synthesis of dopamine, wherein it catalyzes the conversion of tyrosine into L-DOPA (Gaskell et al, 2009). Using T. Gondii-infected mice, Prandovsky et al showed that increased dopamine staining was restricted to tissue cysts containing the parasite. They also observed increased dopamine release in response to T. gondii infection (Prandovszky et al, 2011). Together, the biological effects of T. gondii might include increases in
+the levels of pro-inflammatory cytokines, QUIN, and dopamine, thus altering both glutamatergic and dopaminergic neurotransmission.
+TRAUMATIC BRAIN INJURY
+Traumatic brain injury (TBI) results from sudden trauma to the brain. Immune system activation following TBI usually occurs as a secondary event and further worsens existing neuro-degeneration and other neurological impairments following initial trauma. The neuroinflammation is characterized by microglial activation and release of inflammatory factors such as IL-1^, TNF-a, and IFN-y (Block and Hong, 2005). In a study involving the largest cohort of TBI patients (two million controls and 200 000 Swedish TBI patients), it was determined that those with TBI are three times more likely to die of suicide than controls (Fazel et al, 2014). To determine whether increases in inflammatory mediators are associated with TBI, Jeungst and colleagues measured TNF-a levels in serum and CSF of TBI patients 6 and 12 months following initial injury. Not only did elevated TNF-a levels correlate with TBI but they also found positive correlations between TNF-a levels and disinhibition (a behavioral proxy for impulsivity), as well as disinhibition and suicidal ideation at various time points. Thus, they found an alternative rational for the etiology of TBI, involving immune activation and increase in cognitive deficits associated with impulse control and suicidal behavior (Juengst et al, 2014). A study by Mackay et al (2006) found an increase in activation of the kynurenine pathway, which was sustained for at least 1 year (and possibly longer) following TBI. Meanwhile, another study determined that during the first year after mild-to-severe TBI, suicidal ideation was present in 25% of patients (Mackelprang et al, 2014). These long-lasting effects of the TBI could be, in part, responsible for the increased risk of suicidality observed in veterans (Brenner et al, 2011).
+VITAMIN D DEFICIENCY
+Low levels of vitamin D have been linked with MDD and other psychiatric illnesses (Kjaergaard et al, 2011), and recent studies have begun to explore its role in suicidal behavior. Umhau et al (2013) investigated vitamin D status in a cohort of active duty military service professional and found that low levels of vitamin D correlate with increased suicide risk. Although it is still unclear how vitamin D is associated with suicidal behavior, it is plausible that its immune-modulatory functions might have a role. 1,25-dihydroxyvitamin D3 attenuates immune response by suppressing the effects of IL-2 and IFN-y, produced by Th1 cells, subsequently preventing the activation and proliferation of T-cell populations (Cippitelli and Santoni, 1998). Furthermore, vitamin D also inhibits the release of cytokines such as IL-6 and TNF-a from human monocytes (Zhang et al, 2012). Grudet et al compared vitamin D levels in suicide attempters, non-suicidal depressed patients, and
+healthy individuals, and found that suicidal patients had significantly lower levels of vitamin D, compared with the other groups, and that 58% of the suicide attempters were severely vitamin D deficient. Lower vitamin D levels also correlated with elevated levels of inflammatory cytokines in the suicidal and depressive patients (Grudet et al, 2014).
+THERAPY
+Several clinical trials have found that ketamine robustly decreases depressive and suicidal symptoms in as little as 40 min with effects lasting for several weeks (DiazGranados et al, 2010; Larkin and Beautrais, 2011; Price and Mathew, 2015; Price et al, 2009; Zarate et al, 2012). Animal studies also showed that ketamine is capable of reducing depressive effects in LPS-injected mice, which exhibit activation of the kynurenine pathway in the brain through IDO induction (O’Connor et al, 2009; Walker et al, 2013). As ketamine is an NMDA receptor antagonist, its observed beneficial effects could be due to its competing action with neurotoxic QUIN, an NMDA receptor agonist. In addition, ketamine can be metabolized in vivo via P450 enzymes to produce various metabolites, some of which are biologically active, such as (2R, 6R)-hydroxynorketamine (HNK), and mediate antidepressant effects by activation of AMPA receptors (Zanos et al, 2016). Currently, there are several ongoing clinical trials investigating whether repeated administration or different doses of ketamine are able to increase its efficacy in various psychiatric disorders (US National Institute of Health, 2016).
+Another drug with therapeutic potential to treat MDD and suicidal behavior includes 4-chloro-kynurenine (4-Cl-KYN), which is a brain-penetrating prodrug of 7-chloro-kynurenic acid, and works by blocking the glycine co-agonist site of the NMDA receptor. 4-Cl-KYN exerts similar antidepressant effects of ketamine, but not side effects in animal models (Zanos et al, 2015). Interestingly, these beneficial moodaltering effects are contingent on the activity of AMPA receptors, suggesting an important relationship between NMDA and AMPA receptors in generating antidepressant response. There is, currently, an ongoing clinical trial testing the antidepressant effects of 4-Cl-KYN in MDD patients (US National Institute of Health, 2016).
+Another potentially promising pharmacological target is glycogen synthase kinase-3 (GSK3), which raises the levels of pro-inflammatory cytokines and, thus, is thought to promote the development of aggression and depressive symptoms, which are closely linked to development of suicidality (Beurel and Jope, 2014). Administration of lithium, which exhibits anti-inflammatory effects through inhibition of GSK3, has been found effective in decreasing depressive-like and aggressive behavior in animal models (Beurel and Jope, 2014) and reducing suicidal attempts and suicide completion in patients with bipolar as well as unipolar depression (Baldessarini et al, 2006; Guzzetta et al, 2007).
+Other potential drug targets include cyclooxygenase-2 (COX-2) inhibitors, which are selective nonsteroidal
+anti-inflammatory drugs. Several recent meta-analyses of randomized controlled trials (RCTs) have found a COX-2 inhibitor celecoxib to be an effective add-on treatment for unipolar depression as celecoxib decreases depression severity and increases remission rates (Faridhosseini et al, 2014; Na et al, 2014). A RCT conducted by Nery et al (2008) has found that adjunctive treatment with celecoxib is able to produce a rapid antidepressant effect in bipolar disorder patients during depressive or mixed phases.
+Infliximab, which is a monoclonal antibody against TNF-a, has also shown some antidepressant effects, as it is able to decrease depressive symptoms in patients with treatment-resistant depression and elevated levels of plasma inflammatory biomarkers (Raison et al, 2013). A new, recently started RCT will evaluate the effect of adjunctively administrated infliximab on bipolar I and II depression (US National Institute of Health, 2016). Other clinical studies have shown that infliximab and etanercept, another TNF-a blocker, are capable of improving symptoms of depression in patients with various inflammatory conditions (Ersozlu-Bozkirli et al, 2015; Ertenli et al, 2012; Gelfand et al, 2008; Minderhoud et al, 2007; Tookman et al, 2008).
+Minocycline, which is a broad-range tetracycline antibiotic capable of decreasing microglial activation, is another promising treatment for depressive symptoms. Preliminary data from an open-label study of patients with MDD suggest that adjunctive minocycline treatment is able to reduce the severity of depressive and psychotic symptoms (Miyaoka et al, 2012). Currently, ongoing RCTs are investigating the effect of minocycline add-on treatment for the management of depressive symptoms in patients with treatment-resistant depression (Husain et al, 2015), MDD (Dean et al, 2014), and bipolar depression (Savitz et al, 2012).
+Other anti-inflammatory drugs with therapeutic promise in the treatment of depressive and suicidal symptoms include those targeting cytokine IL-6 and its biological function. An example of such drug is tocilizumab, a monoclonal anti-IL-6 receptor antibody that works by preventing the IL-6 ligand from binding to the IL-6 receptors. Previous studies have found that tocilizumab is able to effectively treat several inflammatory and autoimmune diseases (Choy et al, 2002; Fonseka et al, 2015; Ito et al, 2004) and a recently initiated RCT will access the ability of tocilizumab to decrease depressive symptomology in treatment-resistant depression (US National Institute of Health, 2016). A more recently developed drug is the human anti-IL-6 monoclonal antibody—sirukumab. In previous studies, sirukumab was able to decrease inflammatory symptoms in patients with rheumatoid arthritis (Smolen et al, 2014; Tanaka and Martin Mola, 2014). An ongoing RCT will assess the efficacy and safety of an add-on sirukumab treatment in MDD patients (US National Institute of Health, 2016).
+Pentoxifylline is a phosphodiesterase inhibitor, which, among other effects, decreases the expression of pro-inflammatory cytokines such as TNF-a, IL-1, and IL-6 in blood cells (Ferrari et al, 2010; Gonzalez-Espinoza et al, 2012; Neuner et al, 1994; Pollice et al, 2001). Although it has also
+been shown to decrease depressive-like behavior in animal models (Bah et al, 2011; Elgarf et al, 2014), whether it can be replicated in humans has not yet been determined. A recently started RCT will attempt to shed some light, as it will explore whether pentoxifylline is able to reduce depressive symptoms, as well as improve artery function, in older primary care patients (US National Institute of Health, 2016).
+Most of the above-mentioned anti-inflammatory treatments are in the process of being tested for their effectiveness as anti-depressive action. As discussed in the above sections of this review, irrespective of psychiatric condition, suicidal patients and those at increased suicidal risk often display pronounced inflammatory changes and, therefore, might benefit from anti-inflammatory therapies. Unfortunately, suicidal patients are frequently excluded from the initial phase of investigation due to the safety concerns. Thus, whether the mentioned anti-inflammatory treatments could be additionally used as effective therapies for suicidal behavior remains to be studied in well-designed clinical trials.
+CONCLUSIONS
+Mounting evidence implicates dysregulation of the immune system in pathophysiology of suicidality. The potential upstream triggers of suicidal behavior include various inflammatory conditions (TBI, vitamin deficiency, autoimmune disorders, and infections), which, through raised levels of inflammatory mediators, can cause dysregulation of the kynurenine pathway of tryptophan catabolism, hyperactivation of the HPA axis, and alterations in monoamine metabolism in the patients. These neurobiological effects might cause profound changes in emotion and behavior, which could ultimately lead to suicide in vulnerable individuals. More studies are needed to further characterize the interconnection between upstream triggers of inflammation, downstream mediators, and predisposition factors, which, in the presence of inflammation, confer resilience or vulnerability towards development of suicidality.
+Currently, anti-inflammatory treatments, initially approved for other conditions, are being tested in depressive individuals. Although for safety concerns, actively suicidal individuals and those at increased suicide risk are excluded from such clinical trials, it may be of key importance to include suicidal individuals in the future as this patient group exhibits the most pronounced inflammatory changes, likely to respond to treatment. Ketamine, an NMDA receptor antagonist, which might counteract some of the actions of the kynurenine pathway metabolites, is being specifically tested in suicidal individuals.
+In the future, it may prove to be advantageous to target additional signaling and regulatory mechanisms involved in neuroinflammation. Such targets could include TLRs, activation of which regulates the production of pro-inflammatory cytokines, as some studies have found some abnormalities in brain and blood TLRs’ levels in depressed
+and suicidal patients (Pandey et al, 2014; Wu et al, 2015). Another target could be regulation of the balance between Th1 and Th2-type T-cell populations, as several studies have found an imbalance of these cell types and their corresponding cytokines in the blood of MDD patients with suicidality (Huang and Lee, 2007; Kim et al, 2008; Mendlovic et al, 1999).
+Altogether, as neuroinflammation is gradually becoming more accepted as a cause of behavioral symptoms in suicidal individuals, a wide range of novel treatment options, targeting either upstream or downstream factors in the inflammatory cascade, are showing promise to help affected patients. As such, the future for development of novel therapies in psychiatry looks brighter than in decades.
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+Introduction
+Worldwide, investments in mental health are very meagre. Data from WHO’s Mental Health Atlas 2014 survey1 suggest that most low-income and middle-income countries spend less than US$2 per year per person on the treatment and prevention of mental disorders compared with an average of more than $50 in high-income countries. As a result of this limited investment in public mental health, a substantial gap exists between the need for treatment and its availability. This large treatment gap affects not just the health and wellbeing of people with mental disorders and their families, but also has inevitable consequences for employers and governments as a result of diminished productivity at work, reduced rates of labour participation, foregone tax receipts, and increased health and other welfare expenditures. Findings of several national and international studies2-5 have shown the enormous economic challenge these disorders pose to communities and society at large as a result of foregone production and consumption opportunities as well as health and social care expenditures. In 2010, worldwide, an
+estimated US$2-5-8-5 trillion in lost output was attributed to mental, neurological and substance use disorders, depending on the method of assessment used.2 This sum is expected to nearly double by 2030 if a concerted response is not mounted.2 In view of this concern, the promotion of mental health and wellbeing have been explicitly included in the United Nations’ 2015-30 Sustainable Development Goals.6
+Cost-effectiveness studies have largely restricted themselves to a consideration of the specific implementation costs and health outcomes of an intervention, and have typically not extended to a full estimation of the wider socioeconomic value of investment in mental health innovation and service scale-up. As shown in the Lancet’s Commission on Investing in Health, elucidation and enumeration of these wider economic and social benefits provides a more comprehensive assessment of the returns on investment.7 In particular, increasing attention and emphasis is being given to extending valuation to also include the intrinsic value of improved health (a so-called full income approach to national accounting).7
+Research in context
+Systematic review
+We did a systematic review of studies published until Jan 1, 2015, of the effect of treatment of depression and anxiety disorders on economic outcomes (return to work, absenteeism, and presenteeism rates). We searched an existing database on psychological treatments of depression, which has been described in detail by Cuijpers and colleagues, and has been used in a series of earlier published meta-analyses. We identified abstracts by combining terms indicative of psychological treatment and depression (both medical subject headings terms and text words; search terms listed in appendix p 4). For this database, we examined 17 061 abstracts from PubMed (4007 abstracts), PsycINFO (3147 abstracts), Embase (5912 abstracts), and the Cochrane Central Register of Controlled Trials (3995 abstracts). We included all randomised trials comparing a psychological treatment with a control condition (waiting list, care as usual, placebo), another psychological treatment, pharmacotherapy, or combined treatment. We excluded studies in adolescents, children, and inpatients, and maintenance trials. We scrutinised all 440 studies identified in this database for economic outcome data. Although four studies had data for functioning at work,
+they did not report sufficient detail. Accordingly, we did an additional search on May 21, 2105, to widen our search to include anxiety disorders with greater emphasis on economic outcomes in PubMed, EMBASE, PsycINFO and the Cochrane Library (search terms listed in appendix p 5). We found few useful data and these could not be synthesised meta-analytically. The same conclusion was made in a similar review of the scientific literature.
+Interpretation
+This analysis sets out a model linking the prevalence of depression and anxiety disorders with expected health and economic benefits of scaled-up treatment, including restored labour participation and productivity. Results from the analysis suggest that monetised benefits of better health and labour force outcomes outweigh the costs of achieving them by 2-3—3-0 to 1 when economic benefits only are considered, and 3-3—5-7 to 1 when the value of health returns is also included.
+Treatment of common mental disorders leads to improvements in economic production and health outcomes. Clinicians should increase the detection and management of people with depression and anxiety disorders.
+Here we did a global return on investment analysis for mental health in people aged 15 years and older focusing on depression and anxiety disorders, which are the most prevalent mental disorders. These disorders lead to large losses in work participation and productivity, and yet lend themselves to effective and accessible treatment as part of an integrated programme of chronic disease management.8-10
+Methods
+Analytical framework
+Because depression and anxiety disorders represent a public health challenge worldwide, we did a global investment appraisal in low-income, middle-income, and high-income countries. The 36 countries for which we modelled costs and benefits of scaled-up treatment, which span all six of WHO’s major regions, account for 80% of the world’s population and 80% of the global burden ofdepression and anxiety disorders (appendix p 1). Results for these countries were aggregated and reported by income level (low, lower-middle, upper-middle, high). We set the scale-up period at 2016-30, in line with the timeline of the post-2015 Sustainable Development Goals.
+The economic and social benefits of good mental health include both its intrinsic value (improved mental health and wellbeing) and also its instrumental value, in terms of being able to form and maintain relationships, to work or pursue leisure interests, and to make decisions in everyday life. To assess the value of these benefits, first we estimated the population in need in each country,
+then established the health effects of scaled-up coverage of effective intervention, and finally calculated the economic effect of improved mental health outcomes in terms of enhanced labour participation and productivity. Panel 1 provides more detail on the health and economic benefits captured in, and omitted from, the analysis. The key outputs of the model are year-on-year estimates of the total costs of treatment scale-up and system strengthening (ie, the investment), increased healthy life-years gained as a result of treatment (ie, health return), the value associated with better health (ie, the value of health returns), and enhanced levels of productivity (ie, economic return). The stream of costs incurred and benefits obtained between 2016 and 2030 were discounted at a rate of 3%, to give a net present value. All costs and monetised benefits were expressed in constant US$ for the year 2013.
+Population and disease modelling
+We used the mental health module of the inter-UN agency OneHealth tool to estimate the number of people with depression and anxiety disorders living in the 36 large countries until 2030. Estimates are based on UN population projections and Global Burden of Disease prevalence estimates for 2010.11,12 The global point prevalence rate for anxiety disorders is 7 • 3%;13 for depression it is 3^2% for men, and 5^5% for women.14 The OneHealth tool also links the epidemiology of depression and anxiety disorders (prevalence, incidence, remission, excess mortality, and disability weight)12-14 to country-specific life tables, so that cases averted and
+healthy life-years gained over time at the population level can be estimated. Healthy life-years reflect time spent by the population in a particular state of health with a known degree of disability. Estimation of healthy life-years for depression took into account its association with excess mortality (due to suicide and other causes of death).14
+Intervention effects, costs, and coverage
+Intervention effects
+We restricted the analysis of interventions within the OneHealth tool to treatment because the evidence on prevention of depression and anxiety is quite weak and of uncertain generalisability to low-income and middleincome country settings.15 In line with WHO’s Mental Health Gap Action Programme (mhGAP) intervention guide, modelled interventions included basic psychosocial treatment for mild cases, and either basic or more intensive psychosocial treatment plus antidepressant drug for moderate to severe cases.16 Moderate to severe cases of depression were split into first-episode and recurrent episode cases. We calculated the health effect of treatment in terms of a proportionate improvement in the rate of remission, equivalent to a shortening of the duration of an episode of illness, and also, up to the point of recovery, an improvement in the average level of functioning as reflected in the disability weight for the disorder.8,10 The appendix shows the effect size estimates and their derivation (appendix p 2); these take into account partial response, the lag time between onset of the disorder and treatment, and expected levels of nonadherence in treated populations.
+Intervention costs
+We worked out total costs in a given year for a country by multiplying resource use needs by their respective unit costs to give a cost per case, which was then multiplied by the total number of cases expected to receive a particular intervention. Country-specific unit costs of inpatient and outpatient care were taken from a WHO database, adjusted to 2013 price levels.17 Treatment costs relied on previous cost-effectiveness studies and resource need profiles garnered from existing treatment guidelines and costing studies.10,16,18,19 Key categories of resource use were: medication: 6 months continual antidepressant drug (generically produced fluoxetine) was included for moderate to severe cases; outpatient and primary care: regular visits were needed for all cases, ranging from four per case per year for basic psychosocial treatment, up to 14-18 visits for moderate to severe cases receiving antidepressant drug and intensive psychosocial treatment (half of whom are assumed to receive this on an individual basis, the other half in groups); in line with the mhGAP intervention guide, it is envisaged that this care and follow-up would largely be undertaken in non-specialist health care settings by doctors, nurses and psychosocial care
+Panel 1: Health, economic, and social benefits of scaled-up treatment for depression and anxiety disorders
+Health effects
+To establish the effect of treatment, we used rates of improved recovery or remission and levels of functioning. Improved functioning translates into fewer life-years spent by the population in a state of diminished health, whereas an increased rate of remission leads to a decrease in the prevalence of these disorders over time. Depression is also associated with an excess risk of premature mortality because of suicide and other causes of death. We projected a reduction in excess mortality, amounting to an increase in healthy life expectancy, as a result of averting cases of depression in the population. Although depression and anxiety disorders are often comorbid with each other, and with a range of other health disorders (eg, substance use disorder, other non-communicable diseases and, in certain populations, in people with HIV/AIDS) we were not able to account for these comorbidities in the analysis. Additionally, we were unable to capture the positive effect of treatment on the mental and physical health of close family members, including infants of mothers with perinatal depression, despite robust evidence that depression can adversely affect infant attachment and subsequent child growth and cognitive development.
+Economic effects
+A direct potential benefit of successfully treating common mental disorders is a decrease in overall health-care costs. Although interventions have their own costs, these can be more than offset by a reduction in other services, notably hospital-based inpatient episodes or outpatient visits. Reduced use of informal and indigenous health-care providers, such as faith healers or traditional healers, is a further expected source of cost savings in many countries. Estimation of the predicted extent of these cost offsets is very challenging at the international level because it requires detailed information about both the varying level of comorbidity across diverse populations and the typical use of non-intervention related services. Accordingly, we did not explicitly consider such effects in our analysis. Similarly, we did not have sufficient information across countries to model the reduced need for other welfare-related services potentially available to people with depression and anxiety disorders, including unemployment benefit or income support and social or disability assistance. In the mainly high-income countries where such welfare support is widely available, depression and other common mental disorders account for a significant proportion of overall payments.5 Instead, the analysis focused on the financial benefits flowing from increased rates of workforce participation and productivity. The analysis only considers the contribution to the economy as a whole through increased economic output; it does not estimate the various income shares of this output.
+Social effects
+Conceptually distinct from improvements in clinical functioning (health effect) and the restored ability to do paid work (economic effect), the successful treatment of depression and anxiety disorders leads to improved opportunities for individuals and households to pursue their leisure interests, participate more in social and community activities, and carry out household production roles. The economic worth of these non-market production and welfare gains is incorporated into our estimate of the intrinsic value of mental health.
+providers trained in the identification, assessment, and management of depression and anxiety disorders; and inpatient care: few cases are expected to be admitted to hospital (2-3% of moderate to severe cases only, for an average length of stay of 14 days).
+Additionally, we included an estimate of the expected level of programme costs and shared health system resources needed to deliver interventions as part of an
+integrated model of chronic disease management. These include programme management and administration, training and supervision, drug safety monitoring, health promotion and awareness campaigns, and strengthened logistics and information systems. We expressed estimates as an on-cost to the estimated direct healthcare costs. The baseline value for this on-cost was 10% (and therefore grows in absolute terms during scale-up).
+Intervention coverage
+The appendix provides coverage rates used for each individual intervention at different levels of national income (appendix p 3). Summing across all interventions and their respective populations in need, it is estimated that—depending on the income level of the country— between 7% and 28% of all people with depression currently receive treatment, equivalent to a treatment gap of 72-93% (table 1). A gradual, linear increase in treatment coverage to a third of all cases in low-income countries and to more than half of cases in high-income countries would close the current gap by 29-39%; the use of separate target coverage rates for low-income, middleincome, and high-income countries reflects differences in which they stand now with respect to treatment coverage, and are intended to reflect what has been achieved through programme scale-up efforts in countries such as Chile and the UK.20 Because of even lower starting coverage levels, the modelled gap reduction for anxiety disorders is lower than for depression (16-25%).
+Effect of labour force on treatment
+We modelled the economic effect of decreased morbidity in terms of increased participation in and increased productivity of the workforce. With regards to labour force participation, very few studies have assessed the extent to which effective depression treatments get people back into work, and when measured, estimates have been subject to local factors such as prevailing levels of unemployment in the economy (panel 2).21-24 For our
+base case, we conservatively modelled a 5% restored ability to work as a result of treatment, with half and double that rate used under pessimistic and optimistic scenario analyses. Impaired productivity was assessed both with respect to whole days off work (absenteeism) and also partial days of impaired activity while an individual is at work (presenteeism). Compared with adults without common mental disorders in a range of low-income, middle-income, and high-income countries participating in the World Mental Health Survey, 4-15 more days out of role per year were recorded because of depression and 8-24 days because of generalised anxiety disorders; additional time lost per year due to presenteeism was 11-25 partial disability days for depression and 12-26 for generalised anxiety disorders.25,26 Again, there are few empirical studies upon which to base estimates ofthe effect of effective treatment of depression and anxiety on productivity, and these point towards small differences between intervention and control groups (panel 2).27-33 Expressed as a proportion of total working days per year (220 days), and allowing for both the onset of effect as well as the time lag between improved health and return to work, we modelled a 5% increase in working days as a result of reduced absenteeism, and a 5% increase through reduced presenteeism. Again, these baseline values were varied up and down by a factor of 2 and 0-5, respectively, in an uncertainty analysis. These losses in and returns to productivity were linked to the prevailing rates of labour participation in the working age population (age 15-65 years) and gross domestic product (GDP) per worker in each of the 36 assessed countries3435 to calculate productivity losses at current levels of treatment coverage and productivity gains after scaled-up treatment. The model does not account for potential changes in retirement age or working patterns over time, although an increase in retirement age and more flexible working patterns might enhance the overall productivity gains by people with depression and anxiety with treatment.
+Economic value of health benefits
+Improvements in labour force outcomes represent the instrumental value of improved mental health after effective treatment of common mental disorders. Independent of this instrumental value, being alive and healthy is also valuable in itself. For this analysis, we followed the approach adopted by Stenberg and colleagues,36 who divided the overall value of a life-year into its economic (instrumental) and health (intrinsic) elements. For the Lancet Commission on Investing in health, the value of a 1 year increase in life expectancy in low-income and middle-income countries was estimated to be 2-3 times per person national income, and 1 • 6 times per person national income worldwide (using a discount rate of 3%).7 Stenberg and colleagues36 attributed two-thirds of that derived value to the instrumental components, which are measured here directly via the
+Panel 2: Labour force effects of treatment
+Labour force participation
+There are very few studies showing the extent to which effective depression treatments get people back into work. Two studies undertaken in the USA reported a 6% increase in employment retention in patients with depression whose care was monitored and managed closely.20,21 Findings of another US study22 of patients in primary care showed that, at 6 months, employment rates were 52-5% for patients with no care versus 72-2% for patients with care. For low-income and middle-income countries, programme evaluation data for livelihoods from four countries—China, India, Ghana, and Pakistan—were made available by BasicNeeds, which showed that the proportion of people with depression undertaking income-generating activities increased by more than 50%, and in those with anxiety by more than 30% (Chris Underhill, BasicNeeds, personal communication). These estimates are in line with the assessment of the BasicNeeds programme in Kenya, which for a more mixed caseload showed an 43% improvement in the proportion of enrollees in income generation or productive work.23 Because these data are based on observation rather than under controlled trial conditions, we can infer only a clear association between exposure to treatment and subsequent earnings rather than a defi nitive effect of intervention. For our base case, we therefore conservatively modelled a 5% restored ability to work as a result of treatment, with half and double that rate used under pessimistic and optimistic scenario analyses.
+Labour force productivity
+A comprehensive review of 440 published trials in an existing database of psychological and pharmaceutical interventions in depression24 was specifically undertaken for this project (by researchers at the Vrije Universiteit Amsterdam, Amsterdam, Netherlands, and the Trimbos Institute, Utrecht, Netherlands) to identify the effect of effective treatment on productivity; unfortunately, very few trials reported these effects. However, some treatment trials done in the USA, Korea, and India have estimated the effect of intervention on productivity loss. The decrease in absenteeism reported in these studies was close to 1 day per month.20,29-32 Only two studies reported the findings for presenteeism separately from days lost because of absenteeism: in the Korean study, treated patients had 24 more productive hours per month,29 whereas in the Indian study, patients receiving the collaborative care had 4 fewer partial days lost than controls.30 By conservatively assuming that 1 partial day is equivalent to a third of a whole day, we estimate that almost 1 complete day of unimpaired work is restored per month through reduced presenteeism. Expressed as a proportion of total working days per year (220 days), and allowing for both the onset of effect and the time lag between improved health and return to work, a 5% increase in working days is gained through reduced absenteeism, and a 5% increase through reduced presenteeism.
+labour force outcomes, leaving the remaining third for the intrinsic benefits of health, which is equivalent to 0-5 times per person income.
+Uncertainty analysis
+We assessed the sensitivity of results to plausible variations around these and other key input parameters by constructing optimistic and pessimistic scale-up scenarios. For the upper estimate: total investment costs were assumed to be 20% lower than baseline, as a result of lower than expected use of expensive hospital outpatient and inpatient care or the development of more efficient interventions, including internet-based treatments; and productivity effects were set at double their baseline rate (10% rather than 5%); the intrinsic value of a year of health life was set at 0-7 times GDP per person (rather than 0-5). For the lower estimate: total investment costs were assumed to be 20% higher than baseline, as a result of higher than expected drug prices, service use and programme management; productivity effects were set at half their baseline rate (2-5% rather than 5%); and the intrinsic value of a year of health life was set at 0-3 times GDP per person (rather than 0-5).
+Role of the funding source
+The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full
+access to all the data in the study and had final responsibility for the decision to submit for publication.
+Results
+Across the 36 largest countries in the world, in the absence of scaled-up treatment, it is projected that more than 12 billion days of lost productivity (equivalent to more than 50 million years of work) are attributable to depression and anxiety disorders every year, at an estimated cost of US$925 billion. Assuming the same distribution of costs across lower-income and higher-income countries holds for all other countries (representing 20% of the world’s population), the global cost per year is $1-15 trillion. Compared with people without these disorders, 4-7 billion extra days are lost, at a cost of $592 billion (36% of the total cost); this figure can be termed the excess productivity loss of these disorders (figure 1).
+Table 2 shows the estimated cost of scaling up treatment for depression and anxiety, expressed as the net present value of the total expenditure required over the scaling-up period between 2016 and 2030 (ie, the cumulative cost over 15 years of steady scale-up, but discounted at a rate of 3%). These costs relate to incremental treatment coverage in the population over and above current levels of coverage. For all 36 countries, the total cost amounts to US$91 billion for depression and $56 billion for anxiety disorders. Treatment of mild cases accounts for less than 10% of total costs for depression and 20% for anxiety
+disorders. After standardising for population size, the cost is actually quite low; for depression treatment, the average annual cost during 15 years of scaled-up investment is $0-08 per person in low-income countries, $0-34 in lower middle-income countries, $1-12 in upper middle-income countries and $3-89 in high-income countries (table 2). Per person costs for anxiety disorders are nearly half that of depression.
+Table 2 shows results for two key health outcomes: cases averted (reduced prevalence) and healthy life-years gained (equivalent to disability-adjusted life-years averted). Across the 36 countries represented in the analysis, we recorded a small decrease in the estimated prevalence of depression and anxiety disorders as a result oftreated cases recovering from illness more quickly; in the next 15 years, this gradual decrease in prevalence translates into millions of averted cases (73 million fewer cases of depression, and 45 million fewer cases of anxiety disorder). Weighting these averted prevalent cases by the average level of improved functioning or reduced disability provides a measure of healthy life-years gained. For depression and anxiety disorders combined, the cumulative number of healthy life-years gained over 15 years is 43 million.
+Table 2 also shows the difference in aggregate GDP between a continued current coverage scenario and one reflecting scaled-up treatment and enhanced productivity; again, this and the total economic return for the entire period of scale-up has been discounted at 3% to give a net present value. For all 36 countries combined, the net present value is $399 billion ($230 billion for depression and $169 billion for anxiety disorders). The intrinsic value of health returns show a net present value of more than $250 billion for scaled-up depression treatment and more than $50 billion for anxiety disorders (table 2).
+By summing the discounted costs and benefits for all countries in an income group, we derived a summary measure of the relationship between the benefits of scaled-up treatment and the associated costs of investment (table 2, figure 2). Restricting assessment to the economic returns to investment, benefit to cost ratios for scaled-up depression treatment across country income groupings were in the range of 2-3 to 2-6. For anxiety disorders the ratios were slightly higher (range 2-7-3-0). Extension of the benefit-cost analysis to include the estimated value of health returns increased the ratio of benefit to cost, especially for depression because of the higher health returns for this disorder compared with anxiety disorders. Benefit to cost ratios for depression now exceed those for anxiety disorders (range 4-2-5-7), and were more than double the ratio when only economic benefits of depression treatment scale-up were considered. Benefit to cost ratios for anxiety disorders increased by a third (range 3-3-4-0).
+We did uncertainty analysis to ascertain the sensitivity of results to plausible changes in key study parameters. Benefit to cost ratios fell to or almost reached parity under the more pessimistic scenario when only economic benefits were considered, and did not exceed 3 even when the value of health benefits was included (figure 2). By contrast, the more optimistic scenario produces benefit to cost ratios of 5-5-7-2 (economic benefits only) and 7-5-11-3 when the value of health benefits was added in. As expected, results were quite sensitive to the estimated rate of enhanced labour participation and productivity. We also assessed the effect of changing the rate used to discount future costs and benefits to the present time. At a discount rate of 6%, the net present value of total investments and returns would be 25% less; with no discounting, they would be 35% higher in absolute terms. Because such a change in discount rate was applied to both costs and benefits, the ratio of benefit to cost, our summary return on investment metric, is not affected.
+Discussion
+This analysis sets out, for the first time, a global investment case for a scaled-up response to the massive public health and economic burden of depression and anxiety disorders. Previous international economic studies of mental health have assessed the economic effect of these disorders,2,3 the cost-effectiveness of different intervention strategies,8,10 and the cost of scaling up care,18,19 but not the value of both economic and health benefits of intervention scale up.
+Notwithstanding the general limitations of any projection modelling study, the analysis suggests that the investment needed to substantially scale up effective treatment coverage for depression and anxiety disorders in the 36 countries included in this analysis is substantial; the net present value of all investments between 2016
+and 2030 is $147 billion, equivalent to less than $10 billion per year on average. Extending the scope to the 20% of the world’s population not living in the 36 countries represented in the study would increase the cost by about 25% to $184 billion. However, the returns to this
+investment are also substantial, with benefit to cost ratios of 2-3-3-0 when economic benefits only are considered, and 3-3-5-7 when the value of health returns are also included. To put these findings into context, any benefit to cost ratio exceeding 1 provides a rationale for investment. Compared with some other potential investments in health, ratios of the order reported here can be deemed relatively modest. For example, a return on investment analysis for malaria, also for 2016-30, but using the full value of a statistical life-year, estimated benefit to cost ratios in the range of 28:1 to 40:1.37 An investment case done for maternal, reproductive, neonatal, and child health obtained a benefit to cost ratio of less than 10:1 for 2013-35,36 which is closer to the results obtained in this study. Inclusion of other benefits arising from scaled-up treatment of common mental disorders that could not be captured though the present modelling exercise, notably reduced welfare support payments, and improved outcomes for other affected people (eg, partners and children of women with perinatal depression) would generate higher ratios of benefit to cost. Set against that, treatment programmes might cost more or achieve less than anticipated, as highlighted by the uncertainty analysis.
+One limitation of our study is that although the projected level of overall prevalence of depression and anxiety disorders is quite well-established,12-14 the same cannot be said for treated prevalence. The analysis done here allows for a gradual linear increase in effective service coverage for depression and anxiety disorders in all parts of the world in the next 15 years. However, for this to happen, not only will a new level of political commitment and resource mobilisation be required, but also a significant reorientation of public health systems towards chronic disease identification and management.9 Partial or weak implementation of envisaged treatment programmes, including appropriate management of recurrent cases of depression or insufficient promotion and awareness programmes, will inevitably reduce the number of cases effectively reached and therefore the health and other benefits obtained. It is also possible that as treatment coverage in the population increases substantially, the average cost per case might go up, for example as a result of reaching out to more remote or less well-served parts of a country. Target coverage rates were accordingly set at a modest level in this analysis (an upper value of 56% of depression cases in high-income countries). Aside from projected treatment coverage and effectiveness, a further crucial parameter for this analysis concerns the effect of treatment on labour force participation and productivity, for which there remains a paucity of evidence. As concluded by a systematic review, such data are not hard to collect alongside clinical trials and other studies, and need to be uniformly measured more often.27 More generally, population health models (eg, the OneHealth tool) rely on many input parameters, data sources, and assumptions regarding expected rates
+of disease, demographic change, and intervention effects in the future, which limits their precision.
+Several effects were not included in the analysis. One was the negative effect of maternal depression on early child development, for which there is clear evidence;38 the health, social, and economic benefits of effective treatment of maternal depression on the cognitive and physical development of newly born babies was not assessed, but there is some evidence that this could be substantial over the longer term.39 Likewise, the monetary and non-monetary impact of effective treatment on family and other caregivers has not been factored in. Additionally, no account has been taken of the substantial effect of depression and its treatment on physical health outcomes; depression is a risk factor for disorders such as hypertension, stroke, coronary heart disease, and substance use disorders (just as these conditions are risk factors for depression), and adversely affects outcomes through reduced help-seeking and adherence.40 Inclusion of these additional effects of treatment would bolster identified economic returns. Taking appropriate account of the regular co-occurrence of depression and anxiety in individuals would be expected to lead to strong synergies on the treatment side, leading to potentially reduced investment costs, but health and economic outcomes for these comorbid cases might be slower or harder to achieve.
+Although the analysis accounted for age and sex (eg, in terms of disease prevalence, labour force participation and treatment eligibility), it was not possible to consider the effect of socioeconomic status as a mediator and predictor of good health and economic outcomes. Poverty has an adverse effect on the risk of depression and anxiety disorders through higher levels of stress, social exclusion, violence and trauma, but the evidence base for the mental health effect of interventions targeted at the poor remains insubstantial.41 In many countries, poor people face significant barriers to accessing services, including the financial cost of seeking and paying towards health care. Finally, it should be acknowledged that the workplace itself can be a source of stress for many people, and that there is a consequent need to integrate mental health and wellbeing into new or existing employee support programmes.
+A crucial issue related to but outside the scope of this return on investment analysis is the source of financing for investments required to scale-up services for depression and anxiety disorders. As previously noted, the absolute amount needed for investment (eg, on a per person basis) is modest, but because existing service coverage level is so low in most countries, the gap between current and required spending can be large.18,19 Accordingly, both rich and poor countries need to carefully consider the merits of different health financing mechanisms. For many countries, the first question to address concerns the extent to which domestic financing
+represents a feasible and sufficient method for financing mental health services, perhaps as part of a package of measures to be paid for from enhanced revenue generation. For low-income countries eligible for official development assistance, a second question might be to what extent external funding can complement domestically generated resources to catalyse service development. In countries where domestic or external funding mechanisms are expected to fall short of requirements or pose a risk to fiscal stability, a further question relates to the extent to which market-based financing options such as bonds offer a suitable and feasible approach to generating and providing funds for outcomes-based scale-up for mental health services.
+The pursuit of any of these methods of financing will be affected by other factors, including the amount of investment needed, the level of political will and also fiscal space for raising new resources for health, and eligibility of the country for bilateral or multilateral funding. Faced with a new and broad development agenda,6 governments need to assure themselves that investment in the mental health of their populations represents a sound and equitable investment of society’s resources that leads to clear and definable health, economic, and social benefits. Our return on investment analysis, coupled with an assessment of health-system needs and priorities, and the broader macro-fiscal situation, can contribute to a balanced investment case for common mental disorders and the health sector more generally.
+Articles
+26	Bruffaerts R, Vilagut G, Demyttenaere K, et al. Role of common mental and physical disorders in partial disability around the world. Br J Psychiatry 2012; 200: 454-61.
+27	Cuijpers P, van Straten A, Warmerdam L, Andersson G. Psychological treatment of depression: a meta-analytic database of randomized studies. BMC Psychiatry 2008; 8: 36.
+28	Nieuwenhuijsen K, Faber B, Verbeek JH, et al. Interventions to improve return to work in depressed people.
+Cochrane Database Syst Rev 2014; 12: CD006237
+29	Harvey S, Joyce S, Modini M, et al. Work and depression/anxiety disorders: a systematic review of reviews. 2012. Commissioned review by University of South Wales for Beyond Blue. https://www. beyondblue.org.au/docs/default-source/research-project-files/ bw0204.pdf?sfvrsn=4 (accessed April 16, 2015).
+30	Woo J-M, Kim W, Hwang T-Y, et al. Impact of depression on work productivity and its improvement after outpatient treatment with antidepressants. Value Health 2011; 14: 475-82.
+31	Buttorff C, Hock RS, Weiss HA, et al. Economic evaluation of a task-shifting intervention for common mental disorders in India. Bull World Health Organ 2012; 90: 813-21.
+32	Rollman BL, Belnap BH, Mazumdar S, et al. A randomized trial to improve the quality of treatment for panic and generalized anxiety disorders in primary care. Arch Gen Psychiatry 2005; 62: 1332-41.
+33	Rost K, Smith JL, Dickinson M. The effect of improving primary care depression management on employee absenteeism and productivity. A randomized trial. Med Care 2004; 42: 1202-10.
+34	ILO. 2015 databases and subjects. http://www.ilo.org/global/ statistics-and-databases/lang--en/index.htm (accessed April 2015).
+35	World Bank. World development indicators. 2015. http://databank. worldbank.org/data/reports.aspx?source=world-development-indicators (accessed April 13, 2015).
+36	Stenberg K, Axelson H, Sheehan P, et al, and the Study Group for the Global Investment Framework for Women’s Children’s Health. Advancing social and economic development by investing in women’s and children’s health: a new Global Investment Framework. Lancet 2014; 383: 1333-54.
+37	World Health Organization on behalf of the Roll Back Malaria Partnership Secretariat. Action and Investment to defeat Malaria 2016-2030: For a Malaria-Free World. 2015. World Health Organization, Geneva.
+38	Rahman A, Fisher J, Bower P, et al. Interventions for common perinatal mental disorders in women in low- and middle-income countries: a systematic review and meta-analysis.
+Bull World Health Organ 2013; 91: 593-601.
+39	Cuijpers P, Weitz E, Karyotaki E, Garber J, Andersson G. The effects of psychological treatment of maternal depression on children and parental functioning: a meta-analysis. Eur Child Adolesc Psychiatry 2015; 24: 237-45. DOI:10.1007/s00787-014-0660-6.
+40	Prince M, Patel V, Saxena S, et al. No health without mental health. Lancet 2007; 370: 859-77
+41	Lund C, De Silva M, Plagerson S, et al. Poverty and mental disorders: breaking the cycle in low-income and middle-income countries. Lancet 2011; 378: 1502-14.
+424
+www.thelancet.com/psychiatry Vol 3 May 2016
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+This article was published Online First January 12, 2017.
+Marisa E. Marraccini, Bradley/Hasbro Children’s Research Center of Rhode Island Hospital, Providence, Rhode Island, and Department of Psychiatry and Human Behavior and Pediatrics, Alpert Medical School of Brown University; Zoe M. F. Brier, Bradley/Hasbro Children’s Research Center of Rhode Island Hospital.
+We thank Peg Dawson and Bergljot Gyda Gudmunds-dottir for their contribution to this research study.
+Correspondence concerning this article should be addressed to Marisa E. Marraccini, Bradley/Hasbro Children’s Research Center of Rhode Island Hospital, Coro West Building, One Hoppin Street, Providence, RI 02903. E-mail: marisa_marraccini@brown.edu
+Suicide remains a significant public health concern, globally accounting for 8.5% of deaths among adolescents and young adults between ages 15 to 29 (World Health Organization, 2016). Considering the critical importance of suicide prevention, a large body of literature has investigated the influence of protective factors against suicidal thoughts and behaviors (STB) during adolescence. In recent years, the idea that perceived connectedness may serve as a protective factor against STB during adolescence has garnered considerable attention. Scientific inquiries have focused on adolescent connectedness to parents, family, peers, school, and communities in relation to a wide range of health behavior outcomes (Barber & Schluter-man, 2008). Understanding the influence of school connectedness on STB is especially important given the critical role schools play in adolescent development and because schools are ideally situated to provide interventions reaching the vast majority of youth.
+A rich literature base supports an inverse relationship between adolescent school connectedness and STB (e.g., Whitlock, Wyman, & Moore, 2014). Synthesis of these findings, however, is limited by inconsistences in the way studies have measured and operationalized school connectedness and the wide variability of participant sample characteristics included in studies. This fragmentation limits our understanding of the practical, theoretical, and scientific implications of school connectedness as a protective factor against STB. For example, are there particular categories of school connectedness that school psychologists should prioritize over others? Are there critical subpopulations that school psychologists and researchers should target their intervention efforts toward in order to prevent STB? The meta-analytic study presented here, which elucidates the influence of measurement and sample variation on the association between school connectedness and STB, will help answer these questions.
+School Connectedness: Definition and Measurement
+One of the most widely accepted definitions of school connectedness was initiated by the Wingspread declaration on school connections (Blum & Libbey, 2004). As described by Waters and Cross (2010), school connectedness
+may be defined as “the belief by students that adults in the school community care about students’ learning and about them as individuals” (p. 165). In practice, this includes supportive academic expectations, positive teacher-student interactions, and a safe environment. Based on a review of the literature, Barber and Schluterman (2008) operationalized school connectedness to include three distinct components—interper-sonal relationships, relationship to the school, and attitudes toward school importance. Taken together, school connectedness may include (a) social affiliations: positive school relationships, feeling cared about and/or respected by adults at school, perceiving availability to interact with adults at school; (b) school belonging: feeling part of the school, feeling safe in school, feeling happy at school; (c) attitude about school importance: caring about school, trying to do one’s best at school; and (d) supportive learning environment: clear and appropriate expectations, perceived fairness.
+A wide variety of instruments are available to measure student and staff perceptions of school connectedness, spanning from single-item questions (e.g., “Do you feel like you belong at this school?”) to more complex, multi-item instruments addressing several school connectedness categories. Most of these scales use a unit-weighted approach, averaging equally weighted items together to yield one composite score (Waters & Cross, 2010). One of the first scales to measure school connectedness was the Psychological Sense of School Membership Scale (PSSM; Goodenow, 1993). Although the PSSM was initially designed to yield a single school connectedness construct, factor analyses conducted after its development identified multiple underlying constructs (Lohmeier & Lee, 2011). Thus more recent scales, such as the School Connectedness Scale (Lohmeier & Lee, 2011), have included as many as seven components. The most commonly used instruments, however, yield a unidimensional measure of school connectedness. These include instruments designed to measure multiple constructs with a school connectedness subscale (e.g., the Adolescent Family and Social Life Questionnaire; Yen & Shieh, 2005) and single item queries of school or teacher connectedness (e.g., Seil, Desai, & Smith, 2014). One of the most widely used measures of school connectedness is the 3-to 7-item school connectedness scale developed
+for the National Longitudinal Study of Adolescent Health (Add Health; Resnick et al., 1997). The Add Health Scale has demonstrated satisfactory reliability when examined as a five-item construct (a = .82 to .88; Furlong, O’Brennan, & You, 2011); however, studies that use this scale may include as few as two items. Although the variability across these instruments highlights the richness of the school connectedness literature to date, it also makes the compilation of findings across studies (e.g., meta-analytic analysis) difficult.
+School Connectedness and Health Risk Behaviors
+Empirical evidence overwhelmingly supports the protective role of school connectedness against risky health behaviors. For example, a systematic literature review including 18 studies (Markham et al., 2010) provided evidence that adolescent school connectedness protects against early and frequent sexual activity. Another systematic review examining emotional health (Kidger, Araya, Donovan, & Gunnell, 2012) demonstrated that teacher support, general school connectedness, and additional components of school environment (i.e., happiness with school, feeling safe at school, feeling close to people at school) have an inverse relationship to negative emotional health and suicidal behavior. Finally, a recent review of connectedness and suicidal outcomes (Whitlock et al., 2014), which identified 10 studies focused on school context, revealed that school connectedness was largely associated with reduced STB. As noted by the researchers, however, two studies that used models accounting for multiple interactions and contexts did not indicate an inverse relationship between school connectedness and STB, suggesting more complex interactions may be at play.
+The protective role of school connectedness against STB has also been revealed across more vulnerable groups, such as American Indian youth with a history of sexual abuse (Pharris, Resnick, & Blum, 1997), sexual minority or lesbian, gay, bisexual, and transgendered (LGBT) youth (Duong & Bradshaw, 2014; Whitaker, Shapiro, & Shields, 2016), and students with other risk factors, such as residing in high-risk communities (e.g., Kaminski et al., 2010), experiencing physical or sexual abuse
+(e.g., Eisenberg, Ackard, & Resnick, 2007), having been investigated by child welfare (He, Fulginiti, & Finno-Velasquez, 2015), engaging in sexual activity (Stone, Luo, Lippy, & McIntosh, 2015), and experiencing bullying (ColeLewis, Gipson, Opperman, Arango, & King, 2016). Although these findings are based on diverse participant samples, they underscore the critical importance of enhancing school connectedness to protect against STB.
+Shedding light on how connectedness may protect against STB, Whitlock and colleagues (2014) proposed a model identifying three pathways linking connectedness to STB: (a) intrapersonal responses and processes, encompassing perceived rejection and isolation; (b) collective responsibility and action, supporting more avenues for risk identification; and (c) positive norms and expectations, reinforcing helpseeking behavior and identifies STB risk as problematic. A number of foundational theoretical frameworks support these mechanisms, including ecological systems theory (Bronfen-brenner, 1979) and attachment theory (Ainsworth, 1979).
+The Current Study
+Adolescents who feel connected to their family, peers, and schools are less likely to engage in health risk behaviors. Although the former forms of connectedness (i.e., family, peers, and communities) may have critical implications for preventing STB, they are largely beyond the control of the school. School connectedness, however, is an important protective factor against STB that does fall within the purview of school psychology. Thus, the primary aim of this meta-analytic investigation is to examine the association between school connectedness and STB.
+Although previous reviews have addressed the importance of adolescent connectedness in relation to STB (Whitlock et al., 2014) and additional reviews have explored school connectedness and school environment across a number of health outcomes (Kidger et al., 2012; Markham et al., 2010), the current study is the first to compare pooled effect sizes across studies specifically examining school connectedness in relation to STB. By reviewing findings from cross-sectional and longitudinal studies that examined adolescent (Grades 6-12) school con
+nectedness and STB, findings from this investigation will answer the following questions: (a) What is the strength of association between school connectedness and STB, and (b) How does the magnitude of association differ across varying subpopulations and measures of school connectedness? The primary hypothesis is that high levels of school connectedness will relate to reduced reports of STB across general, high-risk, and sexual minority samples. It is also hypothesized that effect sizes will remain consistent across differing categories of school connectedness.
+Method
+Literature Search
+The systematic search and retrieval process used a standardized review protocol based on Lipsey and Wilson’s (2001) meta-analysis guide and recommendations from Meta-Analy-sis Reporting Standards (APA Publications and Communications Board Working Group on Journal Article Reporting Standards, 2008). The study aimed to identify and retrieve all empirical studies that examined the relation between school connectedness and suicidal ideation or suicide attempts conducted at any time in any geographical location. We conducted a comprehensive search of PsycINFO, Academic Search Premier, and PubMed from June 15, 2016 to July 24, 2016 using the search terms school, connect*, and suicid* and searched for studies in Whitlock and colleagues’ (2014) review article. We conducted a thorough examination of titles, abstracts, and full articles to assess eligibility of studies. Studies were selected for meta-analysis based on the following eligibility criteria:
+1.	The study investigated the association between school connectedness and suicidal ideation (SI), suicide attempts (SA), or a combination of SI and SA, referred to as STB.
+2.	The measure of school connectedness was explicitly referred to as “school connectedness” or “connections at school” and included at least one of the four categories described previously (affiliation, belonging, attitudes, environment).
+3.	The study was published in English.
+4.	The sample included youth attending school in Grades 6-12.
+We excluded studies that did not directly examine the association between school connectedness and SI, SA, or STB or did not report sufficient data to calculate a measure of effect between the variables of interest (e.g., studies examining school connectedness as a mediating or moderating variable only).
+Data Extraction
+Two review authors independently extracted and coded data based on a predetermined standardized coding manual. We selected the following moderator variables of interest a priori to test the potential for methodological factors to influence heterogeneity of effect sizes:
+1.	Region of recruitment (U.S. vs. international);
+2.	Percent Caucasian/White;
+3.	Percent female;
+4.	Timeframe of STB (past 2 weeks, past 12 months, or lifetime); and
+5.	Categories of school connectedness (social affiliation, school belonging, attitude about importance of school, or supportive learning environment). For each study, a dichotomous (yes/no) code was applied for each category resulting in four moderator variables.
+To conduct sensitivity analyses examining effect sizes separately among subsamples, we also coded studies based on the population sampled. After accounting for eligibility criteria, two subsamples with a minimal number of studies to pool effect sizes emerged:
+1. Samples described as risky, including students involved in child welfare, reporting feeling isolated or involvement in bullying, residing in high-risk neighborhoods, and reporting being sexually active.
+2. Samples described as sexual minority or LGBT youth.
+We measured coder consistency for high inference variables (school connectedness categories) with Cohen’s kappa (affiliations, k = 1; belonging, k = .842; attitude, k = 1; environment, k = .842; Yeaton & Wortman, 1993).
+Disagreements were resolved through discussion until consensus was reached between coders.
+Statistical Method
+We conduced meta-analyses using Biostat’s Comprehensive Meta-analysis (www.meta-analysis.com; Borenstein, Hedges, Higgins, & Rothstein, 2015). A random effects model was selected a priori to account for sampling error and random effects variance (Lipsey & Wilson, 2001). The primary meta-analysis examined the average effect size of associations between school connectedness and any STB, including the mean of SI and SA in samples that included both outcomes or SI or SA in samples that included only one outcome. Two secondary meta-analyses examined associations between school connectedness and SI and SA separately. Sensitivity analyses also examined pooled effect sizes separately across studies investigating school connectedness and STB among high-risk and sexual minority youth. Finally, we examined qualitative findings from studies that reported associations between STB and school influences that were not identified as school connectedness but included overlapping measures with the four categories of school connectedness.
+We calculated effect sizes measuring school connectedness and STB from descriptive data, that is, rates of occurrences, means and standard deviations, and inferential statistics, that is, odds ratio (OR) and correlation coefficients. For missing raw data necessary to compute effect size, we made a request to researchers for more information; otherwise, we excluded studies with missing data for effect size computation (k = 1). We converted final results to OR for comparing the association between school connectedness and STB across studies.
+Because meta-analysis assumes that each measure of effect is representative of an independent study, we used a protocol to handle studies with more than one effect size and publications reporting on data from the same dataset. We calculated the average of effect sizes when studies reported findings separately across individual items of school connectedness. In the case of multiple publications reporting data from the same study, we prioritized the most recent publications and those that provided suf
+ficient data. When publications used overlapping data sets but reported effects from different subsamples, we selected the most inclusive sample for the primary analysis and analyzed findings for the subsamples of interest separately. When data were presented separately for subgroups (i.e., males and females) within an individual study, we conducted a meta-analysis to compute the combined effect size under a fixed effects model (Borenstein, Hedges, Higgins, & Rothstein, 2009). Finally, we selected effect sizes that reflected cross-sectional findings over longitudinal findings considering the majority of included studies were crosssectional.
+We analyzed homogeneity of effect size distribution with visual inspection of outliers and forest plots, as well as the Q statistic and 12 (95% confidence interval [CI]) index. Heterogeneity is signaled by a statistically significant Q (Lipsey & Wilson, 2001) and estimated by the I2 statistic, an index between 0% and 100% (Borenstein et al., 2009), which may be interpreted as low (I2 = 25%), moderate (I2 = 50%), or high (I2 = 75%; Higgins, Thompson, Deeks, & Altman, 2003). To measure level of publication bias, we used a combination of Egger’s regression index, the funnel plot, Duval and Tweedie’s trim and fill, and Rosenthal’s failsafe N.
+The study conducted statistical tests of 8 moderators (region of recruitment, percent Cau-casian/White, percent female, STB timeframe, and the school connectedness categories of affiliation, belonging, attitude, and environment) with weighted regression analysis (metaregression) and analog to analysis of variance (ANOVA) using a mixed effects model. We used ANOVA analog to investigate potential effect size differences across studies based on STB timeframe and region. We also conducted two multiple metaregression analyses: The first model included percent Caucasian/White and percent female as moderator variables and the second model included dichotomous variables (yes/no) representing the four categories of school connectedness (affiliation, belonging, attitude, and environment). We contacted authors for more information if specific items measuring school connectedness were not reported. Case analysis for studies with missing data for moderator variables was used.
+Results
+Search Results
+The study identified a total of 1,169 titles via the bibliographic databases PsycINFO, Academic Search Premier, and Pubmed (see supplementary Figure S1 for PRISMA-style flowchart). We reviewed 47 articles in full for eligibility, of which 23 were excluded from the quantitative synthesis. We maintained four of these studies in the qualitative synthesis because they did not identify the measure of interest as “school connectedness” but included items similar to school connectedness. A total of 20 publications and 17 samples met eligibility criteria of which 19 publications and 16 samples with sufficient data to calculate effect sizes were included in this study. Studies examined school connectedness in relation to SI (k = 12) and SA (k = 10), with a total of 16 samples examining any form of STB (see Table 1). For more information about school connectedness and STB measures see supplementary Table S1.
+Primary Analysis
+The primary analysis examined STB, including SI, SA, or a combination of SI and SA, across any sample. The analysis included a total of 16 samples, resulting in between 185,088191,156 participants. The range of participants represents average effect sizes taken from publications that used overlapping samples with varying numbers of participants. Taken together, the studies resulted in a statistically significant mean effect size of odds ratio (OR) = 0.536 (95% CI: 0.460, 0.624), p < .0001 and included effect sizes that ranged between OR = 0.215 to OR = 0.811 (see Figure 1). The heterogeneity of variance analysis was significant, Q(15) = 515.533, p < .0001, I2 = 97.090, signifying between-study variance. None of the moderator analyses, including multiple metaregression examining differences across school connectedness categories, were significant.
+Trim and fill analysis did not recommend the imputation of any studies to reduce bias (see supplementary Figure S2a). Egger’s regression was not significant and Rosenthal’s N indicated a minimum of 4,746 studies to lead to a p value at or above alpha of .05. These findings indicate minimal risk for publication bias.
+Secondary Analyses
+School connectedness and suicidal ideation. When meta-analysis was conducted separately with studies examining SI as an outcome, a total of 53,618 participants from 12 samples were included. The studies generated a statistically significant mean effect size of OR = 0.529 (95% CI: 0.433, 0.647), p < .001 (see Figure 2). The heterogeneity of variance analysis was significant, Q(11) = 297.882, p < .001, I2 = 96.307, indicating between-study variance. ANOVA analog comparing studies conducted in the US, OR = 0.618 (95% CI: 0.520, 0.734), k = 10, to those that were conducted internationally, OR = 0.226 (95% CI: 0.190, 0.269), k = 2, was significant, Q(1) = 64.339, p < .001; however, the small number of studies conducted internationally precludes drawing definitive conclusions about these differences. None of the additional moderator analyses were significant.
+Rosenthal’s N of 2,188 to lead to a p value at or above alpha of .05 and nonsignificant results from Egger’s regression supported minimal risk for publication bias. Trim and fill analysis recommended the imputation of one study resulting in a mean effect size of OR = 0.505 (95% CI: 0.409, 0.623) under the random effects model (see supplementary Figure S2b).
+School connectedness and suicide attempts. A total of 10 studies examined school connectedness and SA across any sample, including a total of 57,637 participants. The mean effect size of OR = 0.589 (95% CI: 0.493, 0.704), p < .0001 was statistically significant (see Figure 3). The heterogeneity of variance analysis was significant, Q(9) = 198.636, p < .0001, I2 = 95.469, indicating significant between-study variance. The multiple metaregression models were not conducted due to missing data and the presence of collinearity; none of the other moderator analyses were significant.
+Trim and fill analysis recommended the imputation of one study to reduce publication bias, resulting in a mean effect size of OR = 0.627 (95% CI: 0.525, 0.749) under the random effects model (see supplementary Figure S2c). Minimal risk for publication bias was indicated by a Rosenthal’s N of 1,827 to lead to ap value at or above alpha of .05 and nonsignificant results from Egger’s regression.
+Sensitivity Analyses
+High-risk youth. Five studies including between 9,707-10,179 participants examined school connectedness and any form of STB in high-risk samples (i.e., high risk communities, youth engaging in sexual contact, youth investigated by child welfare, and youth reporting perceived disconnectedness and/or bullying experiences). The mean effect size taken from high-risk samples remained significant, OR = 0.603 (95% CI: 0.480, 0.757), p < .0001 (see Figure 4). The heterogeneity of variance analysis was significant, Q(4) = 16.249, p = .003, I2 = 75.383, indicating significant between study variance. Note that moderator analyses were not conducted due to the small number of studies included in the analysis. Rosenthal’s N of 99 to lead to a p value at or above alpha of .05 and nonsignificant results from Egger’s regression indicated minimal risk for publication
+bias. Trim and fill analysis recommended the imputation of one study resulting in a similar mean effect size of OR = 0.634 (95% CI: 0.507, 0.792) under the random effects model (see supplementary Figure S2d).
+Sexual minority youth. The analysis pooling effect sizes across studies examining school connectedness and any form of STB within sexual minority samples included four studies with between 2,436-2,485 participants. The mean effect size was statistically significant, OR = 0.608 (95% CI: 0.509, 0.726), p < .0001 (see Figure 5). Heterogeneity of variance analysis indicated minimal between study variance, Q(3) = 3.897, p = .273, I2 = 23.015; therefore, moderator analyses were not conducted. Analyses examining publication bias indicated minimal bias across studies. Rosenthal’s N suggested 37 nonsignificant effect sizes would lead to ap value at or above alpha of .05 and Egger’s
+regression was not significant. Trim and fill analysis did not recommend the imputation of any studies (see supplementary Figure S2e).
+Additional School Influences of Suicidal
+Thoughts and Behavior
+We excluded a total of four studies from the quantitative analyses because they used similar measures of school connectedness but identified them as a different construct (e.g., school attachment, school engagement, school climate). In general, studies examining STB and constructs closely aligned to school connectedness demonstrated significant bivariate associations (Borowsky, Taliaferro, & McMorris, 2013; Carter, McGee, Taylor, & Williams, 2007; De Pedro, 2012; Pharris et al., 1997).
+Discussion
+Findings from the present study, which pooled effect sizes across 18 samples and included nearly 200,000 participants, clearly indicate that students reporting a connection to their schools are significantly less likely to report having suicidal thoughts or report making a suicide attempt. Results support the primary
+hypothesis that higher school connectedness would relate to reduced reports of STB across general (OR = 0.536), high-risk (OR = 0.603), and sexual minority (OR = 0.608) adolescents. This association was consistent across general adolescent samples when analyzed separately for suicidal ideation (OR = 0.529) and suicide attempts (OR = 0.589). This stability across a diversity of samples, as well as the finding that among general samples these associations remained consistent after accounting for variations across ethnic and racial representation and region, underscores the importance of enhancing school connectedness for all students. These findings synthesize a large and fragmented body of literature that has identified school connectedness as an important protective factor against STB during adolescence.
+The nonsignificant results from the moderator analyses support the second hypothesis, that effect size variability would remain stable across four categories of school connectedness (social affiliation, belonging, attitude, and environment). In other words, the association between school connectedness and STB demonstrated comparable magnitudes across studies using a variety of measures of school connect
+edness. Although preliminary, results suggest that a wide variety of measures of school connectedness may be used to support the identification of youth at-risk for STB, contributing to ongoing discussions about the best measurement of school connectedness.
+Limitations
+Although meta-analysis has a number of methodological strengths, particularly for pooling weighted estimates of effects to achieve greater power than individual studies, there are also important limitations to this analysis. Metaanalysis is frequently limited by reduced power for moderator variable detection (Hedges & Pigott, 2004); therefore, the finding that effect size variability did not differ based on the moderators of interest may be a result of limited variability as opposed to consistent findings across studies. Indeed, given the significant heterogeneity between effect sizes indicated by the large I2 statistics, a primary limitation of the present study is that the contributors to variation across effects remain unclear.
+A related limitation of meta-analysis pertains to the influence of study methodology on variability of effect sizes. Although moderator analysis did not support heterogeneity of effect sizes due to region of recruitment, measurement of school connectedness, and timeframe of STB, these represent only a sample of the potential differences across study methodology. For example, variability could be due to participant characteristics (age and grade of students), school characteristics (e.g., private vs. public, size, school climate, etc.), or community characteristics.
+Meta-analysis is also limited by the potential for publication bias, where null effects may not be adequately represented due to the “file drawer” effect. In addition to including dissertations, we used a number of methods to mea
+sure publication bias (e.g., trim and fill analysis, etc.) supporting minimal publication bias within the present study. In an effort to further examine the potential for publication bias, we also calculated effect sizes from the publically available New York City (NYC) YRBS dataset (NYC Department of Health and Mental Hygiene, 2007; 2009) and compared them to the effect sizes we calculated from Seil and colleagues (2014) peer-reviewed article. The mean effect sizes were comparable, reinforcing the study’s statistical findings of minimal publication bias (see supplementary Table S2).
+Results from the present study were also limited by its cross-sectional nature. Although the findings presented here do not allow for temporal inference, it is noteworthy to highlight that effect sizes calculated from the longitudinal analyses part of this study did reflect that school connectedness predicted reduced risk for STB across time, ranging between OR = 0.380 to OR = 0.774 (Kidd et al., 2006; Kidger et al., 2015; Russell & Toomey, 2013).
+A final limitation concerning the present study involves its focus on bivariate analyses. Although a portion of the included studies also analyzed school connectedness as a protective factor against STB accounting for additional covariates, only direct effect sizes pertaining to school connectedness and STB were analyzed. Studies that consider multiple contexts in addition to school connectedness have revealed mixed findings depending on the additional variables examined in the model. In general, however, when multiple forms of connectedness are accounted for, parent and family connectedness appear to be the most salient of the connectedness protective factors against STB, whereas school connectedness is often cited as a powerful secondary protective factor for STB (e.g., Borowsky et al., 2013; Eisenberg et al., 2007). Thus, even after accounting for addi
+tional critical factors associated with STB, school connectedness has shown a positive influence on STB in school aged youth. Considering how well suited schools are for providing prevention efforts at a population level, school connectedness remains a critically important protective factor of STB.
+Implications for Research
+Although preliminary research supports that school connectedness is associated with reduced reports of suicidal ideation and attempts between 1 and 2 years later (e.g., Kidger et al., 2012; McNeely & Falci, 2004), the long-term consequences of school connectedness as a protective factor against suicidal outcomes are less certain than the cross-sectional findings described here. Further research investigating school connectedness as a predictor of STB over time will help elucidate a temporal relationship with STB. Longitudinal research should also identify whether or not there is a critical period of time during development when enhancing school connectedness may be the most effective for preventing suicide.
+Another important avenue of research that remains relatively unexplored is school connectedness within clinical populations, such as youth hospitalized for STB. To date, the only program designed to support school reintegration following hospitalization for STB is Bridge for Resilient Youth in Transitions (White, Langman, & Henderson, 2006). Its intensive model provides ongoing academic and social support following hospitalization, most likely contributing to enhanced feelings of school connectedness. Future research examining school connectedness in clinical populations will be important for the development of school transition programs designed to bolster school connectedness.
+Finally, future research addressing the lack of evidence-based preventions and interventions for improving school connectedness and preventing suicide will support practical steps toward applying theoretical and empirical findings to practice. Although a recent review of the literature identified four programs that demonstrated improvements in school connectedness to reduce risktaking behavior (Chapman, Buckley, Sheehan, & Shochet, 2013), none of the interventions examined STB as an outcome. The vast majority of
+interventions also required systematic schoolwide changes, supporting the need for an increased understanding of the efficacy of simpler intervention designs (Chapman et al., 2013). Future interventions should capitalize on the rich literature base examining school connectedness and STB to inform the most salient inquiries and evaluations should be based on meaningful (i.e., behavioral) outcomes.
+Implications for Practice
+School suicide prevention programs have a long history of promoting a “culture of connectedness” in order to effectively identify youth considering suicide (Lieberman, Poland, & Cowan, 2006, p. 12; Miller, 2011). By fostering trusting relationships between adults and students, students are more willing to break promises or secrecy and seek help when they or their peers experience suicidal thoughts or behaviors (Lieberman, Poland, & Kornfeld, 2014). Although there is a dearth of evidence-based school suicide prevention programs, interventions effective in preventing adult suicide have also targeted enhanced social connectedness and belonging (Miller, 2011). Thus, school psychologists should promote school connectedness not only as a method for intervention, but also as a way to lay the groundwork for suicide prevention efforts that rely on a culture of connectedness (Centers for Disease Control and Prevention, 2012; Lieberman et al., 2014.
+According to the Wingspread Declaration, to foster school connectedness schools should maintain high and supportive academic expectations, fairly apply just disciplinary policies, build trusting school relationships, staff skilled teachers, support high expectations from family, and ensure that students feel connected to at least one adult in the school (Blum & Libbey, 2004). Because of their expertise in assessment and intervention and their collaborative role in the school, school psychologists are well suited to support increased school connectedness. At the whole school level, school psychologists can work closely with administrators and the school problem-solving team to promote activities supporting student and adult interpersonal interactions. Collaborative efforts can also provide opportunities for student ownership over school policies and school facilities (Waters, Cross, & Runions, 2009; Waters, Cross, &
+Shaw, 2010); for example, by way of student government and clubs. As consultants to faculty and staff, school psychologists can foster a collaborative teaching environment and enhanced faculty-student relationships by encouraging faculty and staff to participate in activities outside of the classroom (e.g., collaborating across disciplines, standing in the hallways in between class periods).
+Particular care should also be taken to identify and support students most at risk to suicide, including those who feel disconnected from school and who may be less likely to engage in school activities. In addition to educating students, faculty, and staff about suicide warning signs, schools should consider supplementing existing school-wide surveys with a simple measure of school connectedness (i.e., items from the Add Health Survey; Resnick et al., 1997) to identify high-risk youth. Once high-risk students are identified, the school psychologist or another designated staff member may decide a more thorough suicide risk assessment is warranted. When conducting these assessments, it is important that the practitioner maintain a connection to the student by being empathic, supportive, and respectful (Lieberman et al., 2014).
+Depending on the nature of the student’s risk, school psychologists may implement a targeted intervention designed to increase school connectedness or they may refer the student for outside services. For example, school psychologists may consider interventions like Check and Connect, a program that is used to increase school engagement by using systematic monitoring by way of an assigned mentor (Alvarez & Anderson-Ketchmark, 2010), as well as schoolbased mentoring programs, which have shown potential for improving student connections, reduced absenteeism, and disciplinary referrals (Gordon, Downey, & Bangert, 2013). Even in more extreme cases that may require outside services, school psychologists should continue to support student connectedness given that both quality and accessibility of adult relationships are critical factors in preventing adolescent suicide (Seeley, Rohde, & Jones, 2010).
+Conclusion
+Results from the present study indicate that students reporting a connection to school are less likely to report having suicidal thoughts or
+report making a suicide attempt. Although there are other important protective factors associated with STB, prevention and intervention efforts aimed at bolstering school related influences of STB remain critically important because schools serve the vast majority of youth. Therefore, findings from the present study support recent calls to increase school connectedness across schools worldwide (Blum & Libbey, 2004; Murray & Pianta, 2007). Because findings were stable across multiple categories of school connectedness, schools administering school connectedness assessments to aid with suicide prevention efforts should be encouraged to select the simplest and most accessible instrument. Future research focused on developing and evaluating interventions that target school connectedness to prevent STB will fill a significant gap in the literature.
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+Coronary heart disease (CHD) and depression are leading causes of disability in high-income countries, and are expected to become so globally by 2030 [1, 2]. There are three extensively replicated epidemiological observations regarding CHD and depression. First, these conditions are highly comorbid [3]. Second, depression is associated with increased risk of incident CHD [4, 5] and vice versa [6, 7]. Third, depression is a strong predictor of poor prognosis in people with CHD [4, 8]. However, there are also key unanswered questions particularly regarding potential mechanisms underlying this comorbidity. It is unclear whether the association between CHD and depression arises from largely shared genetic or environmental factors. Additionally, while risk factors for CHD are associated with depression in young [9] and older adults [10, 11], it is unclear whether these associations are causal. It is possible that the two illnesses are underpinned by one (or more)
+G. M. Khandaker et al.
+shared pathophysiologic mechanism, which manifests as distinct conditions in different organs (i.e., brain and heart).
+It was first reported over 50 years ago that around 40% of patients report depression after acute myocardial infarction [12]. Features of mild depression are present in up to two-thirds of patients after acute myocardial infarction [13], while severe depression is found in around 15% of CHD patients [14]. This association cannot be explained simply as a reaction to emotional trauma from a potentially lifethreatening illness. It is clear that the relationship between CHD and depression has bidirectional aspects. Psychological factors including depression were strongly associated with myocardial infarction in the large, multinational, casecontrol INTERHEART study [15]. Meta-analyses of longitudinal studies confirm that depression is associated with incident CHD after controlling for lifestyle and other factors [4, 5]. Risk factors for CHD such as hyperlipidaemia, hypertension, diabetes and inflammatory markers are associated with risk of depression [9, 10]. These findings indicate that CHD and depression may have shared mechanisms.
+However, common illnesses such as depression and CHD tend to cluster at population and individual levels [16], so to what extent this comorbidity is attributable to shared environmental or shared genetic factors is an outstanding question. Residual confounding could be an alternative explanation for previously reported associations between CHD risk factors and depression. Mendelian randomization is an epidemiological approach that uses genetic variants as instrumental variables to untangle the problems of reverse causation (as genetic variants are fixed at conception, hence genetically-predicted levels of risk factors must precede any event) and unmeasured confounding (as genetic variants are often specific in their associations with risk factors) [17]. If genetically-predicted values of a risk factor are associated with a disease outcome, then it is likely the association between the risk factor and outcome has a causal basis [18]. Even if measurement of the genetic variants or the exposure is made after depression diagnosis, genetic variants predispose individuals to lifelong changes in exposure levels, hence the exposure still precedes the outcome. Discovering common causal risk factors is clinically important as they could inform strategies for primary and secondary prevention. They could also inform further research into shared mechanisms for these comorbid conditions.
+We have used UK Biobank, a large population-based cohort study in the United Kingdom, to examine links between probable lifetime major depression (moderate/severe) and risk of CHD. Our investigation consisted of three components. First, we assessed whether family history of heart disease was a predictor of depression. Second, we assessed whether genetic predisposition to CHD risk was a predictor of depression. Third, we performed Mendelian randomization
+analyses for CHD and various CHD risk factors to determine whether any of these was a causal risk factor for depression.
+Subjects and methods
+Data sources
+The UK Biobank cohort comprises around 500,000 participants aged 40-69 years at baseline, recruited between 2006 and 2010 in 22 assessment centres throughout the United Kingdom, and followed up for a variety of health conditions from their recruitment date until February 17, 2016 or their date of death [19]. Informed consent was obtained from all participants. The full dataset includes genome-wide genotyping of baseline samples from all participants, results of clinical examinations, assays of biological samples, detailed information on self-reported health behaviour, and is supplemented by linkage with electronic health records such as hospital inpatient data, mortality data and cancer registries. UK Biobank ethical approval is provided by the UK Biobank research ethics committee and Human Tissue Authority research tissue bank. An independent Ethics and Governance Council oversees adherence to the Ethics and Governance Framework and provides advice on the interests of research participants and the general public in relation to UK Biobank. The current study was approved by UK Biobank (ref no. 26999).
+We restricted our attention to 367,703 unrelated participants of European ancestry that passed various quality control tests. European ancestry was defined using self-reported ethnicity and genomic principal components as described previously [20]. An initial probable European subset was defined using genomic principal components. Genetic variants were then dropped from the investigation if they had low call rate (3 standard deviations away from the mean) or failed Hardy-Weinberg equilibrium (p-value< 10-6 for common variants with minor allele frequency > 0.01, p-value < 10-12 for rare variants with minor allele frequency < 0.01). Individuals were dropped from the investigation if they had low call rate or excess heterozygosity (3 standard deviations away from the mean), or mismatch between genetic and reported sex. Principal components were then recalculated on the European ancestry subset, and a further principal component threshold was applied to exclude nonEuropeans. Finally, we removed related individuals so that only one person in each family (defined as third-degree relatives or closer) was included in the analysis.
+Outcome
+Our primary outcome was self-reported probable lifetime major depression, either moderate or severe, as previously used in UK Biobank [21].
+Probable moderate lifetime major depression was defined using four criteria: (1) answering yes to the question “Looking back over your life, have you ever had a time when you were feeling depressed or down for at least a whole week?” or “Have you ever had a time when you were uninterested in things or unable to enjoy the things you used to for at least a whole week?”, (2) answering 2 weeks or more to the question “How many weeks was the longest period when you were feeling depressed or down?”, (3) answering 2 or more to the question “How many periods have you had when you were feeling depressed or down for at least a whole week?”, (4) answering yes to the question “Have you ever seen a general practitioner for nerves, anxiety, tension or depression?”.
+Probable severe lifetime major depression was defined by the first three criteria and by answering yes to the question “Have you ever seen a psychiatrist for nerves, anxiety, tension or depression?”.
+We also considered moderate depression and severe depression separately as secondary outcomes.
+Family history of heart disease and depression
+Participants were asked about illnesses of their father and mother, and given a list of conditions to choose from. Family history of heart disease was defined as the participant selecting “heart disease” for either their father or mother. This variable was self-reported, and no validation was attempted.
+Genetic predisposition to coronary heart disease and depression
+Genetic risk scores for CHD were calculated using 1.7 million genetic variants and their associations with CHD measured in 60,801 cases and 123,504 controls as described previously [22]. This dataset did not contain any of the UK Biobank participants. This score has been shown to have similar predictive ability to a conventional cardiovascular risk factor score comprising smoking, diabetes, body mass index (BMI), and hypertension (C-index 0.623 for the genetic score versus 0.639 for the risk factor score).
+Mendelian randomization analyses
+Mendelian randomization analyses were conducted for (i) CHD risk; (ii) various conventional cardiovascular risk factors: BMI, waist-hip ratio (WHR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and lipids (low-density lipoprotein [LDL]-cholesterol, high-density lipoprotein [HDL]-cholesterol, and triglycerides); and (iii) inflammatory markers: interleukin-1 (IL-1), interleukin-6 (IL-6), fibrinogen, tumour necrosis factor (TNF) alpha,
+C-reactive protein (CRP), intercellular adhesion molecule 1 (ICAM1), and P-selectin.
+Selection of genetic variants for Mendelian randomization
+For CHD risk, we selected 55 genetic variants previously associated with CHD risk at a genome-wide level of significance in a large meta-analysis of the CARDIo-GRAMplusC4D consortium [23] (Supplementary Table 1). Genetic associations with CHD risk were estimated in up to 60,801 CAD cases and 123,504 controls, mostly (~90%) of European ancestry.
+For BMI, we selected 97 genetic variants previously associated with BMI at a genome-wide level of significance in a large meta-analysis of the Genetic Investigation of ANthropometric Traits (GIANT) consortium [24] (Supplementary Table 2). Genetic associations with BMI are estimated in up to 339,224 participants mostly (95%) of European ancestry. Associations with WHR were obtained for 48 genetic variants associated with WHR at a genomewide significance level from the same participants in the GIANT consortium [24] (Supplementary Table 3).
+For blood pressure, we selected 93 genetic variants previously associated with either SBP, DBP, or pulse pressure, at a genome-wide level of significance in a large meta-analysis of the UK Biobank CardioMetabolic Consortium BP working group [25] (Supplementary Table 4). Genetic associations with SBP and DBP are estimated in UK Biobank, in the same 367,703 unrelated participants of European ancestry as the genetic associations with depression.
+For lipids, we selected 185 genetic variants previously associated with either LDL cholesterol, HDL cholesterol, and triglycerides, at a genome-wide level of significance in a large meta-analysis of the Global Lipids Genetic Consortium [26] (Supplementary Table 5). Genetic associations with lipids are estimated in up to 188,577 individuals of European ancestry.
+For inflammatory markers, we selected genetic variants in the relevant coding gene region previously shown to be conditionally associated with the inflammatory biomarker and only moderately correlated (r2 < 0.6). For interleukin-1, we selected two variants (rs6743376 and rs1542176) in the IL1RN gene region. For interleukin-6, we selected three variants (rs7529229, rs4845371 and rs12740969) in the IL6R gene region [27]. For fibrinogen, we selected one variant (rs7439150) in the FGB gene region [28]. For TNF-alpha, we selected one variant (rs1800629) in the TNF gene region [29]. For CRP, we selected four variants (rs1205, rs3093077, rs1130864 and rs1800947) in the CRP gene region [30]. For ICAM1, we selected five variants (rs1799969, rs5498, rs1801714, rs281437 and rs11575074) in the ICAM1 gene region [31]. For P-selectin, we selected one variant (rs6136) in the SELP gene region [32]. Genetic
+associations with the biomarkers were obtained from the referenced papers and are listed in Supplementary Table 6.
+Statistical analyses
+Associations with the depression outcome (moderate/ severe, moderate only, or severe only) were assessed by logistic regression for family history of heart disease and for the CHD genetic risk score. Associations of genetic variants with the outcome were estimated by logistic regression with adjustment for the first 10 principal components of ancestry. Analyses were performed in all participants, and in men and women separately.
+For each cardiovascular risk factor and CHD, we performed Mendelian randomization using the inversevariance weighted method by weighted regression of the genetic associations with the outcome on the genetic associations with the risk factor [33]. We also performed the MR-Egger [34] and weighted median [35] methods to assess the robustness of findings.
+For lipids, we conducted analyses separately for each lipid fraction including all variants associated with that lipid fraction at a genome-wide level of significance (86 variants for HDL cholesterol, 76 variants for LDL cholesterol, 51 variants for triglycerides), as well as for all the lipid fractions in a single analysis model using multivariable Mendelian randomization [36] with all the 185 genetic variants.
+For the inflammatory markers, when there are multiple genetic variants, we report the inverse-variance weighted estimate with adjustment for correlation between the variants [33]. The correlation matrix was estimated using participants from the 1000 Genomes project of European ancestry. This method combines the genetic associations with the outcome into a weighted average association
+scaled by the genetic association with the biomarker measure. When there is a single genetic variant, we report the per allele genetic association with the outcome.
+Two-sided p-values are reported throughout, with correction for multiple testing in the Mendelian randomization analyses using a p-value of 0.05/15 = 0.003, given that 15 risk factors were included in the analysis. Statistical analyses were conducted using snptest version 2.5.2 or R version 3.3.2 (“Sincere Pumpkin Patch”). Code for analyses is available from the authors on request.
+Results
+Participant characteristics
+A summary of participant characteristics is presented in Table 1. Out of the 367,703 European unrelated participants, 14,701 (4.0%) had probable lifetime major depression (moderate/severe), of whom 8473 (2.3%) were classed as having moderate depression and 6228 (1.7%) as severe.
+Family history of heart disease and depression
+There was evidence for an association between family history of heart disease and depression (odds ratio [OR] for moderate/severe depression 1.20, 95% confidence interval [CI]: 1.16-1.24; moderate only OR 1.26, 95% CI: 1.21-1.32; severe only OR 1.15, 95% CI: 1.12-1.18; p <0.0001 for each outcome). Associations for moderate/ severe depression were similar when restricting the analysis to men (OR 1.22, 95% CI: 1.16-1.29) and women (OR 1.16, 95% CI: 1.11-1.21).
+Genetic predisposition to coronary heart disease and depression
+A 1 standard deviation increase in the CHD genetic risk score was associated with a 71% increase in CHD risk [22]. However, there was only weak evidence for an association between the CHD genetic risk score and depression, and the magnitude of the association was small: OR per 1 standard deviation increase in the genetic risk score for major depression (moderate/severe) 1.01, 95% CI: 1.00-1.03, p = 0.11; moderate only OR 1.03, 95% CI: 1.01-1.05, p = 0.014; severe only OR 0.99, 95% CI: 0.97-1.02, p = 0.66. Associations for moderate/severe depression were almost identical for men (OR 1.01, 95% CI: 0.99 or 1.04) and women (OR 1.01, 95% CI: 0.99, 1.04).
+Mendelian randomization analyses
+For CHD and the conventional cardiovascular risk factors (Table 2), only triglycerides showed any evidence for a causal effect on depression risk, with an odds ratio of 1.18 (95% CI: 1.09-1.27, p = 2 x 10-5) per 1 standard deviation increase in genetically-predicted triglycerides from the inverse-variance weighted method (Fig. 1a). Similar results were observed in the robust methods (Table 2) and for moderate and severe depression considered separately (Supplementary Tables 7 and 8).
+For the inflammatory biomarkers (Table 3), there was evidence for causal effects of IL-6 and CRP, with an odds ratio of 1.35 (95% CI 1.12-1.62; p = 0.0012) per unit increase in genetically-predicted values of log-transformed IL-6 (Fig. 1b), and 1.18 (95% CI: 1.07-1.29, p = 0.0009) per unit increase in genetically-predicted values of log-transformed CRP (Fig. 1c). The alleles associated with increased IL-6 levels are also associated with decreased CRP levels. This provides a discrepancy in the interpretation of results based on variants in the IL6R and CRP gene regions: variants in the CRP gene region associated with increased CRP levels were associated with increased risk of depression, whereas variants in the IL6R gene region associated with increased circulating IL-6 levels but decreased IL-6 activity and decreased CRP levels were associated with increased risk of depression. Further investigation is needed to understand this discrepancy, which could be linked to divergent effects of IL-6 classical and trans signalling on depression risk, and/or CRP-dependent vs CRP-independent effects of IL-6 on depression risk. Again, similar results were observed for moderate and severe depression considered separately (Supplementary Tables 9 and 10). All other results were compatible with the null, although there was nominal significance in the association of increased genetically-predicted IL-1 with increased risk of severe depression (p = 0.037).
+Discussion
+We performed several analyses to understand potential shared mechanisms between CHD and depression. Our analyses suggest family history of heart disease is strongly associated with probable lifetime major depression (moderate/severe) with a 20% relative increase in depression risk associated with reporting at least one parent dying of heart disease, but a genetic risk score that predicts CHD risk almost as well as
+conventional risk factors was not strongly associated with depression. This suggests that the comorbidity between the two conditions arises largely from shared environmental factors. Further investigation into cardiovascular risk factors using the Mendelian randomization paradigm elucidated potential biological pathways contributing to comorbidity. We provide evidence that, out of all cardiovascular risk factors, triglycerides and the inflammatory markers IL-6 and CRP are likely to be causally related to depression.
+Accumulating evidence supports an important role for low-grade systemic inflammation indepression [37]. Meta-analyses of cross-sectional studies confirm that concentrations of inflammatory markers, such as CRP, IL-6, TNF-alpha, IL-1p, are elevated in peripheral blood during an acute depressive episode [38, 39], then tend to subside after recovery but continue to be elevated in treatment resistant patients [38, 40, 41]. Population-based longitudinal studies including our own work have reported that higher concentrations of CRP or IL-6 in childhood or adult life are associated with increased risk of depression or persistent depressive symptoms subsequently at follow-up [9, 11, 42-44]. Furthermore, a genetic variant in the IL6R gene (Asp358Ala; rs2228145 A>C), which is known to dampen down inflammation by impairing IL-6R signalling [45], was previously reported to be protective for severe depression [46]. Depressive symptoms and IL-6 share common genetic predictors [47]. Findings from our Mendelian randomization analyses add to the existing literature by showing that reverse causality or residual confounding are unlikely explanations for previously reported associations between IL-6, CRP and depression. We provide evidence that inflammation, particularly the CRP and IL-6/IL-6R pathways, is causally involved in pathogenesis of depression.
+A previous MR study did not find evidence for a causal association between depression and CRP [48]. Statistical power could be a reason for the null findings (1128 cases of depression compared with 14,701 in the current analysis). Previous Mendelian randomization investigations have suggested that IL-6 is a causal determinant of CHD risk [27, 30], but this is not the case for CRP [49, 50]. Therefore, with regards to shared inflammatory mechanisms underpinning the comorbidity between depression and CHD it is likely that IL-6 is a key driver.
+Consistent with our findings, a recent Mendelian randomization study reported potential causal relationships with depression for LDL cholesterol and for triglycerides [51]. The finding is unlikely to be driven by central obesity, because BMI or WHR are not causally linked with depression. Systematic reviews of observational studies suggest that depression is associated with lower total cholesterol [52], but the evidence for LDL cholesterol is mixed [53]. A systematic
+review of triglyceride concentration in depression compared with controls is currently lacking. Inflammation leads to changes in lipid metabolism including decreased HDL cholesterol and increased triglycerides levels [54]. Antiinflammatory treatment that inhibits IL-6 also inhibits triglycerides [55]. Elevated IL-6 [9] and triglycerides [56] levels in childhood are associated with increased risk of depression in young adulthood. However, lowering of serum cholesterol in middle-aged subjects by diet, drugs, or both, leads to a decrease in CHD but an increase in deaths due to suicide or violence [57]. Mechanisms for this effect are not understood, but it has been proposed that low membrane cholesterol could affect serotonergic neurotransmission by decreasing the number of serotonin receptors [57]. Further work is needed to understand the relationships between lipid alterations and depression.
+How inflammation causes depression and CHD has been studied extensively; see reviews [58, 59]. In brief, direct effects of peripheral inflammation relevant for cardiovascular risk include the development of atherosclerotic lesions in the arterial tree, and effects on endothelial reactivity and myocardial function [59]. Peripheral inflammation also influences the central nervous system. Brain responses to inflammation involve neural systems for motivational and homoeostatic control and are expressed through depressed mood state and changes in autonomic cardiovascular regulation [60]. As for depression, extensive animal and human studies have demonstrated that peripheral immune activation leads to changes in mood and behaviour by increasing turnover of serotonin, oxidative stress, activation of the hypothalamic-pituitary-adrenal (HPA) axis, and by reducing synaptic plasticity [58].
+A strong association of depression with family history of heart disease, but not with a genetic risk score for CHD, suggests that the comorbidity between depression and CVD arises largely from shared environmental factors. Although we did not observe an association between the genetic risk score for CHD and depression, it is likely that there is some contribution from shared genetic predictors, as a genetic correlation between CHD and depression has previously been reported. However, the magnitude of this correlation
+was low [61, 62]. According to a large Swedish twin study, acute environmental factors play a large role in depression-CHD comorbidity in men, whereas in women chronic factors, which are in part genetic, are more important [62]. However, we did not observe any sex-difference in the observational associations of depression with CHD family history or genetic risk. Nearly two-thirds of depression cases in our study were female. The observational association between family history of CHD and depression may also be influenced by dynastic effects. For example, individuals with family history of heart disease will have been exposed to risk factors for heart disease (and potentially also for depression) through their family environment, i.e. confounding by shared environment.
+With regards to shared environmental factors, the depression-CHD comorbidity could be linked with early-life factors influencing inflammatory regulation, such as impaired foetal development or childhood maltreatment. This idea is consistent with the common-cause or foetal programming hypothesis by David Barker [63]. Low birth weight, a catchall marker of suboptimal foetal development, and childhood maltreatment are associated with increased levels of circulating inflammatory markers [64, 65], depression [66] and CHD [67] in adulthood.
+Limitations of the work include the quality of the depression outcome. A working group of the UK Biobank study used self-reported data to create variables for probable recurrent major depression (moderate/severe) named according to Diagnostic and Statistical Manual for Mental Disorders (DSM) terminology. We use the term probable lifetime major depression to describe the same variable as we could not be certain whether criteria for ‘recurrent’ were fulfilled. Nevertheless, based on these criteria the prevalence of probable lifetime major depression (moderate/severe) in our sample was 4.0%, which is unlikely to be an overestimate. Lifetime prevalence of major depression is around 10-20% according to general population studies [68]. Misclassification of cases as controls introduces a bias towards the null, so the associations between genetic variants and depression observed in our analysis are likely to be conservative, and may well under-estimate the true impact of the risk factors on disease risk. Our investigation focused on CHD and cardiovascular risk factors as causes of depression, but the reverse analysis could also be performed. As future work, the association between a genetic risk score for depression and CHD risk could be investigated.
+A better understanding of causal risk factors for depression could inform novel strategies for treatment and prevention. Lifestyle modifications targeting ‘inflammogenic’ risk factors such as physical inactivity, obesity, smoking and alcohol use could improve risks for both CHD and depression. Inflammation is associated with antidepressant resis
+tance [41, 69], so anti-inflammatory drugs may be helpful for patients with depression who show evidence of immune activation. Anti-cytokine drugs that inhibit IL-6 or TNF-alpha signalling improve depressive symptoms in patients with chronic inflammatory illness, independently of improvement in physical illness [70]. Randomized trials testing the efficacy of novel anti-inflammatory drugs in patients with depression are currently ongoing.
+In summary, we provide evidence that the comorbidity between depression and CHD largely arises from shared environmental factors. Certain risk factors for CHD, specifically IL-6, CRP and triglycerides, are likely to be causally linked with depression, so these could be targets for treatment and prevention of the illness.
+Shared mechanisms between coronary heart disease and depression: findings from a large UK general...
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+Introduction
+Recovery from mental illness has been defined as the “personal experience of the individual as he or she moves out of illness into health and wholeness”.1 It is a dynamic process characterized by movement toward conditions of hope, purpose, and wellness. Participation in a meaningful social life is a major goal for many persons in recovery,2 and therefore relationships among recovery, social support, and social activities are important areas of study.
+Social support is a key source of psychological health3 and has been identified as a specific aid to recovery.4,5 Corrigan et al.6 found that size of social networks as measured by number of friends was correlated to recovery, but that satisfaction with social support was not. However, in another study with a larger sample, Corrigan and Phelan7 found that recovery was related to both social network size and perception of network satisfaction. Other studies have examined social support and recovery from a more limited symptoms perspective. Pevalin and Goldberg8 followed a sample of 4,878 people to track episodes of mental illness over time and found that low social support increased the chances of an illness episode onset and decreased chances of recovery. Kendler et al.9 reported that social support predicted shorter time to recovery from symptoms for women with major depression. Similarly, Lara et al.10 found that social support predicted both depression severity and 6-month symptom recovery, and Johnson et al.11 found social support related to better 6-month recovery from depression but not mania among patients with bipolar disorders.
+Less understood than social support is the role of meaningful activities in promoting recovery. The term “meaningful activities” itself has not been clearly defined, but may be expressed as pursuits that allow a person to grow in connection, confidence, and contribution through development of skills, education, vocation, or relationships.12 Meaningful activities has been identified qualitatively as important to promoting recovery,12 but research evidence on the role that involvement in activities may play is limited. Mezzina et al.2 suggest that the role of social support in recovery is not merely that of building more social ties but investing social life with meaning, which may be assisted by participation in community activities. Thus, social support and activities may interact to promote recovery. Although not specifically research on recovery, prior studies have identified volunteering behavior as a correlate of lower depression, especially among older adults;13,14 volunteering is a form of meaningful activity.
+This study examined the roles of social support, social network size, and engagement in activities as they relate to recovery from serious mental illness. Specifically, the study examines whether involvement in activities has benefits beyond social networks and social support and whether activities interact significantly with support in their relationship to recovery. The relevance of this examination for clinical practice and for personal efforts at recovery pertains to the respective contributions of support and activities; for example, if greater involvement in activities is related to better recovery above effects of support, then encouragement of activities, such as setting behavioral goals for activities, may be advanced as a clinical treatment strategy.
+Methods
+Setting
+The study took place at Kaiser Permanente Northwest (KPNW), a non-profit prepaid, group model, integrated health plan serving about 480,000 members in northwest Oregon and southwest Washington State. The study took place as part of a larger longitudinal study funded by the National Institute of Mental Health, Study of Transitions and Recovery Strategies (STARS), that focused on recovery among individuals with serious mental illness. KPNW’s Institutional Review Board and Research Subjects Protection Office approved the study, and all study participants provided informed consent prior to participation.
+Participant identification, inclusion and exclusion criteria, and recruitment
+Study participants were identified using health plan membership and diagnostic records. Inclusion criteria included a diagnosis (for a minimum of 12 months) of schizophrenia, schizoaffective disorder, bipolar disorder, or affective psychosis; at least 12 prior months of health plan membership; age 16 years or older; and plans to stay in the local area for at least 12 months.
+Patients with dementia, mental retardation, or organic brain syndrome were excluded because such conditions interfere with ability to provide informed consent, participate in interviews, and complete questionnaires. Also excluded were those whose mental health clinician felt they were unable to participate. Based on power analyses for the larger study, the target was to recruit at least 170 participants.
+Potential participants (n = 1,827) were recruited through letters signed by the principal investigator (PI) and the member’s mental health clinician or, if no specialty mental health visits were found, primary care provider. The letter was first prepared by the PI and then sent to the clinician or provider for review. At this stage, 289 patients (15.8%) were screened out by clinicians, and 17 (0.9%) additional patients were excluded because clinicians did not return letters. Remaining potential participants were stratified by gender and diagnosis and sampled randomly within these groups to achieve roughly equal representations of men and women, and individuals with mood (bipolar disorder, affective psychosis) or schizophrenia spectrum (schizophrenia, schizoaffective disorder) disorders.
+Recruitment was conducted via letters followed by telephone calls. Attempts were made to contact 418 people before exceeding the enrollment target of 170. Of the 418 attempted contacts, 350 individuals were contacted, and of these, 201 individuals refused (n = 184) or agreed to participate but did not complete the baseline interview (n =17); 22 individuals were ineligible for the study. Overall, 44% (184) of the 418 people were enrolled, 46% of those who were eligible. Of these, three did not complete both baseline interviews and four were excluded because study staff determined that medical record diagnoses had been in error. Data from these seven individuals were not included in analyses, resulting in a final sample of 177. Limited information was available from eligible persons who did not participate, but they did not differ from participants in terms of age, sex, or diagnostic group.
+Participants
+Study participants included 92 women (52%) and 85 men (48%). The average age of participants at baseline was 48.8 (SD = 14.8) years, with a range from 16 to 84 years. The enrolled sample distributions for age, sex, and diagnosis did not differ from the study-eligible population of health plan members. About 75% of the sample had some post-high school education. About 94% were white. The total number of participants was 177, but missing data on items of interest reduced the sample to 153 for the current study.
+Data
+The STARS design employs mixed methods and is longitudinal and exploratory. Questionnaire data were linked to health plan records of diagnoses, service use, and clinician information. The study presented here is limited to quantitative information collected at baseline. Definitions of measures follow:
+Mental and physical health The SF-12 mental health composite (MCS) and physical health composite (PCS) scores were used, which assess general mental health and emotional functioning.15,16 A short version of the SF-36 health inventory,16 the SF-12 is a general measure of health status that is a reliable and valid measure of functioning among people with severe mental illnesses.17 The SF-12 reproduces the SF-36 physical and mental health summary scales with greater than 90% accuracy.16
+Satisfaction with clinicians Satisfaction with primary mental health clinician (psychiatrist, nurse practitioner, counselor, or primary care provider) was measured using the mean of three questions
+rated on 5-point scales from “very satisfied” to “very dissatisfied”. Questions were: “How satisfied are you with:” (a) “the personal interest and attention your (psychiatrist, nurse practitioner, counselor, primary care provider) gives you?” (b) “your (psychiatrist’s, nurse practitioner’s, counselor’s, primary care provider’s) competence, skill, and ability?”, and (c) “the amount of information and explanation your (psychiatrist, nurse practitioner, counselor, primary care provider) gives you?”.
+Recovery The recovery assessment scale (RAS)6 is an easy-to-complete measure developed for use with individuals with serious mental illnesses. It has good test-retest reliability (r=0.88) and high internal consistency (Cronbach’s alpha=0.93).
+Social support and social network The social network question was measured as number of friends and scored in four categories: none, 1-2, 3-5, or more than 5. Social support was measured as the reported support of family and friends in the last month (scored 1=poor, 2=moderate, and 3 = good). The exact wording was, “During the last 4 weeks, you have (check one): (1) been having good relationships with others and receiving support from family and friends; (2) been receiving only moderate support from family and friends; (3) had infrequent support from family and friends or only when absolutely necessary”; this direction was reverse scored so that a higher score meant better support. Both the social support and social network items were taken from the Wisconsin Quality of Life Questionnaire.18-20
+The authors obtained permission to use the Wisconsin Quality of Life Questionnaire, but encountered a problem when scoring the social support subscale. This scale cannot be scored for individuals who have no social support, thus truncating the range of support experiences. For this reason, the authors chose to use two items from the scale that address (1) actual support received, and (2) number of individuals in participants’ support networks. The authors do not have reliability or validity data for these items, although they are similar to other items used in the field and are components of the full subscale, which shows adequate reliability and validity.
+Activities Preliminary analyses were undertaken to identify the optimal way to measure activities. Activities were initially measured through a set of 25 items that asked respondents to indicate the extent to which they engaged in that activity over the last month, scored from 1 (not at all) to 5 (daily). Bivariate correlations were explored between each of the items and the RAS. One item, frequency of television watching, was unrelated to recovery and was dropped from further analysis. An exploratory factor analysis was conducted to examine how the remaining items grouped together; although a 5-factor solution was suggested, the conceptual interpretation of the factors was problematic, and the resulting alpha reliabilities were poor, so these factor scores were not used. A second strategy involved the qualitative grouping of items based on discussion among the research team, but the item groups again demonstrated poor reliability, and all of the groups correlated at a bivariate level with scores on the RAS at approximately the same level—that is, there was no advantage of one group over another. However, a simple mean of total activities across all 24 items resulted in an alpha reliability of 0.77; the research team dropped two additional items (playing cards and playing computer games) which increased alpha to 0.80. The final activities measure is the mean of the 22 retained items (Table 1).
+Other covariates Other variables included in analyses were age group (16-20, 21-30, 31-50, 5164, 65-74, 75, and over), sex, married (yes/no), diagnostic group (schizophrenia spectrum or mood disorder), socioeconomic group (scored 1 to 5 based on participant self-report of group membership as lower, working, middle, upper-middle, or upper class, which was used instead of reported income because they correlated with each other at 0.44 and income was missing in 14 extra cases), experience with negative medication side-effects (no, mild, or severe), and age at
+which the participant “first felt different.” The “age felt different” question is a proxy to early onset, which predicts delay in receiving treatment and less favorable prognosis.21,22 However, preliminary models indicated that the “age felt different” measure was not related to recovery, and would have resulted in the loss of nine additional cases due to missing data, and so it was dropped from further analysis.
+The side-effects variable was also included as a possible risk variable because of its association with discontinuation of treatment23 and with risk of social withdrawal resulting from symptoms of tardive dyskinesia.24 This variable also had missing data because not every patient was taking medications. Preliminary regression models indicated that the side-effects question was not related to recovery and did not change the significance of other variables, and so it was deleted from final models.
+Analyses
+After descriptive analyses, the activity variable was examined with other variables in ordinary least squares multiple regression models where the RAS was the dependent variable. A set of four models was examined. Model 1 contained demographic and risk variables only: age group, sex, married, diagnostic group, socioeconomic group, and counselor satisfaction. Model 2 included all variables from Model 1 plus the social network and social support questions. Model 3 added the total activity score. Model 4 added two interaction terms, namely, social support and total activity and social network and total activity.
+N =153
+Results
+The activity score correlated with the RAS at 0.47. Social support and social network correlated with the RAS at 0.44 and 0.36, respectively. A descriptive summary of the study variables is provided in Table 2. There were 39.9% of participants with schizophrenia or schizoaffective disorder and the remainder were diagnosed with a bipolar or mood disorder. Average provider satisfaction scores were high.
+Results of the regression models are shown in Table 3. Each model includes the unstandardized coefficient, the standard error, and the standardized beta (B). In all models that included them, social networks and social support were positively related to better recovery. Total activities was also related to better recovery. The interaction of social support and activities in the final model was also significant, but the interaction of activity and network size was not. Findings for each model are summarized as follows.
+Model 1 This model had adjusted R2=0.31 (F =9.56, df=8, 144, p<0.0001). Significant correlates of recovery were better MCS and PCS score.
+Model 2 The adjusted R2 increased to 0.39 (F =10.52, df=10, 142, p<0.0001). Significant variables were MCS, social network, and social support. The PCS variable was no longer significant.
+Model 3 The adjusted R2 increased to 0.41 (F =10.74, df=11, 141, p<0.0001). Significant variables were MCS, social network, social support, and total activities.
+Model 4 The adjusted R2 increased to 0.43 (F =9.78, df=13, 139, p<0.0001). Significant variables were MCS, social support, total activities, and the activities-social support interaction. The main
+effect for social networks was no longer significant. The negative sign for the interaction term indicates that the activities variable was relatively more important for people with less social support. The relative size of the standardized betas (B) in the table show that social support, activity, and the interaction had the strongest effects, with the interaction being strongest, and that these three variables were stronger than the MCS score.
+The significant interaction was examined graphically, as shown in Figure 1. The figure shows that the association between activities and recovery is positive for all levels of support, but that the slope is greatest for persons with lower support.
+Discussion
+Results are consistent with previous research7,8 showing that both social network size and social support are correlated to better recovery for persons with serious mental illness. Prior literature suggests that patients with mental illness report a number of benefits from social support, including emotional, material, and psychological benefits, modeling, motivational encouragement, and others.4 But in addition to social support and social networks, our results demonstrate that greater involvement in a wide range of activities is also related to better recovery, especially when levels of social support were lower. These activities may be more or less social in nature, more or less physically active, or occur inside or out of the home. The particular activities related to recovery may be highly individualized from one person to another. Choice of activity may even be a contributing factor in building a sense of control over one’s life. The data suggest that participation in a greater number of activities, regardless of the activity type, is associated with recovery.
+Beyond social networks, social support, and activities, the only other variable associated with recovery in the final model was mental health status measured by the MCS. Recovery was not related to age, sex, diagnostic group, socioeconomic status, or other indicators. Other research, however, has found that recovery over time is related to variables such as physical health and changes in employment and marital status.8 The current results are encouraging in that many of these potential factors are outside people’s ability to control; in contrast, it appears that recovery is potentially amenable to influence through social and behavioral factors.
+In particular, people have the potential to exercise control over the behaviors in which they engage. The results suggest that being involved or active, in any of a wide variety of individualized activities, is related to better recovery. Moreover, to the extent that people do not enjoy strong social support, participation in activities may be of even greater importance. Of course, these data are cross-sectional and so it is unclear if activities promote recovery or if recovery enables people to be more active. There may be effects in both directions.
+Regarding the interaction term, a hypothesis was tested that the combination of social support and activities would relate to higher recovery. This was found to be the case in that the association between activities and recovery was positive for all levels of support, however, rather than a simple cumulative effect, activities were particularly important when social support was low. Either activities or support may work to promote recovery when only one or the other is relatively high.
+In addition to the cross-sectional design, limitations of the study include the way that activities and social support were measured. The measurement of activity was limited to a count, and may not reflect how meaningful specific activities are. In addition, not all potential activities are included, and other forms of physical, social, or spiritual activity may aid in recovery25 but were not assessed here. Social networks and support were measured through only two items asking about network size and levels of support over the last month. The impact of this support and the positive or negative nature of support were not investigated, although the item that was used is suggestive of qualitative feelings about receiving support. The sample was limited to persons who received care through the KPNW health plan. Future analyses may assess changes in recovery over time as they relate to social support, social networks, and activities.
+Implications for Behavioral Health
+From a treatment perspective, the results suggest that clinicians may consider working to encourage social supports and engagement in activities as aids to recovery. Identifying meaningful activities and setting behavioral goals to realize these activities may be a useful strategy. Furthermore, patients may be encouraged that even if their levels of social support are low, finding personally meaningful activities to pursue is a viable approach to taking control of the recovery process.
+Acknowledgements
+This research was supported by a grant from the National Institute of Mental Health (Recoveries from Severe Mental Illness, R01 MH062321). The authors would like to thank interviewers Sue Leung, Alison Firemark, David Castleton, and Micah Yarborough. We also thank analysts Elizabeth Shuster and Michael Leo, who helped prepare the data used in these analyses. In addition, we thank Hannah Cross and Robert Paulson for providing helpful contributions to earlier drafts.
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+Routledge
+Taylor & Francis Group
+According to the Centers for Disease Control and Prevention, National Center for Injury Prevention and Control (2013), suicide is the second leading cause of death among youths aged 15-24 years. Lesbian, gay, bisexual, transgender, and questioning youth (LGBTQ) are more likely to attempt suicide than their non-LGBTQ peers. Empirical research with varying methodologies and geographical locations all demonstrate this unfortunate
+relationship (D’Augelli & Hershberger, 1993; Garofalo, Wolf, Kessel, Palfrey, & Durant, 1998; Lea, de Wit, & Reynolds, 2014; Lewis, 2009; Liu & Mustanski, 2012; Remafedi, Farrow, & Deisher, 1991; Shields, Whitaker, Glassman, Franks, & Howard, 2012; van Bergen, Bos, van Lisdonk, Keuzenkamp, & Sandfort, 2013; Ybarra, Mitchell, Kosciw, & Korchmaros, 2015). Marshal and colleagues (2011) completed a meta-analysis
+of 20 studies on sexual minority youth and found that compared to their heterosexual peers, LGBQ youth were three times more likely to present with suicidal thoughts and behaviors (STB). Research has also demonstrated that transgender youth are at higher risk for STB (Grossman, Park, & Russell, 2016; Liu & Mustanski, 2012; Veale, Watson, Peter, & Saewyc, 2017). However, there are no systematic reviews or meta-analyses that examine how factors vary in their association with STB among LGBTQ youth.
+Most broadly, suicide is thought of as either the act or intention to cause one’s own death. Suicide as a psychological construct, however, can be conceptualized, measured, and analyzed in a myriad of fashions. For example, there is evidence that the cognitive dimensions (i.e., suicidal ideation) and behavioral dimensions (i.e., suicide attempts) are distinct (Klonsky & May, 2014; Taliaferro & Muehlenkamp, 2014).
+Joiner’s (2005) interpersonal theory of suicide (IPTS) is a useful theoretical framework that helps differentiate between ideation and behavior. This theory posits that factors like perceived burdensomeness and thwarted belongingness contribute to thoughts of suicide. Thwarted belongingness is thought of as a lack of social interaction with others, a deficit of caring relationships, and a lack of social support (Joiner, 2005). Perceived burdensomeness emerges when one believes that they are so flawed that they are an encumbrance on important people in their life (Joiner, 2005). Alternatively, the capacity to act on suicidal thoughts is frequently attributable to divergent factors like exposure to painful events, access to lethal means, and fearlessness (Joiner, 2005; Van Orden et al., 2010). Klonsky and May (2014) suggest that exploring whether risk factors predict
+ideation, behavior, or both is vital to advancing our understanding of suicide.
+O’Connor and Kirtley (2018) propose one of the more comprehensive models of suicide. This integrated motivational-volitional model of suicidal behavior illustrates three relevant dimensions including the biopsychosocial context (i.e., pre-motivational phase), the emergence and maintenance of ideation (i.e., motivational phase), and the transition to suicidal behaviors (i.e., volitional phase). The pre-motivational phase is characterized by vulnerability factors and triggering negative events. Both the motivational and volitional phases are delineated into two sections: a higher-order level and a lower-order level. Key higher-order factors for both phases include feelings of defeat and humiliation, feelings of entrapment, and suicidal ideation. The authors suggest that lower-order factors like threat-to-self moderators (e.g., coping, memory biases, rumination processes), motivational moderators (e.g., belongingness, burdensomeness, resilience, social support), and volitional moderators (e.g., access to means, planning, impulsivity, fearlessness) influence higher-order factors and may, therefore, illuminate ideation to behavior pathways.
+The aforementioned complexities, among others, confound our understanding of correlates, predictors, moderators, and psychological mechanisms of suicide (Cash & Bridge, 2009). Franklin et al. (2017) completed a meta-analysis on 365 studies that examined risk factors predicting STB among a general adult population. Their synthesis suggested that risk factors’ ability to predict STB have not significantly improved over 50 years of research. Limitations of the extant literature on STB included rarely exploring multiple predictors, results demonstrating
+significant heterogeneity of effect sizes, and having a narrow scope of methodology (Franklin et al., 2017). These systematic approaches to synthesis offer useful direction for theory, future research, and the prevention of STB. For example, Franklin and colleagues (2017) demonstrated that there is a dearth of STB research concerning specific populations and developmental phases. A synthesis of the research on predictors of LGBTQ youth STB would be a novel contribution which augments the extant literature.
+The goal of this article is to build on our understanding of STB among LGBTQ youth by conducting a systematic review and meta-analyses. Specifically, the synthesis will illustrate strengths and weaknesses in the existing literature, establish how factors vary in their associations with STB, and offer useful insight for the prevention and intervention of STB among this vulnerable group. These aforementioned contributions are an important part of advancing the field in an effort to enhance the well-being of LGBTQ youth.
+MINORITY STRESS, STIGMA, AND UNIQUE
+DEVELOPMENTAL CHALLENGES
+The seminal minority stress theory (MST) posits that health disparities between marginalized groups, like LGBTQ youth, and their peers can largely be explained by social factors such as stigma, discrimination, and victimization (Meyer, 2003). Facing these stigmatizing stressors, in combination with typical daily stressors, leads to diminished psychological well-being. These social stressors influence well-being through psychological mechanisms—some that are unique to LGBTQ people, and others that are not unique to this
+population. Meyer’s (2003) conceptualization of stress among LGBTQ people as encompassing multiple spaces, both distal (i.e., external) and proximal (i.e., internal) is a helpful framework for differentiating psychological processes at play. Exposure to external stigma, for example, may lead to the development and maintenance of issues like internalized homophobia (i.e., negative social attitudes toward self). There are numerous other variables, like identity, personality traits, and community, which are associated with the psychological well-being of LGBTQ people (Meyer, 2003).
+There appear to be two main overarching methodological approaches to understanding the mental health of LGBTQ people. They are exploring within-group processes and between-group differences (i.e., minority and nonminority groups). Understanding psychological processes specific to LGBTQ people or between LGBTQ people would be a within-group approach, for example, researching LGBTQ identity development, stigma-related stressors, intersectionality, and the coming-out process. Meyer (2003) suggested that focusing on within-group differences could clarify processes pertinent to the population of interest. As such, the present review will synthesize within-group studies by focusing on variability of specific correlates and their impact on STB.
+Hatzenbuehler (2009) also developed a valuable theoretical framework for understanding the psychological well-being of groups like LGBTQ youth. He demonstrated that there were two distinct litera-tures—one that focused on group-specific processes (i.e., minority stress) (Meyer, 2003), and the other that explored general psychological processes/mechanisms that explain the development of psychopathology among all people (Diamond, 2003; Savin-Williams, 2001). He presented a
+psychological mediation framework which accounts for both group-specific processes and general psychological processes. This framework was centered on three hypothe-ses—that sexual minorities are exposed to increased distress due to factors like stigma, that this stigma-related stress contributes to the elevation of various processes (i.e., coping/emotional, social/ interpersonal, and cognitive) that translate to risk of psychopathology, and that the aforementioned processes are mediating the relation between stigma-related stress and the associated poor outcomes. In line with Hatzenbuehler’s work (2009), the present meta-analysis will also examine general psychological processes and how they relate to STB among LGBTQ youth.
+The aforementioned theoretical frameworks are essential to our understanding of LGBTQ youth well-being, but there are other complimentary paradigms. It is also important to consider ecological factors. Eco-social theories have suggested that social conditions influence risk for poor outcomes like STB and therefore a focus on only individual psychological processes may be lacking (Krieger, 2001). In an effort to offer the most comprehensive review, this synthesis will also examine factors like family, school climate, and community factors, and how they impact STB among LGBTQ youth.
+Minority stress is especially concerning for LGBTQ youth because adolescence and emerging adulthood are critical transitional phases when well-being may be tenuous (Arnett, 2000; Steinberg, 2008). LGBTQ youth are navigating the development of intimacy, identity, and sexuality all while struggling with unique factors like disclosing their identities (i.e., coming out), fear of victimization, deficient sexual health education, and lacking a sense of community or belonging. Yet,
+findings have illustrated that although LGBTQ status is a risk factor for STB, not all LGBTQ youth present with mental health issues, and the discrepancies are not well understood (Mustanski, Newcomb, & Garofalo, 2011; Robinson & Espelage, 2011; Savin-Williams, & Ream, 2003).
+SUICIDE AMONG LGBTQ YOUTH
+The literature on LGBTQ youth STB has been dichotomized into either positive or negative correlates of suicide for the conceptual and analytic organization of the present synthesis. This structure is a simplification and does not necessarily align with the best classification of factors related to STB. Ideally, we could have explored risk and protective factors in the traditional/longitudinal sense and classified factors based on the nature of their effect (i.e., mechanisms or moderators), but the extant literature on LGBTQ STB is mostly cross-sectional. As such, the literature is summarized in the exploration of positive (risk) and negative (protective) correlations with STB.
+POSITIVE CORRELATES OF SUICIDE
+The existing literature on positive correlates associated with LGBTQ youth STB offers a strong foundation and is still evolving. Factors commonly reported to relate to STB are various forms of victimization, stigma, other mental health issues, and risky behaviors, as well as ecological, interpersonal, and demographic factors (de Graaf, Sandfort, & ten Have, 2006; Hatzenbuehler, Birkett, Van Wagenen, & Meyer, 2014; Hershberger, Pilkington, & D’Augelli, 1997; Lea et al., 2014; Lytle, De Luca, & Blosnich, 2014; Mathy,
+Cochran, Olsen, & Mays, 2011; Mustanski, Andrews, Herrick, Stall, & Schnarrs, 2014; Mustanski, Garofalo, & Emerson, 2010; Paul et al., 2002; Remafedi et al., 1991; Robinson & Espelage, 2011; Russell, Ryan, Toomey, Diaz, & Sanchez, 2011; Russell & Toomey, 2012; Smith, Armelie, Boarts, Brazil, & Delahanty, 2016). Haas and colleagues (2010) completed a literature review on suicide risks associated with LGBT populations. This article offered helpful underpinnings for understanding risk but did not utilize quantitative data nor did it focus on younger populations. The present study will be a unique contribution to the extant literature because it utilizes quantitative and systematic methods to examine how factors unique to LGBTQ youth relate to STB.
+Peer Victimization
+Peer victimization, which is broadly defined as aggression by similarly aged peers, is one of the most studied correlates of STB among LGBTQ youth. Toomey and Russell (2016) completed a meta-ana-lysis on 18 studies which examined the relation between sexual orientation and peer victimization. They found that sexual minorities were moderately more likely to report peer victimization than their peers. Eisenberg, McMorris, Gower, and Chatterjee (2016) completed a crosssectional study on LGBQ youth from 505 schools and found that peer victimization predicted STB. Several other crosssectional studies, mostly conducted in the United States, have demonstrated that peer victimization is positively correlated with STB among LGBTQ youth (Blosnich & Bossarte, 2012; Duong & Bradshaw, 2014; Goodenow, Szalacha, & Westheimer, 2006; Hatchel, Merrin, &
+Espelage, 2019; Hershberger & D’Augelli, 1995; Mustanski, Andrews, Herrick, Stall, & Schnarrs, 2014; Poteat, Sinclair, DiGiovanni, Koenig, & Russell, 2013; Reisner, Biello, Perry, Gamarel, & Mimiaga, 2014; Robinson & Espelage, 2011; Shields et al., 2012; Whitaker, Shapiro, & Shields, 2016; Ybarra et al., 2015). Many of these studies also reported significant variability attributable to demographic and moderating factors. There is a clear paucity of longitudinal studies examining the aforementioned relations over time. Yet, studies have demonstrated that peer victimization can take on different forms which are theoretically more pertinent to LGBTQ youth.
+Bias-based victimization is a form of peer victimization rooted in sexism, homophobia, and transphobia. Examples include homophobic bullying and name calling. Given the stigmatizing nature of this biasbased victimization, it has garnered significant attention because it aligns well with MST (Meyer, 2003). MST suggests that this sort of victimization may be more deleterious to LGBTQ youth well-being than less stigmatizing forms of victimization. Similar to the general peer victimization literature, there are several cross-sectional studies demonstrating the moderate direct effect of bias-based peer victimization on STB (Baams, Grossman, & Russell, 2015; Hightow-Weidman et al., 2011; Lea et al., 2014; van Bergen et al., 2013; Whitaker et al., 2016). Liu and Mustanski (2012) completed a longitudinal study with 246 LGBTQ youth and found that bias-based victimization was a risk factor for future suicidal ideation. It is unclear if the extant literature on various forms of peer victimization are heterogeneous with regards to effect sizes associated with STB. The present synthesis will offer insight into how they may vary.
+Discrimination and Stigma
+Discrimination is conceptualized as the unjust or prejudicial treatment of people based on group membership and stigma speaks to the general disapproval of people based on group membership. These constructs align well with MST and are broader than peer victimization and bias-based aggression. One school-based, cross-sectional study on 1,032 LGBTQ adolescents found that perceived discrimination predicted suicidal ideation (Almeida, Johnson, Corliss, Molnar, & Azrael, 2009). Another cross-sectional study of LGBQ young adults reported that perceived stigma predicted STB (Lea, de Wit, & Reynolds, 2014). Other cross-sectional studies found similar results (Blosnich & Bossarte, 2012; Thoma & Huebner, 2013). In sum, in line with MST, discrimination and stigma are associated with STB among LGBTQ youth.
+Sexual Victimization and Intimate Partner Violence
+There are other forms of victimization, such as sexual victimization and intimate partner violence, that have been explored in the LGBTQ youth STB literature. Examples of sexual victimization include sexual assault, sexual abuse, and sexual harassment victimization. A cross-sectional study on 137 gay and bisexual male youth found that participants who reported a history of sexual abuse were more likely to present with suicidal behaviors (Remafedi et al., 1991). Intimate partner violence is thought of as aggression and victimization which occurs within the context of a romantic relationship. Mustanski and colleagues (2013) completed a study with sexual-minority male youth and found that both sexual victimization and intimate
+partner violence predicted suicidal behavior. Blosnich and Bossarte (2012) also found that both sexual assault and intimate partner violence were associated with STB.
+Mental Health
+Depression is one of the best predictors of suicidal ideation among adults and adolescents alike (Brent, Baugher, Bridge, Chen, & Chiappetta, 1999; Cash & Bridge, 2009; Franklin et al., 2017). The relationship is also found for STB among a wide array of studies on LGBTQ youth (Baams et al., 2015; D’Augelli, 2002; D’Augelli & Hershberger, 1993; Denny et al., 2016; Hershberger et al., 1997; Lytle et al., 2014; Mustanski et al., 2014; Mustanski & Liu, 2012; Silenzio, Pena, Duberstein, Cerel, & Knox, 2007; Smith et al., 2016; Thoma & Huebner, 2013; van Heeringen & Vincke, 2000; Whitaker et al., 2016; Ybarra et al., 2015). This is particularly precarious because LGBTQ youth are more likely to present with depressive symptoms than their non-LGBTQ peers (Kosciw, Greytak, Giga, Villenas, & Danischewski, 2016).
+STB are also associated with other mental health indicators among LGBTQ youth. For example, studies have found the following issues predicted STB: anxiety, obsessive compulsiveness, paranoid ideation, psychosis, somatization, general distress, impulsivity, hopelessness, and oppositional defiant and conduct disorder symptoms (D’Augelli, 2002; D’Augelli & Hershberger, 1993; Hershberger et al., 1997; Liu & Mustanskii, 2012; Mustanski & Liu, 2013; Poteat et al., 2013; Remafedi et al., 1991). These studies illustrate the sprawling and somewhat inscrutable nature of correlates between STB and other mental health issues among LGBTQ youth.
+Internalized phobia—a key aspect of MST—has been examined in only a few studies (Meyer, 2003). Gibbs and Goldbach (2015) completed an analysis of a public dataset with 2,945 LGBTQ young adults. They demonstrated that internalized homophobia mediated the relation between various predictors and STB. A cross-sectional study on LGBQ youth demonstrated that internalized homophobia significantly predicted suicidal thoughts (Lea et al., 2014).
+Ecological and Interpersonal Factors
+Ecological and systems frameworks, or appreciating the context in which youth are developing, is another important dimension to understanding STB (Bronfenbrenner, 1994). One cross-sectional study demonstrated that conflict within and among family members predicted suicidal ideation among gay and lesbian youth (Blosnich & Bossarte, 2012). Other studies have shown that parental rejection of LGBTQ youth’s identities and religious upbringing predicted STB (Gibbs & Goldbach, 2015; Ryan, Huebner, Diaz, & Sanchez, 2009; van Bergen et al., 2013). Parental rejection can be so severe that LGBTQ youth are no longer allowed to live in their homes. A cross-sectional dataset demonstrated that being homeless was associated with suicidal ideation (Hatchel et al., 2019). Concerning schoollevel factors, a few studies have established that unsupportive school climates were associated with STB (Hatzenbuehler et al., 2014; Hatzenbuehler & Keyes, 2013; Whitaker et al., 2016). There is a paucity of research on community-level factors. Poon and Saewyc (2009) completed a cross-sectional study with 6,905 LGBQ adolescents and found that living in rural areas predicted STB. Lastly, a cross
+sectional study with 404 LGBQ youth found that exposure to suicide in one’s environment was correlated with suicidal behavior (van Heeringen & Vincke, 2000).
+In line with the IPTS (Joiner, 2005), a number of studies have examined interpersonal factors and how they relate to STB. Baams et al. (2015) reported that both perceived burdensomeness and thwarted belongingness predicted suicidal ideation. Coming out, sharing one’s identity with others, is a universal part of being an LGBTQ youth. Since being LGBTQ is associated with stigma and discrimination (Meyer, 2003), this process can be distressing. One study found that coming out-related distress was also positively correlated with suicidal ideation (Baams et al., 2015). D’Augelli (2002) found that LGBQ youth who reported a general interpersonal sensitivity also reported more STB. Next, gender nonconformity, often measured as thoughts and behavior not socially perceived as typical for one’s sex assigned at birth, has been found to predict STB among LGBTQ youth (Liu & Mustanski, 2012; Remafedi et al., 1991).
+Behavioral Factors
+There are a number of behavioral factors like drug use, sexual risk, and selfharm which are positively correlated with STB. Several studies with varying samples, mostly with cross-sectional designs, have demonstrated that drug use predicted STB (D’Augelli & Hershberger, 1993; Hershberger et al., 1997; Mustanski et al., 2014; Poteat et al., 2013; Reisner et al., 2014; Remafedi et al., 1991; Silenzio et al., 2007; Whitaker et al., 2016). Drug use is related to chronic experiences of minority stress (Kosciw et al., 2016), association with deviant peers (Huebner, Thoma, & Neilands, 2015), and
+unhealthy coping mechanisms (Goldbach & Gibbs, 2015)—all factors that align well with frameworks like MST (Meyer, 2003). Non-suicidal self-harm behaviors were found to be strongly associated with suicidal behaviors among LGBQ youth (Reisner et al., 2014; Tsypes, Lane, Paul, & Whitlock, 2016). Perhaps one of the best predictors of suicidal behavior among all people is the history of suicide attempts (Bostwick, Pabbati, Geske, & McKean, 2016). Yet, only one study quantitatively examined the relationship between suicidal attempt history and future suicidal ideation (Liu & Mustanski, 2012). They found that previous attempts were associated with suicidal thoughts. Engaging in sexually risky behaviors is also associated with STB among LGBTQ youth (Hershberger et al., 1997; Mustanski et al., 2014; Poteat et al., 2013).
+Demographic Factors
+LGBTQ youth are not a uniform group. They vary in their sexual and gender identities as well as other intersecting identities. The broad theoretical and empirical literature on intersectionality and LGBTQ people is largely mixed. Some have found that multiple minority statuses can increase the risk for poor outcomes (Kosciw et al., 2016). Others have suggested that intersectionality can be conducive to resiliency for LGBTQ youth (Singh, 2013). The quantitative literature on intersectionality and STB among LGBTQ is also mixed. Some studies have found that identifying as female via selfreport measures was associated with an increased risk of STB (Bostwick et al., 2014; Mustanski et al., 2010; van Heeringen & Vincke, 2000; Whitaker et al., 2016). In contrast, others found that identifying as female was not significantly
+predictive of STB (Liu & Mustanski, 2012; Lytle et al., 2014). The literature comparing LGBTQ youth of color to their White peers largely demonstrates that having multiple minority identities is positively correlated with STB (Bostwick et al., 2014; Lytle et al., 2014; Mustanski et al., 2010; Shadick et al., 2015). There is a dearth of within-group studies on LGBTQ youth and STB. For example, most of the existing literature only compared males to females but neglected to measure and/or include transgender youth. This gap may be attributable to the rigid nature of public datasets used by many scholars.
+NEGATIVE CORRELATES OF SUICIDE
+The discourse on protective factors and STB has grown significantly following Savin-Williams’s (2001) call for attention to LGBTQ youth resiliency. Yet, this area is still significantly underdeveloped when compared to the examination of positive correlates. Supportive school climates and a sense of belonging have been found to attenuate STB among LGBTQ youth (Poteat et al., 2013; Reisner et al., 2014). Moreover, LGBTQ youth who report being supported by family and peers also appear to be more resilient to STB (Bouris et al., 2010; Mustanski & Liu, 2013; Needham & Austin, 2010; van Heeringen, Vincke, 2000). Other factors that are negatively correlated with STB include being older (Hershberger et al., 1997; Livingston et al., 2015; Lytle et al., 2014; Mustanski & Liu, 2013), being open with one’s LGBTQ identity (Baams et al., 2015; D’Augelli & Hershberger, 1993; Hershberger et al., 1997; Livingston et al., 2015), high self-esteem (D’Augelli & Hershberger, 1993; Hershberger et al., 1997; Hershberger & D’Augelli, 1995;
+Ybarra et al., 2015), having an adaptive personality (Livingston et al., 2015), selfcompassion (Hatchel et al., 2019), and identifying as bisexual (Mustanski et al., 2010). It is apparent that more work needs to be done in this area, especially when it comes to protective factors that are easily cultivated by clinicians, policymakers, families, and school staff.
+IMPLICATIONS FOR UNDERSTANDING CORRELATES OF SUICIDE AMONG LGBTQ YOUTH
+Countless clinicians and mental health professionals support youth who may be at risk for STB. This is a difficult task as many available interventions have not been promising when studied and compared to control groups (Brown & Jager-Hyman, 2014; Glenn, Franklin, & Nock, 2015; Ward-Ciesielski & Linehan, 2014). It is not unusual for interventions to be developed based on clinical experiences and other easily translatable methods, as opposed to a large body of empirical data. However, a few meta-analyses and reviews have synthesized the extant literature on interventions targeting suicidal behaviors and self-harm among adolescents. Ougrinm, Tranah, Stahl, Moran, and Asarnow (2015) completed a meta-analysis of 19 randomized clinical trials with non-pharmacological interventions and found that the interventions were modest in reducing self-harm. Hawton et al. (2015) found similar results on self-harm by synthesizing 11 clinical trials. In sum, it appears that interventions may be somewhat, albeit modestly, efficacious at reducing self-harm among adolescents, but there is a dearth of literature demonstrating the efficacy of interventions targeting STB among adolescents.
+Having a taxonomy for identifying and treating STB is a useful clinical tool. There are resources that have been developed to estimate risk of STB based on minimal or qualitative data (Beck, Brown, & Steer, 1989; Chronis-Tuscano et al., 2010). Many programs designed to diminish STB offer such lists of risk factors (AAS, 2015; CDC 2015; NIMH, 2015; WHO, 2015). These resources are both helpful and lacking. There is significant variability across resources and they often are not developed to target particular groups like LGBTQ youth, but instead are designed to be generalizable. The cited clinical tools are not precise and are extensive lists of potential risk factors that suggest nearly any person who presents with mental health issues, serious trauma or distress, chronic illness, and/or marginalized identities may be at risk for STB. The heterogeneity of factors, lack of consistency across agencies, and disregard for certain groups makes the tools difficult to translate into practice. There is room for new and innovative approaches to supporting clinicians with tools that are empirically robust and translatable to the diverse clients that they serve. The present study offers a foundation for developing interventions specific to LGBTQ youth by establishing the most pronounced correlates of STB.
+THE CURRENT STUDY
+The primary aim of the present synthesis was to explore the degree that correlates vary in their relationship with STB among LGBTQ youth. These findings will offer helpful foundations for the development of clinical tools and demonstrate shortcomings as well as strengths of the existing research.
+METHODS
+Eligibility Criteria
+The purpose of this synthesis was to examine the relation between STB and various risk and protective factors for youth who identified as LGBTQ. “Youth” was operationalized as having a mean age between 13-24 years and an upper bound not exceeding 25 years. The larger range was incorporated because much of the available LGBTQ research conceptualized youth in this fashion due to sampling and ethical limitations (Mustanski, 2011). Retrospective studies were included as long as the participants reported on their youth. Similarly, longitudinal studies were incorporated if the first time point occurred during the acceptable age range. Although most of the studies found examined only lesbian, gay, bisexual, and questioning (LGBQ) youth, transgender youth were included in a few reports and therefore remain within the scope of this synthesis. All forms of sexuality—identity, attraction, and behavior—were acceptable measurements. Studies that only reported group disparities between LGBTQ and non-LGBTQ youth were not included unless they offered data specific to LGBTQ youth. Some studies only offered a small proportion of LGBTQ youth and, as such, only data concerning the subsample were included (not the whole sample with largely non-LGBTQ-identified youth). Studies needed to examine the relations quantitatively and offer sufficient statistical information for the calculation of an effect size demonstrating the strength of correlation. Published studies and unpublished data were included (i.e., scholars in the field were contacted electronically and asked for additional quantitative information and/or any unpublished data).
+Finally, studies were included if they were published between 1990 and 2017 and written in English.
+Studies that measured at least one correlate with STB among LGBTQ youth were included. STB included ideation, plans, and behaviors associated with ending one’s life. Self-injurious behavior was not an acceptable proxy for suicide. Correlates could be measured in innumerable fashions including cross-sectionally, longitudinally, and/or in models that analyzed mediation/moderation effects.
+Search Strategy
+A number of databases were searched to identify studies, including PsychINFO, EBSCO, and PubMed. Terms were searched within search titles, abstracts, and subject lines. Examples included gay, lesbian, bisexual, sexual minority or transgender and youth, adolescence, teenager, emerging adults and suicidality, suicidal ideation, suicidal behavior or suicide. In addition to these approaches, citation lists of articles that met criteria were explored and other cross-checking methods were utilized such as contacting authors for add-itional/unpublished data (i.e., Google Scholar, scholars in the field).
+Screening and Coding
+Screening and Coding Procedures. Citations found through the search procedures were screened by the first author and added to an Excel spreadsheet. The included studies were then retrieved and screened a second time to ensure their eligibility. From the eligible and included studies, research assistants and the authors extracted simple data such as study and sample-level characteristics. The first
+author extracted and coded other features in consultation with coauthors and senior colleagues well versed in meta-analyses. Disagreements on coding were resolved via discussion between coders.
+Study and sample-level descriptors. Basic information for all studies was extracted and included in the meta-ana-lysis. Study-level factors included authors, year of publication, title, source, sampling strategy and characteristics, measures of suicide, and methodology. Sample-level factors involved sample size, participant age, and distribution of LGBTQ status.
+STB factors. Data concerning STB were extracted and coded. Studies varied in their approach as some measured suicidal ideation, suicidal behavior, and/or both. All available data were extracted and coded (i.e., some studies measured both ideation and behavior, but did not offer correlations with all other measured constructs for both factors).
+Positive (risk) and negative (protective) correlates of STB effect sizes. All relationships of interest were coded in an r effect size metric or converted to an r effect size metric. Much of the available data could be conceptualized and incorporated into risk or protective categories. However, studies coded their own measures in a variety of manners such that risk correlates were expressed as a negative value (i.e., - -r) or protective correlates were expressed as a positive value (i.e., r). For example, some studies measured STB such that higher scores indicated less STB whereas others coded their measures inversely. To offer improved consistency and interpretability, some values were inversely coded so that all “risk” correlates were positive values and “protective” correlates were negative values. Some studies offered effect sizes comparing groups based
+on subsamples via demographics such as age, sexuality, and gender, and these were also included. The measurement of sexuality was coded, as was the measurement of gender. At least one product moment correlation coefficient was taken from each study that demonstrated the relation between positive (risk) or negative (protective) correlates and LGBTQ youth STB. If the correlation coefficients were not available, they were calculated based on reported statistical information (Lipsey & Wilson, 2001). If more than one effect size was offered by a study, it was extracted and included in the analysis.
+To facilitate synthesis and interpretability, some factors were aggregated if they could be conceptualized as being very similar constructs, whereas others were not aggregated, to offer important insight for research and intervention. Theory and empirical findings were used to develop the broad categorization of correlates. The heuristic utilized reduced some precision offered by the existing research, but it enabled the synthesis of otherwise scattered and inscrutable findings. This process resulted in generating 24 correlates of STB that offered sufficient information for a meta-analysis.
+There was no need to modify depression because all of the included studies either measured depressive symptoms or depressive disorders directly. Although measures of other mood disorders and symptoms emerged (i.e., anxiety), they were not included in this category because they are often conceptualized as distinct disorders/symptoms.
+Drug and alcohol use were aggregated because the aforementioned literature often combines them in measurement and conceptualizes them in similar fashion, and the relationship with STB tends to be congruent. Any measurement of drug use,
+regardless of form, past or present, was classified broadly as drug use.
+Three aspects of family dynamics emerged in the studies that met inclusion criteria—family conflict, parental rejection, and parental support. One study measured conflict in the home, which was associated with strain or distress and was conceptualized broadly as family conflict. Other studies measured, more precisely and theoretically anchored, if parents rejected youth due to their LGBTQ identity (i.e., parental rejection). Third, parental support was an aggregation of general parental support and monitoring.
+Gender was another common factor analyzed by included studies. However, the data could only be included if they were binary in nature—comparing two groups due to the r metric. All of the studies that offered sufficient data demonstrated differences based only on comparing self-reported males and females, as opposed to comparing to self-identified transgender youth. As such, only one category for gender emerged.
+General distress was a category comprised of measures that asked youth about their level of distress broadly, that is, not specific to any disorder and/or stigma.
+Internalized homophobia was explicitly measured by two studies and is a distinct construct as per MST.
+Intimate partner violence was indicated if there was victimization or abuse (i.e., verbal, physical, or sexual) within the context of a romantic relationship.
+MST and the extant literature posit that stigma-related stressors are essential to understanding LGBTQ youth mental health and, therefore, bias-based victimization was categorized as distinct from general peer victimization. If a measure indicated that a youth was victimized due to perceived or actual LGBTQ group
+membership, then it was classified as LGBTQ victimization. All other forms of general victimization, whether measured as peer victimization or bullying, were classified as peer victimization.
+Externalizing behaviors and oppositional-defiant and conduct disorders were aggregated, given their similarity.
+Age, similar to gender, was coded and included if data were binary. Although ranges varied, effect sizes were included if they compared across ages such that a coefficient could be generated comparing younger to older LGBTQ youth.
+Outness/openness was a category comprised of whether youth had explicitly shared their LGBTQ identity with others as well as their general openness with their identity. For example, some studies asked youth if they had come out to family or peers and other studies asked youth how likely they perceived others to know they were LGBTQ based on factors like gender expression.
+Stigma and discrimination emerged as a broader category than the various specified forms of victimization. A number of studies measured whether youth felt stigmatized or discriminated against, generally, as opposed to within a certain setting or by a specific group like family or peers.
+Race/ethnicity was another category that was included. Given the constraints of the analyses, data were coded in a binary fashion such that all non-White youth were classified as people of color. This enabled the comparison of White youth to their peers of color.
+Location was only examined by one study which compared urban areas to rural areas.
+Three different school-level factors were developed. Factors were categorized as school belonging if they specifically measured youth’s perceptions of feelings of
+connection/belongingness. Supportive school climate was composed of broader measurements such as the inclusion of policies and resources specific to LGBTQ youth. Unsupportive school climates were comprised of factors indicating that their institution did not incorporate inclusive policies and resources.
+Self-esteem was not modified as all of the effect sizes measured the construct directly. The same is true for self-harm.
+Sexual risk is an aggregate of a variety of measures which can be conceptualized as risky behavior. Examples include reporting several sexual encounters, not using contraception or safe practices, and engaging in sexual behavior while under the influences of drugs or alcohol. Many measures of sexual risk broadly include all of these items, but some of the synthesized effect sizes were composed of only one item.
+Sexual victimization was composed of sexual assault, rape, and sexual abuse. Measures did not examine perpetrators and therefore it was unclear if these factors would be theoretically different. Similar to drug use, their association with STB tends to be congruent.
+All of the other extracted effect sizes that could not be aggregated fell within the purview of the systemic review since there were not enough data to complete a meta-analysis.
+Meta-Analytic Statistical Procedures
+For each correlation synthesis, we estimated a random-effects meta-analysis that accounted for the dependent effect sizes using robust variance estimation via the R package robumeta (Fisher & Tipton, 2015; Polanin, Hennessy, & TannerSmith, 2017). This advanced meta-analytic practice enables the inclusion and synthesis of all estimated effect sizes (Tanner-Smith,
+Tipton, & Polanin, 2016). The robust estimation approach models all of the effects sizes, which eliminates the constraint of averaging or using only one effect size per study. If only two effect sizes were available, then an aggregation of the effects (i.e., average of effect sizes) was completed because the package requires at least three effect sizes for estimation.
+Effect size heterogeneity was also estimated. I2 was calculated as an estimate which enumerates the heterogeneity beyond sample differences (Higgins & Thompson, 2003). Values between 50-70% suggest enough heterogeneity to warrant moderation analyses. In an effort to control for erroneous findings while explaining heterogeneity, three moderating variables of interest were chosen a priori and based on available data—the measurement of suicide (i.e., behavior, ideation, or both), gender (i.e., male, female, combined, or transgender), and sexuality (i.e., lesbian or gay, bisexual, combined, or questioning). These factors were chosen since the extant literature on gender and sexuality tends to be mixed and suicide theory suggests that ideation and behavior are distinct.
+Publication bias was another part of our analysis. Unfortunately, there is no approach to analyzing bias that can handle the robust nested effect sizes. Therefore, the average effects sizes within each study were calculated such that there is a studylevel effect size. Because there is no clearly superior method to exploring publication bias, two methods were employed. The Duval and Tweedie (2000) trim and fill analysis was utilized, which indicates the potential number of missing studies given the asymmetry of a funnel plot. Next, effect sizes that include the potentially missing data are imputed. Possible publication bias is indicated by a large number of missing studies and/or a large disparity
+between estimated and imputed effect sizes. Egger’s regression test was also used (Egger, Smith, Schneider, & Minder, 1997). This method regresses standardized effect sizes on study precision. Publication bias is found if the intercept is significant. The R package metafor was utilized to complete these analyses (Viechtbauer, 2010).
+RESULTS
+Descriptive and Predictor Analysis
+The search procedures yielded 313 potentially eligible articles, but screening for eligibility by the first author resulted in 44 unique and included studies (Figure 1).
+The aggregation of the samples resulted in the synthesis of 671,943 participants. Table 1 delineates the 44 reports and includes authors, sample characteristics, sampling strategies, methodology, and effect sizes. Publication years ranged from 1991-2017 (Figure 2). Sample sizes varied greatly, from 68 to 122,180 (M = 14,149, Mdn = 2,949, SD = 25,969), and were often only partially composed of LGBTQ youth (only LGBTQ youth k = 20). Two-hundred and thirty-four correlation coefficient effect sizes were calculated and categorized. A total of 24 meta-analyses were completed.
+Given the number of factors and effect sizes modeled, a general overview is offered. See Table 2 for initial models and
+Table 3 for syntheses of other effect sizes (i.e., when fewer than 3 effect sizes were available). Table 4 offers a ranking of the top positive and negative correlates of STB among LGBTQ youth. Positive correlates varied substantially in their effect sizes. Perceived burdensomeness, self-harm, and sexual risk emerged as having moderatestrong magnitude. With regard to negative correlates, self-compassion and self-esteem also had moderate-strong effect sizes. However, the correlates also varied considerably in the number of effect sizes available to calculate a meta-analytic mean.
+Moderator Analyses
+Given the high degree of heterogeneity found among some correlates, follow-up meta-regression analyses were conducted if the random effects parameters were estimated by at least ten effect sizes. The a priori variables of interest were included in different models due to the number of available effect sizes. For the factors that
+demonstrated heterogeneity, gender was not available to test as a moderator. Table 5 illustrates the various models.
+LGBTQ Victimization
+LGBTQ victimization random effects were estimated from 11 effect sizes and there appeared to be a significant amount of heterogeneity (I2 = 67%). The metaregression suggested that there were no significant differences between suicidal ideation and behavior (b = .01, SE = .03, t = 0.37, p = ns). However, mean differences were present between suicidal ideation (M = .18, SE = .02, t = 9.59, p < .05) and suicidal behavior (M = .20, SE = .03, t= 7.08, p < .01). Similar results were found for the measurement of sexuality, such that there was no significant relationship between lesbian/gay and bisexual youth (b = —.07, SE = .04, t =—1.48, p = ns) and between lesbian/gay and a combined measurement	(b =—.05,
+SE = .05, t =—1.11, p = ns). Mean
+differences were present among lesbian/gay (M = .23, SE = .04, t = 5.88, p = ns), bisexual (M = .17, SE = .02, t = 10.03, p < .05), and combined (M = .18, SE = .03, t = 7.52,p < .01).
+Supportive School Climate
+Supportive School Climate random effects were estimated from 13 effect sizes and a significant amount of heterogeneity was indicated (I2 = 56%). The analyses indicated that there were not significant differences between suicidal ideation and behavior (b = .08, SE = .05, t = 1.47, p = ns). Mean differences emerged between suicidal ideation (M =—.12,
+SE = .03, t = —3.70, p < .01) and suicidal behavior (M =—.05, SE = .04, t =—1.16, p = ns). There were no significant differences between lesbian/gay and a combined measurement of sexuality (b = .04, SE = .03, t = 1.29, p = ns). There were mean differences between lesbian/gay (M =-.14, SE = .01, t = -NA, p = ns) and combined (M =—.11, SE = .03, t = —3.52, p < .01). There were no data available to examine differences between lesbian/gay and bisexual.
+Publication Bias
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+1.	Introduction
+Some sociological and psychological theories postulated a prominent role of social variables in suicide (Stanley et al., 2016). Firstly, Émile Durkheim speculated that suicide is inversely correlated with social integration, considered as a protective factor (Durkheim, 1897). According to the more recent interpersonal theory of suicide by Thomas E. Joiner, the lack of feeling of belongingness is one of the main risk factors associated with suicide (Joiner, 2005; Van Orden et al., 2010). Particularly, the construct of Thwarted Belongingness, which includes
+self-reported loneliness, living alone, fewer friends, non-intact family, social withdrawal, and family conflict, is one of the core concepts of his theory. Together with Perceived Burdensomeness (i.e., the perception to represent a burden for others), Thwarted Belongingness might induce suicidal ideation. According to Joiner, Thwarted Belongingness and Perceived Burdensomeness constitute the most proximal mental states preceding suicidal ideation, while other factors, such as childhood maltreatment and psychiatric disorders, are relatively more distal in the causal chain of suicide risk factors (Van Orden et al., 2010). The concomitant presence of the Acquired Capability for suicide (due to the
+repeated exposures to painful and provocative events that decrease the fear of death and increase physical pain tolerance) contributes to triggering lethal suicide attempts.
+Cohen and Wills, in a pioneering study, compared two different theories in which social support has either a general, positive effect on health and well-being (main or direct-effect model), or protects individuals from stressful life events (stress-buffering model) (Cohen and Wills, 1985). They found that both models are correct. In the first case, social support corresponds to the degree of social integration of the individual, while, in the second case, social support is related to social resources linked to the needs elicited by stressful events. Hence, in the context of suicide, social support could act as the main direct protective factor and as a protective factor in the presence of adversities. This suggests that risk factors are more likely to be associated with bad outcomes, such as suicide, among individuals with poor social support.
+Social factors (e.g., being single, divorced, or widowed, social isolation, loneliness, alienation, loss of connectedness, and lack/loss of social support) have been repeatedly reported as risk factors for death desire, suicidal thoughts and behaviors among adolescents (King and Merchant, 2008), older adults (Draper, 2014; Minayo and Cavalcante, 2015; O'Connell et al., 2004; van Wijngaarden et al., 2014; Yi and Hwang, 2015), and psychiatric patients (Pompili et al., 2007).
+In agreement, two recent meta-analyses have reported the protective role of social support in depression (Gariepy et al., 2016; Rueger et al., 2016).
+However, to our knowledge, no previous recent (in the last decades) review focused on the link between social isolation, considering all its related constructs, and suicidal thoughts and behaviors, although its modulatory role has been largely established (see this first review on the topic (Trout, 1980)). The aim of this review was to provide a narrative overview on this association, focusing on all main relevant social constructs.
+2.	Methods
+A literature search was independently performed by RC and CF to identify studies on social isolation and suicide. Articles published until April 13, 2018, were retrieved from the PubMed database using broad search terms (living alone OR social isolation OR loneliness OR social alienation) AND (suicid* OR self-harm OR self harm). Any form of suicidal outcome was considered: suicidal ideation (SI), suicidal planning (SP), non-suicidal self-injury (NSSI), deliberate self-harm (DSH), suicide attempt (SA), and suicide. The reference lists of the selected studies and reviews were also checked to identify additional relevant articles.
+Studies were included if: (1) they investigated any form of social isolation or loneliness; (2) they focused on any form of suicidal outcome; (3) they were: (a) systematic reviews, meta-analyses, and narrative reviews, (b) original observational studies, or (c) qualitative studies; (4) they were written in English. Studies were excluded if: their main focus was social support only (e.g., parent support or peer support) without a measure of social isolation/loneliness; they focused on suicidal patients only (e.g., Ferrada-Noli et al., 1995; Haw and Hawton, 2011; Hawton et al., 1996); concerning original observational studies, they had a sample < 500.
+This is not a systematic review but a narrative one. First, (a) it summarized the findings described in the selected systematic reviews, meta-analyses, and narrative reviews. Second, (b) it reviewed the results of original observational studies with large samples (N > 500) that have not been included in reviews/meta-analyses. For example, studies on Joiner's interpersonal theory of suicide were not included because they were discussed in a recent meta-analysis (Chu et al., 2017). Third, (c) it included also qualitative studies.
+Concerning original observational studies, we adopted the criteria of large samples (N > 500) to avoid spurious findings due to small sample size. If studies were performed on the same sample, only the
+most recent one was selected in the case of similar analyses (e.g., between Schinka et al. (2013) and Jones et al. (2011), only Schinka et al. (2013) was retained), or both in the case of different types of analysis (e.g., Brunstein Klomek et al., 2016; Kahn et al., 2015).
+From each selected original observational study, RC and CF independently extracted: the study design, follow-up duration, targeted population, sample size, sex, age, ethnicity, main psychometric scales, suicidal outcomes, social isolation/loneliness outcomes, main results, association metric of social isolation-related results, and presence/ab-sence of the association (Table 1).
+3.	Results
+In the following paragraphs we will present the main literature evidences on the key social constructs: marital status and living alone, social isolation, loneliness, alienation, and belongingness. In each section, we will first define the construct and present the findings of the main reviews and meta-analyses published on its association with suicidal outcomes. Then, we will describe the results of the original observational studies according to the suicidal outcomes (suicidal ideation versus suicidal behaviors) and the different life periods (childhood and adolescence, adulthood and older adulthood).
+We retained 40 observational studies (see Table 1). Most of them concerned adolescents and/or young adults (k = 23, 57.5%), four focused on adults (10%), four on older adults (10%), three investigated the general population (7.5%), one included men who have sex with men (2.5%), two studied prisoners (5%), one was on adults with Human Immunodeficiency Virus (HIV) (2.5%), one on psychiatric patients involuntarily admitted to hospital (2.5%), and one on adults with substance use disorders (2.5%). Only 4 studies (10%) included only men while the majority (k = 24, 60%) have a sex-balanced sample. So we were not able to separately consider men and women.
+3.1.	Marital status and living alone
+Social isolation and lack or poor social support can be assessed in different ways. Social isolation is often measured using objective quantifiable variables, such as socio-demographic data: marital status (being married/widow or cohabitation), living alone, unemployment, frequency of social relationships, or participation to the community life.
+Marital status is frequently considered as a proxy for social support. A first meta-analysis of 54 case-control studies considered different proxies for social relationships (Crawford et al., 2010): marital status (k = 37), living alone (k = 22) and also employment (k = 29). Most of these studies (85.2%) included different age groups. The odds ratios for these three suicide risk factors were correlated with their prevalence among controls, and negative correlations were reported for living alone and unemployment. Moreover, the impact of living alone and unemployment appeared to be heightened when they were less prevalent in the population. This result could be linked to the perception of being different from the majority. However, when studies focused only on older adults (k = 6) and when young people (k = 5) were excluded, the negative correlation with living alone was no more present.
+Seven observational studies focused on marital status/living alone and suicidal outcomes.
+Suicidal Ideation: A study on 4,675 Asian university students found that living without parents was a predictor of SI, but not of SA (Peltzer et al., 2017). Among European older adults from the Study of Health, Ageing and Retirement in Europe (SHARE) cohort, being widowed was associated with SI (Saias et al., 2012). However, data collected from patients involuntarily admitted to hospital have shown the non-predictive role of living alone on SI. Conversely, being unemployed (and probably having less social contacts than employed people) was predictive of SI (Giacco and Priebe, 2016).
+Suicidal Behaviors: Divorced and separated subjects experience higher suicide risk, especially men (Kposowa, 2000). In the context of
+the large Quebec Health Survey, living alone and having no friends were associated with both SI and SA (Stravynski and Boyer, 2001). Among older adults, being unmarried and living alone is a SA predictor (Wiktorsson et al., 2010). Finally, among adults with substance use disorders, living alone and a low level of perceived social support are SA predictors (You et al., 2011).
+3.2.	Social isolation
+The level of social isolation of a person, defined as a state in which interpersonal contacts and relationships are quantitatively disrupted or non-existent (de Jong Gierveld and Havens, 2004), should be assessed by considering the number of individuals with whom this person interacts in a given period, the frequency of social interactions, the number of qualitatively different types of relationships the person has, and the degree of intimacy involved in his/her interactions (Trout, 1980).
+Six observational studies focused on social isolation and suicidal outcomes.
+Suicidal Ideation: Among White and American Indian/Alaska Native adolescents, the sensation of not being socially accepted and the perception of not being part of the school were positively associated with SI (Zamora-Kapoor et al., 2016). Adolescents feeling socially isolated were twice as likely to report SI then those feeling socially accepted. Similarly, among American adolescent girls, being socially isolated from their peers was a risk factor for SI (Bearman and Moody, 2004). Moreover, in both sexes having a dense social network was a protective factor for SI (girls) and SA (males). However, among youths (between 14 and 20 years), friendship problems (social isolation and poor quality friendships) were not linked to SI and SA (Winterrowd et al., 2011). Within the same population, the lack of family support was associated with SI and SA among Mexican-American girls. Finally, among Chinese adults, social isolation did not have any direct effect on SI. Social isolation was only weakly associated with SI in a path model that included depression and self-esteem (Zhang and Jin, 1998).
+Suicidal Behaviors: In contrast with the already mentioned study that found no association between friendship problems and SA (Winterrowd et al., 2011), social isolation was a predictor of SA in adolescent boys and girls (Hall-Lande et al., 2007). Simultaneously, high levels of family connectedness, school connectedness and academic achievement were protective factors against SA. Finally, lack of friends was found to be a suicide predictor in a large Swedish men cohort (Allebeck et al., 1988).
+3.2.1.	Correctional settings
+The specific condition of physical and social isolation of life in prison intensifies suicidal risk. In fact, suicidal behaviors are frequent in this context. In a review focused on suicide prevention in jails and prisons, Pompili and colleagues highlighted that being in isolation or segregation cells is a risk factor for suicide, while contacts with family and inmates might represent a protective factor (Pompili et al., 2009). Similarly, in a more recent systematic review on risk and protective factors related to near-lethal SA among prisoners, social isolation and low social support were included among the risk factors (Marzano et al., 2016). Talking with peers or staff members was indicated by prisoners themselves as a good supportive strategy.
+Two observational studies focused on correctional institutions.
+Suicidal Behaviors: A French prospective study found that suicide rate is higher among male prisoners in disciplinary cells than among those in regular cells, while it is lower among those who receive regular visits from relatives or friends (Duthe et al., 2013). Similarly, in a study performed in New York City, being in solitary confinement could be decisive for predicting self-harm acts, including potentially fatal ones (Kaba et al., 2014).
+3.3.	Loneliness
+Quantitative aspects of social isolation seem to be an insufficient measure of the absence of or poor social support and connection with others. Therefore, recent studies highlighted the importance of taking into account also the feeling of social isolation (Perissinotto and Covinsky, 2014) that is estimated with subjective variables, such as loneliness and low sense of belonging. Indeed, people who live alone are more likely to report loneliness; however, many individuals living alone are not lonely and report effective social support. Moreover, also people who live with others could feel lonely and have poor social support. Therefore, it is broadly agreed that loneliness is not highly correlated with social isolation (Coyle and Dugan, 2012), and seems to be more associated with mental problems. Conversely, social isolation is associated with poor general health in older adults.
+Loneliness, defined as the subjective feeling of being alone or without the desired level of intimate and social relationships (Ernst and Cacioppo, 1999), could be a better proxy of social isolation than living alone. Loneliness is generally assessed using the University of California, Los Angeles (UCLA) Loneliness Scale, a short, 20-item scale to measure the subjective feelings of loneliness and of social isolation (Russell et al., 1980; Russell et al., 1978) (positive: “There are people I feel close to”, “There are people who really understand me”; negative: “I feel isolated from others”). In other words, loneliness is the perception of social isolation, or the subjective experience of being lonely. Weiss distinguished emotional loneliness (i.e., the lack of an intimate attachment) from social loneliness (i.e., the lack of membership in a desired group) that he called "social isolation" (Weiss, 1973).
+A recent meta-analysis of 31 studies considered the influence of structural social relationships (marital status, living alone, familial discord, social contact, social network, social isolation, community participation, unemployment, religious affiliation, social integration) and functional social relationships (perceived loneliness, received social support, perceived social support and mistreatment in late life) on SI in older adults (aged 50 years or above) (Chang et al., 2017). Poor relationships predicted SI, with a higher impact for poor functional measures. Among these measures, mistreatment had the strongest impact, followed by perceived loneliness and poor perceived social support.
+Most of the included observational studies (k = 24, 60%) focused on loneliness.
+3.3.1.	Children/adolescents
+Suicidal Ideation: An in-depth cohort study focused on 832 American children followed until adolescence and reported that chronically high and increasing levels of loneliness early in life predict the presence of SI at the age of 15, together with social skill deficits, depression, and aggression (Schinka et al., 2013). The Global SchoolBased Student Health Surveys (GSHS) have been implemented by the Ministry of Health and Education of many countries. McKinnon et al. analyzed GSHS data from 32 countries and estimated that loneliness is the main risk factor for SI and SP, followed by limited parental support and bullying (McKinnon et al., 2016). Having few friends is also a risk factor, but with lower impact. Specifically, they found a positive association between loneliness and SI among adolescents in Malaysia, Seychelles, China, Philippines and Uganda (Chan et al., 2016; Page et al., 2011; Rudatsikira et al., 2007; Wilson et al., 2012). Conversely, in Zambia, loneliness appeared to be negatively associated with SI (Muula et al., 2007).
+Suicidal Behaviors: Among young boys of a Stockholm cohort, selfrated loneliness and not being a member of voluntary associations were associated with suicide and para-suicide during adolescence or young adulthood (Rojas, 2012). Similarly, among adolescents in Poland, loneliness was positively associated with SI, SP and SA (Pawlowska et al., 2016). Among Chinese adolescents included in the GSHS, loneliness played a significant role on suicidal thoughts and
+behaviors as a mediator between problems in peer relationships (being bullied, having no close friends and physical fighting) and both SI and SA (Cui et al., 2011). Also among adolescents of Benin (GSHS), loneliness was positively associated with SI, SP and multiple SA (Randall et al., 2014), and lack of parental support was linked to SI and SP (Randall et al., 2014). Analysis of data collected in the multi-country study “Saving and Empowering Young Lives in Europe” (SEYLE) showed that loneliness is associated with DSH in univariate analyses, and only with repeated DSH in multivariate analyses (Brunstein Klomek et al., 2016). Moreover, parent support, peer support, and pro-social behaviors were protective factors. Analysis of the French SEYLE cohort highlighted higher level of loneliness, social relationship problems and SI/suicidal behaviors among adolescents referred for treatment because considered at risk (Kahn et al., 2015). Finally, loneliness (i.e., feeling lonely very often and also sometimes) was a risk factor for DSH also among adolescents in a Finnish study (Ronka et al., 2013). On the other hand, loneliness was not associated with SI and SA in univariate analysis in a study on adolescents in the Netherlands (Garnefski et al., 1992). However, this sample was smaller compared with most of the other studies. Moreover, the principal component analysis highlighted among girls, correlations between loneliness, SI/SA, sexual abuse, physical abuse, low self-esteem, depression and spending money on drugs.
+3.3.2.	Adults
+Suicidal Ideation: In the general population loneliness has been associated with SI (Beutel et al., 2017). Moreover, in a sample of men who had sex with men, having five or more psychosocial health problems (including loneliness and poor social support) increased of four times the chance to have reported SI in the previous year (Li et al., 2016).
+Suicidal Behaviors: In the context of the already mentioned Quebec Health Survey, living alone, not having friends, and also loneliness (with a stronger association) were associated with SI and SA (Stravynski and Boyer, 2001). Moreover, SI and SA increased with the degree of loneliness. In another general population survey, loneliness was linked to SI and SA (Stickley and Koyanagi, 2016). The association with SI, but not SA, was particularly strong among individuals with common mental disorders. Furthermore, the interaction between loneliness and high income predicted death caused by fatal accidents or suicide (Patterson, 2016). Finally, in a clinical sample of adults with HIV, loneliness was a major predictor of suicidal risk (defined as SI, SP or SA) (Carrieri et al., 2017).
+3.3.3.	Older adults
+Passive Suicidal Ideation: Analysis of the SHARE data indicated that loneliness and partner's loss increases passive SI, whereas the social network size protects older people from passive SI (Stolz et al., 2016). Similarly, low level of perceived mastery and financial problems, loneliness and small social networks are variables strongly associated with death wishes among older adults after depressive symptoms (Rurup et al., 2011).
+Suicidal Behaviors: In an already mentioned study loneliness was linked to SA (Wiktorsson et al., 2010).
+3.3.4.	Qualitative studies
+Suicidal Ideation: In a sample of 32 older outpatients who reported SI, the feeling of loneliness was listed among the psychological changes that contributed to SI, while loss of family support was identified as an SI trigger (Huang et al., 2017). On the other hand, social support from family and friends was a strategy to deal with suicidal thoughts. An analysis of 17 Tumblr accounts, based on posts connected with depression or suicide (and two other categories: “self-mutilation” and “cutting” ) highlighted the link between these terms and the common themes of loneliness and feeling unloved (Cavazos-Rehg et al., 2017).
+Suicidal Behaviors: Among 10 adolescent girls of Latin American
+origin from low-income families in New York City who attempted suicide, emotional isolation (loneliness or lack of sense of connection with friends or parents) was one of the several themes linked to their suicidal behavior (Gulbas and Zayas, 2015). Among 10 patients after a SA, the experience of connectedness with other and of being accurately listened to by the healthcare personnel and loved ones were among the most crucial resources to maintain their will to live and hope (Vatne and Naden, 2016). Eight older inpatients who attempted suicide described the sense of disconnection and alienation from significant others and the feeling of loneliness as preceding their attempt (Bonnewyn et al., 2014). A study on the life experiences of 35 older Korean adults after SA found having more sadness and loneliness than before among the reported experiences (Kim, 2014). Similarly, 23 patients with a serious mental illness and who attempted suicide described loneliness and isolation as two emotional precursors to the attempt (Montross Thomas et al., 2014). In a photovoice study that included 20 men with previous SI, SP and/or SA, participants were asked to take photographs to describe their experiences of suicidality and perspectives about male suicide (Oliffe et al., 2017). Analysis of the interviews indicated that isolation and feeling of separation from others were factors that increased the suicide risk. Declarations about a sense of solitude, lack of comprehension from parents and the consequent feeling of isolation were particularly associated with suicidal behaviors in a sample of 47 young immigrant women (South Asian-Surinamese, Turkish, and Moroccan) in the Netherlands (van Bergen et al., 2012). Finally, among 20 male veterans with HIV/Acquired Immunodeficiency Syndrome (AIDS), loneliness and social isolation were identified as stressors for self-directed violence, whereas social support was recognized as a protective factor (Signoracci et al., 2016).
+3.4.	Alienation
+The construct of alienation has been sometimes reported as associated with suicidality, although the link is less clear than for other constructs. Three observational studies focused on alienation in ado-lescents/youths.
+Suicidal Behaviors: Among Native American adolescents, alienation from family and community (i.e., the feeling of lack of care from significant others) was associated with SA (Grossman et al., 1991). Moreover, interpersonal alienation reported by young people (early parent-child relationships) predicted NSSI (Bureau et al., 2010). These results concerned the analysis of the entire sample, and then of only girls. Finally, parental criticism predicted a pathway to NSSI via alienation towards parents, especially in boys (Yates et al., 2008). In the last two studies alienation was measured using the Inventory of Parent and Peer Attachment (IPPA) alienation subscale.
+Qualitative approach: From the analysis of interviews with older psychiatric inpatients who reported SI, the sensation of not feeling cared for and to be distant from significant others was among the themes emerged as relevant (Moore, 1997).
+3.5.	Thwarted belongingness/sense of belongingness
+Another frequently used term is the sense of belonging that could be defined as appertaining, relationship, a particular feeling related to the quality and the number of interactions with others. The Sense of Belonging Instrument (SOBI) (Hagerty and Patusky, 1995) includes two subscales: the SOBI-Antecedents (SOBI-A) (i.e., the antecedents of belonging, such as, “I want to be a part of things going on around me”), and the SOBI-Psychological state (“If I died tomorrow very few people would come to my funeral” or “I could disappear for days and it wouldn't matter to my family”). The Interpersonal Needs Questionnaire (INQ) (Van Orden et al., 2008) also can be used to measure Thwarted Belongingness (“other people care about me”, “I feel like I belong”, “I rarely interact with people who care about me”, “I am fortunate to have many caring and supportive friends”, “I feel disconnected from other
+people”, “I often feel like an outsider in social gatherings”, “I feel that there are people I can turn to in times of need”, “I am close to other people”, “I have at least one satisfying interaction every day”).
+A recent meta-analysis including 122 published and unpublished samples supports the interpersonal theory of suicide by Joiner (Chu et al., 2017). When Thwarted Belongingness (measured with the INQ) was considered alone in univariate analyses, it was moderately associated with the risk of SI and suicide, and only weakly associated with history of SA. Conversely, Perceived Burdensomeness seemed to have a stronger impact on suicidal outcomes. Indeed, the authors underlined how the considered constructs and their interaction “appear to not be better predictors of suicide risk than many traditional and often-studied risk factors”. Most studies included in the meta-analysis were performed in young adults (18-24 years; 48.4%) and adults (older than 25 years of age; 37.7%). However, in the meta-analysis the association between Thwarted Belongingness and SI was stronger among older adults (k = 9). Therefore, this construct needs to be better investigated in adolescents and older adults.
+According to a systematic review that included 16 studies, low Sense of Belongingness is associated, even if weakly, with both SI and SA mainly in non-clinical populations (Hatcher and Stubbersfield, 2013). Sense of Belongingness was measured with the INQ (k = 5), the SOBI (k = 7), or other tools.
+3.6.	Additional aspects to be considered in future studies
+Other factors have been linked to loneliness and mental health outcomes: unemployment, living in rural communities, low population density, and sedentary lifestyles.
+A proposed model of the mechanisms linking economic recession to suicide considered the association between unemployment/financial difficulties and social isolation (Haw et al., 2015). Furthermore, suicide rates are higher in rural communities (Fontanella et al., 2015; Helbich et al., 2017). An explanation could be that living in rural areas can lead to social isolation, and this could contribute to suicide. In addition, low population density (under-crowding) has been associated with youth suicide (Seiden, 1984).
+Among adolescents, sedentary lifestyles (i.e., total amount of time spent in front of screens for leisure, TV viewing, computer/internet use, video gaming, and other sedentary behaviors) were investigated, in a systematic review, in relation to mental health outcomes, including depressive symptomatology, SI, loneliness, stress and psychological distress (Hoare et al., 2016). The evidence was insufficient concerning the relationship between screen time and loneliness, although only studies showing absence of associations were included (Donchi and Moore, 2004; Gross, 2004). Moreover, the lack of association could be explained by the fact that time spent online communicating and time spent talking on the phone were among the included behaviors.
+4.	Discussion
+The aim of this narrative review was to provide an overview on the link between social isolation and suicidal thoughts and behaviors. We focused on: a) systematic reviews, meta-analyses, and narrative reviews; b) 40 original observational studies on large samples (N > 500); and c) some qualitative studies.
+The main constructs associated with suicidal outcomes were: marital status (being single, separated, divorced, or widowed) and living alone, social isolation, loneliness, alienation, and belongingness.
+Both the objective condition of being alone (e.g., living alone) and the subjective feeling of being alone (i.e., loneliness) were strongly associated with suicidal outcomes, in particular with SA and SI. However, the subjective feeling of loneliness, which was investigated in most studies (k = 24, 60%), seemed to have a major impact on both SI and SA.
+Remarkably, most of the included observational studies reported a
+positive association between all the constructs of social isolation and suicidal outcomes, with the exception of four (one reported a negative association (Muula et al., 2007), and three a lack of association (Garnefski et al., 1992; Giacco and Priebe, 2016; Zhang and Jin, 1998)). Mula et al. did not propose any explanation concerning the negative association between feeling lonely and SI among in-school adolescents in Zambia (Muula et al., 2007). The hypothesis of a cultural difference is not consistent with other studies performed in Africa (in Benin (Randall et al., 2014) and Uganda (Rudatsikira et al., 2007)). However, Zambia could be classified as a lower-middle income country, while Benin and Uganda are low-income countries, and this factor could have influenced the results. Nevertheless, we must underline that, in the global GSHS analysis, the association was positive. Concerning the three studies with the lack of association (Garnefski et al., 1992; Giacco and Priebe, 2016; Zhang and Jin, 1998), in the first one, living alone was not predictive of SI, but to be unemployed was related to SI, and being unemployed could be a good proxy for reduced or lack of social contacts. In the second one, no association was found between loneliness and SI and SA; however, among girls, loneliness, SI/SA, sexual abuse, physical abuse, low self-esteem, depression and spending money on drugs were inter-correlated. In the third one, social isolation was only weakly associated with SI in a path model including depression and self-esteem. In this Chinese group, interpersonal conflicts and difficulties in interactions had an effect on SI, and this finding could be
+linked to cultural specificities.
+However, overall, results were transculturally consistent. Therefore, to be alone and feeling lonely are associated with suicidal outcomes across different countries and populations.
+4.1.	Future research directions
+We have to underline that the present review is extremely preliminary. The next step should be to perform one or more meta-analyses on this topic, similarly to what has been done with the protective role of social support in depression (Gariepy et al., 2016; Rueger et al., 2016), but including both social isolation and social support. Concerning social support, Rueger et al. underlined that disaggregating the sources of support could be useful to better understand subtle differences in the roles of others in our lives (e.g., family members, teachers, general peers and close friends in the case of young people) (Rueger et al., 2016). We think that this is the case of social isolation as well. The “disaggregation” of social isolation could help the development of more focused interventions with the aim of specifically reducing social isolation and loneliness.
+Moreover, as the majority of studies focused on adolescents and/or young adults (k = 23, 57.5%), additional analyses on different life periods, especially adulthood and also older adulthood, could be useful. Furthermore, since results on sex differences are mixed, their further
+evaluation is warranted. In the suggested future meta-analysis both sensitivity analyses and meta-regressions should be carried out to control for age, sex, and different social isolation/social support constructs. Moreover, the main confounding factors reported in the association between social isolation and suicide, such as temperament/ personality, low socio-economic status, abuse/life events/interpersonal conflicts, unemployment, low self-esteem, depression and other psychiatric disorders, alcohol/drugs abuse/dependence, medical conditions and loss, should be considered (see Fig. 1).
+Finally, it could be useful to distinguish between social isolation and deficits in social functioning present in some neuropsychiatric disorders, such as Alzheimer's disease and schizophrenia, with specific pathophysiological mechanisms (Porcelli et al., 2018).
+4.2.	Clinical perspectives
+Four primary strategies have been identified to reduce loneliness: (1) developing or improving social skills, (2) increasing social support, (3) increasing the occasions for social contacts, and (4) focusing on maladaptive social cognition (Masi et al., 2011). Integrated interventions that combine cognitive behavioral therapy focused on loneliness reduction and short-term adjunctive pharmacological treatments have been recently proposed (Cacioppo et al., 2015). A review on interventions targeted to older adults reported that flexibility, involvement in the development of activities, and the focus on productive engagement were features related to their success (Gardiner et al., 2018).
+In the context of suicide prevention programs, these strategies could be useful for patients who are alone and/or who feel alone. The strengthening of protective factors by increasing/developing social contacts and by modifying the perceived social isolation could be a strategy, particularly of subjects at risk. For adolescents, the activation of prevention programs where the theme of belonging to the peer group is salient (e.g., in schools) could be another strategy. Moreover, the family's involvement could be another useful approach. Finally, the fact that half of suicides communicate their intentions prior to death (Pompili et al., 2016) strengthens the importance of social support as protective factor.
+4.3.	Limitations
+The main limitation of the present review is the non-inclusion of studies on social support. For instance, an American longitudinal study on 72,607 women underlined that the risk of suicide was linked to low level of social integration, formulated as marital status, the size of social network, the frequency of social contacts, and the participation in different social groups (Tsai et al., 2015). Moreover, according to a French study on employees of Electricity of France-Gas of France, low social integration is a predictor of elevated risks of dying not only from suicide, but from cancer and accidents (Berkman et al., 2004). Furthermore, this was neither a systematic review nor a meta-analysis, and we could not determine the extent of the risk or control for confounding factors. Particularly, as already mentioned, confounding factors can limit the weight of the results obtained in observational studies.
+In conclusion, data from observational studies suggest that both objective social isolation and the subjective feeling of loneliness should be incorporated in the risk assessment of suicide. The design of interventional studies targeting social isolation for the prevention of suicide is needed. Furthermore, a meta-analysis on this topic is warranted, considering the modulation of confounding factors such as age, sex, different social isolation constructs, and depression and other psychiatric diagnoses.
+Acknowledgments
+We would like to thank Dr. Elisabetta Andermarcher for her careful linguistic revision of the manuscript. Dr. Raffaella Calati received a
+grant from the FondaMental Foundation, Créteil, France (2015-2016). Dr. Emilie Olié received research grants from AstraZeneca, Servier, Institut UPSA de la Douleur and fees for presentations at congresses from Janssen, Lundbeck, Otsuka, Servier. Prof. Philippe Courtet received research grants from Servier, and fees for presentations at congresses or participation in scientific boards from Janssen, Lundbeck, Otsuka, Servier.
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+Self-harm refers to intentional self-poisoning or self-injury with a nonfatal outcome, irrespective of whether the individual intends to die (National Collaborating Centre for Mental Health, 2011). This definition recognizes that suicidal intent may be low but not absent in many acts of self-harm (Hjelmeland et al., 2002) and that suicidal intentions underlying some selfharm behaviors may be mixed, unclear, or even unknown to individuals (Hawton, Cole, O’Grady, & Osborn, 1982). Self
+harm is a significant public health problem across the world and has considerable impact on the lives of the individual, their family, and on health services.
+At present, rates of self-harm are highest in young people (Bergen, Hawton, Waters, Cooper, & Kapur, 2010). Large community-based surveys within Europe reveal that approximately 10% of adoles
+Glazebrook et al.
+cents report having self-harmed in their lifetime (Hawton, Rodham, Evans, & Weatherall, 2002; Madge et al., 2008). Furthermore, these surveys indicate repetition of self-harm is common; over half of adolescents who had self-harmed reported multiple episodes. There are, however, limits to the inferences that can be made from such cross-sectional or retrospective studies. Prospective studies are likely to provide more robust and reliable information about repetition of self-harm (Hawton, Bergen, et al., 2012). In one of the few studies to prospectively examine the prevalence of self-harm in the community, O’Connor, Rasmussen, and Hawton (2009) found that 6.2% of 15-to 16-year-olds reported self-harm over 6 months, with 2.6% having self-harmed for the first time, and 3.6% with a repeat episode. Prospective studies monitoring adolescent presentations of self-harm in hospitals suggest that approximately 15% of adolescents carry out a further act within the following year (Hawton, Hall, et al., 2003), with up to 27% repeating self-harm when followed-up over a minimum of 2 years (Hawton, Bergen, et al., 2012). However, findings from hospital-admission studies are based on records of individuals who have reattended hospital following self-harm and are likely to reflect an underestimate (Hawton, Saunders, & O’Connor, 2012).
+It is crucial to consider why some adolescents repeat self-harm as repetition may reflect ongoing or recurrent distress and places greater demands on clinical services (Hawton, Kingsbury, Steinhardt, James, & Fagg, 1999), and importantly, a history of self-harm is the strongest predictor of completed suicide (Hawton, zahl, & Weatherall, 2003). The need to reduce the risk of suicide in key high-risk groups, such as those with a history of self-harm, is a target outlined in the most recent UK government suicide prevention strategy (Department of Health, 2012). However, relatively few studies have prospectively investigated the extent to which psychosocial and psychological factors are predictive of repeat self-harm behavior among adolescents
+665
+(Fliege, Lee, Grimm, & Klapp, 2009). Prospective research suggests that family dysfunction and poor parental mental health are risk factors for repeated self-harm (Chitsabe-san, Harrington, Harrington, & Tomenson, 2003; O’Connor et al., 2009); however, little attention has been paid to the role of attachment in repetition of self-harm.
+Attachment theory argues that infants are biologically programmed to form an emotional bond with their caregiver (Bowl-by, 1969/1982), and an attachment figure should act to provide physical security and comfort to an otherwise helpless infant. For securely attached infants, the caregiver will be available and responsive in times of stress and the caregiver provides a “secure base” from which to explore the environment. This exploration promotes the development of emotion-regulation, selfconfidence, and problem-solving skills. Furthermore, favorable interactions with the attachment figure lead to the development of positive representations of the self, of others, and of relationships (Bowlby, 1973). These competencies are believed to increase children’s adaptation to the world around them and are thought to continue to influence adjustment throughout the life span.
+It is proposed that an insecure attachment style can develop when a primary caregiver is insensitive or inconsistent in responding to the child in times of need, predisposing children to become either preoccupied with maintaining contact or disengaging with the caregiver. Insecure attachment styles can, therefore, impede socioemotional development and the development of effective coping strategies and problem-solving skills needed in challenging situations (Mikulincer, Shaver, & Pereg, 2003).
+Although Bowlby’s early work was criticized for potentially blaming mothers (Mead, 1954), careful longitudinal research has confirmed the contribution of the quality of the child’s attachment relationship with the caregiver to children’s long-term developmental outcomes (Sroufe, Egeland, Carlson, & Collins, 2005). Studies investigating the contribution of genetic and
+environmental influences to individual differences in attachment in infants and toddlers have found evidence for an environmental, rather than genetic, influence on attachment, as predicted by attachment theory (e.g., O’Connor & Croft, 2001). A recent twin study (Fearon, Shmueli-Goetz, Viding, Fonagy, & Plomin, 2014), however, has confirmed a significant genetic influence on adolescent attachment; for attachment classification (secure vs. insecure), 35% of the variability was found to be attributable to genes. These findings suggest that a child’s inherited characteristics play a role in their attachment status in adolescence; it is possible that the child’s temperamental characteristics evoke changes in the sensitivity of care provided by the caregiver, which influences security of attachment in the child-caregiver relationship. Hence, parent effects, child effects, and bi-directional parent-child effects may all play a role.
+Insecure attachment has been related with self-harm behavior in adolescent clinical samples (Adam, Sheldon-Keller, & West, 1996), and prospective, longitudinal research has shown insecure attachment to be a significant risk factor for self-harm in community samples of adolescents (Fergus-son, Woodward, & Horwood, 2000; Salzin-ger, Rosario, Feldman, & Ng-Mak, 2007) and young adults (Sroufe et al., 2005). Adolescents with insecure attachment styles demonstrate more dysfunctional anger and avoidance of problem solving during discussions with parents (Kobak, Cole, Ferenz-Gillies, Fleming, & Gamble, 1993) and develop maladaptive ways of coping with negative emotions (Seiffge-Krenke, 2006). Furthermore, adolescent self-harm is associated with poorer problem-solving skills (Pollock & Williams, 2004).
+To our knowledge, however, no published research has explored longitudinally the role of attachment in relation to the course of self-harm in a clinical sample of young people with a history of self-harm. If self-harm can be seen as “extreme attachment behavior” (Adam et al., 1996, p. 265) produced in response to threats in order to
+signal distress and the need for caregiving, it can be theorized that adolescents with secure attachment will have caregivers and peers who will recognize this distress and therefore this behavior will elicit appropriate concern, help, and support. That is, following an incident of self-harm, sensitive caregivers may become more attentive to their child’s needs, or more protective, creating a “safe” environment and encouraging the child to develop more adaptive methods of coping with distress. Thus, securely attached adolescents would be expected to have better outcomes in terms of self-harm behavior and problem solving.
+In this study we aimed to investigate the role of insecure parental and peer attachment in relation to outcomes for selfharm over a 6-month period among a high-risk group of clinically referred adolescents with a history of self-harm. We also examined whether adolescents classified as having insecure parental attachment have poorer outcomes in terms of problem solving and attendance with clinic appointments at 6-month follow-up.
+METHOD
+Participants
+Adolescents aged 12 to 17 years referred to specialist child and adolescent mental health services (CAMHS), with a history of self-harm behavior within the last year were eligible. Those adolescents referred following accidental self-harm were excluded from the study.
+Design and Recruitment
+This was a longitudinal study with assessments at baseline and 6-month followup. Participants were invited to take part in the study before a routine psychosocial assessment conducted by a specialist CAM-HS professional following emergency treatment for self-harm or at a CAMHS clinic appointment. If the researcher was unable
+Glazebrook et al.
+to meet with participants, CAMHS staff gave out information packs and collected contact details on the researcher’s behalf.
+Baseline Measures
+Attachment. The Child Attachment Interview (CAI; Target, Fonagy, & Shmu-eli-Goetz, 2003) was administered. This semistructured interview asks about current experiences with, and perceptions of, attachment figures. Questions are designed to tap into the adolescent’s self-representation and representation of his or her caregivers, particularly during situations in which the attachment system is thought to be activated (e.g., emotional upset, conflict, distress, illness, hurt, separation, and loss).
+The interview, conducted by a trained rater, is filmed and later transcribed verbatim; relevant nonverbal behaviors are noted where appropriate (e.g., marked anxiety and maintenance of eye contact). Transcripts of the interview and nonverbal behavior are coded according to nine scales (Preoccupied Anger, Idealization, Dismissal, Disorganization, overall Coherence, Emotional openness, Use of Examples, Balance of Positive/Negative References to Attachment Figures, and Resolution of Conflict) and based on these ratings a main attachment style can be assigned for the mother and father independently: secure attachment or an insecure attachment style (dismissing, preoccupied, or disorganized). All interviews were conducted and coded by the same accredited researcher (KG), who was trained by the developers to 85% agreement over 20 cases for the secure-insecure split for maternal attachment (k = .7).
+The CAI was originally designed for use with individuals aged 8 to 12 years but has since been adapted and used with adolescents up to 17 years of age (Scott, Brisk-man, Woolgar, Humayun, & o’Connor, 2011) using age appropriate language. For participants in foster care, the modified CAI for adolescents in care was administered. The CAI has demonstrated sound psychometric properties, with good crite
+667
+rion validity, discriminant validity, and test-retest reliability at 1 year (Shmueli-Goetz, Target, Fonagy, & Datta, 2008).
+To establish peer attachment styles, participants completed The Attachment Questionnaire for Children (AQC; Muris, Mayer, & Meesters, 2000). This consists of three descriptions relating to relationships with close friends. Respondents endorse the description that matches their peer relationships most closely giving classifications of secure, insecure-avoidant, or insecure-ambivalent peer attachment. Muris, Meesters, van Melick, and Zwambag (2001) found this brief measure has demonstrated good concurrent validity with the Inventory of Parent and Peer Attachment (Armsden & Greenberg, 1987).
+Anxiety and Depression. Participants completed the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983). The HADS is a well-validated measure of anxiety and depression in clinically referred adolescents (White, Leach, Sims, Atkinson, & Cottrell, 1999). It has 14 items (seven for anxiety, e.g., “I feel tense or wound up”; seven for depression, e.g., “I feel as if I am slowed down”) each with a 4-point verbal rating scale scored from 0 to 3, giving total scores ranging from 0 to 21 for each subscale. Negative items are recoded so that high scores indicate high levels of distress. The HADS demonstrates sound internal consistency; Cronbach’s a of between .78 and .93 have been reported for the anxiety subscale and between .82 and .90 for the depression subscale (Mykletun, Stor-dal, & Dahl, 2001).
+Self-Harm. The self-harm questionnaire was developed from the questions used in Hawton et al.’s (2002) large school-based survey investigating self-harm in adolescence. In the original school study, participants were asked to describe in their own words what they had done to harm themselves. From this, the authors were able to determine whether this met one of the predetermined criteria for self-harm. In this study, we used the same criteria and created a list of self-harm behaviors. Instructions asked participants to indicate yes or no as to
+whether they have engaged in the methods of self-harm behaviors with the intention to harm (e.g., “Have you ever poisoned yourself?” “Have you ever taken more than the recommended dose of a drug?” “Have you ever burned yourself with something?”). Participants were also asked to indicate how frequently they had engaged in self-harm in their lifetime (once/2-5 times/6-10 times/11-15 times/over 15 times).
+Informal and Formal Support. Participants were asked to indicate whom they felt they go to talk about things that really bother them (mother/father/brother or sister/ another relative/friends/teacher/member of staff at CAMHS/somebody else [e.g., a boyfriend or girlfriend]). The number of sources of support selected was summed and higher scores indicated a greater number of perceived individuals available for help. This measure was also used in Hawton et al.’s large school survey (Evans, Hawton, & Rodham, 2005) but was adapted for the clinical sample in this study to include the response option “a member of staff at CAMHS.”
+Problem-Solving Abilities. The means end problem-solving task (MEPS; Platt & Spivack, 1975), a performance-based test of general social problem solving, was used. Participants are presented with scenarios that begin with a protagonist needing or wanting something and end with this need being satisfied by him or her. Participants complete the story by generating potential solutions that could have occurred between the goal being presented and being reached. Individuals are assessed on their ability to appraise the given problem and identify steps or “means” that would adequately result in the given resolution. The MEPS has been used to assess problem solving in adolescents with a history of self-harm (orbach et al., 2007).
+This study used a shortened version of the MEPS designed for use with adolescents (Hawton et al., 1999). Solutions were scored according to the guidelines developed by Steinhardt, Hawton, and Kingsbury (1999). We present findings from the “total relevant means” subscale, which
+refers to the sum of all steps in the story that are relevant to the story process. The MEPS was scored by one of two independent coders. To assess interrater reliability, both coded approximately 10% of the MEPS and across the five stories intra-class correlations ranged from .74-.93 for the “total relevant means” scores.
+Participants also completed questions on their current living situation, previous living situation, and family affluence (Family Affluence Scale II, Boyce, Torsheim, currie, & Zambon, 2006).
+Outcome Measures
+Self-Harm. The self-report self-harm behavior measure was adapted to collect information about self-harm in the 6 months since baseline.
+Problem-Solving Abilities. The MEPS was readministered at 6-month follow-up. Differences in scores from baseline to follow-up were calculated for total relevant means scores to give “change in total relevant means” as an outcome variable.
+Attendance at Clinical Services. The proportion of scheduled CAMHS appointments (including assessment and treatment sessions) attended during the 6-month follow-up period was recorded from RiO, the mental health electronic patient record system.
+Procedure
+At the baseline assessment, informed parental consent and participant assent from adolescents under 16 years of age were obtained. Participant consent was obtained for adolescents aged 16 or older. The CAI was then administered and subsequently participants completed the measures of peer attachment, anxiety and depression, sources of help, and self-harm behaviors via a computer-assisted self interview, as research has shown that adolescents feel more comfortable revealing sensitive information, such as mental health problems, to a computer (Parkin, 2000). Lastly, participants com
+Glazebrook et al.
+pleted the MEPS, which was administered face-to-face by the researcher.
+At the follow-up assessment, 6 months later, participants completed the problem-solving task again as well as the self-harm measure adapted for follow-up.
+Attachment interviews were coded blind to outcomes. Repeat referrals to CAMHS following emergency treatment for self-harm and appointment attendance were recorded from Rio once the coding of the CAIs was complete.
+This study received ethical approval from the “East Midlands—Nottingham 2” NHS Research Ethics Committee.
+Statistical Analyses
+Maternal attachment and peer attachment classifications were dichotomized into secure attachment (0) and insecure attachment (1) for univariate and multivariate analyses. The self-harm variable was dichotomized into no self-harm behavior (0) and self-harm behavior (1). Multivariable logistic regression (enter method) was used to examine those factors that independently and most strongly predicted self-harm behavior at 6month follow-up. Covariates were age, gender, maternal attachment, peer attachment, and levels of previous self-harm at baseline and baseline levels of anxiety and depression. Multicollinearity checks were run on all predictor variables used in the regression analyses. Correlations between variables were less than .7, variation inflation factor scores were below 2.2, and tolerance statistic values were above .5, indicating that there were no strong correlations between predictors in the regression model.
+RESULTS
+During the study period of April 20, 2010-June 29, 2011, 91 adolescents agreed to receive information about the study and supplied contact details, of whom 52 (3 males) consented and were recruited to the
+669
+study. Consenters were older (median age 15 [IQR = 15-16] vs. 15 [IQR = 14-15]; Z = —2.63, p = .009), although there was no difference in gender between consenters and dissenters. As recruitment was through CAMHS staff, it is not known how many young people were approached who declined to supply contact information.
+The final sample consisted of 49 young people who had been assessed following emergency treatment for self-harm and three young people who had been referred to Tier three Community CAMHS and had disclosed a history of self-harm (see Table 1 for sample demographics).
+The most frequently endorsed methods of self-harm were self-poisoning (n = 44, 85%), self-cutting (n = 39, 77%), and battering or hitting oneself (n = 29, 57%). Three quarters of participants (n = 39, 75%) had scores of 8 or above on the anxiety subscale of the HADS, indicating probable clinical anxiety, while a third (n = 17, 33%) had scores of 8 or above on the depression subscale of the HADS, indicating probable clinical depression. A third of participants (n = 17, 33%) met the criteria for probable clinical depression and anxiety.
+Parental Attachment Style
+Thirty-seven participants (71%) were classified as insecurely attached to their mother. One participant did not provide sufficient information to be able to assign an attachment style for maternal attachment.
+The Relationship Between Maternal Attachment Style and Study Variables at Baseline
+At the time of interview, many participants (40%) had infrequent or no contact with their biological father. No participants classified as insecurely attached to their mother had a secure attachment to someone else (including father, a grandparent, or foster carer). Therefore, attachment style to the mother (secure/insecure) was used as
+the independent variable. Participants classified as securely attached did not differ in terms of age, gender, and self-reported family affluence from those classified as insecurely attached.
+Securely attached participants had greater levels of social support (Z = —2.34, p = .019) and were more likely to report having their mother, y2(1) = 12.4, p < .001, a sibling, y2(1) = 4.01, p = .045, and their friends, v2(1) = 5.26, p = .022, as a source of support, compared to insecurely attached participants. Insecurely attached participants reported greater levels of current depression, t(49) = —1.72, p = .035, and a greater frequency of previous self-harm behavior, median = 2-5 times vs. 6-10 times;
+V2(1) = 4.44, p = .034. Over half of the sample (n = 28, 56%) reported having attempted suicide. Securely attached adolescents were as likely as insecurely attached adolescents to endorse having made a suicide attempt. There were no differences between the groups in terms of self-reported secure peer attachment [n = 5 (36%) vs. n = 17 (46%)]. There were also no differences between the securely attached group and the insecurely attached group in terms of “total mean” scores on the MEPS (Table 2).
+Six-Month Follow-up
+Forty-nine (94%) participants completed the study tasks at Time 2. The three
+nonresponders were females with two classified as having secure maternal attachment and another insecure maternal attachment at baseline. None of these participants had been assessed by CAMHS following emergency treatment for self-harm in the 6month follow-up period.
+The Role of Attachment in Repetition of Self-Harm
+Two thirds of participants engaged in one or more episodes of self-harm at follow-up and significantly more were classified as having insecure maternal attachment at baseline, v2(1) = 5.46, p = .019. Therefore, 78% (28/36) of all participants classified as insecurely attached repeated self-harm behavior during the study period. In comparison, 42% (5/12) of the securely attached group went on to repeat self-harm (see Figure 1). A slightly greater proportion of insecurely attached participants had been assessed following emergency treatment for self-harm during the study period [n = 6
+(17%), vs. n = 1 (8%)], although this difference was not statistically significant.
+Bivariate associations indicate that repeated self-harm was related to greater levels of baseline self-harm, y2(1) = 5.10, p = .024; depression, t(44.7) = —2.52, p = .015; and anxiety (Z = —2.12, p = .034) and
+672
+peer attachment, v2(1) = 4.62, p = .032, but was not related to age and gender.
+In the multivariable logistic regression model, insecure maternal attachment (OR = 7.80, 95% CI 1.15, 52.91) and poor peer attachment (OR = 8.01, 95% CI 1.00, 64.20) independently predicted self-harm at follow-up (Table 3). Age, gender, previous self-harm, and levels of anxiety and depression at baseline were not independently associated with the outcome.
+The Role of Attachment in Improvement in Problem Solving
+Overall, participants classified as having secure attachment showed greater improvement in problem-solving skills at follow-up, producing on average one extra step toward the resolution of the problem, t (43) = 2.33, p = .027 (Table 4).
+The Role of Attachment in Attendance at Clinical Services
+Participants with insecure maternal attachment had a greater number of appointments (which included both assessment and treatment sessions) scheduled with specialist CAMHS during the 6-month study period (Z = —2.18, p = .029; Table 4). However, there was no difference
+in the proportion of sessions attended by participants with and without secure maternal attachment. Findings show that those participants with insecure maternal attachment were more likely to be in contact with specialist CAMHS at follow-up [n = 18 (50%) vs. n = 1 (8%); p = .016].
+DISCUSSION
+During this study we investigated the role of attachment in predicting outcomes for clinically referred adolescents who have self-harmed; a hitherto neglected area of research. The findings demonstrated that both insecure maternal and peer attachments independently predicted repeated self-harm at follow-up, while other known correlates of self-harm behavior (age and levels of previous self-harm, anxiety, and depression symptoms) were not associated with repetition of self-harm once attachment was accounted for. Furthermore, of the seven participants who required clinical assessment by CAMHS following emergency treatment for self-harm during the follow-up period, six were classified as having insecure attachment. In addition, participants who were insecurely attached were more likely to be in contact with specialist CAMHS at follow-up, perhaps indicating
+ongoing distress. These findings build on previous prospective longitudinal research that has demonstrated insecure attachment is associated with self-harm behavior in young people (Fergusson et al., 2000; Salz-inger et al., 2007; Sroufe et al., 2005).
+While there were no differences between securely and insecurely attached participants in problem-solving skills at baseline, participants classified as having secure attachment showed improvements in problem solving at follow-up. They produced on average one whole extra step toward the resolution of the problems presented. It is possible that participants with secure attachments had caregivers who recognized their self-harm as a signal of distress and this behavior elicited appropriate concern, help, and support, including encouraging the child to develop more adaptive methods of coping. Adolescents report that the behavior of parents can influence further self-harming (Yip, Ngan, & Lam, 2003) and future research could use qualitative methods to explore adolescents’ perceptions of caregivers’ role in aiding the development of constructive problem-solving skills following self-harm.
+Furthermore, research has shown that genes may play a significant role in adolescent attachment (Fearon et al., 2014) and it is possible that the child’s genetic propensi
+ties and temperament evoke changes in the sensitivity of care provided by the caregiver. It is therefore important to consider that the relationship between attachment and outcomes for self-harm and problem solving may not just be accounted for by parent effects, but also child effects and bidirectional parent-child effects.
+our findings indicate that insecurely attached adolescents could particularly benefit from problem-solving therapy and attachment-based therapy in combination. Problem-solving therapy has been shown to improve problem-solving skills in adults who self-harm (Townsend et al., 2001) and a recent randomized controlled trial (RCT) has indicated that dialectical behavior therapy for adolescents (DBT-A), which in part aims to enhance skills to cope with intense emotions that may precede self-harm, is effective for adolescent self-harm behavior (Mehlum et al., 2014). To date, few RCTs of family-based interventions have been conducted for adolescents who self-harm; however, results from these are promising. Attachment-based family therapy (ABFT) focuses on strengthening parent-adolescent attachment bonds to create a protective and secure base for adolescent development. It works to improve parent-adolescent communication and the adolescent’s confidence in the parent’s availability and support. Fur
+thermore, it aims to improve the family’s capacity for problem solving, affect regulation, and organization in an attempt to strengthen family cohesion. An RCT by Diamond et al. (2010) demonstrated that adolescents who received ABFT reported significantly greater and more rapid reductions in suicidal ideation during the treatment period compared with those adolescents receiving Enhanced usual care (a facilitated referral process with ongoing clinical monitoring). This was supported by clinician ratings of the adolescents’ suicidal ideation. Furthermore, while there was no difference between the two groups in rate of change from the endpoint (12 weeks) to follow-up (24 weeks), those in the ABFT group still reported significantly less suicidal ideation at follow-up. In addition, mentalization-based treatment (MBT), which is grounded in attachment theory, has been shown to be more effective than routine care in reducing repeat self-harm in a clinical sample of adolescents; MBT produced a recovery rate of 44% versus 17% for the treatment-as-usual group (Rossouw & Fonagy, 2012). The mechanism of change was attributable to improved men-talization and reduced attachment avoidance.
+No relationship was found between attachment style and the number of scheduled clinical appointments attended but it may be that electronic records of service uptake, although more accurate than participant recall, are not a sensitive marker for engagement with treatment. However, clinical record data revealed that participants with insecure maternal attachment had more appointments scheduled with specialist CAMHS during the follow-up period compared to those with secure attachment. This could be indicative of greater clinical need or a slower response to treatment.
+A large proportion (67%) of participants reported repeated self-harm behavior during the 6-month study period. However, only seven participants were assessed by
+specialist CAMHS following emergency treatment for self-harm during this period, suggesting that episodes are often unknown to clinical services. Many of the studies that have prospectively studied self-harm among adolescents who have received emergency treatment for self-harm have relied on hospital records to determine repetition of selfharm. The discrepancy between self-report data and hospital admissions for self-harm found in this study illustrates the limitations of relying on hospital data alone.
+Baseline data also suggested a difference between the securely and insecurely attached participants in frequency of previous self-harm behavior. Furthermore, analysis of individual sources of support revealed that securely attached individuals were more likely than insecurely attached participants to report their mother, their siblings, and their friends as someone they could talk to. This is in line with previous research that indicates that adolescents who have selfharmed on only one occasion are more able to talk to relatives and friends than adolescents who have engaged in multiple episodes of self-harm (Evans et al., 2005). It is possible that in this study having a greater range of individuals to turn to for help among securely attached participants contributed toward better outcomes in terms of self-harm behavior. Furthermore, the greater endorsement of maternal support by participants classified as securely attached provides validity for the secure-insecure attachment classifications assigned.
+To date, no published research has explored the role of attachment in a clinical sample of adolescents who have self-harmed using an attachment interview that is suitable for adolescents. The attachment interview is considered the “gold standard” in attachment research; however, previous research in this domain has relied on assessing attachment styles through a self-report measure of attachment (e.g., West, Spreng, Rose, & Adam, 1999) or an attachment interview designed for adults (e.g., Adam
+Glazebrook et al.
+et al., 1996). One novel aspect of this study, therefore, was that adolescent attachment classifications were assigned based on narratives produced during a reliable and valid interview specifically designed for young people.
+Furthermore, a considerable strength of this study was the high number of participants (94%) retained at 6-month follow-up. Longitudinal studies are necessary to investigate the course of complex behavior patterns such as self-harm, yet research with psychologically vulnerable or transient populations is often limited by high rates of attrition (Kleschinsky, Bosworth, Nelson, Walsh, & Shaffer, 2009). Attrition can compromise the external validity of study findings and high follow-up rates allow greater confidence that the findings are representative of the whole sample and more generalizable.
+Limitations
+The longitudinal design of this observational study allows inferences to be made regarding the association between attachment and outcomes for self-harm and problem solving. However, without a randomized experimental design, it is not possible to infer a causal relationship. It was not possible to measure, and control for, all factors that may have affected the outcome of self-harm and problem solving, and it is possible that there may be some residual confounding.
+The present study was limited by a modest sample size and the possibility that participants may not be representative of all adolescents who self-harm. In particular, there were very few males in this study and the findings might not generalize to males. The female:male ratio among adolescents assessed by the CAMHS self-harm team during the study period was 10:1. The ratio in this sample was 17:1; therefore, males were further under represented. This research should be repeated with a sample of males to determine whether these findings are generalizable to both genders.
+A single researcher (KG) administered the Child Attachment Interviews and coded
+675
+all transcripts. Although the researcher is a certified reliable coder, having passed the reliability test, this does not protect against rater drift in reliability. Furthermore, as this sample of adolescents contained individuals with elevated levels of depression and anxiety, there is the potential that level of distress impacted attachment classification. It is noted, however, that unlike self-report measures of attachments, the CAI does not directly assess participants’ interpretations of their caregivers and examines coherence, inconsistencies, and contradictions within the narrative. Furthermore, the CAI was developed for use with clinical samples. In addition, actual distress was not the strongest predictor of self-harm (as measured by levels of anxiety and depression), which would be expected if it played a greater contribution than attachment styles to repeated self-harm.
+Implications for Future Research and Clinical Practice
+The finding that insecure maternal attachment and insecure peer attachment are associated with future self-harm has important implications in research and clinical work. Future research could build on these findings by investigating the relationship between attachment and future selfharm in a large cohort study. Within such a study, it would be important to examine outcomes for those with high levels of repeat self-harm behavior and those who have experienced living in foster care or residential care homes. Furthermore, an attachment-based intervention study with people who have self-harmed for the first time could help shed light on causal relationships.
+Peer and maternal attachment classifications could also be routinely used to help inform assessment and treatment for adolescents who have self-harmed. Insecure attachment is a potentially useful marker of risk of future self-harm and it could be that combined attachment-based and problemsolving interventions are particularly effec
+tive for insecurely attached adolescents who have self-harmed, but this requires further investigation.
+CONCLUSION
+This study offers novel insights into the role of attachment in outcomes for self
+harm and problem solving among clinically referred adolescents who have self-harmed. There were high rates of insecure attachment within this sample and this is of concern as insecurely attached participants were found to be at greater risk of repeated self-harm and showed less improvement in problem solving.
+Glazebrook et al.
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+Madge, N., Hewitt, A., Hawton, K., Wilde, E. J., Corcoran, P., Fekete, S., et al. (2008). Deliberate self-harm within an international community sample of young people: Comparative findings from the Child & Adolescent Self-harm in Europe (CASE) Study. Journal of Child Psychology and Psychiatry, 49, 667-677.
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+Mikulincer, M., Shaver, P. R., & Pereg, D. (2003). Attachment theory and affect regulation: The dynamics, development, and cognitive consequences of attachment-related strategies. Motivation and Emotion, 27(2), 77-102.
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+Muris, P., Meesters, C., van Melick, M.,	& Zwambag, L. (2001). Self-reported
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+Mykletun, A., Stordal, E., & Dahl, A. A. (2001). Hospital Anxiety and Depression (HAD) scale: Factor structure, item analyses and
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+internal consistency in a large population. The British Journal of Psychiatry, 179, 540-544.
+National Collaborating Centre for Mental Health. (2011). Self-harm: Longer term management. NICE Clinical Guidance 133. London: National Institute for Clinical Excellence.
+O’Connor, T. G., & Croft, C. M. (2001). A twin study of attachment in preschool children. Child Development, 72, 1501-1511.
+O’Connor, R., Rasmussen, S., & Hawton, K. (2009). Predicting deliberate self-harm in adolescents: A 6-month prospective study. Suicide and Life-Threatening Behavior, 39, 364-375.
+Orbach, I., Blomenson, R., Mikulincer, M., Gilboa-Schechtman, E., Rogolsky, M., & Retzoni, G. (2007). Perceiving a problem-solving task as a threat and suicidal behavior in adolescents. Journal of Social and Clinical Psychology, 26, 1010-1034.
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+1943278x, 2015, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sltb.12159 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlmelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+678	Attachment and Outcomes for Adolescent Self-Harm
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+White, D., Leach, C., Sims, R., Atkinson, M., & Cottrell, D. (1999). Validation of the Hospital Anxiety and Depression Scale for use with adolescents. The British Journal of Psychiatry, 175, 452-454.
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+Manuscript Received: January 30, 2014 Revision Accepted: January 27, 2015
+1943278x, 2015, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sltb.12159 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlmelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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@@ -0,0 +1,136 @@
+Suicide and
+Life-Threatening
+BEHAVIOR
+Wiley
+INTRODUCTION
+Emerging infectious diseases, such as HIV and 2009’s pandemic influenza A (H1N1), can have significant economic, social, and medical costs (Danziger, 1994; Gasparini, Amicizia, Lai, & Panatto, 2012; Meltzer, Cox, & Fukuda, 1999; Shrestha et al., 2011; Szucs, 1999). In late 2019, an emerging disease called coronavirus 2019 (COVID-19) rapidly spread across the globe and became an unprecedented public health event (Centers for Disease Control and Prevention [CDC], 2020; World Health Organization [WHO], 2020). Indeed, 1140 | © 2020 The American Association of Suicidology
+in 4 months’ time, COVID-19—which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; Lu et al., 2020; Zhou et al., 2020)—has infected over 2 million people and caused nearly 150,000 deaths across 185 countries (CDC, 2020; Dong, Du, & Gardner, 2020). The U.S. has been among the most highly affected countries to date, accounting for approximately 30% of infections and 20% of deaths worldwide (CDC, 2020; Dong et al., 2020). Moreover, due to a combination of COVID-19’s long incubation period, ease of transmission, relatively high mortality rate (compared to the seasonal flu), and lack of pharmacological interventions (Linton et al., 2020; Rajgor, Lee, Archuleta, Bagdasarian, & Quek, 2020;
+Suicide Life Threat Behav. 2020;50:1140-1148.
+Shereen, Khan, Kazmi, Bashir, & Siddique, 2020), extraordinary social distancing interventions have been implemented in many states to slow the spread of the virus, including relatively restrictive shelter-in-place or stay-at-home orders issued in 42 states, the District of Columbia, and Puerto Rico (Mervosh, Lu, & Swales, 2020). These orders, which have shuttered schools, universities, and nonessential businesses, urge individuals to stay at home unless it is absolutely necessary to leave, and promote strict physical distancing to slow the spread of the virus (CDC, 2020).
+From a public health perspective, the reasoning behind such interventions is clear: Physically separating people is an effective strategy for preventing infectious diseases from spreading (Ahmed, Zviedrite, & Uzicanin, 2018; Jackson, Mangtani, Hawker, Olowokure, & Vynnycky, 2014; Qualls et al., 2017), including COVID-19 (Flaxman et al., 2020; Thakkar, Burstein, Hu, Selvajar, & Klein, 2020). Yet, despite the necessity of stay-at-home orders and other social distancing interventions from a disease prevention perspective, these measures are likely to have numerous unintended social and economic consequences that may adversely affect psychological outcomes during this time (Galea, Merchant, & Lurie, 2020; Reger, Stanley, & Joiner, 2020; Thunstrom, Newbold, Finnoff, Ashworth, & Shogren, 2020).
+Indeed, pandemics of this nature have well-documented economic and social consequences (Chen, Huang, Chuang, Chiu, & Kuo, 2011; Reger et al., 2020; Thunstrom et al., 2020)—some of which have been linked to psychological difficulties (Montemurro, 2020; Wang et al., 2020), including suicide risk (see Reger et al., 2020). Currently, in the United States, beyond the immediate physical health consequences of COVID-19 (and related fear and distress associated with these consequences), two consequences of the COVID-19 pandemic that stand out as particularly relevant to suicide risk are the social isolation related to stay-at-home orders and the widespread job loss related to the current economic crisis—both of which have been theoretically and/or empirically linked to suicide risk (e.g., Classen & Dunn, 2012; Oyesanya, Lopez-Morinigo, & Dutta, 2015; Reger et al., 2020). For example, with regard to the economic consequences of this pandemic, both theory and research support an association between involuntary job loss and suicide risk (Classen & Dunn, 2012; Milner et al., 2014), with recent job loss from mass-layoffs in particular (comparable to what is occurring currently in the United States) associated with increased suicide risk (Classen & Dunn, 2012).
+Likewise, the widespread social distancing interventions implemented to slow the spread of the virus (of which stay-at-home orders are the most restrictive) have been proposed to increase suicide risk by increasing social isolation and loneliness (Reger et al., 2020). Specifically, although stay-at-home orders are designed to increase physical distancing in particular (and need not negatively impact social connections and connectedness through remote or virtual means), researchers have suggested that an unintended consequence of social distancing interventions may be an increase in social isolation and related feelings of loneliness (Reger et al., 2020). Loneliness, in turn, is a well-documented suicide risk factor (e.g., Calati et al., 2019; Joiner, Ribeiro, & Silva, 2012; Li, Dorstyn, & Jarmon, 2020) that evidences strong associations with suicidal ideation, suicide attempts,
+I	| 1141
+Wiley 1
+and suicide risk (e.g., Calati et al., 2019; Chang et al., 2017; Li et al., 2020; Stickley & Koyanagi, 2016; Stravynski & Boyer, 2001).
+Beyond just examining the relations of pandemic-related stay-at-home orders and job loss to suicide risk, research is needed to clarify the factors that may account for these relations. The Interpersonal Psychological Theory of Suicide (ITS; Van Orden et al., 2010) provides a particularly useful framework in this regard. According to this theory, the desire for suicide is driven by perceived burdensomeness (i.e., perceptions of being a burden to others) and thwarted belongingness (i.e., feeling disconnected from and lacking meaningful relationships with others). Notably, although thwarted belongingness overlaps with loneliness, it is a broader construct that also captures the nature and extent of supportive and reciprocal interpersonal relationships. A recent me-ta-analysis provides empirical support for this theory and the proposed relations of perceived burdensomeness and thwarted belongingness to suicidal desire (Chu et al., 2017). With regard to the relevance of these factors to the relations of interest in this study, thwarted belongingness would be expected to play a particularly important role in the relation of stay-at-home orders to suicide risk, capturing the proposed unintended negative consequences of social distancing interventions on social connectedness (Reger et al., 2020). Conversely, although a recent job loss could also contribute to thwarted belongingness (particularly if that job was a primary source of social connection), theory suggests the particular relevance of perceived burdensomeness to the relation between job loss and suicide risk. Specifically, the inability to provide for loved ones or support oneself financially could increase the experience of being a burden on others, which, in turn, would increase the desire for suicide and suicide risk (Cukrowicz, Cheavens, Van Orden, Ragain, & Cook, 2011; Van Orden et al., 2010).
+The present study examined the relations of COVID-19-related stay-at-home orders and job loss to suicide risk, both directly and indirectly through thwarted belongingness and perceived burdensomeness. Given that social distancing and related social isolation have been proposed to increase suicide risk through loneliness, we also examined the indirect relation of stay-at-home orders in particular to suicide risk through loneliness. We hypothesized that both recent job loss and stay-at-home order status would be associated with increased suicide risk. We also hypothesized the differential relevance of thwarted belongingness and perceived burdensomeness to the relations of stay-at-home order status and pandemic-related job loss, respectively, to suicide risk. Specifically, we hypothesized that stay-at-home order status would be indirectly related to suicide risk through thwarted belongingness and loneliness, whereas recent job loss would be indirectly related to suicide risk through perceived burdensomeness.
+Method
+Participants
+Participants included a nationwide community sample of 500 adults from 45 states in the United States who completed online measures
+GRATZ et al.
+Suicide and
+Life-Threatening
+BEHAVIOR
+1142 | w. r	I
+-----'Wiley
+through an Internet-based platform (Amazon’s Mechanical Turk; MTurk) from March 27, 2020, through April 5, 2020. The study was posted to MTurk via CloudResearch (cloudresearch.com), an online crowdsourcing platform linked to MTurk that provides additional data collection features (e.g., creating selection criteria; Chandler, Rosenzweig, Moss, Robinson, & Litman, 2019). MTurk is an online labor market that provides “workers” with the opportunity to complete different tasks in exchange for monetary compensation, such as completing questionnaires for research. Data provided by MTurk-recruited participants have been found to be as reliable as data collected through more traditional methods (Buhrmester, Kwang, & Gosling, 2011). Likewise, MTurk-recruited participants have been found to perform better on attention check items than college student samples (Hauser & Schwarz, 2016) and comparably to participants completing the same tasks in a laboratory setting (Casler, Bickel, & Hackett, 2013). Studies also show that MTurk samples have the advantage of being more diverse than other Internet-recruited or college student samples (Buhrmester et al., 2011; Casler et al., 2013). For the present study, inclusion criteria included (a) U.S. resident, (b) at least a 95% approval rating as an MTurk worker, (c) completion of at least 5,000 previous MTurk tasks (referred to as Human Intelligence Tasks), and (d) valid responses on questionnaires (i.e., assessed by accurate completion of multiple attention check items).
+Participants (47% women; 51.8% men; 0.2% transgender; 0.6% nonbinary; 0.4% other) ranged in age from 20 to 74 years (M age = 40.0 ± 11.6). All states in the United States were represented, with the exception of Delaware, New Hampshire, North Dakota, Vermont, and West Virginia (see Table 1 for the distribution of participants across states). Most participants identified as White (85%), followed by Black/African-American (8.4%), Asian/Asian-American (6.6%), Latinx (1.9%), and Native American (1.6%). Regarding educational attainment, 11.8% had completed high school or received a GED, 35.6% had attended some college or technical school, 43% had graduated from college, and 9.6% had advanced graduate/profes-sional degrees. Most participants were employed full-time (69.2%), followed by employed part-time (16.2%) and unemployed (14.6%). Annual household income varied, with 30.6% of participants reporting an income of <$35,000, 33.6% reporting an income of $35,000 to $64,999, and 35.8% reporting an income of >$65,000. With regard to participants’ household composition, 58.6% reported living alone and the remaining 41.4% reported living with at least one other person (ranging from 2-8; mean = 3.2 ± 1.1). Very few participants reported having sought out testing for COVID-19 (1%) or having been infected with COVID-19 (0.8%).
+Procedure
+All procedures received approval from the university’s Institutional Review Board. To ensure the study was not being completed by a bot (i.e., an automated computer program used to complete simple tasks), participants first responded to a Completely Automatic Public Turing test to Tell Computers and Humans Apart
+(CAPTCHA) prior to providing informed consent. On the consent form, participants were also informed that “-.we have put in place a number of safeguards to ensure that participants provide valid and accurate data for this study. If we have strong reason to believe your data are invalid, your responses will not be approved or paid and your data will be discarded.” Data were collected in blocks of nine participants at a time, and all data, including attention check items and geolocations (i.e., geographical coordinates used to identify participants outside of the United States and/or in locations determined to be “bot farms” within the MTurk community; see Kennedy, Clifford, Burleigh, Jewell, & Waggoner, 2018), were examined by researchers before compensation was provided. Attention check items included three explicit requests embedded within the questionnaires (e.g., “If you are paying attention, choose ‘2’ for this question”), two multiple-choice questions (e.g., “How many words are in this sentence?”), a math problem (e.g., “What is 4 plus 2”), and a free-response item (e.g., “Please briefly describe in a few sentences what you did in this study”). Participants who failed one or more attention check items were removed from the study (n = 53 of 553 completers). Workers who
+completed the study and whose data were considered valid (based on attention check items and geolocations; N = 500) were compensated $3.00 for their participation.
+Measures
+COVID-19-related experiences and stressors were assessed via a 20-item measure developed for this study. Participants were asked about a variety of relevant experiences related to the pandemic. Of interest to the present study were questions assessing whether they were currently under a stay-at-home order (“Do you live in a state that has instituted a stay-at-home order?” [0 = no; 1 = yes]) and whether they had experienced a recent job loss as a result of the pandemic (“Have you experienced a recent job loss due to the pandemic?” [0 = no; 1 = yes]).
+The Interpersonal Needs Questionnaire (INQ; Van Orden, Cukrowicz, Witte, & Joiner, 2012) is a 15-item self-report measure with subscales assessing thwarted belongingness and perceived burdensomeness. The 15-item iteration of the INQ was used due to research demonstrating that it outperforms other versions of this measure (Hill et al., 2015). Items assessing thwarted belongingness (e.g., “These days, I feel disconnected from other people.”) and perceived burdensomeness (e.g., “These days I think my death would be a relief to the people in my life.”) are rated on a 7-point Likert-type scale ranging from 1 (not at all true for me) to 7 (very true for me). Higher scores on each subscale are indicative of greater thwarted belongingness and perceived burdensomeness. Research provides support for the reliability and convergent and divergent validity of both subscales (Hallensleben, Spangenberg, Kapusta, Forkmann, & Glaesmer, 2016; Marty, Segal, Coolidge, & Klebe, 2012; Van Orden et al., 2012). Internal consistency of both subscales in the current sample was acceptable (as > 0.91).
+The UCLA Loneliness Scale—version 3 (ULS-3; Russell, 1996; Russell, Peplau, & Cutrona, 1980) is a 20-item self-report measure of perceptions of loneliness and social isolation. Participants rate items (e.g., “No one really knows me well;” I lack companionship;” “There are people I feel close to [reverse scored]”) based on how often they apply to themselves on a 4-point Likert-type scale ranging from 1 (never) to 4 (often). Higher scores are indicative of greater loneliness. The ULS-3 has demonstrated adequate test-retest reliability and good construct validity (Russell, 1996). Internal consistency in the present sample was acceptable (a = 0.94).
+The Depression Symptom Index-Suicide Subscale (DSI-SS; Metalsky & Joiner, 1997) was used to measure current suicide risk. The DSI-SS is a 4-item screening measure that assesses the frequency and intensity of suicidal thoughts, plans, and impulses over the past 2 weeks. Scores on this measure have been found to be positively associated with depression symptoms (Cukrowicz et al., 2011; Joiner, Pfaff, & Acres, 2002) and to be higher among individuals with (vs. without) a history of suicide attempts (Capron et al., 2012). For the present study, a continuous variable assessing the severity of current suicide risk was calculated by summing all four items (a = 0.94 in this sample).
+RESULTS
+Preliminary analyses
+At the time of data collection, 82.4% (n = 412) of participants were under a stay-at-home order and 11% (n = 55) reported a recent job loss related to the pandemic. On the DSI-SS, 11.6% (n = 58) of participants were classified as having high suicide risk (operationalized as a score of >3 on this measure; Joiner et al., 2002). Descriptive data for and correlations among the primary variables of interest are
+Suicide and
+Life-Threatening
+BEHAVIOR
+1144 | -. .	I
+-----'Wiley
+presented in Table 2. Results revealed significant positive zero-order associations between recent job loss (0 = no; 1 = yes) and both suicide risk and perceived burdensomeness. Stay-at-home order status (0 = no; 1 = yes) was not significantly correlated with suicide risk; however, it was significantly positively correlated with thwarted belongingness and loneliness.
+Primary analyses
+The PROCESS (version 3.3) macro for SPSS (Model 4; Hayes, 2017) was used to examine the indirect relations of (a) recent job loss to suicide risk through perceived burdensomeness (thwarted belongingness and loneliness were not examined in this model due to their lack of significant associations with recent job loss); and (b) stay-at-home order status to suicide risk through thwarted belongingness and loneliness (perceived burdensomeness was not examined in this model due to its lack of significant association with stay-at-home order status). In both models, age, sex, racial/ethnic background, income, and household composition (lives alone vs. lives with other people) were included as covariates, given their relevance to suicide risk and/or pandemic-related outcomes. All indirect relations were evaluated using bias-corrected 95% confidence intervals based on 10,000 bootstrap samples.
+With regard to the analysis examining the indirect relation of recent job loss to suicide risk through perceived burdensomeness, the overall model was significant, accounting for 29% of the variance in suicide risk, F (7, 492) = 28.62, p < .001. Although the total relation of recent job loss to suicide risk (including both the direct relation and the indirect relation through perceived burdensomeness,
+represented in Figure 1 as path c) was significant, the direct relation of recent job loss to suicide risk (i.e., the remainder of the relation not accounted for by the indirect relation through perceived burdensomeness, represented in Figure 1 as c’; Preacher & Hayes, 2008) was not significant. Further, although recent job loss was significantly associated with perceived burdensomeness and perceived burdensomeness was significantly associated with suicide risk in the model, the indirect relation of recent job loss to suicide risk through perceived burdensomeness was not significant (see Figure 1).
+As for the analysis examining the indirect relation of stay-at-home order status to suicide risk through thwarted belongingness and loneliness, the overall model was significant, accounting for 12% of the variance in suicide risk, F (8, 491) = 8.21, p < .001. Of note, although stay-at-home order status was significantly uniquely associated with both thwarted belongingness and loneliness, only thwarted belongingness (and not loneliness) was significantly uniquely associated with suicide risk. In addition, results revealed a significant indirect relation of stay-at-home order status to suicide risk through thwarted belongingness, but not loneliness (see Figure 2).
+DISCUSSION
+The results of this study provide preliminary empirical support for the theorized relations of COVID-19-related social and economic consequences to increased suicide risk (Reger et al., 2020). Specifically, the results of this study highlight the differential relevance of thwarted belongingness and perceived burdensomeness to the relations of stay-at-home orders and pandemic-related job
+loss, respectively, to suicide risk. Providing partial support for study hypotheses, although the presence of a stay-at-home order was not significantly associated with greater suicide risk at a zero-order level, it was indirectly related to suicide risk through greater thwarted belongingness. These findings suggest that any association of stay-at-home orders (at least in the short-term) to suicide risk is due to the association these orders have with increased social disconnection (Reger et al., 2020). Interestingly, although the presence of a stay-at-home order was significantly uniquely associated with both loneliness and thwarted belongingness, only thwarted belongingness was uniquely associated with suicide risk and explained the relation of stay-at-home order status to suicide risk in this sample. Together, these results suggest that although stay-at-home orders may very well increase the potential for loneliness among adults in the United States, it is not loneliness specifically but a broader sense of disconnection and absence of meaningful relationships that accounts for the relation of stay-at-home orders to greater suicide risk.
+Results of this study also provide partial support for study hypotheses pertaining to the relation of pandemic-related job loss to
+suicide risk. Specifically, although recent job loss evidenced a significant zero-order correlation with suicide risk, it was not uniquely associated with suicide risk when perceived burdensomeness was included in the model. Likewise, results provided no support for an indirect relation of job loss to suicide risk through perceived burdensomeness. These findings are most consistent with a proxy risk factor model (see Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001), suggesting that the total relation of recent job loss to suicide risk is due to their shared association with perceived burdensomeness. Although inconsistent with our hypotheses, these results are not without support in the literature, as there is some evidence to suggest that involuntary job loss in general is not associated with increased suicide risk in the short-term, outside of mass-layoff events (see Classen & Dunn, 2012). Instead, evidence suggests that the duration of time spent unemployed following a job loss may be more strongly associated with suicide risk (Classen & Dunn, 2012). Thus, it may be that the strength of the relation of pandemic-related job loss to suicide risk will increase over time if new employment is not obtained and financial strain continues.
+Suicide and
+Life-Threatening
+BEHAVIOR
+1146 | -. .	I
+------'Wiley
+Several limitations warrant consideration. First, the use of cross-sectional data precludes any conclusions about the precise nature or direction of the associations examined here. In particular, although theory and research suggest that both job loss and social isolation may increase suicide risk (Classen & Dunn, 2012; Oyesanya et al., 2015; Reger et al., 2020), our data cannot rule out the possibility that elevations in suicide risk reported in this study preceded or occurred concurrently with (but unrelated to) these factors. Prospective, longitudinal studies are needed to clarify the extent to which the social and economic consequences of COVID-19 and related stay-at-home orders increase suicide risk, as well as the mechanisms underlying these relations. Another limitation is the exclusive reliance on self-report questionnaire data, which may be influenced by social desirability biases or recall difficulties. Future research should incorporate structured clinical interviews and/or timeline follow-back procedures to assess suicide risk and its temporal relation to social distancing and economic difficulties. Likewise, although the use of a diverse nationwide community sample is a strength of this study, the generalizability of our findings to particular at-risk groups (e.g., hospitalized patients, individuals with chronic medical conditions, health care workers) remains unclear. Future research is needed to examine the relations of COVID-19 and related social and economic consequences to suicide risk within these vulnerable groups in particular.
+Finally, it is important to note that the results of this study speak to only the early associations of stay-at-home orders and COVID-19-related job loss to suicide risk among individuals in the United States. However, it is likely that the consequences and psychological impact of these factors may change over time. For example, and consistent with the proposed mechanisms through which stay-at-home orders and other social distancing interventions are thought to increase suicide risk (Reger et al., 2020), the psychological impact and negative consequences of these orders may intensify over time, with suicide risk increasing as the duration of these orders increases. Likewise, research suggests that the duration of unemployment following an involuntary job loss is more strongly associated with suicide risk than the initial job loss (Classen & Dunn, 2012); thus, it is likely that the relation between pandemic-related job loss and suicide risk may increase over time, particularly in the context of the current economic crisis and ongoing stay-at-home and shelter in place orders (which decrease the likelihood of obtaining a new job in the near future). Although research examining the early impact of this pandemic and associated factor on suicide risks is important, it is imperative that research continues to track these relations as the pandemic and related public health interventions persist over time.
+Despite these limitations, the results of this study highlight the potential impact of COVID-19 social and economic consequences on suicide risk among adults in the United States, as well as the relevance of thwarted belongingness and perceived burdensomeness to these associations. These results are consistent with theory and research highlighting the relevance of thwarted belongingness and perceived burdensomeness to suicide risk (e.g., Chu et al., 2017; Van Orden et al., 2010), and suggest that these may be important factors to target in the context of focused interventions aimed at decreasing suicide
+risk during this time. In the absence of effective COVID-19 infection prevention efforts and/or pharmacological interventions (e.g., vaccines), large-scale public health interventions such as social distancing or stay-at-home orders are necessary to reduce the spread of the virus and infection-related mortality. However, in the context of these necessary public health interventions, our results speak to the need to also implement interventions aimed at mitigating the negative psychological consequences of both the social isolation and economic problems that can arise from or be exacerbated by stay-at-home orders.
+Specifically, our results provide further support for recent suggestions to focus on increasing social connection and connectedness in the context of stay-at-home orders and other social distancing interventions, in an effort to offset the isolation, loneliness, and disconnection that may inadvertently accompany these orders (see Reger et al., 2020). Likewise, among individuals who have experienced a job loss during this time, our findings suggest that interventions aimed at decreasing perceived burdensomeness and increasing individuals’ awareness of and connection to their contributions to the lives of others may help to decrease suicide risk among this vulnerable population. Finally, given both theoretical and emerging empirical literature suggesting increased suicide risk during this pandemic, it is important that crisis call centers continue to be funded and staffed to ensure that individuals who may have limited social contacts are able to seek help in emergency situations. Likewise, it is imperative that evidence-based tele-mental health services are made available and accessible to vulnerable individuals throughout the duration of stay-at-home orders and other social distancing interventions (Reger et al., 2020).
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+1943278x, 2020, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sltb.12654 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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+INTRODUCTION
+Adolescents and emerging adults who engage in non-suicidal self-injury (NSSI) are at increased risk for considering and attempting suicide (Guan et al., 2012; Klonsky et al., 2013). Although extensive research has identified risk factors for NSSI, NSSI remains a prevalent public health concern among adolescents and emerging adults (Muehlenkamp et al., 2012).
+Suicide Life Threat Behav. 2021;51:741-754.
+Lifetime prevalence estimates of NSSI among adolescents and emerging adults range from 13% to 17% (Swannell et al., 2014), and suicide remains the second leading cause of death among adolescents and young adults (CDC, 2018). Furthermore, a recent national survey conducted June 24-30, 2020, found that over one-quarter of 18- to 24-year-old respondents seriously considered attempting suicide in the previous month. Mentalization-based interventions, along with
+Suicide and
+Life-Threatening
+BEHAVIOR
+dialectical behavior therapy (DBT), are effective for self-injury and suicide-related outcomes (for a meta-analysis, see Ougrin et al., 2015). However, most young adults who engage in selfinjury do not seek professional help (Whitlock et al., 2011). Further, although DBT is useful for people who self-injure, it requires intensive, specialized treatment, and many people do not have access to these resources (McMain et al., 2017).
+Brief interventions that are accessible to a greater number of people, including individuals with subclinical symptoms of psychopathology, are needed. Decentering is a cognitive-affective regulation strategy used in many forms of psychotherapy; however, research on its relation to NSSI and suicide-related outcomes is scarce. The goal of this preliminary study was to examine whether decentering might buffer against the impact of past-year NSSI and suicide ideation via cognitive-affective factors known to increase risk of suicide-related outcomes. By targeting such cognitive-affective risk factors, 1clinicians may be better able to help individuals who engage in NSSI reduce future self-injurious thoughts and behaviors (Witt et al., 2019).
+NSSI and cognitive-affective risk for suicide ideation and behavior
+NSSI refers to deliberate self-injury without intent to die that often results in damage to bodily tissues (Butler & Malone, 2013; Nock, 2009). Emotion dysregulation underlies NSSI (Nock, 2009; Voon et al., 2014). Nock (2009) proposed intrapersonal (affective) and social reinforcement functions that motivate NSSI behaviors. Intrapersonal functions enable people to reduce aversive stimuli or generate desired ones. For instance, NSSI may elicit positive affect by enabling the person to “feel” something or reduce negative affective states by distracting from aversive situations. NSSI may also facilitate positive and negative interpersonal reinforcement (e.g., by eliciting a response from a third party or escaping unwanted social situations, respectively).
+Further, the emotional cascade theory proposes that individuals with high levels of distress may engage in rumination, a non-adaptive cognitive-affective regulation strategy in which they passively and repetitively dwell on negative affect (Nolen-Hoeksema et al., 2008; Treynor et al., 2003).
+1We note a conceptual distinction between cognitive-affective strategies and cognitive-affective risk factors. By cognitive-affective strategies, we refer to strategies aimed at regulating specific thought processes or emotional states. Decentering and rumination either reduce or maintain emotions and cognitions. In contrast, we refer to hopelessness and depressive symptoms as cognitive-affective risk factors or symptoms of psychopathology because they both have a cognitive (thought process) and affective (emotional) component, though neither hopelessness nor depressive symptoms are used to regulate thoughts and emotions.
+Rumination amplifies negative affect, and to escape or distract from negative affect, individuals may engage in dysreg-ulated behaviors, including NSSI (Selby et al., 2008; Selby & Joiner, 2009). In support of this theory, evidence demonstrates that young adults with high levels of rumination and high levels of negative affect are at increased risk of engaging in NSSI (Nicolai et al., 2016). Although NSSI serves to regulate unpleasant emotions and cognitions by alleviating negative affect in the moment (Klonsky, 2007), it increases the risk of future suicidal thoughts and behaviors (Guan et al., 2012; Hamza et al., 2012). Notably, young adults with a history of NSSI report higher levels of hopelessness and depressive symptoms (Fox et al., 2015; Moller et al., 2013). Thus, individuals who engage in NSSI experience a complex dynamic of deficits in emotion regulation and a variety cognitive-affective risk factors that increase risk for subsequent self-injurious behaviors. They may use NSSI to reduce unpleasant affect and the effects of ruminative thinking.
+Rumination is a transdiagnostic factor that also increases risk for suicide ideation (see Rogers & Joiner, 2017 for a meta-analysis). However, even though extensive research demonstrates an association between ruminative thinking and NSSI and suicide ideation, little is known about the mediating effect that rumination may have on the relation between NSSI and suicide ideation. Additionally, extensive evidence has established hopelessness and depressive symptoms as risk factors for suicide ideation (Miranda-Mendizabal et al., 2019; Ribeiro et al., 2018). Even though hopelessness and depressive symptoms are risk factors for NSSI and suicide ideation, whether they might mediate the relationship between NSSI and suicide ideation remains a question. One reason for this gap in research is that studies have focused on risk factors that predict either NSSI or suicidal thoughts and behaviors. Other studies have examined NSSI or suicidal thoughts and behaviors as predictors of each other but not the mechanisms that explain these associations. Given the substantial research aimed at identifying correlates and risk factors for NSSI and suicide ideation, our goal is to inform brief intervention strategies aimed at targeting suicide-related risk among individuals with NSSI. In other words, we are not interested in NSSI as an outcome, per se, because there is already considerable research on the factors that predict NSSI. Rather, because NSSI is often a predictor of future suicidal thoughts and behaviors, our goal is to identify mechanisms that explain the relation between NSSI and suicidal thoughts and behaviors and factors that moderate this relationship.
+Decentering
+Prevention and intervention efforts aimed at reducing NSSI would benefit from identifying effective cognitive-affective protective factors. Decentering may be one protective factor
+ROSARIO-WILLIAMS et al.
+worth investigating in relation to risk for suicide-related outcomes among individuals with NSSI. Decentering, also known as self-distancing and psychological distancing, refers to the ability to distance the self from an internal experience and acknowledge the gap between objective reality and the subjective reality the individual is constructing (Safran & Segal, 1996). This cognitive-emotional process requires that the individual perceives a situation as a distant and “dispassionate observer” instead of viewing the situation from a selfimmersed perspective. By psychologically distancing the self from a distressing situation, an individual may be better able to evaluate the situation and regulate emotions (Denny & Ochsner, 2014; Shahane & Denny, 2019). Safran and Segal (1996) proposed that intellectually grasping the gap between reality and one's constructed reality is not enough to produce meaningful change. Rather, in viewing one's experiences as a "dispassionate observer," the experience itself changes, allowing for sustained change.
+Elements of decentering have been used in different therapeutic interventions, including cognitive therapy and mindfulness (Bernstein et al., 2015; Safran & Segal, 1996). Bernstein et al proposed an integrated model of decentering that consists of three higher-order meta-cognitive processes. Meta - awareness refers to a person's ability to view their internal experience as a process rather than absolute truths (“I am incompetent” vs. “I am having a self-critical thought”). Disidentification from internal experience refers to the ability to observe one's internal experience from a distant perspective and distinct from the self (“I am ashamed,” vs. “a feeling of shame”). Reduced reactivity to thought content refers to the reduced effect that people's thoughts and feelings have on subsequent experiences.
+These elements of decentering are inversely related to symptoms of psychopathology (Bernstein et al., 2015; Naragon-Gainey & DeMarree, 2017), and evidence suggests that decentering may be an adaptive cognitive-affective regulation style. Decentering is associated with lower levels of shame and post-event processing (Kross et al., 2014), higher levels of mindfulness, value-based action, general psychological acceptance (McCracken et al., 2013), and adaptive interpersonal interactions prior to or following a socially stressful task (Kross et al., 2014). Decentering weakens the relationship between ruminative self-focus and automatic negative thoughts among depressed individuals (Lo et al., 2014), enhances recovery after an interpersonal stressor (Kross et al., 2014), and reduces the negative effects of rumination on working memory (Kaiser et al., 2015). Furthermore, decentering enables more adaptive cognitive appraisals of resources and demands under stressful states—that is, perceiving situations as challenges instead of threats—(Kross et al., 2014; Streamer et al., 2017). These findings suggest that decentering is a useful cognitive emotion regulation strategy that reduces negative
+appraisals of highly arousing stimuli and buffers against the deleterious effects of ruminative thinking.
+The present study
+Given the protective effects of decentering on psychological symptoms, this study examined the moderating role of decentering in the relation between NSSI and suicide ideation2 via rumination, hopelessness, and depressive symptoms. Even though decentering has been used in many psychotherapies and examined in relation to psychological symptoms, no study has examined whether decentering attenuates the relation between self-injury and recent suicidal thinking, especially in relation to risk factors known to predict and maintain suicide-related outcomes. Our goal was to address this gap. We propose that if decentering is related to mood improvement, then it may protect against the effects of cognitive-affective factors that increase risk for suicide-related outcomes. However, given the cross-sectional design of our study, we limited our examinations and predictions to the moderating effect of decentering. Our first aim examined the effects of decentering on rumination, hopelessness, and depressive symptoms. We predicted that higher levels of decentering would be associated with lower levels of these cognitive-affective risk factors. Our second aim investigated the moderating effect of decentering in the relation between past-year NSSI history and cognitive-affective risk factors. We predicted that for emerging adults with NSSI, higher levels of decentering would be associated with lower levels of rumination, hopelessness, and depressive symptoms. However, at low levels of decentering, NSSI would predict higher levels of rumination, hopelessness, and depressive symptoms. Finally, the third aim investigated the moderated indirect effect of decentering in the relation between past-year NSSI and recent suicide ideation via rumination, hopelessness, and depressive symptoms. We expected that decentering would moderate the indirect effects via these statistical mediators. Specifically, at low levels of decentering, there would be an indirect effect of past-year NSSI history on suicide ideation via rumination, hopelessness, and depressive symptoms, but these effects would be attenuated at high levels of decentering.
+METHOD
+Participants
+One-hundred twenty-five undergraduate students (ages 18-27) participated in the present study. Participants were
+2Given the low base rate of suicide attempts in our sample, we focused on the relation between NSSI and suicide ideation, both of which are risk factors for suicide attempts (Guan et al., 2012).
+Suicide and
+Life-Threatening
+BEHAVIOR
+recruited from an online portal in the institution's psychology department as part of an introductory psychology research requirement. Students who scored moderate-to-high in levels of anxiety or depressive symptoms were eligible to participate in the study. Of the 125 students who participated in the study, 99 (79%) identified as women (99 at birth), 25 (20%) identified as men (26 at birth), and 1 (1%) identified as questioning. The sample was racially/ethnically diverse: 38 (30%) were Asian; 37 (30%) were Hispanic; 23 (18%) were White; 10 (8%) were Black; and 17 (14%) identified as “Other.”
+Measures and procedure
+Screening and procedures
+Potential participants completed the Depression, Anxiety, and Stress Scale (DASS; Lovibond & Lovibond, 1995) as an initial screener. The DASS consists of 41 items that assess cognitive-affective symptoms of depression (e.g., selfcriticism, pessimism), physiological symptoms of anxiety and worry, and physiological symptoms of stress. Participants who scored an 18 or higher on the DASS were eligible to participate. We intended to recruit participants with at least moderate symptoms of either depression or anxiety (i.e., at least a 14 on the depression subscale or at least a 10 on the anxiety subscale); however, given the restrictions of the online recruitment screening system (only one overall cutoff score could be used), we selected a cutoff of at least 18 for the total combined subscales.
+Eligible participants completed in-person, self-report measures of past-year NSSI, decentering, rumination, hopelessness, depressive symptoms, and suicide ideation, among a battery of other measures and behavioral tasks not related to the present analyses. Upon completion of the study, participants were assessed for suicide risk (and, if needed, referred to the college's counseling center), debriefed, and compensated.
+NSSI
+Participants completed the Functional Assessment of SelfMutilation (FASM; Lloyd et al., 1997), a self-report measure that assesses history and past-year NSSI. The FASM assesses methods, motives, and frequency of NSSI, whether the individual received medical attention for engaging in NSSI, age of onset, how long the individual thought about engaging in NSSI before engaging in the behavior(s), whether the individual was using substances while engaging in NSSI, and severity of pain during the NSSI episode. Items from the FASM were derived from extensive literature reviews
+and interviews of adolescents from both psychiatric and non-clinical samples who engaged in NSSI (see Lloyd et al., 1997). The psychometric properties of the FASM have been established (Guertin et al., 2001; Penn et al., 2003), and other studies have used the scale to assess the functions of NSSI (e.g., Nock & Prinstein, 2004). Previous work has distinguished between minor and moderate/severe NSSI using the FASM (Lloyd-Richardson et al., 2007). Minor NSSI includes pulling hair, hitting self on purpose, picking areas of the body to draw blood, picking at wound, and inserting objects under the skin and nails. Moderate/severe forms of NSSI include cutting or carving the skin, scraping the skin, burning the self, self-tattooing, and erasing the skin to the point of drawing blood.
+Decentering
+The decentering subscale of the Experiences Questionnaire (Fresco et al., 2007) measures the tendency to be aware of and view thoughts and feelings as transient, the ability to control thoughts, and the ability to observe feelings in a non-judgmental manner. The subscale contains 11 items and is rated on a 5-point Likert-type scale ranging from 1 (never) to 5 (all the time). A sample item includes “I can slow my thinking at times of stress.” The scale has good concurrent, convergent, and discriminant validity (Fresco et al., 2007). The decentering subscale captures meta-awareness, reduced reactivity to thought content, and disidentification from internal experience (see Bernstein et al., 2015 for a review). The internal consistency reliability of decentering in this sample was a = 0.78.
+Rumination
+The Ruminative Response Scale (Nolen-Hoeksema, 1999; Treynor et al., 2003) is a widely used 22-item questionnaire that includes 10 items that assess brooding (i.e., the tendency to dwell on a negative mood and on its causes, meanings, and consequences) and reflection (i.e., the tendency to try to understand the reasons for one's dysphoric mood). Sample items for brooding and reflection include “Why do I always react this way?” and “analyze recent events to try to understand why you are depressed,” respectively. Items are scored on a 4-point Likert-type scale, ranging from 1 (almost never) to 4 (almost always). A sum was computed by totaling the 5 items on the brooding and 5 items on the reflection subscale, given that the remaining items overlap with symptoms of depression. The psychometric properties of the subscales have been previously established (Treynor et al., 2003). The internal consistency reliability of rumination in this sample was a = 0.81.
+Hopelessness
+The Beck Hopelessness Scale (BHS; Beck et al., 1974), is a widely used 20-item self-report measure that assesses general negative expectations about the future. Each item is scored as true or false, with some reverse-coded items, and higher scores reflect greater hopelessness. The psychometric properties of the BHS have been established for non-clinical samples (Hanna et al., 2011). The internal consistency reliability of hopelessness in this sample was a = .87.
+Depressive symptoms
+Participants completed the Beck Depression Inventory-II (Beck et al., 1996), a widely used 21-item questionnaire that measures symptoms of depression over the previous two weeks. The items are scored on a 4-point Likert-type scale ranging from 0 to 3, where 0 indicates no symptoms and 3 indicates severe symptoms. Proposed cutoff scores for the BDI-II are as follows: 0-13 (mild), 14-19 (mild), 20-28 (moderate), and 29-63 (severe). This measure has been validated among ethnically diverse college students (Carmody, 2005). In this sample, we excluded the suicide-related item in the total score to reduce overlapping variance with suicide ideation. The internal consistency reliability of the BDI-II in this sample was a = 0.88.
+Suicidal thoughts and behaviors
+The Beck Scale for Suicide Ideation (BSS; Beck & Steer, 1991) measures both passive and active suicidal thoughts,
+frequency and severity of thoughts, and history of suicide attempts. The scale consists of 21 items, using a 3-point Likert-type scale ranging from 0 to 2. Items 1-19 measure severity of suicide ideation in the previous week, while items 20 and 21 measure lifetime suicide attempts and severity of intentions during the last attempt. In this study, we modified the measure instructions to assess suicide ideation in the preceding two weeks. Participants also responded to the single item “In the past 6 months, have you tried to kill yourself?” The internal consistency reliability of suicide ideation (items 1-19 of the BSS) in this sample was.78. Although the BSS is a widely used measure, no validated cutoff scores have been established (Erford et al., 2018).
+Table 1 includes the means, standard deviations, and internal consistency reliability of the above measures.
+Demographic information
+Participants reported on their age, sex, gender, race/ethnic-ity, sexual orientation, and income. They also reported on whether they were receiving treatment and type of treatment.
+Data analytic plan
+Overall analyses focused on comparisons between young adults with and without past-year NSSI. We further classified participants based on severity of NSSI (no NSSI, minor NSSI, and moderate/severe NSSI), with the reference group being young adults with no past-year NSSI. We computed descriptive statistics to examine the distribution of participants who engaged in NSSI in the previous year compared
+to those who did not. We used bivariate correlations to test associations among study variables (Table 1). Next, we conducted independent-samples t-tests to examine differences in main study variables by gender and NSSI (Table 1), and one-way analysis of variance to examine differences by race/ ethnicity. To test the main hypotheses, we conducted a moderated mediation analysis using the Process Macro for SPSS, version 3.1 (Model 7) (see Figure 1). In the moderated mediation analysis, NSSI was the predictor, suicide ideation was the outcome, rumination, hopelessness, and depressive
+symptoms were parallel mediators, and decentering was the moderator. Decentering was centered around its mean prior to computing interactions. The analyses adjusted for sex. Given that our sample was non-clinical and that previous research has distinguished between minor and moderate/severe NSSI (Lloyd-Richardson et al., 2007), we followed up these analyses using three separate regression models distinguishing based on severity of NSSI. We applied a Bonferroni correction (p < 0.017) to these models. Thus, Tables 2-3 present moderated mediation analyses grouped by severity of NSSI.
+We used bias-corrected 95% bootstrapped confidence intervals for the moderated mediation analyses to test significant indirect effects. Bootstrapping procedures do not assume a normal distribution (Preacher & Hayes, 2008).
+RESULTS
+Sample characteristics
+Bivariate associations among main variables are presented in Table 1. Notably, decentering was inversely related to all risk factors. Age was unrelated to any of the study variables and was not included in any of the follow-up analyses. Fifty-five percent of the sample (n = 69) reported any NSSI within the previous year, with 32% (n = 40) reporting minor NSSI and 23% (n = 29) reporting moderate/severe NSSI. Independentsamples t-tests revealed that young adults who reported NSSI within the past year reported higher levels of all clinical symptoms, but lower levels of decentering (Table 1). Sex differences emerged for decentering, with males scoring higher than females (Table 1). We included sex as a covariate in the moderation analyses. There were no racial/ethnic group differences in any of the study variables of interest; therefore, race/ethnicity was not included in moderation analyses.
+Of the 69 participants who reported NSSI in the previous year, most participants reported biting their lips (n = 39; 57%), followed by picking at a wound (n = 33; 48%), hitting themselves on purpose (n = 27; 39%), picking areas of their body to the point of drawing blood (n = 24; 35%), carving their skin (n = 19; 28%), and pulling their hair (n = 16; 23%). The least commonly endorsed items were inserting objects
+under their skin (n = 1; 1%), erasing their skin (n = 2; 3%), and burning their skin (n = 4; 6%). Note that 24 (out of 29) participants who endorsed moderate/severe forms of NSSI also reported minor forms of NSSI. Further, 23% (n = 29) of the sample endorsed minimal depressive symptoms, 17% (n = 21) endorsed mild symptoms, while 26% (n = 33) and 34% (n = 41) endorsed moderate and severe symptomology, respectively. Nineteen (15%) participants reported a lifetime suicide attempt, and 4 participants indicated that they attempted within the previous 6 months. Forty-seven participants (38%) endorsed suicide ideation in the preceding two weeks, though they denied suicide plans. Seventeen percent (n = 21) of the sample reported being in treatment, while 14 participants (11%) left the item blank. Finally, 64 (51%) participants (in the overall sample) reported SI (M = 7.98, SD = 4.87). Among participants who reported SI, scores on the BSS ranged from 1 to 23. The overall sample range was 0-23.
+Primary analyses
+To test our first hypothesis, we examined the direct effects of decentering on rumination, hopelessness, and depressive symptoms. Findings revealed a significant negative effect for all cognitive-affective factors: Higher levels of decentering were associated with lower levels of rumination (b = -0.25, p < 0.01), hopelessness (b = -0.26, p < 0.01), and depressive symptoms (b = -0.77, p < 0.01).
+Our second hypothesis was that decentering would moderate the relation between past-year NSSI history and cognitive-affective risk factors. There was a significant
+interaction between NSSI and decentering in statistically predicting rumination (b = -0.30, p =.03). Specifically, at low and average levels of decentering, young adults with pastyear NSSI reported higher levels of rumination than their peers without NSSI. However, at high levels of decentering, there were no group differences in rumination. The interaction between past-year NSSI and decentering did not predict hopelessness (b = -0.06, p = 0.60) or depressive symptoms (b = -0.49, p = 0.07). We further explored interactions between severity of NSSI and decentering in predicting
+rumination, hopelessness, and depressive symptoms, respectively. There was a significant interaction between moder-ate/severe NSSI and decentering in predicting rumination (b = -0.42, p = 0.03), but no interaction between minor NSSI and decentering in predicting rumination (b = -0.25, p = 0.11). Specifically, at low and average levels of decentering, young adults with moderate/severe NSSI reported higher levels of rumination than their peers without NSSI. These differences were no longer significant at high levels of decentering. There were no significant interactions between
+severity of NSSI and decentering in predicting either hopelessness (Table 3a) or depressive symptoms (Table 4a).
+Our final hypothesis was that decentering would moderate the indirect relationship between past-year NSSI and suicide ideation via rumination, hopelessness, and depressive symptoms, respectively. The index of moderated mediation was statistically significant for rumination, but not for hopelessness or depressive symptoms. Thus, at low and average levels of decentering, there was an indirect effect of NSSI and suicide ideation via rumination; however, at high levels of decentering, there was no indirect effect via rumination. See Figure 1 for a visual schematic of the overall moderated mediation analyses.
+We further explored these moderated mediation effects by severity of NSSI. The index of moderated mediation was significant for moderate/severe NSSI but not minor NSSI via rumination, and not via hopelessness or depressive symptoms. Specifically, moderate/severe NSSI had a direct effect on suicide ideation (b = 2.47, p = 0.03), whereas minor NSSI did not (b = 1.29, p = 0.20). Moreover, at low and average levels of decentering, young adults with moderate/severe NSSI (relative to no NSSI) had increased rumination, which predicted higher levels of suicide ideation. However, at high levels of decentering, the effect of NSSI on suicide ideation via rumination was not statistically significant (see Table 2b). See Tables 3b and 4b for the moderated mediation analyses via hopelessness and depressive symptoms, respectively. Given that results grouped by severity of NSSI were consistent with the overall models, we present these results in Tables 2-4, instead of presenting the overall models.
+Given this study's cross-sectional design, we tested alternative models with suicide ideation as the predictor and NSSI severity (minor vs no NSSI and moderate/severe vs. no
+NSSI) as the outcome; rumination, hopelessness, and depressive symptoms as mediators; and decentering as a moderator. SI did not statistically predict rumination or hopelessness, though it predicted higher levels of depressive symptoms (b = 0.16, SE = 0.06, p = 0.01, 95% CI = 0.04, 0.27). In all models, suicide ideation statistically predicted moderate/ severe NSSI but did not predict minor NSSI. Further, the index of moderated mediation was not significant for any of the indirect effects (i.e., via rumination, hopelessness, or depressive symptoms). Thus, decentering did not moderate the relation between suicide ideation and NSSI via any of the proposed mediators.
+DISCUSSION
+The present study investigated the moderating role of decentering in the relation between NSSI and suicide ideation via cognitive-affective factors proposed to maintain risk for NSSI and predict suicide ideation. Overall, findings revealed that decentering moderated the relationship between NSSI and suicide ideation via rumination but not the relation via hopelessness or depressive symptoms. Further, the moderating effects of decentering were particularly important for youth with moderate/severe NSSI relative to their peers without NSSI. These findings provide preliminary evidence that decentering may attenuate the impact of having a history of NSSI on vulnerability to suicide ideation via cognitive factors like rumination among young adults. We discuss the clinical and theoretical implications within the context of the existing literature and the emotional cascade theory.
+First, results indicated that young adults with past-year NSSI reported higher levels of rumination, depressive
+symptoms, and suicide ideation than their peers without NSSI. These results are consistent with findings from a metaanalysis (Fox et al., 2015) and a systematic review (Moller et al., 2013) indicating higher levels of symptomatology among adults with NSSI. However, both groups reported similar levels of hopelessness, which is inconsistent with research suggesting that individuals with NSSI report higher levels of hopelessness than their peers (Perez Rodriguez et al., 2017). However, this previous research was conducted with a clinical sample, while the present sample included a college-student sample with elevated symptoms of depression and/or anxiety, and this may partially account for the difference in findings.
+Further, young adults who engaged in NSSI reported lower levels of decentering than their peers without NSSI. This finding corroborates evidence that individuals who engage in NSSI have deficits in cognitive-affective regulation (Selby et al., 2008), and provides preliminary evidence that young adults with and without NSSI differ on trait levels of decentering. Young adults who engage in NSSI to regulate emotions may have difficulty viewing their experiences from a third-person perspective, which may increase vulnerability to cognitive-affective symptoms like depression, anxiety, and suicide ideation. Young adults who self-injure may experience difficulty with the three elements of decentering described earlier. For instance, relative to their peers who do not self-injure, those who do self-injure may lack the metaawareness to view their internal experiences as transient rather than absolute truths. They may also have difficulty regulating their behavioral responses to internal thoughts and feelings, thereby engaging in NSSI to self-regulate. Young adults who
+engage in moderate/severe NSSI may have more difficulties with these regulation strategies than their peers who engage in minor forms of NSSI, even though both groups may have more difficulties than their peers without NSSI.
+Consistent with our hypotheses, results also revealed a negative relationship between decentering and rumination, hopelessness, and depressive symptoms. These findings support previous work highlighting the potential benefits of decentering in reducing symptoms of psychopathology (Bernstein et al., 2015; Kaiser et al., 2015; Kross et al., 2014; Lo et al., 2014; Naragon-Gainey & DeMarree, 2017). However, and contrary to expectations, decentering did not moderate the relation between NSSI and hopelessness and depressive symptoms, although it moderated the direct effect of NSSI on rumination. These latter findings support previous research highlighting the effects of decentering in buffering against the effects of ruminative thinking (Kaiser et al., 2015; Lo et al., 2014). What is novel about this finding is that decentering may attenuate the relationship between NSSI and rumination. Recall that the emotional cascade theory proposes that individuals in distress engage in rumination, which amplifies negative affect, resulting in NSSI to regulate the negative affect (Selby et al., 2008; Selby & Joiner, 2009). This creates a positive feedback loop that maintains a cycle of self-injury. Our findings suggest that trait decentering may counteract the effect of trait rumination for people who engage in NSSI. In other words, the tendency to view one's experiences as a distant observer and have the meta-cognition to detach one's objective experiences from interpretations of that experience may supersede the passive tendency to dwell on
+the subjective experience, itself. This might be one reason why decentering moderated the relation between NSSI and ruminative thinking but had no effect on hopelessness and depressive symptoms, though it should be noted that the cross-sectional nature of this study limits any inferences about direction of these relationships.
+Finally, we examined the moderated mediating effect of decentering in the relation between NSSI and suicide ideation via rumination, hopelessness, and depressive symptoms. There was a significant effect only for the indirect effect through rumination. In other words, NSSI was associated with suicide ideation via rumination only when levels of decentering were low or average, but not high. Follow-up analyses also revealed similar effects for young adults with moderate/severe NSSI relative to no NSSI, but no significant effects for young adults with minor NSSI relative to their peers without NSSI. These findings suggest a different relationship between NSSI and suicide ideation via rumination, depending on levels of decentering, among individuals who are already at risk for suicide ideation and attempts, given the severity of their self-injurious behaviors (Lloyd-Richardson et al., 2007). Thus, not only does decentering moderate the direct relation between NSSI and rumination, but it also moderates the indirect effect that NSSI may have on suicide ideation via rumination, especially among those with higher risk. The ability to view distressing situations from a detached vantage point may enable young adults to view their distress more objectively and prevent them from ruminating, thereby decreasing vulnerability to suicidal thinking and potential suicidal behaviors. These interpretations are made with caution, given the cross-sectional and correlational design of our study. We encourage future research with longitudinal or experimental designs to further assess the protective effects of decentering among vulnerable youth who self-injure and are at risk for considering and attempting suicide.
+We note, however, the distinct moderating effect of decentering among young adults with minor NSSI and those with moderate/severe NSSI relative to their peers without NSSI. Young adults with minor NSSI (e.g., lip biting, picking at a wound) appeared more similar to their peers without NSSI than to their peers with moderate/severe NSSI. This is a critical distinction, particularly in the context of SI. Previous research suggests that adolescents with mod-erate/severe NSSI report worse psychiatric outcomes, including history of suicide attempts and current SI, than their peers with minor NSSI (Lloyd-Richardson et al., 2007). Emerging adults with minor NSSI may engage in these “minor” NSSI behaviors for different reasons (e.g., boredom) or more automatic functions other than for emotion regulatory purposes (Lloyd-Richardson et al., 2007). Further research is required to better distinguish the functions of minor vs moderate/severe NSSI and how young
+adults who engage in these types of NSSI behaviors differ on cognitive-affective risk factors and regulation strategies prospectively.
+We note that contrary to our hypotheses, decentering did not buffer against the relationship between past-year NSSI and hopelessness or depressive symptoms, nor did it moderate the indirect relationship between NSSI and suicide ideation via hopelessness and depressive symptoms. One interpretation of these findings could be that both hopelessness and depressive symptoms are already strongly associated with suicide ideation. Individuals with high levels of decentering may already be less likely to consider suicide or self-injure. Thus, state decentering, in which individuals are trained to adopt a different way of viewing their experiences, may undermine hopeless and depression-related cognitions in real time, which may reduce vulnerability to subsequent suicide ideation. Future experimental designs are necessary to test how state decentering across various groups of selfinjury affects psychological symptoms both concurrently and prospectively, particularly in relation to suicidal thoughts and behaviors. Online intervention studies may be of interest for young adults; brief online interventions may increase accessibility to individuals who may self-injure but do not seek professional help and individuals with subclinical levels of psychopathology who engage in NSSI. Given that rumination is a transdiagnostic factor that facilitates dysregulated behaviors such as NSSI, it is an important target of intervention. Research would benefit from identifying whether state decentering moderates the relation between NSSI and future state rumination, and vice versa. Understanding how these factors maintain each other over time, and whether decentering disrupts the relation between them, may aid in designing specific interventions that reduce risk for NSSI and suicide ideation.
+Finally, we tested alternative models in which suicide ideation was the predictor and severity of NSSI the outcome. The moderated mediating effect of decentering was not significant for any of the mediators (i.e., rumination, hopelessness, depressive symptoms). This may be because suicide ideation was measured in the preceding two weeks, whereas participants reported on their NSSI within the previous year, thereby reducing the likelihood of a statistically mediated effect. Alternatively, the findings may suggest preliminary evidence of directionality from NSSI to rumination to suicide ideation. However, these interpretations are tentative, given the cross-sectional and non-experimental design of the study.
+LIMITATIONS, STRENGTHS, AND FUTURE DIRECTIONS
+Some limitations of this study should be noted. First, we used a cross-sectional design, which limits our ability to draw
+causal inferences. Despite the finding of a statistically significant indirect effect through rumination, we cannot infer that rumination causally mediates the relation between NSSI and suicide ideation. Longitudinal designs are necessary to establish these causal relations by delineating the temporal order of measurements. Using panel data and cross-lagged analysis can clarify the causal relation between NSSI, rumination, and suicide ideation.
+We also note that decentering has been shown to protect against maladaptive emotion regulation strategies and is associated with improved mood; a logical next step would have been to examine the protective effects of decentering against both NSSI and suicide ideation. However, given the cross-sectional nature of this study and the time measurement of NSSI (past year) and suicide ideation (past 2 weeks), we thought that too much variability would exist in predicting past-year NSSI. Therefore, we limited our investigation to the moderating effect of decentering in the relation between NSSI and suicide ideation. Future research may consider examining the protective effect of decentering on both NSSI and suicide ideation using a similar time frame to measure both outcomes. Future studies may also investigate how realtime changes in decentering may be directly associated with thoughts of NSSI and suicide ideation. Our hope is that this study has generated new ideas to examine brief interventions that might be more accessible for emerging adults. Further, our sample was non-clinical. Ideally, this research would be replicated with clinical and community samples to establish the moderating effects of decentering among individuals with varying degrees of psychopathology.
+The present study also has several strengths. The sample was ethnically/racially diverse. Further, even though the sample was non-clinical with low levels of suicide ideation (relative to a clinical sample), more than 50% of participants reported moderate-to-severe depressive symptoms. Moreover, a high proportion of participants endorsed suicide ideation, and 15% reported a lifetime suicide attempt. Thus, the clinical characteristics of the current sample are more severe than that of the general population of college students.
+CONCLUSION
+This study suggests that young adults with past-year NSSI have greater difficulty viewing their experiences from a detached, third-person perspective. The ability to decenter was associated with significantly lower cognitive-affective symptoms of psychopathology, including rumination, hopelessness, and depressive symptoms. Further, decentering moderated the indirect relationship of past-year NSSI with recent suicide ideation via rumination, such that at high levels of decentering, the indirect effect via rumination was nonsignificant. This suggests that levels of decentering may have
+a differential effect on some cognitive-affective factors that increase risk for suicide ideation among youth with NSSI. The effects of decentering are most relevant to youth who report more severe NSSI than to youth who report minor forms of NSSI relative to their peers without NSSI. Creating brief interventions that facilitate decentering may help reduce symptoms that increase risk of self-injury.
+| 753
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+How to cite this article: Rosario-Williams B, Kaur S, Miranda R. Examining decentering as a moderator in the relation between non-suicidal self-injury and suicide ideation via cognitive-affective factors. Suicide Life Threat Behav. 2021;51:741-754. https://doi.
+org/10.1111/sltb.12747
+1943278x, 2021, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sltb.12747 by CAPES, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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+The COVID-19 pandemic of 2020 is a major global health challenge. At the time of writing, over 11.6 million people around the world had been registered as infected and 538,000 had died (Worldometers, 2020, accessed July 7, 2020). Public health responses to COVID-19 need to balance direct efforts to control the disease and its impact on health systems, infected people, and their families with the impacts from associated mitigating interventions. Such impacts include social isolation, school closure, health service disruption stemming from reconfiguring health systems, and diminished economic activity. The primary focus of both the United Nations (UN) and the World Health Organization (WHO) has been on addressing COVID-19 as a physical health crisis, but the need to strengthen mental health action, including suicide prevention, is increasingly recognized, as is the need for mental health research to be an integral part of the recovery plan (UN, 2020a). The impacts of the pandemic on physical and mental health will unfold differently over time and will vary depending on the duration and fluctuating intensity of the disease. Research is needed
+© 2020 Hogrefe Publishing. Distributed as a Hogrefe OpenMind article
+under the license CC BY 4.0 (https://creativecommons.Org/licenses/by/4.0)
+to help ensure that decision-making regarding all aspects of health, including mental health (Holmes et al., 2020), is informed by the best quality data at each stage of the pandemic.
+The pandemic poses a prolonged and unique challenge to public mental health, with major implications for suicide and suicide prevention (Gunnell et al., 2020; Reger, Stanley, & Joiner, 2020). A rise in suicide deaths in the wake of the pandemic is not inevitable. There is consensus, however, that the mitigation of risk will be contingent upon a proactive and effective response involving collaborative work between the state, NGOs, academia, and local governments and coordinated leadership across government ministries, including health, education, security, social services, welfare, and finance. Countries have responded in different ways to the pandemic, effectively creating a series of natural experiments. Thus, regions of the world affected later in the pandemic can draw on lessons from countries, such as China and Italy, affected in its early phase. Likewise, lessons learned early in the pandemic (e.g., on the impact of lockdown and physical distancing
+Crisis (2020), 41 (5), 321-330
+https://doi.org/10.1027/0227-5910/a000731
+measures) can be used to inform responses to any future surges in the incidence of COVID-19.
+Although there are important parallels between countries in the course of the pandemic, some stressors, responses, and priorities are likely to differ between high-and low-middle-income countries and between cultures and regions. As COVID-19 appears to be disproportionately affecting Black, Asian, and minority ethnic communities, the response - and suicide prevention research carried out to inform the response - needs to be sufficiently granular and account for the complexity of risks in these groups (O’Connor et al., 2020).
+Throughout this editorial, when we refer to suicide and suicidal behavior, we mean to include both fatal and nonfatal suicidal behaviors and self-harm.
+The Need for Evidence-Based
+Suicide Prevention Responses
+Suicide is the most extreme outcome of a mental health crisis and should therefore be a key priority in any broader mental health response to the pandemic (Gunnell et al., 2020; Reger et al., 2020). Suicide prevention responses need to be informed by research that is as specific as possible to the current situation and takes account of the many mechanisms that have an impact on suicide, as they may vary during the different phases of the pandemic. At the same time, given the risks involved, strategic development of policy and implementation responses cannot wait until all aspects of the epidemiology and consequences of the disease on mental health and risk of suicide are understood.
+The dilemma here is that few studies have investigated the impact of previous pandemics - or even epidemics - on suicide (Cheung, Chau, & Yip, 2008; Wasserman, 1992; Zortea et al., 2020), and none has evaluated suicide prevention measures in the current context. An analysis of the impact of the Spanish Flu epidemic (1918-1920) in the United States indicated that it resulted in a small rise in suicides (Wasserman, 1992). Cheung and colleagues (2008) reported a rise in suicide among older people during the 2003 SARS epidemic in Hong Kong. Similarly, what can be learned from other types of public health emergencies is limited. Much of the related research comes from one-off events, such as terrorist attacks and natural disasters (e.g., earthquakes). Findings from such events might not be applicable to the current situation (Devitt, 2020).
+Early Research Findings Relevant to Assessing the Impact of COVID-19 on Mental Health
+Early publications relevant to the COVID-19 response have largely come from literature reviews, small selective surveys or case reports, often using indirect measures of suicide risk or from modeling approaches to predict the impact of the pandemic. These have addressed issues such as the impact of quarantine (Brooks et al, 2020), highlighted possible high-risk groups (Yao, Chen, & Xu, 2020), and assessed mental health service disruption (Royal College of Psychiatrists, 2020).
+Physical distancing and related measures, which have been at the forefront of the public health response, carry a strong risk of increasing isolation, particularly in vulnerable populations such as older people and people who have been bereaved (Brooks et al., 2020; De Leo & Trabucchi, 2020; Wand, Zhong, Chiu, Draper, & De Leo, 2020; Yip & Chau, 2020). Physical distancing measures may also lead to increases in household stress levels, domestic violence, and alcohol misuse and affect the accessibility of mental health services (Brooks et al., 2020; Reger et al., 2020). The stresses oflockdown may be worse in low- and middle-income countries where extended families tend to live together with limited housing space.
+Concerns have been expressed about increases in demand for psychiatric emergency care (Royal College of Psychiatrists, 2020). In the context of overwhelmed health-care systems and shortages of resources to treat people with COVID-19 in healthcare settings, qualitative findings from China indicate that the intensity of work during the pandemic drained health-care workers physically and emotionally (Liu et al., 2020). In the United Kingdom, the British Medical Association well-being support services have seen a 40% increase in use after the onset of the pandemic (Torjesen, 2020).
+Positive effects of the pandemic on the public, such as increased prosocial behavior (e.g., donating and volunteering) and the strengthening of community ties, may help to mitigate detrimental impacts of physical distancing (Van Bavel et al., 2020). The move of some health and third-sector services into online settings may also have long-lasting benefits in improving service accessibility, particularly to those who find face -to -face consultation difficult. The effect on people with mental illness of replacing face-to-face treatment with remote delivery of care, however, remains unclear. Moreover, in low- and middle-income countries the technology to support remote assessment is limited (De Sousa, Mohandas, & Javed, 2020; UN, 2020a). In these, and other settings, where there is limited
+© 2020 Hogrefe Publishing. Distributed as a Hogrefe OpenMind article
+under the license CC BY 4.0 (https://creativecommons.Org/licenses/by/4.0)
+access to specialist mental health services, community and peer support becomes extremely important.
+The potential for the COVID-19 virus to affect the brain and to cause long-lasting physical morbidity means it might become relevant as a risk factor for mental illness and suicide in the future (Holmes et al., 2020; Rogers et al., 2020; Wu et al. 2020). Review findings indicate that the incidence of psychosis, a major risk factor for suicide and suicidal behavior, appeared to be high in people following SARS, MERS, and H1N1 infection (Rogers et al., 2020). Given emerging evidence that the virus can have severe effects on different organ systems including kidney and liver function (Zhang, Shi, & Wang, 2020), the physical consequences of infections might include a prolonged reduction in functional capacity and disability in some patients, all of which might have potential implications for suicide risk and prevention.
+However, longer-term risks for suicide are likely most closely related to the economic consequences of the pandemic, including financial strain and unemployment. In a study based on suicide data from 54 countries, the recession of 2008 was associated with a 3.3% increase in suicides in men (but not women) in the following year and more prolonged increases in several countries (Chang, Stuckler, Yip, & Gunnell, 2013). The increase varied depending on the regional depth of the recession and the specifics of the social insurance systems (e.g., regulations for unemployment benefits or payed sick leave; Chang et al., 2013; Norstrom & Gronqvist, 2015). The economic downturn associated with the COVID-19 pandemic may be more rapid in onset than the 2008 recession and may push an estimated 500 million people, particularly in low-and middle-income countries, below the poverty line (UN, 2020b).
+Early Research Findings Relevant to Assessing the Impact of COVID-19 on Suicide and Suicidal Behavior
+There is, as yet, no direct evidence of the impact of the pandemic on suicidal behavior. While a number of news stories from Japan, New Zealand, and Germany report a decrease in suicides in the period around the time of lockdown (Deutsche Welle, 2020; New Zealand Herald, 2020; The Guardian, 2020), these are all based on preliminary data/anecdotal reports and unsubstantiated by peer-reviewed publications. General population survey findings from the United Kingdom have shown no clear evidence of a rise in reported self-harm during the weeks following lockdown (after March 23), but no pre-lockdown data are
+© 2020 Hogrefe Publishing. Distributed as a Hogrefe OpenMind article
+under the license CC BY 4.0 (https://creativecommons.Org/licenses/by/4.0)
+available (Fancourt, Bu, Mak, & Steptoe, 2020). Many surveys have been carried out in the wake of the pandemic, these often use convenience samples, which are prone to selection bias (Pierce et al., 2020). In addition, there have been multiple case reports from some low- and middle-income countries highlighting occurrences of suicide thought to be related to COVID-19 (De Sousa et al., 2020; Mamun & Ullah, 2020). These reports must, however, be interpreted with great caution - and even more so when they are based on mass media reports, which are unlikely to have been validated.
+Some researchers have attempted to model the possible pandemic-associated increase in suicides, largely based on predicted rises in unemployment (Kawohl & Nordt, 2020; McIntyre & Lee, 2020; Moser, Glaus, Frangou, & Schechter, 2020). Risk estimates vary widely, from a 1% increase in global suicides (Kawohl & Nordt, 2020) to a doubling of national suicides in a Swiss study, using prison incarceration as a questionable proxy for modeling the social distancing effects oflockdown (Moser et al., 2020). These discrepancies are partly due to differences in modeling assumptions, which are associated with considerable uncertainty and may be very misleading. Given the uncertainty of the baseline assumptions about how events will unfold, the results of these tentative projections can at best provide a guide as to where action should be directed but are largely unhelpful for accurate quantifications of future suicidal behavior and suicide.
+In this regard, access to real-time suicide mortality data is a key priority (Gunnell et al., 2020). Further, active surveillance systems for suicide attempts are warranted (WHO, 2016).
+In the absence of direct evidence about trends in suicide, some researchers have used search behavior on Google Trends for terms related to suicide, as a proxy for suicide risk (Knipe, Evans, Marchant, Gunnell, & John, 2020; Sinyor, Spittal, & Niederkrotenthaler, 2020). Their findings indicate that, although relative search volumes for financial and work-related concerns have increased (Knipe et al., 2020), searches for suicide and suicide methods have not (Knipe et al, 2020; Sinyor et al., 2020). The potential limitations of Google search data for surveillance are well recognized and include uncertainty about the algorithms used and issues with the stability of findings provided by Google Trends, as well of inconsistent associations with suicide (Tran et al., 2017).
+Gaps in knowledge about the epidemiology of suicide and suicidal behavior during COVID-19 and the effectiveness of intervention and prevention measures underline the need for a strategic approach to suicide research and prevention at a global level. The uncertainties regarding the direct and indirect effects of COVID-19 on suicide can only be addressed with good-quality tailored research.
+Crisis (2020), 41 (5), 321-330
+Furthermore, suicide prevention in the age of COVID-19 needs to build on what we know about the effectiveness of various measures, but also needs to take account of the unique challenges posed by the situation in order to develop novel approaches. Our knowledge is currently still very limited and building the evidence base on suicide prevention is crucial.
+Research Considerations
+During COVID-19
+There are several considerations in relation to suicide prevention research carried out during crisis situations and in the present global pandemic (Table 1). These include ensuring the safety of research participants and researchers as well as the need for research to focus on low- and middle-income settings as well as high-income countries, keeping in mind that findings from one setting may not generalize to another. We expand on a few specific issues in the following section. First, the limited research conducted thus far on suicide and its prevention during COVID-19 has focused mostly on high-income countries. While complementary research in this area in low- and middle-income countries should be prioritized, the poor quality of routine mortality and hospital attendance data as well as the limited availability of resources to carry out research
+in many of these settings present very real challenges. In 2014 the WHO considered that only just over one third of member states had good-quality suicide registration data, and such data were largely absent in low- and middle-income countries (WHO, 2014). The establishment of sentinel sites to gather as accurate data on suicidal behavior as possible to supplement those that already exist would be one way forward (WHO, 2016).
+Second, as a result of the pandemic, mental health services have had to develop new ways of working to deliver care to suicidal individuals, including new care pathways, the mass roll-out of remote consultation, and increased use of digital interventions. These new ways of working require real-time evaluation and ongoing adaptation in response to findings. Traditional evaluation approaches, such as randomized trials, may need to be adapted in a manner that is still consistent with making robust inferences about their effectiveness.
+Third, with school and university closures in place in a number of countries, the traditional setting for carrying out research into children and young people’s health is no longer available. Given current concerns about the impact of the pandemic on young people, mental health researchers will need to find alternative routes to studying the impact of the pandemic on this potentially vulnerable group.
+Fourth, for all studies it is vital that those with lived experience of suicide are involved in shaping the research at all stages - from developing the research questions to data
+collection and dissemination of the findings. Fifth, all research needs to comply with ethical standards. Researchers who do not normally work in the area of mental health and suicide prevention but who are now shaping conversations on suicide prevention need to obtain necessary training and background information on how to conduct suicide research, including the need to follow established research protocols and safety considerations that are specific to the field (Townsend, Nielsen, Allister, & Cassidy, 2020). Sixth, it is important that research resources (i.e., staff, funding) are rapidly mobilized to ensure timely research evidence is available. However, this presents tensions between the time researchers have available to write robust funding applications, time-scales for the grant review by funding bodies, and, if funded, the availability ofhigh-quality fieldworkers and analysts as these are likely to be already committed to other projects. Flexibility and clear communication with funders about project delays and re-allocation of resources should help ameliorate these challenges. There is a distinct possibility that research funding may be adversely affected by a post-pandemic recession. Seventh, any proposed research should have a clear pathway to impact to ensure that clinicians and policy-makers can implement the findings of research in their work.
+Lastly, traditional models of research publication, with the need for peer review, introduce delays between article submission and on-line publication, reducing the speed with which evidence is disseminated and recommendations implemented. One solution is the fast-track review processes for selected papers - these were already in place before COVID-19, but have been extended and adopted by more journals since the beginning of the outbreak. Another solution is open science publication models that involve on-line publication of articles while they await peer review, although there is a danger of low-quality research findings being disseminated and acted upon precipitously, without scrutiny of their validity (Armstrong, 2020). In order to mitigate this risk, researchers need to label their findings as preliminary and implement a communications strategy that addresses the preliminary nature of findings.
+The International COVID-19 Suicide Prevention Research Collaboration
+High-quality timely research to understand the suicide-related consequences of COVID-19 and to determine how best to mitigate the risk stemming from these consequences is now needed. The UN highlights the need for “rapid knowledge acquisition,” establishing research priorities, coordinating research efforts, open-data sharing, and funding (UN, 2020a). In response to widespread concerns
+© 2020 Hogrefe Publishing. Distributed as a Hogrefe OpenMind article
+under the license CC BY 4.0 (https://creativecommons.Org/licenses/by/4.0)
+about the impact of the COVID-19 pandemic on suicide and suicidal behavior, a group, initially consisting of 44 suicide prevention researchers and leaders of suicide prevention charities from around 20 countries, came together to pool their expertise about the likely impact of the pandemic on suicidal behavior and to identify prevention priorities. The International COVID-19 Suicide Prevention Research Collaboration (ICSPRC) sought to include at least one representative from many of the most affected countries and also representation from high-, middle-, and low-income countries (https://www.iasp.info/COVID-19_suicide_research. php). The ICSPRC assessment of the risks posed by the pandemic and suggested responses to mitigate these were summarized in a Lancet Psychiatry commentary published in April 2020 (Gunne ll et al., 2020).
+Building on this initiative, the collaborative network has been extended to include suicide researchers from a wider range of countries (including countries in Africa, the Middle East, and South America), with skills ranging from pop -ulation health to biological psychiatry and incorporating expertise in quantitative and qualitative methods, together with ethics. The objectives of the group are to:
+a)	Share early findings (and, where appropriate, data) on the impact of the pandemic, and the public health measures (e.g., physical distancing) to contain its spread, on suicidal behavior in participants’ countries and to provide timely policy advice to those in other countries.
+b)	Facilitate collaboration/avoid duplication through sharing information about ongoing research studies and COVID-19 research tools/questionnaires focused on suicide prevention, as well as advice about study design.
+c)	Harmonize data collection approaches to facilitate pooling of data, where possible, from different settings and contexts.
+An early example of the success of this approach has been the collaboration between two groups working on almost identical systematic reviews investigating the impact of pandemics/epidemics on suicide, self-harm, and suicidal ideation (Zortea et al., 2020). Another group has established real-time surveillance of the emerging literature on COVID-19 and suicide to become a “living review” (John et al., 2020). The global distribution of group members will facilitate rapid combined efforts in response to funding opportunities, where cross-national studies would strengthen the evidence base.
+Conducting high quality suicide prevention research is challenging. Suicide, in population terms, is a low-incidence event and thus studies are often under-powered to identify small but potentially important effects. Furthermore, a focus on intermediate or proxy outcomes (e.g., self-reported suicidal ideation) is sometimes necessary but these have a questionable relationship to suicidal behav-
+Crisis (2020), 41 (5), 321-330
+iors (Mars et al., 2019; May & Klonsky, 2016). The collaboration provides a mechanism to work together, pool data using shared protocols, and investigate different outcomes with a range of research designs. It should also facilitate reaching global consensus on issues such as the impact of lockdown on suicide risk and how best to mitigate risk,
+especially if further periods are required to address the re-emergence of COVID-19, as has recently been reported in countries such as Iran (Worldometers, 2020).
+The collaboration has identified several suggestions for research to help inform responses to the current and future pandemics, formulating these as research questions (see
+Table 2 and Table 3). The proposed research questions link to the gaps in knowledge that we identified earlier. Table 2 highlights research questions relating to whether rates of suicidal behavior increase as a result of the pandemic and what mechanisms may be driving any increase, suggesting specific research for the general population and for high-risk groups. Table 3 presents research questions relating to whether particular responses might help to mitigate any risk of suicide associated with the pandemic. Members of the collaboration have worked with the International Association for Suicide Prevention (IASP) to establish a searchable on-line list of ongoing COVID-relevant studies on suicidal behavior, managing suicidal crises, and suicide prevention (https://www.iasp.info/covid-19/covid-19-sui cide-research-studies) to facilitate collaboration and avoid duplication, similar to the website developed for longitudinal studies on mental health during COVID-19 (https:// www.covidminds.org/longitudinalstudies). The role of the IASP, in collaboration with other international and national organizations (e.g., WHO, International Association of Suicide Research [IASR], American Foundation for Suicide Prevention [AFSP], and others), is to provide up-to-date information on suicide research and prevention in its global network. The IASP is developing a strategic plan to reduce COVID-19-related suicidal behavior and building a central pool of resources (expertise, research, guidelines for good practice, briefings) that will be available to support organizations globally (IASP, 2020a, b). Members of the ICSPRC have contributed to an IASP briefing paper on reporting suicide during the COVID-19 pandemic and IASP members have developed guidance to help workplaces and professional associations through the COVID-19 Crisis (IASP, 2020a, b). The combination of the specific research focus in the ICSPRC and IASP, with its prevention network and links to the WHO, as the leading organization for suicide prevention globally, is a core strength of this collaboration and many members are active in both.
+A key issue the group needs to consider is how best to ensure the rapid dissemination of research and surveillance information to inform policy-making and prevention activities. Furthermore, there is a need to consider the best way of responding to (sometimes unsubstantiated) findings reported in news articles that may be hastily picked up by policy-makers and politicians. Three sorts of information are relevant: (a) routinely available data (e.g., national mortality, survey data, research publications) that not everyone will be aware of - this could be disseminated via regular briefings/updates; (b) pre-publication research data and findings that may inform policy, but are going through peer review - one possible approach to sharing these data is via regular webinars/research presentations; and (c) highly sensitive surveillance data, for example, known only to government officials and individuals on na
+tional suicide prevention strategy groups who have agreed not to disclose them. The latter data are unlikely to be shareable, but it will be important to consider approaches to share broad findings to give those working in different settings the opportunity to act pre-emptively and before local data are available.
+Facilities for sharing data/measures/protocols/pre-peer-reviewed manuscripts (e.g., the Open Science Framework and PsyArXiv) are possible options for building a repository of research that can have a digital object identifier (DOI) and thus are traceable and citable. Crisis now also publishes Registered Reports, which allow authors to submit research protocols for review before the research is conducted.
+Conclusion
+The unique challenges posed by the current pandemic require suicide researchers to collaborate in order to understand the impact of COVID-19 on suicide and suicidal behavior and effective ways of mitigating the risk. We urge colleagues to complete the recently launched register of suicide prevention research studies to facilitate this (https:// www.iasp.info/covid-19/covid-19-suicide-research-stud-ies). In a challenging economic environment, suicide researchers will need to advocate strongly for the importance of the issues we have identified and make sure the research that is conducted is of the highest possible quality and ethical standard to inform public health, policy, and healthcare responses. Lessons learned and subsequent changes made will contribute to improving response plans for future possible waves in this pandemic and future pandemics. The establishment of the International COVID-19 Suicide Prevention Research Collaboration is an important contribution to this effort and we ask suicide researchers particularly from regions currently not represented to join us.
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+Keywords
+suicide, social justice, dignity, political responsibility
+The purpose of this article is to offer the general outlines of a political approach to the analysis and prevention of suicide. By a political approach to suicide I mean an account that (1) seeks to understand suicide in the context of unequal concentrations of “primary goods” (substantive rights, social connections, resources, opportunities, etc.) within an overall scheme of social and political power, and (2) confronts suicide as a normative issue of equal justice and human dignity.1 A political approach to suicide seeks, first, to supplement (not fully dislodge) the dominant psychological and psychiatric approaches to the study of suicide with greater overall attention to the sociocultural dynamics that are part of the enduring conditions of possibility for suicide today. Second, a political approach to suicide will direct concerned citizens to read the rates and specific concentrations of suicide within certain populations as troubling political and ethical questions for our time.
+For example, it is widely recognized that white men account for the majority of suicides in the United States. A recent study that usefully disaggregates U.S. mortality data between 1999 and 2013 points to heightened economic insecurity amid widening inequality as contributing factors in the surprising and marked increase in the all-cause mortality rate (including suicide) of middleaged white non-Hispanic men and women—especially among those with the least amount of education (Case and Deaton 2015, 4). As suicide increasingly plagues the white American working class, it is also the case that Native American men aged eighteen to twenty-four die
+by suicide at much higher rates than the national average (34.3/100,000 deaths compared with the latest national average of thirteen/100,000; Jiang et al. 2015), and among all racial/ethnic groups, the greatest increase in suicide by adults aged thirty-five to sixty-four has been among American Indians/Alaskan Natives (Centers for Disease Control and Prevention [CDC] 2013). Overall, suicide is the second leading cause of death among fifteen- to thirty-four-year-olds and is especially elevated among young American Indian and Alaskan Natives (1.5 times higher for this age group than the national average; CDC 2013); lesbian, gay, and bisexual youth are four times more likely to attempt suicide than their straight peers (CDC 2012); the suicide rate in the U.S. Armed Forces has doubled since the initiation of military operations in Afghanistan and Iraq (Bryan et al. 2013; U.S. Department of Defense 2011); and between 1993 and 2012, there has been a significant increase in the suicide rate among black children (ages five to eleven; Bridge et al. 2015). What political message might be derived from these diverse and complex trends in suicide today?
+My thesis is that suicide is a solitary “answer” to a set of collective questions about the conditions of a dignified human existence that we (i.e., most political societies)
+have not confronted as a matter of equal justice that weighs on all citizens. In my view, there is no categorical duty to sustain one’s life (come what may) such that suicide could be treated as an absolute moral wrong, and this is especially significant in the context of terminal illness and physician-assisted death. However, I believe that there is a compelling collective obligation, grounded in the moral equality and dignity of persons, to ameliorate the social, economic, and material conditions that are correlated with higher rates of suicide (outside of the medical context of end-of-life decisions). This sense of presumptive political responsibility for the conditions and unequal patterns of suicidality is largely missing today. My suspicion is that the lack of a collective political orientation to suicide—and the antecedent unwillingness to conceptualize and discuss suicide as a properly political question that exposes the limits of social jus-tice—is at least partially the product of the dominance of a clinical-psychiatric approach to suicidal ideation and conduct. Reducing the study of suicide to the psychopathology of individuals and therewith restricting issues related to suicide to questions about improving the clinical assessment and treatment of suicidality stymies sustained reflection on suicide as a properly political question and preempts the formation of a collective political response to the sociocultural conditions that help to foster suicide. This is not to challenge the fields of psychiatry or psychology as such, or at least not exclusively; we (political scientists) have a lot to learn from these disciplines. Rather, the worry that is the point of origin for this article is that our professional and disciplinary assortments in relation to the study of suicide facilitates (however unintentionally) a form of collective bad faith wherein we (citizens, public officials, and scholars alike) presume that a collective political response to suicide as a matter of social justice is not possible because one has never been imagined or tried before.2
+To be clear, the purpose of this article is not to present a new theory or an alternative explanatory model of suicide. Instead, the purpose of this article is to consider what a more explicitly political turn within the ongoing confrontation with suicide might amount to today. In pursuing this possibility, I argue that a political account of suicide should ultimately point in the direction of a new right to life movement for the already born, the policy and ethical aim of which is to secure the conditions of human dignity for all persons. To affirm life in a politically serious way requires that political societies confront the formation and distribution of suicidal subjectivities within their population, whether among poorly educated middle-aged white men coping with simultaneous increases in morbidity and decreases in real median earnings (Case and Deaton 2015), or among Native Americans dealing with “historical trauma, alienation, and poor sense of identity” (EchoHawk
+1997, 60). The affirmative-ethical sources motivating this public orientation are gratitude for the diversity of life and a commitment to defending the inherent dignity and plenipotentiary promise of all persons. Liberals, conservatives, theists, poly-theists, and post-Nietzschean nontheists should all find room to coalesce around principles they already claim to honor.
+The remainder of this article proceeds as follows. The section “Why a Political Approach to Suicide?” provides a brief account of the philosophical, sociological, and psychological backdrop that has heretofore conditioned and constrained a more explicitly political turn in the study of suicide. The section “From the Interpersonal to the Intrapolitical” utilizes Thomas Joiner’s (2005) explanatory model of suicide to highlight the wider sociocultural conditions that play a significant role in differential experiences of “perceived burdensomeness” and “thwarted belongingness” and therewith the origins of suicide. “The Formation and Distribution of Suicidal Subjectivities” section argues for the merits of a new social science research agenda focused on the formation and distribution of suicidal subjectivities, and the section “‘To Take Arms against a Sea of Troubles’: A Social Justice Response to Suicide” identifies some of the ethical and policy elements necessary for a social justice response to suicide.
+Why a Political Approach to Suicide?
+The fact of suicide turns many of the most basic assumptions of modern political theory upside down. Since the work of Thomas Hobbes, political theorists and legal philosophers have accepted the idea that the fear of violent death provides both the motivational source and the principle justification for the concentration of legal authority and coercive power within a sovereign state apparatus. While critical questions have been raised about the overall weight that Hobbes placed on the fear of violent death within human psychology and social behavior (especially in relation to the religious devout), the Hobbesian thesis remains a cornerstone within theories of the state, jurisprudence, and international politics.3 Yet, the phenomenon of suicide shows that the highest rates of violent death take place within otherwise stable and legitimate political states through self-initiated acts of destruction. And this has been true for decades. In the sixteen states included in the National Violent Death Reporting System, 62.8 percent of all violent deaths were suicides, with homicides accounting for 24.4 percent of the total (or 12.4 per 100,000 suicides compared with 4.8 per 100,000 for homicides; CDC 2010). Foucault (1990, 139) addressed this perplexing circumstance for modern political states as follows:
+This determination to die [by suicide], strange and yet so persistent and constant in its manifestations, and consequently so difficult to explain as being due to particular circumstances or individual accidents, was one of the first astonishments of a society in which political power had assigned itself the task of administering life.
+If the biopolitical power of the modern state faces its limit—if not its antithesis—with self-targeted death, is suicide even susceptible to political explanation, or at least a politically minded inquiry? The apparent absence of any such investigation would seem to suggest that the answer to this question is no. Nonetheless, if one of the primary tasks of politics is to provide protection against the great evils of human life (Hampshire 2000; Hobbes [1651] 1996), it would seem that on the basis of well-documented evidence about the rates and distribution of suicide in the United States (42,773 in 2014)4 and around the world (over one mil-lion/year), suicide (outside of the context of terminal illness and assisted death) properly belongs among the ills that a socially responsive political theory should confront. But how should it do so, and what can political theory really hope to contribute to this emotionally charged area?
+As students of political thought, we are familiar with the various argumentative moves that have been made in relation to the question of suicide—“the one truly serious philosophical problem,” [Emphasis added] according to Camus (1955, 3). Generally speaking, these replies come in two basic forms. Suicide is a gift, a freedom, and later an individual right: that is how thinkers like Seneca (1969), Nietzsche (1968, 1978), Hume (1985), Améry (1999), and Szasz (1999) have viewed suicide. Alternatively, suicide is an act of metaphysical theft, a sin, and a crime (against God, the sovereign, or the self): that is how Socrates (1977), Aristotle (1953), Augustine (2001), Dante (1993), and Kant (1997) viewed suicide.
+For all of their significant ethical differences, each of these broad orientations to suicide presupposes a naked soul facing the question of self-initiated death in suspended philosophical animation from the sociopolitical conditions within which all souls are ineluctably shaped (Hamlet’s soliloquy without the moral and political rot in Denmark). As a result, each of these orientations to suicide—as gift/ right or sin/crime—purchases existential and metaphysical significance at the price of conceptualizing suicide as a serious political problem about the conditions that imperil or negate human dignity. The tendency to isolate the psyche/soul from its social and material context is further illustrated by studies showing that contemporary moral judgments about suicide (even after controlling for religious and ideological variables) are driven by concerns about “impurity” (Rottman, Keleman, and Young 2014).
+As if to bracket centuries of philosophical agonism about suicide, the overwhelming majority of today’s medical/
+psychiatric profession views suicide as a symptom of severe mental illness. The statistic that is usually cited in this context, based on “psychological autopsies,” is 90 to 95 percent of all suicides entail some form of significant mental illness: depression, bipolar disorder, schizophrenia, and drug and/or chemical dependency.5 In a recent work that maps the historical formation of this “contemporary ‘regime of truth’” about suicide, Ian Marsh (2010) highlights the “compulsory ontology of pathology” that took root in the nineteenth century and continued to make the question of suicide primarily an issue of disciplinary concern for the psychological, psychiatric, and medical professions (see also Battin 2005). As Marsh (2010, 222-23) explains, “Suicide is now mostly constituted as the tragic act of a mentally unwell individual and other ways of conceiving self-accomplished death possess relatively limited currency.” This is not to deny the ongoing relevance of neo-Stoic, Christian, Kantian, or post-Nietzschean orientations to the meaning and status of suicide but to highlight the fact that the dominant discursive frame for explaining (and predicting) suicidal behavior is one of individual psychopathology.
+On one hand, the slow scientific and cultural shift in accordance with which suicide has come to be seen as determined by genetic, neurological, and mental disorders is conducive to relatively more humane and less punitive and moralizing responses to suicide (although there are loud dissenters to this view).6 For example, Western political societies no longer deny burial to suicides, confiscate their property, or hammer a stake in their heart (MacDonald and Murphy 1990; Minois 1999; Watt 2004). On the other hand, by turning suicide into an issue that is almost exclusively played out within the interior psyche of a person, the external sociopolitical conditions that are a constitutive feature of all subject formation are left out of the picture. Even within studies that signal an appreciation for the interaction between the individual and the social environment, the primary stress within these investigations is on the genetic and biological factors that predispose individuals to respond to “environmental stressors” in certain self-destructive ways (Goldney 2000; Traskman-Benz and Mann 2000; Williams and Pollock 2000). The “environmental stressors” themselves are treated as uncontrollable exogenous factors about which suicidology has little or nothing to say. This emphasis on interior predispositions (neurological, biochemical, and personality traits) in turn has a significant influence on the recommended strategies for responding to suicidal ideation and behavior, which are overwhelmingly psychotherapeutic and pharmacological. The following observation from the field of psychiatry is representative of this orientation:
+There is relatively little that a doctor can do to control many of the major stresses in a patient’s life: they occur too randomly, and thus are difficult to predict and even more
+difficult to govern. But there are things that can be done to influence and treat the underlying biological vulnerabilities to suicide, as well as the mental illnesses closely linked to suicidal behavior. (Jamison 1999, 239)
+I will return to the issue of the supposed randomness and ungovernability of the external conditions that facilitate suicidality further below. For now I simply want to indicate that in suicidology today, psyche-analysis has come at the expense of the Platonic appreciation for the constitutive interrelationship between polis and psyche. In this context, it is hardly surprising that the use of antidepressants and other pharmacological treatments would continue to rise, even as general rates of suicide have remained more or less constant (roughly between 10.4 and 13.0 deaths per 100,000 per year). It is perhaps slightly more surprising to discover that public health approaches to suicide—with their focus on surveillance and targeted interventions with specific populations— tend to leave prior structural questions about the constitution and concentration of “at risk” groups within a general population unaddressed (see Potter 2001; World Health Organization 2010). For all of the merits of populationbased public health strategies (see May et al. 2005), the long-term benefits of these programs stand to be improved by a more fundamental political orientation regarding the formation and endurance of the conditions that help to breed suicidal subjectivities in the first place.
+Given the above discussion, it would seem that there are very few new arguments left to make regarding the ontological and moral status of suicide, beyond working at the edges of these diverse philosophic and scientific orientations and carving out precarious intellectual planks upon which to balance some elements of each of these approaches. Here is one such line of thought: we can accept the important finding that suicide is highly correlated with a variety of mental and personality disorders in the way that psychologists insist today, but perhaps this does not completely close the door on one’s agency to “die at the right time,” as Nietzsche (1978, 71) put it. After all, most people with various mental and personality disorders do not commit suicide, and for those who do, suicide might still be seen as an element of human freedom and an expression of a desire for personal autonomy. Can we think mental illness and human autonomy together? Take, for example, the following statement from a suicidal “patient” who spent most of his life in mental institutions:
+If I commit suicide, it will not be to destroy myself but to put myself back together again. Suicide will be for me only one means of violently reconquering myself . . . By suicide I reintroduce my design in nature, I shall for the first time give things the shape of my will. (Alvarez 1971, 131)
+Statements like this—if taken seriously—might raise important questions about the Kantian judgment that suicide necessarily entails an action that puts one in contradiction with one self as a free moral agent.
+Here is another alternative line of thought: perhaps suicide is one dimension of human freedom, but it might still remain a kind of theft, not from a creator God at whose disposal we are morally bound (as Socrates and later Christian thinkers believed) but from interpersonal relations and social connections that are irrevocably diminished through the act (see Hecht 2013). Indeed, it is hard not to feel that the suicidal steal away or significantly diminish the phenomenal world that we are all a part of by removing themselves from that world. Can we make sense of a “right” to social theft? Alternatively, if a suicidal individual feels that he or she has already been stripped of equal moral standing and significant social connections to others, perhaps a prior social “theft” needs to be investigated to fully understand and respond to the vexing scene of suicide—and in this context the language of “rights” to, and the “rationality” of, suicide will provide very little illumination and even colder comfort for those who live on.
+These unlikely synthetic intellectual orientations to suicide and the additional questions they generate are deeply perplexing, to be sure. Yet for all of their intrinsic philosophical and social interest, the preceding lines of thought are all (with the partial exception of the last question) quite unpolitical. Indeed, it is rare to see politics— understood as the structural conditions of collective life and human governance that shape and are shaped in turn by human agency—factor into discussions of suicide in a meaningful way today.7 Even scholars like Battin (2005) and Marsh (2010) who are deeply attuned to the contingent historical dimensions of contemporary understandings of suicide and who are critical of the “medicalization” of suicide assessment and treatment do not attend to these wider political conditions as a factor in the distribution and concentration of suicide among certain populations. The one thinker who came closest to a political-structural analysis of suicide was Durkheim. However, politics entered Durkheim’s analysis only insofar as political and national crises had an effect on general rates of suicide: Durkheim (1951, 208) found that crises reduce rates of suicide owing to temporarily heightened conditions of social integration. For Durkheim (1951, 299), suicidal aptitude was a purely social phenomenon:
+There is . . . for each people a collective force of a definite amount of energy, impelling them to self-destruction. The victim’s acts which at first seem to express only his personal temperament are really the supplement and prolongation of a social condition which they express externally.
+Perhaps because he was as concerned with championing the burgeoning field of sociology as an empirical science of man as he was with explaining the causes of suicide, Durkheim’s analysis did not go any deeper than the “social forces” (i.e., egoism, altruism, and anomie) that determine general rates of suicide. Hence, after establishing, as he thought he had, the sui generis nature of collective social tendencies acting upon individual existence, Durkheim could rest his case against the defenders of psychological and other extra-social causes of suicide (most notably in the work of Jean-Étienne Dominique Esquirol).
+My plea for a more explicitly political approach to suicide is Durkheimian insofar as it understands suicide not simply as an individual act but also as the consequence of wider social and political forces. However, this approach moves beyond or beneath Durkheimian structural sociology by arguing that the “social facts” of suicide (the patterns and trends of suicide rates in certain populations) also tell a political story—a story (or a series of stories) that is frequently punctuated by marginalization, persistent neglect, cultivated indifference, and bad faith. To be sure, the broad social correlates of suicide are extremely complex and cannot by themselves predict or fully explain the causes of individual suicide (see Hawton and van Heeringen 2000; Rudd, Joiner, and Rajab 2000). Yet the properly political question about suicide today is not only why certain identifiable groups are persistently haunted by higher rates of suicide than others, but how it is that these concentrations of suicide can coexist alongside widespread beliefs about the dignity and moral equality of all persons without raising an acute sense of existential and institutional crisis in need of a collective political response. An organized political approach to suicide would not allow a society and its major institutions (legislative bodies, the health care industry, schools and universities, the mass media, etc.) to characterize suicide as a strictly personal or family problem because such an approach would start with the acknowledgment that suicide is also a collective burden of social justice tied to the distribution of primary goods within a political system.8
+While Durkheim’s analysis remains quite influential to this day, the contemporary study of suicide has swung in a decidedly nonsociological and nonpolitical direction. Starting with Henry Murray’s (1938) focus on thwarted psychological needs, Edwin Shneidman’s (1985, 1996) influential discussions of psychological pain (or psychache ), and Aaron Beck’s (Beck et al. 1985; 1990) emphasis on hopelessness, individual psychological mechanisms are now at the center of contemporary theories of suicide. Perhaps the most compelling and parsimonious “model” for explaining suicide today is offered by the clinical psychologist Thomas Joiner (2005; see also Van Orden et al. 2010). Joiner’s research effectively distills the previous
+scholarship of Murray and Shneidman (among others) and explains the origins of suicide with reference to two primary categories: perceived burdensomeness and thwarted belongingness.
+Like previous research on the causes of suicide, Joiner focuses on the thwarted psychological needs of individuals in combination with the acquired ability to enact lethal self-injury (through habituated practices of deliberate self-harm, for example). The specific psychological mechanisms at work in suicidal ideation and behavior are reduced to two: the feeling that one is “ineffective” and a burden to others, and the feeling that one does not belong or is not cared about by others. “Thwarted belongingness” is essentially a Durkheimian category referring to the lack of stable and satisfying forms of social integration and interpersonal connection. “Thwarted effectiveness” is a category that refers to a negative self-image about one’s perceived lack of effectiveness that is tied to the belief that one is also a burden to others (not entirely unrelated to Durkheim’s notion of altruistic suicide). According to this model, two psychological conditions are necessary to support the will to live: feelings of connectedness and effectiveness. A person can, by and large, survive on just one of these, but take both away, and the evidence from psychological studies points to a significant increase in suicidal ideation and behavior (see Van Orden et al. 2008).
+While Joiner’s theory of suicide is clearly intended for the clinical setting and for assisting those who are charged with assessing, intervening, and treating suicidal “patients,”9 I want to use this compelling explanatory model as a starting point for a more explicitly political intervention into the sociocultural conditions of suicide. To do so, I will offer a political interpretation of “perceived burdensomeness” and “thwarted belongingness” as first steps toward a more general sociopolitical inquiry into the formation and distribution of suicidal subjectivities. A political interpretation of the proximal conditions of suicidality requires that we shift our attention from an exclusive focus on the suicidal mind and ask about the constitutive relationships between psyche and polis as this relates to the formation of suicidal subjectivities.
+From the Interpersonal to the Intrapolitical
+What are the sociocultural sources for a conception like “thwarted effectiveness” or “perceived burdensomeness”? That is, how do these specific forms of self-consciousness take root within an individual psyche and within members of larger social groups? According to one influential study, evolutionary-psychological pressures surrounding the idea of suicide suggest that one’s
+perceived liability to one’s relatives undermines selfpreservation motives and is a precursor to suicide (de Catanzaro 1991). An evolutionary framework elucidates part of the link between burdensomeness and suicide through theories of natural selection and inclusive fitness. Yet, in the context of advanced liberal democracies characterized by culturally specific funds of ontological commitments and moral values, at least as important is the normative model of autonomous self-mastery. Perhaps it is true that no one wants to be a burden to others. But given the significant role that this perception seems to have in suicidal ideation and behavior, and given the fact that in advanced liberal democracies perceived burdensomeness is often tied to things like unemployment and physical illness, it is crucial to attend to the cultural persistence of an impossible ideal of sovereign autonomous agency as to the specific life events that prompt the use of these scripts in the first place. In other words, a punitive (and frequently gendered) notion of sovereign, responsible agency is implicated in a category like “thwarted effectiveness,” even if it is also underwritten by wider evolutionary-psychological mechanisms. Perhaps it would be more apt to say that evolutionary-psychological currents flowing through the late-modern self are given more condensed, rigid, and moralized form within the contemporary neoliberal-capitalist conditions of life. As a result, contingent life span events are turned into conditions for the self-enforcement of a conceptual model of moral subjectivity that is in its own right (ontologically) implausible.
+Joiner and others are right to highlight the ways in which perceived burdensomeness is potentially remediable through cognition, perception, and skill-based psy-chotherapies—assuming that these kinds of interventions are actually available to individuals in crisis. Yet individual cognitive strategies are really no match for a wider system of socialization that burns into the psyche a conception of responsible autonomous agency that contains, as its dark underside, self-loathing and potential selfdestruction for failing to live up to its impossible demands. Hence, alongside individualized psychotherapeutic responses to perceived burdensomeness, liberal democratic societies would be well advised to rework the cultural scripts and “master narratives” that sustain these persistent illusions of self-mastery in a world of contingency, rupture, and precariousness. This work needs to take place within numerous institutional sites of human activity: within families, religious assemblies, the workplace, and the armed forces. Yet none of this “work” on collective moral identification is really meaningful, in a material sense, without significant public investment in the kind of practices of care—health care, mental health services, workforce assistance, and family support services, to name a few—that could provide communities
+with the resources to respond humanely and with dignity to physical illness, unemployment, drug/alcohol dependency, and family conflict: the very events that spur the perception of one’s burdensomeness to others. In short, no amount of individual cognitive or perceptual therapy (as valuable as they are) should release a political society from (1) renegotiating the cultural scripts that sustain a punitive model of human agency and (2) addressing the material-institutional conditions that prevent a dignified form of reciprocal social care from forming. Indeed, it would be preferable if a more realistic conception of moral subjectivity and identity were cultivated in tandem with a serious investment in a social safety net for all persons: the former on its own risks turning away from the material conditions that undergird a life of dignity; the latter on its own courts social stigmatization.
+“Thwarted belongingness” or the loss of stable social connections is a dilemma that provides a clear path of connection and collaboration between the fields of psychology, sociology, and political science. Hegel, Marx, Tocqueville, Durkheim, William James, Robert Putnam (2000), and contemporary social and cognitive psychologists have all highlighted the threat that social alienation and disconnection pose to the vibrancy of the human life drive (the same could also be said of the poetry of Sylvia Plath, the novels of Céline and Faulkner, or the plays of Sarah Kane). Yet when it comes to thinking about how to proactively address the contribution that thwarted belongingness makes to suicidality, none of these disciplines have connected the systemic analysis of the sources of social alienation to the protection from suicidality as a feature of social justice and a fundamental element in the protection of human dignity. To begin to move in this direction, we need to take a more explicitly political turn in our approach to suicide prevention. A more explicitly political turn within the study of suicide would (1) bring critical attention to the formation and distribution of suicidal subjectivities as a question of social justice, and (2) generate a social justice agenda directed at securing the conditions of human dignity for all persons. Both of these steps are but preliminaries to the real political work of cultivating a diverse citizen coalition whose aim is to realize a substantive right to a dignified life for the already born and to hold policy makers and political institutions accountable to this goal. Nonetheless, I provide a preliminary outline for each one of these steps in what immediately follows.
+The Formation and Distribution of Suicidal Subjectivities
+What do I mean by the formation of suicidal subjectivity? I refer, first, to the presence of systemic social conditions that foster and sustain the kinds of anguished feelings that
+are highly correlated with suicidal ideation and conduct: hopelessness, burdensomeness, and social isolation. Second, because the social conditions that help to facilitate suicidality are differentially distributed among the population, and because these distributions can be tied— at least in part—to wider sociopolitical patterns and historical practices (like widening income inequality, structural racism, heteronormativity, disproportionate exposure to violence and traumatic stress, and the ongoing withdrawal of a social safety net), the formation of suicidal subjectivity also refers to the social-political conditions that generate these differential distributions. Some groups of people (like the long-term unemployed, the elderly, the members of some Native tribes, young sexual minorities, black children, and combat veterans) are more exposed to life-subverting and dignity-stripping conditions—as part of their everyday existence—than others. A suspicious mind might wonder whether this exposure to the known conditions of suicidality is itself a precondition of life for others who rank higher on a normative scheme of social value.10 While no single human life is completely immune to conditions of social isolation and hopelessness, the enduring concentration of these conditions within certain segments of the population might suggest that individuals do not simply “fall into” certain risk categories for suicide; instead, the risk categories for suicide have been allowed—through malign neglect, willful blindness, and thoughtlessness— to constitute the conditions of subjectivity for some so that others might be (in relative terms) more free from this anguish. These distributions, while not always attributable to the actions of a specific liable agent, nonetheless point in the direction of persistent structural injustices that both mirror and further aggravate wider injustices in relation to socioeconomic class, race, ethnicity, age, sexuality, and disability. The main purpose behind addressing the formation and distribution of suicidal subjectivity is to insert political thinking into the question of sui-cide—not only in relation to the background conditions of possibility for patterns of suicide but also in relation to the formation of an integrated and politically responsible engagement with these patterns.
+How might a social science research agenda form around suicide as a question of social justice? Some studies already point in this direction, albeit insufficiently. In one study of black male suicides in metropolitan areas, the authors found that rates of black male suicide are higher in areas where occupational and income inequalities between blacks and whites are greater. The authors reason that “blocked opportunities within a climate of higher expectations about increased opportunity lead to a greater probability of violence—including a higher risk of suicide” (Burr, Matteson, and Hartman 1999, 1054). Yet, like other studies that show statistical links between
+various dimensions of racial and ethnic inequality and suicide—from residential segregation and income inequalities to unequal opportunities and barriers to acculturation—this study does not interrogate the political-institutional conditions and policy choices that contribute to these differentiated exposures to the known conditions of suicidality. This is not to deny the causal complexity between social correlates (like race and class) and suicide rates but to insist on the need to connect the study of social correlates with the distributions of primary goods that are always the product of policy and budgetary decisions.
+Despite the widespread absence of a political sensibility within the study and prevention of suicide today, the Institute of Medicine (2001) has repeatedly declared that almost all states of health and disease are the product of interactions between individual and environmental factors. “Suicide is a clear example of the interaction of multiple factors including individual biological and psychological factors, life-stressors, and cultural and social factors” (Institute of Medicine 2002, 26). Nonetheless, among the most frequently employed methods for studying suicide are “psychological autopsies” that strive to understand the mind and feelings of those who have committed suicide (see Shneidman 2004). This procedure (carried out by psychiatrists, psychologists, and social workers) entails interviewing anyone who might possess information about the mind and intentions of someone who has completed suicide: spouse, relatives, friends, employers, doctors, and others. This technique is widely regarded as valid for providing an accurate diagnosis of suicide (see Kelly and Mann 1996), and psychological autopsies over the last thirty years have been fundamental in forming the now dominant “common sense” that suicide is the product of various mental disorders (see Cavanagh et al. 2003).
+What would it look like if these researchers also brought a political-institutional eye to the writing of these reports (call it a psychopolitical autopsy)? What might we learn about the formation and distribution of suicidal subjectivities by combining the evaluation of individual suicides with a wider, macroscopic analysis of sociopolitical conditions? How might suicide prevention programs look differently as a result of a more explicit engagement with the reciprocal constitution of psyche and polis ? For one thing, the list of “symptoms” and risk factors for suicide would need to be expanded to incorporate things like social fragmentation (Trovato and Jarvis 1986) and social isolation; community depopulation, blight, and crime; unemployment (Platt and Hawton 2000), measures of union concentration, and real median incomes for working-class citizens; degree of community investment and community programming, especially for youth and LGBTQ and Native American youth in particular; access to mental health services, especially in
+rural areas (where suicide rates are higher); and regulations governing the possession and safe storage of firearms (Brent and Bridge 2003). The point here is not to challenge the finding that mental illness is a central factor in aggregate rates of suicide but to advance the idea—based on what we know about the differential rates of suicide among certain populations—that the formation of suicidal subjectivities (inclusive of mental disorders) is also the complex product of institutional patterns like racial disparities and ethnic segregation, and political actions (or the absence of political actions) in areas like economic inequality, unemployment, firearms regulation, and access to mental health care that deserve to be scrutinized and challenged as matters of social justice. Each one of these policy domains influences the conditions of possibility for a life of moral equality and human dignity, and thus belongs within a properly political approach to the study and prevention of suicide. This does not mean that every case of suicide should be explained with reference to political phenomena (an ecological fallacy), but rather that a more explicit and sustained turn to the political in the study and prevention of suicide will help advance both social science and social justice.
+What can we legitimately expect from this kind of research program? There is no way to know for sure, of course, but psychopolitical autopsies might reveal a mutually constitutive relationship between individual psychopathology and the wider, recurrent pathologies of sociopolitical existence for some citizens. Given widespread commitments to moral equality, human diversity, and personal dignity, this improved understanding of social life and its inequitable distortions might spur advocacy for more thoroughgoing political changes—in both public policy and public morality—as the necessary structural means for preventing suicide. I pursue this last line of thought further in the following section.
+“To Take Arms against a Sea of Troubles”: A Social Justice Response to Suicide
+I have argued that suicide (outside of the context of physician-assisted suicide) properly belongs among the ills that a socially responsive political theory should confront as a matter of social justice.11 Even if we cannot expect widespread agreement about the ontological and moral status of suicide as such, it seems more reasonable to expect agreement about the acceptance of a collective political responsibility, rooted in the moral equality and dignity of all persons, to ameliorate those social and material conditions that can strip individuals of the will to sustain their connections with the living. This point is all the more urgent given the unequal exposure to the known conditions of suicidality among certain populations.
+But what can political theory really hope to contribute in this area? If the history of political thought offers any
+guidance, the answer is, more than we might suppose. As was true of many other domains of human life—from the structure of family life and child care, reproduction and human sexuality, to poverty, workplace organization, and consumer practices—what initially struck social observers as “private” and nonpolitical can, and frequently does, become the site of self-conscious political reflection and mobilization through the efforts of agitating nonconformists: preachers, journalists, teachers, artists, writers, and others. In the long process of this “politicization”—where new questions and agents are brought into the contested domain of public morality—the members of society are prompted, however gradually and incompletely, to think, feel, and judge differently about a set of human practices that were “naturalized” by the flow of accumulated experience; covered by layers of religious, philosophical, and ideological sedimentation; or willfully ignored altogether. Despite, or rather owing to, persistent and powerful forms of organized counter-resistance to these tremors within the fibers of public morality, political theory—along with other associated intellectual disciplines and cultural insti-tutions—can play a socially and politically relevant role by offering new conceptual formations and discursive frames through which to challenge and help reorganize long-accepted practices, norms, and collective identities.
+The dominant approach to suicide prevention in the United States and other countries is focused on understanding how to accurately identify and productively intervene with individuals in crisis. This approach is fully consistent with the overwhelming medical consensus about the psychopathological sources of suicidality. Together both of these orientations to the identification and treatment of suicidal behavior largely assume that the social-political world in relation to which a suicidal person is constituted is already normative. Perhaps this is an understandable assumption for psychologists and psychiatrists to make since they are serving on the front lines of self-destruction. Perhaps to help illuminate the specific symptoms of individual suicidality, the background conditions of life have to be held as the norm against which to judge the severity and degrees of risk for suicide. Yet, a political approach to suicide will require that we open this assumption to critical questioning. For example, what contribution do social pathologies like systemic-institutional racism and social marginalization make to suicidality? To what extent is hopelessness about a sense of social belongingness and personal effectiveness the product of political decisions like repeated deployments to combat zones, community disinvestment, and/or the failure of public policy responses in areas like income inequality, collective bargaining, and access to mental health services? Instead of isolating psychopathology as biologic and genetic challenges within a condition of societal normalcy, a political approach to suicide prevention will look at rates and distributions of completed
+suicides as a form of sociopolitical critique for the living, one that challenges the acceptance of what counts as normal. At the same time, a political approach to suicide will invert all forms of moralism (religious and secular) about suicide today: suicide is debasing of humanity, but the debasement is not generated by individual annihilation but by prior distributions in the unequal opportunities afforded to a life of dignity.12 The harm of suicide therefore includes the social and material disparities that are part of the condition of suicidality as well as the deeper harm of persistently ignoring these unequal conditions. The purpose of a political approach to suicide is to challenge and bring to an end both sets of conditions.
+At this point, a likely response from researchers in psychology will be to point out that even a more politically oriented approach to the structural conditions of suicide will still come down to a personal, psychological question: why, within certain specific sociopolitical conditions, do certain individuals die by their own hands while others (the majority) do not? To this we might respond that while each and every suicide will entail a unique psychological profile, the specific distributions of suicidal subjectivities within a population also signify patterns of policy choice and policy neglect that warrant political scrutiny and organized political resistance. Hence, while psychotherapeutic and pharmacological treatments for individuals will remain a significant line of defense against self-destruction, a political approach to suicide calls for a more active public engagement with the material and institutional conditions that breed suicidal subjectivities in the first place. If this account is persuasive, the claim that nothing politically can be done about a malady of a distressed psyche will begin to look like so much collective bad faith; that is, of denying collective agency in a realm of social life where there might indeed be plenty of room for purposeful and caring action. In this way, suicide might become an issue of social justice as much as it is a question of clinical and pharmacological treatment.
+Conclusion
+Outside of the context of end-of-life decisions and related cases where individuals seek an end to terminal illness and/or irremediable physical pain, suicide, and more specifically, the distribution of suicidal subjectivities, is a proper site of political reflection and mobilization because the despair and hopelessness of the suicidal, and the loss and suffering of their friends and family, should be the kind of things that a decent political society ought to try to prevent. To make suicide a subject of collective responsibility about which the members of a political society can and should be responsive, we should acknowledge that suicide is not simply a “naturally” occurring social bad that is randomly distributed within a population but is something for which human agency (or the lack of human
+agency) plays a causal role. That has been one of the argumentative burdens of this article. However, even if we should fail to make a fully convincing case that the distribution of suicidal subjectivities is something for which the members of a political society bear some responsibility—because the chains of causality are indeed quite complex—the case for a politically minded response to suicide does not hinge on that question alone. For if the despair, hopelessness, loss, and suffering occasioned by suicide are great social ills—as I take them to be—then even in the absence of a clear chain of culpable human agency, a decent political society should do all that it reasonably can to prevent and mitigate these traumas. Most political societies take numerous precautions against all manner of human misfortune and significant loss of life without necessarily troubling themselves over the specific and culpable role that human agency may have played therein, and a political response to suicide ought to do the same; providing the initial outlines of what a collective political approach to suicide might look like has been one of the other tasks of this article.
+A political approach to suicide is concerned with what a collective political response to the formation of suicidal subjectivities might look like. As I have argued, this response will entail a change in both public policy and in political ethics. If this approach were to catch on, it might be the beginning of a newly constituted pro-life coalition for the already born that understood that its members have agency over the conditions that make life bearable or unbearable for their fellow citizens.
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+Research in context
+Evidence before this study
+Evidence on the relationship between the COVID-19 pandemic and suicide before this study predominantly came from studies that relied on unofficial data sources or did not account for pre-existing trends. We have been conducting a living systematic review since the onset of the pandemic, searching the literature (including preprints) on a daily basis via PubMed, Scopus, medRxiv, bioRxiv, the COVID-19 Open Research Dataset by Semantic Scholar and the Allen Institute for AI, and the WHO COVID-19 database. We used over 20 search terms for suicide (eg, “suicid*”), suicidal behaviour (eg, “attempted suicide”), and self-harm (eg, “self-harm*”), in combination with a range of terms for COVID-19 (eg, “coronavirus” OR “COVID*” or “SARS-CoV-2”). Databases were searched from Jan 1, 2020, with no language restrictions. As of Dec 8, 2020, we had identified 21 reports but only five of these accounted for temporal trends in suicides (eg, by using time-series analyses). Three of these studies found no change in suicide numbers in Greece, Queensland (Australia), and Massachusetts (USA), and the fourth identified a decrease in Peru. The fifth highlighted a decrease followed by an increase in Japan, which appeared to be related to pandemic-induced employment shocks.
+Added value of this study
+This study drew on data from 21 countries and used an analytical approach that controlled for pre-existing trends to assess whether patterns of suicide have changed since the COVID-19 pandemic was declared. It is the first study to explore the potential suicide-related effects of COVID-19 at this scale.
+The results of the primary analysis showed that, in general, there does not appear to have been an increase in suicides since the pandemic began, at least in high-income and uppermiddle-income countries. Our study adds value because previous studies have reported findings from single countries or regions and their estimates of effect have often not taken account of trends in suicide before the pandemic.
+Implications of all the available evidence
+Policy responses to prevent the spread of COVID-19 need to balance the benefits of physical distancing, school and workplace closures, and other restrictions against the possible adverse impact of these measures on population mental health and suicide. Our early findings provide some reassurance (at least for high-income and upper-middle-income countries) that COVID-19 risk mitigation measures have not led to population-level increases in suicide rates. Many countries put in place additional mental health supports and financial safety nets, both of which might have buffered any early adverse effects of the pandemic. There is a need to ensure that efforts that might have kept suicide rates down until now are continued, and to remain vigilant as the longer-term mental health and economic consequences of the pandemic unfold. There are some concerning signals that the pandemic might be adversely affecting suicide rates in low-income and lower-middle-income countries, although data are only available in a small minority of these countries and tend to be of suboptimal quality. Even in high-income and uppermiddle-income countries, the effect of the pandemic on suicide might vary over time and be different for different subgroups in the population.
+identified ten studies, focusing on epidemics or pandemics of influenza (1889-93 [UK]; 1918-19 [USA]; 2009-13 [USA]), severe acute respiratory syndrome (2003 [Hong Kong and Taiwan]), and Ebola virus (2013-16 [Guinea]).3,4 These reviews suggested that, although suicide rates might sometimes increase following these sorts of public health emergencies, the changes might not necessarily occur immediately, and that the risk might actually be reduced initially.
+We established the International COVID-19 Suicide Prevention Research Collaboration (ICSPRC) to monitor the global effect of COVID-19 on suicide. We have tracked studies specific to COVID-19 and suicide through a living systematic review,5 and found that most studies have had methodological limitations. Some have relied on data from unconfirmed sources, including reports from Nepal and Thailand based on newspaper articles citing data from the police6,7 and a secondary source,8 respectively. These reports indicated increases in suicide after the COVID-19 pandemic began.
+Other studies have used official suicide statistics for the months since the pandemic began but have made comparisons to equivalent periods without accounting
+for underlying trends. Studies of this kind in Norway,9 Sweden,10 South Korea,11 Tyrol in Austria,12 Leipzig in Germany,13 and Connecticut in the USA14 showed decreases in suicides, and one in the Evros region of Greece found no change.15 Three separate studies used a similar approach to analyse Japanese suicide statistics: one considered children and adolescents only and found no evidence of an increase;16 and the other two considered all age groups and identified a decrease in the pandemic’s early stages,17 but highlighted an upswing in July, 2020.17,18
+Only five studies—from Greece,19 Queensland in Australia,20 Massachusetts in the USA,21 Peru,22 and Japan23—have used official data and accounted for temporal trends. The studies in Greece, Queensland, and Massachusetts found that the observed and expected numbers of suicides did not differ after pandemic responses were introduced.19-21 The Peruvian study reported a decrease in suicides following stay-at-home orders.22 The Japanese study confirmed fluctuations in suicides and identified a positive association between pandemic-induced employment shocks and suicides.23
+The evidence so far is insufficient to indicate what the effect of COVID-19 on suicides has been or will be. It is
+likely that any effect will vary between and within countries, and over time, depending on factors such as the extent of the pandemic, the public health measures instituted to control it, the capacity of existing mental health services and suicide prevention programmes, and the strength of the economy and relief measures to support those whose livelihoods are affected by the pandemic. There are also multiple other population-level influences on suicide (eg, political unrest, economic challenges, and availability of lethal means) that might operate independently of the pandemic or be exacerbated by it, and these factors might differ across countries.
+We did this ICSPRC study to gain a broader understanding of suicide patterns, which we believe is crucial for mitigating the risk of any pandemic-related increases. Specifically we aimed to assess the early effect of the COVID-19 pandemic on suicide rates around the world.
+Methods
+Overview
+Using real-time suicide data from multiple countries and areas within countries, we did an interrupted time-series analysis to ascertain whether trends in monthly suicide counts changed after the pandemic began. Given the importance of questions about COVID-19 and suicide, we believed that it was crucial to provide evidence from the best available real-time data sources. In many countries, there is a time-lag in official suicide data being released because of the way in which suicide deaths are identified and recorded in vital statistics collections. In these countries, suspected suicides are investigated by a coroner, medical examiner, or other official to confirm the cause and manner of death, with or without an autopsy. The investigation process can be lengthy, resulting in data that are not sufficiently timely to guide suicide prevention actions. Consequently, some countries and areas within countries have developed methods for initial death classification while the investigation is ongoing to produce real-time suicide data. Typically, although not always, these approaches rely on police reports or death certificates as their primary source of evidence for the preliminary classification. These alternative or preliminary data sources are crucial for identifying and responding to any changes in patterns of suicide that might be associated with external events.
+Our approach followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER; appendix p 1).24 We received approval from the Swansea University Medical School Research Ethics Sub-Committee (2020-0054).
+Data inputs
+We sought real-time data on suicides from countries as well as from areas within countries to maximise the number of places that could contribute to the overall picture. Establishing real-time suicide data collection
+systems is difficult, especially on a national level, so restricting our efforts to whole countries would have limited the conclusions we could draw. Real-time suicide data were identified through internet searches, recourse to the scientific literature, and contact with our networks.
+We did internet searches between Sept 1 and Nov 1, 2020, to identify relevant data in World Bank countries and economies with more than 3 million residents (n=135).25 We first searched the official websites of these countries’ ministries of health, police agencies, and government-run statistics agencies or equivalents, using the translated search terms “suicide” and “cause of death”. If this search did not yield results, we did a more general internet search using the translated search terms “suicide”, “[name of country]”, “pandemic”, “COVID” and “corona” for publicly reported information (eg, in news reports and on the websites of suicide prevention organisations) that might indicate whether relevant data existed and, if so, how they might be traced.
+We also searched the academic literature for studies reporting on suicides before and after the pandemic began through our living review.5 We extracted data from the publications or their cited sources and contacted the authors. We also drew on the knowledge of ICSPRC members (representing 40 countries) and our contacts at WHO and the International Association for Suicide Prevention (IASP).
+Publicly available data were accessed online and data that were not publicly available were provided by data custodians.
+Data inclusion and exclusion criteria
+To be included, data from a given country or area had to come from an official government source (eg, a government department, agency responsible for collating
+networks. Areas within countries could also be included with populations of 3 million residents or fewer.
+Data storage and management
+We aggregated all data to the monthly level. Data were housed in a safe, secure, password-protected database held at Swansea University using Secure eResearch Platform technology (Adolescent Mental Health Data Platform [ADP]). Per the platform’s data protection protocols, access to the data was limited and only made available to JP, AJ, SS, MDPB, VA, DGu, and MJS.
+Data analysis
+We used interrupted time-series analysis to model the trends in monthly suicides before COVID-19 in each country or area within country, accounting for time trends and seasonality wherever possible. Models were fitted with use of Poisson regression and accounted for possible over-dispersion using a scale parameter set to the model’s %2 value divided by the residual degrees of freedom. We modelled the effect of time as a non-linear predictor, unless this offered no improvement beyond a linear model, in which case we used the linear model instead. Non-linear time trends were estimated by selecting the best fitting model from a series of fractional polynomial models. Seasonality was accounted for with Fourier terms (pairs of sine and cosine functions). We then used each country or area’s model to forecast what the trend in suicides from the beginning of the COVID-19 period would have been had COVID-19 not occurred, calculating the expected number of suicides, which represented the counterfactual. We compared this expected number with the observed number of suicides in the same period by calculating rate ratios (RRs) and 95% CIs. In a small number of countries or areas, it was
+not possible to account for seasonality in the model because we only had pre-COVID-19 data for a single year (Jan 1, 2019, onwards). For these countries, we fitted a model with a linear predictor for time only. Further details of the modelling strategy are provided in the appendix (pp 2-10).
+We did a primary analysis and two sensitivity analyses (figure 1). In each analysis, we included data from all available months in each country or area in the pre-COVID-19 period. In the primary analysis, we treated April 1, 2020, as the start of the COVID-19 period and censored the data beyond July 31, 2020, in order to maximise data quality, in recognition that there might have been under-enumeration of suicides in the later months with figures being subsequently updated. In the first sensitivity analysis, we retained April 1, 2020, as the start of the COVID-19 period but relaxed the end date to include all data available in the COVID-19 period for each country or area up to Oct 31, 2020. In the second sensitivity analysis, we changed the start of the COVID-19 period to March 1, 2020, and used the original censoring date of July 31, 2020, as the end of the COVID-19 period, recognising that the onset of COVID-19 and associated public health measures varied.
+We also did two supplementary analyses. In the first, we repeated the primary analysis using the same methods and date cutoffs, but inflated the number of suicides in each country and area in the months of the COVID-19 period by 5%. In the second, we used data from the Australian state of Tasmania that were aggregated to 3 months (rather than 1 month) but otherwise met our inclusion criteria. In this analysis, we used data from Jan 1, 2019, to Sept 30, 2020, and treated April 1, 2020, as the beginning of the COVID-19 period.
+All analyses were done on the Swansea University ADP Secure eResearch Platform using Stata software (version 16.1). The Stata code is available in the appendix (pp 11-17).
+Role of the funding source
+There was no funding source for this study.
+Results
+We sourced data from 21 countries (16 high-income countries and five upper-middle-income countries), of which ten had data available for the whole country and 11 had data for a specific area or areas within the country. The table summarises the populations of the countries and areas as well as the dates on which the first stay-at-home orders were implemented.26 The appendix contains details of the source and nature of the data for each country and area (pp 18-23) as well as the raw data (pp 24-28).
+The observed and expected number of suicides for April 1 to July 31, 2020, and the RRs based on these numbers are shown in figure 2 (see appendix pp 4-10 for the coefficients and standard errors of the models underlying the expected number of suicides). The 95% CIs
+surrounding the RR for each country or area either include the null value of 1-00 or fall below the null value, indicating that there was no evidence of an increase in suicides relative to the expected number during the COVID-19 period in any country or area. There was statistical evidence of a decrease in suicides in 12 countries or areas: New South Wales, Australia (RR 0-81 [95% CI 0-72-0-91]); Alberta, Canada (0-80 [0-68-0-93]); British Columbia, Canada (0-76 [0-66-0-87]); Chile (0-85 [0-78-0-94]); Leipzig, Germany (0-49 [0-32-0-74]); Japan (0-94 [0-91-0-96]); New Zealand (0-79 [0-68-0-91]); South Korea (0-94 [0-92-0-97]); California, USA (0-90 [0-85-0-95]); Illinois (Cook County), USA (0-79 [0-67-0-93]); Texas (four counties), USA (0-82 [0-68-0-98]); and Ecuador (0-74 [0-67-0-82]).
+Incorporating data up until the latest month available (to Oct 31, 2020) made little difference to the results from most countries or areas (figure 3), with most 95% CIs for the RR estimates below or including 1-00. Victoria, Australia (0-89 [0-80-0-99]); Thames Valley, England, UK (0-82 [0-68-0-98]); and Mexico City, Mexico (0-86 [0-77-0-97]) showed significant decreases that were not seen in the primary analysis. There were three exceptions to the picture of no change or decreases in suicides: Vienna showed statistical evidence of an increase in suicides (1-31 [1-08-1-59]) relative to the expected number when the additional months were included, as did Japan (1-05 [1-04-1-07]) and Puerto Rico (1-29 [1-05-1-58]). In each case, the latest month for which data were available was October.
+The results of the second sensitivity analysis, in which the pandemic’s first day was defined as March 1 rather than April 1, 2020 (figure 4), were also similar to those from our primary analysis. Again, there was evidence of a decreased risk of suicide in several additional countries or areas over and above those observed in our primary analysis: Manitoba, Canada (0-60 [0-48-0-76]); Poland (0-94 [0-90-0-98]); Las Palmas, Spain (0-69 [0-51-0-94]); and Peru (0-73 [0-64-0-83]). There was no evidence of any increase in suicides relative to the expected number during this COVID-19 period for any country or area except Puerto Rico (1-36 [1-07-1-72]).
+Our two supplementary analyses also showed consistent findings. Inflating the suicide numbers in the COVID-19 period by 5% made little difference to the results (appendix p 29), with only two areas showing statistical evidence of an increase in suicides where this had not been the case previously: New Jersey, USA (RR 1-18 [95% CI 1-05-1-34]) and Puerto Rico (1-34 [1-03-1-74]). When we analysed the 3-monthly data from Tasmania, the findings were similar to those from the other Australian states, with no evidence of any increase in suicides in the COVID-19 period (RR 0-74 [95% CI 0-53-1-02]).
+Discussion
+In general, based on the primary analysis, there does not appear to have been an increase in risk of suicide during
+the pandemic’s early months in the 21 countries for which we had data, and a number of countries or areas appear to have seen fewer suicides relative to the expected number.
+Our findings align with those of other published studies from high-income and upper-middle-income countries, in which there were either decreases or no changes in suicide rates as a function of the pandemic.9-15,19-22 Our findings are also consistent with emerging reports in the grey literature from various countries (eg, England).27 In some cases, this consistency is not surprising because we used the same data sources, but the fact that we found similar patterns in many other countries increases our confidence in this finding.
+The lack of increase in suicides since the pandemic began could be attributed to various factors. First, there was an early emphasis on the potential adverse effects of stay-at-home orders, school closures, and business shut downs. Empirical evidence began to emerge from some countries that self-reported levels of depression, anxiety, and suicidal thinking were heightened during the initial stay-at-home periods,1 but this does not appear to have translated into increases in suicides, at least in the countries in our study. In some countries, governments responded rapidly to the threat to mental health, implementing recommended approaches such as bolstering mental health services.28 Maintaining this emphasis on accessible, high-quality mental health care is crucial.
+Second, certain protective factors might have been operating in the pandemic’s early months. Communities might have actively tried to support at-risk individuals, people might have connected in new ways, and some relationships might have been strengthened by households spending more time with each other.28 For some people, everyday stresses might have been reduced during stay-at-home periods, and for others the collective feeling of “we’re all in this together” might have been beneficial.
+Finally, many countries rapidly enacted fiscal support initiatives to buffer the pandemic’s economic consequences. In many cases, this support is now being reduced or withdrawn. As it lapses, previously protected populations might face increasing stress. Suicide rates can rise during times of economic recession,29 so it is possible that the pandemic’s potential suicide-related effects are yet to occur.
+Vienna, Japan, and Puerto Rico were outliers in parts of our analysis. Although they showed no evidence of an increased risk of suicide in our primary analysis, we observed a significantly increased risk in all three when we extended the observation period to Oct 31, 2020, and in Puerto Rico we noted an increase when we brought forward the pandemic’s start date from April 1 to March 1, 2020. Additional contextual factors might have operated in these countries—for example, in Japan, several widely reported celebrity suicides that occurred
+during the pandemic might have exerted an influence; and Puerto Rico has been in a deep recession since 2006, so pre-existing high levels of poverty might have exacerbated the pandemic’s economic effects.
+To our knowledge, this study is the first to combine data from multiple countries to examine the early effects of COVID-19 on suicide, taking account of underlying trends. The study involved a systematic search process and overcame the delays inherent in vital statistic collection by using real-time data from numerous official sources. However, it did not represent low-income or lower-middle-income countries, which account for 46% of the world’s suicides and might have been hit particularly hard by the pandemic. Very few of these countries have good-quality vital registration systems and still fewer collect real-time suicide data.30 In our search, we identified unofficial real-time data from two lower-middle-income countries (Myanmar and Tunisia) and one low-income country (Malawi) that could not be disaggregated to the monthly level. We were unable to verify or use these data in our analyses, but they were concerning for two of these countries. In Malawi, there was reportedly a 57% increase in January-August, 2020, compared with January-August, 2019, and in Tunisia there was a 5% increase in March-May, 2020, compared with March-May, 2019. By contrast, in Myanmar, there was a 2% decrease in January-June, 2020, compared with January-June, 2019.
+Another limitation is that data quality might have been an issue in the countries and areas in our study. Data from the most recent months in any given country or area might have been the least reliable and the most likely to represent undercounts, especially if COVID-19 disrupted data-collection processes. We attempted to overcome this problem by using July 31, 2020, as the end date in our primary analysis, and only using more recent months (to Oct 31, 2020) in the first sensitivity analysis. If the data in the later months were artificially low, we might have expected to see countries or areas that showed no difference in suicides in the primary analysis recording a decrease in this sensitivity analysis, but this only occurred in Victoria, Australia; Thames Valley, England, UK; and Mexico City, Mexico. Similarly, inflating the number of suicides in each month of the COVID-19 period by 5% (which might be the typical magnitude of any increase if later figures were updated) made little difference. Only two areas showed statistical evidence of an increase in suicides where this had not been the case previously: New Jersey (USA) and Puerto Rico.
+In addition, various factors might have influenced the power and precision of our models. In particular, low numbers of timepoints and low numbers of monthly suicides in given countries or areas might have resulted in models with relatively poorer power and precision, with the effect of biasing the findings to the null and suggesting that there was no change in the number of
+monthly suicides from the pre-COVID-19 period to the COVID-19 period when in fact there might have been an increase or a decrease. Only five areas had both the minimum number of pre-COVID-19 timepoints (January, 2019, to March, 2020) and low numbers of monthly suicides and showed no change in suicide risk in our primary analysis: Vienna, Austria; Cologne and Leverkusen, Germany; Frankfurt, Germany; Botucatu, Brazil; and Maceio, Brazil. The findings from these five areas should be interpreted with caution.
+We were unable to stratify the data by age, sex, or ethnicity, and the pandemic might have a differential effect on suicides in certain demographic groups (eg, women and girls,17,18 children and adolescents,17 and ethnic minorities14). We were also unable to explore any temporal changes in suicide methods. Additionally, we could not consider external factors that might have influenced suicide patterns in different countries or areas, including varying public health measures or economic support packages. We are planning future studies to address these questions.
+We relied on area-within-country data for 11 countries. We included these data to ensure representation from as many countries as possible and to avoid generating a picture that was biased towards better-resourced countries. We deliberately did not extrapolate from these areas to whole countries because we were aware that they were sometimes small and might have had unique suicide profiles. However, some of these areas would have been expected to account for a large proportion of the suicides in the given country, based on their population size and their historical suicide statistics (eg, suicides in New South Wales, Queensland, and Victoria typically represent 75% of all suicides in Australia)31 and others had larger populations than some of the other included countries (eg, California had a population of 39• 7 million people). Additionally, data from the areas within these countries showed similar patterns to those from relevant areas studied elsewhere. For example, studies done in Massachusetts and Connecticut, USA, showed no increase in suicide numbers after the pandemic began,14,21 which is in line with our findings from the US jurisdictions for which we had data. Similarly, the 3-monthly data from Tasmania that we analysed separately showed no increase in suicides, consistent with the findings from the other Australian states.
+We used the same date in a given analysis to distinguish the pre-COVID-19 period from the COVID-19 period for all countries (April 1 or March 1, 2020), potentially underestimating any effect of COVID-19 in countries or areas with an earlier onset of the pandemic or public health protection measures. We considered using the date of the initial stay-at-home order to distinguish the pre-COVID-19 and COVID-19 periods, but areas within a given country might have introduced stay-at-home orders at different times. Additionally, because we had monthly
+suicide counts, we would have had to convert the date of the initial stay-at-home order to the beginning of the month in question or the next month. These dates fell between Feb 23 and April 7, so between them the analyses covered all periods.
+Our study is the first to examine suicides occurring in the COVID-19 context in multiple countries. It offers a broadly consistent picture, albeit from high-income and upper-middle-income countries, of suicide numbers remaining unchanged or declining in the pandemic’s early months. This picture is neither complete nor final, but serves as the best available evidence on the pandemic’s effects on suicide so far.
+We need to continue to monitor real-time data and be alert to any increases in suicide, particularly as the pandemic’s full economic consequences emerge. We need to understand what has kept suicide numbers down during the pandemic’s early months, and what drives any increases if they do occur. We also need to recognise that suicide is not the only indicator of the negative mental health effects of the pandemic; levels of community distress are high and we need to ensure that people are supported. We need to redouble our efforts to understand the pandemic’s effects on suicides in low-income and lower-middle-income countries, and we need to make sure that we communicate our findings to governments and communities in safe, non-sensationalist ways.32
+Policy makers should heed the value of high-quality, timely suicide data in suicide prevention efforts, and should prioritise mitigation of suicide risk factors associated with COVID-19 and take decisive action (eg, by resourcing mental health services and providing financial safety nets) to prevent the possible longer-term detrimental effects of the pandemic on suicide.
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+INTRODUCTION
+Suicidal behavior is a global cause of death and disability. Worldwide, suicide is the fifteenth leading cause of death, accounting for 1.4% of all deaths (WHO 2014). In total, more than 800,000 people die by suicide each year. The annual global age-standardized death rate for 2012 is estimated to be 11.4 per 100,000, and the World Health Organization (WHO) projects this rate to remain steady through 2030 (WHO 2013, 2014).
+In addition to suicide deaths, suicidal thoughts and nonfatal suicide attempts also warrant attention. Globally, lifetime prevalence rates are approximately 9.2% for suicidal ideation and 2.7% for suicide attempt (Nock et al. 2008a). Suicide ideation and attempts are strongly predictive of suicide deaths; can result in negative consequences such as injury, hospitalization, and loss of liberty; and exert a financial burden of billions of dollars on society (CDC 2010a; Nock et al. 2008a,b; WHO 2014). Taken together, suicide and suicidal behavior comprise the nineteenth leading cause of global disease burden (i.e., years lost to disability, ill-health, and early death), and the sixth and ninth leading cause of global disease burden among men and women 15 to 44 years of age, respectively (WHO 2008). By any measure, there is urgency to better understand and prevent suicide and suicidal behavior.
+DEFINITIONS AND TERMINOLOGY
+The use of vague or inconsistent terms and definitions has hindered progress in suicide research and theory. For example, some use the term suicidal behavior as a general term encompassing any suicidal thought or action without taking additional steps to distinguish thoughts from plans, from nonfatal attempts, and from attempts that result in death. Similarly, some use the term self-harm to refer to intentional self-injury without intent to die (i.e., nonsuicidal self-injury behaviors such as superficial skin cutting), whereas others use the term to encompass all intentional self-injurious behaviors regardless of intent to die. Because these different aspects of suicidality and self-injury can have very different prevalence rates, functions, clinical correlates, and outcomes, it is critical to be precise with our use of definitions and terminology.
+The scope of this review precludes a comprehensive discussion of issues of terminology and definition, but we emphasize a few key points. We utilize the definitions provided by the US Centers for Disease Control and Prevention (CDC) (CDC 2015a, Crosby et al. 2011), whereby suicidal self-directed violence is distinguished from self-directed violence with undetermined or nonsuicidal intent. Within the domain of suicidal self-directed violence, suicide is defined as death caused by self-directed injurious behavior with an intent to die as a result of the behavior; suicide attempt is defined as a nonfatal, self-directed, potentially injurious behavior with an intent to die as a result of the behavior even if the behavior does not result in injury; and suicidal ideation is defined as thinking about, considering, or planning suicide. The terms completed suicide, failed attempt, nonfatal suicide, successful suicide, suicidal gesture, and suicide threat are considered pejorative or misleading, and the term parasuicide is considered overly broad and vague and therefore unacceptable by the CDC.
+The American Psychiatric Association (APA) has also addressed an important definitional issue with the publication of the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5; Am. Psychiatr. Assoc. 2013). Section III of the DSM-5 includes nonsuicidal self-injury (NSSI) and suicidal behavior disorder as “conditions for further study.” A key reason for proposing a distinct disorder for NSSI was to distinguish the behavior from suicide attempts (i.e., self-harm with intent to die). Although NSSI is strongly correlated with suicide attempts (Klonsky et al. 2013, Wilkinson et al. 2011), the behaviors differ in terms of prevalence (NSSI is more prevalent), frequency (NSSI is often performed dozens or hundreds of times, whereas suicide attempts are typically performed once or a few times), methods (cutting and burning are more characteristic of NSSI, whereas self-poisoning is more characteristic of attempted suicide), severity (NSSI rarely causes medically severe or lethal injuries), and functions (NSSI is performed without intent to die, usually to temporarily relieve overwhelming negative emotion, and sometimes in an effort to avoid suicidal urges) (CDC 2010a, Klonsky 2007, Klonsky & Muehlenkamp 2007, Muehlenkamp 2005, Muehlenkamp & Gutierrez 2004). We believe NSSI has a strong relationship with suicide attempts for two reasons: NSSI correlates with variables, such as depression, known to increase risk for suicidal ideation; and NSSI facilitates habituation to self-inflicted violence and pain, which in turn increases the capacity to attempt suicide (Klonsky et al. 2013).
+CHALLENGES FOR RESEARCH
+The study of suicide is fraught with many challenges resulting from the nature of suicidality itself, the research practices common to the field over the past several decades, and the complicated cultural meaning of suicide (Goldsmith et al. 2002). Five challenges are detailed in this section.
+First, as noted above, the field of suicidology has struggled to establish a set of agreed upon terms over the past 50 years. Although it has become more common for researchers to be clear about the terms they use and their meaning (like we do above), the existing research literature is filled with different terms, which hampers our ability to integrate findings across the various studies published. The field has repeatedly sought to address the issue, including at a meeting hosted in the 1970s by the National Institute of Mental Health (NIMH), and subsequent efforts in the 1990s by multiple organizations including NIMH, the American Association of Suicidology, and the Center for Mental Health Services. These meetings resulted in a seminal article by O’Carroll et al. (1996) that was subsequently revised and updated by Silverman et al. (2007). However, despite these workshops, differences persist in terminology between subfields (e.g., mental health professionals versus school systems versus coroners) and even among mental health professionals and suicidologists (e.g., whether to distinguish NSSI from suicide attempts). Such diversity impedes the ability to combine knowledge from disparate studies and publications and limits the advancement of suicide knowledge and prevention (Posner et al. 2014).
+Second, in part due to the aforementioned inconsistencies in nomenclature, measures of suicidality are numerous and often divergent in their aims and content. For example, assessments of suicide ideation range from simple one- to two-item screenings [e.g., “Did you ever seriously consider suicide?” (CDC 2015b)] to full assessments that capture frequency, severity, planning, communication, and intent (Nock et al. 2007). Though versatility in measurement approaches allows for assessments in different settings and time frames, it also leads to confusion in the literature. For example, the presence of ideation is at times operationalized as fleeting thoughts about suicide and at other times requires heightened severity or frequency. A history of suicide attempt may be determined by a single question (e.g., “Have you ever attempted suicide?”) or may explicitly require intent or a certain degree of lethality. The diverse measurement approaches make it difficult to compare findings and integrate knowledge across studies.
+A third challenge to research is the variability across studies in whether suicidal ideation and attempts are treated as states or traits. In other words, is suicide ideation and attempt better conceptualized as an experience someone has at a moment in time (e.g., studies of ideation or attempts) or as an individual difference variable attached to anyone who has thought about or attempted suicide at least once (e.g., studies of ideators or attempters)? For most, ideation is a relatively rare experience isolated to a particular period of one’s life rather than a chronic experience (Kessler et al. 2012). Similarly, most individuals who attempt suicide only do so once (Kessler et al. 2012). Thus, it may be most accurate to consider suicidality a state and to study it accordingly. However, because previous suicide attempts strongly predict future attempts (Borowsky et al. 2001, O’Connor et al. 2013) and because some ideators, often with early onset, experience persistent ideation (Kessler et al. 2012), there is also reason to view suicidality as a trait-like variable, especially in the context of clinical risk assessment. Different perspectives on this issue imply different research designs and questions, and yield different types of knowledge (e.g., when is an individual at risk versus who is at risk). Unfortunately, the basis for the approach taken is rarely explicitly considered or rationalized in published studies, and knowledge about suicide and suicide risk suffers as a result.
+Fourth, even when clear definitions are agreed upon and standardized measures are used, the heavy stigma surrounding suicide can influence reporting. For example, individuals in countries strongly influenced by religions that prohibit suicide may underreport suicide attempts and deaths. It is even possible that individuals with a history of suicidal thoughts or attempts are less likely to identify as such and agree to participate in research studies, although for obvious reasons it would be extremely difficult to recruit a representative sample of suicidal individuals to examine this possibility. Nonetheless, it is likely that cultural differences in the stigma around suicide affect the accuracy of the rates reported in global epidemiological studies (Mars et al. 2014, Nock et al. 2008b).
+Finally, the nature of suicidal thoughts and behaviors themselves presents a variety of obstacles for research. To begin with, low base-rate behaviors such as suicide are hard to study for both practical and statistical reasons. Even in high-risk populations, where suicide deaths are more common than in the general population, thousands of participants are needed to obtain reliable results (Goldsmith et al. 2002). Moreover, unlike many other clinically relevant behaviors, such as binge drinking or occurrences of panic attacks, a suicide death precludes the possibility of reporting about the event retrospectively. Instead, examining suicide as an outcome means utilizing large longitudinal studies and psychological autopsy studies. Longitudinal studies present challenges for the inclusion of large sample sizes, comprehensive clinical assessment, and sufficiently frequent assessments so as to ensure that any suicide death that occurs is likely to have been preceded by an assessment relatively close in time. Psychological autopsy studies are limited by their reliance on the memories, knowledge, and interpretations of informants and medical records.
+Because of the difficulty in studying suicide as an outcome, researchers instead often study suicidal thoughts and/or behaviors as proxies for suicide. These behaviors make good research targets because they are strongly related to suicide but occur far more frequently and are thus easier to study. However, these studies have their own practical and ethical limitations. For example, researchers have an ethical responsibility to intervene should they believe a suicide attempt is imminent, which means that researchers often must impact the participants they are studying precisely when, from a scientific perspective, it would be most important to observe and assess the natural course of suicidal thoughts and attempts. In addition, a few studies suggest that suicidal thoughts and behaviors have some different predictors and correlates than suicide death (Daigle 2004, DeJong et al. 2010), which means that studies of suicidal thoughts and behaviors may not fully generalize when it comes to understanding suicide itself. Although these challenges will remain for the foreseeable future, suicide research is also poised to benefit from creative advances in psychological research, including using social networking analysis, ecological momentary assessment, and big data approaches. It will be important for suicidologists to use these and other methodological innovations to combat the challenges inherent to the study of suicide.
+SOCIODEMOGRAPHIC CORRELATES
+A comprehensive examination of correlates of suicide, suicide attempts, and suicidal ideation is beyond the scope of this review; however, we briefly emphasize some key points. Most notably, suicide rates are not distributed evenly across people or places.
+For example, high-income countries have higher suicide rates than low- and middle-income countries (LMICs; 12.7 versus 11.2 per 100,000, respectively). LMICs, however, account for over 75% of all suicides worldwide. Suicide rates also differ by gender and age (Nock et al. 2008a; WHO 1999, 2014). Men account for roughly three times the number of suicides than women, and this gender disparity is even greater in high-income countries (WHO 2014). When stratified by age, suicide rates are highest in adults aged 70 and older across both men and women. However, although overall rates of suicide are lower in children and young adults, suicide accounts for a disproportionately large number of deaths in these age ranges. For example, suicide is the second leading cause of death among those 15 to 29 years old, and the leading cause of death among young women aged 15 to 19 (Patton et al. 2009). Notably, sex and age patterns often differ across countries. For example, in high-income countries, middle-aged men have a higher suicide rate than their LMICs counterparts, whereas in LMICs, young adults and elderly women have higher suicide rates compared with young adults and elderly women in high-income countries.
+Changes in suicide rates over time also differ across peoples and places (WHO 2014). Between 2000 and 2012, age-standardized suicide rates decreased worldwide by an average of 26%. However, this decrease was far from uniform. For example, during this period suicide rates decreased by 69% among women in Malta but increased by 416% among men in Cyprus. Meaningful variability was even observed between neighboring countries. Whereas Canada experienced an 11% decrease in suicide rates from 2000 to 2012, the United States experienced a 24% increase.
+Rates of nonfatal suicidal behavior also differ by region, age, sex, and sexual orientation. For example, the United States has higher rates of suicide ideation (15.6%), plans (5.4%), and attempts (5.0%) than the global average (Nock et al. 2008a). In addition, rates of lifetime suicidal ideation, suicide plans, and suicide attempts are higher in females than males (Kessler et al. 1999; Nock et al. 2008a, 2013) and higher in adolescents than adults (Nock et al. 2008b). It is also recently becoming clear that individuals reporting sexual- or gender-minority orientations (i.e., lesbian,
+gay, bisexual, and transgender) are at increased risk for suicidal ideation and suicide attempts, a trend that appears to hold constant worldwide (Figueiredo & Abreu 2015).
+MENTAL DISORDERS AND OTHER CLINICAL CORRELATES
+It is often stated that over 90% of individuals who die by suicide have mental disorders (Bertolote & Fleischmann 2002). However, it is also true that the overwhelming majority of individuals with mental disorders—more than 98%—do not die by suicide (Nordentoft et al. 2011). In addition, some mental disorders confer higher risk for suicide than others.
+In developed countries, the disorders that most strongly predict a subsequent suicide attempt are bipolar disorder, posttraumatic stress disorder, and major depression; in developing countries, the most predictive disorders are posttraumatic stress disorder, conduct disorder, and drug abuse/dependence (Nock et al. 2009). Importantly, additional analyses of these data showed that the associations between these disorders and suicide attempts are mostly due to the disorders predicting the development of suicidal ideation. When limiting analyses to individuals with suicidal ideation, mental disorders became very weak predictors of suicide attempts. This tendency of potential risk factors to predict suicidal thoughts better than attempts is a key theme that is revisited throughout the remainder of this article.
+Besides mental disorders, numerous clinical and psychological variables have been demonstrated to influence suicide risk. A recent paper on the psychology of suicide by O’Connor & Nock (2014; see panel 2) lists more than 30 psychological risk and protective factors. Here, we focus on three psychological variables often considered to be particularly important predictors of suicidal thoughts and attempts: depression (measured as a continuous variable rather than a discrete mental disorder), hopelessness, and impulsivity. Indeed, there is evidence that each of these variables exhibits statistically reliable relationships to measures of suicidality and suicide risk. However, the literature for each of these variables has important nuances. Depression appears to be one of the strongest predictors of suicidal ideation but does not appear to distinguish those who have attempted suicide from those who have experienced suicidal ideation without attempts (May & Klonsky 2016). Hopelessness is well known for demonstrating prospective prediction of suicide and suicide attempts in very-long-term studies; however, the magnitude of prediction in this research is actually quite small, similar to a correlation of about 0.2 (Beck et al. 1989). In addition, like depression, hopelessness is elevated in those who have experienced suicidal ideation but is not higher in attempters compared to ideators (May & Klonsky 2016).
+The role of impulsivity in suicide is particularly noteworthy because impulsivity has long been conceptualized as a key risk factor for suicide attempts. Indeed, because impulsivity is thought to hasten the transition from thoughts to action, it has often been conceptualized as a critical clinical factor in the progression from suicidal thoughts to attempts (Bryan & Rudd 2006, Mann et al. 1999). However, recent research disputes these long-held clinical beliefs. For example, a recent meta-analysis found that impulsivity is a relatively modest predictor of suicide attempts (Anestis et al. 2014). Other studies find no connection between measures of trait impulsivity and more “impulsive” suicide attempts (e.g., attempts made with little planning or forethought) (Wyder & De Leo 2007). Research has also found that most measures of impulsivity are no higher in suicide attempters than in those who have experienced ideation without attempts (Klonsky & May 2010), although this same study found higher impulsivity in those who have experienced either ideation or attempts compared to those without histories of suicidality.
+Taken together, most clinical correlates of suicidality appear to be best conceptualized as correlates of suicidal ideation. These variables appear to predict suicide attempts or deaths only to the extent that they predict ideation. This pattern and its implications are discussed further below in the section titled The Ideation-to-Action Framework.
+MOTIVATIONS FOR SUICIDE
+Whereas most studies on suicide focus on correlates, another way to improve suicide knowledge and prevention is to better understand the motivations for suicide attempts. Understanding the most common motivations for suicide attempts can inform conceptual models of suicide and facilitate the development of intervention and prevention programs that are most likely to resonate with and help those at risk. Clinically, identifying the motivation for a specific client’s attempt allows the clinician and the attempter to find alternative solutions that may solve the problem and reduce the likelihood of future attempts. Though a desire to die is, by definition, a motivation common to all suicide attempts, research suggests that individual attempts may be motivated by a myriad of reasons such as escape, communication, altering one’s environment, and dealing with an unbearable state of mind (Brown et al. 2002a, Chapman & Dixon-Gordon 2007, Holden et al. 1998, May & Klonsky 2013, Schnyder et al. 1999).
+Different theories of suicide offer different hypotheses about why people attempt suicide. Edwin Shneidman’s (1993) theory of suicide describes psychache (i.e., emotional or psychological pain) as the primary motivator of an attempt. He posits that suicide occurs when an individual’s threshold for tolerating psychological pain is surpassed and that this threshold varies across individuals. Roy Baumeister presents a theory of suicide based on constructs from cognitive, social, and personality psychology. His escape theory suggests that many suicide attempts are motivated by a need to reduce aversive self-awareness (Baumeister 1990). Thomas Joiner’s (2005) interpersonal theory states that two domains, perceived burdensomeness and thwarted belongingness, interact to confer the desire for suicide. Other theories highlight the roles of hopelessness (Abramson et al. 1989), problem-solving (Baechler 1979), impulsivity (Simon et al. 2001), and interpersonal communication (Farberow & Shneidman 1961, Kobler & Stotland 1964, Kreitman 1977) in motivating a suicide attempt.
+Interestingly, and perhaps unfortunately, most instruments designed to assess suicide motivations have been developed with little regard for the theoretical work described above. Early efforts to assess motivations for suicide were carried out by John Bancroft and colleagues in the 1970s. Potential motivations for overdoses were generated by researchers and study participants, resulting in a list of 14 possible reasons (Bancroft et al. 1976, 1979). Twenty years later, Ronald Holden and collaborators (1998) used these items to construct the Reasons for Attempting Suicide Questionnaire. Shortly thereafter, Marsha Linehan and colleagues (Brown et al. 2002a) included reasons for self-injurious behavior as part of their Parasuicide History Interview. More recently, the Inventory of Motivations for Suicide Attempts (IMSA; May & Klonsky 2013) was developed. Unlike for previous measures, development of the IMSA was informed by prevailing theories of suicide, and the IMSA consists of nine scales assessing motivations emphasized by these different theories.
+Some important lessons can be drawn from studies utilizing the above measures. Across both rationally and empirically derived measures, two superordinate dimensions of attempt motivations arise (Brown et al. 2002a, Holden & DeLisle 2006, May & Klonsky 2013, May et al. 2016). The first represents internal (self-oriented) motivations, such as hopelessness, extreme emotional pain, a need to escape, and other distressing emotional or cognitive states. The second domain captures communication (other-oriented) motivations, such as a desire to communicate with, influence, or seek help from others. The fact that multiple independent lines of inquiry converge on these two factors increases confidence in the validity and clinical utility of these domains.
+Internal motivations for suicide, particularly overwhelming pain and hopelessness, are more often endorsed than communication motivations. Numerous studies find that a majority of suicide attempters report internal motivations (Brown et al. 2002a, Hjelmeland et al. 2002, Holden et al.
+1998, May & Klonsky 2013, May et al. 2016), and to our knowledge there are no exceptions. A smaller subset of participants report communication motivations, almost always in addition to, rather than instead of, internal motivations. Importantly, the types of motivations endorsed have implications for the type of suicide attempt made. Relative to internal motivations, communication motivations appear to be protective. For example, among a sample of undergraduates and outpatients with recent attempts, greater endorsement of communication motivations was associated with lower suicidal intent and a greater likelihood the attempt would be interrupted, whereas greater internal reasons were correlated with a greater desire to die (May & Klonsky 2013). These findings are consistent with earlier studies reporting that internal reasons were correlated with higher intent and preparation, whereas communication motivations were not (Hjelmeland et al. 2002, Holden et al. 1998).
+A possible explanation for this pattern is that the presence of socially oriented motivations signifies a continued connection to people, a desire to maintain these relationships, and thus a continued investment in living. This connection to people may counterbalance a desire to die, whereas the absence of communication motivations may signify less connection and thus less ambiguity about the desire to die. In addition, individuals who attempt suicide with communication motivations, particularly help-seeking, may be more interested and engaged in the treatment options that are often offered postattempt. It is important to remember that all research on suicide motivations has been conducted with suicide attempters who survived, limiting our knowledge of whether these same motivations generalize to suicide decedents.
+EVIDENCE-BASED CLINICAL ASSESSMENT
+Suicide research and prevention require accurate evaluation of suicide phenomena. Therefore, reliable, valid, and comprehensive assessments are essential. For in-depth reviews of such measures, including scope and psychometric properties, see Brown (2001), Goldston (2003), and Nock et al. (2008c). Here we summarize some of the more widely used and better-validated measures.
+The Suicide Attempt and Self-Injury Interview (SASII; Linehan et al. 2006a), formerly the Parasuicide History Interview (Linehan et al. 1989), is a structured interview composed of 31 items designed to assess the intent, context, and topography of nonsuicidal and suicidal behaviors. The SASII subscales were factor-analytically derived using three medium-sized cohorts. The SASII demonstrates excellent internal consistency and high interrater reliability (Linehan et al. 2006a), and has been repeatedly used in samples with borderline personality disorder (Brown et al. 2002b, Crowell et al. 2012, Harned et al. 2010).
+The Self-Injurious Thoughts and Behaviors Interview (SITBI), developed by Nock et al. (2007), is another structured interview that comprehensively assesses both nonsuicidal and suicidal selfharming behaviors. The SITBI’s 169 items assess characteristics associated with NSSI, suicidal ideation, plans, gestures, and attempts including their frequency, severity, methods used, function, perceived cause, and age of onset. The SITBI was developed and has been primarily used with adolescent samples (Barrocas et al. 2012, Janis & Nock 2008, Nock et al. 2007), where it has been found to have strong psychometric properties, including high interrater and test-retest reliability, and has demonstrated concurrent validity by overlapping with established measures of NSSI, suicide ideation, and suicide attempts (Janis & Nock 2008, Nock et al. 2007).
+The Scale for Suicide Ideation (SSI; Beck et al. 1979) is a long-standing semi-structured interview assessing the presence, frequency, and severity of suicidal thoughts using 21 items. The SSI has been found to have high internal consistency and test-retest reliability (Beck et al. 1979, 1997) and strong concurrent validity (Beck et al. 1979, 1985, 1997), and it is one of the few clinician-administered measures to have been shown to predict suicide attempts. Specifically, participants
+who obtained scores at or greater than 3 on the SSI were found to be seven times more likely to attempt suicide over a ten-year period than those who scored less than 3 (Brown et al. 2000). Amore recently developed semi-structured interview, the Columbia-Suicide Severity Rating Scale (C-SSRS; Posner et al. 2008, 2011), has also demonstrated predictive validity. The C-SSRS assesses lifetime presence of suicide ideation, plans, intensity of ideation, and attempts as well as NSSI, and it has been shown to predict suicide attempts during a 24-week follow-up period (Posner et al. 2011).
+A variety of self-report measures assessing constructs related to suicide (such as depression and hopelessness) and aspects of suicidality (ideation, intent, lethality) have been developed over the past 40 years. However, only a few of these measures have been shown to predict future suicide attempts. The Beck Hopelessness Scale (Beck et al. 1974) assesses participants’ sense of hopelessness using 20 true-or-false items. Psychiatric outpatients who obtained scores at or above 9 on the Beck Hopelessness Scale were found to be 11 times more likely to die by suicide than were outpatients scoring 8 or below. Question nine on the Beck Depression Inventory-II (Beck et al. 1961, 1996) assessing suicidal thoughts/wishes has demonstrated sensitivity to future suicide attempts in three psychiatric samples (Beck et al. 1990, Brown et al. 2000, Oquendo et al. 2004). Patients scoring at or above 2 on this question were found to be 6.9 times more likely to die by suicide than those who scored below 2 (Brown et al. 2000). Similarly, baseline scores obtained by a psychiatric sample on the 25-item Adult Suicidal Ideation Questionnaire (Reynolds 1991), a measure of the frequency of suicidal ideation, predicted suicide attempts over a three-month period (Osman et al. 1999).
+Virtually all clinical interviews and self-report measures rely on participants self-disclosing information regarding their past suicide attempts and current suicidal thoughts and plans. Suicide, however, is an extremely personal and sensitive subject that is often stigmatized and difficult to discuss. In response to these challenges, objective measures free of reporting biases have been developed to assess suicide risk. One such measure is the death/suicide implicit association test (IAT) developed by Nock et al. (2010). Administering the death/suicide IAT to patients in an emergency department revealed that the death/suicide IAT correctly distinguished participants admitted following a suicide attempt from those who were admitted for reasons other than a suicide attempt. Furthermore, and critically, performance on the death/suicide IAT predicted future suicide attempts, over and above both the patients’ own predictions and clinicians’ predictions of the likelihood of future suicide attempts (Nock et al. 2010). The death/suicide IAT therefore is promising for predicting suicide attempts, although further study of these findings and their clinical utility is required.
+EVIDENCE-BASED CLINICAL INTERVENTION
+Suicidal thoughts and behaviors remain difficult to treat. Unfortunately, no gold-standard, highly effective treatments exist. However, some treatments have better evidence than others for reducing suicidal thoughts and behaviors, and we summarize these below. We specifically focus on clinical interventions that target individuals at risk for suicide and that seek to reduce suicidal thoughts and behaviors; we address community-level suicide prevention efforts separately in a subsequent section.
+Dialectical behavior therapy (DBT; Linehan 1993) is a multimodal treatment that combines behavioral and acceptance-based strategies. DBT was developed for populations with extensive histories of self-injurious and suicidal behaviors, and it has been primarily used and studied in samples with borderline personality disorder. Randomized controlled trials (RCTs) have found that patients who received DBT engaged in less self-harm (suicidal intent not always assessed
+or reported; Koons et al. 2001; Linehan et al. 1991, 1993; van den Bosch et al. 2005; Verheul et al. 2003), attempted suicide less often (Linehan et al. 2006b), and experienced improvements in disability and quality of life (Carter et al. 2010).
+Another treatment, cognitive therapy for suicide prevention (CT-SP; Brown et al. 2002a), is based on Beck’s cognitive theory (Beck 1976). CT-SP views suicide as resulting from patients’ sense of hopelessness and dysfunctional automatic thoughts. CT-SP therefore focuses on mitigating hopelessness, evaluating and challenging the accuracy of patients’ assumptions, and providing patients with coping strategies and problem-solving skills. RCTs have found that patients who received CT-SP experienced greater reductions in suicidal thoughts (Slee et al. 2008) and made fewer suicide attempts at 6-month (Evans et al. 1999) and 18-month (Brown et al. 2005) follow-up.
+The collaborative assessment and management of suicide risk (CAMS; Jobes 2006) is a relatively new treatment of suicidal behavior. CAMS uses a collaborative, nonjudgmental approach and focuses on developing a strong therapeutic patient relationship as the basis for working with patients to design and implement a treatment plan. Studies have found that CAMS can quickly reduce suicidality broadly defined (Jobes et al. 2005) and that treatment gains are sustained at 50 days (Ellis et al. 2012). An RCT found CAMS to be effective in treating suicidal ideation and that CAMS patients had continued to improve 12 months after treatment (Comtois et al. 2011). These studies suggest that CAMS might be an effective treatment for suicidal ideation. Additional and larger CAMS trials are currently under way.
+EVIDENCE-BASED PREVENTION
+Treatments for suicidality tend to focus on individual and/or group modalities. However, some key approaches to suicide prevention can be implemented at the level of the community or government. These approaches include means restriction, physician education, and school-based programs.
+There may be no more effective approach to suicide prevention than to reduce access to means on a large scale. Access to firearms in the United States represents a prime example. Firearms are the leading cause of suicide death in the United States, and laws regulating the availability of firearms vary by state. In two important studies, Anestis and colleagues found that laws restricting access to handguns, such as those requiring permits, registration, licenses, background checks, and gun locks, were associated not only with reductions in suicides by handgun, but also with lower suicide rates overall (Anestis & Anestis 2015, Anestis et al. 2015). In addition, evidence indicates that states with higher self-reported gun ownership have higher rates of firearm suicide as well as overall suicide (Miller et al. 2007). These patterns are not due to an association of gun ownership with mental health or suicidal thoughts; in fact, there is no relationship of gun ownership to either mental health or suicidal thoughts, and the relationship between gun ownership and suicide persists after controlling for these variables (Betz et al. 2011, Hemenway & Miller 2002, Miller et al. 2009).
+Means restriction applies beyond the United States and beyond firearms. For example, when particularly lethal pesticides became a common method of suicide in Sri Lanka, regulations restricting the availability of these pesticides resulted in a halving of the overall suicide rate (Gunnell et al. 2007). A similar story took place in the United Kingdom. Up until the 1950s, domestic gas came from coal and included 10% to 20% carbon monoxide. During this time, gas inhalation was the leading method of suicide. Starting in the late 1950s and through the 1970s, natural gas, which contains very little carbon monoxide, was introduced, and its use became increasingly common. As the carbon monoxide levels in domestic gas decreased between the 1950s and 1970s, rates of suicide by carbon monoxide poisoning as well as overall suicide rates decreased substantially (Kreitman 1976).
+There is a common assumption that if someone seeking to attempt suicide has a method of choice blocked, he or she will simply find another method. The data described above provide strong evidence disputing this assumption. A probable explanation is that suicidal crises are motivated by extreme pain, hopelessness (May & Klonsky 2013), and other distressing affective and cognitive states that, by their nature, ebb and flow over time. The suicidal crisis occurs when these states are at a peak. If someone can be kept alive during a suicidal crisis, it is quite likely the individual will not seek to attempt suicide again in the near or even far future. In fact, a review of 90 studies found that most individuals who make a severe but nonfatal suicide attempt never attempt again and have a 93% survival rate (i.e., 7% eventually die by suicide; Owens et al. 2002). Means restriction can take many forms and should be a key component of suicide prevention worldwide.
+Evidence indicates that suicide prevention approaches other than means restriction can be effective. Programs to educate physicians about depression assessment and management have led to improved detection of patients with suicidal ideation and a reduction in suicides (Mann et al. 2005). Thus, like means restriction, physician education programs should be considered a key component of suicide prevention efforts worldwide. In addition, school-based programs designed to increase knowledge about suicide, suicide risk, and ways to help those at risk have received increased attention, with some promising results for improved knowledge about suicide and reduced suicide ideation and attempts (Aseltine et al. 2007, Schilling et al. 2014). However, not all of the evidence for these programs is high quality, and there is no direct evidence that these programs reduce suicide rates (Cusimano & Sameem 2011).
+THE IDEATION-TO-ACTION FRAMEWORK
+Although many promising approaches to treatment and prevention are described above, suicide remains a leading cause of death worldwide and is projected to remain so through 2030 (WHO 2013). We believe a key reason for the limited success in reducing suicides is inadequate knowledge, particularly about why and when suicidal thoughts progress to potentially lethal attempts. In this section, we elaborate on this knowledge gap and describe the ideation-to-action framework, a framework that we believe can address this gap and guide the next generation of suicide theory, research, and prevention.
+As noted previously, it is becoming increasingly clear that most oft-cited risk factors for suicide—including depression, hopelessness, most mental disorders, and even impulsivity—predict suicidal ideation but do not distinguish those who have made suicide attempts from those who have experienced ideation without attempts (Klonsky & May 2014, May & Klonsky 2016). This pattern is apparent both in large epidemiological studies and in a recent meta-analysis. For example, a large epidemiological study in the United States found substantially higher rates of mental disorders in suicide ideators compared to those who had never been suicidal; however, the same study found that mental disorders minimally or negligibly distinguished suicide attempters from ideators without attempts (Kessler et al. 1999). More recent and worldwide epidemiological studies have found similar patterns (Nock et al. 2012, 2013). In fact, the variables examined in the WHO World Mental Health Surveys explain more than 60% of the variability in suicidal ideation, but only 7% of the variability in suicide attempts among ideators (Glenn & Nock 2014). Moreover, this pattern was reported in a recent meta-analysis that examined mental disorders as well as other clinical variables (May & Klonsky 2016). For example, the meta-analysis found that depression and hopelessness were robust predictors of suicidal ideation, but when attempters were compared to ideators without attempts, the effect sizes for depression and hopelessness dropped to near zero.
+The fact that most oft-cited risk factors for suicide predict ideation but not behavior is of great import because most individuals with suicidal ideation do not go on to make attempts (Nock
+et al. 2008a). It thus becomes critical for both theoretical and clinical purposes that the field better understand suicide and suicide risk, in particular the progression from suicidal ideation to behavior. In response to this need, Klonsky & May (2014) proposed the ideation-to-action framework. From this perspective, (a) the development of suicidal ideation and (b) the progression from suicide ideation to attempts should be viewed as distinct processes with distinct predictors and explanations.
+One implication of the framework concerns research design. Most studies of suicide compare attempters to a nonsuicidal group. Because all (or virtually all) attempters have also experienced ideation, this design allows predictors of ideation to masquerade as predictors of attempts. It is crucial that future studies stop this practice. No longer should the studies that compare attempters to ideators (e.g., Kessler et al. 1999, Klonsky & May 2015, Nock et al. 2008a) be the exception rather than the rule.
+The ideation-to-action framework also represents a departure from traditional approaches to suicide theory. Theories of suicide have emphasized many different factors, including psychache (overwhelming psychological pain; Shneidman 1985, 1993), social isolation (Durkheim 1897), escape from aversive self-cognitions (Baumeister 1990), and hopelessness (Abramson et al. 2000). Although these theories have been tremendously helpful for stimulating thought, motivating research, and advancing the field, they also share a particular limitation: They do not offer separate explanations for the development of suicidal ideation and the progression from ideation to attempts.
+An important theoretical advance occurred when Thomas Joiner proposed his interpersonal theory of suicide (Joiner 2005). This theory proposed explanations for suicidal desire and for acting on suicidal desire. In particular, the theory stipulated that the combination of perceived burdensomeness and low belongingness (and hopelessness about these perceptions) creates desire for suicide, whereas the capability to act on suicidal desire requires that one overcome fears of death and pain that are a natural part of attempting suicide. While the specifics of the interpersonal theory have received significant study, we propose that the framework itself is at least as important a contribution to the field. Thus, we view Joiner’s theory as the first theory of suicide to be positioned within the ideation-to-action framework.
+Indeed, Joiner’s theory appears to have spawned additional theories grounded in the ideation-to-action framework (see Table 1). To our knowledge, Rory O’Connor’s integrated motivational-volitional theory (IMV; O’Connor 2011) represents the second ideation-to-action theory. The IMV suggests that defeat and entrapment are the primary causes of suicidal ideation and that acquired capability along with other factors (e.g., access to lethal means, planning, impulsivity) predict and explain the progression from ideation to attempts.
+In addition to guiding research and theory, the ideation-to-action framework should also inform applied domains, such as prevention and risk assessment. For example, prevention and treatment programs should distinguish which intervention targets and mechanisms of change address ideation and which are meant to impede progression from ideation to attempts. We believe the framework should also inform the field’s approach to risk assessment. To illustrate, we consider efforts to identify and label suicide risk factors. Table 2 contrasts the traditional approach to suicide risk with the approach suggested by the ideation-to-action framework. The traditional approach treats suicide risk as a unitary construct; all risk factors are listed in a single column. In contrast, the ideation-to-action framework distinguishes predictors of ideation from predictors of the progression from ideation to behavior. Variables such as depression, most mental disorders, hopelessness, and most forms of impulsivity are included only in the ideation column on the basis of evidence suggesting that these are strong correlates of ideation but that they poorly distinguish attempters from ideators without attempts (Kessler et al. 1999, Klonsky & May 2010, May & Klonsky 2016). In contrast, variables such as access to and comfort with lethal means and a specific impulsivity-related trait are listed in the behavior column on the basis of evidence that these variables reliably distinguish attempters from ideators who have never attempted (Klonsky & May 2010, 2015). Finally, some variables, such as a diagnosis of posttraumatic stress disorder and a history of NSSI, appear in both columns because research indicates that they are correlates of both suicidal ideation and behavior (Klonsky et al. 2013, Nock et al. 2009). Importantly, the research summarized above is mainly correlational, and it will be necessary for future research to specifically identify risk factors for suicidal ideation and attempts using prospective designs.
+THE THREE-STEP THEORY OF SUICIDE
+Recently, we developed the three-step theory (3ST) of suicide (Klonsky & May 2015), which we feel has the potential to improve understanding and prediction of suicide, suicidal behavior, and suicide ideation. The 3ST utilizes the ideation-to-action framework, is informed by previous research and theory, and provides a parsimonious and testable model of suicide. The key constructs of the 3ST are pain and hopelessness, connectedness, and suicide capacity. The theory is summarized below and illustrated in Figure 1.
+Step 1. Development of Suicidal Ideation
+According to the 3ST, the first step toward ideation begins with pain. Pain typically (but not necessarily) means psychological or emotional pain. All people are shaped by behavioral conditioning (Skinner 1953). We engage in behaviors that are rewarded and avoid those that are punished. If someone’s experience of living is characterized by pain, this individual is essentially being punished for living, which can decrease desire to live.
+It is intentional that the theory does not specify the nature of the pain. Just as any sufficiently aversive stimulus can effectively punish behavior (Mazur 2012), whether it be electric shock, a loud noise, or social exclusion, different sources of pain in daily life can all lead to a decreased desire to live. These can include many of the factors emphasized by others as playing roles in suicidal ideation, such as physical suffering (Ratcliffe et al. 2008), social isolation (Durkheim 1897), burdensomeness and low belongingness (Joiner 2005), defeat and entrapment (O’Connor 2011), and negative self-perceptions (Baumeister 1990), as well as numerous other aversive thoughts, emotions, sensations, and experiences. The first step toward suicidal ideation begins with pain, regardless of its source.
+However, pain alone will not cause suicidal ideation. If someone in pain has hope that his situation can improve and that the pain can be diminished, the individual will strive to achieve a future with diminished pain rather than consider suicide. For this reason, hopelessness is also required for the development of suicidal ideation. That is, if someone’s life includes considerable pain, and he feels hopeless that the pain will improve, he will consider ending his life. In short, the combination of pain and hopelessness is what leads to suicidal ideation.
+This first tenet of the 3ST is consistent with some key recent research findings. First, as reviewed above, studies on suicide motivations find that suicide attempts are prompted by overwhelming pain and hopelessness more than by other factors, including burdensomeness, thwarted belongingness, desire for help or to communicate, and impulsivity; moreover, this pattern has replicated in both clinical and nonclinical samples, and in both adults and adolescents (May & Klonsky 2013, May et al. 2016). In addition, a recent study surveyed two groups—loved ones who lost someone
+to suicide, and individuals who had made a suicide attempt requiring hospitalization—about which of 42 variables appeared different in the minutes, hours, or days leading up to the suicide death or attempt (Wintersteen 2014). The list of 42 variables was diverse and included items such as sleep problems, agitation, giving away possessions, family conflict, disengagement from social activities, anger and hostility, and guilt or shame. The results of the investigation were very much in line with the 3ST. Aggregating across both groups, the two factors most commonly observed to precede suicide deaths and attempts were pain and hopelessness, specifically “emotional misery or pain” and “feelings of hopelessness about the future.”
+Importantly, the 3ST emphasizes that it is the combination of pain and hopelessness that brings about suicidal ideation. Someone in pain but with hope for a better future will continue to engage with life. Likewise, someone who feels hopeless about the future but lives without pain will not feel suicidal. To illustrate this latter case, consider the example of a young man who has recently graduated from university and moved back home with his parents. If this young man lacks a marketable degree, strong grades, and career goals, he may feel hopeless about the future. However, if day-to-day he is comfortable and without pain, if his food and shelter are provided and he has ample free time for friends and activities he enjoys, then he is unlikely to consider suicide. Pain and hopelessness in combination are what lead to suicidal ideation.
+Step 2. Strong Versus Moderate Ideation
+According to the 3ST, the second step toward potentially lethal suicidal behavior occurs when pain exceeds connectedness. The term connectedness is used in a broad sense. Connectedness can mean connection to other people as well as to an interest, role, project, or any sense of purpose or meaning that keeps one invested in living. The 3ST stipulates that someone who experiences pain and hopelessness and considers suicide will only have moderate ideation (e.g., “Sometimes I think I might be better off dead”) if connectedness remains greater than the pain. However, ideation becomes strong (e.g., “I would kill myself if I had the chance”) if pain overwhelms any sense of connectedness. Consider the example of a parent who experiences daily pain and hopelessness but who also feels invested in and connected to his or her children. If the parent’s connectedness exceeds the parent’s pain, this individual may still have passive ideation but will not progress to active desire for suicide. However, if both pain and hopelessness are present, and connectedness is dwarfed by pain, the individual will experience strong ideation and actively consider ending his or her life.
+Disrupted connectedness is similar to low belongingness and burdensomeness, as described in Joiner’s interpersonal theory, but operates differently in the 3ST. In the interpersonal theory, belongingness and burdensomeness are thought to directly cause suicidal ideation. In the 3ST, the primary role of connectedness is to protect against escalating suicidal ideation in those at risk due to pain and hopelessness. Although disrupted connectedness can contribute directly to pain and hopelessness, it is not viewed as necessary for the development of pain or hopelessness, or for the development of suicidal ideation. From the perspective of the 3 ST, many people with suicidal ideation do not have disrupted connectedness, and many with disrupted connectedness do not develop suicidal ideation.
+Recent research supports the second step of the 3ST (Klonsky & May 2015). Specifically, in a large online sample of ideators and nonideators, connectedness was protective against ideation in those high on both pain and hopelessness but was negligibly related to ideation in everyone else. Moreover, in this same study we created a difference variable indexing the extent to which pain exceeds connectedness (i.e., we subtracted standardized scores on a measure of connectedness from standardized scores on a measure of psychological pain). As predicted by the 3ST,
+this variable robustly predicted ideation in the combined pain and hopelessness group but was a negligible predictor of ideation in everyone else. In short, findings support the 3ST’s tenet that connectedness is most relevant to suicidal ideation as a protective factor among those high on pain and hopelessness, especially when one’s connectedness exceeds one’s pain.
+It is important to be clear that the 3ST’s emphasis on pain, hopelessness, and connectedness does not suggest that other oft-cited suicide risk factors are unimportant. On the contrary, we believe that numerous disorders (e.g., depression), states of mind (e.g., self-criticism), personality traits (e.g., borderline personality), temperaments/dispositions (e.g., negative emotionality) and experiences (e.g., interpersonal loss) are highly relevant to suicidal ideation. However, the 3ST suggests they are relevant in a particular way, through their contributions to pain, hopelessness, and/or connectedness. For example, depression would be expected to contribute to suicidal ideation to the extent it contributes to pain, hopelessness, and/or disrupted connectedness, but not beyond.
+Step 3. Progression from Ideation to Attempts
+Most individuals with ideation do not make a suicide attempt; therefore, the final step of the 3ST addresses the conditions under which strong ideation leads to a suicide attempt. We agree with Joiner (2005) that the key determinant is whether the individual has the capacity to make a suicide attempt. Joiner suggests that fear of death is a powerful instinct that makes it extremely difficult to attempt suicide, even if experiencing strong suicidal ideation; thus, individuals can only attempt suicide if they have developed the capacity to overcome this barrier. The 3 ST echoes this point but expands it in two ways.
+Joiner’s theory emphasizes acquired capability. In short, this ability is developed and increased through experiences with painful and provocative events that increase one’s tolerance for pain, injury, and death. The 3ST broadens the concept and proposes three categories of variables that contribute to suicide capacity: dispositional, acquired, and practical.
+Dispositional refers to relevant variables that we are born with. For example, some individuals are born with higher or lower pain sensitivity (Young et al. 2011). Someone born with lower pain sensitivity will have a higher capacity to carry out a suicide attempt. The concept of dispositional contributors to capacity is supported by recent research from Joiner and others suggesting that capability for suicide is largely genetic (Smith et al. 2012). The second contributor to suicide capacity, acquired variables, refers to the same concept Joiner describes. That is, habituation to experiences associated with pain, injury, fear, and death can, over time, lead to higher capacity for a suicide attempt.
+Finally, practical variables are concrete factors that make a suicide attempt easier. There are many kinds of practical factors. For example, someone with both knowledge of and access to lethal means, such as a firearm, could act on suicidal thoughts much more easily than someone without knowledge of and access to lethal means. Practical contributors to capacity may explain findings that anesthesiologists and other medical professionals have elevated suicide rates (Swanson et al. 2003). These individuals have both easy access to the necessary drugs and extensive knowledge of how to end one’s life painlessly, which makes their practical capacity extraordinarily high. In summary, dispositional, acquired, and practical factors contribute to the capacity for attempted suicide, and individuals with strong suicidal ideation will only make suicide attempts if and when they have the capacity to do so.
+This third step of the 3SThas also been supported by recent research (Klonsky & May 2015). In a US-based online sample, which included large numbers of attempters and ideators, dispositional, acquired, and practical contributors to suicide capacity each related to suicide attempt history, and
+they continued to relate to attempt history in analyses controlling for current ideation and for past ideation. Thus, consistent with the 3 ST, all three components of suicide capacity matter, and they each predict suicide attempts above and beyond ideation.
+FUTURE DIRECTIONS
+The adoption of the ideation-to-action framework and the proliferation of ideation-to-action theories of suicide are promising developments that will help meaningfully advance suicide knowledge and prevention. At the same time, key knowledge gaps remain. These gaps limit our ability to understand and reduce suicide and should be the focus of intensive research efforts in the coming years.
+First, it is imperative that we better understand progression from suicidal ideation to attempts. As discussed above, the vast majority of oft-cited suicide risk factors predict who is at risk for suicide ideation but not which ideators are at risk for attempting or dying by suicide. Suicide capacity, which is emphasized by all three ideation-to-action theories, does appear to be an important factor, but its predictive ability remains moderate (Klonsky & May 2015). In short, there are unknown factors that explain when and why individuals transition from suicidal thoughts to action, and it is imperative that they be identified and understood.
+Related to this point, it is crucial to better understand the time-course of suicide risk. For example, few studies examine the factors that predict suicide attempts in the minutes or hours or days before the attempt (Glenn & Nock2014; but see Bagge et al. 2013,2014). There is likely both important overlap and important divergence between the factors that predict suicide at longer (years/months) versus shorter (weeks/days/hours) time scales. This information is particularly important for clinical risk assessment and is also likely to be highly relevant to treatment for individuals with histories of suicidal thoughts and behavior.
+A third future direction is to apply evidence-based theories of suicide to risk assessment and treatment/prevention. For example, the 3ST suggests that brief but valid measures of pain, hopelessness, connectedness, and capacity could be combined to form a measure that targets variables most central to suicide risk. Likewise, the 3 ST suggests four clear objectives for suicide prevention: decrease pain, increase hope, improve connectedness, and/or reduce capacity.
+A final critical knowledge gap concerns the difference between those who make nonfatal and those who make fatal suicide attempts. Most studies on suicide assess those who have experienced suicide attempts or ideation, which means the participants are still alive. It is much more difficult, and thus much more rare, for studies to examine individuals who have died by suicide. An implicit assumption is that studying individuals who have made nonfatal attempts provides knowledge that is also relevant to understanding and preventing suicide death. However, only a minority of attempters die by suicide, and the majority of those who do die by suicide do so on their first attempt (DeJong et al. 2010, Fushimi et al. 2006, Suominen et al. 2004). Therefore, future research must consider and aggressively investigate the possibility that, compared to those who make nonfatal attempts, those who die by suicide have important differences in clinical presentation, motivation, and other characteristics that could meaningfully inform risk assessment and prevention.
+SUMMARY POINTS
+1.	Many commonly cited risk factors for suicide, including depression, hopelessness, most mental disorders, and impulsivity, are best conceptualized as predictors of suicidal ideation.
+2.	These same risk factors struggle to differentiate those who have attempted suicide from those who have experienced suicidal ideation without making an attempt.
+3.	The ideation-to-action framework stipulates that (a) the development of suicidal ideation and (b) the progression from ideation to attempts should be viewed as distinct processes with distinct predictors and explanations and should guide the next generation of suicide research, theory, and prevention.
+4.	The capacity to attempt suicide (including the capacity to tolerate the fear of pain and death that accompanies suicide attempts) plays a key role in the progression from ideation to attempts.
+5.	Means restriction is a practical way to reduce capacity and a powerful way to block progression from ideation to attempts.
+6.	The interpersonal (Joiner 2005), integrated motivational-volitional (O’Connor 2011), and three-step (Klonsky & May 2015) theories are the first theories of suicide to utilize an ideation-to-action framework.
+7.	The three-step theory posits that (a) the combination of pain and hopelessness leads to suicidal ideation, (b) ideation escalates if pain exceeds connectedness, and (c) dispositional, acquired, and practical contributors to suicide capacity facilitate the transition from ideation to attempts.
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+Open access
+Protocol
+INTRODUCTION
+Despite various suicide prevention and intervention programmes, there has not been a commensurate significant decrease in suicide rates. WHO reports approximately 800 000 suicides annually.1 Over 70% of global suicides are individuals who are aged 30 years or older,2 53% of suicides in the
+USA are from individuals aged 45 years or greater3 and more than half of all suicides in Australia occur between the ages of 30 and 59 years.4 It is estimated that the number of people who attempt suicide is much greater, ranging from 20 to 40 attempts per suicide.5 6 Within the USA, there is one suicide attempt every 27 s3 and over 65 000 people attempt to take their own life in Australia each year.7 Suicide attempts that do not result in death create aftereffects that impact the survivor and family members, friends and society. These include suicide stigma and emotional strain8 as well as bodily disfigurement and/ or permanent disability.9 Suicide attempts may also lead to the development of psychological disorders such as post-traumatic stress disorder.10 Family members often suffer significant emotional distress and become panicked and stressed believing that another attempt is imminent.11 Furthermore, there are large financial costs to society associated with suicide attempts, in excess of US$5.2 billion in the USA.12 Thus, suicide attempts place a high burden on individuals, families and society as a whole.
+Given the above, better understanding the movement from thinking about suicide to attempting suicide becomes critical. The ideation-to-action framework is a theoretical framework that focuses on this movement and includes several contemporary theories of suicide that differentiate the development of suicide ideation from the movement from suicide ideation to suicide attempt. This framework has been criticised for reiterating previous conclusions; that there are differences in risk factors for suicidal ideation and suicide attempt.13 However, Klonsky and May14 argue that the framework goes beyond previous conclusions because of its theoretical implications. That is, the theories take the position that risk factors need to be categorised by ideation, attempt or both and new-generation theoretical models of suicide should address the development of ideation, movement and attempt as related but distinct processes. This distinction is important as the majority of individuals who experience suicidal ideation do not necessarily make the progression to suicide attempt.15 Additionally, frequently identified risk factors for suicidal ideation, such as depression and hopelessness, do not differentiate between suicide ideators and suicide attempters.16 Moreover, from a meta-analysis of 50 years of research on risk factors for suicidal thoughts and behaviours, no category of risk factors associated with suicide attempts were found to predict an attempt much greater than random guessing.17 Given the poor utility of previously associated risk factors with suicide attempts, it is hoped that shifting research to examine factors within an ideation-to-action framework that differentiates between non-attempting ideators and suicide attempters will help towards understanding the movement from ideation to action.18 Within this framework, a core facilitator of the transition from suicidal thoughts to suicide attempt appears to be the individual’s capacity for suicide. This is defined as the combination of contributing factors that enable an indi-.i-i-r'19 20
+vidual to make an attempt on their life.
+The ideation-to-action framework and capacity-for-suicide concept is one of the most recent influential theoretical innovations within the field of suicidology and has generated a considerable amount of research.21 Three suicide theories that feature suicide capacity are positioned within the framework. These are displayed in table 1 and include the Interpersonal Theory of Suicide (IPTS),22 23 the Integrated Motivational-Volitional model (IMV)24 25 and the Three-Step Theory of Suicide (3ST).19 The oldest of the three theories developed in 2005, the IPTS, innovated suicidology research. It proposes that suicide ideation alone is insufficient for a suicide to occur as an individual has to overcome the evolutionary and biological will to remain alive.
+The IPTS hypothesises that the factor of acquired capability for suicide is needed in addition to suicide ideation. The IPTS postulates that the more an individual experiences painful and provocative events, such as non-suicidal self-injury (NSSI), the more they habituate to the fear and pain of attempting suicide. The individual thus acquires the capability to make a suicide attempt. The second theory within the ideation-to-action framework, the IMV, builds on the acquired capability factor within its action construct. This concept, developed by O’Connor,24 is referred to as the volitional phase. Although the volitional phase retains the acquired capability factor from the IPTS, it also introduces other factors to the concept of suicide capacity, such as access to lethal means, intention and imitation. This differs from the IPTS as it suggests that the acquired capability factor alone is not sufficient for an individual to progress from ideation to action and acknowledges that there are other factors involved. The most recent theory within the ideation-to-action framework, the 3ST,19 expands the necessary combination of factors required to transition from suicidal thoughts to suicide attempt. The 3ST posits that to progress from suicidal ideation to suicide attempt, an individual must possess the capacity to make an attempt. According to the
+3ST, suicide capacity contains three contributing factors. The single acquired capability factor is retained from the IPTS and the IMV, acknowledging that repeated experiences involving fear, pain, injury and death, increase an individual’s capacity to attempt suicide. A second factor refers to dispositional variables that are largely genetic, such as pain tolerance where low pain sensitivity increases suicide capacity and personality traits. The final factor includes practical variables that are also included in the IMV, such as access to and knowledge of lethal means. For example, easier access to firearms or pesticides increases suicide capacity, likewise exposure to a family member or friend who has attempted suicide increases suicide capacity. Suicide capacity as suggested by the 3ST retains factors suggested by the IPTS and the IMV but adds genetic factors. Importantly, the 3ST proposes that it is the combination of factors that facilitates a suicide attempt.
+Since the introduction of the IPTS in 2005, there has been an increase in studies relating to the ideation-to-action framework26 and this suggests that the concept of suicide capacity has the potential to advance our understanding of suicidal behaviours. However, results have been varied regarding the factors comprising suicide capacity. A previous systematic review and a meta-analysis on the factor of acquired capability has found partial support for associations between the factor of acquired capability and suicide attempts,27 and weak relationships between acquired capability and suicide attempts.28 Furthermore, a narrative review concluded further research is needed to understand factors that contribute to an individual’s capacity for suicide.29 In addition to these reviews, individual studies have reported support for the volitional phase of the IMV,30 31 and support for suicide capacity as suggested by the 3ST.32 33 The diversity of results on the contributing factors of suicide capacity led May and Victor20 to conclude that despite the increase of research on the construct, further work is needed to continue the refinement and understanding of suicide capacity and suicide attempts.
+There have been two previous systematic reviews,27 28 however both of these focused on the single factor of acquired capability rather than suicide capacity as a whole. In this sense, the other reviews by the nature of their design and focus have produced a limited perspective on suicide capacity, although one consistent with their research questions. Given this limitation and the recent increase in suicidology publications as evidenced by a recent bibliometric analysis,26 it is timely to review and report current research as well as map a broader range of literature and variables. The proposed scoping review does this by including literature that was previously excluded from other reviews in order to identify and map research that has focused on the contributing factors (vs singular factor) of suicide capacity. This focus on suicide capacity within the ideation-to-action framework is based on the substantial amount of research that this concept has generated.21 For refinement and continued
+understanding of suicide capacity to occur, there needs to be a clear conceptualisation of the current status of research on suicide capacity within the ideation-to-action framework. Having this will provide researchers with an empirical foundation on which to embark on future research that is clearly aligned with furthering the refinement and understanding of suicide capacity. In order to do this, prior research on suicide capacity needs to be scoped for commonality of findings, gaps in evidencebased findings, and future directions for research.
+An appropriate methodology to achieve the above and for mapping developing concepts, such as suicide capacity, is a scoping review.34 A scoping review is a literature review technique that synthesises research from an array of sources to provide an overview of a topic in response to a broad research question.35 We are proposing to undertake a scoping review that will synthesise the literature on suicide capacity and contributing factors within the ideation-to-action framework. Currently, there is no registered or completed systematic review of the literature including all contributing factors of suicide capacity. This scoping review aims to produce a broader, more holistic overview of the suicide capacity literature incorporating all recent literature to conceptualise suicide capacity by classifying factors. It brings together in one review studies, variables and foci that are broader than the other two reviews.27 28 Without an extensive review of the literature and pinpointing limitations of previous research, suicide prevention and intervention programmes may not be based on empirical evidence which can negatively impact on programme efficacy. Furthermore, a scoping review will provide an empirical foundation that future research can be based on. In addition, by publishing a clearly articulated a priori protocol with inclusion and exclusion criteria, decisions such as what studies are included in the review are made transparently and not arbitrarily thus limiting reporting bias.36 According to Moher et al,3 the gold standard for identifying reporting bias in a completed review is to compare it with its protocol.
+METHODS AND ANALYSIS
+This review will follow the five-stage scoping review methodology presented by Arksey and O’Malley38 that has been further enhanced by Levac et al.39 Adding to the methodology are recommendations from the Joanna Briggs Institute (JBI),40 including the development of an a priori protocol, using the PCC mnemonic that stands for Population-Concept-Context in the construction and clarification of the research question, and adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRIS-MA-ScR).41 A scoping review has been selected as opposed to systematic literature review as the aim is not to address a relatively precise research question, but to explore the breadth of the literature and map conceptual bound-aries.42 Moreover, suicide capacity is at a stage where it would be untimely to ask specific research questions
+because without an empirical overview of the literature it is unclear what research questions need to be asked.
+The stages of the proposed review are: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data and (5) collating, summarising and reporting the results.
+Stage 1: Identifying the research question
+To identify the research question, the following elements of the protocol have been clarified using the PCC mnemonic.
+Population
+Individuals attempt suicide from all age groups, however adolescents and children may have additional factors that impact their decision-making capabilities and the mechanisms involved in the movement from ideation to action.43 44 Including these populations alongside adults could impact the clarity of the review. Therefore, the population for this review focuses on adults that are aged 18 years or above who have attempted suicide.
+Concept
+Identifying what is and what is not known about the concept of suicide capacity within the ideation-to-action framework. This will include all studies that reflect factors that contribute to an individual’s capacity for suicide as suggested by each of the three theoretical models.
+Context
+There will be no restriction on location or type of research design. However, a quality appraisal checklist tool will be used in stage 5 to assess the studies. Based on the authors’ language competencies, only studies published in English or translated to English will be included.
+Thus, the aim of the scoping review is to map the empirical literature on the concept of suicide capacity within the ideation-to-action framework for adults. To achieve this aim, the following questions will guide the review: 1. What is currently known about the concept of suicide capacity within the ideation-to-action framework?
+2.	Through what methods has this knowledge been obtained?
+3.	What are the limitations of the research?
+4.	What research opportunities are present due to gaps in the research?
+Stage 2: Identifying relevant studies
+The search strategy and database selection were developed in consultation with a research librarian with the express aim to comprehensively capture and identify relevant studies that meet the eligibility criteria. Initially, Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews and Effects, the International Prospective Register of Systematic Reviews and the JBI Evidence Synthesis journal will be searched for any previous systematic reviews on suicide capacity. This was planned to begin in December 2020. Additionally, the following 11 electronic academic databases have been
+selected as they ensure the most adequate and sufficient coverage of the literature relating to suicide attempts while minimising repetition of results.45 The electronic databases to be searched independently of each other are: Academic Search Ultimate, APA PsycArticles, APA PsycINFO, CINAHL, Psychology & Behavioural Sciences, & Sociology Source Ultimate via EBSCOHost Megafile Ultimate; PubMed; Science Direct; Wiley Online; Taylor & Francis and ProQuest dissertations and theses.
+The following search strategy has been devised to be broad as it aims to capture all relevant studies and will include title and abstract searches using the following search string that can be found with limiters in the search strategy online supplemental file: suicid* AND attempt* AND capa* OR “access to means”.
+However, for databases that advise against the use of truncations such as PubMed, searches will include permutations of several terms related to the words “suicidal behaviours”, “attempt”, “capability” and “capacity”. Complete terms can be found in the search strategy online supplemental file.
+This search string has been piloted in the APA PsycINFO database and no modifications have been required as no additional keywords were identified from the returned studies. Besides using databases, a search of the grey literature will also be conducted. Grey literature, for the purpose of this study, is referred to as documents published by non-commercial entities.46 Sources will include a grey literature database (www.opengrey.eu), websites of key suicide organisations that publish research from Australia, the USA, Europe and Google Scholar. The identified suicide organisations to be searched include:
+Australia:
+► Australian Institute for Suicide Research and Prevention.
+► Australian Suicide Prevention Foundation.
+► Beyond Blue.
+► Black Dog institute.
+► Lifeline.
+► National Mental Health Commission.
+► Suicide Prevention Australia.
+USA:
+► American Association of Suicidology.
+► American Foundation for Suicide.
+► American Medical Association.
+► National Institute of Mental Health.
+Europe:
+► International Association for Suicide Prevention.
+► Samaritans.
+Initial database searches will be completed independently by two reviewers with search results exported and collated in the reference management software EndNote (V.9.2).47 Reviewers will compare results after each database search to ensure homogeneity. Any discrepancies between search results will be discussed between reviewers and if no agreement can be reached, a third reviewer will resolve the difference before progressing to study selection. Duplicates will be removed after the
+completion of all database searches. Key articles, that is, recommended papers from updates on the ideation-to-action theories of suicide or brief reviews, reference lists will also be hand-searched for missing literature.48 Following the academic databases search, grey literature will be searched, starting with suicide organisations. Then the Google Scholar search will be completed using the title search function as opposed to full-text search as more grey literature is returned in Google Scholar via title searches than full-text searches.49 In addition, as the search engine displays results by relevance, the search will be limited to the first 200 references as recommended by Bramer et al.45 To keep track of search history and search results, a Microsoft Excel spreadsheet will be used by each 44
+reviewer.
+Stage 3: Study selection
+The criteria mentioned in table 2 will determine whether or not a study is eligible for a full review. While the criteria exclude studies that only contain individuals outside the specified age range, it is possible that studies may include participants from both outside and inside the age range. If so, the study will be included. In addition, studies that focus exclusively on assisted suicide/euthanasia or NSSI will be excluded as per the suicide attempt definition that is included in table 2. It is necessary to include suicide attempts as an inclusion criterion because each of the theoretical models suggest that to attempt suicide an individual must have the capacity to do so. Therefore, while individuals with suicidal ideation may have some capacity towards attempting suicide, there is no evidence that they have reached a level of capacity required to attempt suicide. It is necessary to include studies that may not compare the two groups, such as case studies or psychological autopsies. Because the goal is to map the literature on factors identified within the ideation-to-action framework that contribute to suicide attempts, it is possible that articles solely including suicides or suicide attempters will
+be useful for exploring factors that contribute to suicide attempts. Articles will initially be screened via title and abstract independently by each reviewer. Following this, the remaining articles will undergo a full-text review for eligibility as per the inclusion and exclusion criteria. At the end of the review phase, the reference lists of eligible texts will also be searched for any additional sources that were not identified through the database and grey literature searches. Both reviewers will compare lists and resolve any discrepancies through discussion with respect to the inclusion and exclusion criteria. However, if consensus cannot be reached, a third reviewer will resolve the difference. The final list of full-text studies to be charted will be recorded in EndNote (V.9.2).47
+Stage 4: Data extraction
+Extracting the data involves the production of a logical and descriptive summary of the results in line with the objective and research question.50 Included studies will be reviewed and charted independently by the first reviewer using a modified version of the JBI data charting template, which extracts information such as the study citation details, study characteristics, factors of suicide capacity, limitations and author(s) suggestions for future research.51 As charting the results can be an iterative process, the template may need to be updated throughout the process if reviewer 1 encounters additional unforeseen data pertaining to the research question. Therefore, to test the template reviewer 1 will trial the extraction form for five studies and then discuss the outcome of the trial with reviewer 2. If reviewer 1 decides that the template needs to be reviewed throughout the charting process and changes are necessary, discussion will take place with reviewer 2 and consensus will need to be reached before any changes are made. However, a third reviewer is available to adjudicate if consensus cannot be achieved. Once the data have been charted, the template details will be entered into Microsoft Excel and sorted
+by commonalities. In order to check the validity of the charted data and act as a first quality check, reviewer 2 will audit a random selection of articles (20% of final article total) to identify any potential charting errors and/or biases. The outcome of this review will be discussed with reviewer 1 with a view of reaching consensus over the charted data. Should consensus not be reached between reviewers 1 and 2, reviewer 3 will resolve the disagreement to address any inaccuracies in the charting of data with respect to the four questions guiding the review.
+Stage 5: Collating, summarising and reporting the results
+To clearly present the amount of available literature on suicide capacity and the stages of article selection for the review, a flow chart and a checklist will be used. This includes the PRISMA flow chart52 and the PRISMA-ScR checklist.41 It is expected that the results will include both quantitative and qualitative studies subsequently restricting the methodological options to arrange, analyse and display the results. The first author will complete the analysis and synthesise the results. Although quality analysis is not imperative to a scoping review, an appraisal of the included research will be completed in the analysis to enhance the conclusions drawn.53 Full texts will be collated in NVivo (V.12),54 allowing for analysis via the synthe-sisation methodology of textual narrative synthesis.55 However, this methodology may change due to a greater awareness of the results.56 As a stepwise method that has previously been used to map concepts in a scoping review (eg, children’s therapeutic footwear),57 textual narrative synthesis includes quality appraisal as part of the analysis, addressing limitations such as study bias and design.58 The first step involves grouping the studies into subgroups. For this review, it is anticipated that the subgroups will include the three contributors to suicide capacity as suggested by the theories within the ideation-to-action framework.16-21 The second step involves producing commentaries for each study regarding key variables and themes while addressing limitations such as study design and bias. To systematically appraise the quality of each study, an adaptation of a JBI59 critical appraisal tool checklist will be used addressing participant groups, confounding factors, measures used and analytical techniques. Finally, the third step requires discussion of differences and similarities among subgroups to synthesise and report the studies coherently.
+ETHICS AND DISSEMINATION
+To our knowledge, this is the first scoping review to synthesise the literature on suicide capacity beyond the single factor of acquired capability. This review will identify gaps in knowledge, suggest research opportunities for further advancement and clarification of the concept and may inform intervention and prevention strategies. The results of the scoping review will be published in a peer-reviewed journal, a thesis, presented at conferences and shared with suicide organisations by emailing a
+summary of the results coupled with a copy of the peerreview published article. Ethics approval is not necessary for this review as no data is being collected from human participants.
+Patient and public involvement statement
+The current project is a scoping review that will derive data from previously published studies. It does not involve the acquisition of new information. Patient and public involvement is not applicable in this situation.
+Limitations
+We will only include English-language articles potentially introducing language and cultural biases. This may result in the exclusion of relevant articles that may contain contributing factors which are not primarily Eurocentric. Another limitation is the exclusion of individuals aged 18 years or below and because of this limitation the results can only be interpreted within the context of adults. Only including articles published after January 2005 may result in some contributing factors of suicide capacity to be overlooked. However, much of suicide research prior to 2005 did not distinguish risk factors for suicidal ideation from risk factors for suicide attempts. The IPTS started a resurgence in suicidology and new-generation theoretical models of suicide differentiated risk factors between the two groups. It is this research that specifically targets risk factors for suicide attempts that the scoping review aims to synthesise. In addition, factors that contribute to a capacity for suicide not yet incorporated within ideation-to-action models of suicide may not be captured by this review. This may result in factors that can contribute to an individual’s capacity for suicide not being included. Therefore, our findings will be restricted within the context of the ideation-to-action framework.
+Open access
+Open access This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:http://creativecommons.org/licenses/by-nc/4.0/.
+ORCID iD
+Luke T Bayliss http://orcid.org/0000-0001-6076-801X
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+7
+Open access
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+56	Peters MDJ, Godfrey CM, Khalil H, etal. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc 2015;13:141-6.
+57	Hill M, Healy A, Chockalingam N. Key concepts in children’s footwear research: a scoping review focusing on therapeutic footwear. J Foot Ankle Res 2019;12:25-38.
+58	Lucas PJ, Baird J, Arai L, et al. Worked examples of alternative methods for the synthesis of qualitative and quantitative research in systematic reviews. BMC Med Res Methodol 2007;7:4-10.
+59	Joanna Briggs Institute. Appendix 7.1 Critical appraisal checklist for cohort studies [online], 2020. Available: https://wiki.joannabriggs.org/ display/MANUAL/Appendix+7.1++Critical+appraisal+checklist+for+ cohort+studies [Accessed 6 Aug 2020].
+60	Silverman MM, Berman AL, Sanddal ND, et al. Rebuilding the tower of babel: a revised nomenclature for the study of suicide and suicidal behaviors. part 2: Suicide-related ideations, communications, and behaviors. Suicide Life Threat Behav 2007;37:264-77.
+BMJ Open: first published as 10.1136/bmjopen-2020-043649 on 15 February 2021. Downloaded from http://bmjopen.bmj.com/ on December 23, 2022 by guest. Protected by copyright.
+8
+Bayliss LT, et al. BMJ Open 2021;11 :e043649. doi:10.1136/bmjopen-2020-043649
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+Suicide is among the top 20 causes of death worldwide.
+The World Health Organization estimates that the global age-standardized suicide rate was 11.4 per 100 000 person-years in 2012.1 Most suicides occur in individuals with mental illness,2 and virtually all mental disorders are associated with increased suicide-associated mortality.3 Mentally ill persons who have been discharged from psychiatric hospitals and wards seem to have a greater risk for suicide than other mentally ill persons.4
+The rate of suicide after discharge from psychiatric hospitals and wards (referred to herein as postdischarge suicide) is very high. A recent US study5 reported a suicide rate of 178 per 100 000 person-years in the first 3 months after discharge, a figure that is approximately 15 times the US national suicide rate. Studies6-9 from the United Kingdom and Nordic countries with similar durations of follow-up after discharge have reported higher suicide rates. Currently, there are no accepted benchmarks for postdischarge suicide rates.
+A synthesis of the existing literature about rates of postdischarge suicide would help quantify the extent of this issue and would complement an earlier meta-analysis10 of risk factors for postdischarge suicide by estimating expected base rates. A meta-analysis could help clarify the time course of postdischarge suicide risk and examine progress in reducing postdischarge suicide.
+The first aim of this study was to calculate a pooled estimate and statistical dispersion (range, median, and interquartile range) of postdischarge suicide rates. The second aim was to explore whether the observed heterogeneity in postdischarge suicide rates was associated with the duration of follow-up after discharge and the year in which the samples were collected. We also explored potential associations between suicide rates and a predetermined set of moderator variables de-finedby demographic characteristics, clinicalfactors, and study methods.
+Methods
+We conducted a registered meta-analysis of rates of postdischarge suicides according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE)11 and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)12 guidelines.
+Search Strategy
+Two of us (D.T.C. and M.M.L.) independently searched MEDLINE, PsychINFO, and EMBASE for relevant articles published in English from January 1,1946, to May 1, 2016, with the search terms ((suicid*).ti AND (hospital or discharg* OR inpatient or in-patient OR admit*).ab and ((mortality OR outcome * OR death *) AND (psych * OR mental *)). ti AND (admit * OR admis * or hospital * OR inpatient * OR in-patient * OR discharg*).ab (Figure 1). Electronic searches were supplemented by hand searches of the relevant review articles. Gray literature was not considered. Two of us (D.T.C., M.M.L.) independently winnowed titles, abstracts, and full-text publications.
+Inclusion and Exclusion Criteria
+We included studies that reported the number of suicides among patients discharged from psychiatric hospitals or wards and the number of person-years in which the suicides occurred. We also included studies from which these data could be calculated using the reported suicide rate, the mean length of patient follow-up, or the duration of follow-up.
+Studies of suicide attempts, community patients, and current inpatients; conducted before 1950; and with discharge from nonpsychiatric settings were excluded. Studies were excluded if the number of suicides and number of person-years were not reported, could not be calculated, or could not be obtained by email from the authors. When 2 studies reported completely overlapping samples (eg, when one study was conducted at a national level and another study was from a single hospital in that country in the same period), the study with fewer person-years was excluded. When 2 studies had partial overlap, both studies were included if less than 20% of the participants in the smaller study were included in the larger study.
+Data Extraction
+Two of us (D.T.C., M.M.L.) independently extracted the data. When studies reported multiple samples, nonoverlapping samples were selected according to a hierarchy of sex, age group (adolescents, adults, and those >65 years of age), duration of follow-up, year of the suicide, and diagnostic group, such that no participant in any single study was included more than once. In some publications, the number of person-years could be calculated using the number of suicides and reported suicide rate. In other publications, the number of person-years was estimated using the reported duration of follow-up.
+A predetermined list of moderator variables was extracted for each sample: the duration of follow-up, the year or midyear of data collection, the country in which the study was conducted, sex (male, female, or mixed), age at discharge (adolescent, adult, unspecified by age, >65 years of age), whether the patients were discharged from long-term or forensic facilities, whether the patients were defined by an admission for suicidality (defined as an admission associated with a suicide
+included open verdicts were regarded as being of higher quality because of evidence that open-verdict cases are often regarded as suicides by researchers.14,15
+Statistical Analysis
+The suicide rate per patient-year was the effect size measure. The pooled effect size was calculated using a random-effects model because we considered that the different patient groups in different countries during different periods were unlikely to have a common effect size. The studies were weighted according to their inverse variance, t2 was calculatedusingthe Paule-Mandel method, and the suicide rates were log transformed and reported in rates per100 000person-years. The significance test of the pooled suicide rate was conducted with a null hypothesis of 11.4 events per 100 000 person-years. Between-study heterogeneity was assessed using the Cochran Q and 12 statistics. Possible publication bias was tested by examination of the funnel plot and by an Egger regression.16 The analysis was conducted using R packages meta and metafor.17
+Sources of between-sample heterogeneity were investigated using subgroup analysis with categorical moderators and meta-regression with continuous moderators. Continuous moderators examinedby meta-regression and categorical moderators examined using subgroup analysis and Cochran Q that were significantlyassociatedwithheterogeneityinsuicide rates (a<.05) were entered into a mixed-effects multiple metaregression model. The length of follow-up was dichotomized (<12 months coded as 1, >12 months coded as 0) for the purpose of the multiple meta-regression.
+attempt or suicidal ideation), whether the samples were of patients followed up after their first psychiatric admission (first-admission patients), and the psychiatric diagnosis (psychosis, affective disorder, or mixed and other diagnoses). The corresponding national suicide rate in the country during the year of the study was obtained using World Health Organization data for each sample.1
+Strength of Reporting Scale
+The strength of reporting of each study was assessed using a 5-point scale derived from the Newcastle-Ottawa Scale for assessing the quality of nonrandomized studies.13 One point was awarded ifthe study identified suicides by using coroners’ records or a national mortality database (rather than using hospital records), included all the postdischarge suicides in a defined geographic region (rather than suicides from a particular care setting), included open verdicts in suicide numbers, reported the number of individuals (rather than the number of discharges), or reported the number of person-years directly or the mean length of patient follow-up from which the number of person-years could be calculated directly. Studies that
+Results
+Searches and Data Extraction
+A total of 142 full-text peer-reviewed publications met our inclusion criteria before being examined for possible overlapping participants (Figure 1). Two of us (D.T.C., M.M.L.) identified 113 of the 142 publications (79.6%). A total of 100 publications were included after the removal of overlapping studies (eTable 1 in the Supplement).6-9,18-113 These studies reported 183 separate samples of discharged patients. Forty-two publications were excluded because of overlap with larger publications (eTable 2 in the Supplement). Of the 183 patient samples, 50 were of females, 49 were of males, and 84 were of mixed sex (eTable 3 in the Supplement). A total of 129 samples were of adults or unspecified patients, 20 were of adolescents (<18 years of age at the time of discharge), and 19 samples were of older patients (>65 years of age at the time of discharge). Fifteen samples were of patients discharged from long-term or forensic facilities, and 23 samples were of first-admission patients. Nine samples were of patients who had been admitted with suicidality. There were disagreements about 40 of the 366 data points relating to effect size. All disagreements were resolved by a second examination of the data (D.T.C., M.M.L.).
+Sample Characteristics
+The 100 studies with 183 samples reported 17 857 suicides during 4 725 445 person-years. The mean (SD) number of
+suicides per sample was 97.6 (321.6) (median, 12; range, 0-2822), and the mean (SD) number of person-years per sample was 25 822 (111914) (median, 3534; range, 17-1393 800). The median sample suicide rate was 461 per 100 000 person-years, with a range of 0 to 6259 per 100 000 person-years. The interquartile range was 207 to 919 per 100 000 person-years.
+The median duration of follow-up was 60 months (range, 1-432 months; interquartile range, 12-112 months). The earliest study68 reported a sample with a midpoint in 1967, the median year of the samples was 1992, and the most recent study99 reported suicides that occurred in 2014.
+Twenty-seven samples were from Asian countries, 10 samples were from Australasia, 30 from mainland Europe, 40 from Nordic countries, 32 from North America, and 42 from the United Kingdom or Ireland. There was 1 sample each from Israel and Brazil.
+Meta-analysis
+The pooled rate of suicide after discharge was 484 per 100 000 person-years (95% CI, 422-555 per 100 000 person-years; prediction interval, 89-2641; P < .001, assuming a null hypothesis of 11.4 per 100 000 person-years) with very highbetween-sample heterogeneity (n = 183, Q182 = 9768, P < .001,12 = 98%, t2 = 0.86). The pooled suicide rate among 18 samples with a follow-up of 3 months or less was 1132 per 100 000 person-years (95% CI, 874-1467 per 100 000 person-years; 12 = 93%) and decreased thereafter in studies with follow-up of 3 to 12 months (654 [95% CI, 533-802]; 43 samples; 12 = 95%), 1 to 5 years (494 [95% CI, 354-688]; 44 samples; 12 = 96%), 5 to 10 years (366 [95% CI, 283-472]; 44 samples; 12 = 97%), and greater than 10 years (277 [95% CI, 218-352]; 34 samples; 12 = 97%). The duration of follow-up was significantly associated with between-group heterogeneity (Q4 = 73.1; P < .001) (Figure 2).
+jamapsychiatry.com
+More recently conducted studies had higher rates of suicide per 100 000 person-years than older studies (20052016: 672 [95% CI, 428-1055]; 27 samples; 12 = 98%; 1995 to 2004:656 [95% CI, 518-831]; 51 samples; 12 = 99%; 1985to1994: 404 [95% CI, 322-508]; 46 samples; 12 = 98%; 1975 to 1984; 373 [95% CI, 279-498]; 47 samples; I2 = 96%; before 1975; 423 [95% CI, 316-567]; 12 samples; 12 = 75%). The period of sample collection was significantly associated with between-sample heterogeneity (Q4 = 14.7; P = .005).
+Difference in postdischarge suicide rates according to geographic region were not statistically significant (Q6 = 10.6; P = .10) (Asia: 632 [95% CI, 434-921]; 27 samples; 12 = 95%; Australasia: 423 [95% CI, 164-1090]; 10 samples; 12 = 96%; Mainland Europe: 502 [95% CI, 375-672]; 30 samples; 12 = 96%; Nordic countries: 562 [95% CI, 430-735]; 40 samples;12 = 97%; North America: 308 [95% CI, 220-430]; 32 samples; 12 = 94%; United Kingdom and Ireland: 513 [95% CI, 410-642]; 42 samples; 12 = 98%; other: 261 [95% CI, 7-9940]; 2 samples; 12 = 98%).
+The suicide rate was lower among cohorts of adolescents (158 per 100 000 person-years) compared with samples of adults (555 per 100 000 person-years), patients from longterm or forensic discharge facilities (487 per 100 000 person-years), and older patients (496 per 100 000 person-years) (Table 1). Samples of persons discharged after an admission for suicidality (2078 per 100 000 person-years) had more than 4 times the suicide rate of other samples (452 per 100 000 person-years) (Table 1). Rates of suicide were lower among first-admission patients (305 per 100 000 person-years) (Table 1). Samples of patients with psychosis (599 per 100 000 person-years), affective disorder (524 per 100 00 person-years), and mixed and other diagnoses (463 per 100 000 person-years) had similar suicide rates (P = .40). There was no significant
+JAMA Psychiatry July 2017 Volume 74, Number 7	697
+difference in suicide rates according to sex (533 for males, 412 for females, and 503 for mixed per 100 000 person-years; P = .34) or total strength of reporting scores (488 for lower scores and 526 for higher scores per 100 000 person-years; P = .30) (Table 1).
+Publication Bias, Meta-regression,
+and Multiple Meta-regression
+The funnel plot was symmetrical and the Egger regression was not significant (intercept, -0.27; 1181 = -38; P = .70), suggesting an absence of publication bias. Samples with longer durations of follow-up were more likely to report lower suicide rates (coefficient = -0.0049; 95%CI,-0.0064to-0.0034; z = -6.34;
+P < .001). Samples reporting suicides from more recent studies reported higher suicide rates than older studies (coefficient = 0.02; 95%CI,0.008-0.031; z = 3.40; P = .007).Thegen-eral population suicide rate was not associated with rates of suicide among discharged patients (coefficient = 0.0064; 95% CI, -0.019 to 0.032; z = 0.49; P = .60).
+Five moderators that were associated with between-sample heterogeneity were included in a mixed-effects multiple meta-regression model (Table 2). More recent samples, samples with follow-up of a year or less, and samples of suic idal patients were independently associated with higher postdischarge suicide rates. Samples of adolescent patients were independently associated with lower rates.
+This model accounted for 39% of the observed between-sample variance.
+Discussion
+This study synthesizes more than half a century of research into postdischarge suicide rates. We identified a large number of studies reporting more than 17 000 suicides in almost 5 million person-years at a pooled rate of484 per 100 000 person-years. This figure is more than 3 times the suicide rate estimated in a comparable study114 of the suicide rate among inpatients and 44 times the global suicide rate of 11.4 per 100 000 patients per year in 2012.1 The suicide rate of studies that followed up patients for no more than 3 months was 100 times the global suicide rate. Studies with follow-up periods of 3 to 12 months had almost 60 times the global suicide rates, and the suicide rate among discharged patients was more than 30 times that in the general population even for periods of follow-up of 5 to 10 years.
+Nordentoft et al115 recently described the phenomenon of postdischarge suicide as a “nightmare and disgrace.”115(p 1) We agree; however, they formed this view in the light of a recent study116 that found a rate of suicide of 178 per 100 000 patients per year during the first 3 months after discharge. Of note, our meta-analytic estimate during the same duration of follow-up is more than 6 times higher.
+Our data suggest that the suicide rates among discharged patients have not decreased in the past 50 years. This is a disturbing finding considering the increase in community psychiatry and the availability of a range of new treatments during this period. The increase that we observed in postdischarge suicides can be seen in the context of the recent finding of a more extreme increase in the suicide rate among current inpatients from 68 per 100 000 person-years in the 1960s and 1970s to 646 per 100 000 person-years since 2000.114 An increase in the suicide rate of admitted and discharged patients might be attributable to multiple factors, including changing legal and other criteria for admission, shorter lengths of inpatient treatment, increased prevalence of substance use, and a greater acuity of illness among those admitted in the era of deinstitutionalization.114 Publication bias in favor of recent studies from regions with a higher suicide rate might have also contributed to the observed increase in suicide rates over time.
+The marked variation in postdischarge suicide rates was not fully explained by the duration of follow-up or the year of the sample. Studies with similar periods of follow-up and studies conducted in the same or similar years have between-study heterogeneity that is similar to that of the whole sample.
+Limitations
+Our study has a number of limitations, most relating to the high between-sample heterogeneity. Although we were able to explain some heterogeneity by using moderator variables, further unexplained heterogeneity might be attributable to factors that were not reported in the primary research. Few of the included studies reported comparisons of those who committed suicide with those who survived, and information about
+jamapsychiatry.com
+the psychiatric care that the patients received in hospital or after discharge was absent. For example, the included studies did not report on the extent of any association between readmission and suicide. The association between readmission and suicide might matter because we found that samples of first-admission patients had a lower postdischarge suicide rate than samples of patients with a mix of first-time and previous patients. This finding suggests that readmission might be an important suicide risk factor.
+Other limitations relate to the representativeness of the included studies. Almost all the research came from high-income economies of Asia, Australasia, North America, and Europe, and our results might not be representative of postdischarge suicide in low- and middle-income countries. Even the pooled results that we obtained from our 6 regions were heterogeneous and should not be considered to be a generalizable benchmark for all psychiatric hospital settings in high-income countries.
+Furthermore, factors that are associatedwithincreasedsui-cide risk at an aggregate level should be interpreted cautiously and are not necessarily applicable to individual patients. For example, we found that samples of older patients had a higher suicide rate than samples of adolescents, whereas a meta-analysis10 of risk factors for suicide after discharge found that older age was not associated with an increased suicide risk. Likewise, we found that samples of patients with affective disorders did not have a particularly high suicide rate, whereas the earlier meta-analysis10 found an association between suicide and depressive symptoms and disorders.
+Finally, our study did not estimate the suicide rate in the days immediately after discharge. Some studies117,118 have suggested that the days immediately after discharge are the period of the highest suicide risk, but our methods yielded insufficient numbers of samples to robustly estimate suicide rates during periods of less than 3 months. It is likely that rates of suicide in the initial period after discharge are substantially higher than the rate we report during the 3 months.
+Conclusions
+It has been argued that a way of combatting postdischarge suicide is to focus on individual patients with clinical characteristics that signify a high suicide risk.119,120 However, the very high suicide rates calculated in this study and the known limitations of suicide risk assessment116,121 suggest that a focus on clinical risk assessment might mislead clinicians into thinking that some patients can be regarded as having low risk after discharge.115 Our findings better support the views of authors who believe in a more universal approach to suicide prevention that might focus on periods of high risk but that extends for periods of years.116 However, the findings should curb enthusiasm for restrictive interventions directed at patients labeled as having high risk of suicide by virtue of demographic or clinical variables. Our figures suggest that 0.28% of all discharged patients can be expected to commit suicide during the first 3 months after discharge. The modest statistical strength of suicide risk assessment means that even patients
+who are classified as having high risk because of their suicide risk factors122 will have a low absolute probability of suicide over clinically meaningful time frames, whereas patients with a low risk for suicide will still have a probability of suicide that is many times that in the general community.
+Discharged patients have suicide rates many times that in the general community. Efforts aimed at suicide preven
+tion should start while patients are in hospital, and the period shortly after discharge should be a time of increased clinical focus. However, our study also suggests that previously admitted patients, particularly those with prior suicidality, remain at a markedly elevated risk of suicide for years and should be a focus of efforts to decrease suicide in the community.
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+NEWS
+Suicide rates continue to rise in England and Wales
+Gareth Iacobucci
+The Royal College of Psychiatrists has called for more research to understand why numbers of deaths by suicide in certain groups are increasing in England and Wales, after new figures showed a continuing rise last year.
+Data published by the Office for National Statistics on 1 September showed that in 2019 the suicide rate among men and boys was 16.9 deaths per 100 000, the highest since 2000 and slightly above the 2018 rate of 16.2 per 100 000. The suicide rate among women and girls was 5.3 deaths per 100 000 in 2019, up from 5.0 per 100 000 in 2018 and the highest since 2004.
+Overall, 5691 suicides (4303 in men and boys) were registered in England and Wales in 2019, giving an age standardised rate of 11 deaths per 100 000 people. A total of 5420 were registered in 2018 (10.5 per 100 000).
+Among men and boys the age group with the highest suicide rate was 45 to 49 years (25.5 deaths per 100 000), while among women and girls 50 to 54 year olds had the highest rate (7.4 per 100 000).
+Despite a low number of deaths overall among people aged under 25 years, the data showed that rates of suicide in this age group have generally increased in recent years, particularly in the case of 10 to 24 year old females, whose rate has increased by 94% since 2012, from 1.6 deaths per 100 000 (81 deaths) to 3.1 per 100 000 in 2019 (159).
+Adrian James, president of the Royal College of Psychiatrists, said it was crucial to identify the people most at risk and to provide tailored care and support to them. He said, “This data provides real insight into particular groups in society who are at higher risk. We need more research to understand the reasons behind the increased rates of suicide in teenage girls and young women, as well as middle aged men.
+“The current pandemic and its impact on people’s mental health reinforces the need for substantial and sustained government funding to ensure that there is a mental health system where no one, including those at risk of suicide, is unable to access the care they need.”
+The Office for National Statistics said, “Generally, higher rates of suicide among middle aged men in recent years might be because this group is more likely to be affected by economic adversity, alcoholism, and isolation. It could also be that this group is less inclined to seek help.”
+Rosena Allin-Khan, Labour’s shadow mental health minister, said, “Suicide is both a public health and social inequality issue, and with the right interventions it is preventable. Today’s figures must be a wake-up call for the government.”
+The ONS also published provisional data for the second quarter of 2020, the peak of the covid-19 pandemic. It showed that there were 6.9 deaths by suicide per 100 000 people in England. This was the lowest of any quarter since 2001, but the ONS said that the lower number between April and June should be “interpreted with caution,” because the pandemic meant that inquests were delayed.
+It said, “Given the length of time it takes to hold an inquest (around five months), we do not currently know the total number of suicides that occurred during the coronavirus pandemic.”
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diff --git a/Suicide-risk-and-the-economic-crisis-An-exploratory-analysis-of-the-case-of-MilanPLoS-ONE.txt b/Suicide-risk-and-the-economic-crisis-An-exploratory-analysis-of-the-case-of-MilanPLoS-ONE.txt
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+Introduction
+In the past five years, several research reports [1, 2] and scientific articles [3,4], have highlighted the increasing number of suicides linked to economic reasons since the economic crisis started in 2008.
+According to Link Lab [2], in Italy the number of suicides linked to economic reasons increased to as much as 40% of the total during the last four months of 2013. This increase is particularly striking as this type of suicide accounted for 6.1% of the total in 2010, 6.6% in 2009 and 5.3% in 2008. At the same time, several scientific papers and international research [3, 4, 5, 6, 7] have claimed that the increase some countries have registered in suicides since 2008 is somehow related to the economic crisis.
+Stuckler et al. [7] analyzed suicide trends in ten European countries and noted that in all but one the suicide rate increased between 2007 and 2009. Recent national studies in England
+[8], Italy [3] and the United States [9] also revealed significant increases in suicide rates between 2008 and 2010. An analysis of the relationship between suicides and unemployment in 27 European countries, demonstrated that there were 4900 extra suicides in 2009 compared with previous years (2000±2007) [5].
+According to EURES [1] and De Vogli et al. [3], Italy has also witnessed an increase in suicide rate since the beginning of 2008, especially among men. Crevallo et al. [10] noted that in the Italian province of Turin the percentage of suicides linked to economic reasons doubled during the period of the economic crisis (2011±2013) in comparison with previous years (2002±2010).
+All these studies claimed that the economic crisis somehow influenced—through the increase in the unemployment rate or prevalence of economic difficulties±the suicide rate in a way which cut across other macro and micro factors.
+Differently, other international and national research suggests that during a period of economic crisis the impact of specific economic problems on the probability of suicide is often mediated by other individual-level factors, mainly psychological and physical, whose negative influence is exacerbated by reductions in the availability of health and social care [6,11,12]. Economic crises can, indeed, have a strong negative impact on the quality of social and health services because they often influence the public funding for these sectors. Many countries privatized health services, reduced staffing levels in the public sector and reduced public expenditure on social care and social assistance during the economic crisis and this created a situation of social inequality in which less wealthy people could not afford necessary medicines and services [11,13, 14].
+Social inequality worsened as a consequence of austerity policies, this was reflected at individual level in reduced wellbeing and increased incidence of anxiety and depression syndromes. This also contributed to an increase in chronic physical conditions, such as circulatory diseases, hypertension, strokes, etc. which are known to be influenced by stress [15, 16, 17, 18].
+Countries where public investment in health services and public spending on social care was maintained, such as Iceland, Finland, Sweden and Germany, did not experience these problems during the economic crisis [11]. Uutela [19] noted that the effects of the economic crisis on the psychological health of the population were less serious in those countries where both formal and informal social networks remained solid and easily accessible.
+The above presentation of evidence is not intended to minimize the negative effect of economic crises on suicides; rather to emphasize that their impact on the suicide is not solely attributable to the worsening of individuals' economic conditions.
+The recent World Health Organization [12] report on prevention of suicide demonstrated that the interaction between biological, psychological, social, environmental and cultural factors has a significant influence on the variation in suicide rate across countries [12].
+McLean et al. [20] classified factors contributing to risk of suicide into two main categories: 1) societal (i.e. macro-level, structural) and 2) individual (i.e. micro-level, biological, psychological and behavioral). Based on a systematic review of risk and protective factors for suicide they [20] also identified a third group of determinants: psychosocial factors. These factors represent an interaction of behavioral and social factors; the influence of social factors on an individual state of mind and behavior [21]. Societal risk factors become psychosocial factors only if they influence individuals' health [21]. Family structure, school environment and employment status are societal factors which can have psychosocial effects on individuals. Martikainen et al. [21] argued that unemployment “is not a psychosocial risk factor when the impact on the individual is limited access to income and material goods, it becomes a risk factor only when it impacts on feelings of self-esteem that then impact on the health of the individual through
+modified behavior or psychobiological processes” (20: 15). Employment status should be considered a psychosocial factor if it affects health by influencing behavior and psychological or physical state at the individual level.
+With this regard, several studies have suggested that unemployment can precipitate suicide, especially in interaction with psychological or physical illness, rather than being their main cause [22, 23, 24, 25, 26]. The WHO report [27] and other research on the effects of economic crisis, concluded that physical and psychological health are the individual variables which are most sensitive to economic changes [19, 28, 29, 30, 31]. In particular, according to the WHO [12] people suffering chronic pain or chronic disease have two to three times higher risk of suicide than the rest of the population. All illnesses associated with pain, physical disability, neu-rodevelopmental impairment and distress increase the risk of suicide (e.g. cancer, diabetes and HIV/AIDS) [12]. Preti and Miotto [32] investigated the trend in suicide rate during the economic crisis and they argued that psychological disease is the main predictor of suicide, although stressful events, such as losing one’s job, can act as enablers. In related research, Stuckler et al. [6] demonstrated that unemployment has a negative impact on psychological health, especially in the short-term.
+According to Istat [33] 59.5% of suicides in Italy over the previous 10 years happened as a consequence of a psychological disease, 17.5% of a physical disease, 15.9% for sentimental reasons and only 6.3% for economic reasons. The presence of psychological or physical disease appears to be the main reason for suicide and together these motives accounted for more suicides (77%) than any other motive.
+Losing one’s job, having difficulty paying for adequate housing and financial instability are all factors that can increase suicide risk in interaction with other issues, such as depression, anxiety, substance abuse, physical disease, difficult social relationships.
+Based on this evidence the aim of this study was to test the hypothesis that the probability of suicidal behavior during an economic crisis is influenced by the interaction between an individual’s employment status and the presence of psychological or physical disease. To achieve this we analyzed data collected by the Institute of Forensic Medicine on suicides in the province of Milan. Milan is part of the region with the highest suicide rate in Italy [34]. Information about the relationships among the main individual and structural factors which influence suicide rate during an economic crisis provides vital evidence for determining public policy and identifying individuals at greater than average risk.
+Materials and Methods
+This study addressed the following research questions:
+1. Does employment status influence suicide risk at the individual level during an economic crisis?
+2. Is individual suicide risk during an economic crisis influenced by the interaction between employment status and the presence of psychological or physical disease?
+In order to answer these questions we compared the influence of these two factors (employment status and presence of psychological or physical disease) on suicide risk during the period of the economic crisis (2008-2013) and the period immediately preceding it (20022007). In order to test this relationship we used the chi-square test and binary logistic regression.
+The analysis considers individual suicides in the province of Milan and benefits from data collected by the Institute of Forensic Medicine, University of Milan. This database has the
+advantage of containing detailed information about victim characteristics, including their socio-economic and medical background, as well as the characteristics of the suicide event.
+The dataset
+The database of the Institute of Forensic Medicine, University of Milan is updated monthly and includes all deaths registered as suicides in autopsy reports by the Milan mortuary since 2001. The autopsy report includes several groups of variables: personal information on the victim (e.g. gender, age, residence, educational level, marital status, type of job, consumption of alcohol, tobacco and drugs, medical history, use of medicines), information on the suicide event (location, date, method etc.) and several other variables based on the autopsy (date of death, type of injury, site of injury, cause of death).
+The database includes data on all suicides registered in the 91 municipalities under the authority of the Milanese public prosecutor.
+The data recorded by the Institute of Forensic Medicine, University of Milan are consistent with the data collected by Istat through the 'Survey on causes of death’, which is the main official source of data on suicides in Italy. Data from these two sources, classified by gender and age group, are 99% correlated for the years common to both datasets (2009-2012). This demonstrates the reliability and completeness of the information gathered by the Institute of Forensic Medicine, University of Milan.
+The main contributions of this study are the analysis of individual-level data and the analysis of detailed information about the suicide event.
+Defining the dependent variable: Suicides in the province of Milan
+Cases of suicide in Milan were divided into two categories, those which occurred in the period immediately preceding the economic crisis (2002-2007) and those which occurred during the economic crisis (2008-2013).
+The date for the start of the economic crisis was defined by inspecting the trend in unemployment rate in Italy as a whole and Milan in particular. Unemployment rate is a clear and straightforward indicator of economic downturn and is often used in the scientific literature to define economic crisis [7, 4]. Gross domestic product (GDP) is also often used as an indication of a country’s economic status, but as GDP changes more slowly it is a less sensitive indicator of the temporal parameters of an economic crisis. In fact, the majority of European countries registered increases in GDP during the worse part of the economic crisis (between 2010 and 2011), with GDP only starting to decline in 2012 [28]. For these reasons we chose to use unemployment rate to define the start of the economic crisis in Italy.
+Fig 1 shows that, in the province of Milan, unemployment started to increase in 2008 and continued to do so until 2013 [4,7, 33]. This data justifies our decision to classify suicides occurring between 2008 and 2013 as having occurred during the economic crisis.
+Our analysis categorized suicides according to whether or not they occurred during the economic crisis. This was represented as a dichotomous variable (0 for the period preceding the crisis, 2002-2007; 1 for the economic crisis, 2008-2013). Both periods were of equal length (six years).
+Table 1 presents descriptive statistics for suicides occurring in the province of Milan between 2002 and 2013, based on data from the Institute of Forensic Medicine.
+The annual mean number of suicides in Milan is 158, which represents 6.2 per 100,000 population. The lowest rate during the period we analyzed was registered in 2007; the rate was highest in 2009 and in 2013.
+As shown in Fig 2, the suicide rate in Lombardy (the Italian region of which Milan is the capital city) and Italy show a very similar trend. In Milan the average suicide rate was 6.2 per 100,000 between 2002 and 2013 whereas in Lombardy it was 6.7 per 100,000 and in Italy as a whole it was 6.8 per 100,000. The temporal trend in suicide rate in the province of Milan reveals that there is not a significant difference between suicide rate during the economic crisis (2008±2013) and in the period preceding it (2002±2007) (Chi-square = 0.385, p = 0.535).
+Defining the independent variables: Employment status, psychological disease and physical disease
+As already mentioned in the introduction, several studies highlighted that economic crisis should be considered a risk factor for suicides if it affects—through unemployment or other
+5/13
+economy-related factors—specific individual problems, such as psychological or physical diseases. Drawing on this evidence, we decided to analyze the effects of three independent variables: employment status of the suicide victim (employment status), presence of physical or psychological disease (disease) and the interaction of these two variables (employment status * disease).
+Employment status was represented as a dichotomous variable (0 = unemployed; 1 = employed). Cases where the victim was retired at the time of suicide were excluded from the analysis because it is believed that retired people are less affected by an economic crisis than those of working age and they might have psychological diseases related to senile dementia. Information about the victim’s employment status was collected at the time of autopsy by interviewing the victim’s partner, relatives or other persons close to him or her.
+Disease was represented as a dichotomous variable capturing whether a suicide victim was or was not affected by physical or psychological disease at the time of suicide (0 = not affected; 1 = affected). For the purposes of this analysis, psychological disease was defined as psychological conditions such as anxiety and depressive syndromes, psychosis, schizophrenia, anorexia and other eating disorders. Drug dependency and alcohol dependency, which McLean et al. [20] and Rockville [35] considered to be among the main risk factors for suicide, were also considered among psychological diseases.
+Physical disease included both chronic physical disease (e.g. diabetes, viral hepatitis, epilepsy, auto-immune diseases, etc.) and other serious diseases (e.g. cancer, cardiopathology
+etc.). Information about the presence of physical or psychological disease was obtained by the Institute of Forensic Medicine from inspection of the victim's medical history and, where this was not available, by interviewing relatives, partner or other persons close to the victim, and from the results of toxicological tests.
+We controlled for the influence of individual factors, such as gender, age, marital status (partnered; un-partnered) and residence context (urban; non-urban), on the relationship between the abovementioned variables and suicide risk.
+Gender and age are the most important determinants of suicide risk. According to the WHO [12], men are 3.5 times more likely to be victim of suicide than women in developed countries and 1.6 times more likely to do so in developing countries. Data from Istat [32] confirmed that this pattern is observed in Italy: men have three times higher suicide risk than women. Gender was included in the analysis as a binary variable (0 = male; 1 = female). Istat [32] also found that the people older than 65 years were eight times more likely to commit suicide than people younger than 25 years. Age was represented as a binary variable (1 = victims aged between 25 and 34 years old; 0 = victims of all other ages). This classification was adopted on the grounds that people aged between 25±34 years are of working age and thus vulnerable to the effects of an economic crisis. The variable marital status distinguished between individuals who were married or cohabiting at the time of suicide (1) and individuals who were single, divorced or widowed (0). A systematic review [20] concluded that being married or living with a partner has a strong protective effect against the socio-economic risk factors for suicide.
+The variable urban context was used to capture whether the suicide victim lived in the municipality of Milan (1) or outside the city (0). According to van Hooijdonk et al. [36] urban areas have a higher suicide rate than non-urban areas, mainly because of differences in population structure and the greater physical and social complexity of urban environments. However, the impact of living in an urban area is moderated by gender and age. Living in an urban area reduces the risk of suicide for young men but increases it for women [37].
+Results
+The bivariate analysis reported in Table 2 shows that there was no relationship between employment status or health status and suicide risk during the economic crisis. However, it is important to note that more than 80% of suicide victims in the province of Milan between 2002 and 2013 were affected by a physical or psychological disease. This figure is in line with the data published by the WHO [12] and Istat [32].
+The control variables age and marital status were negatively correlated with suicide risk during the economic crisis (Table 3). In the province of Milan people aged between 25 and 34 years had a lower suicide risk during the economic crisis than those in other age categories.
+Similarly, married and cohabiting couples also had lower suicide risk during the economic crisis than those who were unpartnered.
+Binary logistic regression was performed to obtain a better understanding of how employment status affected suicide risk in the province of Milan during the economic downturn and to investigate whether the interaction between employment status and health status was an important factor in suicide risk. The binary logistic regression model included the two main independent variables (employment status and health status), another independent variable representing the interaction term for these variables (employment status * health status) and the control variables (gender, age, marital status and urban context).
+The results of the binary logistic regression are summarized in Table 4.
+The results of the binary logistic regression demonstrate that, among suicide victims in the province of Milan, the likelihood of suicide during the economic crisis is three times higher for persons affected by a severe disease, either physical or psychological, than for people who were not affected. The presence of severe disease was a significant contributor to suicide risk during the economic crisis.
+Neither employment status nor the interaction between employment status and health status contributed to the difference between the suicide rate before and during the economic crisis. Living with a partner can be considered a protective factor with respect to suicide risk during the economic crisis. The likelihood of suicide during the economic downturn compared to the pre-crisis period was 1.6 times lower for those who were married or cohabiting than for people who were divorced, widowed or single. Age also helped to account for suicide risk during the economic crisis. People aged 25±34 years old were 1.5 times less likely to
+commit suicide during the economic downturn than people in other age categories. Neither gender nor urban context contributed to the difference between the suicide rate during and before the economic crisis.
+The binary logistic regression model was significant and accounted for 3% of the variance in suicide rate in the province of Milan. Sensitivity and specificity analysis is reported in Tables A-B and Fig A in S1 Annex.
+Discussion
+Generally speaking, it is not easy to identify and quantify the effects of the economic crisis on suicide rate and health at population level, mainly due to the lack of up-to-date, reliable data. According to the WHO [12, 7] “since suicide is a sensitive issue, and even illegal in some countries, it is very likely that it is under-reported. Even in countries with good vital registration data, suicide may often be misclassified as an accident or another cause of death. Registering a suicide is a complicated procedure involving several different authorities, often including law enforcement. And in countries without reliable registration of deaths, suicides simply die uncounted”. For this reason official statistics often under-report suicide. In addition, suicide statistics very often do not include information about the method or other important characteristics of the event which are fundamental to developing effective prevention strategies.
+Improving the availability and reliability of demographic statistics, public health statistics, and forensic institutes statistics, as well as developing sample surveys on the causes of death, is the prerequisite for developing effective suicide prevention programs [12]. The data collection protocol followed by the Institute of Forensic Medicine, Milan represents a good practice in this area. Their data covers a long time period (2001-2013) and is updated every month. It includes detailed information not only on the suicide event but also on the characteristics of the victims, including their socio-economic and health status. This database is useful for sociological research as well as forensic analyses. It enables the sociological researcher to analyze patterns in the main variables relevant to suicidal events, from the method used, to the location and the characteristics of the victims.
+It is important to note that the analyses reported in this paper are exploratory and have some weak points. The main weakness relates to the difficulty of obtaining data and the problems inherent in this type of information, which might limit the reliability of the statistical analysis.
+Although there is a 99% correlation between the data collected by the Institute of Forensic Medicine, Milan and the Istat data, some variables—such as that on the employment and health status of victims—remains problematic. This information is usually collected from preexisting clinical documentation on the victim or, if this documentation is not available, by questioning relatives of the victim or other people close to him or her.
+Preti and Miotto [32] noted that official statistics on the employment status of suicide victims might be biased. Relatives, and perhaps even the public authorities, might be more inclined to cover up the suicide of people in employment than unemployed people, for various reasons. Relatives might use the victim’s lack of employment as a justification for an event they cannot otherwise explain. Other people might be influenced by public opinion and the media, who often use unemployment and the economic crisis as scapegoats for these tragic events.
+These types of bias could explain why employment status appears to influence suicide risk and should be borne in mind when considering evidence on trends in suicide.
+In addition to these factors, the validity of the analysis of the relationship between suicide and the economic crisis in the province of Milan is limited by the failure to control for the
+influence of contextual variables such as investments in health and social care and income distribution, for which micro/individual level data are not available.
+Given all these points, extreme caution should be exercised with respect to claims that of a causal relationship between economic crisis and suicides. This is true not only for the analyses included in this paper but also for the information provided by the media to the public.
+These shortcomings notwithstanding this exploratory analysis may serve as a good starting point for similar research in other Italian provinces or at national level.
+It would also be interesting, and in line with the recommendations of the WHO [12], to use data collected by all the forensic institutes in Italy in order to obtain a larger sample and hence more reliable statistical results. The information collected by these institutes should be well-suited to a pooled analysis because it is all based on standardized autopsy reports.
+Conclusions
+The analysis of suicides in the province of Milan suggests that the relationship between the suicide rate and the economic crisis is mediated by individual factors other than unemployment, such as the presence of physical or psychological disease. In particular, this exploratory analysis of the data for Milan indicated that structural economic issues had less influence on suicide risk in people who were in good psychological and physical health. Such people can probably rely on solid cognitive and emotional barriers against external threats. If these barriers are weakened by severe psychological or physical disease, or by the lack of a stable emotional life (being without a partner), people become more vulnerable to structural economic pressures. This pressure, often worsened by the media, can instill feelings of social distress and insecurity.
+Interestingly, the lack of an interaction between health and employment status suggests that suffering from severe disease during a period of economic crisis has a strong impact on suicide risk, independently from employment status. This may be due to the reduced availability and quality of health care during the economic crisis. The lack of effect of employment status may reflect the contemporary perspective on jobs. Jobs are regarded as more and more insecure and precarious and a job is viewed more as something with practical utility than as something with symbolic value that contributes to one's identity. Other factors, such as age, health status and marital status contribute more to personal identity and are more strongly related to vulnerability to the negative effects of a socio-economic crisis.
+In this context, it is important to emphasize that the economic crisis resulted in severe financial cuts to the public health sector and thus limited the availability of health care services. This may have exacerbated the difficulties faced by people affected by physical or psychological diseases, for example dealing with everyday stress or interpersonal problems [28].
+The increase in suicide rate probably represents only the tip of the iceberg of health problems linked to the economic crisis. Several studies in various countries have demonstrated that the incidence of depression and anxiety syndromes and consumption of psychotropic drugs increase during periods of economic downturn [28].
+The impact of austerity policies on the public health sector limited access to certain health care services and made them more expensive for patients (see Introduction). National governments, which are struggling to limit public expenditure, usually fail to meet the increased demand for health and social services which results from the increase in social distress and physical and psychological disease during an economic crisis. This results in a vicious circle, where the lack of proper health and social care during an economic crisis exacerbates social distress and health problems and thus increases the economic and human costs of the crisis.
+The presence of a robust and efficient social and health care system is the key to limiting the negative impact of economic crisis on the psychological and physical health of a population. The WHO reported that the impact of an economic crisis on the health of a country's population depends on action in five key areas: 1. active labor market programs; 2. family support programs; 3. control of the pricing and availability of alcohol; 4. debt relief programs; 5. primary care for the people at high risk of mental health problems [18].
+This point further highlights the need, during a period of economic downturn, to identify population groups at high risk for suicide in order to focus scarce public resources on their needs. For this reason, research into suicide risk should be promoted and data on suicide rate and the factors which influence it, including psychological and physical health problems, should be widely disseminated.
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+Suicide Risk and the Economic Crisis
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+13.	Busse R. Health system reforms in time of crisis: EU countries. Berlin University of Technology/WHO Collaborating Centre for Health Systems Research and Management/European Observatory on Health Systems and Policies; 2013.
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+16.	Costa G, Marra M and Salmaso S. Gli indicatori di salute ai tempi della crisi in Italia”. Epidemiologia & Prevenzione. 2012; 36 (6):337±366.
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+19.	Uutela A. Economic crisis and mental health. Current Opinion in Psychiatry. 2010; 23:127±30. doi: 10. 1097/YCO.0b013e328336657d PMID: 20087188
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+24.	Jin RL. The impact of unemployment on health: A review of the evidence. Canadian Medical Association Journal. 1995; 153:1567±8.
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+27.	World Health Organization—European Observatory on Health Systems and Policies. Health, health
+systems and economic crisis in Europe. Impact and policy implications. World Health Organization: Geneva; 2013. http://www.euro.who.int/en/media-centre/events/events/2013/04/oslo-conference-on-health-systems-and-the-economic-crisis/documentation/working-documents/health,-health-systems-and-economic-crisis-in-europe-impact-and-policy-implications. Accessed 22 October 2015.
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+33.	Istat. I suicidi in Italia: Tendenze e confronti, come usare le statistiche. Nota Informativa Istat, 8 agosto 2012. http://www.istat.it/it/files/2012/08/nota-informativa-SUICIDI.pdf. Accessed 6 November 2015.
+34.	Istat. I.Stat. http://dati.istat.it/. Accessed 9 December 2015.
+35.	Rockville MD. National Strategy for Suicide Prevention: Goals and Objectives for Action. U.S. Dept. of Health and Human Services, Public Health Service, 2001. www.mentalhealth.org/suicideprevention. Accessed 15 November 2015.
+36.	van Hooijdonk C, Droomers M, Deerenberg IM, Mackenbach JP and Kunst AE. Higher mortality in urban neighbourhoods in the Netherlands: who is at risk? Journal of Epidemiology and Community Health. 2008; 62(6):499±505. doi: 10.1136/jech.2007.060145 PMID: 18477748
+37.	Scocco P. Epidemiologia del suicidio nelle aree urbane. Simposio: Aree Urbane e Salute Mentale. 13°-Congresso della SOPSI. Psichiatria 2009: Clinica, Ricercae Impegno Sociale. Roma, 10±14febbraio 2009. http://www.soproxi.it/wp-content/uploads/2012/12/EPIDEMIOLOGIA-DEL-SUICIDIO-nelle-aree-urbane.pdf. Accessed 29 November 2015.
+PLOS ONE | DOI:10.1371/journal.pone.0166244 December 29, 2016
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+ABSTRACT A variety of nutrition education interventions and social marketing initiatives are being used by the Food Stamp Program to improve food resource management, food safety, dietary quality, and food security for low-income households. The Social-Ecological Model is proposed as a theorybased framework to characterize the nature and results of interventions conducted through large public/private partnerships with the Food Stamp Program. In particular, this article suggests indicators and measures that lend themselves to the pooling of data across counties and states, with special emphasis on systems, environment, and public policy change within organizations at the community and state levels.
+(JNE 33:S4-S15, 2001)
+a social-ecological model
+Sustained improvements in dietary behavior often benefit from long-term, repeated exposure to behaviorally focused nutrition education through a variety of channels and in ways that can compete in today’s marketplace.1-6 These range from small groups that participate in interactive education to large-scale social marketing campaigns.These efforts feature multiple channels of communication, along with system, environmental, and policy change as a way to reinforce healthy nutrition behavior.7-9 Reviews of research efforts suggest that multiple approaches to health and nutrition education com
+plement one another.2-6,10-14 However, this range of methods and interventions presents particular challenges for evaluation.
+This article presents a theoretical framework for planning and evaluating nutrition education programs with low-income populations. Because it provides a framework for describing individual change within the context of social change, a Social-Ecological Model15 may provide a conceptual framework that can assist in the planning and evaluation of multiple-component nutrition education programs. This particular model conceptualizes the social world in five spheres, or levels, of influence (Fig. 1).These levels of influence are (1) social structure, policy, and systems; (2) community; (3) institutional/organizational; (4) interpersonal; and (5) individual.
+The sections that follow describe each of the five spheres of influence in the Social-Ecological Model as they may be applied to conducting and evaluating large-scale nutrition education programs. A summary of theories and indicators appropriate for each sphere is found in Table l.Theories and examples of the science-based indicators that can be used to identify and evaluate change in each sphere are described. Indicators are the theoretical constructs, activities, and behaviors that the evaluator would operationalize to identify change at the particular level. Measures are the specific tools (surveys, dollar amounts, etc.) that can be used to evaluate the change; they are described in the sections that follow. This article will concentrate on the broadest three spheres (see Fig. l) as they pertain to large-scale nutrition education campaigns for low-income consumers. It should be noted that interventions involving the first three spheres may occur at the community, state, or national level. The two innermost spheres (see Fig. l),interpersonal and individual,are not discussed in as much depth because they are treated in more
+detail in the other articles of this supplement. Also, although nutrition education may ultimately need to be evaluated in terms of the effects of behavioral and dietary change, attention to the effects of nutrition education activities at the system, community, and organizational levels is important because changes at those levels can enable and reinforce changes at the individual level.
+Social structure, policy, and systems sphere of influence. The broadest level of influence in the Social-Ecological Model is social structure, policy, and systems. This sphere includes local, state, and federal policies that regulate or support organizational or individual behavior, including protection of or attention to children and special populations. Policy includes more than laws and regulations. The Food Guide Pyramid and U.S. Department ofAgriculture (USDA) guidelines for nutrition education in the Food Stamp Program are part of this level, and, in turn, they influence entire systems of service delivery and consumer communications. Organizational mission statements, position papers, and industry standards that are enforced administratively or followed voluntarily are other examples of policy decisions.15
+Policy changes tend to be the culmination of incremental steps. Policy decisions are affected by customs and traditions as well as situational improvisations and political negotiations.16 Approaches to policy and systems change often include the
+components of public education, policy-maker education, and advocacy.17-19 In addition to program-specific process measures, some theories, such as Crespi’s summary of the public opinion process, are useful for tracking policy and public opinion change as a measure of the social environment.18
+Indicators. Indicators of progress for policy change include process measures such as the amount and content of educational outreach by concerned groups and documentation of consistent advocacy over time, as well as descriptions of the political climate of policy makers and their constituents.19 Small steps such as the development of educational materials geared toward policy makers and of efficient methods of materials distribution are an example of one step of the complex process of policy change.An endpoint indicator of policy change is the policy document itself, such as a copy of a new law, regulation, or position statement; however, focusing only on the end result does not account for development.19 Should a policy be adopted, it is likely helpful to estimate the size of the population that will be affected.
+Changes can also result from interactions among individuals, organizations, and government, as suggested by Crespi.18 For instance, a community program in Wisconsin worked with the transportation agency to alter bus routes and improve service to local supermarkets in low-income neighborhoods.20 Similarly, in New Jersey, the nutrition education program
+negotiated a policy change that allowed the delivery of nutrition education to participants in state employment and training programs,7 thereby increasing outreach efforts.
+Measures. Measures of policy change will generally be descriptive owing to the nature and specificity of what is studied. Two examples of descriptive measures often applied are process measures and narrative case studies.
+Process measures. A tracking system for measures of longterm policy change might include the number of informational documents created and disseminated to educate constituents, requests from the public for information, requests for state budget appropriations, or the proportion of legislators, employers, or other gatekeepers who received educational information from constituents.Tracking can be useful to review the kinds of messages that are emphasized in advocacy efforts or to whom the efforts are targeted.
+Narrative case studies. Policy changes sometimes occur suddenly, such as after negative public exposure about a
+problem. These events may better be described using a narrative case study to capture the more subtle nuances of the political environment. “Suggestions for Writing a Success Story” (Table 2) is a template that can be useful in writing accounts of effective programs. This template is an example of a best-practices summary used by Food Stamp Nutrition Education Programs (FSNEPs) in Wisconsin.29 Useful measures include information on legislation or regulation that was passed and a description of how passage was secured.This information can be useful to others who want to effect similar changes. Case study research can also be conducted across programs to identify features associated with the implementation of nutrition education programs that deserve closer investigation.30
+Community sphere of influence. The community level includes social networks, norms, and standards that exist formally or informally among individuals, groups, partnerships, and organizations. Community-level theoretical models hold
+that collaboration is a process of participation through which people, groups, and organizations work together to achieve desired results.23 Models of community organization emphasize active participation by residents so that communities can better evaluate and solve health and social problems. Organizational change theories examine the process of health and nutrition promotion policies being adopted and institutionalized within formal organizations.
+Broad community support for nutrition education creates a more positive environment for behavior change and a shared commitment to improving the nutritional status of members of the local community. Furthermore, the greater the number and variety of community partnerships and the deeper the collaborations with these partners, the greater the learners’ access will be to education, nutritious low-priced food, and community recognition of nutritional success. An example of a community-level activity is the Maine Nutrition Network, which has been working with community farmers markets to allow redemption of food stamps to help increase access to low-cost fresh fruits and vegetables by low-
+income families. Analysis of redemption data found a 15% increase in the dollar value of food stamps redeemed at farmers markets and roadside stands.7
+Indicators. Community-level models are critical for comprehensive, multichannel health and nutrition promotion programs because they provide a framework for understanding how people interact, how social systems function, and how communities can be mobilized.23 In communications and marketing, “channels” are simply any means through which persuasive messages are delivered.22 Indicators of community-level change may include assessments of partner-ships,31 changes in social norms and the community environment, and documentation of social marketing activities (see Table 1).32
+Partnerships and coalitions. The number, type, depth, and strength of partnerships involved in the social marketing of nutrition education efforts can be important indicators of change.33 The greater the number and variety of community partnerships and the deeper the collaborations among these
+partners, the greater will be the exposure of target audiences to social marketing messages and affordable and nutritious food.33
+The nature and scope of partnerships, such as those formed by programs providing nutrition education to participants in the Food Stamp Program, are indicative of either an empowered or an engaged community. Kretzmann and McKnight asserted that the basic element of community organization is mobilizing communities to create associations and build community capacity to decide on a common problem, share in developing a plan to solve the problem, and take action to implement the problem-solving plan.34 Other models of community collaboration also propose a continuum of involvement starting with networks and progressing through to cooperation, coordination, and, finally, collaboration.24,35-39
+Social norms and the community environment. The community environment tempers the thoughts, values, mores, and actions of individuals. Social norms are guidelines that govern our thoughts, beliefs, and behaviors.40 Shared assumptions or norms of appropriate behavior are reflected in everything from laws to expectations and are manifestations of the prevailing social values within a community. In the tobacco control movement, for example, the normative change started in the social environment at the grass-roots, community level.41 The goal of an effort to change social norms is to create a social milieu and legal climate in which a particular behavior becomes more or less desirable, acceptable, or accessible. One means of influencing norm changes is through community organizing.42 With skilled leadership, efforts that start at the community level can be transferred to higher levels of government. For example, in Los Angeles, a grass-roots advocacy project assisted groups of parents to assess the nutrition and physical activity situation in their low-income community and take their concerns to their school board and state legislators. As a result, legislation has been introduced proposing significant improvements in schools and communities statewide.43 In Sacramento, California, advocates and parents were instrumental in the school board’s decision to reject a soft drink contract and to initiate a study of the districts’ nutrition and physical activity policies (Purcell A, personal communication with second author, September 18, 2000).
+Social marketing approaches: publicity, advertising, and public relations. Publicity via free news coverage gives visibility to a program, frames the program’s issues, and initiates conversation related to those issues. Free publicity, paid advertising, unpaid advertising through public service announcements (PSAs), public relations activities, and news editorial activities can help shape public opinion. Documenting the nature and frequency of these types of media coverage can indicate the importance of an issue to community members.
+Specifically, publicity has been described as advertising, informational messages, and promotional events,25 but conceptualizing it as free coverage in print and electronic media to distinguish it from other types of advertising has also been an effective approach for planning and evaluating large-scale social marketing programs.26 Paid and unpaid advertising, or
+PSAs, for electronic media are usually conceptualized as radio and television commercials.44 In the print medium, both paid and unpaid advertising can appear outside the home, such as on billboards, on transit (e.g., buses), or in newspapers.44
+Public relations is news and news media outreach activities about an issue or service that is not guaranteed to appear in print or electronic media but is likely to appear if the topic is newsworthy enough.27 Public relations activities are conducted to shape the content and type of news coverage.28 Public relations includes press conferences, community events covered by the media, media tours with trained spokespersons, deskside briefings, visits with editorial boards, feature articles, and the creation of educational materials for media use.
+Media promotions are often conducted using a combination of social marketing components. Spanish-language outlets in California, for example, employ multiple marketing elements, such as on-air promotion, live remotes, billboards, airing of PSAs during premium air times, preferential coverage during public affairs segments, interviews with media spokespersons, and tie-ins with station and community events to get health messages to the public.45 In Kent County, Michigan, a multimedia nutrition education campaign combined cable television advertisements, billboards, bus posters, newsletters, and take-home information on the back of school menus.This 3-month local campaign achieved higher unaided recall of related media messages in low-income neighborhoods than the national “Got Milk?” campaign, which had been airing for more than a year.46 The Kent County campaign specifically targeted low-income families, and aided recall of the nutrition campaign was significantly greater among households with incomes below $20,000 than among higher-income households (52% vs.40%,p < .05).46This suggests that carefully executed nutrition education media campaigns can effectively target low-income populations.
+Measures. Measures at the community level include measures of partnerships among organizations involved or interested in nutrition education efforts, measures of social norms of the community environment, and measures of social marketing activities.
+Partnerships. Measures important to assessing change in partnerships include the actual number and types of organizations in a partnership, the depth of relationship between and among partners, time and resource contributions by each partner, and the fiscal resources leveraged by each partner.
+Scales describing the depth of collaboration within partnerships build on work in community development.37,38 There is some evidence nationally from 22 nutrition education networks7 and from community-based programs in Illinois and Wisconsin33 that state or county nutrition networks with deeper degrees of partnership launched more extensive social marketing programs than networks with weaker part-nerships.As shown in Table 3, progress over time can be monitored in terms of the number and type of partners, the depth of partnerships, and the types of contributions that partners make toward a social marketing effort in nutrition.
+Contributions to the partnership relationship may focus on improving education, access to food, or public policy.The approximate dollar value of resources can include in-kind contributions—both in-kind contributions from governmental agencies used as state match and in-kind contributions from private organizations.
+The authors of this article helped adapt a partnership profile, originally developed by the National Collaboration Network and revised by Wilson.33 It summarizes the nature and scope of partnerships in a community and provides a standardized way to assess progress in developing nutrition promotion partnerships in communities, states, and the nation (Fig. 2). Collection of these data over time can help describe changes in maturation of partnerships, in access to nutrition education, and in allocation of resources (e.g., in-kind monies that are eligible for match vs. other in-kind monies). This type of measurement tool can show where partnership efforts should be targeted to yield the most benefit.
+The partnership profile grid (see Fig. 2) was used in 1998 with 2600 community partners in the Wisconsin and Illinois
+nutrition education programs.33 This qualitative study examined the depth of relationships among participating extension units and organizations.The Wisconsin program, which had been operating longer than the one in Illinois, had many more collaborators. Organizations participating for 2 or more years in Illinois and 3 or more years in Wisconsin were significantly (p < .05) more likely to provide access to food than organizations that had not participated as long.33 The results of this study indicate that organizations participating at the partnership, coalition, and collaboration levels of maturity provide significantly (p < .05) more monetary contributions per organization than those with less integrated partner-ships.33 These findings suggest that the investment in longterm relationships has very real benefits.
+In addition to gathering data on the strength of organizational relationships in a structured format such as the partnership profile, partners and programs can prepare qualitative narratives, data from which can be used to construct case studies of community action. Qualitative data are useful for explaining the why or how of research questions and can complement data collected in the partnership grid by clarifying how the partner collaboration developed and its perceived effect on a target audience. Case studies can focus on the partnership and program development and include specific documented changes in policy and behavior. For example, a case study may describe a program that has been very effective in reaching the elderly food stamp audience, detailing how the program formed partnerships with the Department of Aging to involve more participants in planning and food preparation of Title III© senior meals. Case studies allow sharing of lessons with other states and communities to help increase overall program impact.
+Social norms and the community environment. Measures of social norms can be both quantitative and qualitative. Measuring the community environment can emphasize geographic and social features that could influence specified outcomes. Perceptions of the structure of the community environment can be measured in surveys of community members and opinion leaders.8 Environmental assessment, such as community mapping or site observations, can measure how the community is structured to promote or inhibit behaviors.15,16 For example, mapping bus routes and supermarket locations, in comparison to major housing sites, provides a way to measure societal structures that can influence nutrition.The social community environment can be assessed from a community organizing perspective that considers community competence, empowerment, participation in issue selection, and raising of critical consciousness.24
+Social marketing components: publicity, advertising, and public relations. Because so many social marketing activities are implemented through the community, it is often a challenge to measure small-scale community-level or nonprofit-initiated campaigns in the same way as larger-scale commercial marketing. (Publicity in social marketing can be operationalized as the general amount of free news coverage that a program receives.) Advertising in a social marketing cam
+paign can include PSAs and paid advertising on television or radio. Commercial statistics of the viewing/listening audience for each media outlet are presented in measures known as media impressions, which estimate how many opportunities there were to see or hear a message.44 Commercial monitoring can also be used to estimate the level of exposure achieved by a nutrition education campaign in terms of “reach,” the proportion of a target audience that actually saw or heard a message, and “frequency,” the number of times members of a target audience saw or heard a message.25,44
+Public relations can be news oriented but supplemented by promotions or contests. Public relations through news outlets can be tracked by the number and nature of press releases or interviews. Electronic news coverage can be quantified by seconds of airtime and the dollar value estimated from the amount of coverage. Print news can include the number of news articles, inches of column space, and estimated circulation of the newspaper. Public relations activities with the public, such as giveaways, are measured in “hits” (e.g., the estimated number from the amount of materials disseminated) and reach.
+Institutional/organizational sphere of influence. The institutional/organizational level includes factors that influence organizational behavior in the private, public, and nonprofit sectors. Organizations or channels include businesses, schools, churches, public agencies, service organizations, and professional or trade associations that have common policies and procedures and reach large population segments. It is also at this level that many research-based intervention programs are available for settings such as worksites, schools, churches, and grocery stores. Examples of institutional-level programs include North Carolina’s Black Churches United for Better Health Research Project.47,48 A number ofAfrican-American churches in California adapted methods from the Black Churches Project and added interventions dealing with the media and public relations, farmers markets, buying clubs, food pantries, after-school programs, and community outreach.48 Also, in California, 10 retail chains worked with their competitors through the state health department to conduct seasonal 5 A Day promotions targeted to low-income shoppers in more than 1500 supermarkets and grocery stores.49 A community-based program in the Washington Heights area of New York City worked to persuade local groceries to stock low-fat milk in conjunction with a nutrition education effort to promote the use of low-fat dairy products.50 Depending on the organization that is targeted by an intervention and the type of intervention, theoretical models such as Diffusion of Inno-vation51 and Theories of Organizational Change52 may be applied at this level of influence.The social marketing components applicable to this level include advertising, publicity, and promotions.32
+Indicators. Indicators of institutional or organizational behavior change include adoption of new programs and
+policies and the effectiveness of new programs and policies. Institutional behavior can include activities that occur within an institution and the rules, regulations, policies, and informal structures that govern the behavior of people within the institution. Theories of institutional behavior often focus on the adoption of new programs. Many of these can be tracked through process measures such as documenting organizational efforts toward working with a nutrition program to promote its message by building awareness (Initiation of Action, Organizational Change Theory52), modifying the institution’s physical environment related to food and exercise (Implementation of Change, Organizational Change Theory52), and adopting policies that intentionally promote objectives of nutrition education in the Food Stamp Program (outcomes, Organizational Change Theory52).
+Indicators of program effectiveness at the institutional level are usually specific to the intervention being tested. An example of an outcome evaluation of an institution-level program is evaluation of a retail grocery store promotion featuring interactive kiosks in the produce section of supermarkets serving low-income populations in Arizona and California.This evaluation measured the percentage increase in produce sales and found significant increases in purchases of fruits and vegetables during the months the kiosks and interactive promotions were used compared with the months when they were not.49 This study demonstrated the effectiveness of the interactive promotions in increasing produce sales.
+Indicators of social marketing activities at the institutional level are similar to those used at the community level and include advertising, publicity, and promotions. These social marketing components can be implemented at the institutional level and be part of larger health promotion projects. Documentation of the number of nutrition education events or promotions and feedback from participants, gatekeepers, and intermediaries are other possible approaches to program evaluation at this level.
+Measures. A wide variety of methods and measures can be used to monitor institutional change in research settings.5 Practical measures of program adoption in applied settings tend to be process oriented and can be guided by constructs of institutional behavior theories. For example, the constructs of Organizational Change52 can be operationalized as steps toward an institutionalized program. Other examples of channel-specific measures for adoption include assessing the number of farmers markets that accept food stamps or EBT cards or the number and amount of participation in food safety training for volunteers working at local food pantries. Measures of program effectiveness should be specific to the evaluation question, but important questions to ask are whether the program changed dietary intake behavior, purchasing behavior, food safety behavior, or some other behavioral outcome. For example, institutional behavior change measures of effectiveness for implementing EBT cards at
+farmers markets would be the rate at which the markets adopted and the rate at which people used them.
+Measures of the social marketing components can include documenting the number of locations and prevalence of cues to action at point-of-choice locations (advertising), displays and demonstrations (public relations), and price promotions such as sales, in-store managers’ specials, and cents-off coupons (promotions).The number of geographic locations and descriptive statistics of program activities will provide estimates of the reach of an intervention. Measures of implementation are important to programs, but measures of outcome should also be assessed when feasible.
+Interpersonal sphere of influence. The interpersonal level of influence includes primary groups, such as peers, family, and friends, that provide social identity, support, role delineation, and interaction for an individual.15 Individuals exist in a dynamic social environment in which the attitudes and actions of others influence an individual’s behavior. Examples of theories at the interpersonal level include Social Cognitive Theory, which posits that people and environments continuously interact to form social meaning.1 The social marketing component (most likely to be applicable) is the delivery of one-on-one or small-group nutrition education.
+Indicators. Assessment of individual attitudes and other variables based on the constructs of Social Cognitive Theory or other interpersonal level theories can be used to indicate program effectiveness. Constructs of Social Cognitive Theory that could be measured include behavioral capability (the knowledge and skills to influence behavior), expectations (the beliefs about the likely results of actions), self-efficacy, observational learning, and reinforcement.1 Indicators of interpersonal influence include the type and quantity of nutrition education that is delivered.
+Measures. Interpersonal indicators are commonly measured by surveys, which may be designed for statewide monitoring, targeted to a specific population, or incorporated into program evaluation forms. Survey questions may be designed to assess an individual’s interaction with others or the perception of the immediate social environment. Repeated surveys can measure changes over time. Specific examples of survey items are “I eat fruits and vegetables because I want to set a good example for my family” (modeling) and “There are a lot of fruits and vegetables I don’t know how to fix” (behavioral capability). Survey items like these, combined with dietary assessment, help monitor the intermediate effects of a program.
+On a direct service level, the reach of a program can be assessed by monitoring the types, amount, consumer attendance, and geographic distribution of materials and the number of people contacted through these materials and programs. Tracking total classes offered or attendance can quantify the educational interaction. Assessing the content of curricula and messages will determine which messages are most frequently promoted and identify reasonable expecta
+tions for program effectiveness. Instruments may include peer educator logs, descriptive reports of intervention activities, attendance counts, and key informant interviews with opinion leaders or gatekeepers.
+Individual sphere of influence. The individual level is the most specific level of influence.This level focuses on expressed behavior choices and psychological and cognitive factors such as knowledge, attitudes, beliefs, and personality traits. Measurement can be informed by theories that examine behavior change at the individual level, such as the Transtheoretical Model, commonly called Stages of Change,53 which has been used by state nutrition programs, such as the Maryland Special Supplemental Nutrition Program for Women, Infants and Children (WIC),54 to develop targeted consumer messages. Other theories that focus on the individual tend to be rational choice models, such as the Health Belief Model.55 A large amount of health education research has focused on the individual, and numerous other theoretical foundations that are appropriate for this level are in the published literature.
+Indicators. At the individual level, indicators reflect the cognitive decisions and thought processes that occur within the mind of an individual (such as knowledge, beliefs, attitudes, cues to action, perceived barriers and benefits) and that are associated with an individual’s behavior. The Transtheoretical Model identifies five stages of psychological readiness to adopt a new behavior, ranging from precontemplation (not aware of the need to change) to “maintenance” of a behavioral change over time.53 Typically, the stage of readiness is identified through an initial assessment and then used to tailor intervention messages, materials, and skill building to the individual. Other examples of attitudinal indicators from Social Cognitive Theory or the Health BeliefModel include perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action.55 When applied to nutrition education, one, some, or all of these indicators can be useful for understanding the individual’s perceptions of food, nutrition, and disease, depending on the program focus and available resources.
+Measures. Individual indicators are readily measured by surveys, interviews, or other assessments of individual behavior. Operationalizing a theoretical construct in a survey question or a module of questions is one way of assessing that construct in an empirical manner. For example, the Maryland WIC 5 A Day Program used a survey question module to determine an individual’s stage of readiness for eating five or more fruits and vegetables a day and for eating two or more servings a day.54 In California, an item on the California Dietary Practices Survey56 to determine perceived severity of a poor diet is “What I eat or drink will not make any difference in whether or not I get cancer.”
+Although antecedents to behavior can be easier to assess, behavioral change itself is the ultimately desired outcome of nutrition education programs, including the behaviors associ
+ated with food resource management, food safety, dietary quality, and food security. Behavioral antecedents can provide early indicators of program effectiveness. However, evaluations are typically most persuasive when they demonstrate changes in actual behavior.Telephone and self-administered surveys can often be used to gather information. The efficiency of these methods and the ability to replicate them across large populations can make them an attractive method of data collection. However, the reliability and validity of measures should be considered when designing survey data collection from low-income populations. Qualitative research approaches, such as group or individual interviews, may also be used to gather more in-depth data than are available from a survey.
+IMPLICATIONS FOR RESEARCH AND PRACTICE
+The magnitude of social and environmental change needed to make and sustain healthy eating and a physically active lifestyle can be profound, and the challenge of eliminating disparities experienced by low-income individuals can be even more daunting. System, environmental, and policy changes at local, state, and national levels may occur slowly, but research from tobacco control suggests that attention to these levels of influence is necessary when individual and interpersonal behaviors are not enough to overcome negative environmental influences.8 Since such changes make healthier living easier for large numbers of people, they are also very efficient and ultimately may be the only way to sustain healthful environmental and behavior change in a dynamic, competitive marketplace environment.
+Historically, nutrition policy has been driven at the national rather than state or local level, so there is relatively little experience and a very small body of literature dealing with factors and strategies that influence systems and environmental and policy change at the state and local levels. For this reason, as well as the urgency of correcting widespread nutrition and physical activity problems, nutrition education in the Food Stamp Program is addressing promising areas of intervention activity. Applying an integrative framework such as the Social-Ecological Model in a disciplined manner holds tremendous potential for assessing the effects of nutrition education and social marketing activities, improving the quality of programs, and accelerating needed public health change. This will be especially critical if it turns out, as it did for tobacco control, that diverse local community projects become the engine of larger-scale change.41
+That said, evaluating programs from a social-ecological approach presents a number of challenges. It will be important to assess the reliability, validity, and ease of data collection within each of these levels of influence. A particularly exciting research challenge will be to understand the relationships and capture the synergy among the various levels. For instance, although it is logical to think that nutrition partnerships promote system, environmental, and policy change and assist in the delivery of nutrition education, their
+potential for fostering change is not yet known.When quantitative data are available, statistical approaches that can examine one component nested within a larger component, such as hierarchical modeling or network analysis, may be particularly useful for understanding the relationships between levels.
+Exploring the possibilities for evaluating multilevel programs through the Social-Ecological Model is the first step toward developing a universal reporting system that will provide comparable data from different states related to each level of the model. The next step will be the challenge of developing ways to synthesize information from multiple sources to draw conclusions that are broad enough for generalization yet specific enough to be useful to federal, state, and community stakeholders.
+S14
+Gregson et al./SOCIAL-ECOLOGICAL MODEL FOR EVALUATING NUTRITION EDUCATION
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+16.	U.S. Department of Health and Human Services. Healthy People 2010: understanding and improving health.Washington, DC: Government Printing Office, 2000.
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+20.	Jones L.Wisconsin nutrition education program annual report: 1998. Madi-son,WI: University of Wisconsin Cooperative Extension Service, 1998.
+21.	Leiderman SA, Dupree DM. Project change evaluation research brief. Albuquerque, NM: Center for Assessment and Policy Development, 2000.
+22.	McGuire WJ. Theoretical foundations of campaigns. In: Rice RE, Atkin CK, eds. Public communication campaigns. Newbury Park, CA: Sage, 1989:39-43.
+23.	London S. Collaboration and community. Pew Partnership for Civic Change [online], 1995-2001.Available at http://www.scottlondon.com/ reports/ppcc.html.
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+32.	Walsh DC, Rudd RE, Moeykens BA, Moloney TW. Social marketing for public health. Health Affairs 1993;12:104-19.
+33.	Wilson L. Organizational relationship through the Family Nutrition Program as affected by time and rural and urban settings. Doctoral dissertation, University of Illinois, Urbana, IL, 1999.
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+45.	Backman D, Sotello-Armijo R, Fierro MP. Evaluation study of Latinospecific social marketing activities using mass media, festival, and farmers’ market/flea market interventions. Centers for Disease Control and Prevention, Grants to Evaluate State Nutrition Programs, Grant #H75/CCH917500-01.Sacramento, CA: Cancer Prevention and Nutrition Section, Public Health Institute, 1999.
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+47.	Black Churches United for Better Health 5 A Day Project, North Carolina Department of Health and Human Services, Division of Public Health, Chronic Disease Prevention and Control. Available at http:// www.communityhealth.dhhs.state.nc.us/hlthprom/fiveaday.htm.
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+52.	Goodman RM, Steckler AB. Mobilizing organizations for health enhancement: theories of organizational change. In: Glanz K, Lewis FM, Rimer B, eds. Health behavior and health education. San Francisco, CA:Jossey-Bass, 1990:314-41.
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+54.	Feldman RH, Damron D,Anliker J, et al.The effect of the Maryland WIC 5 A Day promotion program on participants’ stages of change for fruit and vegetable consumption. Health Educ Behav 2000; 27:649-663.
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+Approximately one million individuals worldwide died by suicide in 2000, and estimates suggest that 10 to 20 times more individuals attempted suicide (World Health Organization, 2008). Only two interventions have been shown to prevent deaths by suicide (Fleischmann et al., 2008; Motto & Bostrom, 2001), and only one form of psychotherapy has been shown to prevent suicide attempts in more than one clinical trial (Linehan et al., 2006). Why is the state of knowledge for such a devastating psychological phenomenon relatively lacking?
+One answer may be that suicidal behavior is difficult to study (for a discussion of this issue, see Prinstein, 2008). First, very large samples are needed because the base rates of suicide attempts and deaths are low in the general population (Moscicki, 2001). Second, individuals with suicidal behaviors are often excluded from clin-
+ical trials due to safety concerns on the part of researchers (Rudd, Joiner, & Rajab, 2001). Finally, individuals who die by suicide are not available for psychological assessments, thus limiting methods researchers can use.
+Another explanation may lie with the status of theory in the suicide literature. Prinstein (2008) noted,
+few theoretical models have been offered to help understand selfinjury in the manner that other manifestations of psychopathology have been examined. In particular, few studies have considered integrative models that address interplay between dynamic systems within the individual and between individuals and their environments. (p. 2)
+Thus, another explanation for the relatively low number of empirical advances in understanding the causes and correlates of suicide, as well as methods for suicide prevention, may be the absence of a theory that can comprehensively explain known facts about suicide, as well as reliably and precisely identify risk for future occurrences of suicidal behavior.
+Here, we propose the interpersonal theory of suicide to explain heretofore unexplained facts about suicide and to increase our understanding of the etiology of suicide. Briefly, according to the theory, the most dangerous form of suicidal desire is caused by the simultaneous presence of two interpersonal constructs—thwarted belongingness and perceived burdensomeness—and further that the capability to engage in suicidal behavior is separate from the desire to engage in suicidal behavior. The model is depicted graphically in Figure 1, with the relatively small area of overlap in the Venn diagram representing the small minority of individuals who possess both desire and capability for suicide. In the current article, the theory’s hypotheses are more precisely delineated than in previous presentations (Joiner, 2005), with the aim of inviting
+scientific inquiry and potential falsification of the theory’s hypotheses. We first ground our theory in the context of previous empirical research and theorizing on suicidal behavior. In reviewing and integrating the literature, we reflect on the status of theory regarding suicidal behavior and indicate areas of relative weakness. Next, we describe our interpersonal theory of suicide and indicate how we address (or fail to address) areas of need delineated above. Finally, we close with future directions for theory-based research on suicidal behavior.
+Defining Suicidal Behavior
+An ongoing task for the field involves refining definitions of key suicide-related constructs to increase precision of measurement and standardize usage of terms across studies (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007a). Our discussion below draws on—and is consistent with—a recently revised nomenclature (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007b), which posits that suicide-related behaviors (previously referred to as suicidality) can be classified as ideations (i.e., thoughts), communications, and behaviors. The authors of the nomenclature further posit that all suicide-related behaviors are self-initiated. Further, these behaviors can vary in terms of the presence or absence of intent to die and the presence or absence of physical injury sustained. In the absence of intent to die, the term self-harm is used (e.g., self-cutting in the service of emotion regulation). As the current theory is concerned with ideations, communications, and behaviors that involve some degree of intent to die, we use the term suicidal behavior rather than suicide-related behaviors. Our primary focus in the current theoretical account is on near-lethal and lethal suicide attempts. The nomenclature states that suicide attempts possess the following qualities: (a) self-initiated, potentially injurious behavior; (b) presence of intent to die; and (c) nonfatal outcome. The term suicide is reserved for those cases in which a suicide attempt results in death. As this distinction is potentially confusing, whenever possible, we refer to nonlethal
+suicide attempts (i.e., suicide attempts with nonfatal outcomes) versus lethal suicide attempts (i.e., suicide attempts with fatal outcomes), with the latter term synonymous with deaths by suicide. The theory defines serious suicidal behavior as lethal and near-lethal attempts. Near-lethal attempts are a type of nonlethal suicide attempts (i.e., death does not result) for which the person presumably survived by chance (e.g., individuals do, on occasion, survive jumps from the Golden Gate bridge).
+One could argue that a phenomenon is not yet ready to be studied empirically until all definitional issues have been addressed. We take a different stance and believe that theory-driven, empirical research could—and should—be used to inform and define the nomenclature and taxonomy of suicide-related behaviors. The definitional issues reviewed above regarding distinctions among ideations, attempts, and deaths highlight the multidimensional nature of suicide and suicide-related behaviors (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007a).
+The value of theory-driven investigations of the taxonomy of suicidal behavior becomes clear on consideration of the prevalence of suicidal behavior. Arguably the most difficult aspect of the prediction of suicidal behavior is the finding replicated worldwide and over time: Only a small subset of those who think about suicide will go on to attempt, and even fewer will die by suicide (World Health Organization, 1998). Both suicidal ideation and nonlethal attempts are vastly more common than lethal attempts. As described in more detail below, current theories of suicidal behavior are inconsistent with this striking aspect of the phenomenon of suicidal behavior and instead address the phenomenology of suicidal behavior broadly, as a unitary construct. Without clearly differentiating among thoughts about suicide, nonfatal suicidal behaviors, and fatal suicidal behaviors, current theories are inconsistent with the taxonomy of suicidal behavior. This results in a number of theories being overly sensitive in the prediction of suicidal behavior at a severe cost in specificity (see Empirical and Theoretical Foundations section below).
+In defining a phenomenon, the boundaries of the construct must also be considered—what is, and what is not, suicidal behavior? Our theory is primarily concerned with what might be termed the prototype of suicide-related behaviors—near-lethal and lethal suicide attempts. At the same time, our theory assumes that a core set of processes and conditions underlie near-lethal and lethal suicide attempts and that these processes are operative, to varying degrees, in other suicide-related behaviors on the periphery of the construct. The extent to which our assumption of core processes in suicidal behavior is supported may help delineate the boundaries of suicidal behavior, thus indicating a role for theory in clarifying definitions and taxonomies.
+Empirical and Theoretical Foundations
+Risk factors are variables that are associated with an increased probability that an outcome will occur, whereas causal processes explain an outcome. However, when a risk factor is shown to temporally precede the outcome and is shown to be nonspurious, a causal relation may be present (Wagner, 1997). In addition, risk factors can be conceptualized as indicators of underlying causal processes that lead to outcomes (Cicchetti & Cohen, 1995); in this way, examining risk factors can be viewed as a stepping-stone to the construction of etiological models. Finally, a useful theory of
+suicide must be consistent with—and able to account for— empirically documented risk factors for suicide.
+In the section that follows, we examine the literature on risk factors for suicidal behavior and follow it with a discussion of theoretical perspectives. As practicalities limit the inclusion of all variables associated with suicidal behavior, we limit our discussion to those risk factors shown to be associated with increased risk for lethal suicidal behavior, as this is the primary outcome of the theory. Table 1 lists these risk factors, grouped by the degree of support that has been found for the association between each factor and lethal suicidal behavior (i.e., number of studies documenting such an association). The literature indicates the most consistent and robust support for the following as risk factors for suicide: mental disorder, past suicide attempts, social isolation, family conflict, unemployment, and physical illness. However, the literature also indicates the presence of other risk factors for suicide, which are also listed in Table 1. Some of these risk factors with less empirical support may in fact be robust predictors of suicide but have not yet been studied as frequently as other risk factors (e.g., there is consensus in the field that hopelessness is an important predictor of suicide), thus we discuss these risk factors as well, but in relatively less depth.
+Family Conflict
+Indices of family conflict are robust risk factors for lethal suicidal behavior across the lifespan, with studies listed in Table 1 documenting associations among suicide and familial discord, domestic violence, familial stress, and perceptions that one is a burden on one’s family.
+Mental Disorders
+The vast majority of people who die by suicide (i.e., approximately 95%) suffer from mental disorders (Cavanagh, Carson, Sharpe, & Lawrie, 2003)—and it is quite possible that the remaining 5% suffer from subclinical variants of mental disorders or presentations of disorders not detected by methodologies such as psychological autopsies (Ernst et al., 2004). In addition, certain mental disorders confer higher risk for suicidal behavior than others. The studies listed in Table 1 indicate that the following disorders are associated with particularly elevated rates of suicide: major depressive disorder, with suicide rates between 2% and 6% (Bostwick & Pankratz, 2000); bipolar disorder, with estimates suggesting a 15-fold increased risk for suicide (Harris & Barra-clough, 1997); borderline personality disorder, with suicide rates between 4% and 5% (Duberstein & Witte, 2008); anorexia nervosa (AN), with a suicide rate 58 times that which was expected (Herzog et al., 2000); schizophrenia, with suicide rates between 1.8% and 5.6% (Palmer, Pankratz, & Bostwick, 2005); substance abuse, with a suicide rate 5.7 times that of the general population (Harris & Barraclough, 1997); and conduct disorder in youth, with a sixfold increase in risk for suicide, compared with community controls.
+However, the studies listed in Table 1 also indicate that the vast majority of individuals diagnosed with the above disorders do not die by suicide. For example, among depressed individuals, approximately one quarter make a nonfatal suicide attempt during their lifetime (Verona, Sachs-Ericsson, & Joiner, 2004), and at least
+25% report having experienced suicidal ideation in the past 2 weeks (Goldney, Dal Grande, Fisher, & Wilson, 2003). Further, although depression greatly increases risk for suicidal ideation, it does not increase risk for suicide attempts above and beyond its association with ideation (Nock, Hwang, Sampson, Kessler, An-germeyer, et al., 2009). Disorders with anxiety and agitation as characteristic symptoms (i.e., generalized anxiety disorder, social phobia, posttraumatic stress disorder, bipolar disorder) or impulsecontrol deficits as characteristic symptoms (i.e., conduct disorder, intermittent explosive disorder, substance use disorders) are predictive of the transition from suicidal ideation to nonlethal suicide attempts (Nock, Hwang, Sampson, & Kessler, 2009). These data indicate that depression is likely associated with the development of desire for suicide, whereas other disorders, marked by agitation or impulse control deficits, are associated with increased likelihood of acting on suicidal thoughts.
+Previous Suicide Attempt(s)
+The studies listed in Table 1 indicate that one of the most reliable and potent predictors of future suicidal ideation, attempts, and death by suicide across the lifespan is having a prior history of this type of behavior. The presence of multiple past attempts is an especially strong predictor of lethal suicidal behavior in both adolescents (Kotila & Lonnqvist, 1987) and adults (Christiansen & Jensen, 2007; Haw, Bergen, Casey, & Hawton, 2007; Suominen, Isometsa, Haukka, & Lonnqvist, 2004; Zonda, 2006), as is a previous attempt with high medical lethality (S. J. Gibb, Beautrais, & Fergusson, 2005). A 37-year longitudinal study indicated that the elevation in risk for lethal suicidal behavior conferred by a history of a previous attempt persists over the lifetime (Suominen, Isometsa, Suokas, et al., 2004).
+Physical Illness
+A review by Whitlock (1986) demonstrated that more than one third of people who die by suicide had a medical illness at the time of their death, and numerous studies (listed in Table 1) have documented a relation between the presence of physical illness and suicide. However, the majority of medical illnesses do not appear to actually increase risk for suicide, including rheumatoid arthritis, diabetes, and hypertension (Harris & Barraclough, 1997; Stenager & Stenager, 1992). One disease with a particularly high risk for suicide is HIV-AIDS, which has been shown to confer approximately a sevenfold risk for suicide as compared with the general population (Conwell, 1994; Harris & Barraclough, 1997). Another illness that appears to confer suicide risk is brain cancer, which has a ninefold increased risk for suicide as compared with the general population and a fourfold risk as compared with individuals with other forms of cancer (Harris & Barraclough, 1997). Finally, amyotrophic lateral sclerosis has an estimated sixfold increased risk (Fang et al., 2008), and multiple sclerosis has a twofold risk (Harris & Barraclough, 1997).
+The relationship between physical illness and suicide is likely indirect and accounted for by a multitude of other risk factors, including comorbid mental disorders (O’Mahony, Goulet et al., 2005; Rasic, Belik, Bolton, Chochinov, & Sareen, 2008), functional limitations (R. D. Goodwin, Marusic, & Hoven, 2003; Kaplan, McFarland, Huguet, & Newsom, 2007), and social isola-
+tion (Carrico et al., 2007; Flavin, Franklin, & Frances, 1986; Rasic et al., 2008). As one example, among patients with end-stage cancer, the will to live was negatively associated with patients’ apperceptions of burdensomeness on others and was positively associated with perceived social support (Chochinov et al., 2005).
+Social Isolation
+Social isolation is arguably the strongest and most reliable predictor of suicidal ideation, attempts, and lethal suicidal behavior among samples varying in age, nationality, and clinical severity (Conwell, 1997; Dervic, Brent, & Oquendo, 2008; Joiner & Van Orden, 2008; Trout, 1980). Numerous empirical studies (listed in Table 1) have demonstrated associations between lethal suicidal behavior and various facets of social isolation, including loneliness, social withdrawal, living alone and having few social supports, living in nonintact families, losing a spouse through death or divorce, and residing in a single prison cell. In contrast, marriage, children, and a greater number of friends and/or family are associated with decreased risk for lethal suicidal behavior.
+Unemployment
+Numerous studies (listed in Table 1) have demonstrated that unemployment is a common factor among individuals who have died by suicide and is associated with elevated risk for lethal suicidal behavior. However, several studies examining associations between unemployment and suicide rates at the population level have failed to show an association, whereas studies examining smaller, more homogeneous populations (that also tend to be higher risk samples) tend to show an association (Lester & Yang, 2003; Stack, 2000). This pattern of findings indicates that many individuals who die by suicide are unemployed and that the vast majority of unemployed individuals do not die by suicide. Thus, it may be that unemployment is associated with elevated risk only among vulnerable individuals or only when it results in certain negative outcomes. Similarly, economic recessions are associated with increased suicide rates, but only those recessions with marked elevations in negative outcomes such as job losses and home foreclosures (American Association of Suicidology, 2009).
+Other Risk Factors
+Several warning signs for suicide (i.e., signs of acute risk) have empirically demonstrated associations with lethal suicidal behav
+ior—agitation, hopelessness, and sleep disturbances including nightmares (see Table 1 for references). Consistent and robust support has been found for hopelessness and sleep disturbances. Less research has been conducted on agitation, but available data indicate that agitation is a pernicious sign of elevated risk. Several severely stressful life events are associated with elevated risk for lethal suicidal attempts: studies listed in Table 1 indicate that individuals who have experienced childhood abuse, military combat, homelessness, and incarceration are at elevated risk for lethal suicidal behavior. The studies listed in Table 1 also include several psychiatric and medical history variables that are associated with elevated risk for lethal suicidal behavior. Finally, the literature also indicates the presence of environmental factors that are associated with elevated risk for suicide, including easy access to lethal means, clustering or exposure to suicidal behavior, and seasonal variation in rates of suicidal behavior, as well as increases in connectedness through pulling together effects that are protective against suicide.
+Theoretical Perspectives
+Although much research on suicidal behavior has been conducted in an atheoretical context, theories of suicide spanning diverse perspectives—including biological, psychodynamic, cognitive-behavioral, and developmental and systems etiologies— have been proposed. In biological theories, it is proposed that suicidal behavior results from the dual presence of a biologically based diathesis (such as dysregulation of the serotonergic system in the ventromedial prefrontal cortex) and an activating psychosocial stressor (Mann, 2003; Plutchik, Van Praag, & Conte, 1989; van Pragg, 2001). In psychodynamic theories, it is proposed that suicide is caused by unconscious drives (Menninger, 1938), intense affective states (Hendin, 1991), desire for escape from psychological pain (Baumeister, 1990; Shneidman, 1998), existential drives for meaning (Rogers, 2001), and disturbed attachment (Bowlby, 1973). Cognitive- behavioral theories posit causal roles for hopelessness (Beck, Brown, Berchick, Stewart, & Steer, 1990; Beck, Steer, Kovacs, & Garrison, 1985), the suicidal cognitive mode (Beck, 1996; Rudd, Joiner, & Rajab, 2001), autobiographical memory deficits and perceptions of entrapment (Williams, 2001; Williams, Van der Does, Barnhofer, Crane, & Segal, 2008), and emotion dysregulation (Linehan, 1993). Developmental/systems theories posit causal roles for disturbed social forces (Durkheim, 1897) and family systems (Richman, 1986; Sabbath, 1969).
+Each of these theories is able to explain part of the landscape of suicidal behavior. For example, theories on hopelessness are readily able to account for the relationship between hopelessness and later death by suicide, biological theories are able to explain the association between serotonergic functioning and suicide, and family systems theories are able to explain the association between family conflict and suicide. However, individuals who die by suicide present with numerous risk factors, rather than a single risk factor in isolation (Maris, Berman, & Maltsberger, 1992). Thus, theories of suicide should be able to account for the diverse array of factors associated with lethal suicidal behavior. Our review of risk factors indicates the most robust support for associations with suicide and mental disorders, previous suicide attempts, social isolation, family conflict, unemployment, and physical illnesses. Thus, a theory of suicide should illuminate how these diverse factors are related to suicidal behavior.
+Theories must also be able to account for the imprecision of the risk factors listed above—each risk factor in isolation is limited as a predictor of suicide, and each risk factor has complex relations with suicidal behavior. To take a couple of prominent examples, a history of past attempts (see references in Table 1), especially multiple past attempts (Christiansen & Jensen, 2007; Haw et al., 2007; Kotila & Lonnqvist, 1987; Suominen, Isometsa, Haukka, & Lonnqvist, 2004; Zonda, 2006), is a robust predictor of death by suicide, yet many individuals who die by suicide do so on their first attempt (i.e., up to half; Rudd, Joiner, & Rajab, 1996). The vast majority of those who die by suicide suffer from mental disorders (Cavanagh et al., 2003); however, the vast majority of those with mental disorders, including disorders with the highest suicide rates, will not attempt or die by suicide, though many will think about suicide (Bostwick & Pankratz, 2000; Hawton, Sutton, Haw, Sinclair, & Harriss, 2005; Herzog et al., 2000; Palmer et al., 2005).
+A comprehensive theory of suicidal behavior must also be able to account for other demographic differences in suicide rates, including that suicide rates vary by gender, age, and culture. One of the most consistent findings with regards to the epidemiology of suicidal behavior is its gender distribution. With the exception of China, male suicides outnumber female suicides in every nation, worldwide (World Health Organization, 2003). In the United States, the ratio of male to female deaths by suicide is 4 to 1, whereas for nonlethal attempts, female attempts outnumber male attempts at a 3 to 1 ratio (American Association of Suicidology, 2006). Nonlethal suicide attempts greatly outnumber lethal attempts (at a ratio of 25 to 1), which means that every day, far more women and girls than men and boys are engaging in (ultimately nonlethal) suicidal behavior. These data indicate that although women die by suicide at much lower rates than do men, it is more common for women to desire and thus attempt suicide. Suicide rates also vary by age, with the highest suicide rate (in the United States) among the elderly (Gould, Shaffer, & Greenberg, 2003). However, suicidal behavior does occur in children: In 2003, 250 children between the ages of 5 years and 14 years died by suicide (Hoyert, Heron, Murphy, & Hsiang-Ching, 2006). Suicide rates have also been found to differ by race and ethnicity. In the United States, European Americans die by suicide approximately twice as often as members of other minority groups, with the exception of Native Americans (American Association of Suicidology, 2006). Suicide rates also vary by occupation, with, for instance, female
+physicians having a particularly elevated rate compared with the general population (Lindeman, Laara, Hakko, & Lonnqvist, 1996).
+Finally, the observation about suicidal behavior that is arguably the most difficult to grapple with theoretically is the fact that only a small subset of those who think about suicide go on to attempt it, and even fewer will die by suicide (World Health Organization, 2008). Estimates from nationally representative studies indicate that each year, 3.3% of Americans seriously consider suicide (i.e., active suicidal ideation), 1.0% develop a plan for suicide, and 0.6% attempt suicide (Kessler, Berglund, Borges, Nock, & Wang, 2005). Yet, each year, only 0.01% of Americans die by suicide (American Association of Suicidology, 2006). It is this observation about suicide that underlies many of the limitations of the risk factors listed above. At a minimum, theories of suicide must be able to account for these data on the epidemiology of suicidal behavior and those factors shown to be associated with elevated risk for suicide. Optimally, theories could build on this empirical foundation by delineating putative etiological connections among risk factors and the outcome of interest, suicidal behavior.
+Several comprehensive models of suicide have been presented that can account for several risk factors simultaneously, as well as the prevalence of suicidal behavior. These models are structured to describe (i.e., model) suicidal behavior, compared with theories that are structured to explain (i.e., predict) suicidal behavior. Blumenthal and Kupfer (1986) propose that suicidal behavior results from the joint presence of risk across five domains: biology, psychosocial life events and chronic medical illness, personality traits, family history and genetics, and psychiatric disorder. This model can be graphically depicted as a Venn diagram with five overlapping circles, with the greatest risk for suicide represented by the area of overlap from all five circles. Maris (1991, 2002) proposes a comprehensive model of suicidal behavior from a developmental (or life-course) perspective that emphasizes the study of multiple interacting factors within life histories of individuals who die by suicide, which he denotes as suicidal careers. The interaction of factors across several domains of risk (including time) allows these models to account for differential prevalence of suicide ideation, nonlethal attempts, and deaths by suicide, with the assumption that deaths by suicide occur at the intersection of numerous facets of risk, that nonlethal attempts occur with fewer facets of risk, and that ideation occurs with even fewer.
+However, although the models described above are comprehensive and thereby able to account for the prevalence of suicidal behavior, they are not structured with a degree of precision that would allow for the falsification of the model and the prediction of suicidal behavior. Thus, what is needed to improve prediction of suicidal behavior is a theory that is both precise—allowing scientific falsifiability and clinical usefulness—and comprehensive— allowing the theory to account for both suicidal ideation and suicide attempts. Shneidman’s (1998) psychache theory involves the proposals that the simultaneous presence of three factors is necessary for lethal suicidal behavior to occur—psychache, press, and perturbation—and that the presence of these factors will create the strongest, and most lethal, level of desire for suicide. It is this assumption about suicidal behavior—that individuals who think about suicide versus those who attempt suicide differ in terms of how much they desire suicide—that is challenged by the interpersonal theory of suicide. It is this level of empirical precision and
+opposing hypotheses open to falsification that is needed to advance the scientific study of suicidal behavior.
+Constructs of the Interpersonal Theory of Suicide
+The foundation of the interpersonal theory, as discussed above, is the assumption that people die by suicide because they can and because they want to (Figure 1). Within the framework of our theory, three constructs are central to suicidal behavior, two primarily related to suicidal desire—thwarted belongingness and perceived burdensomeness—and one primarily related to capability— acquired capability for suicide. The theory also includes a specification of the relations between these constructs in the form of four hypotheses (listed in Table 2) and thereby includes a specification of a causal pathway for the development of the desire for suicide and the capability to engage in serious suicidal behavior (i.e., lethal or near-lethal attempts). Below, we describe both the theory’s constructs and its hypotheses with a level of detail that opens it to possible falsification and invites tests of its hypotheses and comparisons with other theories of suicidal behavior.
+We begin by describing the constructs of thwarted belongingness, perceived burdensomeness, and acquired capability. To do so, we use Figures 2-4, each of which graphically depicts components of the constructs, as well as relations with the empirically demonstrated risk factors discussed in the previous section. The figures feature many conventions of structural equation modeling for ease of interpretation, including that latent variables are denoted by circular shapes and observable variables are denoted by rectangles. In addition, hierarchical latent variables “cause” the lower level latent variables (as well as observable variables) they are connected to; thus, arrows point from the latent variable to the observable and lower level latent variable. In contrast, emergent variables (i.e., those caused by variables depicted earlier in the causal chain of the model) are depicted with causal arrows pointing toward them from other variables. To illustrate how individuals experiencing these constructs might describe their experiences, sample items from self-report measures designed to measure the constructs (i.e., the Interpersonal Needs Questionnaire for thwarted
+belongingness and perceived burdensomeness and the Acquired Capability for Suicide Scale for the acquired capability) are included in italics within the circles representing the constructs. The behavioral (observable) indicators of the constructs in these figures are the empirically supported risk factors for lethal suicidal behavior (discussed above) that according to the theory’s definitions of the constructs represent behavioral indicators of thwarted belongingness, perceived burdensomeness, or acquired capability.
+Thwarted Belongingness
+As noted above, social isolation is one of the strongest and most reliable predictors of suicidal ideation, attempts, and lethal suicidal behavior across the lifespan. Social isolation can be conceptualized as measuring one facet of the higher order construct of social connectedness (or social integration), which can be measured at multiple levels (Berkman, Glass, Brissette, & Seeman, 2000). Our review also indicated that other facets of social connectedness (e.g., loneliness and loss of a spouse) are also predictive of lethal suicidal behavior. We propose that these social connectedness variables are associated with suicide because they are observable indicators that a fundamental human psychological need is unmet; this need is described by Baumeister and Leary (1995) as the “need to belong” (p. 1). According to the theory, when this need is unmet—a state we refer to as thwarted belongingness—a desire for death develops (also referred to in the suicidology and clinical literature as passive suicidal ideation). Other suicide theorists have also proposed a central role for social connectedness (see below),
+though the proposed mechanisms for the relations between social connectedness and suicide differ across theoretical accounts.
+According to Durkheim (1897), dysregulation of social forces— specifically, degrees of social integration—results in suicide. He proposed that too little social integration leads to an increase in suicide because individuals lack a connection to something that transcends themselves. When examining changes in suicide rates for a population over time, Durkheim’s theory could provide explanations for—and facilitate prediction of—patterns and shifts in suicide rates. However, in his theory, Durkheim pays little attention to individual factors: if all individuals in a society are exposed to the shifts in social forces, why then do only particular individuals, and a very small subset of them at that, die by suicide?
+In contrast, Shneidman (1987) articulated a theory of suicide focused on individual factors, with psychache—psychological and emotional pain that reaches intolerable intensity -as the primary factor causing suicide. Shneidman (1985) further posited that psychache is intolerable because it results from basic needs that have been thwarted. Shneidman (1998) proposed an extensive list of basic needs, seven of which he argued are most commonly thwarted in suicidal individuals, ranging from “affiliation” to “shame avoidance” to “order and understanding.” In contrast to Shneidman’s model, we propose that the need to belong is the need central to the development of suicidal desire, consistent with the wealth of findings linking social connectedness to suicidal behavior.
+Thus, the interpersonal theory is consistent with past theoretical accounts of suicidal behavior through its proposal for a key role for social connectedness. However, the interpersonal theory diverges from previous theories in its proposal that an unmet “need to belong” (Baumeister & Leary, 1995, p. 1) is the specific interpersonal need involved in desire for suicide. The theory also differs in that we propose that thwarted belongingness is a multidimensional construct. Figure 2 depicts in greater detail the proposed definition of the construct of thwarted belongingness (analogous to a latent variable measurement model). The figure depicts a hierarchical latent variable model, with thwarted belongingness as a higher order latent variable with two subordinate factors. Consistent with this, Baumeister and Leary (1995) proposed that the need to belong comprises two facets: “People seem to need frequent, affectively pleasant or positive interactions with the same individuals, and they need these interactions to occur in a framework of long-term, stable caring and concern” (p. 520). We conceptualize these two dimensions of interpersonal functioning that are posited to compose thwarted belongingness as loneliness and the absence of reciprocally caring relationships. These constructs are depicted in Figure 2 as latent variables caused by (i.e., components of) the latent construct of thwarted belongingness.
+Drawing on Russell’s (1996) and Joiner and colleagues’ (Joiner, Lewinsohn, & Seeley, 2002) conceptualization of the construct, loneliness is conceptualized as an affectively laden cognition that one has too few social connections, which also maps onto Baumeister and Leary’s (1995) first facet of the need to belong (i.e., frequent and positive interactions). For example, an individual experiencing the mental state of thwarted belongingness might express the loneliness component of the construct by stating, “I did not have a satisfying social interaction today,” or “I feel disconnected from other people.” The second component of thwarted belongingness according to the interpersonal theory is the absence
+of reciprocally caring relationships (i.e., ones in which individuals both feel cared about and demonstrate care of another). For relationships to meet the need to belong, they must be characterized by positive feelings and must occur in a supportive context (Baumeis-ter & Leary, 1995), and when they are not, relationships cease to meet the criteria as reciprocally caring. An individual with an absence of reciprocally caring relationships might express the experience by stating, “I am not a support for others,” or “There are no people I can turn to in times of need” (see Figure 2).
+In addition to depicting the multidimensional nature of thwarted belongingness, Figure 2 also further clarifies the definitions of these constructs by including observable indicators of the constructs of loneliness and reciprocally caring relationships—all of which are associated with elevated risk for lethal suicide attempts (and were discussed above in the Other Risk Factors section). The loneliness factor is posited to give rise to six observable risk factors for lethal suicidal behavior (citations for the elevation of risk for lethal attempts are provided in Table 1): self-report loneliness, pulling together effects, caring letters interventions (interventions designed to increase social contacts through long-term follow-ups, thereby decreasing loneliness and thus lowering risk for suicide), seasonal variation (reductions in social interactions that lead to increased feelings of loneliness have been posited as the mechanism whereby the spring peak in lethal suicidal behavior occurs), presence of marriage and number of children and friends, and living alone and reporting few to no social supports. The absence of reciprocally caring relationships factor is posited to give rise to six observable risk factors for lethal suicidal behavior: social withdrawal, low openness to experience, residing in a single jail cell, domestic violence, childhood abuse, and familial discord.
+The interpersonal theory includes the assumption that thwarted belongingness is a dynamic cognitive-affective state rather than a stable trait, which is influenced by both interpersonal and intrapersonal factors. These include an individuals’ actual interpersonal environments (e.g., number of individuals in the social network; Hawkley et al., 2008), activated interpersonal schemas (e.g., proneness to interpret others’ behavior as indicative of rejection; Downey & Feldman, 1996), and current emotional states (e.g., depressed mood; Cacioppo et al., 2006). Thus, the theory presumes that an individual’s degree of belongingness is likely to vary over time.
+The theory also includes the assumption that the need to belong is a dimensional phenomenon rather than a categorical phenomenon. This is in line with Baumeister and Leary (1995) who proposed that “partial deprivation” (p. 511) occurs when the need to belong is partially, but not fully, met. At what point and under what conditions does a thwarted need to belong lead to suicidal thoughts? Research from our laboratory group demonstrated a significant linear relationship between self-reported thwarted belongingness and suicidal ideation among participants who also endorsed high levels of perceived burdensomeness (Van Orden, Witte, Gordon, Bender, & Joiner, 2008). This study indicated that even among participants with high levels of thwarted belongingness (i.e., those at the 90th percentile in the sample), elevations in suicidal ideation were not evident unless high perceptions of burdensomeness were also present. These data suggest that one condition under which thwarted belongingness may cause suicidal ideation is when it is experienced concurrently with perceptions of burdensomeness. We return to this idea in subsequent sections.
+However, numerous studies have documented independent associations between indices of thwarted belongingness and suicide. To what degree must the need to belong be thwarted in order for suicidal ideation to result? Empirical studies have not directly examined this question, but data on the association between thwarted belongingness and other deleterious health outcomes speak to this question. Chronic feelings of loneliness (i.e., one facet of thwarted belongingness) are associated with elevated salivary cortisol levels, suggesting higher levels of a physiologic stress response (Cacioppo et al., 2000). Chronic feelings of loneliness are also associated with numerous negative emotional and interpersonal states, including elevations in negative emotions (i.e., anxiety and anger), pessimism, fear of negative evaluation, and shyness, as well lower levels of social support, agreeableness, and sociability (Cacioppo et al., 2006). These data suggest that a significant parameter along which thwarted belongingness may vary with regards to psychological and health outcomes is chro-nicity, as chronic loneliness was the key variable in the two preceding studies. Thus, we propose that when thwarted belongingness is prolonged, suicidal ideation is more likely to result.
+Thwarted belongingness can also vary in terms of its magnitude. A robust association has been documented between social isola-tion—a relatively severe manifestation of thwarted belongingness as it involves few to no social relationships—and suicide (as reviewed above). Thwarted belongingness has also been experimentally induced in the laboratory by randomly assigning participants to receive feedback that they are likely to end up alone later in their lives. This experimental manipulation has been shown to cause numerous deleterious effects on cognition and behavior, including self-regulatory impairments (Baumeister, DeWall, Cia-rocco, & Twenge, 2005), executive functioning impairments (Baumeister, Twenge, & Nuss, 2002; Campbell et al., 2006), reduced prosocial behaviors (Twenge, Baumeister, DeWall, Cia-rocco, & Bartels, 2007), aggressive behaviors (Twenge, Baumeis-ter, Tice, & Stucke, 2001), hostile social-cognitive processing biases (DeWall, Twenge, Gitter, & Baumeister, 2009), unintentional self-defeating behaviors, including risky behaviors (Twenge, Catanese, & Baumeister, 2002), and a state of inner numbness (Twenge, Catanese, & Baumeister, 2003). However, the same experimental manipulation of thwarted belongingness has also been shown to cause increased attention to stimuli relevant to belongingness (DeWall, Maner, & Rouby, 2009) as well as increased motivation to connect with others (Maner, DeWall, Baumeister, & Schaller, 2007). This raises the question: What conditions tend to elicit positive versus negative behavioral outcomes? To answer this question, in the latter study (Maner et al., 2007) researchers also investigated “boundary conditions” (p. 52) for the increases in affiliative behaviors. The authors found that those with thwarted needs to belong did not tend to engage in affilitative behaviors under two conditions: (a) if the person with whom affiliation could be increased was the person who caused the lowered belongingness (i.e., someone who rejected the participant) or (b) if the persons with whom affiliation could be increased would not be available for in-person interactions. It follows then that individuals who perceive others in their social networks as individuals who rejected them or as unavailable for in-person interactions would be less likely to engage in affiliative behaviors but would be just as likely (or more likely) to engage in the self-defeating behaviors that accompany thwarted belonging
+ness. The data from the cited studies suggest that this more extreme form of thwarted belongingness may result in a more extreme form of self-defeating behavior, of which suicidal behaviors are an example. The theory is concerned with this most severe form of thwarted belongingness that involves perceptions that meaningful and mutually supportive connections are completely absent, with chronicity likely increasing these perceptions.
+Perceived Burdensomeness
+Family conflict, unemployment, and physical illness were three of the risk factors for suicide (discussed above) with the most robust support for their association with suicide. These three factors are all types of negative life events. Why might these three types of negative life events be particularly associated with suicide? Recall that one form of family conflict that has been shown to be associated with lethal suicidal behavior is the perception that one is a burden on family members. We propose that the elevated likelihood of developing perceptions of burdensomeness on others is the common thread among family conflict, unemployment, and physical illness that can account for the associations with suicide.
+Perceptions of burdensomeness on family are also the key factor in Sabbath’s (1969) family systems theory of adolescent suicidal behavior. The theory emphasizes adolescents’ perceptions that they are expendable members of the family. The causal factors leading to adolescent suicidal behavior, according to the theory, are pathogenic parental attitudes toward the adolescent that are interpreted by the adolescent that he or she is not needed in the family and, in fact, that the family would be better off if the adolescent were dead. In a direct test of Sabbath’s theory, perceptions of expendability in the family were found to be positively correlated with suicidal behavior in adolescents (Woznica & Shapiro, 1990). Converging results were found in a sample of preschoolers: Suicidal children were significantly more likely to be the product of unwanted pregnancies (Rosenthal & Rosenthal, 1984). However, Sabbath’s theory does not account for the fact that the majority of youth who perceive that their families would be better of without them do not die by suicide.
+The interpersonal theory is consistent with past conceptual work (e.g., Sabbath, 1969), as we posit a key role for perceptions of burdensomeness in the etiology of suicide. However, the interpersonal theory differs in that the construct is broader and in that perceptions of burdensomeness on close others, including but not limited to family members, are associated with desire for suicide. Further, according to the theory, perceived burdensomeness comprises two dimensions of interpersonal functioning— beliefs that the self is so flawed as to be a liability on others and affectively laden cognitions of self-hatred. These two dimensions are depicted as subordinate latent variables in Figure 3. An individual experiencing the mental state of perceived burdensomeness might express the liability component of the construct by stating, “I make things worse for the people in my life,” whereas someone expressing self-hatred might directly state, “I hate myself” or “I am useless” (also depicted in Figure 3).
+As was done for thwarted belongingness, observable indicators of the dimensions of perceived burdensomeness are depicted in Figure 3. The liability factor is posited to give rise to six observable risk factors for lethal suicidal behavior (citations for the elevation of risk for lethal attempts are provided in Table 1):
+distress caused by unemployment (the theory is able to account for mixed findings regarding the relation between unemployment and suicide: unemployment should elevate risk for suicide, according to the interpersonal theory, only when the stress of unemployment results in perceptions that one is a liability to oneself and others), distress from incarceration (in this way, the theory is consistent with the fact that markedly elevated rates of suicide are found in incarcerated [and recently incarcerated] populations), homelessness, serious physical illnesses, and direct statements in suicide notes or verbal communications that individuals perceive that they are expendable, unwanted, or burdens on others. It should be noted that we posit that in the vast majority of cases (if not all), these perceptions of liability are misperceptions amenable to therapeutic modification. The other dimension of perceived burdensomeness is the affectively laden construct of self-hate, with three corresponding observable indicators with empirically demonstrated associations with lethal suicidal behavior: low self-esteem, self-blame and shame, and mental state of agitation (in part, because it indicates that an individual may be experiencing a degree of self-hatred and anguish that is so elevated as to manifest physiologically).
+As with thwarted belongingness, perceived burdensomeness is presumed to be a dynamic cognitive affect state, as well as a dimensional phenomenon. Thus, individuals’ levels of perceived burdensomeness are likely to vary over time, over relationships, and along a continuum of severity. Thus, it is necessary to define the point at which perceptions of burdensomeness are relevant to suicidal behaviors. What does previous research on the construct suggest with regards to the critical level?
+A psychological autopsy study of terminal cancer patients who died by suicide indicated that self-perceptions of being a burden on others was a key characteristic likely contributing to desire for suicide (Filiberti et al., 2001). In a comparison of suicide notes of individuals who made lethal versus nonlethal attempts, the presence of perceptions of burdensomeness on others differentiated between those who attempted and survived and those who attempted and died—with perceptions of burdensomeness characterizing the notes of those who died (Joiner, Pettit, et al., 2002). In addition, in the same study, greater perceptions of burdensomeness in the notes predicted the use of more lethal means among the sample of notes from individuals who died. A prospective study following psychiatric patients at high risk for suicide found that statements about feeling like a burden on others significantly elevated risk for suicide during a 60-day follow-up period after an evaluation (Motto & Bostrom, 1990).
+Self-perceptions that one is a burden on others also differentiate between individuals with histories of suicide attempts and individuals with no attempts (R. M. Brown, Dahlen, Mills, Rick, & Biblarz, 1999; Van Orden, Lynam, Hollar, & Joiner, 2006) and are also associated with suicidal ideation (R. Brown et al., 2009; de Catanzaro, 1995; Van Orden, Lynam, Hollar, & Joiner, 2006). Similar to perceiving that one is a burden, the desire to make others better off was shown to be a more common reason given for suicide attempts versus episodes of self-harm without suicidal intent (M. Z. Brown, Comtois, & Linehan, 2002), and the belief that someone wishes one dead was shown to differentiate between suicidal and nonsuicidal individuals (Rosenbaum & Richman, 1970). Recall also that perceptions of expendability have been shown to characterize suicidal adolescents (Woznica & Shapiro, 1990). Similarly, suicidal preschoolers (compared with preschoolers with behavior problems) have been shown to be more likely to be unwanted by their parents (Rosenthal & Rosenthal, 1984).
+These studies indicate that perceptions of burdensomeness on multiple others, rather than on a single individual, may be particularly deleterious. It may also be that extreme perceptions of burdensomeness within a single relationship are most strongly related to suicidal ideation. We take the former stance (while acknowledging that this is ultimately an empirical question) and propose that when an individual holds perceptions of burdensomeness for all significant others in his or her life and the person endorses some degree of self-hate regarding those perceptions, a critical threshold is crossed—and it is this severe level of perceptions of burdensomeness that is relevant to the theory.
+Relations Between Thwarted Belonging and Perceived Burdensomeness
+The theory involves the proposal that other more distal risk factors exert their influence on desire for suicide by increasing levels of thwarted belongingness, perceived burdensomeness, or some combination of the two. Childhood maltreatment and mental disorders are not conceptualized as indicators that an individual is currently experiencing thwarted belongingness or perceived burdensomeness (and are not caused by either construct); thus, they are not included in Figures 2 or 3. However, these risk factors are relevant to the development of both constructs. For example, both are life experiences that may increase an individual’s risk for
+developing social isolation and/or feelings of loneliness. Indeed, social alienation (cf., thwarted belongingness) has been proposed as the mechanism whereby the experience of childhood abuse increases risk for suicidal behavior (Twomey, Kaslow, & Croft, 2000). Insofar as childhood maltreatment and mental disorders predispose individuals to perceive that they are unwanted or expendable, these experiences may also elevate risk for perceptions of burdensomeness. Thus, we posit that these risk factors increase risk for suicide through their relationship with both thwarted belongingness and perceived burdensomeness.
+Thwarted belongingness and perceived burdensomeness are presumed to be distinct but related constructs. This assumption raises important issues with regard to the definitions of the constructs. One could argue, for example, that if an individual’s need to belong is completely thwarted, perceptions of burdensomeness are not possible because human connections are a prerequisite for the development of perceived burdensomeness. We suggest that this is not the case because the presence of perceptions of connections to others does not equate with meeting the need to belong. In other words, the construct of thwarted belongingness is not synonymous with a lack of human connections, and conversely, the need to belong is not fulfilled by the mere presence of perceptions of connections to others.
+Another question arises concerning relationships that are characterized by perceptions of burdensomeness—can those relationships satisfy the need to belong? Does the presence of perceptions of burdensomeness preclude the satisfaction of the need to belong? Imagine a prison inmate whose wife and children come to visit each month. This individual might feel cared about by his family and experience positive interactions during these visits. However, imagine also that this individual believes that the stress of his incarceration on his family is too much for them and that they would be better off if he were gone. In this instance, a degree of belongingness is evident in the presence of strong perceptions of burdensomeness.
+But what about the prison inmate who has no family or friends—what about individuals for whom meaningful connections are absent? Does this condition preclude the development of perceptions of burdensomeness—as raised above—is some degree of belonging necessary for the development of perceptions of burdensomeness? Again, we suggest that this is not the case because the presence of human connections does not in itself satisfy the need to belong; thus, even the most isolated individuals typically possess some degree of connection to others (e.g., estranged family members, health care providers, neighbors), and those connections could be characterized by perceptions of burdensomeness. In addition, those individuals who perceive total isolation from others are most likely alienated to the point that they perceive themselves as completely inconsequential and/or unwanted—a state akin to perceptions of expendability posited to function as behavioral indicators of perceived burdensomeness. Thus, according to the theory, thwarted belongingness and perceived burdensomeness are related but distinct constructs. Empirical findings are supportive, with prior studies indicating a significant correlation of moderate magnitude between the two constructs (e.g., zero order correlation coefficient of .58; Van Orden, Witte, Gordon, Bender, & Joiner, 2008).
+Acquired Capability for Suicide
+The models of suicide described above assume that suicide is multifactorially caused, such that suicidal ideation results from the fewest number of co-occurring risk factors, suicide attempts result from a greater number, and death by suicide results from the co-occurrence of the greatest number. These models also assume that risk for suicide is elevated due to greater risk for suicidal desire and, perhaps, increasingly severe forms of suicidal desire. These assumptions remain unchallenged by current theories and models of suicide. In contrast, according to the interpersonal theory, desire to die by suicide is not sufficient for lethal suicidal behavior to result because, simply put, dying by suicide is not an easy thing to do. Consider the following case example of a woman who died by suicide (Holm-Denoma et al., 2008):
+Case #7 was described as being socially isolated when she attempted suicide with an unknown quantity and type of pain medication and also opened her wrist arteries. This action led to some degree of unconsciousness, from which she awoke .... She then threw herself in front of a train, which was the ultimate cause of her death. (p. 233)
+In this case, initial behaviors were not lethal, and to bring about death, the individual engaged in another method of suicide. These are frightening and painful behaviors.
+According to the theory, to die by suicide, individuals must lose some of the fear associated with suicidal behaviors, and it would be very uncommon (if not impossible) to find someone born with a level of fear low enough to engage in suicide. Why might this be the case? Ohman and Mineka (2001) proposed an evolutionarybased model of fear grounded in the hypothesis that natural selection processes have shaped the human fear system so that it functions as a signal for the presence of “potentially lifethreatening situations in the ecology of our distant forefathers” (p. 484). Thus, these authors posit that the adaptive value of fear (i.e., why humans who possess the fear system are more likely to survive and thus reproduce) lies in its potential to aid humans in the identification of stimuli associated with threats to survival. The interpersonal theory draws on—and extends— evolutionary models of fear and anxiety by proposing that humans are biologically prepared to fear suicide because suicidal behavior involves exposure to stimuli and cues that have long been associated with threats to survival.
+And yet, some individuals die by suicide. According to the theory, it is possible to acquire the capability for suicide, which is composed of both increased physical pain tolerance and reduced fear of death through habituation and activation of opponent processes in response to repeated exposure to physically painful and/or fear-inducing experiences. In other words, through repeated practice and exposure, an individual can habituate to the physically painful and fearful aspects of self-harm, making it possible for him or her to engage in increasingly painful, physically damaging, and lethal forms of self-harm. Further, acquired capability is presumed to be a multidimensional emergent latent variable that involves the dimensions of lowered fear of death and increased physical pain tolerance, as depicted in the top of Figure 4.
+Lowered fear of death. Fear of suicide is one category of reasons that individuals give when asked why they do not engage in suicidal behavior (Linehan, Goodstein, Nielsen, & Chiles, 1983). Further, an investigation of reasons for living, including
+fear of suicide, found that individuals who reported a history of “past serious ideation” (p. 280) about suicide but who had not attempted suicide reported higher levels of fear of suicide, compared with individuals with serious ideation who had acted on this ideation through suicidal behaviors (Linehan, Goodstein, Nielsen, & Chiles, 1983). These data suggest that suicidal ideation (cf., suicidal desire) is not sufficient for suicide attempts to result; rather, suicidal desire must occur in the context of reduced fear of suicide. Fear of suicide is presumed to be a dimensional construct varying from very high levels to negligible levels of fear, and further, for active suicidal desire to progress toward more severe manifestations of suicide risk (i.e., intent for suicide), fear must be reduced to the point that individuals endorse a nonzero degree of fearlessness regarding suicidal actions. To operationalize this construct and thus potentially falsify the theory, fearlessness about suicide can be measured by asking, “Do you think you have the capability or courage to kill yourself?” Any response other than a definitive “no” indicates nonzero fearlessness. In support of this threshold for fear reduction, self-reported fearlessness about engaging in suicide is strongly associated (i.e., r = .79) with a self-report measure of acquired capability (Van Orden, Witte, Gordon, Bender, & Joiner, 2008).
+Elevated physical pain tolerance. Dying by suicide is not only frightening, but physically painful. Consider the following case example of a woman who died by suicide (Holm-Denoma et al., 2008): “Case #1 ingested an unknown quantity of chloral hydrate and 354.9 mL of Lysol Toilet Bowl Cleaner [which contains hydrochloric acid (HCl)] . . . and died 4 hours after being transported to the emergency room due to gastric hemorrhaging” (p. 233). Swallowing hydrochloric acid requires a tolerance for physical pain that most do not possess. The empirical literature concurs with this case example: Individuals with recent suicidal behavior demonstrate elevated physical pain tolerance (as mea
+sured by electric shock and thermal pain), compared with nonsui-cidal psychiatric patients and individuals in the community (Orbach, Mikulincer, King, Cohen, & Stein, 1997; Orbach, Palgi, et al., 1996) and compared with individuals admitted to the emergency room due to accident injuries (Orbach, Stein, et al., 1996). The latter finding indicates that elevated pain tolerance is likely specific to suicidal behavior rather than physical injury. In addition, more serious levels of suicidal ideation have been shown to predict higher levels of self-administered shock (Berman & Walley, 2003).
+Pain tolerance is conceptualized as a dimensional phenomenon. What level of pain tolerance is necessary to allow lethal (or near lethal) suicidal behavior to occur? First, this construct is likely highly method-specific, thus someone gaining the requisite pain tolerance to engage in cutting behaviors will not necessarily have gained the same tolerance for other methods, such as jumping. In this way, we are able to provide an explanation for data indicating that method substitution does not typically occur. In addition, the type of actions involved must also be considered. For example, cutting one’s wrists requires sustained behavior on the part of the suicidal individual, and this individual must continue cutting his or her wrist in spite of the physical pain endured. An individual swallowing pills must continue to do so in spite of feelings of nausea or dizziness. In contrast, pulling the trigger on a gun or jumping off a building typically require a single action. We propose that both expectations about pain-to-be-experienced (e.g., “I won’t feel anything once I pull the trigger”), physiological habituation to physical pain sensations, and cognitive appraisals of the tolerability of expected and/or experienced pain are key factors in determining individuals’ tolerance for the pain involved in a specific suicide method. According to the theory, the common and proximate factor among all methods that serves as either a barrier to or a facilitator of lethal (or near lethal) suicidal behavior is the presence of a cognitive appraisal that the pain involved in the chosen method of suicide is tolerable. For lethal (or near lethal) suicide attempts to result, this cognitive appraisal must be nonam-bivalent and held with a strength of conviction of 100% (with duration of nonambivalence varying by method).
+Habituation and opponent processes. The theory also includes a description of mechanisms whereby individuals acquire the capability for suicide; it is for this reason that the acquired capability latent variable is depicted in Figure 4 as an emergent variable (i.e., one that is caused by other variables in the model). How does the acquired capability develop? We propose that the mechanisms whereby individuals acquire the capability for lethal self-injury are habituation (to fear and pain involved in self-injury) and the strengthening of opponent processes (in response to fear and pain); both processes are described by opponent process theory (Solomon & Corbit, 1974). Opponent process theory states that observed emotional responses are a function of the summation of two underlying, oppositely valenced processes (i.e., opponent processes). Further, with repeated exposure, the emotional effects of the opposite process become amplified (whereas the primary emotional effects of a stimulus remain stable). This results in a net change in the observed response to be more similar to the valence of the opponent process, which behaviorally manifests as habituation. For example, an individual’s initial, primary response to a stimulus such as bungee jumping will likely be fear. However with repeated exposure to bungee jumping, the effect of the primary
+process (e.g., fear) will remain stable, whereas the effect of the opponent process (e.g., exhilaration) will become amplified, yielding a net observed emotional response of decreased fear. If the process is continued long enough, eventually the strength of the opponent process will be such that the valence of the observed emotional experience shifts from negative to less negative to positive.
+The interpersonal theory involves an application of Solomon and Corbit’s (1974) ideas to self-harm behaviors such that that the primary effect of painful and provocative stimuli (e.g., self harm) is fear and pain and that the opponent processes are relief and analgesia. However, the interpersonal theory differs in that it includes a proposal that the primary process also weakens. Thus, through repeated practice, what was originally a painful and/or fear-inducing experience (i.e., self-injury) may become less frightening as well as a source of emotional relief, thereby rendering individuals capable of engaging in what were previously painful and frightening behaviors. Although it has been observed that increases in positive affect may occur subsequent to self-harm without suicidal intent (Brain, Haines, & Williams, 1998; Muehlenkamp et al., 2009), data about changes in positive affect during or after suicidal behaviors are not available.
+Painful and provocative experiences. The risk factors depicted in Figure 4—childhood maltreatment, clustering, combat exposure, impulsivity, and previous suicide attempts—are posited to increase risk for lethal suicidal behavior because they are physically painful and/or sufficiently frightening to engage habituation and opponent processes with regards to the pain and fear involved in self-harm. In addition, factors such as limiting access to lethal means may serve to block acquired capability, thus reducing rates of suicide.
+Our review of risk factors for suicide indicates that a history of a past suicide attempt is another of the strongest and most reliable predictors of suicidal behavior; however, the literature also indicates that the majority of individuals who attempt suicide will not eventually die by suicide and that many individuals (i.e., up to half) who die by suicide do so on their first attempt (Rudd, Joiner, & Rajab, 1996). The construct of acquired capability provides a framework for understanding the complex relations between a history of past attempts and risk for future suicidal behavior. According to the theory, the most direct route (but not the only route) to acquiring the capability for suicide (i.e., the most potent painful and provocative experience) is by engaging in suicidal behavior, either through suicide attempts, aborted suicide attempts (preparing for the attempt and nearly carrying it out), or practicing and/or preparing for suicidal behavior (e.g., tying a noose; buying a gun with intent to engage in suicidal behavior; imagining one’s death by suicide). Suicide attempts are the most potent of these behaviors with regards to acquiring the capability for suicide; thus, a potential way to falsify the theory would be to show that individuals with histories of past attempts have equivalent levels of acquired capability to those without past attempts.
+Initial tests conducted by our laboratory group on acquired capability opened the theory to falsification by examination of the association between number of past suicide attempts and selfreported level of acquired capability, as measured by a self-report measure, the Acquired Capability for Suicide Scale (ACSS; Van Orden et al., 2008). The ACSS assesses fearlessness about lethal self-injury as well as self-perceived ability to tolerate the pain
+involved in self-injury (e.g., “I can tolerate more pain than most people” and “The pain involved in dying frightens me” [reversed]). Results indicated that number of past attempts positively correlated with levels of acquired capability, with highest levels of acquired capability reported by individuals with multiple past attempts. Further, a study on military personnel found that the branch of service (e.g., Army vs. Navy) was associated with a specific method of suicide (i.e., guns for Army; hanging and knots for Navy; falling and heights for Air Force). These data suggest that habituation to the pain and fear in suicide may be method specific and is acquired through exposure.
+However, the capability for self-harm can be acquired through behaviors other than suicide attempts; thus, the theory would not generate the prediction that all individuals who die by suicide would necessarily have histories of prior attempts. This aspect of the theory is depicted graphically by the presence of other painful and provocative experiences in Figure 4 that are posited to activate habituation and opponent processes, thereby giving rise to acquired capability. In addition to previous suicidal behavior, other less potent pathways may also exist through the experience of other fear-inducing, risky behaviors. This aspect of the theory organizes a great deal of the literature on risk factors (described above) that otherwise appears disparate. Childhood maltreatment that involves physical and sexual abuse may activate habituation with regards to fear of self-injury as well as increased pain tolerance. Exposure to others who have engaged in suicidal behavior may activate habituation to the fear of suicidal behavior, thus accounting for clustering of suicidal behavior as a byproduct of elevated acquired capability. Combat exposure, which involves exposure to the fear of one’s own possible death, as well as killing others, represents a relatively direct pathway, according to the theory.
+In support of a less direct route to acquired capability, one study showed that individuals who reported engaging in more painful and provocative experiences (e.g., shoplifting, promiscuous sex, played contact sports, got a piercing, shot a gun, intentionally hurt animals, physical fights, jumped from high places) also reported higher acquired capability scores (Van Orden e al., 2008). These results persisted after controlling for potentially confounding variables, including current level of suicidal ideation, age, gender, and depressive symptoms. Suicidal ideation was controlled for in this particular analysis because, according to the theory, it is possible to be capable of suicidal behavior without desiring suicide.
+Veterans are at increased risk for lethal suicidal behavior and are more likely to use firearms as the suicide method (Kaplan, Huguet, McFarland, & Newsom, 2007); the increased risk for use of firearms is notable, given that this population has extensive exposure to firearms, thus providing ample opportunities to habituate to the fearsome aspects of the use of firearms. A psychological autopsy study showed that retrospectively assessed levels of acquired capability—sample items included the prior engagement in suicide-preparatory behaviors, past suicide attempts, and problems with impulsivity— discriminated between living controls and those who died by suicide in a military sample (Nademin et al., 2008).
+The theory does not preclude more complex relations between risk factors. Studies document a relationship between the trait of impulsivity and suicidal behavior: Impulsive people do not necessarily make impulsive suicide attempts; in fact, people who have engaged in more impulsive behaviors have been shown to engage
+in more prior planning for suicide attempts and to use more medically serious methods. The construct of acquired capability provides a parsimonious explanation for this array of facts: impulsive and/or aggressive individuals are more likely to engage in behaviors that are painful and provocative (e.g., physical fights, injecting drugs). Because of this, we propose that impulsive individuals have higher levels of acquired capability for suicide, and it is this consequence of impulsivity that elevates risk for suicidal behavior. Regarding the relationship between the planfulness of suicide attempts and their lethality, we propose that this is also related to habituation to painful and provocative experiences. Specifically, an individual who spends a great deal of time planning an attempt is not only making pragmatic arrangements for his or her death but is also habituating to the fear associated with making a suicide attempt (i.e., is engaging in mental practice). Thus, although the capability for suicide is conceptualized as a capability that is gained over time, we also propose that through genetic and/or temperamental predispositions to fearlessness, im-pulsivity, or greater physical pain tolerance, some individuals are more susceptible to acquiring the capability for suicide, given exposure to painful and provocative events, or are even more likely to seek such events out.
+The proposal that mental practice is an element of acquiring the capability for suicide provides one possible explanation for the fact that method substitution—when access to a means for suicide is blocked—does not occur. Working up to the act of suicide is quite difficult to do; habituating to one means does not necessarily result in habituation to another. Theoretical accounts of suicide that focus only on suicidal desire struggle to account for these data on method substitution (and for many other findings too).
+The Proximal Causal Pathway to Suicide
+Hypothesis 1: Passive Suicidal Ideation
+The theory involves four hypotheses that are listed in Table 2. These hypotheses are depicted graphically in Figure 5, which illustrates the etiology of suicide according to the interpersonal theory. The causal process is depicted from left to right, beginning with thwarted belongingness and perceived burdensomeness and ending with lethal (or near lethal) suicidal behavior at the far right. The model also contains latent variable interactions, moderating effects, and emergent variables, each of which are discussed in detail. The interpersonal theory is concerned with proximal risk factors—the mental states and behavioral capacities that will be evident in an individual at varying degrees of nonzero risk for lethal suicidal behavior, ranging from individuals evidencing passive suicidal ideation to those demonstrating imminent risk for lethal suicidal behavior. The causal process depicted in Figure 5 also illustrates those factors that are present at varying degrees of risk, with the lowest level of risk for suicide toward the left of the figure and risk incrementally increasing toward the right end of the figure.
+The theory’s first hypothesis is that thwarted belongingness and perceived burdensomeness are proximal and sufficient causes of passive suicidal ideation. Individuals who possess either complete thwarted belongingness or complete perceived burdensomeness will experience passive (versus active) suicidal ideation, which may manifest as cognitions such as “I wish I was dead” or “I would be better off dead.” In contrast, active suicidal ideation is marked by an active desire to engage in behaviors to take one’s life (e.g., “I want to kill myself”). This hypothesis is depicted in the far
+left of Figure 2, which also illustrates independent causal paths between thwarted belongingness and passive suicidal ideation and perceived burdensomeness and passive suicidal ideation. Note, however, that these causal paths do not continue beyond passive suicidal ideation, a point addressed by the next hypothesis.
+The theory could be falsified if studies do not document independent associations for both thwarted belongingness and perceived burdensomeness with suicidal ideation or behaviors. Two studies have opened the theory to falsification by examining the hypothesized relation between perceived burdensomeness and indices of suicidal behavior. First, perceived burdensomeness measured by self-report in a clinical sample was shown to cross-sectionally predict greater severity of suicidal ideation and greater numbers of past suicide attempts, while controlling for age, gender, personality disorder status, depressive symptoms, and hopelessness (Van Orden et al., 2006). Second, an examination of the content of suicide notes showed higher levels of perceived burdensomeness in notes from individuals who died by suicide, compared with notes from individuals who attempted but survived (Joiner, Pettit, et al., 2002). Results of these studies indicate an association between perceptions of burdensomeness and suicidal ideation (though thwarted belongingness was not measured in either study, thus precluding conclusions regarding the effects of the presence of both constructs).
+In several studies, researchers have examined the relation between thwarted belongingness and indices of suicidal behavior. In one study, researchers examined the relation between thwarted belongingness measured by self-report and likelihood of having a past suicide attempt (Conner, Britton, Sworts, & Joiner, 2007) among methadone maintenance patients at an urban university hospital. Results indicated that a one-point increase on the belongingness subscale (indicating greater belongingness) decreased the odds of having a past suicide attempt by 6%; the same association did not hold for accidental overdoses, supporting the specificity of thwarted belongingness to suicidal behavior. The relationship between thwarted belongingness and suicidal desire was also supported by a series of studies investigating the phenomenon of pulling together after positive collective experiences, in this case, sporting events (Joiner, Hollar, & Van Orden, 2006). These studies indicated that lower suicide rates were associated with sports team successes, which is consistent with the hypothesis that sporting events may foster increased belongingness and thereby buffer suicide rates.
+Regarding thwarted belongingness, this hypothesis could be falsified if studies do not demonstrate the presence of passive suicidal ideation among all individuals with a completely thwarted need to belong (i.e., those holding perceptions that all meaningful and reciprocally caring relationships are absent). Regarding perceptions of burdensomeness, this hypothesis could be falsified if studies do not demonstrate the presence of passive suicidal ideation among all individuals with global perceived burdensomeness (i.e., those who perceive themselves as a burden on all significant others and who experience a nonzero degree of self-hate secondary to these perceptions). Note, however, that this hypothesis does not posit that these factors are necessary but does instead allow for situations in which passive suicidal ideation may result from other causes. The degree to which the theory’s constructs are necessary for suicidal behavior remains an empirical question and is addressed in later sections.
+Hypothesis 2: Suicidal Desire
+The literature on suicide indicates that among those who have passive thoughts of suicide (e.g., “I would be better off dead”), most will not experience active suicidal ideation involving thoughts of killing themselves (Thomas, Crawford, Meltzer, & Lewis, 2002). Consistent with this fact, the theory includes the hypothesis that in order for a passive desire for suicide to intensify into an active desire for suicide, a completely thwarted need to belong must be accompanied by global perceived burdensomeness, as well as hopelessness regarding these two painful states. Thus, the theory’s second hypothesis states that a mental state characterized by the simultaneous presence of thwarted belongingness, perceived burdensomeness, and hopelessness about one’s interpersonal connections is a proximal and sufficient cause of suicidal desire. This hypothesis is graphically depicted in the middle of Figure 5 with the intersection of thwarted belongingness, perceived burdensomeness, and hopelessness regarding these states causing desire for suicide (e.g., thoughts such as “I want to kill myself”).
+According to the theory, the absence of either thwarted belongingness (perceived as unchanging) or perceived burdensomeness (also perceived as unchanging) is likely to be lifesaving, as active suicidal desire develops only in the confluence of both factors. Thus, this hypothesis could be falsified if individuals who are elevated on only thwarted belongingness or perceived burdensomeness demonstrate more severe (or equivalent) suicidal ideation than do individuals who are elevated on both. Two studies have opened the theory to falsification by examining this prediction. In the first study (Van Orden et al. 2008), the most severe levels of suicidal desire (operationalized as suicidal ideation on the Beck Suicide Scale) were reported by undergraduates at the most severe levels (in the sample) on both thwarted belongingness and perceived burdensomeness, relative to individuals with severe levels of only thwarted belongingness or perceived burdensomeness. This result was observed above and beyond age, gender, and level of depression. In the second study (Joiner et al., 2009), of an ethnically diverse community sample, young adults with low levels of both family support (cf. low belongingness) and mattering to others (cf. perceived burdensomeness) demonstrated the most severe levels of suicidal ideation. The study included both six-month and lifetime histories of depression as covariates, indicating that the theory’s variables predicted suicidal desire above and beyond the contribution of depression. Findings from these studies suggest that when people hold two psychological states in their minds simultaneously—low belongingness and perceived burdensomeness—risk for the development of suicidal desire is elevated (and this occurs beyond the effects of depression).
+However, in order for active suicidal desire to develop, individuals must perceive their levels of belongingness and burdensomeness to be stable and permanent—in other words, they must be hopeless about their perceived interpersonal status. This hypothesis builds on the empirical and theoretical literature on the association between hopelessness and lethal suicidal behavior (see Table 1). A meta-analytic review showed that the Beck Hopelessness Scale (BHS; (Beck, Weissman, Lester, & Trexler, 1974), with a cutoff score of 9, is predictive of both lethal and nonlethal suicide attempts (McMillan, Gilbody, Beresford, & Neilly, 2007). The sensitivity (i.e., the probability of a positive test result among
+individuals who went on to die by suicide) of the BHS was .80 for predicting death by suicide and .78 for nonfatal suicide attempts. This indicates that approximately 80% of people who engaged in serious suicidal behavior score above this cutoff point on the BHS. This finding is certainly supportive of the notion that hopelessness is associated with suicidal behavior. Nevertheless, the specificity of the BHS (i.e., the probability of a negative test result among those who did not engage in suicidal behavior) was .42 for both fatal and nonfatal suicide attempts. This indicates that nearly 60% of those who did not make a suicide attempt had a cutoff score above 9 on the BHS. Taken together, these data indicate that hopelessness is sensitive in the prediction of suicidal behavior, but it performs less well regarding specificity: Most hopeless individuals will not die by suicide.
+We propose that one explanation for this well-replicated finding is that the content of hopeless beliefs—what individuals are hopeless about—is relevant in the prediction of suicidal behavior. We propose that it is only hopelessness regarding complete and pervasive thwarted belongingness and perceived burdensomeness that will cause active suicidal desire because it is only at this juncture of mental states that individuals see no possibility of positive change. This hypothesis could be falsified if individuals demonstrating hopelessness about thwarted belongingness and perceived burdensomeness are more likely to report passive rather than active suicidal ideation; for example, a counterhypothesis could propose that hopelessness causes individuals to shut down so that active behavior and ideation, including suicide-related, are suppressed.
+Hypothesis 3: Suicidal Intent
+Intent to engage in suicidal behavior has been found to be part of a pernicious group of suicidal symptoms termed “resolved plans and preparation” (Joiner, Rudd, & Rajab, 1997; Witte et al., 2006) and has been shown to predict death by suicide in adults (Conner, Duberstein, & Conwell, 1999; Harriss, Hawton, & Zahl, 2005; Obafunwa & Busuttil, 1994). Current intent for suicide is a key component of suicide risk assessment protocols (Brent, 2001; Jobes, 2006; Joiner, Walker, Rudd, & Jobes, 1999; Linehan, Com-tois, & Murray, 2000; Reynolds, 1991; Shea, 1999; Simon, 2006) and is conceptualized as a necessary component of serious, imminent risk for suicide. Thus, the presence of intent can also be conceptualized as the level of suicidal desire that is most likely to translate into behavior. However, we posit that in order to possess suicidal intent, individuals must have habituated to the fear involved in suicide to an extent that they are able to imagine, plan, or decide to engage in suicidal actions. Thus, it is hypothesized (as depicted in Figure 5) that the simultaneous presence of suicidal desire and the first component of acquired capability—lowered fear of death—serves as the condition under which suicidal desire will transform into suicidal intent.
+This hypothesis would be falsified if, among those with suicidal desire, studies documented a lack of an association between fear of suicide and suicidal intent. Stronger tests could examine the sensitivity and specificity of decreased fear of suicide (e.g., a nonzero response on a measure of confidence regarding suicide) in the identification of suicidal intent among individuals with suicidal desire. The strongest test would involve examining the prediction
+that among individuals with suicidal intent, all would demonstrate fearlessness of suicide.
+Hypothesis 4: Lethal (and Near Lethal) Suicide Attempts
+The theory’s final hypothesis directly addresses the relative rarity of lethal suicidal behavior, compared with nonlethal attempts and suicidal ideation. As depicted in Figure 1, the theory involves the assumption that there are relatively large numbers of people who desire suicide and moderate numbers of those who have developed the capacity for suicidal behavior, but the presence of both should be comparatively rare. The theory’s fourth hypothesis is that the outcome of serious suicidal behavior (i.e., lethal or near lethal suicide attempts) is most likely to occur in the context of suicidal intent (which results from thwarted belongingness, perceived burdensomeness, and hopelessness regarding both), reduced fear of suicide, and elevated physical pain tolerance. This hypothesis is depicted in Figure 5 as the final causal arrow from suicidal intent to lethal (or near lethal) suicide attempts. Notice that this causal path is moderated by the presence of increased pain tolerance: suicidal intent does not result in lethal (or near lethal) suicide attempts unless increased pain tolerance that allows an individuals to endure the pain involved in dying by suicide is present.
+To examine the theory’s prediction regarding the role of physical pain tolerance as a final barrier to the outcome of lethal suicidal behavior, longitudinal studies could examine physical pain tolerance and follow individuals over time to see whether physical pain tolerance is elevated among individuals who die by suicide. Another approach could compare individuals who survived attempts that are almost always lethal (e.g., jumping from the Golden Gate Bridge) to survivors who called for help after engaging (or beginning to engage) in suicidal behavior. Although this approach would necessarily involve retrospective reports (thereby introducing recall biases), cognitive appraisals of the degree to which pain associated with the chosen suicide method was tolerable at the time of the intent could be compared between the groups. The theory’s hypothesis would be falsified if individuals who called for help reported greater appraisals of tolerability, compared with the individuals whose survival could be attributed to chance.
+The theory’s final hypothesis can also be empirically examined with regard to the prediction that the greatest risk for suicide is conferred by the simultaneous presence of thwarted belongingness, perceived burdensomeness, hopelessness in relation to both, and acquired capability for lethal self-injury. Psychological autopsy methods, whereby the characteristics of individuals who died by suicide are assessed, could be used to examine the proportion who evidenced all of the theory’s constructs. This proportion could be compared with the proportion from a sample of nonlethal suicide attempters. The theory’s hypothesis would be falsified if a greater proportion of nonlethal attempters evidenced all of the theory’s constructs, compared with lethal attempters.
+This hypothesis would also be falsified if greater risk for suicide was not found among those demonstrating more severe levels of the theory’s constructs. One study with a sample of clinical outpatients had self-reports of perceptions of burdensomeness and acquired capability as (partial) indices of suicidal desire and ca
+pability for suicide, respectively, and measured risk for suicidal behavior, as rated by clinicians, with a standardized risk assessment framework (Joiner et al., 1999; Van Orden et al., 2008, Study 3). Consistent with Hypothesis 4, results indicated a significant interaction between perceived burdensomeness and acquired capability in the prediction of clinician-rated risk for suicide, above and beyond the effects of other risk factors (i.e., depression scores, gender, and age). In line with predictions, the form of the interaction indicated that individuals high on both perceived burdensomeness and acquired capability were rated at highest risk for suicide by clinicians. The limitation of this finding, of course, is that desire was only partially measured—belongingness was not included.
+To remedy this, members of our research group studied a sample of young adults experiencing a suicidal crisis (Joiner, Van Orden, Witte, & Rudd, 2009, Study 2). Some participants’ crises involved a suicide attempt, whereas others experienced serious suicidal desire in the absence of attempts. Results were consistent with the theory and indicated that the three-way interaction of thwarted belongingness, perceived burdensomeness, and acquired capability (measured by number of past suicide attempts) predicted whether participants’ current suicidal crises involved suicide attempts. Results indicated that the combination of high levels of both thwarted belongingness and perceived burdensomeness was most likely to translate into suicide attempts in the presence of higher levels of acquired capability (i.e., greater numbers of past attempts). Results were obtained above and beyond the contribution of numerous documented risk factors for suicidality, including depression, hopelessness, and borderline personality disorder features.
+If empirically supported with lethal suicidal behavior, the final hypothesis of the theory could provide an explanation for the imprecision of the individual risk factors: unless a risk factor causes both desire and capability for suicide, its specificity in the prediction of lethal suicidal behavior will be low. The interpersonal theory’s final hypothesis provides a parsimonious account for mechanisms whereby risk factors for suicide confer risk: risk factors—such as mental disorders and childhood abuse— confer risk for suicidal behavior indirectly by increasing— or indicating the presence of— experiences of thwarted belongingness, perceived burdensomeness, and pain and/or provocation.
+Consider the role of mental disorders: Individuals who suffer from mental disorders are at elevated risk for suicide (with some disorders conferring greater risk than others), but the vast majority of these individuals will not demonstrate suicidal behaviors. As one example, consider borderline personality disorder. A hallmark feature of BPD is repeated nonlethal, self-injurious behavior, which according to the theory represents a relatively direct pathway to the acquired capability to lethal self-injury. This hypothesis that individuals with BPD may be more likely to have acquired the capability for lethal self-injury is consistent with the fact that approximately 60%-70% of people with BPD have made at least one severe suicide attempt (Gunderson, 2001). Another hallmark feature of BPD is extreme fear of abandonment (American Psychiatric Association, 2000), a symptom that likely relates to thwarted belongingness. In fact, it is often during threatened or real abandonment episodes when individuals with BPD engage in self-injurious behavior (American Psychiatric Association, 2000). Research also suggests that individuals with BPD may be more prone to perceptions of burdensomeness: self-hate, self-blame, and
+strong feelings of shame are common among individuals with BPD (Linehan, 1993; Rizvi & Linehan, 2005), and it is likely that feelings of shame accompany perceptions that one is a burden on others. Thus, according to the interpersonal theory, high rates of suicidal behavior among individuals with BPD may be due to the fact that these individuals are more susceptible to thwarted belongingness and perceived burdensomeness and more likely to engage in painful and provocative events that lead to an acquired capability.
+Consider also that AN is associated with a high suicide rate (Keel et al., 2003). Members of our research group examined methods of suicide among a sample of 9 women with AN who died by suicide, to investigate competing hypotheses about the relation between AN and suicidal behavior (Holm-Denoma et al., 2008). One possibility is that these women were physically fragile due to AN and died using methods that would be relatively less lethal among physically healthy adults (i.e., those without compromised body weight). Another possibility is that these women died using methods that would be highly lethal among physically healthy adults because they had acquired the capability for suicidal behavior through the painful experience of self-starvation. Results were consistent with the latter hypothesis: Of the 9 women with AN who died by suicide, all used methods that would be lethal for physically healthy individuals (e.g., jumping in front of a train), and 7 were unlikely to be rescued following their attempts. This study suggests that the high rate of suicidal behavior among individuals with AN may be accounted for, in part, by experiences of pain and provocation that are a central component of the disorder (e.g., self-starvation) and that may foster acquired capability for suicidal behavior.
+Conclusions and Future Directions
+In the current article, we examined the literature on empirically demonstrated risk factors for suicidal behavior and demonstrated how the interpersonal theory is able to account for these facts about suicidal behavior. The theory involves the assumption that to a large extent, the same mental processes underlie all forms of suicidal behavior. Thus, when looking to the literature on suicidal behavior, available data should be consistent with the role of all constructs in the development of suicidal desire. Figures 2-4 depict the hypothesis that empirically supported risk factors for suicide elevate risk because they are indicators of thwarted belongingness, perceived burdensomeness, or acquired capability. Our discussion illustrated mechanisms whereby risk factors influence the constructs of the theory. This description of mechanisms underlying proximal risk for suicidal behavior provides a parsimonious account for why the majority of individuals who possess a given risk factor will not attempt or die by suicide—few risk factors increase all components of the interpersonal theory. The theory also provides explanations for heretofore difficult to explain epidemiological facts about suicide—including the gender distribution and prevalence of different forms of suicidal behavior— facts that available theories are unable to explain fully.
+One of the most consistent findings with regards to the epidemiology of suicidal behavior is its gender distribution. Male suicides outnumber female suicides worldwide, yet far more women than men are engaging in ultimately nonlethal suicidal behavior. Studies suggest that women may be more likely to experience
+many risk factors that increase—or indicate the presence of— thwarted belongingness and perceived burdensomeness, including major depression (i.e., women are approximately twice as likely as men to suffer from major depression; Nolen-Hoeksema, Larson, & Grayson, 1999). In addition, data indicate that women rank helping others, having a close family, and being loved by loved ones significantly higher than do men as sources of happiness (Crossley & Langdridge, 2005), suggesting that when these potential sources of happiness are absent, women are particularly likely to perceive thwarted belongingness and high burdensomeness and, thus, suffer greater emotional pain than do men in the same situations. However, because women on the whole have fewer experiences that inure them to fear of self-injury (e.g., exposure to guns, physical fights, violent sports, etc.) and because they have lower pain and fear tolerance than men (Berkley, 1997), women may be less able to develop the acquired capability for suicidal behavior than are men. Therefore, although women may be more likely than men to desire suicide, they are less likely to die by suicide.
+The interpersonal theory is also able to explain the prevalence of suicidal behavior. The theory involves three conditions that when present simultaneously are sufficient to result in lethal (or near lethal) suicide attempts. As each of these conditions is relatively rare and their confluence more so, the theory is consistent with the rarity of suicidal behavior itself.
+Available theories are unable to explain these aspects of suicidal behavior, as these theories assume that risk for suicide is elevated solely through increasingly severe levels of desire for suicide. This assumption is exemplified in several descriptive models that account for the prevalence of suicidal behavior by positing the necessary presence of numerous risk factors for suicidal desire. These models, however, are unable to explain facts about suicide such as gender distribution, seasonal variation, and lack the level of precision needed to prospectively predict suicidal behavior. In addition, as noted by Prinstein (2008), available theories do not, for the most part, address both intraindividual and interindividual factors. The interpersonal theory emphasizes the role of acquired capability—a primarily intraindividual factor—as well as the role of thwarted belongingness and perceived burdensomeness— intrapersonal factors (i.e., emphasizing perceptions) that transact with the interpersonal environment.
+Thus far, we have posited that the simultaneous presence of the theory’s constructs is sufficient but not necessary for suicidal behavior to occur. Thus, other pathways to suicidal behaviors are possible. However, a testable alternative is that the theory’s constructs represent the etiological mechanisms that underlie all forms of suicidal behavior. This alternative contrasts with many existing theoretical accounts. Consider, Baechler’s (1979) taxonomy of suicide that proposes all suicidal behavior seeks to solve a problem and that the problem “solved” by suicide varies and results in types of suicide, each with different etiological origins, including escapist suicides (i.e., escape from grief or punishment), aggressive suicides (i.e., vengeance or blackmail), oblative suicides (i.e., sacrifice), and ludic suicides (i.e., proving oneself; Shneidman, 2001). A recent review of the theoretical literature on suicide (Maris, Berman, & Silverman, 2000) addresses this assumption by asking, “Is suicide one thing or many things?” and answering, “it seems clear that the answer is ‘many’” (p. 50). We suggest that this assumption has been accepted because of the relative inability of previous theories to comprehensively explain and predict suicidal
+behavior. We also acknowledge that the question proposed by Maris and colleagues (2000)—“Is suicide one thing or many things?”—is an empirical one and is in need of scientific scrutiny. Further, the assumption that the same mechanisms underlie all suicidal behaviors—if it were supported—would greatly enhance the clinical usefulness of the theory. It is this application of the theory— clinical applications—to which we now turn.
+One of the primary tasks facing clinicians working with suicidal patients is the assessment of the degree of risk faced by individual patients. Suicide risk assessment frameworks are formalized procedures for clinicians that synthesize the research on the many documented predictors of suicide and provide structured ways to assess both current and more long-standing risk. Applying the interpersonal theory to risk assessment suggests that risk assessment frameworks should explicitly address the degree to which patients are currently experiencing thwarted belongingness and perceived burdensomeness, as well as the degree to which they have acquired the capability for lethal self-harm. Risk assessment grounded in the interpersonal theory, if supported empirically, will allow for a more parsimonious and clinically useful conceptualization of the etiology of suicide because this conceptualization does not presume that to assess individuals’ degree of risk for suicide requires measurement (or estimation) of a vast number of risk factors. For more specific recommendations on use of the interpersonal theory in suicide risk assessment (as well as treatment and prevention), readers are referred to Joiner et al. (2009).
+Clinical care for suicidal patients also involves treatment (i.e., psychotherapy and pharmacotherapy) aimed at reducing risk for engaging in suicidal behavior. Public health campaigns also aim at preventing suicidal behavior by targeting all individuals or those at elevated risk for developing thoughts about suicide or engaging in suicidal behavior. We propose that thwarted belongingness and perceived burdensomeness (as well as hopelessness concerning these states) are dynamic (i.e., frequently changing) factors, whereas acquired capability, once acquired, is relatively stable and unchanging. These aspects of the theory are relevant for treatment. The theory includes a clearly delineated danger zone at the intersection of perceived burdensomeness, thwarted belongingness, and the acquired capability and, thus, yields a clear prediction about what components of suicide interventions will be most effective at treating suicidal symptoms. According to the theory, interventions that directly or indirectly address perceived burdensomeness and thwarted belongingness should produce the best outcomes among suicidal individuals. The acquired capability would be relatively difficult to effectively address in treatment because a therapist is not able to modify a patient’s history, but this aspect of the theory does provide a clear prediction regarding who may benefit most from suicide focused preventive interventions: specifically, those who have a history fraught with painful and provocative experiences. The theory also suggests that prevention efforts targeting thwarted belongingness and perceived burdensomeness may be effective. For example, public health campaigns promoting the importance of maintaining social connections and social contributions could impact suicide rates. Use of the interpersonal theory to improve clinical care for suicidal patients and as a basis for suicide prevention efforts would, we suggest, support Lewin’s (1951) claim that “there is nothing so practical as a good theory” (p. 169).
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+Part 1:	Physical health disparities for people with mental illness
+Introduction
+The premature mortality of people with mental illness has been recognised by the medical community for more than half a century.1,2 Although premature mortality was initially shown in patients with severe mental illnesses such as schizophrenia and bipolar disorder,3-5 there is now evidence that individuals who have diagnoses across the entire spectrum of mental disorders have a substantially reduced life expectancy compared with the general population.3-11 Although suicide contributes to a considerable proportion of these premature deaths (with approximately 17% of mortality in people with mental illness attributed to unnatural causes),12,13 the majority of years of life lost in people with mental illness relate to poor physical health, specifically due to comorbid noncommunicable and infectious diseases.11,14-19 The consequent poor physical health outcomes of people with mental illness have been alluded to as a human rights issue,20 and the amount of research on this topic has increased substantially over the past two decades (appendix p 2).
+Despite the increasing amount of research in this area and more general advancements in health care and medicine, the poor physical health outcomes (and the associated decrease in life expectancy) of people with mental illness have not improved.3,12,21 In fact, the number of years of life lost due to physical health conditions in people with mental illness might be increasing.3,21-23 The premature mortality of people with mental illness reflects a large number of health inequalities between people with and without mental illness throughout the life course. Although the psychiatric literature is largely unified on the consensus that physical comorbidities have a lifeshortening effect for people with mental illness, the prevalence and specific effects of the physical comorbidities that can potentially affect individuals with diagnoses across the spectrum of mental disorders (not only severe mental illness) have not yet been widely examined.
+Comorbidity of mental and physical diseases: a literature meta-review
+To provide an overview of the literature in this field, we systematically identified all systematic reviews and meta-analyses of chronic physical disorders in people with common mental disorders, severe mental illnesses, alcohol and substance use disorders, and various other mental health disorders, published between Jan 1, 2000, and Oct 26, 2018. In particular, we sought to identify the top-tier evidence on the prevalence of chronic conditions in comparison with the general population (generally defined as individuals without mental illness). Further details on our search strategy and selection criteria are in the appendix (pp 2-5). We considered this body of meta-research and key recent reports from health-care and governmental bodies in developing the scope,
+priorities, and recommendations of this Commission (figure 1).
+As detailed in table 1, since 2000, almost 100 systematic reviews and meta-analyses have been published on the physical health comorbidities associated with mental illness. The findings from the most recent systematic reviews and meta-analyses on the prevalence or risk of physical illness for each category of mental illness are shown in the appendix (pp 6-14). In common with another review,121 we found a shortage of evidence from low-income and middle-income countries. Most metaresearch on the physical health of individuals with mental disorders has focused on cardiovascular or metabolic diseases in high-income countries. Overall, the available evidence shows that for individuals with diagnoses across the entire spectrum of mental health disorders, the risk for cardiometabolic disease is increased by 1-4-2-0 times compared with individuals without mental illness (appendix pp 6-14). For instance, for patients with depression, the risk of developing
+cardiac disease, hypertension, stroke, diabetes, metabolic syndrome, or obesity is around 40% higher than in the general population. Similarly, 16 reviews ofcardiovascular and metabolic health in patients with severe mental illness14,54-58’77-80’91-95’100 showed clear evidence of an increase in risk of 1-4-2-0 times across all cardiovascular and metabolic diseases examined. Although fewer studies have been done for other mental disorders, the existing reviews of anxiety disorders,46’76’87’98 substance use disorders,81,96 attention-deficit hyperactivity disorder,101 and personality disorders104 consistently find evidence of poor cardiometabolic health in patients with these diagnoses, with substantially higher rates of obesity, diabetes, and metabolic syndrome than in the general population (appendix pp 6-14). The only inverse relationship that has been identified between cardiometabolic health and mental disorders is the reduced incidence of diabetes in patients with anorexia nervosa (odds ratio [OR] 0-71) compared with those without anorexia nervosa.82 However, because of the
+physically damaging behaviours that are inherent to the disorder, individuals with anorexia nervosa are at a much higher risk for other health issues, such as a 12 times greater incidence of osteoporosis,108 and one of the highest rates of premature mortality across all mental disorders (all-cause standardised mortality ratio 5-9, 95% CI 4-2-8-3).12 Furthermore, individuals with other eating disorders, such as bulimia, have a much higher risk of diabetes (OR 3-45) than people without eating disorders.82
+The relationship between mental illnesses and cancer risk is uncertain. Although some reviews have found that mental illnesses are associated with a small increase in overall cancer risk,59 other reviews have found no relationship, or a decreased cancer risk.63,68 The risk of cancer associated with mental illness might vary for different cancer types. For instance, whereas patients with common or severe mental illnesses have an increased risk of lung cancer, the risk of colorectal cancer appears to be similar to (or even lower than) that in the general population.59,63 Further research is required to understand these relationships, but a possible explanation is that people with mental illness have a reduced life expectancy, resulting in a reduced lifetime rate of cancer in this group. Another area requiring further investigation is the relationship between psychiatric and neurological disorders,
+because the tendency to separate these two types of See Online for appendix illness into different categories, despite their overlapping characteristics, could result in underestimations of the true burden of mental illness on a global level.122 A recent meta-analysis has shown that for people with depression, the risk of developing Parkinson’s disease is doubled compared with people without depression,109 but the relationships between other psychiatric and neurological disorders have yet to be established.
+Gaps in the meta-research
+Our meta-research showed an absence of meta-analyses on chronic obstructive pulmonary disease (COPD) in people with mental disorders, although individual health database studies19,123 have found an increased prevalence of COPD in people with severe mental illness.
+The harmful effects of infectious diseases on the physical health of people with mental disorders might also be underestimated, because they have largely been unexplored in mental health disorders other than severe mental illnesses (table 2). The reviews that we identified on infectious diseases in populations with severe mental illness found that the average prevalence (across multiple countries) for hepatitis B infection, hepatitis C infection, and HIV was 15-63%, 7-21%, and 7-59%, respectively,111 and the prevalence of syphilis was 1-1-7-6%.112 Within
+specific settings or countries, prevalence data highlight that individuals with mental illness have an increased risk of infectious disease compared with the general population. For instance, in the USA, the prevalence of both hepatitis B and hepatitis C infections in patients with severe mental illness is around 20%, whereas the prevalence of these infectious diseases in the US population is estimated to be 0-3% and 1-0%, respectively.146,147 Similarly, the median prevalence of HIV among people with severe mental illness in the USA is 1-8%, which is almost four times greater than the general US population.146 In low-income and middle-income settings, infectious diseases are a major cause of mortality in people with severe mental illness. For example, in a 10-year follow-up study in Ethiopia,148 individuals with severe mental illness died 30 years prematurely compared with the general population, and half of the deaths among individuals with severe mental illness were from infectious diseases. Further scientific and governmental attention is required for infectious diseases among people with mental illness in low-income and middle-income settings, particularly given that rates of infection are highest in these settings, and inequalities between people with and without mental illness are most pronounced.149 Furthermore, despite the compelling evidence for increased risk of infectious diseases in adults with severe mental illness, the prevalence of infectious diseases in other mental disorders, and the extent to which this increased risk applies to young people with mental illness, is not well established. Future research should also aim to identify the underlying factors resulting in an increased rate of infectious diseases among people with mental illnesses so that more appropriate and targeted solutions can be developed (as discussed in Part 2).
+Much of the published literature assessing physical health in mental illness to date has examined the prevalence of specific health outcomes or disorders in isolation. The prevalence and specific effects of physical multimorbidity (ie, the presence of more than one chronic physical disorder) in people with mental illness are not fully understood. Some large-scale, multinational studies have shown that people with severe mental illness,123,150 common mental disorders,151,152 and substance use disorders18,153 are at a greatly increased risk of physical multimorbidity from the point of onset of the mental illness.154 The average age of onset of multimorbidity is younger in people with mental illness compared with the general population.123,154 Multimorbidity greatly increases the personal and economic burden associated with chronic conditions, and reduces life expectancy compared with a single morbidity.155,156 Urgent attention is required to address the onset and accumulation of physical multimorbidity, particularly in low-income and middleincome settings, where physical multimorbidity is increased among people with mental illness compared with the general population,81,83,151 but services do not have the resources to deal with the burden and complexity
+of these cases. Additionally, the development of costeffective approaches that address the root causes of multimorbidity is needed to prevent long-term disability in people with mental illness.
+Further considerations
+Although the impact of physical comorbidities on the life expectancy of individuals with mental illnesses is well established,13,14 further research is needed to examine whether the psychological distress associated with mental illness is compounded by the additional burden of these chronic conditions. For instance, in the general population, diabetes is commonly associated with distress, which can have a considerable effect on the person’s quality of life and their ability to manage their overall health.157 Diabetes-related distress also affects people with common mental disorders,157 severe mental illness,158 and substance use disorders.159 The prevalence of obesity is considerably increased across most classes of mental disorder compared with the general population (appendix pp 6-14). Weight gain can be distressing and negatively affect an individual’s quality of life and self-esteem, and might impede treatment-seeking behaviour because an individual is concerned about further weight gain.160 Similarly, obesity can perpetuate lifestyle behaviours, such as social withdrawal161 and sedentary behaviour,126 that are characteristic of many mental disorders, and are also key risk factors for poor cardiometabolic health.162 Emerging evidence suggests that obesity and metabolic syndrome are independent predictors of relapse and rehospitalisation for those with severe mental illness.163,164 This relationship could be explained by the inflammatory effects of abdominal obesity; inflammation has also been associated with worse mental health165 and increased suicidality.166 In addition to the personal burden, physical comorbidities in people with mental illness result in an increased financial cost, the extent ofwhich requires further research (panel 1).
+To address physical health inequalities in people with mental illness compared with those without mental illness, we must focus on both reducing the prevalence of chronic health conditions, and lessening their effects across the life course. In particular, cardiometabolic diseases are a relevant and transdiagnostic target for improving physical health outcomes across a broad spectrum of mental illnesses. Although schizophrenia is typically associated with the greatest degree of cardiovascular risk (partly due to the side-effects of drugs for psychosis), there is compelling evidence that the risk of obesity, metabolic syndrome, diabetes, and cardiometabolic disease is similarly increased in other mental disorders, including common mental disorders.46,76,83,85,87,97,98,175-177 Given the high prevalence of these mental disorders, developing strategies for improving health outcomes that can be applied across many different mental health diagnoses (including severe mental illness) could considerably reduce premature mortality and the lifelong burden of poor physical health for people with mental illness. The effects and prevalence
+of other non-communicable diseases and infectious diseases in low-income, middle-income, and high-income countries cannot be neglected. As such, understanding the epidemiology of mental and physical comorbidities in low-income and middle-income countries,178 and developing evidence-based interventions that integrate mental and physical health care in these settings,179 is increasingly recognised as a major research priority for global health. The following parts of the Commission discuss key modifiable factors that drive mental and physical health comorbidities, describe strategies for improving the management and prevention of these conditions, and present directions for both immediate clinical action and future research to reduce physical health inequalities for people with mental illness (figure 1).
+Part 2:	Key modifiable factors in health-related behaviours and health services
+Introduction
+Part 1 identified cardiometabolic diseases as a category of physical comorbidities that is particularly pervasive and has profound effects on patient wellbeing, morbidity, and mortality, across many mental disorder diagnoses.46’76’81’83’85’8796-98’157-159’175-177’180 In addition to the sideeffects of psychotropic medications (described in Part 3), the reasons for increased cardiometabolic morbidity and mortality in people with mental illness can be separated into patient-related factors and provider-level or system-level factors.121
+Lifestyle risk factors, such as smoking, poor diet, and inactivity’ are modifiable’ patient-related factors that are known to be associated with cardiometabolic disease,108’128’138’141 as well as affecting many other aspects of physical health.46’76’81’83’85’87’96-98’175-177 However, the extent to which lifestyle risk factors in patients with various mental disorders differ from the general population is not fully established. As a result, current lifestyle interventions for people with mental illness could be imprecise, or could focus too much on one behavioural modification at the expense of other important risk factors (eg, increasing physical exercise without considering the impact of diet, or focusing on smoking over alcohol intake).
+We applied a systematic hierarchical approach (appendix pp 15, 16) to identify top-tier evidence on lifestyle risk factors for non-communicable diseases in people with mental illness. We focused on behavioural risk factors in affective and psychotic disorders, rather than on mental health illnesses that are characterised by physically damaging behaviours, such as eating disorders and substance or alcohol use disorders (in which the greatest behavioural risks to physical health are the behaviours that define the conditions). Table 2 summarises findings from meta-analyses, systematic reviews, and population-scale studies, published since 2000, on lifestyle risk factors in various mental health populations.
+Panel 1: Adding up the costs of physical comorbidities in people with mental illness
+Cost-of-illness studies, which assess the economic burden of a diagnosis or group of diagnoses, have found that people with combined physical and psychiatric comorbidity have higher hospital costs, increased readmission rates, and higher total health sector costs compared with people without psychiatric diagnoses.163-167-171
+Although cost-of-illness studies are important for describing economic burden, only economic evaluations can estimate the cost-effectiveness of interventions to support decision making on the investment of limited health-care (and other sector) resources. Economic evaluations are used to assess pharmaceuticals and health technologies in many countries. Evidence regarding the cost-effectiveness of referral programmes and lifestyle interventions for people with mental illness and increased cardiovascular disease risk factors is mostly positive, but little evidence is available.172-174 Further economic evaluations that collect cost and outcome data, and that are done alongside clinical trials, will be needed to provide convincing evidence of the economic benefits of these programmes in people with mental health diagnoses.
+Challenges to trial-based economic evaluations include excessive respondent burden and respondent bias in collecting cost information, although these might be overcome by using administrative data systems. Fragmentation of information and poor availability of data for some populations present additional challenges. Trial-based evaluations, which often use intermediate efficacy endpoints (eg, LDL cholesterol levels), will be an important source of data for modelled economic evaluations. Modelled evaluations will be crucial to establish the long-term cost-savings and improvements in outcomes (eg, quality of life and mortality) through the avoidance of future health consequences, such as metabolic syndrome and cardiovascular disease events. As this area of research develops, both trial-based and modelled economic evaluations will need to adhere to published methodology standards, including presenting health-care and societal perspectives to assist policy makers.
+Lifestyle risk factors across various diagnoses
+Although the initial aim of our hierarchical evidence synthesis was to determine key lifestyle risk factors that are associated with individual mental disorders, most of the published literature showed that all psychiatric diagnoses are associated with a wide spectrum of lifestyle risk factors (table 2). People with mental illness tend to have more unhealthy lifestyles compared with the general population, and among people with mental illness, those with schizophrenia have a particularly high risk of smoking, sedentary behaviour, and poor diet.128,137’138 Socioeconomic factors could partly mediate this trend, because the incidence of schizophrenia is higher in low-income communities,181 and such communities also have higher rates of behavioural risk factors compared with
+high-income communities.182 However, lifestyle risk factors are still greater in patients with schizophrenia than those with other mental health disorders, even when controlling for socioeconomic factors. For instance, a population-scale study from 2018 that used data from the UK Biobank128 found that individuals with severe mental illness ate more obesogenic food than the general population, particularly those with schizophrenia (figure 2), and the differences in diet persisted after adjusting for social deprivation and education.128 The use of second-generation antipsychotics (SGAs) could contribute to changes in diet, because trials in healthy volunteers found that SGAs such as olanzapine can reduce satiety, increase appetite183 and lethargy, and have sedative effects.184-186 Although some SGAs, such as olanzapine, have the most obvious cardiometabolic sideeffects, other more widely prescribed psychotropic medications also have cardiometabolic side-effects that accumulate over time. Thus, early intervention strategies for managing lifestyle and cardiometabolic risk for patients treated with psychotropic medications are important for preventing cardiometabolic diseases from arising (panel 2) The side-effects of SGAs and other psychotropic medications (such as drugs for depression) are discussed further in Part 3.
+Lifestyle risk factors in low-income and middle-income settings
+Although most of the data presented in table 2 are from high-income countries, similar trends have been found in low-income and middle-income counlries.'' For instance, data from the WHO Study on Global Ageing and Adult Health and the WHO World Health Survey show that individuals with depression in low-income and middle-income countries are more likely to smoke (OR 1-41),194 to not meet physical activity guidelines (OR 1-42),195 and to have sedentary behaviour for 8 h or more per day (OR 1-94)196 than individuals without depression. Similarly, low levels of physical activity are found in individuals with anxiety and psychotic disorders in low-income and middle-income countries.137,141,197-199 Despite the differences in sociocultural factors in low-income and middle-income countries compared with high-income countries, people with mental illness in both settings have more lifestyle risk factors compared with the general population. In low-income and middle-income countries, there are new challenges to maintaining a healthy lifestyle caused by the spread of fast-food restaurants, new technologies that allow for reduced physical inactivity, and tobacco promotion and legislation.200-202 Because lifestyle risk factors, such as physical inactivity and poor diet, are elevated in people with mental illness (table 2), further efforts are needed to develop lifestyle interventions that address these factors appropriately for those with mental illness living in low-income and middle-income settings (see Part 5).
+In addition to non-communicable diseases, other behavioural risk factors, such as intravenous drug use and high-risk sexual behaviours, are also over-represented in people with severe mental illness in low-income, middle-income, and high-income settings (see Part 1), and can lead to infectious disease. Most available data are for adults with severe mental illness, so the prevalence in other age groups and for other diagnoses might be underestimated. For instance, a recent meta-analysis203 of 3029 adolescents with a range of psychiatric diagnoses showed a 15% (95% CI 3-50) lifetime prevalence of sexually transmitted illnesses, and found that 40% (95% CI 23-78) of the adolescents had shown high-risk sexual behaviour during their most recent sexual encounter. Furthermore, recent alcohol use increased the likelihood of having unprotected sex (OR 1-66, 95% CI 1-09-2-52).203 The interactions between risk factors for non-communicable diseases and infectious diseases should not be overlooked, and suggest that screening for multiple lifestyle factors, rather than single factors or biological markers alone, will be the most efficient method for improving health outcomes for people with mental illness.
+Interventions for multiple lifestyle risk factors in mental illness
+In summary, although our evidence synthesis process aimed to identify key behavioural risk factors for specific mental disorders, the evidence suggests that simultaneously considering multiple lifestyle factors is more appropriate in understanding and managing risk factors across all mental health diagnoses. However, such transdiagnostic, multifactorial approaches are not widely reflected in the published literature, which generally focuses on specific factors for individual disorders. Furthermore, no suitable tools are available for clinicians to comprehensively assess lifestyle factors as part of standard care. The sole use of biological markers for physical health assessment (such as >7% increase in bodyweight, high blood pressure, and an abnormal lipid profile) could mean that interventions are applied only when it is too late to protect metabolic health or pre-empt obesity (panel 2). Clinical guidelines are increasingly recommending that assessments of diet, physical activity, and health risk behaviours are done alongside assessments of anthropomorphic parameters and blood markers of metabolic status,204 to more accurately assess current physical health and future risk.
+To comprehensively promote the physical health of people with mental illness, a positive first step would be developing quick and widely applicable tools for lifestyle screening. These tools could be used across different diagnoses, settings, and services, to assess a range of behavioural risk factors (eg, exercise, diet, substance use, and sleep) at once, and thus identify key drivers of poor physical health on a case-by-case basis. A comprehensive lifestyle assessment would give patients more actionable
+Panel 2: Why wait for weight? Tipping the scales towards prevention
+Clinical guidelines for metabolic screening upon initiation or continuation of second-generation antipsychotics recommend that blood pressure, body-mass index, blood glucose, and lipid profile should be checked at least every 6 months.187-189 This is a positive example of considering physical health outcomes for people with severe mental illness. However, a large body of research in the general population has shown that preventing conditions such as obesity and metabolic syndrome from arising is considerably more efficient than attempting to reverse their long-term consequences.190 Thus, proactive lifestyle interventions in mental illness might not have their maximal effect if the interventions are only provided after large changes in biological or clinical markers of adverse metabolic health are found during screening.
+Individuals with first-episode psychosis are at considerable lifestyle risk from illness onset (table 2), because they tend to be less physically active and have a higher rate of alcohol use disorders than those with long-term schizophrenia, and also have nutrient deficits and a high rate of smoking (around 60% for both first-episode psychosis and schizophrenia, which is much higher than in the general population). Many other behavioural risk factors also seem to precede, rather than accompany, the onset of psychotic disorders,191 and metabolic disturbance might be present from illness onset.192
+Although treatment with second-generation antipsychotics (SGAs) can be important for stabilising mental health, taking these drugs can further increase metabolic risk (see Part 3). Given the high likelihood of physical health deterioration while taking SGAs, clinicians who prescribe them to patients have a duty of care to ensure that the patients are given access to evidence-based lifestyle interventions (as detailed in Part 4) from the start of treatment. Lifestyle interventions should be made available even to those with intact metabolic health. Although health screening should continue, more timely and effective strategies for improving health outcomes will require intervening on the basis of lifestyle plus pharmacological risk, rather than waiting for visible weight gain or metabolic dysfunction to happen.193
+physical health information than that which is typically provided from screening for biological markers, because patients will be informed of specific lifestyle changes they could make to protect their physical health. Selfreport questionnaires are often burdensome and inaccurate, reducing their suitability for capturing lifestyle factors in people with mental illness.205 Thus, a priority for future research is to examine if digital technologies (including smartphones and wearable technologies) could provide feasible and accurate methods of broad lifestyle assessment.205,206
+In addition, more efficient care pathways are needed to help people with mental illness minimise behavioural risk factors (see Part 4). For instance, multidisciplinary referral pathways (available through both primary and secondary care) could provide access to specialised physical activity, smoking cessation, dietetics, and other allied health services, depending on the individual’s specific behavioural profile and health goals. The dissemination of risk behaviour interventions in low-income and middle-income countries is an urgent challenge, because individuals with mental illness in these countries are disproportionately affected by an increased risk for infectious diseases and noncommunicable diseases.
+Health provider-level and system-level factors
+Lifestyle-related factors are unlikely to be the only explanations for poor physical health outcomes in people with mental illness.121 For severe mental illness in particular, mortality remains high even after adjusting for behavioural risk factors such as smoking, physical activity, and body-mass index.207 Increasingly, evidence suggests that the poor physical health outcomes of people with mental illness are partly driven by differences in the availability and quality of health care that they receive. For instance, people with severe mental illness are less able to access adequate health care than the general population. In the USA, people with severe mental illness are twice as likely as those without mental disorders to have been denied medical insurance because of a pre-existing condition.208 These disparities exist at all levels of health services. In primary care, people with severe mental illness are less likely to have a physical examination (eg, weight and blood pressure),209 or to be assessed and treated for hyperlipidaemia, than people without mental illness.210,211 People with mental illness also have more emergency department visits and more avoidable admissions to hospital for physical conditions that with appropriate primary care should not require inpatient treatment.212 Patients with a range of psychiatric diagnoses, including depression, anxiety, substance use disorder, and severe mental illness, have reduced access to oral health care.213,214
+In secondary health services, physical health might also be poorly managed for people with mental illness.215 In particular, people with mental illness are less likely to receive medical or surgical interventions that are commonly given in the general population. For example, people who have had prior contact with mental health services are less likely to receive cardiac catheterisations and coronary artery bypass grafting than people who have no prior contact, which contributes to the higher mortality for circulatory disease among people with a history of mental illness.216-218 People with mental illness are also less likely to receive appropriate medications, such as P blockers and statins, at discharge after myocardial infarction.219 The incidence of many cancer types (including common types, such as breast, colorectal, and prostate cancer and melanoma) among patients with psychiatric illness is only slightly higher than that of the general population (see Part 1), but mortality is markedly higher.220,221 Disparities at the health-service level are thought to be responsible for increased cancer mortality, because people with mental illness are less likely to be offered cancer screening,222 have a reduced likelihood of surgery for all types of cancer, and wait longer for surgery.223
+A possible explanation for disparities in care for people with mental illness is that clinicians attribute emerging somatic symptoms to the patient’s underlying
+psychiatric disorder, resulting in missed diagnoses (sometimes known as diagnostic overshadowing).224,225 In addition, people with mental illness can have difficulties with reporting medical problems, distinguishing physical symptoms from the symptoms of mental illness, and engaging with health services (ie, attending follow-up appointments), particularly if the services are non-inclusive, or perceived as non-inclusive, of people with mental illness.224,226
+Physicians might be reluctant to offer some medical procedures to people with mental illness because of the ensuing psychological stress, difficulties with obtaining informed consent or compliance with postoperative care, or contraindications, such as substance misuse and smoking.226 However, contraindications to specialised interventions, such as smoking or problems with informed consent, are not relevant to the prescription of vascular drugs, such as angiotensin-converting enzyme inhibitors, P blockers, or statins, that are known to reduce morbidity and mortality.227 Furthermore, people with schizophrenia are as adherent to diabetes medication as the general population.228 Access to secondary health care for people with mental illness might be restricted by financial costs, fragmentation of care, and social stigma.224,226,229 Although health-care providers should recognise that challenging behaviour can be a symptom of illness, evidence shows that some health-care providers have stigmatised views towards people with mental illness.224,225 Nonetheless, health services should routinely offer health screening and lifestyle interventions for people with psychiatric disorders, in the same way as for patients with chronic physical conditions.230
+In conclusion, people with mental illness are likely to receive a poorer standard of health care compared with people without mental illness who have the same physical health problems. To address this discrepancy, changes need to be made in the training of health providers and to the overall health system (see Part 5). Greater integration of physical and mental health care in primary care settings is a key recommendation for improving the management of physical comorbidities in people with mental illness. Mental health clinicians should be wary of attributing emerging somatic symptoms solely to an underlying mental illness, and refresher training on the detection, management, and prevention of chronic medical conditions needs to be available to mental health staff.229 Furthermore, developing clinical tools for comprehensive lifestyle assessment, and improving referral pathways to targeted interventions, will enable practitioners to identify and manage cardiometabolic risk factors in a timely manner. At the service level, screening procedures need to be improved to support prevention initiatives, alongside investment in the integration of physical health within mental health services, and vice versa.
+Part 3:	Interplay between psychiatric medications and physical health
+Introduction
+As discussed in Part 1, a broad range of psychiatric diagnoses are associated with high comorbidity for physical conditions (particularly cardiometabolic diseases). Although lifestyle risk factors for chronic illness seem to be consistent across a wide range of mental illnesses (Part 2), the physical health risks associated with individual mental health diagnoses are modified by the types of psychotropic medications that are given to treat each condition. In this section, we present research on the interactions between psychotropic medications and physical health, and discuss pharmacological strategies for managing the physical health risks associated with mental illness and avoiding psychotropic adverse drug reactions (ADRs).
+ADRs associated with psychotropic medications
+Antipsychotic medications are a key component of treatment for psychotic disorders, because they reduce acute symptoms,231 and reduce the risk of relapses,232 emergency hospital admissions,233 rehospitalisation,234,235 and mortality.21,236 Antipsychotic medications are also used for bipolar affective disorder.237,238 However, the long-term effects of ADRs related to physical health are a major concern, and can be broadly divided into the following categories: cardiometabolic, endocrine, neuromotor, and other ADRs. The ADRs associated with specific antipsychotics are described in the appendix (p 17).
+Cardiometabolic ADRs
+Weight gain is an important ADR because it mediates other cardiometabolic outcomes, such as type 2 diabetes and cardiovascular diseases. Weight gain is the most distressing side-effect reported by callers to mental health helplines,188 and is associated with poorer quality of life239-241 and barriers to social engagement.242 As a result, patients who gain weight have a reduced adherence to treatment, which can lead to relapse and poor mental health outcomes.163,164 Although most antipsychotic medications lead to weight gain, clozapine and olanzapine have the highest propensity, and haloperidol, lurasidone, and ziprasidone have the lowest propensity.243,244 Weight gain pathways induced by antipsychotic medication include those involving histamine H1 receptors, D2 dopamine receptors, blockade of 5-hydroxytryptamine receptor 2C, and dysregulation of glucagon-like peptide-1.243,245 Metaanalyses (table 1) have found that the risk of metabolic syndrome and type 2 diabetes is at least twice as high in people with schizophrenia, bipolar affective disorder, and major depressive disorder compared with the general population (appendix pp 6-13).
+Endocrine ADRs
+Antipsychotic-induced hyperprolactinaemia is the most common endocrine ADR.246 Antipsychotic medications
+block dopamine in the tuberoinfundibular pathway, leading to reduced inhibition of prolactin synthesis and secretion. Hyperprolactinaemia is most commonly found with first-generation antipsychotics, as well as risperidone, paliperidone, and amisulpride.247 Hyperprolactinaemia can be asymptomatic, or can lead to complications, such as menstrual disturbance and sexual dysfunction (including reduced libido, erectile dysfunction, vaginal dryness, and orgasmic dysfunction248) in the short-term,247 and osteopenia in the long-term.249
+Neuromotor ADRs
+Extrapyramidal side-effects are the most common neuromotor ADRs of antipsychotics. These side-effects can be socially stigmatising and are associated with poor quality of life, treatment dissatisfaction, and nonadherence to treatment.239,240 Extrapyramidal side-effects include dystonia (muscle spasm), Parkinsonism (tremor, rigidity, and bradykinesia), akathisia (subjective restlessness), and tardive dyskinesia (abnormal involuntary movements). The detailed mechanisms of these sideeffects are unknown, but they are likely to be related to blockade of dopamine receptors in the nigrostriatal pathway.250 The annual incidence of tardive dyskinesia is lower among patients taking SGAs compared with those taking first-generation antipsychotic medications.251 Neuroleptic malignant syndrome is a rare but serious condition (incidence of one to two cases per 10000 people per year) that can be life-threatening.252 It is characterised by fever, severe rigidity, autonomic disturbances, and confusion.252 The incidence of neuroleptic malignant syndrome has reduced since SGAs became more widely used.252
+Other ADRs
+Antipsychotics are associated with varying degrees of cardiac conduction delay, indicated by a prolonged QTc interval, that can predispose the patient to torsade de pointes and lead to sudden death.253 Therefore, cardiac conduction should be monitored in patients at risk.
+Anticholinergic effects are common side-effects of antipsychotic medications, particularly chlorpromazine, clozapine, and olanzapine.254 Anticholinergic effects are mediated by antagonism of acetylcholine by inhibition of the muscarinic receptors. They can be either central (eg, impairment of cognition, memory, and concentration, and sedation) or peripheral (eg, constipation, dry eyes, mouth, and skin, blurred vision, tachycardia, and urinary retention). These effects are particularly burdensome in the older population and can have cumulative effects when multiple anticholinergic agents are used.254
+Somnolence, sedation, and hypersomnia are also common side-effects of antipsychotics.244 Although sedation might have short-term benefits for an acutely exacerbated or agitated patient, in the long term, somnolence and sedation can affect physical activity,
+bodyweight, concentration, and the ability to participate in daily activities or psychosocial rehabilitation, and could lead to medication non-adherence.239
+Most antipsychotic medications can reduce the seizure threshold. The greatest dose-related risk for seizures is associated with clozapine.253
+Clozapine
+Clozapine is the only approved antipsychotic medication for people with treatment-resistant schizophrenia.255 It is the most effective antipsychotic medication for reducing positive symptoms256 and hospitalisations.257 However, clozapine is associated with severe neutropenia (agranulocytosis; incidence 0-9%; 95% CI 0-7-1-1), usually in the first month after commencement, that can rarely cause death (0-013%; 0-010-0-017).258 Cardiac ADRs can be life-threatening and include myocarditis (incidence of 0-03-1-00%, usually within the first month) 259,260 and cardiomyopathy (incidence of 0-06-0-12%, usually after the first year).259,261 Other ADRs of clozapine include weight gain, type 2 diabetes, sedation, sialorrhoea, constipation, tachycardia, postural hypotension, gastro-oesophageal reflux, nocturnal enuresis, seizures, and obsessivecompulsive symptoms.262
+Mood stabilisers
+Mood stabilisers are prescribed for bipolar affective disorder263 and adjunctively for refractory schizophrenia.264,265 Individuals who are prescribed lithium have a mean weight gain of4 kg over 2 years.266 Lithium is also associated with thyroid disease,267 including development of goitre (in up to 50% of patients268), hypothyroidism,269 or hyper-thyroidism.270 Lithium is also associated with polydipsia, polyuria, diabetes insipidus, and other forms of renal dysfunction.269 Sodium valproate is associated with metabolic effects, with at least half of individuals gaining weight in the first 3 months after initiation,271 with a mean weight gain of 6-4 kg over 3 months.272 It is also associated with insulin resistance, which increases the risk of developing type 2 diabetes.273
+Antipsychotic medications are often prescribed concurrently with mood stabilisers; additional caution is required in this situation because the metabolic effects of the two classes of medication could be additive.93 Although lithium and sodium valproate are the two most widely prescribed mood stabilisers, other mood stabilisers have a lower propensity for weight gain (eg, carbamazepine)271 or have no effect on weight (eg, lamotrigine).274 All mood stabilisers are associated with teratogenic effects and should be avoided in pregnancy and lactation (appendix p 18).
+Drugs for depression
+Common ADRs with newer-generation drugs for depression include headache, nausea, agitation, sedation, dizziness, sexual dysfunction, hyponatraemia, weight gain, and metabolic abnormalities.275 Gastrointestinal
+side-effects, headache, and sexual side-effects are associated with all proserotonergic drugs for depression, whereas sedation, weight gain, and metabolic effects vary across agents. Antihistaminergic agents (eg, mirtazapine) are more associated with cardiometabolic effects and sedation. Less commonly, drugs for depression can have cardiac (eg, arrhythmias), neurological (eg, seizures), and hepatic ADRs.275 Treatment with tricyclic antidepressants is associated with anticholinergic effects, including dry mouth, sedation, blurred vision, constipation, and urinary retention, as well as increased appetite, weight gain, and hyponatraemia (especially in older patients).276 Furthermore, tricyclic antidepressants are associated with a risk of orthostatic hypotension and falls.277 They also have a known arrhythmogenic effect; electrocardiogram (ECG) changes can include prolongation of PR interval, QRS interval, and PT (appendix p 19).
+Pharmacological management of ADRs and physical health comorbidities
+For the physical comorbidities associated with serious mental illness that are also commonly seen in the general population (eg, cardiovascular disease), national and international prescribing guidelines developed for the general population should be followed. By contrast, conditions that are secondary to psychiatric pharmacological treatment (eg, extrapyramidal side-effects) require a specialised approach. Close monitoring of physical health parameters is required for people taking antipsychotic medications, and evidence-based pharmacological treatments are needed.278 If it is safe and feasible, modifying psychiatric medications that are associated with an ADR (eg, by reducing doses or switching medications) should be considered, in consultation with the patient. Here, we provide a targeted, evidence-based approach to addressing commonly observed physical health ADRs in patients with severe mental illness.
+Type 2 diabetes
+Pharmacological management of type 2 diabetes for patients with severe mental illness should follow guidelines for the general population (appendix p 20). The first-line pharmacological therapy is metformin monotherapy, and second-line therapies are listed in the appendix (p 20). The relative risks and benefits of different type 2 diabetes treatments for patients with severe mental illness are presented in table 3. Metformin reduces the risk of transition from prediabetes to type 2 diabetes,283,284 and should be considered for individuals with severe mental illness and prediabetes. Glucagon-like peptide 1 receptor agonists also reduce the transition from prediabetes or non-diabetes to type 2 diabetes, as well as leading to clinically significant weight loss.282
+Weight gain
+When behavioural interventions are ineffective, pharmacological methods for attenuating weight gain in patients
+with severe mental illness should be considered. Pharmacological agents are described in detail in the appendix (p 21); the most evidence in individuals treated with drugs for psychosis is for metformin and topiramate.285 Bariatric surgery can also be considered as a last-resort treatment if both behavioural and pharmacological interventions are not effective. Weight gain associated with drugs for psychosis is not usually dose-dependent, so dose reduction will not be effective in reducing weight.286
+Arterial hypertension
+Pharmacological management of hypertension in patients with severe mental illness should follow guidelines used for the general population (appendix p 20).
+Dyslipidaemia
+Data on dyslipidaemia treatments that are specific for people with mental illness are scarce. Therefore, the best guidance available comes from general population studies. Statins reduce the risk of coronary heart disease events by 20-30%.287-289 Cardiovascular risk calculators that incorporate factors such as age, hypertension, and type 2 diabetes diagnosis, and particularly those that include diagnosis of severe mental illness and use of drugs for
+psychosis (eg, QRISK3 calculator),290 inform decisions about the initiation of statin therapy.291 The pharmacological management of dyslipidaemia in patients with severe mental illness should follow guidelines used in the general population (panel 3). No strong evidence is available to support targeting hypertriglyceridaemia therapeutically to decrease cardiovascular risk.
+Sinus tachycardia
+Sinus tachycardia in patients with severe mental illness could be a feature of the illness, of drug withdrawal, or of an acute drug reaction (eg, serotonin syndrome or neuroleptic malignant syndrome). Psychotropic-related tachycardia is persistent, and usually dose-related.295 If dose reduction or switching medication is not feasible, and inappropriate sinus tachycardia has been confirmed (including a 24-h ECG), the first-line treatment is a cardioselective P blocker (eg, atenolol 25-100 mg per day) with uptitration until the heart rate normalises (60-100 beats per min). If P blockers are not tolerated (eg, in patients with postural hypotension), or are ineffective, then ivabradine (5-0-7-5 mg twice a day) can be introduced.296 Ivabradine has been shown to be effective and tolerated in clozapine-induced tachycardia.297
+Panel 3: General principles for prescribing antihypertensives and statins to people with severe mental illness
+Antihypertensives
+•	If the patient has no indications for a specific medication, then any of the following four medication classes can be used as first-line treatment:292 thiazide diuretics, long-acting calcium-channel blockers (eg, amlodipine), angiotensin-converting enzyme inhibitors, and angiotensin II receptor antagonists
+•	A thiazide-like diuretic or long-acting dihydropyridine calcium-channel blocker should be used as the initial monotherapy for black patients293
+Statins
+•	Consider using a cardiovascular disease risk assessment tool (eg, QRISK3 calculator)290 to guide whether statins should be used; measure total and HDL cholesterol to achieve the best estimate of cardiovascular disease risk294
+•	Before offering statins to the patient for primary prevention of cardiovascular disease, discuss the benefits of lifestyle modification, and optimise the management of other modifiable cardiovascular disease risk factors, if possible
+•	Offer statin therapy (eg, atorvastatin 20 mg once a day) for primary prevention of cardiovascular disease if the QRISK3 assessment tool shows that the individual has a 10-year risk of developing cardiovascular disease of 10% or higher294
+Postural hypotension
+In addition to the causes of postural hypotension that exist in the general population, it can be related to taking psychotropic medication, notably clozapine and quetiapine.298 If increased fluid intake and salt consumption are ineffective, a dose adjustment or switch of the responsible psychiatric medication should be considered if safe to do so. If dose adjustment or medication switch is not feasible, non-pharmacological therapy (appendix p 22) with regular blood pressure monitoring should be undertaken.
+Extrapyramidal side-effects
+Around 10% of individuals who are taking antipsychotic medications have acute dystonia.299 It is more common in antipsychotic-naive individuals, and can occur rapidly after the initiation ofthe drug for psychosis. Acute dystonia can be treated with an anticholinergic medication (eg, benzatropine), which is given orally, intramuscularly, or intravenously, depending on urgency. Parkinsonism is seen in approximately 20% of individuals taking antipsychotic medications.300 If changing medication or reducing the dose is not effective or feasible, patients can be given an anticholinergic medication. The risk of akathisia varies for different drugs for psychosis, but is estimated to occur in 25% of individuals taking
+first-generation antipsychotics.301 If dose reduction of the causative medication is unsuccessful, a switch to quetiapine, olanzapine, or clozapine can be con-sidered.302,303 Other treatments include P blockers (eg, propranolol 30-90 mg per day),304 5-hydroxytryptamine receptor 2 antagonists (eg, mirtazapine 15 mg per day, mianserin 30 mg per day, or cyproheptadine 16 mg per day),304-306 antimuscarinics (eg, benzatropine 6 mg per day),307 and benzodiazepines (eg, clonazepam 0-5-3-0 mg per day).304 Tardive dyskinesia occurs in 5% of patients per year of exposure to drugs for psychosis.251 If tardive dyskinesia occurs, it is recommended that anticholinergics are stopped and treatment is rationalised (ie, stopping the causative drug or reducing the dose), with clozapine most likely to provide symptomatic relief.308 Adjunctive treatments include tetrabenazine,309 and novel vesicle monoamine transporter type 2 inhibitors that have been approved by the US Food and Drug Administration, such as valbenazine and deutetrabenazine.310
+Anticholinergic effects
+The first-line management of anticholinergic ADRs of drugs for psychosis is dose reduction, if it is feasible.298 For constipation caused by an anticholinergic-related reduction in gastric motility,311 stool softeners (eg, macrogols or docusates) and a stimulant laxative (eg, senna) might be effective.312 For patients taking clozapine, sialorrhoea is common. Augmentation with diphenhydramine or benzamide antipsychotics (eg, amisulpride) can ameliorate sialorrhoea.313
+Sexual side-effects
+Sexual side-effects can include reduced libido, delayed or blocked ejaculation, erectile dysfunction, decreased orgasm, persistent genital arousal, lactation, and numbness of the vagina or nipples. Patients with sexual side-effects should be assessed by examining prolactin concentration, concomitant medications, and comorbid causes (which can be psychological or physical—eg, diabetes or cardiometabolic disease).298 If prolactin is elevated, the antipsychotic dose might need to be reduced or the drug might need to be switched. Alternatively, low-dose aripiprazole could be prescribed.298 Patients who are taking SSRIs and have sexual dysfunction could be switched to another drug for depression, or given a trial of bupropion or sildenafil, if appropriate.314
+Thyroid disease
+In patients with hyperthyroidism who are taking lithium, a pertechnetate scan might be required to determine the cause of the thyroid disorder. Graves’ hyperthyroidism or toxic multinodular goitre can be treated with thionamides, radioiodine, or surgery, whereas if the patient has lithium-induced thyroiditis, cessation of lithium should be considered.267 Lithium-induced hypothyroidism can occur in the presence or absence ofgoitre. When lithium-induced
+hypothyroidism is present, treatment with levothyroxine is indicated, according to general guidelines for the management of primary hypothyroidism.315 Lithium-induced goitre requires an ultrasound examination to assess for diffuse versus nodular enlargement, and where appropriate, fine needle aspiration should be done to guide diagnosis. Levothyroxine might stabilise or reduce lithium-induced goitre.316 Because of the high incidence of thyroid disease in patients who are taking lithium, baseline clinical thyroid examination and serological assessment of thyroid function is recommended, with at least annual monitoring during treatment. The development of thyroid dysfunction while taking lithium does not usually require lithium therapy to be stopped; the risks and benefits of continuing treatment should always be considered.
+Renal disease
+Lithium-induced nephrogenic diabetes insipidus, with associated polyuria and polydipsia, can affect a patient’s quality of life. It is usually at least partially reversible with cessation of lithium, although it can be permanent after prolonged therapy.317 If ongoing lithium treatment is required and the patient only has a mild-to-moderate renal-concentrating defect, the introduction of amiloride (which is thought to reduce the accumulation of lithium in collecting tubule cells) can reduce urine volume, increase urine osmolality, and improve responsiveness to antidiuretic hormone.318 Thiazide diuretics with a low-sodium diet have also been found to have a paradoxical effect of reducing urinary output in nephrogenic diabetes insipidus.319 For patients with chronic kidney disease secondary to chronic interstitial nephritis, lithium cessation might be indicated if renal insufficiency progresses. Some renal function might be recovered after discontinuation of lithium, although progressive renal failure can occur.320 Regular monitoring of renal function is required, and monitoring of other risk factors for renal failure (eg, hypertension and diabetes) is also important.
+Nicotine and smoking cessation
+Smoking, and its associated physical morbidity, is a key contributor to the excess mortality of individuals with mental illness.321,322 Therefore, reducing smoking rates is a priority. However, clinicians should be aware that abrupt smoking cessation can change the pharmacokinetics and pharmacodynamics of many psychotropic medications (eg, increasing blood concentrations of clozapine, and to a lesser extent olanzapine and fluvoxamine). Patients who are planning to stop smoking should be followed up closely; plasma concentrations of medications should be monitored, if possible, and appropriate dose adjustments should be made.
+In the general population, nicotine replacement therapy increases the odds of successful smoking cessation by 1-5-2-0 times, with good evidence of efficacy in patients
+with mental illness.323 Nicotine replacement therapies should be used for approximately 8-12 weeks. Different preparations are available, including sublingual tablets, gum, patches, nasal spray, inhalators, lozenges, and electronic cigarettes (e-cigarettes). Bupropion and varenicline can increase the likelihood of successful smoking cessation without increasing the risk of neuropsychiatric events in people with severe mental illness.324
+In conclusion, the burden of ADRs associated with psychotropic medications is important to consider in the context of treatment effectiveness and patient acceptability. Drugs for psychosis (or antipsychotics) are the best evidence-based treatments for psychotic disorders, and lead to lower all-cause mortality in schizophrenia than giving no treatment.325 Mood stabilisers are the most effective treatment for bipolar affective disorder,263 and drugs for depression (or antidepressants) have an important role in the treatment of depression.326 Careful and regular monitoring of laboratory and clinical parameters could help to identify ADRs early, and prevent the development of iatrogenic comorbidities. We would advise against ceasing or switching psychotropic treatments to modalities that are less effective without careful consideration ofthe risk ofrelapse. Involvement ofthe patient in treatment decisions is important when balancing the effectiveness of a medication against its ADRs.327
+Part 4: Multidisciplinary approaches to multimorbidity
+Lifestyle interventions: what works?
+Modifiable lifestyle factors, such as physical activity, diet, and smoking, are increasingly recognised as being fundamental to both physical and mental health. Interventions targeting these modifiable risk factors, delivered by practitioners with specific expertise, are referred to as multidisciplinary lifestyle interventions. The efficacy of such multidisciplinary lifestyle interventions in reducing the risk of cardiometabolic-related morbidity in the general population is well established.283 Accordingly, the 2018 WHO guidelines328 recommend that lifestyle interventions are considered as first-line strategies for the management of physical health (including weight management, cardiovascular disease and cardiovascular risk reduction, and diabetes treatment and prevention) in adults with severe mental illness. However, a broad spectrum of mental disorders, not only severe mental illness, are associated with high rates of cardiometabolic diseases (Part 1) and lifestyle risk factors (Part 2) that are compounded by the medications that are commonly used to treat mental illnesses (Part 3). Thus, a first step in reducing physical health disparities for people with mental illness is the adoption, translation, and routine provision of evidence-based lifestyle interventions as a standard component of mental health care. However, not all lifestyle interventions are equally useful. The efficacy
+Panel4: Key components of lifestyle interventions
+Smoking cessation
+Challenge: general population approaches have not worked for people with mental illness
+•	Although smoking rates have substantially decreased for the general population since the mid-1990s, they have remained high for people with mental illness;329 as a result, people with mental illness now consume around half of all cigarettes sold in the USA, Australia, and the UK321-330
+•	People with mental illness are as motivated to stop smoking as people without mental illness, but they are more nicotine-dependent and less likely to seek out and receive appropriate interventions tailored to their needs331-332
+•	Smoking-related deaths disproportionally affect people with mental illness, and smoking is a leading cause of the premature mortality observed in this population321,322
+Emerging solution: specialised cessation interventions
+•	Evidence on pharmacological interventions shows that they could be effective; for instance, a 2016 meta-analysis324 showed that bupropion and varenicline were the most effective interventions for smoking cessation for people with severe mental illness, and both resulted in a five times increase in smoking cessation compared with placebo treatments
+•	For non-pharmacological interventions to be effective, they must account for the additional barriers to treatment that people with mental illness can have (eg, cognitive impairments);333 for instance, the SCIMITAR+ programme is a candidate model of a bespoke smoking cessation intervention for people with severe mental illness, which was developed with service users to address the needs of this population334
+•	Policy-level interventions can also be implemented; for instance, in 2016 NHS England announced that all mental health services would become smoke free, which included a ban on smoking on mental health wards and hospital premises, and the dissemination of smoking cessation interventions throughout community care.335 Initial data suggest that smoking bans and bespoke smoking cessation programmes are well received in inpatient settings, and they could have broader benefits by supporting a culture of physical health and wellbeing within mental health services336
+Future research priorities: improve the accessibility and timing of cessation interventions
+•	Training on smoking cessation is now freely available online for health-care professionals, which could increase access to evidence-based interventions for people with mental illness; for instance, a concise e-learning tool on smoking from the National Centre for Smoking Cessation and Training330 could help front-line mental health staff to deliver smoking cessation advice
+•	Electronic cigarettes (e-cigarettes) are already widely used among people with a range of mental health disorders,337 and are a potentially useful tool for reducing smoking-related deaths. The UK Science and Technology Committee has advised mental health trusts to allow e-cigarette use on their premises; however, e-cigarettes are not authorised or available in many countries, and further research is required to establish the health outcomes of using e-cigarettes as a smoking harm-reduction intervention338
+•	Early intervention for smoking is feasible,339 and could improve cessation rates and long-term physical-health outcomes340
+(Continues on next page)
+and effectiveness of multidisciplinary lifestyle interventions are impacted by both their content and timing of delivery. Some key considerations for the individual components of multidisciplinary interventions are presented in panel 4.
+Although it might seem counterintuitive to dedicate intensive resources to individuals with relatively good metabolic health, focusing on cardiometabolic protection in at-risk populations could be the optimal approach for lifestyle interventions (panel 2). The Diabetes Prevention Program (DPP),283 developed and evaluated in the USA, is an example of a gold-standard lifestyle intervention (panel 5). The key features of DPP include individual case managers; frequent face-to-face contact with participants; a structured educational component that includes behavioural self-management strategies; supervised physical activity sessions; a maintenance intervention that combines group and individual approaches, motivational strategies, and individualisation through a so-called toolbox of adherence strategies;
+tailoring of materials and strategies to address ethnic diversity; and an extensive network of training, feedback, and clinical support.283
+The primary study on DPP284 recruited 3234 adults without diabetes who were at risk of developing type 2 diabetes (established via multiple risk factors); patients were assigned to receive placebo, metformin, or a lifestyle intervention that involved at least 150 min of physical activity per week with the goal of at least a 7% weight loss. The lifestyle intervention resulted in a 58% reduction in the development of type 2 diabetes over the 3-year study, with 4-8 cases of diabetes per 100 person-years observed in the lifestyle intervention group, compared with 11-0 cases in the placebo group (incidence in the metformin group was 7-8 cases per 100 person-years).283-284 Furthermore, both the clinical benefits and costeffectiveness of the DPP lifestyle intervention were maintained over a 10-year follow-up, as compared with metformin as the control condition.361362 These results show that lifestyle interventions with beneficial
+(Panel 4 continued from previous page)
+Physical activity
+Challenge: patients find it difficult to stay motivated
+•	Weight loss is often a primary motivation factor for physical activity,341 but exercise alone in the absence of dietary modification will not reliably reduce a patient’s bodyweight, particularly in the short term;342 exercise can attenuate further weight gain, but weight maintenance might not be a strong motivator for people with mental illness, particularly if they were overweight before the onset of mental illness, which can result in disengagement with exercise
+Emerging solution: fitness goals designed by fitness professionals
+•	Rather than focusing on weight loss, improving fitness might be a more motivating341 and achieveable343-344 goal for exercise interventions for people with mental illness; improving fitness can also have important health benefits, because even a modest improvement is associated with a 15% decrease in mortality in the general population345
+•	Exercise interventions delivered by qualified exercise professionals (with a university qualification in exercise prescription, such as physiotherapists or exercise physiologists) have significantly greater physical and psychological benefits and adherence compared with interventions delivered by non-specialised practitioners.346-348 In addition, the integration of qualified exercise professionals into mental health services could ensure that mental health staff have the knowledge and training to give clear advice on exercise
+Future research priorities: varied and personalised exercise programmes
+•	Although most research on physical activity has focused on aerobic exercise, evidence from the general population increasingly shows that strength and resistance training or so-called high-intensity interval training can have beneficial effects for both metabolic and mental health349-351
+•	Given that enjoyment and satisfaction are key factors in determining exercise adherence,352 offering a range of exercise options that accommodate patient preferences and goals will be important for establishing sustainable and engaging exercise routines
+Diet
+Challenge: additive effect of medication and diet
+•	Dietary risks are a leading risk factor for cardiometabolic disease identified by the Global Burden of Disease Study;353 for people with mental illness, the risk is exacerbated128-138 because of the side-effects of psychotropic medications (eg, excessive or insatiable hunger, cravings for high-calorie, low-nutrient foods),183,354 an insensitive reward system and poor cognitive control,355 and food insecurity and financial constraints356
+Emerging solution: dietary support
+•	Improved diet quality357 and reduced bodyweight358 are both associated with decreased mortality in the general population
+•	Dietary interventions in people with mental illness are more effective if they are delivered by specialist clinicians, such as dietitians, and at an early stage of treatment;359 cardiometabolic care and subsequent dietary intervention should be implemented within a multidisciplinary framework360
+Future research priorities: personalised pathways to health and fitness
+•	As with exercise, the most effective dietary regime for people with mental illness will be one that is sustainable; future research might identify strategies that alleviate the obesogenic effects of psychotropic medications, and that address the insensitive reward system and poor cognitive control of some people with mental illness
+•	Links between dietary intake, the microbiome, inflammation, and obesity are increasingly becoming clear, and could provide new ways to improve physical outcomes for people with mental illness
+components (panel 5) can reduce the incidence and burden of cardiometabolic diseases when used as a preventive strategy in at-risk populations. Notably, the DPP has also been adapted and successfully delivered in primary-care settings.363
+Considering the increased metabolic and lifestyle risk observed across multiple classes of mental health disorder (Parts 1 and 2), the DPP could be adapted for people with mental illness and made available through primary care, on a referral basis. The use of such transdiagnostic, evidence-based, and cost-effective lifestyle interventions could help to protect the cardio-metabolic health of people with mental illness who are
+treated in primary care settings. Furthermore, evidence increasingly shows that supervised exercise training (a key component of the DPP) can improve psychiatric symptoms, cognition, and functioning across a range of mental health diagnoses.346-364-365 Therefore, integrating the DPP principles into mental health care could improve overall recovery, not only metabolic health. However, the majority of DPP studies to date have excluded individuals with a “major psychiatric disorder which, in opinion of clinic staff, would impede conduct of the DPP”.284 The DPP needs to be analysed as a transdiagnostic lifestyle intervention for people with mental illness through primary care services and specialised mental health
+Panel5: Lifestyle intervention guidelines adapted from the Diabetes Prevention Program283
+Measurable and specific goals
+•	Maintain bodyweight or reduce by between 5% and 7% of total bodyweight
+•	Reduce calorie intake (500-1000 kcal less than the calorie intake needed for weight maintenance per day, and a maximum of 25% of calories from fat), and improve diet quality
+•	Increase the number of minutes of physical activity (meet recommendations of 150 min per week of moderate-to-vigorous physical activity)
+•	Replace sedentary behaviour with light intensity activity as often as possible
+•	Increase cardiorespiratory fitness
+•	Cessation of smoking
+Case managers or lifestyle coaches with university (or equivalent) training in nutrition and dietetics, exercise prescription, or behavioural change
+•	Allow for individualised programme design and delivery
+•	Offer a combination of group sessions and one-on-one sessions
+•	Provide supervised exercise and nutrition sessions at least two times per week (eg, community centre sessions, neighbourhood group walks, or one-on-one personal training)
+•	Do relevant assessments at regular intervals
+•	Ensure lifestyle coaches have training in psychopathology and the basic principles of working with people with mental illness
+Frequent contact and ongoing intervention
+•	Deliver core curriculum on topics including nutrition (modifying energy intake), physical activity (and sedentary behaviour), and behavioural self-management (barrier identification and problem solving)
+•	Provide a flexible maintenance programme with supplemental group classes
+•	Provide motivation campaigns and restart opportunities
+Individualisation through a toolbox of adherence strategies
+•	Self-monitoring of outcomes and behaviours, such as weight, physical activity, sedentary behaviour, and dietary intake (fat and calorie intake)
+•	Barriers to treatment are identified and addressed with simple, individualised resources (eg, a cookbook might be given to a patient trying to improve their diet)
+Strategies that are adapted for culturally and ethnically diverse groups
+•	Translation of documentation to local languages
+•	Identification of culturally appropriate resources and intervention approaches
+•	Cooking groups that allow for dietary restrictions or religious requirements
+Local and national network of training, feedback, and clinical support
+•	Appropriate training of existing and emerging mental health staff
+•	Clear referral pathways and the integration of lifestyle coaches into a standard multidisciplinary mental health team
+•	Monitoring and evaluation of effectiveness and adherence
+services. Although the core principles of the DPP are crucial to its design and delivery, more support is likely to be required by people with severe mental illness compared with the amount needed to effect change in the general population. A randomised controlled trial of an adapted version of the DPP for people with severe mental illness found significant reductions in obesity and other metabolic risk markers associated with antipsychotic treatment compared with usual care.366
+Conversely, in some situations, adaptation of evidencebased programmes for people with mental illness can threaten their effectiveness. For instance, reducing the amount or frequency of interventional components, because of conflicting demands on the priorities and workload of mental health staff and diagnostic overshadowing,20 could mean the programme is insufficient to effect change for those patients. The challenge for policy makers, clinicians, and service providers is to apply established, effective principles of behaviour change to people with mental illness, particularly with regards to adopting a framework of early intervention and prevention.13
+Implementing lifestyle interventions for severe mental illness
+A 2019 meta-review285 aggregated data from 27 metaanalyses of physical health interventions for people with schizophrenia. Exercise, diet, and broader lifestyle interventions (eg, sleep hygiene, smoking cessation strategies, motivational interviewing) had significant benefits for multiple cardiometabolic outcomes (including bodyweight, waist circumference, blood pressure, and glucose and lipid markers), with a similar efficacy to pharmacological management of metabolic health.285 However, the clinical trials from which these efficacy data were predominantly derived could reduce the generalisability and external validity of the findings, because trials are rarely done under real-world conditions and are typically resourced differently to routine clinical care.367
+Few studies have been done on the effectiveness, pragmatic implementation, or sustainability of lifestyle interventions in people with mental illness.368 Furthermore, several large-scale clinical trials in people with mental illness have had null findings. To provide guidance on effective implementation of lifestyle interventions within mental health services, the interventions that are associated with negative and positive outcomes in trials should be considered. Trials of lifestyle interventions in mental health care often do not meet all the principles of programmes such as the DPP (appendix p 23). Specific aspects of the DPP that have been poorly implemented in trials are: (1) using qualified exercise professionals and dietitians to deliver lifestyle interventions, (2) providing sufficient access to supervised exercise services, and (3) ensuring that existing mental health staff are familiar with the lifestyle interventions. Large-scale clinical trials of lifestyle interventions addressing multiple risk factors in people with mental illness are described in the appendix (pp 24—30).
+The high acceptability of lifestyle interventions among patients365,366,369,370 means that they are a novel route to reach typically disengaged service users in more traditional mental health treatment. For example, providing gymbased resistance exercise is a potential clinical pathway to care for young people with early psychosis,369 or veterans
+with post-traumatic stress disorder.371 However, an important consideration is how such programmes are applied across different clinical and broader public health settings. Flexibility in delivery, a focus on practical exercise and dietary advice, and provision of support to integrate the lifestyle measures into daily life are highly recom-mended.372,373 Further research is needed on how interventions are delivered; a mixed model that involves both online and face-to-face delivery is a potentially balanced and cost-effective way forward (appendix pp 31-34).206,193
+Training health professionals for a culture shift
+Multidisciplinary teams in mental health settings are rapidly evolving to include allied health professionals with expertise in nutrition, physical activity, behaviour change, and other aspects of mental health, such as psychoeducation and mindfulness training. For this transition to be successful, allied health practitioners should receive at least introductory training in psychopathology and in the principles of working with patients with mental illness. Accordingly, the curriculum for health professionals, including dietitians, physiotherapists, and exercise physiologists should be updated to reflect the increasing role for such professionals within mental health teams.374
+In addition, medical and mental health professionals should receive training on working with allied health professionals in an integrated manner, and understanding the principles of lifestyle interventions. The importance of training medical students in so-called lifestyle medicine is increasingly being recognised globally.375 Efforts towards integrating lifestyle interventions within routine mental health care should avoid an isolated focus on individuallevel behavioural changes, and should also include broader changes to service structure, delivery, and culture (see Part 5). For instance, evidence suggests that medical and nursing practitioners who have healthy lifestyle behaviours are more likely to recommend such behaviours to patients.376 Advances in implementation science could also provide ways to ensure that lifestyle interventions have meaningful benefits for patient outcomes.368
+Barriers, opportunities, and future research
+Some of the issues, emerging solutions, and research priorities for smoking cessation, physical activity, and dietary interventions for people with mental illness are presented in panel 4. For all types of lifestyle intervention, a gradient of intervention intensity, or so-called stepped care, needs to be considered. For example, intervention intensity might vary between individuals, treatment settings, and cultures, and could depend on the readiness to provide lifestyle interventions, particularly in low-resource settings.
+Even in high-resource settings, only providing intensive lifestyle interventions through mental health services could cause issues for individuals who do not attend mental health centres frequently; those
+who have been discharged might find it difficult to stay engaged with lifestyle changes. One strategy for sustaining engagement with health behaviour interventions is the use of primary care referral schemes. For example, exercise referral schemes for people with mental illness typically involve health-care providers referring individuals to community-based organisations to provide free (or discounted) access to a wide range of fitness activities, facilities, and expertise through community leisure centres and services. Community-based interventions might also be a non-resource-intensive strategy for maintaining physical activity behaviour in a way that complements and supports clinician-led strategies. Exercise referral has already been introduced through multiple large-scale implementation projects for sedentary adults in primary care in the UK, although only small beneficial effects have been found to date.172,377 However, preliminary data show that community exercise can be beneficial and engaging for young people with mental illness, including for those with severe conditions.378,379 Community-based diet programmes, such as Weight Watchers, are cost-effective weight-loss interventions when delivered via primary care to obese individuals.370,380 Research is now warranted to determine the suitability and effectiveness of such programmes for psychiatric populations.
+Mobile device health (or mHealth) technologies could provide new routes for applying adapted versions of programmes such as the DPP in patients with mental illness. For example, a pilot study381 found that FitBit activity trackers could potentially be used alongside fitness applications (apps) in people with schizophrenia to deliver DPP-based interventions, with features such as daily prompts, motivational messages, and selfdetermined step-count goals. Participants found the technology to be engaging, motivating, and empowering,381 but a small sample size (n=25) makes it difficult to determine efficacy. Although they have only been evaluated in small-scale pilot studies to date, mHealth technologies present potential opportunities to deliver a wide range of novel, scalable, and sustainable lifestyle interventions for people with mental illness. mHealth interventions could also be disseminated easily, even in low-resource settings. Therefore, further development and evaluation of evidence-based mHealth interventions for improving physical health in people with mental illness is warranted.
+In conclusion, the principles of existing gold-standard prevention programmes, such as the DPP, can be used as a benchmark for the implementation and maintenance of lifestyle interventions as an integrated, routine component of mental health care (panel 5). However, programmes might need to be adapted to specific care settings, and for particular patient needs. Efforts are required to translate the DPP principles into both (1) preventive, transdiagnostic lifestyle interventions
+available through primary care, and (2) intensive interventions for specialist services. If these efforts are successful, effective programmes for protecting the cardiometabolic health of people living with mental illness could be implemented.
+Part 5: Innovations in integrating physical and mental health care
+Introduction
+Social determinants, including poverty, poor education, unemployment, homelessness, and childhood abuse,
+increase the risk for both mental and physical illnesses.182,382 The relationships between adversity, physical health, and mental health are complex, and risk factors can act synergistically to reinforce disadvantage and disability.182 For instance, people with mental illness are more likely to be in poverty and to have cardiometabolic and infectious diseases (see Parts 1 and 2), and conversely, chronic physical health conditions and social deprivation are key risk factors for mental illness.182,383,384 A 2017 Lancet Series385 on the co-occurrence of chronic health conditions described how syndemic frameworks could be used to understand how health risks and comorbidities interact with one another within the broader environmental context. For instance, epidemiological research has applied syndemic frameworks to characterise the complex relationships between poverty, diabetes, mental illness, and infectious diseases in low-income settings.178 This syndemic approach highlights that national and local conditions affect the interplay between physical and mental health, and shows the importance of taking social, political, and economic factors into account when designing public health interventions, or implementing changes to health services (table 4).179
+Numerous national and international health-care and advisory bodies are now focusing on health inequalities in people with mental illnesses. Resources from these organisations (table 5, appendix pp 35-42) present new ideas and best practice approaches for improving the integration of physical and mental health care at the individual, health service, and societal levels. Several key health organisation guidelines149,330,386 and academic articles179 have included case studies of new local and national initiatives that account for the surrounding environmental conditions and improve the integration of physical and mental health care. As well as detailing required improvements to health care for existing patients, some sets of guidelines discuss approaches to prevention of chronic physical and mental health conditions.330,386 Wide-scale adoption and implementation of strategies that aim to prevent chronic conditions (physical or mental), multimorbidity, and risk of premature mortality are required to reduce health inequalities for patients with mental illness in the future. Some examples and considerations for prevention at the primary, secondary, and tertiary levels are presented in panel 6.
+Improving integrated care for people with mental illness Effective management of multimorbidity requires integrated care to be provided in a holistic manner,391 so that common risk factors and the bidirectional interaction between physical and mental health disorders, and the treatments for each, can be addressed together.386 Internationally, health organisations agree that primary care is the optimal setting for addressing and coordinating the management of multimorbity.392,393 In many countries, most people with mental illness first
+Panel 6: Prevention of physical health morbidity and mortality in individuals with mental illness
+Primary prevention
+Primary preventive strategies aim to provide people with the tools needed to live a healthy lifestyle190 by avoiding smoking, alcohol and substance misuse, poor diet, and physical inactivity. Among those with mental illness, a healthy lifestyle should ideally be adopted in the early stages of illness to build healthy habits, and to protect physical health as much as possible. Primary prevention strategies need to be adapted for people with mental illness, because public health strategies that are effective in the general population are not always as effective for those with mental illness. Separation of patients into diagnostic categories (eg, depression, anxiety, and schizophrenia) is not an effective way of determining the best primary prevention strategies for physical health. Instead, transdiagnostic approaches that account for individual-level differences (eg, gender, cultural and ethnic identity, lifestyle risk factors, medication use, and social circumstances) will be more effective (see Part 2).
+Secondary prevention
+Secondary preventive strategies, such as screening and preventive treatments, are often underused in people with mental illness.216-218,222 Many people with mental illness are affected by comorbid physical diseases, which can be present from illness onset (see Part 1). Population-scale data from NHS England390 indicate that physical health intervention is required even from childhood for those with mental illness. At the age of 11-19 years, children with mental illness are three times as likely to be obese as children without mental illness.
+Tertiary prevention
+Tertiary preventive strategies involve improving treatment and recovery from disease. To be engaging and responsive, integrated care services require flexibility from individual clinicians and service planners. For example, cardiac mortality among patients with severe mental illness is significantly reduced by efficient administration of cardioprotective medications after first cardiac events.227 This supports the claim made in new guidelines328,387 that tertiary preventive measures for people with mental illness are underused, despite their potential to improve health and reduce premature mortality.
+present to the health system through primary care, and most mental health care is delivered in primary care.179 Patients requiring specialist mental health services still need ongoing engagement with primary care to deliver and coordinate other aspects of their health care, including prevention and management of comorbid physical illness. The aim of primary care is to provide equitable, accessible, safe, effective, comprehensive, person-centred care that meets the needs of individuals, families, and communities throughout life.394 Therefore, primary care is ideal for managing multimorbidity,
+which requires an individualised approach that not only addresses the increased burden of multimorbidity, but also manages competing or conflicting treatment needs by accounting for individual preferences and treatment priorities.392 Further discussion on how primary care settings should provide physical health care for people with mental illness is presented in the 2018 guidelines from NHS England387 (appendix p 38).
+As a minimum level of integration, health providers should communicate with each other frequently to ensure the safety and effectiveness of treatment. Ideally, services should take further steps towards integration, aiming for multidisciplinary care that is structured, comprehensive, and proactive. However, integration of this type usually involves overcoming bureaucratic barriers at the service level, such as difficulties in sharing medical records. Governance and funding issues can also restrict the provision of coordinated health care (figure 3). A 2016 report386 from the King’s Fund in the UK presents an aspirational approach towards improving integrated care across a range of physical and mental health conditions, with advice on overcoming common barriers to implementation. For instance, the report recommends a curriculum redesign to give all health professionals a common foundation in mental and physical health and encourage a whole-person approach, and creating opportunities for skills transfer between professionals
+(appendix p 35). Some examples of integrated care models, and their evaluated outcomes, are described in panel 7.
+Managing substance comorbidity and promoting smoking cessation
+Across many mental illnesses, the use of alcohol, tobacco, and illicit drugs is more prevalent than in the general population, and is associated with worse physical and mental health outcomes (table 2).405-407 A bidirectional relationship exists between substance misuse and mental illnesses, because substance misuse can cause and exacerbate mental illness, yet it is often used by patients as a way of reducing anxiety, dysphoria, and other symptoms.408 Genetic risk factors for schizophrenia also appear to predispose individuals towards illicit drug use.409
+Addressing substance misuse within mental health services should be a high priority.408 However, many services have no standardised screening for substance use, and mental health clinicians are often not trained to treat substance misuse.410 For example, in high-income countries, people with severe mental illness report wanting to quit smoking as much as the general population, but are unlikely to be supported to do so.331,332 Furthermore, patients are sometimes excluded from drug treatment programmes or mental health
+services if they have comorbid drug or alcohol use disorder.411
+Because of the complexity of comorbid mental health and substance use disorders, patients need individualised treatment that has an emphasis on overcoming the barriers associated with mental illness and enhancing engagement with evidence-based treatments. Readiness for change, cognitive ability, and cognitive distortions resulting from mental illness need to be taken into account. Evidence-based treatments include motivational interviewing, cognitive behavioural therapy, and family interventions (also known as family therapy).408
+Evidence-based interventions can be a challenge to implement in mental health services that are already struggling to meet demand. Notably, little evidence is available to recommend integrated interventions as compared with sequential or parallel treatment programmes, particularly in alcohol use disorders.412 Each approach has advantages and disadvantages. One advantage of an integrated approach is that the patient does not need to receive care from two services, whereas a disadvantage is that it requires substantial resources and investment from within the mental health system to train mental health clinicians in the treatment of substance use disorder. An advantage of sequential or parallel treatments is that the interventions are delivered within a highly specialised substance use programme. However, the approach requires coordination and sharing of information between agencies. A clear referral policy between mental health and substance misuse treatment services (including those in primary care) should be developed so that a programme of patient care is delivered consistently and in full.
+Regardless of how interventions are provided, investment in screening within mental health services is a priority. Mental health clinicians should be trained to do regular assessments of comorbid substance use, to assess patients’ readiness for change, and to provide motivational interviewing. An emphasis on a so-called no wrong door policy for accessing substance misuse treatments, in which everyone is accepted and offered treatment wherever they present, and the development of clear referral policies between mental health and substance misuse treatment services should be a priority.330
+If cessation of substance misuse is not possible, harmminimisation strategies should be adopted. For instance, patients might be able to switch to alternative, safer forms of the drug (eg, e-cigarettes, methadone, or buprenorphine and naloxone) or access could be provided to safe injecting facilities. The challenges and innovations regarding smoking cessation interventions for people with mental illness are presented in panel 4. The Royal College of Physicians published a report in 2016 on harm minimisation for those who are unable or find it difficult to quit, which recommended e-cigarettes, nicotine replacement therapy, and other non-tobacco nicotine products.413
+Panel7: Examples of integrated care for physical and mental illness
+Within the broad category of integrated care, collaborative care models are emerging as effective approaches that can simultaneously reduce costs and improve clinical outcomes and treatment adherence in the management of both mental illness and chronic physical conditions.395-398 A core component of collaborative care models is the involvement of several health-care professionals working as a team, including a physician, a case manager, and a mental health clinician.395,396 Although the specific actions vary between models, all collaborative care approaches use structured management plans, scheduled patient follow-ups, and extensive interprofessional communication.395 Figure 3 shows the potential components of a collaborative care model for improving health management in people with physical and mental comorbidities.
+The TEAMcare intervention399,400 in 14 primary care clinics in Washington, USA, is an example of a collaborative care approach within primary care. TEAMcare was designed for adults with depression plus diabetes, heart disease, or both, and comprised pharmacological care management with integrated behavioural change support delivered by a nurse. Compared with usual care, the TEAMcare intervention resulted in significant improvements in metabolic health over 12 months, with a decrease in the percentage of glycated haemoglobin of -0-56% (95% CI -0-85 to -0-27), a decrease in LDL cholesterol of -9-1 mg/dL (-17-5 to -0-8), and a decrease in systolic blood pressure of -3-4 mm Hg (-6-9 to 0-1). A reduction in Symptom Checklist Depressive Scale score of more than 50% was found in more than three times as many patients in the TEAMcare group compared with usual care (odds ratio 3-37, 95% CI 1-84 to 6-17), as well as improved perceived self-efficacy, and greater patient satisfaction with medical care.399-401
+The COINCIDE trial397 tested a psychological intervention for people with depression and comorbid diabetes or cardiovascular disease that addressed behavioural activation, healthy lifestyle, exercise, and diet. This integrated approach resulted in significant improvements in depression and patient satisfaction at 4 months.173 Health benefits were sustained at a 24-month follow-up, and the intervention was found to be cost-effective.173 Additionally, evidence from the RAINBOW trial, published in 2019, supports the use of collaborative care models for improving both physical and mental health outcomes in people with common mental disorders and cardiometabolic comorbidities.402 However, these evaluations of collaborative care models have all been done in high-income settings; similar evaluations in low-income and middle-income settings are needed (see Part 5).
+Although collaborative care models have been shown to be effective for people with common mental disorders, the evidence for their use in people with long-standing severe mental illness is conflicting,174,403,404 and optimal models of integrated care in this group are yet to be found. The PRIMROSE study174 compared integrated primary care with usual care in 327 people with severe mental illness, and found no significant benefits for HDL cholesterol over 12 months. However, integrated care did have a 12-month mean cost difference of -£824 (95% CI -568 to 1079) compared with usual care, and was found to be cost-effective because of fewer hospital readmissions over a 12-month period.174
+Innovations in integration for low-income and middle-income countries
+In most low-income and middle-income countries, less than 1% of the health budget is spent on mental health,414 including mental health care within specialist mental health services, general health services, and social care services.414 As a result, mental health services are poorly resourced; 90% of people who need treatment do not receive any care.415 Mental health services in low-income and middle-income countries predominantly rely on expensive psychotropic drugs, which are seldom
+available, and are associated with various side-effects that require close management (see Part 3).416 Previously, little attention has been given to the complex bidirectional relationship between physical and mental health, and the relevance of screening, in low-income and middle-income settings.416,417
+WHO guidelines from 2018 state that health inequalities for people with severe mental illness could be worse in low-income and middle-income countries than high-income countries, because “the resources are inadequate, the institutions are not well managed and access to quality mental health care and physical care is limited”.328 The largest gaps in life expectancy for people with severe mental illness compared with the general population are observed in low-income settings.5,149 Mental health care systems in low-income and middleincome countries need to be reoriented towards integrated models. However, many low-income and middle-income countries do not have integrated physical health and mental health services, and have poorly developed community-based services, resulting in overreliance on institutional psychiatric care.416 In many countries, mental health legislation and policies are outdated.417 Specific barriers to the development and implementation of integrated mental and physical health policies include: insufficient coordination across different government levels; a shortage of trained staff at all levels of care; a need for commitment from health services; governmental bureaucracy; and shortage of funding. In addition, funding for health services is provided by several different sources, which makes the sharing of decisions and responsibility challenging.179 As a consequence, in daily clinical practice, mental health providers in community settings do not generally ask about or test for physical health issues because they are not considered to be a priority, and time and resources are limited.418
+In low-income and middle-income countries, there is an urgent need to increase awareness that patients with mental health illness could have physical health needs, and vice versa.416 For example, public health campaigns could increase awareness of the links between chronic physical and mental disorders. In a 2016 review419 of interventions for mental disorders at the population and community levels in low-income and middle-income countries, mass public awareness campaigns and schoolbased awareness programmes were considered to be good practice, with limited but promising evidence to support their use.
+At the system level, the physical health of people with mental illness could be improved by increasing the competencies of existing staff at all levels of care. Although education campaigns on the links between chronic physical and mental health conditions are important tools, bringing about changes to skills and behaviour will require a long-term approach. Multiple training sessions and subsequent top-ups will usually be
+required, with rolling programmes to support staff turnover.420 In addition, mental health policies in low-income and middle-income countries need to be changed to make an integrated care model the central focus of mental health care action plans. A review across high-income, middle-income, and lower-income settings179 presents clear evidence for the rationale and effectiveness of integrated care. The Practical Approach to Care Kit (PACK), which comprises a guide, a training strategy, a health system strengthening intervention, and monitoring and evaluation, is an example of a bestpractice approach towards providing universal integrated primary health care.421 PACK has been successfully implemented in several low-income and middle-income countries, including Botswana, Brazil, Ethiopia, Nigeria, and South Africa.422 Development of clinical practice guidelines that build on best-practice examples such as PACK and consider the local context, including staff attitudes and available resources, will be crucial in encouraging policy uptake in low-income and middleincome countries. The local context, including prevalent knowledge, behaviours, and attitudes towards mental health conditions, is a good predictor for the success of implementing changes to clinical practice.420
+Clinical practice guidelines should also incorporate strategies for collaboration between formal primary care and mental health services, and community-based providers, such as traditional healers. Approximately half of individuals seeking formal health care for mental disorders in low-income and middle-income countries choose traditional and religious healers as their first care provider, and this choice is associated with delays in accessing formal mental health services.423 Based on research into collaboration between traditional healer and biomedical health systems in Uganda,424 strategies should involve improving clinicians’ understanding of traditional healers’ explanatory models for illness, and vice versa. Trust between the two types of health-care providers needs to be improved so that they can interact, rather than operating in isolation. In particular, negative attitudes of clinicians towards traditional healers need to be addressed. The quality of care provided by traditional healers needs to be enhanced by improving hygiene practices and eliminating unethical practices.
+Task sharing with key community-based providers is a potentially effective implementation strategy in low-resource settings. Task sharing is the process of transferring a task usually delivered by a scarce resource, such as a physician, to a rapidly trained and less scarce resource, such as a health-care worker.425,426 Research on the implementation of task-sharing collaborative-care models is being done,427-429 and the findings could improve our understanding of the quality, safety, effectiveness, and acceptability of such strategies for mental health disorders in low-income and middle-income countries. Case studies from non-governmental organisations show that inefficient health system structures can present
+barriers to successful task sharing,427 indicating a need for more collaborative care services. However, whether such approaches will be successful in reducing premature mortality, improving wellbeing, and achieving better social outcomes in low-income and middle-income settings has yet to be fully established.427
+Digital technologies for people with mental illness
+Digital technology plays an increasing role in promoting health, addressing risk factors, and managing physical disease, with growing evidence for its effectiveness. Mobile phones provide a particularly convenient platform for digital health-care delivery (also known as mHealth). WHO estimates that 95% of the global population lives in an area covered by mobile networks, and over 7 billion mobile contracts have been issued, which is one for almost every person on the planet.430 Smartphone technologies are closing the so-called digital divide (ie, between those who have easy access to computers and the internet, and those who do not) that was previously present in low-income and middleincome countries.431 Unlike traditional health services that require attendance at a specific time and location, digital technology is available at a time and place that suits the patient.
+Technologies as simple as text messaging have been shown to support lifestyle improvement. For example, in the TEXT ME trial of 710 patients with coronary heart disease,432 patients in the intervention group received four personalised text messages per week for 6 months that provided advice, motivation, and support to change lifestyle behaviours. After 6 months, levels of LDL cholesterol were significantly lower in intervention participants compared with patients who received usual care, with concurrent reductions in systolic blood pressure and body-mass index, significant increases in physical activity, and a significant reduction in selfreported smoking. Further studies to assess the sustainability of these positive changes, and the effectiveness of text messaging in participants who have not yet experienced a cardiovascular event, are underway.433 Text messaging can also support other important health behaviours, such as medication adherence for people with chronic conditions.434
+Smartphone apps might promote healthy lifestyle change, but they vary in quality, and the quality of reported evaluation research is also inconsistent.435 To date, few studies have examined clinical effectiveness or cost-effectiveness.436 In addition, user engagement could be lower in everyday clinical practice than in trial settings.437,438 Key strategies for effective user engagement include designing interventions in collaboration with patients, personalisation of interventions, and just-intime adaptation (in which an intervention supports an individual's changing behaviours and contexts over time).439 An example is the Australian FoodSwitch app, which uses a smartphone camera to scan the barcode of a
+food item, and recommends healthier alternatives from a crowd-sourced database of nutritional information.440
+Several smartphone functionalities could be valuable for improving health, including the recording and analysis of data from sensors measuring activity or biological variables; access to health information via the internet; and the ability to engage with social media campaigns on lifestyle change.441 Increasingly, people can access elements of their electronic health records via their smartphone or other portable device, providing an important opportunity for partnership between patients and health professionals, and for empowerment of patients to be more involved in decisions about their health care. However, because smartphones are more expensive than basic mobile phones and require an internet or data connection, text messaging might be required to reach the wider population in some low-income settings.442
+To date, most studies using mHealth to promote healthy behaviours have recruited from the general population. Increasing numbers of individuals with severe mental illness also want to use technology to manage their health.443 Although few evaluations of mHealth for physical health in mental illness have been done, emerging evidence indicates that online peersupport platforms, smartphone apps, and fitness trackers can successfully increase walking and physical activity in people with severe mental illness.381,444,445 Furthermore, a review of digital health technologies for people with depression446 found that online lifestyle interventions can have positive effects on various health behaviours, including alcohol use, sleep, and physical activity. Although the evidence is only preliminary, mHealth is a promising route towards reducing physical health disparities for people with mental illness globally, and further research is warranted (figure 1). Widespread adoption ofmHealth will depend not only on technological advances, but also on rigorous evaluation of digital health interventions and overcoming of common limitations, such as consumer perceptions (particularly around safety, reliability, and trustworthiness) and ethical risks, such as the potential for intrusion, coercion, and data privacy breaches.408,447
+Who is responsible?
+To turn ideas into actions, governments, health commissioners, and care providers must acknowledge their respective responsibilities for improving physical health for people with mental illness, and clear accountabilities must be established. For instance, primary prevention is often regarded as the duty of governments, rather than health services.448 The increased risk for physical disease among people with mental illness, which can be present even before the first diagnosis of mental illness, could represent a failure at the public health level, and perhaps even wilful abandonment of educational and health promotion
+initiatives to reach this marginalised group. However, socioenvironmental factors that contribute to poor physical health, such as a shortage of green spaces and walking routes, the affordability and accessibility of fast foods compared with healthy foods, and tobacco and alcohol advertising (and associated legislation), are all areas that could feasibly be targeted by local and national health policy to improve the physical health of people with mental illness.
+Furthermore, increasing evidence suggests that obesity,449,450 smoking,451,452 and physical inactivity453,454 are dual risk factors for both chronic physical conditions and mental illnesses. Because these risk factors are also associated with social deprivation,182,455 greater investment in public health schemes and policy to proactively address them in at-risk groups, particularly in young people, could potentially reduce the incidence of both physical and mental illnesses. However, the effectiveness of such schemes has yet to be demonstrated, and should be considered a promising area for future research (figure 1).
+The risk of physical disease in people with mental illness is further compounded by barriers to health care at the personal, service, and social levels for this population. As a priority action, governments must address the inequalities in health insurance and access to care for people with mental illness, to provide a suitable environment for effective medical and lifestyle interventions. Additionally, health commissioners must acknowledge the shortage of resources allocated to the protection of cardiometabolic health in mental health services, and the broad neglect of physical health risks in the treatment of mental illness.
+Clinical staff should also reflect on the duty of care that they have to people with mental illness, both at an individual level and through their national associations. Given the foreseeable nature of poor physical health outcomes, protecting the physical health of people receiving treatment for mental illness should be regarded as within the scope of clinical duty of care. Within sufficiently resourced settings, this duty of care must include: (1) measuring and addressing the physical health of the patient; (2) clearly explaining the risks associated with treatment; and (3) taking appropriate action to mitigate those risks and protect the physical health of the patient. As demonstrated in this Commission, and evidenced in guidelines (appendix pp 35—42), good clinical practice in mental health care is increasingly considered to include monitoring the physical health of service users.
+The allocation of research funding is another pathway through which systemic discrimination affects the health and wellbeing of people with mental illness. Major research councils must aim to provide more funding to address the physical health disparities that affect people with mental illness. As a solely economic justification, the allocation of resources should at least correspond with the demonstrated financial cost of physical and
+mental comorbidities (see panel 2). This economic burden must also be considered alongside the unresolved (and worsening3,21-23) personal burden of comorbid physical diseases that disproportionately affect people with mental illness across the entire life course. Substantial research investment in this area is now required to eliminate physical health inequalities, and to develop novel methods that will prevent these disparities from arising in future generations.
+Conclusion
+Large disparities in physical health for those with mental illness are an ongoing health issue, and might even be worsening in some regions. Although this inequity is increasingly gaining attention, further investment, intervention, and research are urgently required to address the premature mortality and lifelong burden of poor physical health associated with mental illness.
+Nonetheless, our Commission takes an optimistic approach, and describes how disparities could be reduced through evidence-based prescribing and better integration of physical and mental health care. Our priority actions for health policy, clinical services, and future research are presented in figure 1. Promisingly, multiple national and international guidelines now present feasible actions for improving the integration of physical and mental health, across various health and social care settings. Broader implementation of lifestyle interventions for mental illness is also required to reduce elevated cardiometabolic risk and attenuate medication side-effects. Whenever possible, interventions should maintain the core principles of evidence-based lifestyle programmes (such as the DPP) and be made accessible to those who do not have current physical comorbidities, with the aim of protecting cardiometabolic health from the earliest stages of mental health treatment. From a public health perspective, further exploration of population-scale strategies for primary prevention of co-occurring physical and mental disorders is warranted. Additionally, more government action is required to prevent discrimination and ensure equitable access to all aspects of health care for those with mental illness. Overall, protecting the physical health of people with mental illness should be considered an international priority for reducing the personal, social, and economic burden of mental health conditions.
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+1.	Background
+People with schizophrenia have a reduced life expectancy compared to the general population, primarily because of premature cardiovascular disease (Bobes et al., 2010; Laursen, 2011; Wahlbeck et al., 2011). This condition may be explained in terms of a high rate of metabolic syndrome in these patients (Arango et al., 2008), which carries with it a series of risk factors for cardiovascular disease, such as central obesity, atherogenic dyslipidemia, hypertension, and impaired insulin and glucose metabolism. Furthermore metabolic syndrome is also associated with an elevated prothrombotic and proinflammatory state which may in
+turn increase the mortality from cardiovascular disease (Hasnain et al., 2010).
+The mechanism underlying the increased prevalence of metabolic syndrome in patients with schizophrenia is still unclear. A number of explanations have been hypothesised: genetic predisposition (Gough and O’Donovan, 2005), antipsychotic drug action on metabolism and heart function (Stahl et al., 2009), alterations of the hypothalamic—pituitary—adrenal axis activity due to high levels of stress (Anagnostis et al., 2009), and unhealthy lifestyle (Brown et al., 1999; Mushtaq et al., 2008). Some studies have reported a greater likelihood of metabolic problems in first-degree relatives of patients with schizophrenia (Spelman et al., 2007), and the presence of common susceptibility alleles for both schizophrenia and diabetes (Gough and O’Donovan, 2005); however, the genetic link between schizophrenia and metabolic problems has still not been clearly elucidated and would need further investigation. In contrast, it is widely acknowledged that antipsychotic treatment is associated with several metabolic side effects such as weight gain, insulin and leptin resistance, glucose intolerance,
+dyslipidemia and alterations of cardiac function (Stahl et al., 2009). However, some studies have reported metabolic alterations, such as abnormal glucose tolerance and increased fasting glucose, also in drug naive patients (Ryan et al., 2003). Indeed, investigating metabolic abnormalities and dietary habits in first episode psychosis is particularly relevant as these studies are less likely to be biased by effect of antipsychotic treatment and long duration of illness. The findings of metabolic abnormalities in first episode psychosis have been hypothesised to be related to high levels of stress and the consequent hyperactivity of the hypothalamic—pituitary—adrenal axis. Indeed, high levels of stress in both childhood and adulthood have been consistently reported in patients with psychosis (Fisher et al., 2009; Laursen et al., 2007), and more recently at the time of, or before, the onset of the first psychotic episode (Aas et al., 2011; Aiello et al., 2012; Belvederi Murri et al., 2012; Mondelli and Pariante, 2010; Mondelli et al., 2010a,b; Pariante et al., 2004). The hyperactivity of the hypothalamic—pituitary—adrenal axis enhances circulating levels of cortisol and causes glucocorticoid resistance, which in turn increase the deposit of visceral fat and cause alterations of leptin signals, insulin resistance and glucose tolerance (Anagnostis et al., 2009). Moreover, repeated episodes of psychological stress in both childhood and adulthood may induce a chronic inflammatory process (Di Nicola et al., 2012; Mondelli and Pariante, 2010), characterized by increased inflammatory markers such as C-reactive protein (CRP) (Hepgul et al., 2012), which in turn may predispose to the development of metabolic abnormalities (Danese et al., 2009). At the same time, the unhealthy lifestyle of patients with schizophrenia may also play an important role in the development of metabolic syndrome. These patients are more likely to be heavy smokers, have high rates of alcohol consumption and substances abuse, and low levels of physical activity and poor diet (Brown et al., 1999). Interestingly, it has been reported that diet is a major and modifiable cause of cardiovascular disease, as a poor diet may be strongly linked to insulin resistance, dyslipidemia and hypertension. Past studies concerning eating habits in nonpsychiatric subjects have suggested a strong link between the metabolic syndrome and the consumption of some macro-nutrients such as saturated and unsaturated fats, fruits, vegetables, sugar and salt. Apart from the obvious association between high calories intake and obesity, it has also been reported that a diet rich in high saturated fats and poor in unsaturated fats is associated with obesity, increased concentration of LDL cholesterol and insulin resistance (Siri-Tarino et al., 2010). A high intake of carbohydrates, especially refined sugars with elevated Glycemic Load (GL), is related to high fasting triglycerides and low HDL cholesterol levels (Hu and Willett, 2002). A poor fibre and fruit consumption is associated with increased food intake and reduced control on glucose homeostasis and plasma lipid levels (Delzenne and Cani, 2005). Finally, high salt intake may have a key role in the development of hypertension (Chen et al., 2010).
+Therefore, given that diet may represent a key factor in the development of metabolic syndrome, this paper aimed to review studies on dietary habits in patients with schizophrenia-spectrum disorders, to clarify whether these dietary patterns may be associated with the development of metabolic syndrome, and understand which factors may influence them.
+2.	Materials and methods
+We conducted a systematic review of the literature reviewing studies in English language published from 1950 to the 1st of November 2011, and reporting dietary habits in patients with schizophrenia spectrum disorders, identified by searches on Pubmed, The Cochrane Library, Scopus, Embase, Ovid of Medline, Psychoinfo and ISI web of Knowledge. Search terms over full text included the combination of the following keywords: DIET or DIETARY or NUTRITION or NUTRITIONAL or EATING or FOOD, crossed with SCHIZOPHRENIA or PSYCHOSIS or PSYCHOTIC and with LIFESTYLE or METABOLIC or HEALTH HABITS. More than 700 articles (n = 783, excluding duplicates) were found through the investigation of such databases. After title, abstract or full-text reading, we selected a total of 89 papers; after checking references from these studies other 9 articles were added, obtaining a total of 98 papers. Among these, we excluded papers focused on: 1) eating habits and their relationship with prevention, psychopathology, treatment and outcome of schizophrenia (n = 22); 2) trials to improve dietary habits and manage weight gain (n = 25); 3) reviews on dietary habits of patients with schizophrenia (n = 3); 4) studies dealing with food habits of patients with schizophrenia, which matched cases and controls for diet or did not provide any information on the consumption of macro-nutrients or groups of food (n = 17). Eventually, we reviewed thirty-one (n = 31) studies concerning the dietary patterns and their effects on metabolic parameters in patients with schizophrenia. Table 1 summarizes our methods.
+3.	Results
+Most studies were focused on the assessment of unhealthy lifestyle, metabolic abnormalities and cardiovascular risk in chronic patients with schizophrenia. In the majority of the studies, the results were compared with data from the general population, instead of data from a matched control group. Four studies focused on first-episode psychosis patients (Hepgul et al., 2011; Ryan et al., 2003, 2004; Samele et al., 2007); only few (n = 10) papers reported a detailed medication history. Results from papers focused on first-episode patients were usually concordant with those from studies involving chronic patients. A summary of the details and findings of the studies is shown in Table 2.
+3.1.	Assessment of dietary profile
+The diet was assessed retrospectively in most of the studies. Five papers (Brown et al., 1999; Hepgul et al., 2011; Osborn et al., 2007; Ryan et al., 2003, 2004) assessed eating habits of both cases and controls using the DINE, a short structured questionnaire which provides a brief initial assessment of intake of only total fat and dietary fibre; additional nutrients of interest, such as sugars, are not included due to the need to keep the assessment tool short. The DINE categorises respondents into high, medium or low intake of fibre, saturated fat and unsaturated fat. Similarly, other six papers (Amani, 2007; Archie et al., 2007; Chuang et al., 2008; Gupta and Craig, 2009; Roick et al., 2007; Samele et al., 2007) used short food frequency questionnaires aimed at giving only a general overview of fat and fibre consumption. In contrast, another study (McCreadie et al., 1998) obtained more detailed information on dietary habits of patients (caloric intake, protein, fat, fibre, retinol and vitamins consumption) through a modified version of a known food frequency questionnaire. Two studies (Strassnig et al., 2003, 2006) conducted a 24-h diet recall using standardized food models to collect the nutritional information and estimate portion sizes. In this 24-h recall method, nutritional values (fibre, saturated fat, mono and polyunsaturated fat and vitamins intake) were compared to nutritional data for the general population of similar age from NHANES III surveys (National Health and Nutrition Examination Survey, Cycle III). A similar 24-h recall method was used by two other papers (Ellingrod et al., 2011; Wallace and Tennant, 1998). Six papers (Blouin et al., 2008; Gothelf et al., 2002; Henderson et al., 2005, 2006, 2010, 2011) applied a perspective assessment of diet; among these, four studies measured eating habits through a 4-day dietary record, in which patients recorded their food and beverage consumption for 4 consecutive days (Henderson et al., 2005, 2006, 2010, 2011). Nutritional variables included total energy intake, fat, protein, carbohydrate, cholesterol, fibre, sucrose, folate, calcium, sodium, zinc, alcohol and caffeine; the findings were compared to the general population using data matched for age, gender, and ethnicity from the NHANES III surveys. One study (Blouin et al., 2008) recorded food intake and food preferences of patients by offering them a buffet-type meal which provided a large diversity in protein, lipid, and carbohydrate sources in order to facilitate the detection of macronutrient preferences. After the record, the authors calculated macronutrients intake by Canadian Nutrient File (Canadian Nutrient File. Tape and User’s Guide. no. 58-42-1997E-MRed. Health and Welfare Canada: Ottawa, 1997) or information on food labels. Another paper (Gothelf et al., 2002) used a 2-day record in which patients were allowed free choice of the type and amount of food. All food products and beverages consumed by patients were weighed before and after the meal and the total daily caloric intake, as well as carbohydrate, fat, and protein contents, were calculated from computerized food tables.
+Finally, some authors (Arango et al., 2008; Bobes et al., 2010; Fusar-Poli et al., 2009; Treuer et al., 2009) assessed the diet through a series of simple verbal questions regarding fat, fibre, carbohydrates, salt and alcohol consumption without applying a proper-scientific-structured model.
+3.2.	Differences in dietary pattern between patients with schizophrenia and controls
+The most common finding was that patients with schizophrenia were more likely to consume a diet poor in fibre and fruit intake (Brown et al., 1999; Fusar-Poli et al., 2009; Gupta and Craig, 2009; Henderson et al., 2006; McCreadie et al., 1998; McCreadie, 2003; Osborn et al., 2007; Roick et al., 2007; Ryan et al., 2004; Wallace and Tennant, 1998) and rich in saturated fat (Amani, 2007; Archie
+et al., 2007; Brown et al., 1999; Henderson et al., 2010; Osborn et al., 2007; Ryan et al., 2003,2004; Strassnig et al., 2005b). A few studies also reported a significantly increased intake of calories (DeMyer et al., 1968; Strassnig et al., 2003) and a low consumption of both monounsaturated and polyunsaturated fatty acids (Henderson et al., 2006). Five studies did not report any significant difference in the diet of patients with schizophrenia compared with healthy subjects (Blouin et al., 2008; Henderson et al., 2005; Saarni et al., 2009; Strassnig et al., 2003; Suvisaari et al., 2007) or with other patients affected by depression or bipolar disorder (Chuang et al., 2008).
+3.3.	Dietary pattern and metabolic abnormalities
+Most papers assessed cardiovascular risk and unhealthy lifestyle such as smoking, caffeine and alcohol consumption and poor physical activity in patients with schizophrenia. Subjects with a poor diet and an unhealthy lifestyle were more likely to be overweight or obese (Archie et al., 2007; Brown et al., 1999; Fusar-Poli et al., 2009; Gupta and Craig, 2009; Henderson et al., 2006; McCreadie, 2003; Strassnig et al., 2003), with high LDL-c (Fusar-Poli et al., 2009; McCreadie, 2003; Osborn et al., 2007; Ryan et al., 2003) and low HDL-c blood levels (McCreadie, 2003; Osborn et al., 2007), along with increased fasting glucose (Arango et al., 2008; Fusar-Poli et al., 2009). The cardiovascular risk was mainly measured with the Framingham assessment, and patients showed a significant increased risk due to their unhealthy lifestyle (Bobes et al., 2010; Fusar-Poli et al., 2009; Gupta and Craig, 2009; McCreadie, 2003; Osborn et al., 2007).
+3.4.	Factors influencing dietary pattern in schizophrenia
+Only few studies investigated which factors may influence the diet of individuals with schizophrenia and the findings appear to be inconsistent. Results from two studies suggest that the poor diet associated with schizophrenia is influenced by socioeconomic status (Roick et al., 2007; Samele et al., 2007); however, another study was not able to replicate this finding (Osborn et al., 2007). The effects of gender and smoking status on diet revealed that men tend to have a less healthy diet than women, eating significantly less fruit, vegetables, milk and pulses (McCreadie, 2003), or were less likely to consume healthy foods in general and more likely to consume alcohol (Roick et al., 2007). On the other hand, other authors noted that female patients consumed more fat, carbohydrates and calories (Strassnig et al., 2003), less fruit, vegetables and nuts (Amani, 2007), less fibre and more fats (Archie et al., 2007). Regarding smoking habits, a significant difference in dietary patterns was reported between smoking and non-smoking patients, as non-smokers showed healthier eating habits than smokers (Bobes et al., 2010; McCreadie, 2003). However, another study did not find any correlation between smoking and diet (Strassnig et al., 2006). The link between stress and diet has been only briefly investigated in previous studies. Indeed, it is acknowledged that the hypothalamic—pituitary—adrenal axis may influence both metabolic parameters and dietary pattern. In a preliminary study by our team, we found that first-episode psychosis patients with childhood sexual abuse had a less healthy diet, as indicated by lower fibre intake, than patients without a history of abuse (Hepgul et al., 2011). As childhood trauma is also linked to hypothalamic—pituitary—adrenal axis and immune activation, it is possible to hypothesise that the association between stress and dietary patterns of first-episode psychosis patients is mediated by these biological systems. However, only two of the reported studies investigated cortisol levels (Ryan et al., 2003, 2004), but these studies did not explore the correlation between
+cortisol secretion and diet. None of the other papers provided information about stress levels or the association between stress and diet in these patients. Similarly, very little has been reported on the influence of antipsychotic treatment on diet. Patients treated with olanzapine showed an increased consumption of candy/sweet food after six months of treatment (Treuer et al., 2009) or a significant increased caloric intake without any change of diet macronutrient composition after four weeks of treatment (Gothelf et al., 2002). In a study which compared two groups of patients treated with either clozapine or risperidone (Henderson et al., 2010), clozapine-treated subjects had a significantly higher intake of saturated fat and protein, along with a lower fibre and carbohydrates consumption than risperidone-treated individuals; both groups of patients expressed higher fats consumption than what is recommended by National Cholesterol Education Program (NCEP). However, in another paper (Henderson et al., 2005) the same author did not report any difference in dietary habits between quetiapine-treated patients and olanzapine-treated patients. At the same time, another author did not report any significant difference in macronutrient and caloric intake between patients treated with atypical antipsychotics and patients treated with first generation antipsychotics (Ellingrod et al., 2011). Moreover, it has been reported a high saturated fat and low fibre intake in a sample of drug naive patients when compared with healthy controls; however, the same patients did not show any significant change in their dietary patterns after six months of antipsychotic treatment (Ryan et al., 2004). In agreement with these findings, another paper (Osborn et al., 2007) showed that antipsychotic treatment did not influence diet, as patients showed unhealthy dietary habits also after controlling for medications. Finally, one study (Arango et al., 2008) compared the dietary patterns of antipsychotic-treated patients with and without the metabolic syndrome; surprisingly, the subjects affected by metabolic syndrome were more likely to follow a healthier diet, to control their salt intake and to avoid saturated fats and cholesterol than those without metabolic syndrome.
+4.	Discussion
+4.1.	The influence of diet on metabolic abnormalities
+Despite the authors used a number of different instruments for the assessment of dietary habits, most of these studies agreed that subjects with schizophrenia tend to have an unhealthy diet, rich in saturated fats and poor in fibre and fruit. These patients also show a high caloric intake. As reported above, these dietary patterns are known to be linked to the development of metabolic syndrome in non-psychiatric individuals. Moreover, both high saturated fat intake and low fibre and fruit consumption are related with high levels of inflammatory markers, especially tumour necrosis factor (TNF)-alpha, interleukin (IL)-6 and C reactive protein (CRP), which in turn may promote the development or the worsening of the metabolic syndrome in genetically or metabolically predisposed individuals (Esposito et al., 2004). This is particularly harmful in patients with schizophrenia, who show an elevated oxidative stress (Minutolo et al., 2012), high levels of pro-inflammatory cytokines and several metabolic abnormalities, also at the onset of the illness (Mondelli et al., 2011; Pennington et al., 2008; Ryan et al., 2003).
+Despite these findings, in the studies we reviewed, the link between poor diet and the metabolic abnormalities was not clearly elucidated. Surprisingly, most papers did not explore the correlation between diet and metabolic variables. Indeed, these studies were more focused on investigating whether the lifestyle, the metabolic parameters and the cardiovascular risk of people with schizophrenia were actually different from those of healthy subjects. However, it has been reported that higher saturated-fat
+intake of both clozapine- and risperidone-treated patients was proportionally associated with impaired glucose homeostasis (Henderson et al., 2010); in particular, the consumption of saturated fats was negatively correlated with disposition index (an index of b-cell function) and glucose effectiveness (the net fractional glucose clearance rate due to the increase in glucose, independent of any increase in circulating insulin concentrations above baseline). According to the authors, saturated fat might reduce both glucose transporter (GLUT)-2 and glucokinase function and, at the same time, induce oxidative stress and apoptosis of beta-cell mass, thus compromising the insulin sensitivity. Another study (Ryan et al., 2003) did not correlate diet and metabolic abnormalities; however, the patient group showed a diet higher in saturated fat along with lower fasting plasma levels of total cholesterol and LDL cholesterol than the normal comparisons subject, thus suggesting that poor diet is not associated with the development of metabolic abnormalities. However, as most studies did not report a clear link between diet and metabolic pattern, we have not been able to clarify whether these metabolic abnormalities may be due to diet, to other unhealthy lifestyle aspects, such as alcohol abuse, lack of physical exercise, smoking, or to other factors, including stress or antipsychotic treatment. Therefore, on the basis of the findings from the studies reviewed and previous literature, we can only suggest that a poor diet represents one of the factors involved in the development of metabolic abnormalities. With this view, it is important to clarify which factors may influence diet and consequently have a role in the development of metabolic syndrome.
+4.2.	The role of smoking habits and socio-economic status in abnormal dietary pattern in schizophrenia
+Past studies in non-psychiatric patients reported that smokers, when compared with non-smokers, presented a worse dietary pattern (de Castro and Taylor, 2008); at the same time, it is well known that people with schizophrenia have more than five times the odds of current smoking than the general population (de Leon and Diaz, 2005). In the studies we reviewed, smoking patients were more likely to consume alcohol, caffeine, salt, saturated fats and less likely to avoid the consumption of salt, saturated fats and to follow a low-caloric diet (Bobes et al., 2010); they usually consumed fewer portions of fruit and vegetables (McCreadie, 2003). In contrast, another study (Strassnig et al., 2006) did not show any influence of smoking habits on diet: however, none of these studies discussed possible mechanisms behind the relationship between smoking habits and diet in patients with schizophrenia, which are still far from being properly clarified.
+It has been reported that the metabolic syndrome is associated with lower socioeconomic status (Manuck et al., 2010) and subjects with lower socioeconomic status are more likely to report unhealthy eating habits (Darmon and Drewnowski, 2008). Individuals with schizophrenia usually belong to lower socioeconomic classes (Agerbo et al., 2004; de Souza and Coutinho, 2006). In the papers we reviewed, the importance of socioeconomic status may have been underestimated as only few studies used a carefully matched control group, or adjusted the outcomes for socioeconomic status. Despite unemployment seems to predict a reduced consumption of healthy groceries (Roick et al., 2007) and no differences in dietary pattern were reported between patients and controls after adjusting for unemployment and educational qualification (Samele et al., 2007), some authors (Brown et al., 2000; McCreadie, 2003) found that patients with schizophrenia have a worse diet than the lowest social class of the general population sample used as a comparison group. According to these authors, their findings might be related to the presence of negative symptoms of schizophrenia, like apathy, which may decrease both energy
+and motivation, thus inducing the preference for easily obtainable and less healthy food. Similarly, the negative symptoms of schizophrenia may also worsen poor dietary patterns. These mixed findings make it difficult to understand whether socioeconomic status affects the diet of patients with schizophrenia.
+4.3.	The role of stress in the abnormal dietary pattern in schizophrenia
+Stress is thought to influence eating behaviour and this association has been examined both in animals and humans (Dallman et al., 2005; Morley et al., 1983). In humans, stress is more likely to increase food intake, along with a high consumption of palatable food, rich in saturated fat and sugar (Rutters et al., 2009). These abnormalities may be related to an excessive activation of the hypothalamic—pituitary—adrenal axis, which usually occurs in subjects who may have a higher sensitivity to stress.
+Patients with schizophrenia during the acute phase of illness present a higher sensitivity to stress (Myin-Germeys and van Os, 2007), along with hyperactivity of the hypothalamic—pituitary— adrenal axis (Pariante, 2008), which in turn causes glucocorticoid resistance (Mondelli et al., 2008; Pariante, 2009) and increased circulating levels of adrenocorticotropic hormone (ACTH) and cortisol.
+Being more sensitive to stress has been associated with an increased high palatable food intake, through a complex mechanism involving elevated levels of ACTH, cortisol, leptin, insulin, neuropeptide Y, and activation of the reward system (Adam and Epel, 2007): this mechanism may explain the high saturated fat consumption of patients with schizophrenia, at least at the onset of illness.
+However, in our review, we have not been able to test this hypothesis as most studies have not assessed or reported cortisol and stress levels. Two studies including drug naive first-episode psychosis subjects (Ryan et al., 2003, 2004) have found both hyper-cortisolemia and a significant high intake of saturated fat, but unfortunately these studies did not make any correlation between these two variables. Moreover, it has been found a similar poor dietary pattern in the same patients before and after six months of antipsychotic treatment, although the patients showed a decrease in cortisol levels after treatment (Ryan et al., 2004). This finding suggests that high levels of stress and hyper-cortisolemia may be more likely to influence diet only at onset, prior to the implementation of the effects of antipsychotic treatment. Indeed, it has also been reported that chronic patients, especially those on antipsychotic treatment, may not express high levels of stress and/ or hyper-cortisolemia (Zhang et al., 2005) but, as revealed by the outcomes of the studies reviewed in this paper, they may still report poor dietary habits.
+Thus, although stress is likely to affect the diet at the onset of psychosis, it might have a less important role in the development or in the maintenance of the abnormal dietary pattern in chronic patients. In order to clarify this hypothesis, future studies should take into account the influence of stress on diet in longitudinal studies.
+4.4.	The role of antipsychotic treatment in abnormal dietary pattern in schizophrenia
+The mechanism by which antipsychotic medications increase food intake has been explained by several alterations of neurotransmitters and hormonal pathways. The blockade of both serotonin 2C (5HT2C) and the histamine 1 (H1) receptors (peculiar of some second-generation antipsychotics such as olanzapine and clozapine) is most frequently associated with metabolic risk
+because of its influence on the hypothalamic hormones involved in the satiety control (Stahl et al., 2009), such as neuropeptide Y (NPY), agouti-related peptide (AGRP), pro-opiomelanocortin (POMC) and, especially, leptin (Porte et al., 2002). The blockade of histamine 1 (H1) receptors in the hypothalamus reverses the action of leptin on satiety (Stahl et al., 2009), thus leading to further leptin secretion, leptin resistance, increased appetite and food intake. At the same time, the antagonism on serotonin 2C (5HT2C) may interfere with leptin activity and may also cause increased NPY levels which in turn stimulates food intake (Reynolds et al., 2006).
+In addition to the effect on food intake, it is still unclear whether these mechanisms influence also food choices. Because of the scarce information on detailed medical histories, it is difficult to evaluate whether the antipsychotic treatment has influenced the poor dietary habits reported in the studies reviewed in this paper. Indeed, our review has focused especially on studies involving chronic patients, with a long history of antipsychotic treatment. It has been reported a less healthy dietary pattern in clozapine-treated patients than risperidone-treated ones (Henderson et al., 2010), an increased consumption of candy/sweet food in patients after six months of treatment with olanzapine (Treuer et al., 2009) and an enhanced caloric intake in olanzapine-treated patients after four weeks of treatment, without any change of food choices (Gothelf et al., 2002). In contrast, other authors (Ellingrod et al., 2011; Henderson et al., 2005; Osborn et al., 2007; Ryan et al., 2004) did not find any significant influence of antipsychotic treatment on diet. However, these results seem to be in contrast with what has been reported in the literature concerning the influence of antipsychotic drugs on food intake (Basson et al., 2001). Future studies should elucidate the influence of antipsychotic treatment on diet of patients with schizophrenia and mechanisms underlying this possible association.
+5.	Limitations
+Although the literature has shown that stress, socioeconomic status and antipsychotic treatment may affect diet, we have not been able to assess whether these factors may be accountable for the poor dietary habits of patients with schizophrenia, as the results of the studies reviewed appear inconsistent. This inconsistency is probably due to methodological issues. Indeed, most studies involved chronic patients, without reporting stress levels and antipsychotic treatment and without adjusting the results for socioeconomic status. Few studies were also conducted on relatively small samples of patients (less than 50) and using particular selection criteria that might have partially influenced their findings. Moreover, only few studies used a carefully matched control group and the diet was usually assessed retrospectively, often through self-report questionnaire. Surprisingly, most papers did not make any statistical correlation between diet and metabolic abnormalities. To overcome some of these methodological issues, future studies should focus on first-episode psychosis patients, include a carefully matched control group, report medication history, and possibly assess stress, cortisol levels and socioeconomic status.
+6.	Conclusions
+To our knowledge, this is the first review that has attempted to analyse the key features that may influence the dietary habits of patients with schizophrenia. Past reviews (Peet, 2004; Strassnig et al., 2005a) have taken in account fewer studies and focussed more on the metabolic abnormalities associated with unhealthy lifestyle of these patients. Our findings confirm those of past reviews. We have found that people with schizophrenia have a poor
+diet, mainly characterized by a high intake of saturated fat and a low consumption of fibre and fruit. Such diet is likely to increase the risk of developing metabolic abnormalities, and may worsen metabolic abnormalities induced by other factors (e.g. antipsychotic treatment, hypothalamic—pituitary—adrenal axis hyperactivity, low physical activity, smoking, and alcohol and substances abuse). The diet and factors underlying poor dietary patterns may represent an important therapeutic target to control metabolic abnormalities in patients with schizophrenia.
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+S. Dipasquale et al. / Journal of Psychiatric Research 47 (2013) 197—207
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+Introduction
+Studies are increasingly identifying the importance of early life experiences to people’s health throughout the life course. Individuals who have adverse childhood experiences (ACEs; during childhood or adolescence) tend to have more physical and mental health problems as adults than do those who do not have ACEs and ultimately greater premature mortality.1,2 ACEs include harms that affect children directly (eg, abuse and neglect) and indirectly through their living environments (eg, parental conflict, substance abuse, or mental illness). Physiological and biomolecular studies are increasingly establishing how childhood exposure to chronic stress leads to changes in development of nervous, endocrine, and immune systems, resulting in impaired cognitive, social, and emotional functioning
+and increased allostatic load (ie, chronic physiological damage).3,4 Thus, individuals who have ACEs can be more susceptible to disease development through both differences in physiological development and adoption and persistence of health-damaging behaviours.
+Although studies linking childhood experiences to health go back decades,5 examination of multiple ACEs enables a better assessment of the breadth of childhood adversity and its relation with adult health than does examination of single ACEs. The first major ACE study1,6 examined relations between the number of ACEs reported by more than 17 000 individuals in the USA and their health as adults. It found that the more ACE types that individuals reported, the greater their risks of health-harming behaviours (eg, smoking or sexual risk taking) and both infectious and non-communicable
+Research in context
+Evidence before this study
+Previous reviews have synthesised evidence for the long-term health effects of individual adverse childhood experience (ACE) types. However, ACEs often cluster in children’s lives and a growing body of research is identifying cumulative relations between multiple ACEs and poor health. Initial evidence of this relation was published in the 1990s. Since then, an increasing number of studies have used similar methods to identify how multiple ACEs affect health-harming behaviours and development of health conditions, including non-communicable diseases.
+Added value of this study
+To our knowledge, no previous attempt has been made to synthesise evidence for the risks of poor health associated with multiple ACEs across various health-related behaviours and conditions. We found that individuals with at least four ACEs were at increased risk of all outcomes examined. Associations were weak or modest for physical inactivity, overweight or obesity, and diabetes (ORs of less than two), moderate for smoking, heavy alcohol use, poor self-rated health, cancer, heart disease, and
+respiratory disease (ORs of two to three), strong for sexual risk taking, mental ill health, and problematic alcohol use (ORs of more than three to six), and strongest for problematic drug use and interpersonal and self-directed violence (ORs of more than seven)
+Implications of all the available evidence
+This systematic review and meta-analysis highlights the pervasive harms that ACEs place on health throughout the life-course and the importance of addressing the various stressors that can occur in children’s lives, rather than limiting attention to any one type. Although further work is required to establish causality, the strong relations between multiple ACEs and poor health suggest that a reduction in ACEs and building of resilience to enable those affected to avoid their harmful effects could have a major effect on health. International resolutions, including the Sustainable Development Goals, provide crucial opportunities to address ACEs and our findings offer key information to advocate and inform development of more sustainable life-course approaches to health and health care than at present.
+diseases (NCDs). Supported by international work to standardise measurement of ACEs and their effects on health, these findings have since been replicated in studies from low-income and middle-income7,8 and high-income2,9 countries. However, although previous reviews10,11 have collated literature on the health effects of any ACE exposure or specific ACE types, no systematic attempt has been made to synthesise findings from studies of the effect of multiple ACEs across multiple outcomes. Consequently, no global estimates have been made of the strength of associations between multiple ACEs and adoption of health-harming behaviours, occurrence of conditions such as obesity and chronic health conditions, or risk of further exposure to violence in adult years.
+In this study, we present findings from a systematic review and meta-analysis of studies measuring associations between multiple ACEs and health outcomes. The primary outcomes of interest were pooled measures of relations between multiple ACEs and health outcomes. Following precedent in the literature,1,6 we restricted analyses to exposure to at least four types of adversity during childhood, with individuals reporting no ACEs as the comparator.
+Methods
+Search strategy and selection criteria
+The search strategy of this systematic review and metaanalysis is summarised in the panel. Searches focused on six categories of health outcomes: substance use, sexual health, mental health, weight and physical exercise, violence, and physical health status and
+conditions. We excluded studies based on high-risk (eg, the homeless or those in prison) or clinical populations because of often few individuals with low ACE exposure in such populations. Included studies met the following criteria: cross-sectional, case control, or cohort study, using a cumulative measure of at least four ACEs spanning both direct (eg, maltreatment) and indirect (eg, household dysfunction) types, focused predominantly on adults aged at least 18 years, a sample size of at least 100, and reported odds ratios (ORs), comparable statistics (hazard ratios or prevalence ratios), or data to enable their calculation for a health outcome. We also excluded outcomes with fewer than four studies reporting results suitable for meta-analysis. The initial literature search was done by two reviewers (KH and KAH), who then also retrieved and independently screened full-text articles. Conflicts over inclusion were resolved through discussion with MAB. Data were extracted by one reviewer (KH) and checked by two others (KAH and MAB).
+Data analysis
+Included articles were independently assessed for quality by two reviewers (KH and KAH) using criteria based on the standard principles of quality assessment.12 Studies received a point for each quality criterion that they met, for a maximum score of 7. For each article, we extracted data for study type, setting, participants, ACEs, and outcomes. We extracted ORs or equivalent measures for participants with at least four ACEs versus those with none. When such data were not published, we included studies when adequate information was available to allow their calculation, including sample sizes within
+each ACE category and adequate ACE categories for recalculation of pertinent ORs, linear relationships between ACE counts and ORs, or changes in prevalence with ACE count. One article13 combined data from eight studies; for this article, original data were available to us because we were authors of the article, allowing ORs to be calculated for each sample. However, our study was not an independent-participant-data meta-analysis. When multiple studies reported data for the same outcome and sample, we included one study on the basis of largest sample size or data presentation (closest fit to study requirements).
+We calculated pooled ORs with 95% CIs for the risk of health outcomes among individuals with at least four ACEs (vs no ACEs) using a random-effects model in StatsDirect version 2.8.0. When ORs were presented at a subgroup level within samples, we pooled ORs before analysis. We used the 12 statistic to estimate the effect of heterogeneity among pooled studies. We explored risk of publication bias using the Begg-Mazumdar and Egger tests and visual inspection of funnel plots when sufficient studies were included in the meta-analysis (at least ten samples; appendix). We generated forest plots showing ORs and 95% CIs for each study and the overall random-effects pooled estimate. We did sensitivity analyses by excluding outlying studies (so that study 95% CIs did not overlap those of pooled measures). We further explored potential sources of heterogeneity by visual inspection of data and forest plots and, when possible (for outcomes with at least ten samples and high heterogeneity between estimates), by meta-regression. We did univariate analyses using Stata version 14 to test the individual association of the following covariates (when relevant) with pooled estimates: sample size, country income level (low-income or middle-income vs high-income), ACEs measured (fewer than ten vs ten or more), sample age range (old [age >35 years] vs other), outcome timeframe (recent vs lifetime), quality score (<5 vs >5), OR data (adjusted vs unadjusted), and data collection point (past decade [2006 onwards] vs older [pre-2006]).
+Role of the funding source
+Members of the funder contributed to study design, data collection, data analysis, data interpretation, and writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
+Results
+From a total of 11 621 references identified through the literature search, full-text copies of 2334 (20%) articles were retrieved and screened; 194 (8%) of these articles were considered for inclusion, with 37 (19%) articles116-9’13-43 selected to contribute to the review, with a total of 253 719 participants (figure, table 1). 21 used population samples from the USA,1’6’14-32 seven used samples from
+the UK,2’9,33-37 two used samples from Finland,38,39 and one See Online for appendix study each used samples from Canada,40 China,41 New Zealand,42 the Philippines,8 Saudi Arabia,7 and Sri Lanka.43 One article13 included data from eight studies done in Albania, Latvia, Lithuania, Macedonia, Montenegro, Romania, Russia, and Turkey; we treated these samples separately in analyses. Most studies were done in high-income countries, with nine samples from middle-income countries and none from low-income countries. 26 articles used data from cross-sectional studies and 11 used data from cohort studies; however, all studies used retrospective, self-reported ACEs. 21 studies used general population (predominantly household) samples, with other samples from primary care, education, community, military, and workplace settings.
+Sample sizes ranged from 210 to 53 998 individuals, with most studies covering broad age ranges and both sexes, although young, old, and single-sex samples were included. 144725 (57%) of 252 467 participants across all studies reported at least one ACE and 31 795 (13%) of 244 979 reported at least four.
+The mean number of ACEs measured was nine, with most studies using a similar core set (table 2), and all using a similar timeframe for exposure (from up to age 16 years to up to age 18 years). Prevalence of zero ACEs ranged from 12% to 67% and prevalence of at least four ACEs ranged from 1% to 38% (table 1). Included outcomes and associated definitions are shown in table 3,
+along with the number of studies, samples, and countries for each outcome.
+Pooled ORs for individuals with four or more ACEs (vs individuals with no ACEs) for each outcome are presented in table 4. Corresponding forest plots are provided in the appendix. Funnel plots showing risk of publication bias for outcomes with at least ten samples are also given in the appendix. Smoking, alcohol use, and drug use ORs ranged from 2-20 (95% CI 1-74-2-78) for heavy alcohol use to 10-22 (7-62-13-71) for problematic drug use, all with high heterogeneity between estimates (except for problematic drug use, which had low heterogeneity). Although statistical tests were nonsignificant, visual assessment ofthe funnel plot indicated that smaller studies tended to report greater risk of smoking than did larger studies (appendix). Despite variation in outcome measurement for heavy alcohol use (table 3), this variation had no visible effect on study estimates. We did not note any evidence of reporting biases for illicit drug use (appendix).
+Pooled ORs for sexual health outcomes ranged from 3-64 (95% CI 3-02-4-40) for multiple sexual partners to 5-92 (3 • 21-10 - 92) for sexually transmitted infections (table 4). Heterogeneity between estimates was low for multiple sexual partners and high for other outcomes. For teenage pregnancy, four studies9,33-35 had measured unintended teenage pregnancy and these studies
+reported higher estimates than did those measuring any teenage pregnancy8,14,21 (appendix). For early sexual initiation, visual assessment of the funnel plot suggested that small studies showing significant effects were missing (appendix).
+Physical inactivity had the weakest relationship with multiple ACEs (table 4). We noted moderate heterogeneity between estimates, but no evidence of reporting biases (appendix). For overweight or obesity, the pooled OR was slightly higher, with high heterogeneity between estimates. Higher ORs were reported by studies using higher body-mass indices (appendix). We noted an about four-times higher risk in individuals with at least four ACEs across the three indicators of mental distress or disorders (table 4). Heterogeneity between estimates was low for low life satisfaction and high for anxiety and depression. For anxiety, estimates were lower for studies measuring more recent anxiety than for those measuring longer-term anxiety (appendix). We did not identify this difference among estimates for depression, with no evidence of asymmetry in the funnel plot (appendix).
+Pooled ORs were 7-51 (95% CI 5-60-10-08) for violence victimisation and 8-10 (5-87-11-18) for violence perpetration (table 4). Heterogeneity was moderate between estimates. Suicide attempt had the strongest relation with ACEs. However, five of the seven samples comprised students aged 18-25;13 pooling of the remaining two samples8,18 resulted in an OR of 12-53 (6-71-23-37; appendix). Across the five chronic diseases examined, the lowest pooled OR was for diabetes and the highest was for respiratory disease. Other diseases had between a two-times and three-times increase in odds with at least four ACEs (table 4). Heterogeneity was low between estimates pooled for all chronic diseases. A similar increase in risk was identified for poor selfreported health, with low heterogeneity.
+We assessed studies against seven quality criteria (table 1). Summed quality scores ranged from 2 to 7, with three articles obtaining the maximum 7 points. 11 articles reported on studies that had not used random or wholepopulation approaches. All included studies are likely to be affected by bias given relations between ACEs and harms that remove people from population surveys (eg, homelessness, institutionalisation, and premature death). Thus, studies scored positively if they did not appear to include further bias in their recruitment strategies, with 11 articles not meeting this criterion. All articles used an appropriate control group and all but four used validated or clearly defined ACE measurement tools. 15 had response rates of less than 50% and although only four did not adequately describe study participants, 34 provided no information about nonparticipating individuals.
+Sensitivity analyses (excluding outlying studies) reduced pooled ORs for physical inactivity, diabetes, heavy alcohol use, smoking, and illicit drug use and increased those for early sexual initiation, depression,
+problematic alcohol use, and suicide attempt (table 4). We identified no outliers for other outcomes. For physical inactivity, both outlying studies were student samples, with estimates tending to be higher among such samples than among general population samples. For problematic alcohol use, the outlying study used a past year measurement (vs lifetime elsewhere). We identified no clear explanatory factors for other outcomes.
+We did meta-regression for smoking, problematic alcohol use, illicit drug use, early sexual initiation, and depression. We noted no significant relationships between ORs and any measured covariate for illicit drug use, problematic alcohol use, or depression (appendix). For smoking, studies focusing on old participants
+(>35 years) reported lower odds of smoking than did those including general or young samples (P=-0-54; se[P]=0-26; p=0-05). For early sexual initiation, studies measuring fewer ACEs reported significantly higher odds (P=-0-58; se[P]=0-22; p=0-03).
+Discussion
+To our knowledge, this study is the first to synthesise evidence for the effect of multiple ACEs and measure the relative magnitude of associations with many of the lifestyle behaviours and health conditions that challenge public health globally. For all outcomes examined, pooled ORs indicated increased risk among individuals with at least four ACEs compared with those reporting none.
+Associations were weak or modest for physical inactivity, overweight or obesity, and diabetes; moderate for smoking, heavy alcohol use, poor self-rated health, cancer, heart disease, and respiratory disease; strong for sexual risk taking, mental ill health, and problematic alcohol use; and strongest for problematic drug use and interpersonal and self-directed violence. We found considerable heterogeneity between estimates for almost half of the outcomes.
+This study supports substantially increased health risks to adults who report multiple ACEs, but others identify how having such ACEs is common globally.44 A billion children aged 2-17 years were estimated to have been victims of violence worldwide in 2014.45 Across the
+east Asia and Pacific region, the health consequences of child maltreatment have been estimated to cost around 2% of gross domestic product.46 Global estimates of the prevalence and costs of many other ACEs among children, such as witnessing of domestic violence, are not yet available. Despite accumulating knowledge about the lifelong effects of ACEs, their prevention and the development of resilience and support for those affected have been slow to move up political agendas. International attention is increasingly focusing on prevention of violence against children, often emphasising protection of girls.47 Although girls are especially vulnerable to certain ACEs (eg, sexual abuse), both sexes are routinely victims of multiple ACEs and both feel their long-term effects.48 In fact, the high prevalence of ACEs combined with their effect on lifecourse health suggests a substantial but largely hidden contribution to Global Burden of Disease estimates, which include childhood sexual abuse, yet not many other ACEs.49 Thus, smoking and alcohol use are leading risk factors for burden of disease,49 and in this study, individuals who had had at least four ACEs were more than twice as likely to be current smokers or heavy drinkers and almost six times as likely to drink problematically than were those who had had no ACEs. Consistent with such elevated risks, NCDs including respiratory disease, diabetes, cancers, and heart disease (the leading cause of death globally50), were also substantially more likely in those with at least four ACEs than in those with none.
+Most studies included in this systematic review and meta-analysis were done in high-income countries, with nine samples from middle-income countries and none from low-income countries. The World Mental Health Surveys across 21 countries found little variation in ACE prevalence between country income groups, with 38-39% of participants reporting at least one ACE and the prevalence of at least four ACEs being 2-3%.51 These levels are lower than those measured by studies in this systematic review and meta-analysis, with 57% of participants across all studies reporting at least one ACE and 13% reporting at least four. Little is known about how ACEs predict health outcomes in low-income, high-violence settings, where exposure to adversity is widespread across the life-course. However, evidence suggests that ACEs are associated with substance abuse, mental illness, and HIV risk in such settings.52
+To date, efforts to prevent NCDs, for instance, have focused predominantly on tackling of proximal determinants (eg, behavioural modifications, advertising, or pricing).53 Sustained prevention gains might require a shift in focus to include the early drivers of poor health. Policies that capture the environmental and societal causes of adversity in childhood offer new opportunities to address ACEs rather than just their consequences. Specifically, through the UN 2030 Agenda for Sustainable Development, countries have committed to action to
+meet 17 global Sustainable Development Goals (SDGs) by 2030. Although several SDGs (eg, Goal 5 [gender equality] and Goal 16 [peace and justice]) address violence directly, many others support focus on broad ACEs and their risk factors (eg, Goal 3 [good health and wellbeing], Goal 4 [quality education], and Goal 10 [reduced inequalities]). Crucially, the SDGs also place major focus on early childhood development as a means of securing lifelong health and provide strong political endorsement and a multisectoral framework for this approach.54
+Along with the outcomes covered in this analysis, studies are now identifying associations between multiple ACEs and broad harms to life prospects, including education, employment, and poverty.55 Strengthening understanding of the combined effect of ACEs across multiagency priorities should catalyse multidisciplinary prevention focused on early intervention. Thus, work to address a single ACE in children exposed to many might have little effect,51 with treatment and prevention of many health conditions requiring multiple underlying ACEs to be addressed. Collaborative, trauma-informed services can address the various adversities that affect individuals and families across the life course, providing integrated services to support individuals and reduce the likelihood that their own children in turn will be affected by ACEs. A body of evidence suggests that many different agencies can contribute to prevention of ACEs and reduction of their effects.56,57 In health settings, for example, primary prevention can be supported through maternity and home visiting services that strengthen parenting skills58 and screening of families for risk factors for ACEs as part of routine child health care, providing support and referral.59 Screening of adult patients for a history of ACEs can help both patients and professionals understand the underlying causes of health problems and enable better-informed treatment options than without this approach.60 ACE-informed practice can be developed across multiple settings, including schools, criminal justice agencies, and social care. Although eradication of ACEs remains aspirational, development of children’s personal resilience to enable them to overcome adversity and avoid its harmful effects is crucial. Resilience programmes to develop problem solving and coping skills, for example, can be delivered universally in schools and tailored to meet the needs of vulnerable children in youth justice, social services, and community settings.57,61
+This systematic review and meta-analysis has several limitations that could contribute to heterogeneity between study estimates. All included studies incorporated retrospective ACE reports, which could be affected by recall or reporting biases, although retrospective reports of major, easily defined ACEs are deemed to have acceptable psychometric properties.62,63 The number and types of ACEs recorded by studies varied and although summing of ACEs is a recognised approach,1,9,51,63 it does not account for potential variations
+in effects of different combinations of ACEs. Equally, although most studies measured ACEs at any point in childhood or adolescence (typically <18 years of age), the approach does not account for variation in age at or length of exposure. Furthermore, although many risk estimates controlled for confounding factors (mainly sociodemographics), such factors varied and some studies included no such adjustments. Genetic variation and environmental risks (eg, drinking during pregnancy or parental smoking), which are likely to influence relations between ACEs and health, were largely unmeasured. These limitations suggest a need for greater standardisation in ACE studies, and work to support this standardisation has already begun,64 with many studies now using consistent measurement and analytical approaches. However, our criteria also meant that studies were excluded because of alternative data analysis methods (eg, analysis of the World Mental Health Surveys51). A strength of our systematic review and meta-analysis is that it highlights consistency between studies in the links between exposure to multiple ACEs and poor health, despite likely variation in type and extent of exposure. Further work to synthesise dose-response relations between ACEs and poor health and better understand the relative effects of specific ACE types and combinations are needed to better inform effective targeting of prevention.
+We focused on studies in community settings, which are likely to exclude those with the most complex health problems (eg, homeless populations) and those who have already had ACE-related premature mortality. Interpretation of results is also challenged by variation in measurements within grouped outcomes and in the prevalence of included outcomes, with rarer outcomes (eg, suicide attempt) less well covered by population surveys than those such as smoking. Furthermore, in the case of prevalent outcomes like smoking, increased odds will represent substantial increases in absolute risk, whereas increased odds for rare outcomes represent small increases in absolute risk. Included outcomes probably also differ in validity, with difficulties in measurement of physical inactivity, for example, potentially contributing to its low association with multiple ACEs in this study. Finally, this study was only able to measure associations; biomedical evidence is increasing to support plausible causal relations between childhood trauma and poor health, with studies identifying neurological, hormonal, immunological, and epigenetic changes in those exposed to ACEs.3,65,66 Future studies would benefit from designs that allow stronger causal inference and control for factors that attenuate or amplify observed relations.
+This systematic review and meta-analysis identifies the pervasive effects that childhood adversity can have on health across the life course, with exposure to multiple ACEs affecting all 23 of the health outcomes examined, including some of the leading causes of the global
+burden of disease. Outcomes showing the strongest relations with multiple ACEs (violence, mental illness, and problematic substance abuse) can represent ACEs for the next generation (exposure to parental domestic violence, mental illness, and substance use) and thus are indicative of the intergenerational effects that can lock families into cycles of adversity, deprivation, and ill health. Although research into ACEs is far from complete, a compelling case exists for increased international focus on prevention of ACEs, development of programmes to bolster resilience, and implementation of policies that support a sustainable life-course approach to health.
+Articles
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+Bellis MA, Ashton K, Hughes K, Ford K, Bishop J, Paranjothy S. Adverse childhood experiences and their impact on health-harming behaviours in the Welsh adult population. Cardiff: Public Health Wales, 2015.
+Ford K, Butler N, Hughes K, Quigg Z, Bellis MA.
+Adverse childhood experiences (ACEs) in Hertfordshire, Luton and Northamptonshire. Liverpool: Liverpool John Moores University, 2016.
+Hughes K, Lowey H, Quigg Z, Bellis MA. Relationships between adverse childhood experiences and adult mental well-being: results from an English national household survey. BMC Public Health 2016; 16: 222.
+Leung JP, Britton A, Bell S. Adverse childhood experiences and alcohol consumption in midlife and early old-age. Alcohol Alcohol 2016; 51: 331-38.
+Harkonmâki K, Korkeila K, Vahtera J, et al. Childhood adversities as a predictor of disability retirement. J Epidemiol Community Health 2007; 61: 479-84.
+Pirkola S, Isometsâ E, Aro H, et al. Childhood adversities as risk factors for adult mental disorders: results from the health 2000 study. Soc Psychiatry Psychiatr Epidemiol 2005; 40: 769-77 Chartier MJ, Walker JR, Naimark B. Separate and cumulative effects of adverse childhood experiences in predicting adult health and health care utilization. Child Abuse Negl 2010; 34: 454 64.
+Xiao Q, Dong MX, Yao J, Li WX, Ye DQ. Parental alcoholism, adverse childhood experiences, and later risk of personal alcohol abuse among Chinese medical students. Biomed Environ Sci 2008; 21: 411-19.
+Goodwin RD, Fergusson DM, Horwood LJ. Asthma and depressive and anxiety disorders among young persons in the community. Psychol Med 2004; 34: 1465-74.
+Fonseka RW, Minnis AM, Gomez AM. Impact of adverse childhood experiences on intimate partner violence perpetration among Sri Lankan men. PLoS One 2015; 10: e0136321.
+Stoltenborgh M, Makermans-Kranenburg MJ, Alink LRA, van Ijzendoorn MH. The prevalence of child maltreatment across the globe: review of a series of meta-analyses. Child Abuse Rev 2015; 24: 37-50.
+Hillis S, Mercy J, Amobi A, Kress H. Global prevalence of past-year violence against children: a systematic review and minimum estimates. Pediatrics 2016; 137: e20154079.
+Fang X, Fry DA, Brown DS, et al. The burden of child maltreatment in the east Asia and Pacific region. Child Abuse Negl 2015; 42: 146-62.
+Matzopoulos R, Cornell M, Bowman B, Myers J. 67th WHA Resolution on violence prevention misses the mark. Lancet 2014; 384: 854 55.
+Cavanaugh CE, Petras H, Martins SS. Gender-specific profiles of adverse childhood experiences, past year mental and substance use disorders, and their associations among a national sample of adults in the United States. Soc Psychiatry Psychiatr Epidemiol 2015; 50: 1257-66.
+GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388: 1659-724.
+GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388: 1459-544.
+51	Kessler RC, McLaughlin KA, Green JG, et al. Childhood adversities and adult psychopathology in the WHO World Mental Health Surveys. Br J Psychiatry 2010; 97: 378-85.
+52	Jewkes RK, Dunkle K, Nduna M, Jama PN, Puren A. Associations between childhood adversity and depression, substance abuse and HIV and HSV2 incident infections in rural South African youth. Child Abuse Negl 2010; 34: 833-41.
+53	Mendis S, Armstrong T, Bettcher D, et al. Global status report on noncommunicable diseases 2014. Geneva: World Health Organization, 2014.
+54	Daelmans B, Darmstadt GL, Lombardi J, et al. Early childhood development: the foundation of sustainable development. Lancet 2017; 389: 9-11.
+55	Metzler M, Merrick MT, Klevens J, Ports KA. Adverse childhood experiences and life opportunities: shifting the narrative. Child Youth Serv Rev 2017; 72: 141-49.
+56	Hughes K, Bellis MA, Hardcastle KA, et al. Global development and diffusion of outcome evaluation research for interpersonal and selfdirected violence prevention from 2007 to 2013: a systematic review. Aggress Violent Behav 2014; 19: 655-62.
+57	Ungar M. Resilience after maltreatment: the importance of social services as facilitators of positive adaptation. Child Abuse Negl 2013; 37: 110-15.
+58	Avellar SA, Supplee LH. Effectiveness of home visiting in improving child health and reducing child maltreatment. Pediatrics 2013; 132: S90-99.
+59	Dubowitz H, Feigelman S, Lane W, Kim J. Pediatric primary care to help prevent child maltreatment: the Safe Environment for Every Kid (SEEK) model. Pediatrics 2009; 123: 858-64.
+60	Glowa PT, Olson AL, Johnson DJ. Screening for adverse childhood experiences in a family medical setting: a feasibility study.
+J Am Board Fam Med 2016; 29: 303-07
+61	Center on the Developing Child at Harvard University.
+Supportive relationships and active skill-building strengthen the foundations of resilience: working paper no. 13. 2015.
+http://developingchild.harvard.edu/resources/supportive-relationships-and-active-skill-building-strengthen-the-foundations-of-resilience/ (accessed Jan 22, 2017).
+62	Hardt J, Rutter M. Validity of adult retrospective reports of adverse childhood experiences: review of the evidence.
+J Child Psychol Psychiatry 2004; 45: 260-73.
+63	Reuben A, Moffitt TE, Caspi A, et al. Lest we forget: comparing retrospective and prospective assessments of adverse childhood experiences in the prediction of adult health.
+J Child Psychol Psychiatry 2016; 57: 1103-12.
+64	Anda RF, Butchart A, Felitti VJ, Brown DW. Building a framework for global surveillance of the public health implications of adverse childhood experiences. Am J Prev Med 2010; 39: 93-98.
+65	Teicher MH, Samson JA. Annual research review: enduring neurobiological effects of childhood abuse and neglect.
+J Child Psychol Psychiatry 2016; 57: 241-66.
+66	Kundakovic M, Champagne FA. Early-life experience, epigenetics, and the developing brain. Neuropsychopharmacology 2015;
+40: 141-53.
+www.thelancet.com/public-health Vol 2 August 2017
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+s Brazil transforms itself into a more viable market in the inescapable context of economic globalization (Cardoso 1998, W 1999; Lamounier and Figueiredo 2002), how are Brazilian citizens, particularly the urban poor, struggling to survive and even prosper? What happens in the process to polity and social relations on the ground? Scholars of contemporary Brazil argue that the dramatic rise in urban violence and the partial privatization of health care and public security have deepened divisions between the marketable and the socially excluded (Caldeira 2000, 2002; Escorel 1999; Fonseca 2000, 2002; Goldstein 2003; Hecht 1998; Ribeiro 2000). All the while, newly mobilized patient groups continue to demand that the state fulfill its biopolitical obligations (Biehl in press a; Galvao 2000). As economic indebtedness, ever present, transforms communities and revives paternalistic politics (Raffles 2002), for larger segments of the population, citizenship is increasingly articulated in the sphere of consumer culture (Edmonds 2002; O’Dougherty 2002). Overburdened families are suffused with the materials, patterns, and paradoxes of these processes, which they are, by and large, left alone to negotiate (Scheper-Hughes and Bourgois 2004:3; see also Comaroff and Comaroff 2000 and Lamont 2000).
+I find in this ethnography that the family is increasingly the medical agent of the state, providing and at times triaging care, and that medication has become a key instrument for such deliberate action. Free drug distribution (including psychopharmaceuticals) is a central component of Brazil’s search for an economic and efficient universal health care system. As this work illustrates, people’s claims to health coincide with dramatic cuts in funding for health infrastructures and with the proliferation of pharmaceutical treatments (on the social life of pharmaceuticals, see Geest et al. 1996; Nichter and Vuckovic 1994). In engaging with these new regimes of public health, families learn to act as proxy psychiatrists. Illness becomes the ground on which experimentation with changes and breaks in intimate household relations can occur.1 Families can dispose of their unwanted and unproductive members, sometimes without sanction, on the basis of
+American Ethnologist, Vol. 31, No. 4, pp. 475-496, ISSN 0094-0496, electronic ISSN 1548-1425. © 2004 by the Regents of the University of California (or the society name). All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press’s Rights and Permissions website, at http://www.ucpress.edu/ journals /rights.htm.
+individuals’ noncompliance with their treatment regimes. Thus, psychopharmaceuticals become central to the story of how personal lives can be made or unmade in this moment of socioeconomic transformation and how people create life chances vis-a-vis what is bureaucratically and medically available to them. The foreclosure of life chances for some is part of an elaborate if unconsidered common sense that is mediated by the widespread availability of psychopharmaceuticals. Such foreclosures run parallel with gender discrimination, market exploitation, and a managerial-style state that is increasingly distant from the people it governs.
+In the early 1990s, anthropologists began to follow the production of new bioscientific knowledge and the making of biotechnologies, inquiring into their multiple deployments and their interactions with old and new forms of power relations and ethical models (Rabinow 1999; Rapp 1999; Strathern 1992). Paul Rabinow (1996), for example, notes a dissolution of the traditional social domain and the emergence of new forms of identity and moral reasoning around the technical possibility of the literal remodeling of life (what he calls ‘‘biosociality’’). The recent work of anthropologists Veena Das (1997, 1999), Arthur Kleinman (1999), Allan Young (1995), Nancy Scheper-Hughes (2000), Margaret Lock (2002), Lawrence Cohen (1998), and Adriana Petryna (2002), among others, shows how medical and technical interventions affect— sometimes for better, sometimes for worse—the etiology, experience, and course of disease. The appearance and distribution of disorders such as drug-resistant tuberculosis and AIDS are also closely correlated with poverty and social and technological inequality. They are ‘‘pathologies of power’’ (Farmer 2003) mediated by biological, social, and technical and political-economic mechanisms. Concrete biological phenomena are thus intertwined with environmental conditions that are part of a larger context. And it is in this complicated web that the individual’s life possibilities take shape. As Michael M. J. Fischer notes, ‘‘We are embedded, ethically, as well as existentially and materially, in technologies and technological prostheses. [Our] technological prostheses are also taking us into models of ethics which our older moral traditions have little experience or guidance to offer ... we are again thrown ... to ungrounded ways of acting, to new forms of social life’’ (2003:51).
+In their longitudinal study of symptom management in several neighborhoods in Delhi, Veena and Ranendra Das, for example, explore how illnesses are combined with new familial, economic, medical, and pharmaceutical circumstances and assemblages—empirical processes that are both open ended and stabilizing. They argue that illness is ordinarily conceptualized and practiced as a ‘‘relational testing ground’’ and as an ‘‘experiment with life’’ (Das and Das in press). The individual’s nego
+476
+tiation for health within ‘‘local ecologies of care’’ recasts illness categories, kinship textures, and patterns of social exclusion and inclusion, giving occasion to a ‘‘domestic citizenship’’ (Das and Addlakha 2001:511; see also Greenhouse 2002).2 In this study, I am interested in the place of psychopharmaceuticals in local ecologies of care and in the ways they affect changes in human values and subjectivity. Pharmaceuticals used allegedly to treat mediate the cost-effective decision of abandonment of the unwanted and create moral distance. Zones of abandonment such as I describe below accelerate death and fail to account for individual destinies.3 In this bureaucratically and relationally sanctioned register of social death, the human, the mental, and the chemical are complicit: Their entanglement expresses a common sense that authorizes the lives of some while disallowing the lives of others, and it is also the actual means through which the abandoned claim experience, past and present, and articulate desire.
+Ex-humans
+Vita, which means ‘‘life’’ in Latin, was founded in 1987 by Ze das Drogas, a former street kid and drug dealer in Porto Alegre, a comparatively well-off city of some two million people in southern Brazil. After his conversion to Pente-costalism, Ze had a vision in which the Holy Spirit told him to open an institution in which people like him could find God and regenerate. Ze and his religious friends squatted on private property near the downtown, where they founded a precarious rehabilitation center for drug addicts and alcoholics. Soon Vita’s mission was enlarged. An increasing number of people who had been cut off from social life—the mentally ill and the sick, the unemployed and the homeless—were left at the center by relatives, neighbors, hospitals, and the police. Vita’s team then opened an infirmary where the abandoned waited with death.
+Vita is a place in the world for persons who have, de facto, been stripped of their humanity and terminally excluded from reality. Before biological death, they experience social death.4 What are the various interpersonal, medical, and institutional interactions that turn humans into ex-humans and make their abandonment not only possible but also ordinary?5
+Consider a woman the size of a young child, completely curled up in a cradle and blind. As she grew older and could not work to feed her family—‘‘and worse,’’ I was told, ‘‘she was still eating the family’s food’’—relatives kept her in a dark basement for several years. Then they sent her to Vita, where she was ‘‘adopted’’ by Angela. ‘‘Now she is my baby,’’ said the former intravenous drug user, most likely infected with AIDS. Angela long ago lost custody of her two children and spent her days caring for the old woman, who no longer spoke. ‘‘I found God in Vita. ... When I first came here I wanted to kill myself, now I feel
+useful. ... Today I have still not discovered this grandma’s name. She shouts things that I can’t understand.’’
+I had traveled through and worked in several poor neighborhoods in the north and south of Brazil (Biehl 1999). I thought I knew the country. But nothing I had seen before prepared me for the desolation of Vita when I first visited it in 1995. A local human rights activist had told me to go there if I really wanted to understand ‘‘what it means to be human these days.’’ Vita is indeed the end station on the road of poverty, the place where the unwanted become unknowables. Beyond any kind of accountability, most of the 200 people in Vita’s infirmary in 1995 had no formal identification and lived in abject abandonment that had acquired a haunting stillness. For the most part, Vita’s staff consisted of residents who had improved their mental well-being enough to administer care to newcomers and to those considered absolutely hopeless. Lacking funds, training, and proper equipment and medication, these volunteers were as ill equipped to deal with Vita’s more debilitated residents as those running the establishment. Even though Vita’s existence was acknowledged by officials and the public at large, it was not the object of any remedial policy.6 Over the years, Vita became a key site informing my thinking about Brazil’s changing sociopolitical environment and new regimes of personhood and ethics (Biehl in press b).
+Literally left to wither away, many in the infirmary had open wounds filled with maggots and lice. One 50-year-old man had the maggots drawn from his eyes by an application of Pine Sol and bleach. The inhabitants of the infirmary were treated as beings distinct from humans, argued Oscar, the resident-volunteer who guided my initial visits there: ‘‘Hospitals think that our patients are animals. Doctors see them as indigents and pretend that there is no cure. The other day we had to rush old Lucas to the emergency room. They cut him open and left surgical materials in him. He died from infections.’’ What makes these humans turned animals unworthy of affection and care is their inability to pay, added Luciano, another volunteer: ‘‘The hospital’s intervention is to throw the patient away. If they had sentiment, they would do more for them ... so that there would not be such a waste of souls. Lack of love leaves these people abandoned. If you have money, then you have treatment, if not, you fall into Vita. O Vita da vida (the Vita of life).’’
+As I see it, Oscar and Luciano were not using the same concept of ‘‘human’’ that is invoked in human rights discourses, with their notion of shared corporeality or shared reason (Ignatieff 2001). Neither were they opposing it to ‘‘animal.’’ Rather than referring to the animal nature of humans, they spoke of an animal nature of medical and social practices and of the values that shape the ways the abandoned were addressed by supposedly superior human forms.
+In the wake of World War I, Sigmund Freud wrote an essay entitled ‘‘On Thoughts for the Times on War and Death’’ (1957b). Freud spoke of a generalized wartime confusion and disillusionment that he also shared and of people being without a glimmering of the future being shaped: ‘‘We ourselves are at a loss as to the significance of the impressions which press in upon us, and as to the value of the judgements which we form ... the world has grown strange to [us]’’ (1957b:275, 280). This sense of an ethical and political void experienced by ‘‘helpless’’ citizens had been provoked by ‘‘the low morality shown by states which pose as the guardians of moral standards’’ and by the brutality demonstrated by individuals who, ‘‘as participants in the highest human civilization, one would not have thought capable of such behavior’’ (Freud 1957b:280). At stake, in Freud’s account, was not the citizen’s failure to empathize with the suffering of fellow humans but his or her estrangement from imaginaries gone awry. This anxiety over the discredited imaginaries of the nationstate and of a supposedly inexorable human progress stood for people’s incapacity to articulate the function that the Other’s death had in the organization of reality and thought.
+We moderns—that is how I read this melancholic Freud—are time and again faced with a void in what constitutes the human. One’s worthiness to exist, one’s claim to life, and one’s relation to what counts as the reality of the world all pass through what is considered human at any particular time, and this notion is itself subject to intense scientific, medical, and legal dispute as well as political and moral fabrication (Asad 2003; Kleinman 1999; Povinelli 2002; Rabinow 2003). It is between the loss of an old working concept of ‘‘humanness’’ and the installment of a new one that the world is experienced as strange and vanishing to many in Vita.
+‘‘There was no family, we ourselves buried old Lucas. A lone human being is the saddest thing, worse than being an animal.’’ In emphasizing the ‘‘animalization’’ of people in Vita, Oscar and Luciano also conveyed a critical understanding of the relationality of the terms human and animal. The negotiation over these relations, particularly in the medical realm, allows some human-animal forms to be considered inappropriate for life.7
+Zones of social abandonment like Vita proliferate in Brazil’s large cities. They are not directly regulated by legal, welfare, or medical authorities or institutions. Yet these very institutions nonetheless direct the unwanted to such zones, where they are sure to become unknowables with no human rights and no one and nothing to account for them. Here one is confronted with realities that lie between and beyond formal governance and that determine the life course of an increasing number of poor people who are not part of mapped populations and who are reduced
+477
+to struggling, with no prospect of survival. What makes them die?
+The abandoned in Vita know of death, and, when listened to, they offer insights into its fabrication. Their abandonment is part of a larger context—it was realized in many domestic and public sites and through intricate medical transactions coexisting with already entrenched strategies of nonintervention. In tracing the plot of a single life history, I illuminate both the common sense that lets persons die and the language, desire, and hope for life that remain in Vita.
+Catarina
+‘‘In my thinking, I see that people forgot me.’’ Catarina said this to me while peddling an old exercise bicycle and holding a doll. A woman with kind manners and a piercing gaze, she was in her early thirties; her speech was lightly slurred. I first met Catarina in March 1997, during a return visit to Vita. She stood out from the others, who lay on the ground or were crouched in corners, simply because she was in motion. She wanted to communicate. Adriana, my wife, was there with me. In one simple stroke, Catarina told us this story:
+I have a daughter called Ana, she is eight years old. My ex-husband gave her to Urbano, his boss. I am here because I have problems in my legs. To be able to return home, I must go to a hospital first. It is very complicated for me to get to a hospital, and if I were to go I would worsen. I will not like it because I am already used to being here. My legs don’t work well. Since I got here I have not seen my children. My brothers and my brother-in-law brought me here. Ademar, Armando ... I exercise ... so that I might walk. No. Now I can no longer leave. I must wait for some time. I consulted a private doctor, two or three times. When needed, they also give us medication here. So, one is always dependent. One becomes dependent. Then many times one does not want to return home. It is not that one does not want to ... In my thinking, I see that people forgot me.
+Later I asked the volunteers if they knew anything about Catarina. They knew nothing about her life outside Vita. I told the volunteers some of the names and events Catarina had mentioned, but they said that she spoke nonsense, that she was mad (louca). She was a person apparently lacking common sense, her voice was annulled by psychiatric diagnosis. Without an origin, she had no other destiny than Vita.
+I was left with Catarina’s seemingly disjointed account, her story of what had happened. From her perspective, she had not lost her mind. She was trying to improve her condition, to be able to stand on her feet. She
+478
+insisted that she had a physiological problem and that her presence in Vita was the outcome of various relational and institutional circumstances that she could not control. Catarina evoked these circumstances in the figures of the ex-husband, the boss, the hospitals, the private doctor, the brothers, and the daughter who was given away. ‘‘To be able to return home, I must go to a hospital first,’’ she reasoned. The only way back to her child, now living with another family, was through a clinic. The hospital was on the way to a home that was no more. And adequate health care, Catarina suggested, was impossible for her to access. She also suggested that medicine had worsened her condition. While seeking treatment, she had learned about the need for medication. This form of care operated in Vita, as well: ‘‘When needed, they also give us medication here.’’ She was referring to a pharmaceuticalization of disarray that made persons in Vita ‘‘always dependent.’’ Something had made it impossible for Catarina to return home. But the desire was still there: ‘‘It is not that one does not want to.’’
+Catarina’s exercising and her recollection in the context of Vita’s stillness stayed in the back of my mind. I was intrigued by the ways her story commingled elements of a life that had been, her present abandonment in Vita, and her desire for homecoming. I tried to think of her not in terms of mental illness, but as an abandoned person who, against all odds, was claiming experience on her own terms. She knew what had made her so—but how was I to verify her account? Catarina thought through what had foreclosed her life, but the degree to which her thinking and voice were inarticulate did not depend on her alone—we, the volunteers and the anthropologist, did not have the means to understand them. As George Marcus points out, ‘‘Life histories reveal juxtapositions of social contexts through a succession of narrated individual experiences that may be obscured in the structural study of processes as such’’ (1998:94; see also Fischer 1991). Following the plot of a single person can help one to identify the many networks and relations— call it the ‘‘in-betweeness’’—in which regimes of normalcy and ways of being are fashioned and, thus, to capture both the densities of localities and the rawness of uniqueness (Behar 1993; Crapanzano 1980; Das 2000; Desjarlais 2003; Goldstein 2003; Pandolfo 1998; Panourgia 1995; Shostak 1981).8
+Life codes
+As I kept returning to Vita, more and more of the infirmary’s inhabitants said that they wanted to tell me ‘‘minha vida’’ (my life), as they put it. Like crippled Iraci:
+I came from Lages, state of Santa Catarina. I was raised in the interior and like it better than the city. I
+lost my father and my mother. We had cows and pigs and planted corn and beans. I have ten siblings, all scattered. My sister put me in a bus to Porto Alegre. Nobody wanted to take care of me. I was already paralyzed. I got paralyzed when I was one and a half years old. I lived in the streets for five years. Now I am 41 years old and have been here for more than five years. Better to live in the streets than in a place like this for the rest of life. ... Vita makes me nervous ... here one dies. ... There’s a sadness here. I want to get out of this place. This is not life. It’s the end of life. The one who is ill gets even more ill, and one gets nervous. I am a nervous man.
+I was struck by the similarity of the accounts. Most of them mentioned having been banned from the family, the rupture of relations, and the dangerous and now impossible desire for homecoming. These were not illness narratives channeling a search for meaning (see Good 1994; Kleinman 1988; Mattingly 1998). Neither were they the ‘‘schizophrenic recording codes’’ that Gilles Deleuze and Felix Guattari saw as opposed to or as simply parodying social codes, ‘‘never giving the same explanation from one day to the next’’ (1983:15). They were not a ‘‘diffuse and external rain of distractions’’ that Robert Desjarlais (1994:897) says marks the being-in-the-world of the homeless in the Boston shelter he chronicled. As I came to hear and see over time, the accounts of many of the so-called mad in Vita were not ever shifting. Rather, I was impressed by the steadiness, contextuality, and truthfulness (as I was to learn by tracking Catarina’s account) that they maintained in spite of being repeatedly told by caretakers that they were ‘‘nonsense.’’
+Instead of seeing these condensed accounts as proofs of ‘‘a retreat from the world’’ (Desjarlais 1994:897), I began to think of them as pieces of truth—let me call them ‘‘life codes’’—through which the abandoned person attempts to hold onto the real (see Agamben 1999; Caruth 1996). As I listened to them, I was challenged to treat them as evidence of the reality the abandoned are cut off from and of their failed attempts to reenter it. The accounts of Catarina, Iraci, and their neighbors represent a sense of exclusion. As these bits and pieces give language to a lived ex-humanness, they also work as the resources and means through which the abandoned claim experience—they are sites in which destinies are thought through and desires reframed.9
+According to the Oxford English Dictionary, the adjective ex means ‘‘former, outdated’’; the preposition ex is used to mean ‘‘out of’’ in reference to goods; and the noun ex refers to ‘‘one who formerly occupied the position or office denoted by the context,’’ like a former husband or wife. Ex also means ‘‘to cross out, to delete with an x,’’ and the letter X stands for the unknown.
+The dictionary
+At the end of December 1999, I returned to southern Brazil to continue observations of life in Vita. Vita’s infrastructure had improved with new government funding, particularly in the recovery area. In the infirmary, conditions were pretty much the same, but it now had fewer people. Catarina was still there when I arrived, this time seated in a wheelchair. Head down, she was holding a pen and scribbling with much effort. She looked up and recognized me. Her health had deteriorated considerably; she insisted that she was suffering from rheumatism. Like most residents there, Catarina was being given antidepressants at the whim of the volunteers.
+‘‘What are you writing?’’ I asked.
+‘‘This is my dictionary,’’ she said. ‘‘I write so that I don’t forget the words.... I write all the illnesses I have now, and the illnesses I had as a child.’’
+Her handwriting was uneven and betrayed a minimal literacy. The words were composed in block-shaped letters and formed very few full sentences. I was amazed by what I read:
+Divorce
+Dictionary Discipline Diagnostics Marriage for free Paid Marriage
+Operation Reality To apply an injection To get a spasm In the body A cerebral spasm
+‘‘Why do you call it a dictionary?’’ I inquired.
+‘‘Because it does not require anything from me, nothing. If it were mathematics, I would have to find a solution, an answer. Here there is only one subject matter, from beginning till the end. ... I write it and read it.’’
+Catarina let me peruse the dictionary: ‘‘In the womb of pain.’’ ‘‘I offer you my life.’’ ‘‘The present meaning.’’ Amid recurring references to medical consultations, hospitals, and public notaries, she wrote of a working woman and a wanderer, of sexual emotion and mental disturbance, of medication and food for a baby, of misery and abundance, of governmental officers and indebtedness. Blended with allusions to muscular spasms, menstruation, paralysis, rheumatism, and paranoia and a listing of all possible diseases from measles to ulcers to AIDS were names like Ademir, Nilson, Armando, Anderson, Alessandra, and Ana. Here and there, she wrote of motherhood, divorce, a rustic
+479
+life with pigs and insects, and veterinarians and a rural workers’ association. I read striking statements from a lost but enduring world:
+Question, answer, problem to solve, the head Who contradicts is convicted
+The division of bodies
+Dead alive, dead outside, alive inside
+There were expressions of longing:
+Recovery of my lost movements A cure that finds the soul The needy moon guards me
+With L I write Love, with R I write Remembrance
+I returned to talk to her several times during that visit. Catarina engaged in long recollections of her life outside Vita, always adding more details to those she had told me in our first meeting in 1997. The story thickened as she elaborated on her origin in a rural area and her migration to Novo Hamburgo to work in one of the city’s shoe factories. She mentioned having more children, fights with her ex-husband, names of psychiatrists, experiences in mental wards—all told in bits and pieces.
+‘‘When my thoughts corresponded with those of my ex-husband and his family, everything was fine. But when they disagreed with them, I was mad. It was like a side of me had to be forgotten. The side of wisdom. My brothers want to see production and progress. They wouldn’t dialogue and the science of the illness was forgotten. My legs weren’t functioning, working well. I didn’t want to take the medication.’’
+‘‘Did the doctors ever tell you what you had?’’
+‘‘No, they said nothing. ... I am allergic to doctors. They want to be knowledgeable, but they don’t know what suffering is. They don’t touch you where it hurts.’’
+According to Catarina, her physiological deterioration and abandonment had been mediated by a shift in ways of thinking and meaning making in the context of novel domestic economies related to migrant labor and her own pharmaceutical treatment. Subjectivity had become the conduit by which her exclusion was solidified. The forceful erasure of ‘‘a side of me’’ made it impossible for her to find a place in a changing family life. What mediations effected her turning from reality and her reconstruction of it in madness—what guaranteed their success?
+‘‘My brothers brought me to Vita. For some time I lived with my brothers ... but I didn’t want to take medication when I was there. Why was it only me who had to be medicated?’’
+Psychopharmaceuticals seemed to have played a key role in altering Catarina’s sense of being and her value for others. And through these changes, family ties, interper
+480
+sonal relations, morality, and social responsibility were also reworked. As she later wrote in her dictionary, ‘‘To want my body as medication, my body.’’
+‘‘Why,’’ I asked Catarina, ‘‘do you think that families and doctors send people to Vita?’’
+‘‘They say that it is better to place us here so that we don’t have to be left alone at home, in solitude ... that there are more people like us here. ... And all of us together, we form a society, a society of bodies.’’
+Catarina insisted that there was an organized realm to her abandonment (Kleinman et al. 1997). As I tried to find out how her supposed nonsensical thoughts and words related to a now-vanished lifeworld and to identify the empirical conditions that made hers a life not worth living, I found Clifford Geertz’s work on common sense illuminating. ‘‘Common sense represents the world as a familiar world, one everyone can, and should, recognize, and within which everyone stands, or should, on his own feet’’ (Geertz 2000:91). Common sense is an everyday realm of thought that helps one to effectively make decisions as one faces everyday problems. In the absence of common sense, one is a ‘‘defective’’ person. ‘‘There is something of the purloined-letter effect in common sense; it lies so artlessly before our eyes it is almost impossible to see’’ (Geertz 2000:92). What is unique to the anthropological endeavor is to try to apprehend these colloquial assessments and judgments of reality—which are more assumed than ana-lyzed—as they determine ‘‘which kinds of lives societies support’’ (Geertz 2000:93).
+Catarina resisted Vita’s enclosure, and in ways that I initially could not grasp, she voiced an intricate ontology and the wish to untie it: ‘‘Science is our consciousness, heavy at times, burdened by a knot that you cannot untie. If we don’t study it, the illness in the body worsens. ... Science. ... If you have a guilty conscience you will not be able to discern things. I think that people fear their bodies.’’10 How was I to enlarge the possibilities of social intelligibility that Catarina was left alone to resolve in her ‘‘withdrawal’’ from reality (Corin 1998; Corin and Lauzon 1992; Corin et al. 2003; see also Good 2001; Jenkins and Barrett 2003)?
+Absence is the most concrete reality in Vita
+I did not have a structured method to begin with, but I kept returning to Vita and engaging with Catarina on her own terms and then proceeded from there. Catarina refused to be seen as a victim or to hide behind words: ‘‘I speak my mind. I have no gates in my mouth.’’ Clearly, it was not up to me to give her voice but, rather, to find an adequate understanding of what was going on and the means to express it.11 The only way to the Other is through language, but language is not just a medium of communication or misunderstanding but also an experience that,
+in Veena Das and Arthur Kleinman’s words, allows ‘‘not only a message but also the subject to be projected outward’’ (2001:22).
+In the essay ‘‘Language and Body’’ (1997), Das observes that women who were greatly traumatized by the partition of Pakistan from India did not transcend this trauma, as, for example, Antigone did in classic Greek tragedy, but they incorporated it into their everyday experience. In Das’s account, subjectivity emerges as a contested field and a strategic means of belonging to traumatic large-scale events and changing familial and political-economic constellations. Inner and outer states are inescapably sutured. Tradition, collective memory, and public spheres are organized as phantasmagoric-like scenes, for they thrive on the ‘‘energies of the dead’’ that remain unaccounted for in numbers and law. Das scrutinizes this bureaucratic and domestic machinery of inscriptions and invisibility that authorizes the real—a machinery with which people have to forcefully engage as they look for a place in everyday life. In her work on violence and subjectivity, Das (2000) is less concerned with reality structuring psychological conditions than with the production of individual truths and the power of voice: What chance does speaking have to be heard? What power does it have to make truth or to become action?
+In Vita, one is faced with a human condition in which voice can no longer become action. No objective conditions exist for that to happen. The human being is left all by herself, knowing that no one will respond, that nothing will crack open the future. Catarina had to think of herself and of history alongside the fact of her absence in the things she remembered. ‘‘My family still remembers me, but they don’t miss me.’’ Absence is the most pressing and concrete reality in Vita. What kind of subjectivity is possible when one is no longer marked by the dynamics of recognition or by temporality? What are the edges of human imagination that Catarina keeps expanding?
+In posing these questions, I am not concerned with finding a psychological origin (a thing I do not think exists) for Catarina’s condition or with simply tracking down the discursive templates of her experience. I understand the sense of psychological interiority as ethnological, as the whole of the individual’s behavior in relation to her environment and to the measures that define boundaries, be they legal, medical, relational, or affective. It is in family complexes and in technical and political domains, as they determine life possibilities and the conditions of representation, that human behavior and its paradoxes belong to a certain order of being in the world.12 How does one become another person today? What is the price one pays? How does this change in personal life become part of memory, individual and collective?
+As I engaged Catarina, I was also informed by Byron Good’s (2001) work on the social course of psychosis in
+contemporary Indonesia. As he directs attention to the way epidemic-like experiences of acute brief psychoses are entangled with the country’s current political and economic turmoil, the ghostliness of its postcolonial history, and an expanding global psychiatry, Good emphasizes the ambiguities, dissonances, and limitations that accompany all attempts to represent subjectivity in mental illness. He suggests three analytic moves: the first, working inward through cultural phenomenology to get at how a person’s experience and meaning making are woven into the domestic space and its forceful coherence; the second, bringing to the surface the affective impact and political significance of representations of mental illness and subjectivity; and the third, interpreting outward to the immediate economic, social, and medical processes of power involved in creating subjectivity. Good unremittingly resists closure in his analysis, challenging one to bring unfinishedness into view. How is one to address this agonistic openness of lived experience methodologically? How can one incorporate this openness into the analysis of a person’s estrangement from reality?
+Noninstitutional ethnographic spaces
+Taking Catarina’s spoken and written words at face value took me on a journey into the various medical institutions and homes of people who had abandoned her. With Catarina’s consent, I retrieved her records from psychiatric hospitals and local branches of the universal health care system. I was also able to locate her family members—her brothers, ex-husband, in-laws, and children—in the nearby industrial town of Novo Hamburgo. Everything she had told me about the familial and medical pathways that led her into Vita matched the information I found in the archives and in the field.
+Had I only stayed with Catarina’s utterances in Vita, the whole field of tensions and associations that existed between her family and medical and state institutions and that had shaped her life would have remained invisible. Catarina did not simply fall through the cracks of these various domestic and public systems. Her abandonment was dramatized and realized in the juxtapositioning of several social contexts. Following her plot was a way to delineate the powerful, noninstitutionalized ethnographic space in which a family gets rid of its unwanted and unproductive members (on the politics of death, see Agamben 1998; Biehl 2001; Mbembe 2003).
+The fabric of this domestic activity of valuing and deciding which life is worth living remains largely unreflected on, not only in everyday life, as Oscar pointed out, but also in the literature on transforming economies, states, and civil societies in contexts of inequality and democratization.13 As this work unfolded, I was challenged to devise ways to approach this unconsidered
+481
+infrastructure of decision making, which operates independent of the law in close proximity to the household. Fieldwork reassembled this decision making at various points and in various public interactions that defined normalcy and, ultimately, displaced Catarina onto the register of social death, where her condition appeared to have been self-generated. So, this is also a story of the methodological, ethical, and conceptual limits anthropology faces as it goes into these knotty fields and tries both to verify the sources of a life dissociated from her humanity and to capture the density of a locality without leaving the person behind. When I say ‘‘her humanity,’’ I do not mean a circumscribed and definable thing but, rather, the ordinary and real-time efforts Catarina made to constitute herself as daughter, sister, woman, worker, lover, wife, mother, and citizen in institutions and exchanges that are meant to constitute humanness but that have deemed her efforts worthless.
+Catarina embodies a condition that is more than her own. While reconstructing Catarina’s pathways into abandonment, I developed a better understanding of Vita’s imbrication in family and city life and of the ordinariness of the abandonment Catarina experienced. Catarina’s life force was unique, but the human and institutional intensities that shaped her destiny were also familiar to many others in Vita. Despite appearing like a no-man’s-land cut adrift, in terms of its history and maintenance, Vita is in fact entangled with several provincial, municipal, medical, and philanthropic institutions. As a ‘‘total fact,’’ Vita captures the political, moral, and affective densities of that world (Mauss 1979:53; see also Kleinman 1999). On many levels, Vita is not exceptional. There are more than 200 institutions like Vita in Porto Alegre. These precarious places house the abandoned in exchange for their welfare pensions, and many also receive state funds or philanthropic donations. Some 50 million Brazilians (more than a quarter of the population) live far below the poverty line (25 million people are considered indigent).14 Although in many ways a microcosm of such misery, Vita is also distinctive. Many of its residents came from working- and middleclass families and once had been workers with families of their own. Others had lived in medical or state institutions and, at some point, had been evicted to the streets. Zones of abandonment are symbiotic with changing households and public services—they absorb those who do not have ties or resources left to sustain themselves.
+An actual redistribution of resources, power, and responsibility is taking place locally amid large-scale political and economic changes (see Almeida-Filho 1998). Catarina’s life is suffused with the elements, patterns, and contradictions of these processes. Her body and language are overwhelmed by their force; her personhood is made and unmade. This ethnography explores the
+482
+diffusions and contradictions of these larger processes with which families and individuals are ultimately left to cope. What are the political and cultural grounds of a state that continues to play its part in the generation of human misery and of a society that forces increasingly larger groups of people who are considered valueless into such abandonment zones, where it is virtually guaranteed that they will not improve?
+Again and again, I heard Catarina conveying subjectivity both as a battleground in which separation and exclusion had been authorized and as the means through which she hoped to reenter the social world. ‘‘My exhusband rules the city. ... I had to distance myself. ... But I know that when he makes love to other women he still thinks of me. ... I will never again step in his house. I will go to Novo Hamburgo only to visit my children.’’ Charting one human destiny helps one to understand the strategies and values people develop as they try to create life possibilities from whatever the changing institutions of state and market make available to them.
+How can one restore context and meaning to the lived experience of abandonment?
+Catarina is subjected To be a nation in poverty Porto Alegre
+Without an heir Enough
+I end
+How can an observer produce a theory of the abandoned subject and her subjectivity that is ethnogra-phically grounded? To begin with, in her verse, Catarina places the individual and the collective in the same space of analysis, just as the country and the city collide in Vita. Subjection has to do with having no money and with being part of an imaginary nation gone awry. The subject is a body left in Vita without ties to the life she generated with the man who, as she states, now ‘‘rules the city’’ she is banished from. With nothing to leave behind and no one to whom to leave it, Catarina has only her subjectivity—the medium through which a collectivity is ordered in terms of lack and also a way for her to distance herself from the messiness of the world. In her writing, she faces the limits that a human being can bear, and she makes polysemy out of those limits—‘‘I, who am where I go, am who am so.’’
+The sense of exclusion
+‘‘These people in the infirmary represent the putrefaction of the street. They don’t exist as a juridical fact. They have AIDS, tuberculosis, all these things that don’t exist in statistics,’’ explained Captain Osvaldo. Since 1997, Vita
+has been administered by Captain Osvaldo, a civilian policeman working for the state of Rio Grande do Sul. Ze das Drogas was evicted from the establishment by a philanthropic coalition called Friends of Vita, headed by Jandir Luchesi, the region’s most famous radio talk show host and a state representative. During Ze das Drogas’s administration, daily life in the rehabilitation area had been structured around worship and Bible studies; now the emphasis is on personal hygiene, civic values, eating well, total abstinence from smoking and drinking, work therapy, and group self-reflection. As for the abandoned in the infirmary, the captain was straightforward: ‘‘We cannot bring them back to society. As horrible as it is, here one sees a truth.’’
+As I talked to city administrators, public health officers, and human rights activists, I was able to identify some of the institutional networks through which Vita emerged and has been integrated into local forms of governance as well as some of the everyday practices that help to constitute its residents’ nonexistence. With Brazil’s new democratic constitution of 1988, health care became a public right, and many of the country’s discourses and practices of citizenship during the following decade were related to guaranteeing this right.15 The activism of mental health workers was exemplary (Tenorio 2002). They engaged in lawmaking that shaped the progressive closure of psychiatric institutions and their replacement by local networks of community and family-based psychosocial care (Amarante 1996; Goldberg 1994; Moraes 2000).16 This deinstitutionalization of the mentally ill was pioneered in the state of Rio Grande do Sul (Porto Alegre is its capital), where it was well under way by the early 1990s. In practice, however, the mental health movement’s demands and strategies became entangled in and even facilitated local government’s neoliberalizing moves in public health: The mad were literally expelled from overcrowded and inefficient institutions, and little new funding was allocated for the alternative services.
+On the one hand, this local psychiatric reform confirmed the role of the Worker’s Party (Partido dos Trabalhadores, PT) as representative of a politics of social inclusion, occasioned a few exemplary services that treated ‘‘citizens burdened by mental suffering,’’ and realized, if only partially, a socialized form of self-governance. As I later learned, Catarina was treated in one of the model services in the city of Novo Hamburgo. On the other hand, this psychosocial politics shifted the burden of care from state institutions back to the family and communities, which failed to live up to their idealized representations in the reform movement’s discourse. People had to learn new techniques to qualify for services and to live with what were, by and large, the failures of new ideologies and institutions. Increasing numbers of mentally ill people began to live in the streets along with the other leftovers
+of the country’s unequal and exclusionary social project. Many ended up in places like Vita.
+Everyday life in the 1980s and 1990s in that region was marked by high rates of migration and unemployment, the rise of a drug economy in the poorest outlying areas, and generalized violence (see Ferreira and Barros 1999). As police forces were increasingly engaged in erasing signs of misery, begging, and informal economies from the city, pastoral and philanthropic institutions took up the role of caregiver, albeit selectively. Simultaneously, families frequently responded to the growing burdens posed by new responsibilities of care and narrowing options for employment by redefining their functional scope and value systems. As a corollary to all of these institutional, economic, and familial processes, unemployed health technicians began opening their own care centers (modeled after Vita) for patients who had welfare benefits or some remaining assets. If, around 1976, there were some twenty-five ‘‘geriatric houses’’ in Porto Alegre (Bastian 1986), there are now more than 200, about 70 percent of which operate as clandestine businesses hosting the elderly, the mentally ill, and the disabled in the most precarious of conditions (Comissao de Direitos Humanos da Assembleia Legislativa do Rio Grande do Sul 2000; Ferreira de Mello 2001).17
+‘‘People are confined and have no adequate care. Some of these businesses are surrounded by barbed wire like camps,’’ Mariane Gross, a journalist and human rights activist, told me. On July 2, 1999, a 58-year-old man in the Auxiliadora geriatric house that is located next to Vita (and was previously part of it) was bitten to death by dogs. ‘‘Bits of skin were all over the ground,’’ Mariane and her colleagues stated in the annual report of the state’s human rights commission. Novel human rights rhetoric, however, is not strong enough to close Vita and similar institutions down. Recently, the city’s sanitary surveillance service had also begun to investigate these businesses and, according to health technician Jaci Oliveira, it was having a very difficult time getting support from judges to force them to close down. ‘‘The judges tell us that these houses are doing good after all. Where would these people go to if they were freed?’’ And had Vita been shut down, it most certainly would have reemerged elsewhere in the city. As a top city administrator admitted to me, the need to produce quick results for the alternative administration of the Worker’s Party (see Pont and Barcelos 2000) has often led to the constitution of new commissions and the writing of new reports: ‘‘In truth, problems are identified, things are not solved.’’
+In practice, the experimental mental health plan has also faced the widespread availability of new biochemical treatments. Brazil is the eighth biggest market for pharmaceutical products in the world (see Bermudez 1995 and Bermudez et al. 1999). In 1998, there were some 15 thousand drugs being sold in the country, and sales
+483
+reached $11.1 billion (Luiza et al. 1999). With new patent legislation and no import restrictions, the Fernando Henrique Cardoso administration has successfully attracted multinational pharmaceutical industries and also made medication distribution a key element of public health. As part of the decentralization and rationalization of universal health care, the government began in the mid-1990s to implement a nationwide pharmacy program, whereby municipalities distribute basic medication (including psychopharmaceuticals) to the general population. This pharmaceutical policy is said to contribute to cuts in hospitalizations and to making families and communities stronger participants in therapeutic processes.18
+In chronicling how the urban poor tinker with these developments, I observed the routine medicating of affective crisis within households, and families working as proxy-psychiatrists.19 That is, patients’ family members find ways to dictate prescriptions, adjusting the dosages as they see fit. Psychiatrists in private practice are described as regularly telling families, ‘‘Try this; if it does not work, double the dosage.’’ In the process, disturbed and unproductive family members are excluded and disposed of on ‘‘reasonable’’ terms—as mad, noncompliant, or beyond repair, like Catarina.
+When I visited the Public Ministry in Porto Alegre, attorneys told me that they have the power to subpoena family members of abandoned people and to negotiate care or financial responsibility. But as Vita’s history shows, that happens quite rarely, and a few cases then become emblematic of a supposed enforcement of human rights. The state is reborn empirically as it restores family ties for a few.
+Seen in this light, Vita is a social symptom, not a solution. It is an outcome of recent political and economic readjustments that have driven large segments of the population further into poverty and despair. This harshness is amplified by a malfunctioning universal health care system—a supposed democratic gain of the late 1980s— and complicated by new pharmaceutical possibilities. The question of what to do with pauper and surplus bodies, with no apparent value and without ways to survive and prosper, is no longer a question at the core of sovereignty and its bygone populist welfare rhetoric. The destinies of the useless, so to speak, are determined by a whole new array of networks, and as formal institutions either vanish or become nonfunctional and as government becomes increasingly remote from the citizenry, the household is further politicized.
+That so many are regarded as socially and morally superfluous testifies to the further dissolution of the country’s moral fabric. The Brazilian middle class, for instance, has historically acted as a buffer between the elite and the most vulnerable, as both guardian of morality and advocate for progressive politics. In the wake of the country’s democratization and fast-paced neoliberal
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+ization, however, this vein of moral sensitivity and political responsibility has been largely replaced by sheer contempt, sociophobia, or sporadic acts of charity like the ones that sustain Vita (Caldeira 2002; Costa 2000).
+The continuum of life and death
+Yes, treating the abandoned as dying matter might release individuals and institutions from the obligation of some response. But after many visits to Vita, I also saw that the abandoned (os abandonados)—with their daily rations of bread and bean soup and hot water—are not kept alive in vain. While dying in Vita, they still have a final social function. Under the new regime, everyone admitted for rehabilitation (men only) has to spend a few days living in the infirmary as part of the initiation into Vita. Additionally, throughout their stay, rehabilitating men must come to the infirmary and take care of some of the abandoned, clean their feces, move their bodies back and forth. As one of Vita’s new coordinators explained, the infirmary is useful as ‘‘a platform of information for the ones in rehabilitation. It is useful for getting the addict to fall back into reality, for if they don’t change, that’s their end.’’ The captain was more straightforward: ‘‘These people in the infirmary are cobaias [experimental guinea pigs]. Their life is over, they show to the young ones what will happen to them.’’
+Oscar and Luciano had told me that the abandoned in the infirmary had been made inappropriate for living by medical, familial, and state institutions. It was now evident to me that the negotiation over the human-animal boundary that had produced them had become a subjective technique. Lauro had been in Vita for three weeks when I met him. The 30-year-old man sat next to Lucas, formerly known as Vaquinha (little cow), about whom nothing was known. Lauro said that he had adopted and baptized ‘‘the poor thing’’ as Lucas. ‘‘Now he has a name. He is mentally retarded. I am responsible for him.’’ As part of his initial rehabilitation therapy, Lauro has to take care of Lucas, bathe him, change his clothes, watch him as he crawls around, sit silently next to him. ‘‘I help him, so automatically he helps me too.’’
+‘‘How so?’’
+‘‘By helping him, I am helping myself.’’ Lauro then spoke of Lucas and himself in the plural, as belonging to two distinct collectivities: ‘‘They give force to us. Only to look at them already helps us to walk forward, to not stay in the same condition they are in.’’ He voiced an impersonal feeling: ‘‘One develops a tenderness toward him, he is a well-behaved guy, right?’’ He then asked Lucas to speak: ‘‘Show him that you can talk.’’ In that most disturbing encounter, Lucas became a spectacle, not meant to be heard or addressed. His worth as a human socially and medically devalued, Vaquinha - Lucas remains the
+animal form through which the salvageable human, Lauro, constitutes himself.
+The new role of these abandoned men and women as negative citizens stems precisely from their alleged incapacity to produce anything but bodily infections, parasites, and silent suffering. Their social death is the imago of the future. In the end, the negative ones are object lessons for potential citizens—or, better, they provide a ground for the appearance of a distinct concept of ‘‘citizenship.’’ I say concept of citizenship, because the state does not provide the means needed for this regenerated citizenship to become a structural possibility. Philanthropic sites like Vita make the personal regeneration of a marginal individual as citizen possible and livable either for a limited period of time or in the form of fiction. This concept of citizenship enlivens the image of the state as universal and life enhancing. Yet, empirically, citizenship remains a matter of triage and, of course, money. As some are being healed in that simultaneously ‘‘militarized’’ and philanthropic setting, they wake up next to those who are socially dead, without name, without origin, without ties. Like Cida, a nameless young woman with AIDS who, according to volunteers, ‘‘now and then asks us to tie her to her bed. She does that when she feels like killing herself. ... Then a few hours later she mumbles to be untied. How do you understand such a person?’’
+Throughout the years, I have come to understand Vita as the negative side of a political and subjective paradigm increasingly familiar in late modern settings where changes in state and medical institutions, labor regimes, and the household all meet. Against an expanding discourse of human rights, one is confronted with the limits of infrastructures whereby these rights are realized, biologically speaking, but only on a selective basis. Social death and selective life extension are the poles of a continuum on which the state, the community, the family, and the citizen forge their presence these days. The negotiation over the human and the nonhuman forms part of a complex set of intermediary relations through which individual bodies are linked to the political body. In Vita, one sees that even life is achieved through death. The Other’s dying makes it possible for one to belong to a family-like institution, to a new population and subjective economy. The ethnographic challenge is to find these empirical relations and link-ages—technical, political, conceptual, affective—and to bring them out of thoughtlessness.
+Family complexes
+I have worked with Catarina and her family for the past four years. During that time, she has written 21 volumes of her dictionary. I have read all of her writing and her medical records and have discussed them with her. I have
+also scheduled medical examinations and brain imaging for her and others in her family and discovered that, along with immunodeficiency, Catarina has, in a doctor’s words, ‘‘the cerebellum of an 80-year-old woman.’’ In what follows, I want to give you a sense of what I found in this reconstructive work, particularly regarding how inner worlds are remade under the impress of economic pressures, the domestic role of pharmaceuticals as moral technologies, and the common sense that creates a category of unsound and unproductive individuals who are allowed to die.
+Catarina was born in 1966 and grew up in a very poor place in the western region of the state. In fourth grade, she was taken out of school. The father abandoned the family, and Catarina became the housekeeper as her youngest siblings aided their mother in agricultural work. In the mideighties, two of her brothers migrated and found jobs in the booming shoe industry in Novo Hamburgo. At the age of 18, Catarina married Nilson Moraes and a year later gave birth to her son Anderson.
+‘‘When Nilson first brought her photo home,’’ said Sirlei, Nilson’s sister, ‘‘she was so beautiful.’’ It was not Catarina the person but her appearance that her in-laws first remembered when I introduced myself to them. Sirlei was adamant that Catarina’s present paralysis could not be read in the past: ‘‘She was then a perfect person, like us.’’ When she no longer mirrored the family’s image, Catarina had been consigned to the past and was now associated with a dismembering body: ‘‘Her mother also lost the legs and the hands.’’
+Catarina’s brothers told me that they, too, were beginning to have problems walking, but they did not know what the disease was: ‘‘It’s a mystery.’’ In Armando’s words, ‘‘When we were kids, Catarina was normal.’’ His wife again referred to Catarina’s appearance: ‘‘She was very normal. I remember the wedding photos.’’ I wondered about this definition of normality and what in one’s life or interests determined its application to another family member.
+Shady deals, persistent bad harvests, and indebtedness to local vendors forced Nilson and Catarina to sell the land they had gotten to take care of Catarina’s ailing mother, and in the mideighties the young couple decided to migrate and join her brothers in the shoe industry. In the 1970s, shoe companies took advantage of subsidies made available by the military government to further expand their production for export. These were the years of Brazil’s ‘‘economic miracle.’’ Novo Hamburgo became an El Dorado of sorts, attracting many in search of work and social mobility. City officials went to the state’s western region, where Catarina and Nilson were born, to recruit a semiliterate and cheap labor force. Statistics show that at the end of the 1980s the city actually had one of the highest per capita income rates in the state but
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+also that at least one-fourth of its growing population lived as squatters. This situation worsened in the early 1990s, when the city experienced an abrupt economic decline and acute impoverishment mainly because of the country’s inability to articulate a more lucrative export policy and because of the growing competition with China in the global shoe market. As yet, there has been no historical accounting of the massive migrant labor force that radically altered the economic and social landscape of the former German colony of Neu Hamburg.
+Catarina recalls enjoying her work in the factory. ‘‘I had my worker’s ID and made my money.’’ Her husband found a job as a security guard in the city hall. Soon the couple had a second child, Alessandra. Catarina also took care of her ailing mother, who had moved in with them. Complicating her life further, at that time Catarina began having difficulties walking. ‘‘They fired her at the factory, because she began to fall there,’’ said the sister-in-law. At the same time that she lost her value as a worker, she also discovered that Nilson was seeing another woman and her mother passed away.
+Overwhelmed, at times Catarina left the house and wandered through the city. Her husband deployed his contacts at city hall and made sure that the police went after her: ‘‘They had to handcuff her ... in the emergency ward they gave her shots and she calmed down,’’ he told me. This happened a few times and then Nilson began confining her in psychiatric units in Porto Alegre. In the turbulent year of 1992 Catarina gave birth prematurely to her third child, a girl named Ana. Most of her hospitalizations took place between 1992 and 1994, when she and Nilson were no longer living together. ‘‘They gave her the best medication,’’ said Nilson. ‘‘But she threw it into the toilet and flushed it down. At home, she didn’t continue the treatment. She didn’t help herself.’’
+Nilson now works in a shoe factory and has a new family. Like other family members, he spoke openly about Catarina. ‘‘It’s all past,’’ he said, ‘‘it is not even in my mind.’’ In the end, for him, Catarina’s mental disturbance is the penalty she must pay for her evil behavior. ‘‘After her mother died Catarina began saying things that didn’t correspond with reality ... she said that her mother appeared to her. Her mom required lots of work. ... She hit her mom and the old lady cursed Catarina. She said that Catarina would pay for her evil.’’ When I later spoke to Ademar, Catarina’s middle brother, he mentioned that their mother had, indeed, been a very strong-willed woman, but he knew nothing of Catarina’s violence toward her.
+Pharmaceutical ties
+As I accessed Catarina’s medical records, I saw something similar to what Roma Chatterji (1998), in her work
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+with dementia patients in the Netherlands, calls the ‘‘file self.’’ Notes on medical treatment and family discussions enable the retrieval of the patient’s voice and, more importantly, provide the narrative of its alteration and the conditions of its intractability. At the Caridade and Sao Paulo Hospitals, the diagnosis given to Catarina varied from ‘‘schizophrenia’’ to ‘‘postpartum psychosis’’ to ‘‘psychogenic psychosis’’ to ‘‘mood disorder.’’ In tracing Catarina’s passage through these medical institutions and treatments, I saw her not as an exception but as a patterned entity.
+Catarina was subjected to the precarious mental health treatment reserved for the masses, the urban and working poor. Like many, she was conceived a priori as aggressive and was overly sedated, enabling the continued functioning of institutions in the absence of adequate care. Caught in struggles for deinstitutionalization, lack of public funding, and the proliferation of new classifications and treatments, this local psychiatry did not account for her singularity or social condition. Even though her diagnosis had softened over the years (mimicking the psychiatric trends), she continued to be overmedicated with powerful antipsychotics and all kinds of drugs to treat neurological side effects. On several occasions, nurses reported that Catarina experienced hypotension, a clear indicator of drug overdose. For Catarina, as for others, treatment began with a drug surplus and was then scaled down, or not, through trial and error. As I read her files, it was difficult for me to separate the symptoms of the psychiatric illness being treated from the effects of the medication, and I was struck that doctors actually did not bother to differentiate between the two in Catarina. To say that this is ‘‘just malpractice,’’ as a local psychiatrist puts it, misses the productive quality of this unregulated medical automatism and experimentalism: Pharmaceuticals are literally the body that is being treated. And in the process of Catarina’s overmedicating the symptoms that she calls ‘‘rheumatism’’ were being expressed. As doctors remained fixated on her supposed hallucinations, the etiology of her walking difficulties, which were actually reported by the nurses, remained medically unaddressed. The medical records also show that her husband and family were difficult to contact, that they left wrong telephone numbers and addresses, and that on several occasions they left Catarina in the hospital beyond her designated stay.
+According to Ludwik Fleck, medical science in general defines the morbid as an entity by rejecting some observed data and by guessing at nonobserved relations. That is how the irrational becomes rational in its details, says Fleck (1986:39,40), but that is also how some phenomena remain unremarked and unexplained. In Catarina’s case, much was disregarded or subtracted from clinical theory or reasoning: the patterns of rationality that shape common
+sense, the normative ideals of her family and neighborhood as well as of the health professionals, and her agonistic struggle for or against moral adaptation, not to mention her references to physical pain. And the ‘‘nonobserved’’ was subsumed under the neighbors’ and husband’s reports and the automatism of public psychiatry. In this context, medication did most of the work. The truth is, in this intersection of overmedication, medical automatism, and negligence, a different disease was emerging.
+Catarina’s dictionary is filled with references to deficient movement, to pain in the arms and legs, to muscular contractions. At times, Catarina relates these symptoms and her growing paralysis to a kind of biological marker and alludes to a certain ‘‘blood type becoming a physical deficiency’’ or to an ‘‘expired brain and aged cranium’’ that ‘‘impedes change.’’ Most of the time, however, Catarina refers to her condition as ‘‘rheumatism,’’ as I alluded earlier, and speaks of the manmade character of her affections. I followed the word rheumatism as it appeared throughout the dictionary, paying close attention to the words and expressions clustered around it. In the following inscription, for example, Catarina depicts rheumatism as a mangling of threads:
+People think that they have the right to put their hands in the mangled threads and to mess with it.
+Rheumatism.
+They use my name for good and for evil. They use it because of the rheumatism.
+The symptom ties various life threads together. It is an untidy knot, a real matter that makes social exchange possible. It gives the body its stature and is the conduit of a morality. It is Catarina’s bodily effects and not her name that are exchanged in the social world: She becomes a symptom. ‘‘What I was in the past does not matter.’’ Catarina disappears and a religious image stands in her place: ‘‘Rheumatism, Spasm, Crucified Jesus.’’ In another fragment she wrote: ‘‘Acute spasm, secret spasm, rheumatic woman, the word of the rheumatic is of no value.’’ Catarina knows that there is a rationality and a bureaucracy to symptom management: ‘‘Chronic spasm, rheumatism, must be stamped, registered.’’ All of this happens in a democratic context, ‘‘vote by vote.’’
+As I saw it, the ‘‘secret’’ of Catarina’s condition stemmed from an unknown biology and the unconsidered experience of what had been made of it over time. The acute pain Catarina described and the authoritative story she became in medicine and in common sense—as mad and ultimately of no value—have to be considered and deciphered side by side. The names of the antipsychotic drugs Haldol (haloperidol) and Neozine (levomeproma-zine, the stronger and more sedating of the two) are also words in Catarina’s dictionary. In one fragment, she writes
+defiantly that her pain reveals the experimental ways science is embodied:
+The dance of science.
+Pain broadcasts sick science, the sick study. Brain, illness.
+Buscopan, Haldol, Neozine. Invoked spirit.
+An individual history of science is being written here. Catarina’s lived experience and ailment are the pathos of a certain kind of science, a science that is itself sick. As Catarina sees it, in the current Brazilian context, the pursuit of wisdom has broken down and commerce enables ad hoc medical practice. The goods of psychiatric science, such as Haldol and Neozine, have become as ordinary as Buscopan (an over-the-counter medication for stomachache relief) and have become a part of familial treatment practices. As Catarina’s experience shows, the use of such drugs produces mental and physical effects apart from those related to her illness. These pharmaceutical goods—working, at times, like rituals—realize an imaginary spirit, rather than the material truth they supposedly stand for: Objects are then supposed subjects. There is a money-making science to Catarina’s afflictions. As transmitters of this science, her symptoms are of a typical kind.
+The sense of symptoms, Catkini
+In the lecture ‘‘The Sense of Symptoms,’’ Freud (1957a:271) hinted at the existence of a kind of symptom that could not be traced to an individual’s idiosyncratic history and that the science and skills of psychoanalysis failed to satisfactorily explain. He spoke of‘‘typical symptoms of an illness’’ that are more or less the same in all cases: ‘‘Individual distinctions disappear in them or at least shrink up to such an extent that it is difficult to bring them into connection with the patient’s individual experience and to relate them to particular situations they have experienced’’ (Freud 1957a:270). Freud had in mind, for example, the repetition and doubt that would be common to all obsessional neurotics. Instead of biologizing these typical symptoms, Freud saw them as another level of experience, reflecting, perhaps, a kind of universal culture: ‘‘If the individual symptoms are so unmistakenly dependent on the patient’s experience, it remains possible that the typical symptoms may go back to an experience which is itself typical— common to all human beings’’ (Freud 1957a:271).
+Freud admits that the symptom that makes people similar actually enables the work of medical science: ‘‘And we must not forget that it is these typical symptoms, indeed, which give us our bearings when we make our diagnosis’’ (1957a:271). But rather than elaborating further on how the expert uses the symptom to produce science, Freud
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+shifts attention back to the individual’s tinkering with it. Insightfully, he notes that the typical symptom activates a subjective plasticity: ‘‘On this similar background, however, different patients nevertheless display their individual requirements—whims, one is inclined to say—which in some cases contradict one another directly’’ (Freud 1957a: 270). Through typical symptoms patients actively project— manufacture, one could say—their own individual conditions and moods. But, instead of exploring the materiality and historicity of this prosthetic agency, Freud refers to it as a kind of nucleus around which the patient refashions his or her given neurosis.
+In the end, not surprisingly, Freud universalizes. He suggests that this affect actually makes the individual and typical symptom one and the same: ‘‘I will try to console you, therefore, with the reflection that any fundamental distinction between the one kind of symptom and the other is scarcely assumed’’ (Freud 1957a:271). Thus, the repetition and doubt that are common to obsessional neurotics can be read as ‘‘general reactions which are imposed on the patients by the nature of their pathological change’’ (Freud 1957a:271). The problem with this interpretation in present times is that the subject is not simply the reflection of unconscious processes but is literally composed by morbid scientific-commercialpolitical changes.20
+In Catarina’s writing and thinking, global scientific-pharmaceutical things are not simply taken as new material for old patterns of self-fashioning. These universally disseminated goods are entangled in and act as vectors for new mechanisms of sociomedical and subjective control that have a deadly force. In this sense, it is not the symptom per se that is ahistorical but our understanding of how these scientific identifications became so widely available and the concrete ways in which they replace social ties, voiding certain forms of human life in family and medicine.
+One can now more fully understand what Catarina meant when she first said that she was writing a dictionary so as ‘‘not to forget the words, all the illnesses I had as a child and that I have now.’’ The illnesses she experiences now are the outcome of a relational and medicoscientific engineering of the person she had learned to become. She has literally become the words Haldol and Neozine. The drug name Akineton is embedded in the new name Catarina gave herself: ‘‘Catkini.’’
+Social psychosis
+As I disentangled the facts of Catarina’s existence, the ordinariness of her abandonment and how it was forged in the interactions of family, psychiatry, and other public services came into sharp relief. In the process, I also learned that the overpowering phenomenology of what is
+488
+generally taken and treated as psychosis lies not in the psychotic’s speech (Lacan 1977) but in the actual struggles of the person to find his or her place in a changing reality vis-a-vis people who no longer make his or her words and actions meaningful.
+Catarina’s human ruin is, in fact, symbiotic with several social processes: her migrant family’s industrious adherence to new demands of progress and its eventual fragmentation, the mental automatism of doctors, the increasing pharmaceuticalization of affective breakdowns, and the difficult political truth of Vita as a death script. Adopting a working concept, I began to think of Catarina’s condition as ‘‘social psychosis.’’ By social psychosis, I mean those materials, mechanisms, and relations through which the so-called normal and minimally efficient order of families and neighborhoods—the idea of reality against which the patient appears psychotic—is effected and of which Catarina is a leftover.
+‘‘Did Catarina tell you what happened in the hospital?’’ I asked her ex-husband.
+‘‘No, she didn’t remember.’’
+For Nilson, Catarina had no memory. Having been screened by the police and by psychiatrists, placed on all sorts of antipsychotic medication, and mocked by family and neighbors, Catarina lost touch with the reality of the changing family. I asked Catarina about the voices she was said to be hearing: ‘‘It’s true,’’ she said. ‘‘They were cries. ... I was always sad. ... I thought the voices came from the cemetery, all those dead bodies.’’
+A complex plot had developed. After talking to all parties, I understood that, given certain physical signs, her husband, her brothers, and their respective families believed that Catarina would become an invalid, as her mother had been. They had no interest in being part of that genetic script. Catarina’s ‘‘defective’’ body then became a kind of battlefield in which decisions were made within local family-neighborhood-medical networks about her sanity and, ultimately, about whether ‘‘she could or could not behave like a human being,’’ as her motherin-law put it to me. Depersonalized and overmedicated, something stuck to Catarina’s skin—the life determinants she could no longer shed.
+As Catarina’s situation worsened, Nilson found another woman, with whom he had a child, and he had a judge grant him legal separation from Catarina. She never signed the divorce papers herself. Her ex-husband also signed over his youngest daughter Ana to his boss in the city hall, but he insists that Catarina ‘‘gave her away.’’ Nilson and his mother each kept one of Catarina’s other two children, who still help in their respective domestic economies. At the height of Catarina’s despair, her brother and sister-in-law made her accept a deal in which they took her house and she was moved into their shack, deeper into the slum.
+Given that Catarina had ‘‘been given away’’ to Nilson and that the young couple had squandered the family’s land, Catarina’s brothers felt no obligation to her. This was the economic and gendered fabric of their moral thinking, beyond the domain of the blood tie. In more than one way, Catarina was repeating the script of her mother’s illness experience: In both cases the development of the disease was entangled with spousal separation, the abandonment of the women who had the disease, and predatory claims over available goods.
+Ethics
+I located Catarina’s records in the Novo Hamburgo psychosocial clinic where she was serviced before and between hospitalizations. On December 12, 1994, nurse Lilian Mello drove Catarina home and registered the intensity of the affective modes and practices that made her a double of sorts and empty of all concrete possibilities:
+As she now lives alone, I left her at the house of her mother-in-law. Catarina was badly received. The mother-in-law said that Catarina should die. Because she was stubborn and aggressive, didn’t obey anyone, and didn’t take the medication. The mother-in-law made it clear that she will not be responsible for Catarina. I told her that the family should take Catarina to the general hospital for a clinical evaluation. She told me to call Nilson. I went to talk to him. He only said that, like other times, Catarina should be taken to Porto Alegre and hospitalized.
+To Catarina’s complete devastation, a few weeks later, at the end of December 1994, her shack burned down and she was hospitalized again. This time a Dr. Viola wrote, ‘‘I am against admission; patient should have a neurological evaluation.’’ Nevertheless, she was locked up and treated, as I learned, with haphazardly combined antipsychotic medication. On discharge, she wandered from one relative’s house to another. At some point, ‘‘I slept a whole month,’’ recalls Catarina. Backed by a local psychiatrist, family members and neighbors experimented with all kinds of drugs and dosages. As the adoptive mother of Catarina’s daughter said, ‘‘Dr. Gilson told us how to deal with her ... if one dosage of the medication wouldn’t help, then we should double the dosage.’’
+Medication has become a family tool, and families have become psychiatrists by proxy. Inseparable as pharmaceuticals are from our biomedical regime of truth, one could say that in their deployment they constitute the register of the true. One who is medicated within the family is, then, in Catarina’s words, ‘‘on a path without an exit.’’ The abandonment of unproductive and unwanted family members is mediated and legitimated by pharmaceuticals, both through the scientific truth
+value they bestow and through the chemical alterations they occasion. Pharmaceuticals work as moral technolo-gies—they actually make the loss of social ties irreversible.
+‘‘Bottom line, the ethics that the family itself installs around mental suffering,’’ Simone Laux, the director of the Novo Hamburgo psychosocial service, told me, ‘‘guarantees their own physical existence.’’ One of her colleagues agreed that ‘‘the family quite often replaces a state that does not care.’’ The family is thus a ‘‘state within the state.’’ Freud used this very expression to reiterate the constraining features of neurotic pathologic processes vis-a-vis ‘‘external reality’’ (in Loraux 2002:84). I take the interplay of political power and individual subjectivity to be more than analogical. The decision to make persons and relationships work or to let them die is at the center of family life. Medically known, Catarina was left without the choice to live, in her words, was ‘‘almost killed.’’
+In sum, as I charted the various relational, medical, and institutional networks and practices that mediated Catarina’s abandonment in Vita, I found a deadening language with a force of its own, and, as such, the link to her words, as if she and they were dead objects. Catarina had become a leftover in a domestic world that was being disassembled and reassembled in intricate interactions. She was the negative value, the unnecessary component of a migrant and urban poor culture. Finally, in 1996, after learning about Vita from a Pentecostal pastor who had heard of it on the radio, her brothers left her there.
+Catarina’s destiny is the outcome of a structure that operates like the law and that is close to home. Under such dire circumstances, how can a family be expected to make medical decisions in the best interest of an ill member? In this context, how does one speak of the ‘‘evil’’ that is done and the ‘‘good’’ one must do? Armando and other family members respond with a rhetorical question to which the unspoken answer is always ‘‘nothing’’: ‘‘It’s tough, but what to do?’’ In the end, Catarina is a failed medication regime that, paradoxically, allows the lives, sentiments, and values of others to continue in a constantly changing social field marked by economic pressure and violence.
+In her thinking and writing Catarina reworks this literalism that makes possible a sense of exclusion. Her subjectivity is actually constructed in relation to this tinkering. She demonstrates the possibility of rethinking reality and the subject from the absence she became. Abandoned in Vita to die, Catarina writes that her desire has been betrayed, it is now a pharmaceutical thing with no human exchange value:
+Catarina cries and wants to leave. Desire. Watered, prayed, wept. Tearful feeling, fearful, diabolic, betrayed.
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+My desire is of no value. Desire is pharmaceutical. It is not good for the circus.
+Biology and the unknown
+At first glance, Catarina was just one more lost life in Vita, part of an indigent population with whom the country and its people had become accustomed to coexisting or placing out of sight and thought. But as this inquiry progressed, I began to see Catarina as embodying a specific genetic population that had been made medically and socially invisible. As I continued to work with her family, interviewing aunts and cousins and mediating Catarina’s and her brothers’ medical examinations, I discovered an elaborate culture around the disease entity that slowly mangled the bodies of many in that extended family. Family secrets and anecdotes of this unknown disease point to the existence of unconsidered social practices and an embedded moral economy that, given the local state of science and medicine, determine the supposed humanity of the afflicted as well as reproduction patterns and abandonment—I refer to these determinative elements as a ‘‘biological complex.’’ Affective, relational, and economic arrangements are plotted and realized around the visible carriers of the disease and, ultimately, impact the course of dying.
+I was able to get the genetics team of the Clínicas Hospital in Porto Alegre to see Catarina. Fourteen years after Catarina entered the maddening psychiatric world, molecular testing revealed that she suffers from a genetic disorder called Machado-Joseph Disease, which causes degeneration of the central nervous system (Coutinho 1996:15; Jardim et al. 2001b:899). It is inherited as an autosomal dominant disease (Jardim et al. 2001a:224) and was first reported in North American families of Portuguese-Azorean ancestry (Jardim et al. 2001b:899; Sequeiros 1996:3-13; see also Boutte 1990). The disease is characterized by a progressive cerebellar ataxia affecting gait, limb movements, speech articulation, and deglutition. I was extremely happy to hear the geneticists say that Catarina ‘‘knew of her condition, past and present, and presented no pathology.’’ Dr. Laura Jardim, one of Brazil’s leading young geneticists, who has seen hundreds of Machado-Joseph patients, is adamant that ‘‘there is no mental illness, psychosis, or dementia linked to this genetic disorder. In Machado-Joseph your intelligence will be preserved, clean, and crystalline.’’ Of course, biopsychiatrists could argue that Catarina may have been affected by two concomitant biological processes, but, for me, the discovery of Machado-Joseph was a landmark in the overwhelming disqualification of her as mad and shed light on how her condition had evolved over time.
+The high incidence of Machado-Joseph in the south of Brazil is due to a founder effect, says Dr. Jardim: Porto Alegre was founded in the 18th century by Azorean immigrants who apparently carried the genetic mutation (Jardim 2000). I also learned that after the onset of the disease, patients survive an average of 15 to 20 years, most dying from pneumonia in wheelchairs or bedridden. But there are ways to improve quality of life through physical and speech therapy as well as pain relief, which the Clínicas staff has made available to Catarina. She was also invited to participate in an emergent association of Machado-Joseph patients and families.
+Scientists have established that the more serious the gene mutation is, the likelier the disease will manifest early. Among those with a severe mutation, 60 percent are likely to experience early onset, and 40 percent are not: ‘‘So, seeing the person’s genome we could say a likely time of onset, but not with total certainty,’’ Dr. Jardim told me. ‘‘There is a protective factor, however, that postpones the onset in some individuals in spite of the gene mutation. These can be genetic or environmental factors, social and psychological stressors.’’ Among siblings, she continued, ‘‘the age of onset is almost always the same.’’ How does one explain Catarina’s much earlier onset (late teens) compared with, for example, her brother Armando (late twenties)? Variation in age of onset like this might be due, according to Dr. Jardim, to ‘‘environmental reasons, even due to issues related to a difference in personality. Who knows? We will remain searching for an answer to this question. We know that environmental influences are embodied, but we don’t know how to get to them. We don’t have the instruments to study how the history of the subject influences her own life.’’
+The various relational and medical processes in which Catarina’s biology was embedded and tinkered with, I thought, point to the materiality of this ‘‘unknown 40 percent’’—the social science of the biological mutation. I was happy that there was room in this local scientific milieu to openly consider these social, relational, economic, and technical variables.21 Not only were the genetic researchers and I producing a broader and more complex understanding of Catarina’s condition, but this collaborative process also seemed potentially generative of a science that could address some of the environmental unknowns and the implicit practices that affect the actual course of biology and of dying. As Dr. Jardim commented on Catarina’s case, ‘‘At the peak of her suffering, they were dismembering her ... this dying flesh is all that remained.’’ Rather than the residue of obscure times, Catarina’s condition was part of a regularity, forged in all those public spaces and hazy interactions in which a rapidly changing country, family, and medicine met.
+490
+Coda
+Catarina spends the days in Vita assembling words that give form to her being, both at the present time, such as it is, and in the past. Her writing is not only an extension of the refuse she has become in family life, in medicine, and in Brazil but also a reflection on her: naked and displaced ideals, deadlike objects with no ties, only a few verbs here and there containing the chronology of a life castaway.22
+‘‘I am not a pharmacist,’’ she once told me. ‘‘I cannot say which medication heals an illness, I cannot say the name of the pharmakon, but the name of my illness I know. ... How to say it?’’
+Silence.
+She then said, ‘‘Mine is an illness of time.’’
+‘‘What do you mean?’’ I asked.
+‘‘Time has no cure.’’
+Although Catarina’s external functions are almost dead, she retains a puzzling life and language within her body. She refuses erasure, and through apparently disaggregated words she gives the anthropologist and the reader a sense of how her condition as a body not adequate for reality and of how the society of bodies that is Vita have evolved in time. Catarina thinks through her condition and forces her violent exclusion from affection, care, law, and the possibility of life into writing.
+‘‘There is so much that comes with time ... words ... and the signification you will not find in the book. It is only in my memory that I have the signification. And this is for me to discern. So many words that have to be deciphered ... with the pen, only I can do it... in the ink, I decipher.’’
+The pen between my fingers is my work
+I am convicted to death
+I never convicted anyone and I have the power to This is the major sin A sentence without remedy
+The minor sin is to want to separate My body from my spirit
+Notes
+Acknowledgments. I am deeply grateful to Catarina and to Vita’s administrators and volunteers for allowing me to take part in their everyday life and work. I thank Robert Kimball, Paul Rabinow, Michael M. J. Fischer, Arthur Kleinman, Byron Good, and Adriana Petryna for helping with this project since its beginnings and for their extraordinary support throughout. Thank you to Luis Guilherme Streb, Laura Bannach Jardim, and the team of the Casa de Saude Mental for their help in the field and beyond. I greatly benefited from discussions with and the critical readings of Veena Das, Carol Greenhouse, Burton Singer, Arvind Rajagopal, Ian Whitmarsh, Eugene Raikhel, William Garriott, and Leo Coleman. My undergraduate students at Princeton also carefully engaged with these materials and helped me to find a way to tell the story. Ethnographic research was made possible by the Crichton Fund (Department of Anthropology of Harvard
+University) and by the Committee on Research in the Humanities and Social Sciences and the Program in Latin American Studies of Princeton University. The article was written while I was a member of the School of Social Science of the Institute for Advanced Study at Princeton, and I am thankful for the support of the school’s faculty, fellows, and staff as well as for suggestions of the anonymous reviewers of American Ethnologist. Thank you to Virginia Dominguez for her wonderful editorial guidance and to Linda Forman for her fine editing work. I am solely responsible for the interpretations expressed here.
+1.	See Nancy Scheper-Hughes’s (2001) study of how shifting domestic economies impacted family ties and mental illness in rural Ireland in the 1970s. See Luiz Fernando Dias Duarte 1986 for an analysis of ‘‘nervousness’’ among the urban poor in Brazil.
+2.	Veena Das and Renu Addlakha argue that the domestic, ‘‘once displaced from its conventionally assumed reference to the private, becomes a sphere in which a different kind of citizenship may be enacted—a citizenship based, not on the formation of associational communities, but on notions of publics constituted through voice. The domestic sphere we present, then, is always on the verge of becoming the political’’ (2001:512). On the politics of kinship and caring, see Borneman 2001 and Butler 2001.
+3.	Names of people and institutions have been changed to protect their anonymity (unless requested otherwise).
+4.	In his essay ‘‘The Physical Effect on the Individual of the Idea of Death Suggested by the Collectivity’’ (1979), Marcel Mauss shows that in many supposedly lower civilizations, social death, unaccompanied by any physical illness or injury, could ravage a person’s mind and body. Once removed from society, people were left to think that they were inexorably headed for death, and many died for this reason. Mauss argues that such cases are uncommon or nonexistent in ‘‘our own civilization,’’ for they depend on institutions and beliefs such as witchcraft, prohibitions, and taboos that ‘‘have disappeared from the ranks of our society” (1979:38). As I shall argue throughout this article, however, there continues to be a place for social death in the contemporary city. In the face of increasing economic and biomedical inequality and the breakdown of family structures, human bodies are routinely separated from their normal political status and abandoned to the most extreme misfortunes.
+5.	Consider Clifford Geertz’s discussion of the Yanomami as ex-primitive. Geertz provides some chilling reflections on the Yanomami’s technically and politically engineered demise as well as on a general public blindness to this modern form of life-cum-disappearance: ‘‘Now that their [the Yanomami] value as control group ... is diminished or disappeared and the experiments upon them have ceased and the experimenters departed, what sort of presence in our minds, what sort of whatness, are they now to have? What sort of place in the world does an ‘ex-primitive’ have?’’ (Geertz 2001:21, 22; see also Fischer 2001a, 2001b).
+6.	Historically, Brazil’s welfare system has been structured in such a way that the state’s intervention varies according to the population segment claiming social protection. Citizenship has been conceived as universal for the minority rich, regulated according to market inception for the working class and middle class, and denied to poor and marginal multitudes. According to Sonia Fleury, these ‘‘non-citizens’’ might be entitled to some minimum form of social assistance and charity as long as they renounce political rights—this is their ‘‘inverted citizenship’’ (in Escorel 1993:35; see also Escorel 1999). Those occupying the upper strata of society not only live longer but their right to live longer is also bureaucratically decreed or biomedically ensured through the mechanisms of the market. For a review of Brazilian welfare policies since the 1930s, see Oliveira and
+491
+Teixeira 1986. For a critical review of current social policies developed by the Brazilian state, see Fiori 2001 and Lamounier and Figueiredo 2002. Also see Hoffman and Centeno’s (2003) review of persistent inequality in Latin America.
+7.	On the symbolics of the animal, see Geertz’s (1973) essay on the Balinese cockfight. On historical and contemporary debates over the human-animal boundary in science, see Haraway 1989 and Creager and Jordan 2002. Giorgio Agamben (2004) also explores the relationality of the human and the animal.
+8.	For ethnographically grounded accounts of self and experience drawing on theory of ritual and religion theory, see Csordas 1994, 2002, and Nabokov 2000. Ochs and Capps (1996) review the expansive literature relating notions of the self to practices of narration, and Desjarlais (1994) and Chatterji (1998) discuss how far such ideas carry in interpreting the lives and words of the mentally ill; Scheper-Hughes and Lock (1987) expand on the ‘‘mindful body.’’ An influential narrative of ‘‘the modern self” can be found in Taylor 1989; see Rose 1998 for a Foucault-inspired reinterpretation of this history. Two recent collections of ethnographic essays focusing on the contemporary condition that examine selfhood and identification in the contexts of crisis and drastic social change are Greenhouse et al. 2002 and Holland and Lave 2001.
+9.	Culture is not a variable, it is relational, writes Michael M. J. Fischer: ‘‘It is elsewhere, it is in passage, it is where meaning is woven and renewed, often through gaps and silences, and forces beyond the conscious control of individuals, and yet the space where individual and institutional social responsibility and ethical struggle take place’’ (2003:7). On ‘‘the work of culture,’’ see Obeye-sekere 1990.
+10.	See Hannah Arendt’s discussion on thinking and ethics in her book The Life of the Mind (1981). Could the activity of thinking, asks Arendt (1981:5), be among the conditions that make men abstain from evildoing or even actually ‘‘condition’’ them against it?
+11.	Ethnography is challenged to identify the ‘‘political economic order that reproduces sickness and death at its very base’’ and to listen to, collect, and inscribe the histories of lives ‘‘whom the state hardly thinks worth counting at all’’ (Scheper-Hughes 1992:30).
+12.	In dealing with psychosis, Jacques Lacan (1977:216) urged psychiatrists and psychoanalysts to halt diagnosis, to question their own trust in an order of reality, and he let patients define their own terms. ‘‘There is intuitive intelligence, which is not transferable by speech,’’ said a patient in a conversation with Lacan, and ‘‘I have a great deal of difficulty in logifying. ... I don’t know if that is a French word, it is a word I invented’’ (1980:27). One is faced here with the patient’s making of meaning in a clinical world that would rather assign it (see Corin 1998; Corin et al. 2003). One is also faced with Lacan’s important insight (coming not just out of intellectualization but also out of his psychoanalytical practice) that the unconscious is grounded in rationality and in the interpersonal dimension of speech: ‘‘It is something that comes to us from the structural necessities, something humble, born at the level of the lowest encounters and of all the talking crowd that precedes us ... of the languages spoken in a stuttering, stumbling way, but which cannot elude constraint’’ (1978:47, 48). For Lacan, subjectivity is that failed, renewable, and all-too-human attempt to access the truth of oneself. For a more detailed discussion on truth production, subjectivity, and ethics in the works of Lacan and Foucault, see my article ‘‘Technology and Affect’’ (Biehl, with Coutinho and Outeiro 2001).
+Philosopher Ian Hacking follows Foucault (1980, 2000) in asserting that subjects are constituted in and by the mechanisms
+of knowledge and power and the ethical templates in which they are entangled and which generate the potentials for individual experience. Hacking has identified scientific and technical dynamics that mediate among processes by which ‘‘people are made up’’ (1990:3; see also Hacking 1999). Categories and statistical counting engender new classifications within which people must think of themselves and of the actions that are open to them, says Hacking. As classes of people have their ways of being in the world normalized, this process also has consequences for the ways in which people conceive of others and think of their own possibilities and potentialities (Hacking 1990:6).
+13.	As Foucault wrote, politics has been increasingly played out in modern human physiology: ‘‘What might be called a society’s ‘threshold of modernity’ has been reached when the life of the species is wagered on its own political strategies’’ (1980:143). Agamben builds on these insights and argues that the original political element of sovereign power in Western democracies is ‘‘not simple natural life, but life exposed to death’’ (1998:24). The determinant structure of modern ways of ordering public spaces and political relations is the ban, argues Agamben: ‘‘The ban is essentially the power of delivering something over to itself, which is to say, the power of maintaining itself in relation to something presupposed as nonrelational. What has been banned is delivered over to its own separateness and, at the same time, consigned to the mercy of the one who abandons it— at once excluded and included, removed and at the same time captured’’ (1998:109 - 110).
+14.	The Fundacao Getillio Vargas estimates that some 50 million Brazilians earn less than a dollar per day. For official data on inequality in Brazil, see www.ibge.gov.br.
+15.	See Caldeira 2000 for a discussion of democratization and human rights in Brazil and Paley 2001 for a discussion of health movements and democratization. See Das 1999 for a critique of the measures, practices, and values related to international health interventions and Appadurai 2002 for a discussion of the urban poor and new forms of activism and governmentality in India.
+16.	For the broader literature on antipsychiatry debates and movements, see Laing 1967 and Scheper-Hughes and Lovell 1987. For interpretations of psychiatry and psychology in the United States and Western Europe, see Goffman 1961, Luhrman 2000, Lunbeck 1994, and Rose 1998, 2001; in Brazil, see Costa 1976. On new taxonomies of mental illness and psychopharmaceuticals and their clinical and politicoecononomic imbrications, see Young 1995 and Healy 1999; on the braiding of imaging technologies with new regimes of personhood, see Dumit 2004.
+17.	‘‘A man is no longer a man confined but a man in debt,’’ wrote Gilles Deleuze (1995:181) as he developed his idea of the fate of anthropos within the developments of late capitalism. In addition to the erosion of disciplinary and welfare institutions, Deleuze spoke of the concurrent emergence of new forms of control in affluent contexts, which no longer operate by confining people but through continuous control and instant communication. Family, school, army, and factory are increasingly ‘‘transformable coded configurations of a single business where the only people left are administrators’’ (Deleuze 1995:181). The market, however, is not universalizing and homogenizing but keeps generating both wealth and misery. ‘‘One thing, it’s true, hasn’t changed—capitalism still keeps three quarters of humanity in extreme poverty, too poor to have debts and too numerous to be confined: control will have to deal not only with vanishing frontiers, but with mushrooming shantytowns and ghettos’’ (Deleuze 1995:181). That is, there are too many people to be included in the market, and, as I show in this article, the fate of the unproductive and unwanted is ultimately determined by complex
+and largely unconsidered practices and networks that link the changing institutions of state, market, family, and medicine.
+18.	I elaborate on this pharmaceutical form of governance in the article ‘‘The Activist State: Global Pharmaceuticals, AIDS, and Citizenship in Brazil’’ (Biehl in press a). See also the discussion by Ferguson and Gupta (2002) of new forms of neoliberal govern-mentality.
+19.	See Lawrence Cohen’s (1998) discussion of how neuropsychiatric diagnostics work as new technologies of the person in Indian households.
+20.	Jacques Lacan wrote that in 1960 science was already occupying the place of desire in the human: ‘‘During this historical period the desire of man, which has been felt, anesthetized, put to sleep by moralists, domesticated by educators, betrayed by academies, has quite simply taken refuge or been repressed in that most subtle and blindest of passions ... the passion for knowledge. That’s the passion that is currently going great guns and is far from having said its last word’’ (1992:324). The science Lacan had in mind was physics, specifically, the development of the atomic bomb and the nuclear arms race. Political powers, he said, had been taken in by science’s propaganda and had provided the money for new machines, gadgets, and contraptions, ‘‘as a consequence of which we are left with this vengeance’’ (Lacan 1992:325).
+21.	Carol Ryff, Burton Singer, and colleagues (2001) explore the ways the cumulative wear and tear of lived experience—‘‘allostatic load’’—impacts disease-health outcomes.
+22.	Our grammars, writes George Steiner, make it difficult, even unnatural, to phrase a radical existential negativity, ‘‘but the failure of the human enterprise makes the doubt inescapable’’ (2001:39).
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+1.	Introduction
+This paper describes the role of the nurse navigator as a forward step in the evolution of nursing models of care. The nurse navigator role is embodied within the philosophy of primary health care (PHC), wherein nurses work in partnership with individuals, families, communities to enable access to the type, level of services, support they need for optimal health outcomes (McMurray & Clendon, 2015). Some PHC nurses work in primary care (PC), predominantly
+in general practice, while others are attached to, or lead post-acute, community or long-term health services. All have a commitment to the health of the population and, use their knowledge, skills to make a significant contribution to health reform (Carryer, Halcomb, & Davidson, 2015; Keleher, Parker, Abdulwadud, & Francis, 2009). As in many other western nations, the Australian health reform agenda is aimed at improving access, equity, efficiency, effectiveness of services (Bennett, 2013; Australian Government Department of Health, Ageing (DoHA), 2010; National Health, Hospitals Reform Commission, 2009). Existing models of PHC, either in general or community practice, are recognised as being unsustainable, particularly with population ageing, and an exponential growth in the number of people with complex, chronic conditions (Garling, 2008; Hall, 2015; Primary Health Care Advisory Group, 2015). Patients with complex or chronic conditions often have unmet needs as they typically have to access sequential or simultaneous services from multiple providers in different locations with culturally appropriate care provisions. Their care is costly, and usually poorly coordinated with inadequate communication from care providers (Burgers, Voerman, Grol, Faber, & Schneider, 2010; Kuluski et al., 2013). In the Australian system, with its combination of public and private health providers, people with chronic conditions may also be subject to situations where clinicians and services lack the capacity to work effectively together; or where there is a lack of structures or clinical governance systems to support integration of services (Australian Medicare Local Alliance, 2012).
+Coordination of services can be helpful in improving the patient journey if the services are provided in a way that is collaborative, holistic, inclusive, and responsive to people’s needs and preferences in the contexts of their lives. As the most frequent users of the healthcare system those with complex and chronic conditions rely on guidance from health professionals to help them make appropriate choices through the many touch points of service. The health professional at the initial point of service is often the nurse, who, from a primary care position in general practice, a hospital discharge service, or a nurse-led clinic must identify realistic, local resources to help meet their immediate and long-term needs. In this role, primary health care nurses seek to ensure the advice they provide is tailored to the patient’s condition, their expectations across the health trajectory, their level of health literacy and the social determinants of their lives (McMurray & Clendon, 2015). This is person-centred care (PCC), an important objective of PHC nursing. Nurses providing PCC do so as hands-on caregivers, case managers, or care coordinators, and nurse navigators may incorporate all or some of these roles in their practice. The major focus of their role is to enhance care transitions by building people’s capacity for decision-making and selfmanagement as they learn to navigate the complexities of the health and social services most appropriate to meet their needs. As outlined below, it is a unique and evolving role that accentuates nursing’s contribution to PHC.
+2.	The patient navigator
+The patient navigator role was first documented in the 1990s. Freeman (2013), a medical practitioner, coined the
+term 'patient navigator’ in working with cancer patients in Harlem, New York who were poor, uninsured and underserved. He began addressing discontinuities in services for his cancer patients across their journey from diagnosis to treatment by lobbying policy-makers and service managers for patient navigators who would help patients across the 'discovery-delivery disconnect’ (Freeman, 2013, p. 73). The objective of the new role was to help people understand and journey through the healthcare system so they would receive the treatment they required during all transitions across the continuum of care. His campaign was successful, and in 2005 the United States (US) Government signed into law the Patient Navigator and Chronic Disease Prevention Act (United States Congress, 2005). This was followed by an American College of Surgeons decree that by 2015 all cancer programmes in the US must have in place a patient navigator process (Freeman, 2013). With major changes in the American healthcare system impacting on many patients and their families, navigators are now considered crucial in helping the uninsured learn how to access appropriate insurance and the requisite services for their condition (Ingram, Scutchfield, & Costich, 2015). The Swedish government followed suit, establishing the patient navigator role as part of the National Swedish Cancer Strategy (Bau Berglund, Gustafsson, Johansson, & Bergenmar, 2015). Lay patient navigators tend to be community health workers or outreach workers who develop trusting relationships that can overcome system barriers (Cantril & Haylock, 2013). They come from a range of backgrounds, including occupational therapists, medical assistants, social workers or nurses, with nurses being the most common among these groups (Bodenheimer & Smith, 2013; Doolan-Noble et al., 2013; Enard, 2013; Ferrante, Cohen, & Crosson, 2010; Lindsay, Tetrault, Desmaris, King, & Pierart, 2014). Implementation of their role has been so successful in helping cancer patients the patient navigator role has been adapted to help high users of services, such as those with chronic conditions, to develop adequate knowledge to navigate the healthcare system (Dent, 2013; Doolan-Noble et al., 2013; Kuluski et al., 2013; Leaver, 2014; Plant et al., 2013).
+In Australia, a navigator role has been developed in the context of a Queensland pilot programme evaluating integrated care for people with complex and chronic conditions. This programme, the Gold Coast Integrated Care (GCIC) programme, is designed to link primary and secondary health services through a shared care record (SCR) and collaboration between local General Practitioners (GPs) and other health service providers by enrolling them in a type of patient-centred medical home similar to a multidisciplinary primary health care clinic, to ensure comprehensiveness of care planning. Similar models of care have been developed in the US and the UK with the intention of improving continuity of care and preventing unnecessary hospitalisations (Friedberg, Rosenthal, Werner, Volpp, & Schneider, 2015; Willard & Bodenheimer, 2012), and this model has been supported in principle by the Royal Australian College of GPs and the Australian government (Janamian, Jackson, Glasson, & Nicholson, 2014). In the GCIC model, the navigators contact all patients who have been identified by their GPs as appropriate for the programme, conducting a telephone health assessment for each patient and explaining the processes within which they can navigate through the system
+without referrals back and forth between the GPs, specialists and the hospital. An initial contact by a service navigator from the coordinating centre signals the beginning of a four-stage holistic assessment process, which culminates in the development of a care plan and pathway underpinned by the SCR. This record is an electronically enhanced information and communication technology (ICT) system that houses clinical informatics, patient registers, referral networks and ultimately, will provide telehealth and remote monitoring capability. Having the patient and service providers share electronic information increases the efficiency and effectiveness of services in a patient-centred way, given that patient preferences are included at each step. Current studies have shown that when medical, social, behavioural and financial information is available to inform patient decisions, the system is more likely to be empowering and personalised (Koster, Stewart, & Kolker, 2015).
+In the GCIC model the navigators launch the first stage of information sharing. This Evaluation stage is a telephone conversation wherein patients are encouraged to share demographic, social and cultural information as well as complete a structured assessment of the way they see their health and quality of life. These data are collected using a combination of open-ended questions and survey instruments to create a baseline of clinical and demographic data. Included is an assessment of patient activation, goal-setting, problem-solving and coordination, which creates a foundation for discussions with the patient to help tailor their health guidance to individual needs. For patients unable to communicate by phone for language, cognition or preference reasons, the navigator organises a visit to the patient at home or at the GP clinic. Both interactions are aimed at promoting health literacy, described by Redding (2013) as building knowledge and mobilising the patient and family’s social and cultural capital to support their health decisionmaking. A diagnostic review/risk assessment is included in this step, which can include a medication review, mental health and frailty assessment, establishment of health goals and the need for extra supportive resources. The second step is Discovery where the patient meets with members of the multidisciplinary team to help tailor their shared care plan (SCP) to their individual needs. This step is based on relationship building to encourage mutual decision-making, thereby entrenching the partnership as instrumental to care. Step three, the Patient-Centred Care Planning stage, sees a review by the coordination team in collaboration with the patient’s GP to ensure completeness of the information base for planning. At this stage, a member of the multidisciplinary team, a nurse or other team member, is appointed as the care coordinator. In the fourth, Communication stage, the designated care coordinator ensures that the patient and family understand and agree with the SCP, ensuring that all elements of the plan are documented for the SCR, the GP, the Hospital and Health Service record, and any other organisations or resources as appropriate, including addressing any guardianship issues such as advance care planning or power of attorney when required. This stage also focuses on health promotion and ascertaining patients’ and carers’ health literacy; that is, the extent to which patients and family members are able to access appointments, manage risks and undertake any self-directed management of their condition as mutually agreed. At this stage, patients assume
+control over their personal communication strategies, such as deciding who will have access to their SCR or other pertinent elements of care planning. Evaluation of the role is ongoing but early comments from the navigators indicate that they find their advice has helped people better understand the system and the roles of the multidisciplinary resources to which they are being referred. Patients have also reported being extremely satisfied with the service, and plans for the future include role redesign as a nurse navigator role.
+Other navigator roles elsewhere require special skills. In many cases, navigators are appointed for their skills in helping families with acutely ill children navigate safe passage through the myriad of services they require, particularly for families with low health literacy (Jimenez, Barg, Gievara, Gerdes, & Fiks, 2013). Some navigators are required to have special language skills, depending on the needs of the population, but many have in common a specific focus on health literacy; helping people develop a level of knowledge for active engagement in their care by using such tools as motivational interviewing to encourage shared decisionmaking and successful transitions through the health journey (Betancourt, 2014; Lindsay et al., 2014). Enard (2013) reports that bilingual community health workers in the navigator role have been extremely effective, especially when they have been trained in peer-to-peer counselling. She describes their role as including communication, psychological, financial and social support. Evaluation of their role in case management shows that they provide more comprehensive management than usual case management, including ongoing follow-up (Enard, 2013). In the cross-cultural context, the role is one of culture broker, translating language or cultural customs or acting as a multilingual case manager, establishing person-centred and culturally relevant goals to help those struggling to understand new ideas and health practices (Lindsay et al., 2014). For example, researchers in Queensland developed the role of patient navigator for a culturally and linguistically diverse (CALD) regional area where residents were having difficulty understanding the healthcare system. They were able to provide training for lay multilingual residents who were interested in helping people of their cultural group learn to identify their health needs and access appropriate care in their community (Henderson & Kendall, 2011, 2014). The research team and service managers fostered close relationships with the navigators and the local GPs to coordinate services and undertake health promotion. Evaluation of the programme revealed that the navigators felt they were acting as knowledge brokers; that they had been successful in building bridges within the community to improve health literacy and empower local CALD families (Henderson & Kendall, 2011). However, these Australian researchers also recommended maintaining a navigator-centric, grassroots role to avoid the programme becoming overly bureaucratic (Henderson & Kendall, 2014).
+3.	Nurse navigators
+Some navigator roles are nursing-specific, requiring the clinical knowledge of a registered nurse (RN). For example, some nurse navigators in Canada are cancer nurses, whose role involves helping bridge the service gap for cancer
+patients (Pederson & Hack, 2011). They act as 'pivot nurses’, providing disease specific information and practical advice, emotional support, facilitate decision-making, create links to resources, and help identify and develop community supports (Pederson & Hack, 2011). In Western Australia, cancer care coordinators are the focal point of contact throughout the patient care trajectory, coordinating care and providing patient education, but their role often expands to helping people navigate transitions through the cancer support system (Monterosso, Platt, Krishnasamy, & Yates, 2011). This role is somewhat similar to the Jane McGrath Breast Care nurses who practise throughout Australia, sharing care and expertise while helping patients and families navigate through their journey. Another version of the nurse navigator role is the 'emergency journey coordinator’, introduced in New South Wales in 2013 to help ease patient transitions through the emergency department (Asha & Ajami, 2014). Nurse navigators have been found to be particularly helpful for rural people and, in New Zealand (NZ) they have been used to support individuals in need who have few resources and multiple barriers. The NZ navigators focus on engaging with patients and their families to improve access to social support services, enhance health literacy and selfcare ability. Evaluation data showed that they have reduced disparities and improved health outcomes. The researchers concluded that this model of care can be adapted to a range of population groups if it is based on a strong and predictable implementation strategy (Doolan-Noble et al., 2013). Their recommendations also included the need to appoint those best suited to the role; that is, irrespective of their background, the nurses should have essential skills in nurturing relationships with health and social care professionals (Doolan-Noble et al., 2013). Other navigator models have not yet generated sufficient research knowledge to guide role development, with the exception of Monterosso et al.’s (2011) cancer care coordinator role, which attracted high satisfaction ratings from patients and multidisciplinary team members.
+In 2015 the Queensland government announced a major initiative to position nurse navigators throughout various hospitals and health services to help people transition between their GPs and other primary care services, through their hospital and community health journey to home (Queensland Health, 2015). The expectation of this programme is that nurse navigators will be able to redirect many patients to existing programmes, such as the Hospital in the Home (HiTH), and other community supports with a view to reducing fragmentation of services, length of hospital stay and readmission rates (Queensland Health, 2015). A number of health service managers from various service units have successfully applied for the Queensland Health nurse navigators, including the GCIC programme described above. The nurse navigators in this programme will function in a different role to the lay navigators who enrol patients into the GCIC programme. Their roles will be conjoint positions between general practices providing primary care, and Gold Coast Hospital and Health Services (HHS), which provides acute care. Because the SCR is integral to the programme the nurse navigators will have full access to patient information at both the practice and the HHS level — a limitation often faced by primary care nurses in general practice. Once these nurses are appointed it will be
+important to evaluate the impact of these roles on a range of outcomes for patients, families and the community, as previous researchers have suggested (Plant et al., 2013). Other research will investigate the impact of the nurses’ responsibilities, potentially informing the dimensions of the role in future and the requisite educational preparation for various navigator roles in the Australian context.
+A previous systematic review of this type of role outlined the broad and varied dimension of the role, which included care planning and coordination, home visiting, community service provision, and patient and family education, with advocacy as the common feature (Manderson, Mcmurray, Piraino, & Stolee, 2012). The researchers also found variation among evaluation outcomes, with some studies focusing on economic feasibility of the role, and others on patient and caregivers’ experiences and satisfaction (Manderson et al., 2012). Although all studies have shown wide agreement on improvements in timely care and transitions between services for patients with access to a nurse navigator, researchers have concluded that the lack of data on cost effectiveness may hamper widespread rollout of the role (Manderson et al., 2012; Simon et al., 2015).
+In Australia, such a role, subsidised by the state or territory governments would be ideal in addressing the unmet needs of the rural population, as has been demonstrated in the rural populations of Canada and New Zealand (Cantril & Haylock, 2013; Doolan-Noble et al., 2013; Pederson & Hack, 2011). Nurse navigators also have the potential to make a significant impact on transitional care for older people, whether or not they suffer from complex chronic diseases. A number of transitional care programmes have implemented the role in other countries, finding that nurse navigators collaborating with other members of the multidisciplinary team can play an important part in early discharge planning, skilled home visiting or phone support, medication management, advocacy to remove barriers to care, patient and caregiver education, and assessment and management of health status (Abrashkin, Cho, Torgalkar, & Markoff, 2012). This model of practice is now well developed in cancer care, where nurse navigators have moved from general service navigation to focusing on a specific disease such as breast cancer, sharing their in-depth knowledge of cancer care, the side effects and latest evidence-based interventions, as well as building referral alliances to strengthen the partnership between patients, nurses, and other health professionals (Cantril & Haylock, 2013). These nurses coordinate diagnostic evaluations and provide disease specific education and symptom support (May, 2013). Table 1 illustrates the common and unique features of the nurse navigator role in relation to the roles of case manager and care coordinator.
+4.	A new model of care or old wine in new bottles?
+Nurse-led models of care have made significant inroads into improving the health of populations, particularly in the context of general practice, where nurses and nurse practitioners are leading the way in helping their populations manage chronic illness (Carryer & Halcomb, 2015; Harvey, Fisher, & Green, 2012; Parker, Clifton, Shams, & Young, 2012). Some nurses’ roles are described as care
+coordinators, while others are case managers, but both tend to focus simultaneously on the individual and family as they help people navigate the health care system with a single point of entry (Anderson, St. Hilaire, & Flinter, 2012; Watts & Lucatorto, 2014). Their roles are clearly entrenched in the primary health care ethos, where patient and family-centred planning and intersectoral collaboration are crucial to successful patient outcomes. Like the case manager and coordination role, the nurse navigator helps bridge the gap between acute, post-acute and community care. The uniqueness of the navigator role lies in the fact that nurse navigators are specifically appointed with the autonomy to choose how best to help people transition through the system; whereas many care coordinators or case managers are often tied to a single service and the processes embedded in that service. All of these roles have a common aim of demonstrating the convergence of hospital and community care in a way that is distinctively
+person-centred, exemplifying the patient-as-partner approach to care. Partnering with patients reflects the contemporary global rhetoric in health service policy and planning, based on the need for care to be respectful of and responsive to individual patient preferences, needs, and values and ensuring that their values guide all clinical decisions (Prey et al., 2014). PCC is now mandated by the Australian Safety and Quality Health Service Standards, which requires all Australian health services to employ a system-wide PCC focus (Australian Commission on Safety and Quality in Health Care, 2011). This requirement is based on studies showing that patients who actively participate in their health decisions as they transition across services can help prevent the risk of adverse events caused by incomplete information on such things as medications, falls risks, wound infections or cognitive difficulties (ledema, Allen, Britton, & Gallagher, 2012; Longtin et al., 2010; Rathert, Huddledston, & Pak, 2011). In practice, most primary health
+care nurses use a PCC model of care, and often act as service navigators, although the role description is a relatively new addition to our professional lexicon. The nurse navigator helps people identify resources, including general, specialist and multidisciplinary care in a way that reduces service duplication, tailors advice to individual and family needs, and provides timely, seamless, culturally appropriate access to appropriate and acceptable care, all of which meet the philosophical and practical elements of health reform, as well as the Quality and Safety Standard. Helping people learn to navigate the system through advocacy and respect can help them become health literate and build the capacity to manage their condition, irrespective of where they are in the continuum of care. Shared decision-making, expedited by appropriate technologies, can align patients’ and clinicians’ expectations, thus reducing unwarranted variations in clinical practice (Legare et al., 2012). In this respect, the nurse navigators are not only working towards quality and safety and responsive care, but sustainability of the health system, which is more likely when services are integrated around patient needs rather than the needs of service providers (Australian Government, 2010; Ferrer & Goodwin, 2014; Lillrank, 2012; Minkman, 2012).
+5.	Implications for future practice and research
+Managing chronic care requires role clarity, particularly in the context of interprofessional collaborative structures (Brault et al., 2014). In their current form, the roles of case manager, care coordinator and nurse navigator have considerable overlap, yet there remains a dearth of research into the relative effectiveness of these roles (Sutherland & Hayter, 2009). In future, nursing roles in managing chronic conditions can be expected to undergo considerable transformation, evolving with patients’ needs, providers’ experiences, technological and health system developments, and, as researchers have found, within various practice contexts and collegial interactions (Carmel & Baker-McClearn, 2012). It is important from the outset to engage the nursing research community in tracking the outcomes of the nurse navigator model of care, particularly the patient outcomes that can be linked to embedding the role in general practice, where patients transition between acute and community settings. Future studies will also need to examine the efficiency and cost effectiveness of general practices where practice nurses (PNs) have decided to undertake the role of nurse navigator, and whether these roles generate satisfaction for the nurses, other practice staff and patients. One could expect that, especially with enabling information technologies such as the SCR, the roles will reduce the number of referrals between primary and secondary care providers. This research evidence will help inform service policies, health reforms, and validate the need for smart technologies, as well as linking nursing role redesign to patient outcomes. Undoubtedly, the evidence generated from these studies could also help reframe educational preparation for future nursing practice, which given population ageing, will most certainly focus on integrated care for those with chronic and complex conditions.
+6.	Conclusion
+This article has outlined an important new PHC role for nurses. This unique model of care is responsive to one of the common complaints of a 'demand-capacity mismatch’ that pervades contemporary healthcare systems (Bodenheimer & Smith, 2013). Our healthcare systems clearly need renewed consideration of existing models of care. As Bodenheimer and Smith (2013) suggest we should be empowering nurses and other members of the multidisciplinary team to reallocate clinical responsibilities for health promotion, coaching for self-care, medication management and a range of other functions that will help allay shortages of physicians while providing the best and most coordinated care possible. These issues suggest an urgent and critical need for nursing leadership to help find solutions to unstable and disconnected health services (Weberg, Braaten, & Gelinas, 2013). The nurse navigator may be one innovative solution for a smoother journey into and through the health system of the future.
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+Dovepress
+open access to scientific and medical research
+Introduction
+Many people with long-term physical health conditions also have mental health problems.1 These issues can further interact with medical comorbidities and result in reduced quality of life, cardiovascular risk, frailty, and increased mortality.2 Both higher costs and poorer health outcomes have been reported with the increasing medical comorbidity burden for depression,3-5 delirium,6 and dementia.7-9
+Worldwide, populations are aging, and the number of patients aged 80 years or older is growing faster than any younger segment of the older population.10 In the United Kingdom, older people occupy two-thirds of National Health Service (NHS) beds, and 60% of older people11 admitted to general hospital will have or develop a mental disorder. There are about 750,000 people in the United Kingdom with dementia, and this number is expected to double during the next 30 years. This will have a wide effect on health care and social care costs.
+Our objective was to review recent published evidence on the Rapid Assessment Interface and Discharge (RAID) service model, examining the strengths and weakness of the service design, outcome, and effectiveness. We also review the existing evidence on other psychiatry liaison services in dementia care.
+Older people’s liaison psychiatry services
+The traditional acute medical wards have limited access to a staff team with psychiatric expertise or specialist training, and hence, mental illness (particularly dementia) in older people can sometimes go undetected and untreated. Guidelines for the development of liaison mental health services for older people recommend that acute hospital trusts, older peoples’ mental health services, and commissioners of health care and social care work together to improve outcomes of older people with mental health problems who are in general hospitals.11 The commissioning guide published by the National Institute for Health and Care Excellence (NICE) details the potential resource implications for commissioners of dementia care.12
+Active psychiatry liaison intervention in older people with hip fracture has been shown to reduce length of stay and provide substantial cost savings.13 Dementia was significantly overrepresented in patients with hip fracture,14 and therefore an effective psychiatry liaison service supporting the medical team and diagnosing dementia, as well as improving the quality of dementia care and providing continuing care in the community, is essential.
+The National Service Framework for older people was published in 2001 - standard seven aims to promote good mental health in older people and to treat and support those older people with dementia.15 The framework recommended that the NHS and local councils review their processes of early detection and diagnosis and their assessment care and treatment plans, including arrangements for health promotion. However, there has been slow progress and little effect on people suffering with dementia and their carers. There is a nationwide need to launch the Welsh concept of a dementiafriendly community. This concept features as one of the main drivers of the National Dementia Strategy (NDS) policy.16
+RAID service
+NHS is the publicly funded health care system of the United Kingdom. There are a number of regional NHS trusts, including primary care trusts, foundation trusts, and mental health services trusts or health boards, which provide
+various services. Integrated care is essential to meeting the needs of the aging population, transforming the way care is provided for people with long-term conditions, and enabling people with complex needs to live healthy, fulfilling, and independent lives.17
+The RAID model is a modern example of moving toward this goal. It is a specialist, multidisciplinary, mental health service in a large, acute, city hospital in Birmingham in the United Kingdom.18 The RAID service is provided by the Birmingham and Solihull Mental Health NHS Foundation Trust and commissioned jointly by Heart of Birmingham and Sandwell Primary Care Trusts. The service was launched in December 2009 as a pilot project to offer a comprehensive range of mental health specialties. It is a multiskilled liaison psychiatry service that includes nurses, adult psychiatrists, psychologists, specialists in mental disorders of older people, and physician assistants who are experienced at working in mental health. The team works closely with other hospital psychologists and alcohol practitioners, as well as the acute hospital clinicians, to provide a comprehensive assessment of a person’s physical and psychological well-being. The RAID service is for people older than 16 years who have mental health or substance misuse needs who access accident and emergency (A&E) departments or acute hospitals in Birmingham.19
+The service provides a single point of contact for the acute hospitals and A&E 24 hours a day, 7 days a week. A rapid response is offered, within an hour for A&E and within 24 hours for other hospital departments. Advice is given on a wide range of issues, including alcohol problems, detoxification, substance misuse treatment, and assessment of care needs of older people with mental health problems. In addition, team liaison supports the early detection of mental health problems to enable rapid and appropriate intervention. The team can also provide continuity of care for people who are already known to mental health services and can help with discharge planning, general advice, and support.19
+The RAID service is an innovative new approach in mental health that has not only resulted in holistic patient care but has also shown improved outcome and significant savings by avoiding unnecessary admissions onto busy medical wards.20
+Implications of the RAID service for older patients with dementia
+Evidence suggests that investing in services for people at an earlier point in the care pathway can improve the well-being of people with dementia and their carers and can prevent
+crises and unplanned admission to acute hospital beds, in addition to delaying the need for institutional care.21 The NDS aims to increase the awareness of dementia, ensuring early diagnosis and intervention, as well as improving the quality of care for people with dementia and their carers.16 The NDS has identified four priority areas: good-quality early diagnosis and intervention for all, improved quality of care in general hospitals, living well with dementia in care homes, and reduced use of antipsychotic medications.
+Improved quality of care
+Two-thirds of beds in general hospitals are occupied by older people, most of whom have multiple and complex health problems.11 Two-thirds of these patients either have or are at risk of developing a mental disorder during their admission, the most common conditions being delirium, depression, and dementia. The prevalence of dementia in acute hospitals was reported as 48% in men and 75% in women older than 90 years.8 In patients in their 70s or older, delirium has been reported in 27%, and 8%-32% of patients admitted to acute hospitals were found to be depressed.21 People with dementia and concurrent physical conditions have poor-quality care, higher mortality, and worse clinical outcomes than people with the same conditions without dementia.7’8’23’24 The hazard ratio of death increased from 1.82 for the very mildly demented to 9.52 for severely demented patients.9
+The RAID service has shown quality improvement in the care of older people by reducing their length of stay, avoiding their admission to acute hospital beds, and discharging them in increased numbers back to their original place of residence, rather than an institution or care home. In addition, the RAID model has been shown to reduce the readmission rate after discharge by 65% in comparison with a pre-RAID group.25
+The RAID service has received special interest from the Department of Health and NHS Confederation for achieving savings by improved quality of mental health in acute hospitals. The service has received accreditation from the Psychiatric Liaison Accreditation Network of the Royal College of Psychiatrists and also won a prestigious Health Service Journal Award for innovation in mental health in 2010.20 Patients and providers have welcomed the concept of the RAID service having an effect on the health and quality of life of patients.
+Patient safety and early diagnosis
+Dementia is particularly challenging in general hospitals, as it is under-recognized, and 42%-50% of people older
+than 70 years admitted as an emergency case are cognitively impaired.8,22 Improving the rate of early diagnosis is a cornerstone of dementia care and safeguarding patients. Without appropriate diagnosis, effective treatment and timely support cannot be accessed by the older person.
+The RAID service puts an emphasis on diversion and discharge from A&E and on facilitating early, safe, and supportive discharge from general medical wards. Older people accounted for 23% of total referrals received by the RAID service, and 60% were from a general medical ward.20 Cognitive impairment and dementia represented 18% of RAID referrals.25
+Patients were given follow-up support through their general practitioner, community services (including mental health), home treatment teams, and a RAID service follow-up clinic. The involvement of the RAID service led to an increase in the detection and diagnosis of dementia (an increase of 22% was seen in the coding of dementia).25 Dementia is an independent risk factor for falling, and increased detection of dementia could be helpful in undertaking prevention strategies for in-patient falls.
+In addition, people with dementia all experience behavioral and psychological symptoms at some point, which can often be prevented or managed without medication. However, people with dementia have frequently been prescribed antipsychotic drugs as a first resort, and it has been estimated that around two-thirds of these prescriptions are inappropriate. The evidence suggests that these drugs have limited positive effects in treating these symptoms for 70% of patients but can cause significant harm, including increased mortality and stroke. It is a national priority in England and Wales to reduce the use of antipsychotic drugs for people with dementia.26 The psychiatric liaison service can support the management of behavioral and psychological symptoms in patients with dementia; an audit of antipsychotic prescriptions for people with dementia has showed a 52% reduction in antipsychotic prescriptions for people with dementia between 2008 and 2011.27 The RAID service could have contributed to reduced antipsychotic prescriptions, but this was not actually studied as part of the evaluation.
+Cost benefit
+The Kings Fund 2012 report suggested there was a 45% rise in total health cost for each person with a long-term condition and comorbid mental health problem.1 The report also has shown that 12%-18% of all NHS expenditures on long-term conditions are linked to poor mental health and well-being.1
+The total cost of dementia care in 2007 for England was estimated to be GB£14.8 billion, and this amount is projected to rise to £34.8 billion by 2026, for an increase of 135%.11 The Alzheimer’s Society has reported that people with dementia who are older than 65 years occupy one quarter of hospital beds at any one time, and the excess cost is estimated to be £6 million to the average general hospital.28
+The cost-benefit of the RAID service is centered on the ability of the service to promote faster discharge from hospital and fewer readmissions, resulting in reduced numbers of in-patient bed-days. The service has been economically evaluated by the London School of Economics,20 which noted that the mean length of stay was reduced by 3.2 days, and 14,000 bed-days have been saved over the course of 12 months. The estimated cost savings before and after introduction of the RAID service are in the range of £3.4-£9.5 million a year.20
+The RAID evaluation showed a total savings of43-64 beds per day; most of these savings have come from reduced bed use and lower readmission rates among older patients, who formed one-third of total referrals.25 The RAID service has also shown it can reduce the mean length of stay for patients with dementia by at least 7.5 days per admission.
+The elderly care wards provided the majority of bed-day savings by reducing length of stay and preventing readmissions; therefore, the hospital was able to close down 60 beds by incorporating the reduction in bed use.25 The additional cost of the RAID service was around £0.8 million a year, but it generates incremental benefits in terms of reduced bed use valued at £3.55 million a year, implying a benefit-cost ratio of more than 4:1.20
+Teaching and training
+The training of both psychiatry staff and general hospital staff is essential in the detection and basic management of common psychiatric conditions, particularly dementia and delirium. The NDS and national policies recommend that all staff working with older people in the health, social care, and voluntary sectors have access to dementia care training.16,29 Most nurses working with people with dementia want more training and support to help them deliver the best possible care: 33% of nurses had received some training, but 54% of nursing staff had not received any preregistration training in dementia.29 This startling lack of dementia education and training can lead to health professionals feeling unskilled and stressed in dealing with patients with dementia, putting the patients at increased clinical risk.29 The National Audit Office reported that over half of the community mental health
+teams felt that acute hospital nurses were inadequately trained in dementia.30 This perception among nurses working with people with dementia was reflected in the fact that 85% of the nurses also felt they do not have the required knowledge and skills.31 There is existing evidence that staff training helps to eliminate discrimination of those with mental health problems,32 and some suggest that regular teaching of geriatric giants, including dementia and delirium, to general nurses reduces their stress level.33
+The RAID service provides both formal and informal training on mental health difficulties, which included 2 days training on dementia, depression, delirium, and dignity, which was repeated every 3 months to acute staff throughout the hospital. In addition, other mental health issues were discussed in a weekly teaching session. The RAID influence group (referrals not directly seen by the RAID service but managed by acute colleagues who had received training/ support from it) showed improvement in both length of in-patient stays and avoiding readmission, suggesting staff training is helpful. In addition, the RAID service provided training and education to 27% of patients or carers or family of those diagnosed with dementia, and 60% of patients were either given information on dementia or directly referred onto other services in the community.
+Carer and staff satisfaction
+A national study of older people’s mental health services found that carers expressed general dissatisfaction with the care their relatives received on the general wards in acute hospitals.34 In particular, they referred to the staff in hospitals not being trained or equipped to deal with patients with mental health problems, especially dementia.
+The RAID evaluation suggested that the staff felt more confident with training and service provision; however, there was no stated formal evaluation.
+Accessibility
+Delay in psychiatric consultations continues to be associated with longer lengths of stay in the general hospital.35 The RAID data evaluation showed that patients were reviewed, on average, within 24 minutes (A&E referral) and 16 hours (ward referral), and targets in these areas were met in 91% and 89% of cases, respectively.25
+Discussion
+In the United Kingdom, service models for the provision of mental health input on physical health care wards are variable. Traditionally, the dominant model has been one
+of consultation that relies on the medical staff to not only detect but also appropriately refer relevant patients. This is a reactive model, present in 73% of services according to a UK survey in 2002.36 This survey further indicated that 71% of participants considered the service they delivered to be poor.36 The National Service Framework for England, published in 2001, highlighted disparity of care for the elderly,15 yet 3 years later, Tucker et al were only able to report there was “some suggestion that liaison services were developing.”37 A strikingly disappointing comment in the Royal College of Psychiatrists document Who Cares Wins states that where liaison mental health services for older people have developed, it is usually the result of a local champion with a particular interest, rather than the result of strategic planning.11 Who Cares Wins, dating from 2005, sets out the range of service models and illustrates how a proactive liaison model has more advantages, and it urges the standard consultation model services to shift to a liaison approach.11
+The comprehensive geriatric assessment, with an emphasis on cognitive assessment in older people admitted to an acute hospital, has shown good outcome.38 Despite this knowledge, the 2011 National Audit of Dementia Care showed a wide variation in the quality and approach of care for people with dementia who were in general hospitals.39
+The provision of specialist liaison psychiatrists or mental health liaison nurses with time dedicated to this service represents a shift toward more focused support. However, a nurse-led mental health liaison service for medically ill older people was not effective in reducing general psychiatric morbidity.40
+Psychiatrists have sessions for general hospital work, and nurses are often based in the hospital. Models based on more integrated multidisciplinary working include the shared care ward, in which patients with complicated physical and mental health needs are managed by both relevant teams. There are increasing rates of referral of older people to consultationliaison psychiatry services, which is an effect likely to be experienced in all nations with an aging population.41 Old age liaison remains in its relative infancy, and there is a lack of qualitative and quantitative studies worldwide in this area of service.
+A meta-review on liaison psychiatric services outlined the need for more evidence-based research to guide liaison service development and planning.42 A recent systematic review suggested that liaison mental health services in general hospitals have the potential to be effective in improving outcomes such as length of hospital stay, discharge destination, and hospital costs, but concerns were raised about the reliability and validity of the studies included.43
+There was evidence of improved accessibility, but services were heterogeneous, and there was a high level of missing data. The conclusion evidenced a lack of ownership and responsibility for these services, and further evaluation of liaison mental health service for older people was highly recommended.43 A recent quantitative prospective review of referrals to a psychiatry liaison service (Newcastle, United Kingdom) showed a significant increase in cognitive assessments (from 19% to 49%).44 The effect of service on psychological support to patients, cost, staff training, and outcome, including length of stay and readmission, were not evaluated.
+The prevailing view in the United Kingdom is that old age psychiatrists have the main responsibility for the diagnosis and management of dementia. In many hospitals, both psychiatric and medical notes are not easily accessible and are mostly kept separately. Clearly, there is a need for more collaborative and liaison work between geriatricians and old age psychiatrists for the prompt diagnosis and management of dementia. The hospital liaison multidisciplinary mental health team is the model advised in the United Kingdom to offer a general hospital the most complete service, and the RAID service model is most closely linked with this structure of service.
+The RAID model highlights that for an effective psychiatry liaison service model, it is imperative to have multidisciplinary staff working together on these often-complex patients with both physical and mental health needs. The average UK psychiatry liaison service at present does not reflect the level of professional input afforded by the RAID service. However, given statistics related to the aging population, there surely has to be priority to increase the attention, number of resources, and level of service for this vulnerable group of patients.
+The RAID model has overcome organizational barriers across traditional specialty boundaries. It has demonstrated that closer and collaborative multidisciplinary working between mental health specialists and other professionals provides better support for comorbid mental health needs. The care for large numbers of older people with multiple comorbidities could be improved by better integrating mental health support with primary care and chronic disease management programs. The timely response and immediate triage to relevant professionals reduces physical health care costs in the community and acute hospitals. Those with the necessary expertise define appropriate cases and begin the management of the patients at the point of entry to the hospital. The service’s other strengths include rapid access, data collection, and evaluation.
+Such innovative forms of psychiatry liaison services could reduce the cost by early diagnoses and formulating care plans for people with dementia, thus avoiding prolonged admissions and unnecessary readmissions to hospital. The RAID service has influenced other services, such as the Pennine Care NHS Foundation Trust, which established their psychiatry liaison services to provide more support to adult patients presenting at A&E with mental health problems, alcohol misuse, and dementia.
+There are a few limitations in different areas of the published RAID model data that may represent a missed opportunity. The RAID service showed improved overall outcomes, better health care at lower cost, and enhanced quality of care provided to patients with dementia. The cost per quality-adjusted life-year was overall negative but was not evaluated in people with dementia or specifically published as part of this RAID evaluation. Given the focus on reduction of antipsychotic prescribing in the elderly27 in the United Kingdom, it is disappointing that there are no published data available from the recent RAID evaluation.25
+The RAID evaluation showed that 90% of total benefits, in terms of reduced bed use, were related to older people, who formed one-third of the referrals.20 There was no subanalysis of the prevalence of dementia in older people, or comment on how dementia diagnoses related to outcome. The severity of and prevalence of delirium were not evidenced in the published data.
+Staff education and training have addressed the dearth of dementia training in medical nurses. Various factors such as a favorable patient-to-nurse ratio, work environment, nurse’s education, and communication skills training improve patient’s quality of life and their satisfaction with health care professionals.45,46 The RAID service model aimed to provide timely staff education and training, but no specific data on staff competence, satisfaction, or feedback were available. The effect of staff training, including reduction in stress level; reduced complaints; and fewer aggressive incidences toward staff, might have been helpful indicators. The multidisciplinary staff were said to feel confident in dealing with people with dementia, but a formal evaluation of the outcome of staff training was lacking.
+The RAID model has shown success in a city hospital, but the generalizability of its effectiveness in different circumstances (eg, rural areas) and a widely distributed population is questionable. Its multidisciplinary base creates several difficulties: It may produce management barriers with a risk of destabilization if an element of service is withdrawn; financially, it is a challenging resource; and data capture
+and analysis are important if its cost-effectiveness is to be evidenced adequately. It represents a complex organizational task, and the choice and strength of the clinical lead are of prime importance.
+Remodeling the basic general hospital care and geriatric services to develop dementia nursing care plans, dementia nurse champions, memory assessment clinics, and regular audit are the key to meeting the needs of increasing numbers of people with dementia. The development of integrated dementia crisis support and intermediate care services provided by geriatricians would support people with dementia in the community. However, there still remains the wider challenges of managing the two thirds of patients in care homes who have dementia with complex needs, providing care home staff training, and improving end-of-life care for people with dementia.
+Conclusion
+Liaison psychiatry service models have been widely published, but the interpretation of their outcomes has its limitations because of service variability in terms of age, regions, accessibility, and resources. The Royal College of Psychiatrists explains that each model should be carefully considered in light of local factors: one size does not fit all, and there are scarce data from the United Kingdom comparing different models.11 The RAID service has shown an effective, enhanced service model for older people who are at risk for dementia and has shown good outcomes with quality improvements in the care of older people. The development of a rapid response and comprehensive psychiatric team integrated in an acute hospital can lead to significant savings in health service provision. Similar services worldwide could improve dementia care both in community and acute hospitals and open new areas of research and development. A multicentered, randomized controlled trial of psychiatry liaison models to measure their effect on improved quality of life, independent living, and mortality may help dementia care.
+Disclosure
+All authors declare no conflict of interest.
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+1.	Introduction
+Suicides are currently a major public health threat and increased understanding of risk factors is important. Suicidality (e.g. suicidal thoughts, suicidal self-harm and suicide attempts) is one of the most important risk factors for completed suicides (Christiansen & Jensen, 2007; Kim et al., 2018). The lifetime prevalence of suicidality in the general population has been shown to be 9% for suicide ideation, 3% for suicide planning and 3% for suicide attempts (Nock et al., 2008). Non-suicidal self-harm is generally not considered as suicidal behaviour, although a strong relationship between selfharm and suicide has been shown (Hawton, Zahl, & Weatherall, 2003; Zahl & Hawton, 2004). Studies have demonstrated a lifetime prevalence for self-harm of 6-24% in the general population, varying between different study groups and definitions of self-harm (Cipriano, Cella, & Cotrufo, 2017; Klonsky, 2011). Even though some risk factors for suicidality are known (e.g. young age, female gender) (Nock et al., 2008; Zalsman et al., 2016), the interaction among social, psychological and behavioural risk factors is complex. Mental disorders are, for example, known to be among the strongest predictors of suicidal behaviour (Harris & Barraclough, 1997; Nock, Hwang, Sampson, & Kessler, 2010). Yet, a large cross-national analysis from the World Health Organization (WHO) world mental health surveys (n = 108,664) found that only close to half of individuals who reported having had serious suicidal thoughts actually reported a previous psychiatric disorder (Nock et al., 2009). For effective prevention of suicidality and suicide risk, this highlights the need to understand more about other risk factors, such as exposure to traumatic events.
+A majority (60-90%) of individuals will experience a traumatic event in their lifetime (Kessler et al., 2017; Kilpatrick et al., 2013; Thordardottir et al., 2015). While most individuals adjust to the trauma and recover from the emotional strain that follows, it remains unexplained why some suffer more than others and experience mental health decline, even to the point of suicidal risk . A minority may experience post-traumatic stress disorder (PTSD) following trauma, which has been linked to suicidality (Ford & Gomez, 2015; Krysinska & Lester, 2010; Panagioti, Gooding, Triantafyllou, & Tarrier, 2015). The risk of PTSD may, however, vary according to trauma event type (Kessler et al., 2017; Ozer, Best, Lipsey, & Weiss, 2003). The risk of suicidality may also vary according to type of traumatic event. For example, a study based on the WHO’s mental health surveys implemented in 21 countries (n = 102,245) and investigating a range of traumatic events and suicidal behaviour (Stein et al., 2010) found that the strongest associations were found for violence-related events. In addition, previous studies have shown increased risk of suicidal behaviour subsequent to adverse and traumatic life events during childhood (Afifi et al., 2016; Bruffaerts et al., 2010), for both suicidal ideation (Stansfeld et al., 2017) and suicide attempts (Dube et al., 2001; Enns et al., 2006; Ford & Gomez, 2015). Furthermore, studies have found that non-interpersonal events such as the loss of a loved one can increase the risk of self-injury (Bylund Grenklo et al., 2013), suicide attempts and suicides (Jakobsen & Christiansen, 2011; Niederkrotenthaler, Floderus, Alexanderson, Rasmussen, & Mittendorfer-Rutz, 2012). Knowledge on how various types of traumatic event may predict suicidality (Yoo et al., 2018) is, however, still scarce, especially with regard to gender.
+Studies have shown that men are more likely than women to experience various types of trauma, except for sexual and violent trauma (de Vries & Olff, 2009; Tolin & Foa, 2006). Women are, however, more likely to engage in self-harm and suicide attempts than men (Nock et al., 2008; World Health Organization, 2014).
+The knowledge on trauma event exposure is limited in Iceland and, to our knowledge, no study has studied its association with suicidality. With the overall aim of enhancing current understanding of suicidal behaviour, the objective of this study was to increase knowledge on the association of traumatic life events and suicidality, focusing on type of event and gender.
+2.	Methods
+2.1.	Study design and population
+With the principal aim of significantly advancing current understanding of the effects of stress, lifestyle and inheritance on health, the Stress And Gene Analysis (SAGA) cohort study was launched with a pilot phase in February to April 2014. We invited 1640 individuals, aged 20-69 years, to participate in the pilot study. Women were invited through the cancer screening programme at the Icelandic Cancer Society (ICS), where the majority of all women accept a screening invitation whether or not they have a history or increased risk of cancer. A sample of women who had accepted a screening invitation and were attending regular breast and cervical cancer screening at the ICS were invited to participate in the study (n = 742). For men, we invited a random sample from the Icelandic population registry living in the area of the capital, Reykjavik, to participate (n = 898). Apart from the method of invitation, the enrolment procedure was the same for both genders. Participants received an invitation letter containing information about the questionnaire and study details. The invitation letter was followed by a telephone call from a professional working at the study centre, introducing the study aims and procedure and offering further information. All participants received a secure link to the questionnaire via e-mail.
+2.2.	Measurements
+2.2.1.	Stressful life events
+We evaluated stressful and traumatic life events with the assessment instrument Life Stressor Checklist -Revised (LSC-R) (Wolfe, Kimerling, Brown, Chrestman, & Levin, 1996). This 30-item questionnaire covers various types of life stressor such as loss of significant others, exposure to natural disasters, accidents, and interpersonal, physical or sexual assaults. We used the Diagnostic and Statistical
+Manual of Mental Disorders, Fifth Edition (DSM-5) definition of trauma-related disorders to evaluate events as traumatic (where trauma is defined as direct exposure to actual or threatened death, serious injury and/or sexual violence, witnessing these events happening to others, learning that they happened to a loved one, or repeatedly being exposed to details of such events) (American Psychiatric Association, 2018). In total, 11 types of event from the LSC-R were classified as traumatic, which we subcategorized into: (1) all traumatic events, classified into (A) noninterpersonal traumatic events and (B) interpersonal traumatic events. We further divided the interpersonal traumatic events into (B1) childhood trauma and (B2) sexual trauma (see Table 3 footnotes).
+2.2.2.	Assessment of suicidality
+For the outcome measurement, we asked participants about current suicidal thoughts, as well as lifetime history of suicidal thoughts, self-harm, suicide planning and suicide attempts. The question on current suicidal thoughts came from a validated depression questionnaire, the Patient Health Questionnaire (PHQ-9), while questions on suicide planning, selfharm and suicide attempts were single-item questions (see Appendix for detailed prescription). We combined all suicidal outcomes as one outcome of lifetime suicidality (present suicidal thoughts, lifetime suicidal thoughts/planning and suicide attempts) and included self-harm with suicidal intent in that measure of suicidality.
+2.2.3.	Other measures
+We asked whether participants had experienced a 2 week depressive period in their lifetime, and whether they had a history of psychiatric morbidity such as depression or PTSD (see Appendix).
+2.2.4.	Sociodemographic factors
+The SAGA questionnaire included questions on participants’ gender, age, education, place of residence, marital status, employment and social support (Loucks, Berkman, Gruenewald, & Seeman, 2006). Before conducting the analyses, we divided age into four categories: 20-35 years, 36-45 years, 46-55 years, and 56 years and older. We categorized educational level into: basic (elementary), middle (high school), university education (completed) and other/not stated; and divided residence by postal codes into habitation in the centre of Reykjavik, suburbs of Reykjavik and other municipalities surrounding the capital area. Marital status was divided into: married/cohabiting, in a relationship, single, widow/widower and not stated. We categorized employment status as: employed (including being a student and being on parental
+leave), unemployed, disabled/on sick leave, retired and not stated.
+2.3.	Statistical analysis
+We used descriptive statistics to evaluate the demographic background of the participants, using the chi-squared test to evaluate the differences between the groups with and without a history of trauma. We calculated the prevalence for suicidal thoughts, suicidal self-harm, suicide planning and suicide attempts, and evaluated the prevalence for each characteristic category. We calculated the prevalence for the classified groups of traumatic life events, and to evaluate the risk of lifetime suicidality we used Poisson regression for each group with a comparison group experiencing no trauma (or non-equivalent trauma type), overall and by gender. With the same measures, we conducted a sensitivity analysis to evaluate the risk of current suicidality. We performed all statistical analyses with the R statistical program (R Core Team, 2013).
+The study was approved by the National Bioethics Committee in Iceland (reference: VSNb2013010025/ 03.7) and announced to the Data Protection Authorities in Iceland.
+3.	Results
+Individuals who had a listed address and telephone number and spoke Icelandic (n = 1398, 689 women and 709 men) met the inclusion criteria, and out of these, 1038/1398 (74%) started answering the SAGA cohort study questionnaire. We excluded individuals who did not answer the question on gender and those who did not complete the questionnaire, leaving 922 participants (66%). Slightly over half of the participants were female (56%). The total response rate was 58% for men (403/689) and 73% for women (519/ 709). Female participants had similar educational levels, employment and marital status to women in the general population (Statistics Iceland, 2018). The mean age was 52.6 years for females in the study and 45.6 years for males.
+Characteristics of the total study population are listed in Table 1. Characteristics are also listed by whether or not participants had experienced trauma. A vast majority (667/872, 76%) had experienced a traumatic event in their lifetime. Participants with no history of trauma (205/872, 23%) had a lower prevalence of psychological morbidity than the group with trauma history (16% vs 26%, p < 0.05), as well as a lower prevalence of having experienced a 2 week depressive period in their lifetime (27% vs 45%, p < 0.05) or a period of loss of interest (26% vs 37%, p < 0.05) (Table 1).
+3.1.	Mental disorders and gender
+Sixteen per cent of participants reported having had a depressive disorder during their lifetime. Women were more likely to report having had a depressive disorder compared to men (18% vs 13%, p = 0.02), and more likely to have experienced 2 week periods of depressive symptoms (women 46% vs men 33%, p = 0.0002) and a period of loss of interest (women 39% vs men 30%, p = 0.001). Among those who had a history of trauma, the difference between the genders was similar; men had a lower prevalence of previous depression compared to women (15% vs 22%, p = 0.03), as well as a lower prevalence of experiencing a 2 week depressive period (38% vs women 53%, p = 0.009) and a period of loss of interest (35% vs 44%, p = 0.01).
+3.2.	Suicidality and gender
+Out of 893 individuals answering the question on present suicidal thoughts, 44 (5%) reported having current thoughts. As shown in Figure 1, the prevalence of current suicidal thoughts was not higher among men than women (6% vs 4%, p = 0.47), while a lifetime history of having had serious
+thoughts of dying by suicide was higher among men than women (15% vs 8%, p = 0.001), as was having planned a suicide (8% vs 5%, p = 0.02), but not lifetime deliberate self-harming (1% vs 1%) or having attempted suicide (3% vs 2%, p = 0.42). Table 2 presents the demographics of individuals who reported any suicidality, including current suicidal thoughts, lifetime suicidal thoughts (thought and planning) and suicidal actions (suicidal self-harming or attempting suicide). The overall prevalence for lifetime suicidality was 13% (men 16% and women 11%, p = 0.017). Among those reporting lifetime suicidality, 42% reported a previous mood affective disorder and 36% reported having had PTSD (all women; no men reporting suicidality reported previous PTSD).
+3.3.	Traumatic life events and suicidality
+In total, 76% of participants had experienced an event in their lifetime classified as traumatic, 64% had experienced events classified as non-interpersonal trauma (men 68% and women 61%), 40% interpersonal trauma (men 38% and women 43%), 23% trauma during their childhood (men 17% and women 28%) and 19% sexual trauma (men 11% and women 25%). Table 3 presents the association between having experienced traumatic life events and lifetime suicidality. After adjusting for sociodemographic factors, we found that any traumatic life event increased the overall risk of lifetime suicidality [relative risk (RR) 2.05, 95% confidence interval (95% CI) 1.21-3.75], as did non-interpersonal trauma (RR 2.03, 95% CI 1.153.59). After stratifying by gender, the risk was found to be increased for men (RR 3.14, 95% CI 1.25-7.89 and RR 3.27, 95% CI 1.30-8.25), but not for women (RR 1.45, 95% CI 0.70-2.99 and RR 1.27, 95% CI 0.59-2.70). We furthermore found that the experience of an interpersonal traumatic life event increased the risk of lifetime suicidality for both
+genders, with higher risk for men (RR 4.30, 95% CI 1.68-10.98) than for women (RR 2.25, 95% CI 1.084.70). This further applied to childhood trauma (men RR 7.32, 95% CI 2.77-19.31, and women RR 2.82, 95% CI 1.33-5.99) and sexual trauma (men RR 7.66, 95% CI 2.51-23.51, and women RR 2.48, 95% CI 1.15-5.36).
+4.	Discussion
+In this study, we found an increased risk of lifetime suicidality among individuals reporting lifetime interpersonal, childhood and sexual trauma, with stronger associations observed for men than for women. We furthermore found an association between experience of non-interpersonal trauma and suicidality among men. In addition, we found that while women more frequently reported lifetime depressive periods, men had a higher prevalence of suicidal outcomes.
+4.1.	Traumatic life events and suicidality
+Among those who had experienced interpersonal traumatic life events, we found increased risk of suicidality for both genders. Similarly, studies have found strong associations between interpersonal trauma and suicidality, especially sexual trauma (Stein et al., 2010) and childhood trauma (Afifi et al., 2016; Dube et al., 2001). Among those who had experienced sexual trauma or childhood trauma in our study, we found an association with suicidality in both genders, which was stronger for men.
+For non-interpersonal traumatic events, such as the sudden loss of a loved one and experiencing a natural disaster, we found increased risk for suicidality for men only. Similarly, some studies have indicated elevated risk of suicide for both genders following the loss of a loved one, although this was significantly higher for men (Li, 1995; Luoma & Pearson, 2002). Other studies have furthermore indicated that men may be at more risk of suicidal behaviour associated with natural disasters (Chou et al., 2003; Vehid, Alyanak, & Eksi, 2006). To minimize the risk of suicidality, preventive measures aiming at psychological health after traumatic societal events as well as personal trauma may be beneficial, especially for men.
+4.2.	Gender and suicidality
+The total prevalence of any lifetime suicidality was 13% in our study, which largely matches previous research, indicating a lifetime suicidality prevalence of 13-20% in a general population (De Leo, Cerin, Spathonis, & Burgis, 2005; Kessler, Borges, & Walters, 1999; Nock et al., 2008). The observed higher prevalence of suicidality among men than women (men 16% vs women 11%, p = 0.02)
+is, however, unusual. Despite this difference in suicidality, women in our study had a higher prevalence of reported lifetime depressive symptoms and PTSD. The underlying mechanisms for these unexpected findings of higher risk of suicidality but not depressive symptoms in association with exposure to trauma among men are probably multifactorial. First, it has been suggested that traditional diagnostic criteria for depressive symptoms may not detect men’s depression (Martin, Neighbors, & Griffith, 2013), leaving untreated and/or unreported symptoms more likely to develop to suicidality. Secondly, men may find it more difficult, and find different ways, to regulate their emotional feelings than women (Beautrais, 2002; Nolen-Hoeksema, 2012). Furthermore, they seem less likely to seek help for mental health problems after trauma (Moller-Leimkuhler, 2002), which may leave untreated symptoms more likely to develop to suicidality. Thirdly, following trauma, women are more likely than men to meet criteria for PTSD (Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995; Tolin & Foa, 2006). PTSD has frequently been reported to be associated with suicidality and may be an important mediator in further development of suicidality after trauma (Ford & Gomez, 2015; Panagioti et al., 2015; Wilcox, Storr, & Breslau, 2009). In our study, among individuals reporting suicidality, only women reported having been diagnosed with PTSD in their lifetime. The reasons for gender differences in PTSD development are unclear. If men are more reluctant to express their emotional feelings following trauma, they may possibly be less likely to be diagnosed with PTSD and, in turn, less likely to receive help. Our results of suicidality risk associated with non-interpersonal trauma (such as natural disaster), only for men, may be due to higher risk of PTSD among men after such trauma. A study by Arnberg et al. (2015), for example, found increased risk of PTSD in individuals exposed to the 2004 South-East Asian tsunami compared to unexposed individuals, and that the risk was higher for male survivors [hazardratio (HR) 11.5,95% CI 6.77-19.47] thanfor female survivors (HR 6.30, 95% CI 4.25-9.34). In addition, a study on stressful and traumatic life events found that men had higher levels of PTSD after stressful life events than traumatic events, while women had similar levels of PTSD for both type of events (van den Berg, Tollenaar, Spinhoven, Penninx, & Elzinga, 2017).
+If men are more reluctant to acknowledge psychiatric morbidity and seek help, it may result in unrecognized PTSD and psychological morbidities, possibly affecting more serious psychological outcomes for men, such as suicidality. If so, this emphasizes the clinical importance of focusing on adequate psychological follow-up after traumatic events and even screening for trauma history among individuals with psychological morbidities, with a special awareness of the importance of reaching both men and women.
+4.3.	Strengths and limitations
+A strength of our study is that it is based on a sample with a relatively high participation rate (66%). In the questionnaire, we used a validated checklist on exposure measurement (LSC-R), using the newest DSM-5 diagnostic codes as a guideline to evaluate the type of traumatic event. Having questions on psychological morbidity after receiving questions on lifetime trauma may lead to differential misclassification when comparing participants with a history of traumatic events to participants with no such history (Hauksdottir, Steineck, Furst, & Valdimarsdottir, 2006). To avoid this potential bias, we placed questions on psychological morbidity and suicidal behaviour earlier in the questionnaire.
+Some limitations should be noted; for example, owing to the cross-sectional design of the study, we cannot conclude whether the exposure (specific life event) occurred before suicidality. However, when evaluating the association for traumatic events and restricting the outcome measures for current suicidality only, we found similar significant results. We have no information on those who did not participate in the study or did not complete the questionnaire, and it is possible that such selection affects our observed point estimates. Furthermore, even though the question on current suicidal thoughts is a part of the validated questionnaire PHQ-9, we do not have validated or standardized measurements on self-harm and suicide attempts, which limits our generalization and comparison to other studies. Regarding gender differences, all female participants in the study were women who were already attending a cancer screening clinic, while men were a random population sample. On the one hand, women who have experienced serious trauma, especially sexual trauma, may be more reluctant to attend such a screening programme, and therefore not participate in our study, but on the other hand, women who have experienced trauma in their lifetime may be more likely to seek medical care, especially those with psychiatric disorders. We may therefore possibly have an oversampling of women with traumatic life exposure except for sexual trauma. This may limit the generalizability of findings for women. In addition, the findings may underestimate the prevalence of self-harm with suicidal intent since only individuals answering ‘yes’ on lifetime depressive symptoms received questions on self-harm (see Appendix). This may be true especially for men, who may be more reluctant than women to report depressive symptoms. The use of retrospective self-reported measures of lifetime trauma and suicidal behaviour is one of the study’s limitations raising the risk of recall bias, especially with older age and longer time passed since the traumatic event. The main results did, however, not change significantly after
+we restricted the outcome measurement to current suicidality. This source of error would be non-differ-ential with respect to suicidality status. In this regard, the mean age was higher for women in our study, which may further explain our gender-specific result. Yet, adjustment for age, education and other sociodemographic factors did not considerably affect the main results on the relationship between trauma and suicidality, for either gender.
+5.	Conclusion
+This study emphasizes the importance of interpersonal trauma as a major risk factor of suicidality and further indicates that trauma, especially non-interpersonal trauma, may be likely to be associated with suicidality among men. To reduce the risk of suicidal thoughts or behaviours, it may thus be beneficial for clinicians to routinely assess trauma history among patients seeking care for psychological problems but also to implicate preventive measures in society in relation to traumatic events.
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+EDITORIAL
+ASTHMA
+Introduction
+In Finland (population 5,5 million), a 10-year national campaign to treat allergic diseases was initiated in 2008. It was carefully planned and based on consensus among experts [1-3] because the long-term strategy of allergen avoidance had not reduced the burden or stopped the “epidemic”. New insights into immune development in modern, urban societies have challenged conventional thinking. A public health programme has now been implemented, and an avoidance strategy was replaced with a tolerance strategy [4]. This course of action was supported by the Ministry of Social Affairs and Health and the National Institute for Health and Welfare.
+The burden and the epidemic
+In the early 1990s, asthma was recognised as an inflammatory condition with variable airflow limitation [5, 6]. The new paradigm emphasised first-line anti-inflammatory treatment [7], which was implemented in practice by the Finnish Asthma Programme 1994-2004. Step 1 involved cutting the disease burden for
+individual patients as well as for society by improving early diagnostics and medication [8-10]. The programme served as a model for others to follow [11, 12].
+However, the main question remained: how to stop the epidemic? The asthma programme reduced the burden markedly but had no effect on the increased prevalence. Clearly, the true reasons for the increased incidence of allergy and asthma should be better understood in order to move from treatment to prevention. The Karelia Allergy Study, among others, provided some answers [13]. The contrast of prevalence was striking both among children and adults living in Finnish and more rural and traditional Russian Karelia. It seemed likely that the reasons for allergy increase were not so much the new risk factors, characteristic of a modern environment and lifestyle, but the loss of protective factors.
+Non-communicable diseases (NCDs), which include allergy, have been increasing everywhere in urban environments. The human immune system seems to be experiencing an adaptation crisis which does not comply with the fast-changing lifestyle and living conditions. Effective gene-environment interaction is the key issue in tolerance development [14]. Reduced connection with natural environments and, for example, the increased use of processed food may have impoverished human microbiota (dysbiosis), caused immune dysfunction ( poor tolerance) and led to inappropriate inflammatory responses. The manifested clinical disease is then largely dependent on individual genetic architecture.
+Again, there was a place for a paradigm change. The Finnish Allergy Programme 2008-2018 was introduced to test new thinking in practice, and regarded the improvement in allergy health, including asthma health, as step 2.
+Programme testing and implementation
+The key messages of the programme are shown in Box 1. They targeted the general population, patients with allergies and asthma and their families, and public-health and patient organisations, as well as experts and authorities. The more specific goals and indicators for healthcare professionals were quantitative, such as that allergy diets should drop by 50% and asthma emergency visits by 40% within 10 years. Each of the six goals had specific tasks, tools and evaluation methods [1].
+The relevance and acceptance of the messages were tested by email in 2008 among 744 asthma contact persons (response rate 71%; 38% were doctors, 62% nurses, 77% working in primary care) [15]. The messages were well received. For example, GPs scored strengthen tolerance as 9.1 on a scale from 4 to 10. However, allergy-management practice left much room for improvement, e.g. the availability of specific immunotherapy was poor (score 5.4).
+The expert non-governmental organisation (NGO), the Finnish Lung Health Association (Filha), was responsible for the education of healthcare professionals (doctors, nurses, pharmacists, care-givers). The key issue was to improve allergen tolerance, and simple guidance was provided (table 1). Between 2008 and 2016, more than 20000 professionals participated in the various learning activities. The lay-public has been targeted by three NGOs for: 1) allergy and asthma, 2) respiratory health, and 3) skin disorders. Patient organisations arranged regional education for their key personnel and peer workers; this had a major impact upon direct patient counselling and distribution of educational materials.
+Education continued also for the personnel of pharmacies, day-care centres, and schools. From 2009 to 2012, the Association of Finnish Pharmacies produced material and ran campaigns for allergic rhinitis and atopic eczema. The Association of Kindergarten Teachers in Finland planned a pilot campaign called “Go to nature!” for 2014-2015 in southern Karelia, incorporating various outdoor activities into the day-care routine. New guidelines for early childhood education are to be introduced throughout the country.
+Contact-person network
+Finland has 21 hospital districts ( five university hospitals). Primary healthcare services are provided by about 250 primary care centres, including at least three times as many maternity and child health clinics and approximately 1000 units offering occupational health services, one-third of which are private.
+BOX 1 The Finnish Allergy Programme 2008-2018 messages for both healthcare professionals and lay-public; allergy health is promoted
+Key messages
+Endorse health, not allergy
+Strengthen tolerance
+Adopt a new attitude to allergy, and avoid allergens only if mandatory
+Recognise and treat severe allergies early; prevent exacerbations
+Improve air quality; stop smoking
+TABLE 1 Practical advice for building and improving tolerance as well as preventing symptoms and exacerbations
+Primary prevention
+Support breastfeeding, with solid foods from 4-6 months onwards
+Do not avoid exposure to environmental allergens (foods, pets), if not proven necessary
+Strengthen immunity by increasing contact with natural environments (e.g. by taking regular physical exercise and following a healthy diet such as a traditional Mediterranean or Baltic diet)
+Antibiotics should only be used in cases of true need (the majority of microbes are useful and build a healthy immune function)
+Probiotic bacteria in fermented food or other preparations may balance the immune function
+Do not smoke (parental smoking increases the risk of asthma in children)
+Secondary and tertiary prevention
+Regular physical exercise is anti-inflammatory
+Healthy diets are anti-inflammatory (a traditional Mediterranean or Baltic diet may improve asthma control)
+Probiotic bacteria in fermented food or other preparations may be anti-inflammatory
+Respiratory/skin inflammation should be treated early and effectively; maintenance treatment titrated for long-term control
+To stop symptom exacerbations proactively, instructions for guided self-management are provided for
+10 allergic conditions (available in both paper and electronic formats)
+Allergen-specific immunotherapy is recommended for more severe symptoms, e.g:
+allergens as such (for foods)
+sublingual tablets or drops (sublingual immunotherapy, or SLIT) (for pollens)
+subcutaneous injections (for pollens, pets, mites, insect stings)
+Smoking should be strictly avoided (the effectiveness of asthma and allergy drugs is reduced in smokers)
+The educational action plan took advantage of the contact-person network created early in the 1994-2004 asthma programme. At baseline, in 2008, about 1500 appointed asthma contact persons (doctors, nurses, pharmacists) were employed in the healthcare sector, originally recruited for the asthma programme. This network was reactivated and strengthened for the allergy campaign. The network has been complemented by some 200 nurses in maternity and child health clinics as well as in schools. Fourteen regional expert allergy groups have begun to coordinate the local implementation of new recommendations via educational activities.
+Measuring outcomes
+For outcome evaluation, the Finnish healthcare registers provided invaluable data sources - in particular, the hospital admission register of the National Institute for Health and Welfare and the drug reimbursement register of the Social Insurance Institution of Finland. For occupational diseases, Finland has strict legislation, and verified cases are registered by the Finnish Institute of Occupational Health.
+The Finnish anaphylaxis register was established in 2000 at the Skin and Allergy Hospital of the Helsinki University Hospital [16]. Physicians (mostly allergists) from the whole country voluntarily report cases of severe allergic reactions independent of a causative agent. A one-page questionnaire for medical professionals is available on the internet.
+Allergy and asthma costs in 2013 were analysed from all data sources in collaboration with government officials.
+Are we reaching the goals?
+The mid-term outcome is summarised in table 2. The burden of allergy and asthma has been further reduced since the beginning of the asthma programme [17]. In the 2000s, asthma emergency visits decreased by 46% (children 62%) and hospital days by 67%. According to a country-wide pharmacy barometer survey, self-reported asthma has become a milder disease or a better controlled disease [18]. At the start of the Finnish asthma programme in 1994, it was estimated that 20% of the patients had a severe (or uncontrolled) condition. This figure decreased to 10% in 2001 and to 4% in 2010. In 2016 this figure was 2.5% (P. Kauppi, J. Jantunen; unpublished, personal communication). Compared with the 2001 and 2010 cohorts, emergency visits during the previous year had dropped by 86% and hospitalisations by 88%. Asthma mortality has been very low: in 2006-2013, on average seven annual deaths (range 3-12) under the age of 60 years.
+TABLE 2 Six programme goals, indicators and main outcomes at 5 years
+1)	Prevent allergy
+Indicator: asthma, rhinitis and atopic eczema prevalence reduced by 20% Outcome: no information yet
+2)	Improve tolerance
+Indicator: food allergy diets reduced by 50%
+Outcome: allergy diets in day-care settings -40%
+3)	Improve allergy diagnostics
+Indicator: skin prick testing practised in certified testing centres
+Outcome: 30 hospitals and other centres educated, audited and certified
+4)	Reduce work-related allergies
+Indicator: occupational allergies reduced by 50%
+Outcome: occupational allergies reduced by 40%
+5)	Focus on severe allergies and treat in time
+Indicator: effective allergy practice; asthma emergency visits reduced by 40% Outcome: emergency visits -46%; asthma hospital days -67%
+6)	Reduce allergy and asthma costs
+Indicator: allergy costs reduced by 20%
+Outcome: total costs in the 2000s -15%; in 2007-2013 -5%
+In 13 years (2000-2013), the number of emergency visits caused by anaphylaxis almost doubled from 297 to 582 cases. In children (1999-2011), hospitalisations due to allergic reactions increased (from 7.8 to 15.8 per 100000 person-years), but the numbers were somewhat lower than those for Sweden [19]. The anaphylaxis campaign has improved awareness and explains much of the increase. Only a few anaphylaxis-related deaths per year occurred in 1996-2013, and no deaths occurred in children [20].
+In the Helsinki capital region, 40 Finnish day-care centres were educated to follow simple pragmatic allergy guidelines [21]. In 2013-2015, the prevalence of allergy diets decreased 43%, from 7.6% to 4.3%. Parent-reported allergies to nuts, fruits and vegetables decreased among first graders in the first year of elementary schools [22].
+In 2007-2013, verified occupational allergies (asthma, allergic rhinitis, allergic contact dermatitis) fell by 40%, as registered by the National Institute of Occupational Health. The reduction could not be explained by changes in the workforce.
+In the first decade of the 2000s, the direct allergy and asthma costs, together with costs for disability pensions, fell by15% [17]. During the early years of the programme (2007-2013), these costs have fallen from €386 million to 367 million (-5%). Asthma costs decreased by 9% and comprised 63% of all costs.
+Conclusions
+There are no reports of nationwide, comprehensive public health programmes for allergic disorders that include set goals and systematic follow-up. The mid-term results of the ongoing Finnish campaign indicate that the burden of allergic conditions in that particular society has started to decline. Focusing on severe allergies and emphasising allergy health rather than mild problems has encouraged a more efficient use of healthcare resources. It also seems that prevalences are levelling off. However, it is too early to give credit to the programme for any biological changes in the population.
+The outcome of the present real-life intervention, including all Finnish citizens, without effective controls is also open to criticism. This should not, however, prevent medical communities from taking reasonable action to improve public health, in this case lessening the disability and costs resulting from allergy and asthma. Inadequate allergy care seems to be a global problem and leads to delays in patient management and poor outcomes [23].
+Revisiting the asthma and allergy paradigms has led to actions relevant to society and healthcare as a whole.
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+terms of standardised mortality rates and mortality rate ratios, but other measures can be used, such as potential years of life lost,7 average age at death, and life expectancy. As mortality rates in people with mental illness vary with time since onset of the disorder and age of onset, one disadvantage of using mortality rate ratios is that the composition of the cohort studied and the follow-up time can affect the outcome.8 Life expectancy can be a useful alternative. Because it is calculated by cumulating across all ages, life expectancy can reflect changes in mortality rates across ages. It also expresses the results in a metric that is intuitively easy to understand. Life expectancy is most commonly used to describe the mortality rates of geographically defined populations, but the technique has also been used for populations defined by demographic characteristics or diagnosis.
+Of the few studies of life expectancy in people with mental illness, some have been restricted to inpatients and others to people with severe mental illnesses, such as schizophrenia and bipolar disorder. One study reported a reduction in life expectancy of 14 years for males and six years for females treated by the Massachusetts Department of Mental Health.9 Another study reported a reduced life expectancy in nine diagnostic groups from patients in contact with Swedish psychiatric clinics.10 More recently several reports on life expectancy in Nordic countries have been published, with the life expectancy of psychiatric patients reduced by 20 years in males and by 15 years in females compared with the general population.11 12 A group of patients with severe mental illness from a secondary care case register in London were found to have a reduced life expectancy of between eight and 15 years for males and 10 and 18 years for females.13 These were generally cross sectional studies and little is known about whether these benefits have extended to those with mental illness
+Introduction
+The excess mortality associated with mental illness has been extensively documented.12 Much of the attention has focused on the increased risks of suicide,3 even though most of the risk of excess mortality is due to physical health illnesses, such as cardiovascular disease, respiratory disease, and cancer.4-6 Excess mortality in people with mental illness is generally reported in
+and whether the life expectancy gap between people with mental illness and the general population has changed over time. Although some research has shown that the mortality rate for people with schizophrenia has increased in the past three decades,1415 only one study has examined this relation longitudinally using measures of life expectancy.12 This study showed only a modest narrowing of the life expectancy gap in Denmark, Finland, and Sweden, countries with arguably some of the best and most equitably distributed healthcare in the world.16
+We examined the mortality experience of psychiatric patients in Western Australia compared with the general population. Using a population based register of contacts with mental health services (including inpatient, outpatient, and community care based clients), we calculated trends in life expectancy among psychiatric patients compared with the total Western Australian population. We also examined causes of excess mortality in psychiatric patients and calculated the contribution of major causes of death to excess mortality rates, including cancer, heart disease, respiratory disease, and unnatural causes of death.
+Methods
+We extracted the data for this study from population wide databases covering the state of Western Australia. This state is well suited to population based record linkage studies because of its relative geographical isolation. The population of Western Australia increased from 1.4 million in 1985 to 2.02 million in 2005.17
+Data sources
+Mental health information system
+The mental health information system records contacts with mental health services in Western Australia. This database started as a register of patients in psychiatric hospitals in 1966, and its scope was expanded in the 1970s to include all other hospitals and community mental health services. Since 1980 it has covered all inpatient admissions to private or public hospitals in Western Australia where a diagnosis of mental disorder or self harm has been made or where contact has occurred with a specialist psychiatric service, along with all outpatient and community based contacts with public mental health clinics.5 The system records basic personal data about each patient, including date of birth and sex, and service use data including dates of admission and discharge, periods of leave, and primary diagnoses. The register is comprehensive in its coverage of contacts with these services in Western Australia18; a validation study of selected diagnoses in the register found a high sensitivity and specificity for schizophrenia and affective psychosis diagnoses.19
+Death registrations
+All deaths occurring in Western Australia are registered by the registrar general. Cause of death is coded by the Australian Bureau of Statistics based on information provided on the death certificate or by the coroner’s determination of cause of death. Cause of death was coded using ICD-9 (international classification of diseases, ninth revision)20 until 1998 and then ICD-1021 from 1 January 1999.
+Record linkage
+The mental health information system and death registrations in Western Australia have been linked using probabilistic record linkage techniques as part of the Western Australian data linkage
+system.20 Because there are no unique identification numbers common to both datasets, probabilistic matching is undertaken using name, residential address, date of birth, and sex as the principal matching fields. Probabilistic linkage allows for the possibility of errors or changes in the identifying information used for matching. Once record linkage has been undertaken, deidentified linked files are provided to researchers and all analysis is undertaken on anonymised data. The proportion of invalid and missed links using this method has been estimated at0.11%.22
+Measures
+Principal psychiatric diagnosis
+The mental health information system identifies mental disorders using codes: ICD-8 (international classification of diseases, eighth revision),23 ICD-9-CM (Australian version of the international classification of diseases, ninth revision, clinical modification)24 or ICD-10-AM (international statistical classification of diseases and related health problems, 10th revision, Australian modification).25 ICD-9 was introduced in 1979, followed by ICD-10-AM in 2000. We identified hospital admissions and contacts with mental health clinics for mental disorders if an ICD-8 or ICD-9 chapter 5 diagnosis or ICD-10 chapter F diagnosis was made. For the purposes of this study, we excluded patients with all types of dementia, because of the typically older age of onset of this disorder. On the mental health information system, a separate diagnosis is recorded for each episode of care. Where patients have had multiple episodes of care, more than one principal diagnosis may have been assigned over the course of those admissions. We assigned a principal psychiatric diagnosis by using the most recent diagnosis subject to a hierarchy that gave preference to an earlier diagnosis if the later one was less informative or likely to refer to a comorbidity. Full details of this method are described elsewhere.5 Briefly, if the last episode of care recorded a diagnosis of alcohol dependence but a previous episode recorded schizophrenia, then we would assign a diagnosis of schizophrenia. This method was designed to give precedence to more severe disorders and to allow for certain conditions, such as substance dependence, to be considered as a potential comorbidity. We then grouped diagnoses into eight categories: alcohol or drug disorders, schizophrenia, affective psychoses, other psychoses, neurotic disorders, stress or adjustment reaction, depressive disorders, and other mental disorders. At this level, more than 70% of people on the mental health information system had only one diagnosis recorded.
+Major causes of death
+We coded deaths according to both ICD-9 and ICD-10. To ensure maximum comparability over time, we identified major causes of death using comparable ICD-9 and ICD-10 codes as recommended by the US Centers for Disease Control.26 The supplementary table shows the ICD-9 and ICD-10 codes used to define each major cause of death. Cause of death is coded by the Australian Bureau of Statistics. Where there is more than one contributing cause of death, the disease or injury that initiated the train of morbid events directly leading to death is coded as the main cause of death.27 Accidental and violent deaths are classified according to the external cause of death. Suicide is initially determined by coronial verdict. Where the coroner is unable to make a finding as to intent because of the high legislative standard for determining this or because of cultural or religious sensitivities, the Australian Bureau of Statistics undertakes further investigation of the death using information
+No commercial reuse: See rights and reprints http://www.bmj.com/permissions
+Subscribe: http://www.bmj.com/subscribe
+on the national coronial information system and codes the death as a suicide where there is evidence of intent. Deaths where intent cannot be established are coded as other accident or injury.28
+Statistical analysis
+Life expectancy
+We calculated life expectancy at birth using the abridged life tables method as previously described.29 The concept of life expectancy in any given year is based on what would be expected to happen to a hypothetical cohort if each individual was subjected throughout his or her life to the same age-sex specific mortality rates that were observed during that year. As such life expectancy does not refer to any specific individual, as mortality rates change over time.
+The abridged life tables method uses mortality rates in a specific cohort within five year age groups to estimate life expectancy. This method is used by the UK Office for National Statistics to estimate life expectancy for regional areas owing to its suitability for smaller cohorts.30 31 To smooth out variability from smaller cohort sizes, we used deaths within five year periods rather than single year figures to calculate age specific mortality rates. For example, the life expectancy estimate for 1985 was based on deaths occurring in 1983-87, the estimate for 1986 was based on deaths occurring in 1984-88, and so on. As a result, although data were available from 1983 to 2007, the results presented in this study cover the period 1985 to 2005. Because of the small numbers of psychiatric patients under the age of 15 years, we assumed that the mortality rate in psychiatric patients before age 15 was equal to the mortality rate in the general population for that age group.
+Mortality rates within five year age groups formed the basis on which we constructed abridged life tables. We calculate these mortality rates by dividing the number of deaths within the cohort within the appropriate age group and year span, by the total number of person years contributed by people in the cohort by age group and year range. For the eight disorder groups we calculated mortality rates and life expectancy at birth separately for males and females.
+We compared life expectancy in the cohort of psychiatric patients with life expectancy at birth for the total Western Australia population, which is published by the Australian Bureau of Statistics.32 The bureau uses a moving three year average for calculating the mortality rates that underpin the population wide life tables.
+As the mental health information system only started in 1966, and as life expectancy in the general population has increased and the nature of mental health service delivery has expanded with greater emphasis on community based care, the tendency has been for the proportion of the population with a history of contact with mental health services to increase over time. To tackle this problem we defined a cohort of “active” cases on any given date as people with ongoing contact with mental health services or who had contact with services in the five years preceding that date. This gave a more constant basis for observing changes in life expectancy over time. Therefore our primary cohort definition was based on people who had contact with mental health services in the past five years for each reference year. Within each five year window, we calculated person years at risk from date of first contact with mental health services if this date was within the past five years, or from the start of the five year period if first contact with mental health services was before that date.
+Excess mortality by cause of death
+We calculated the expected numbers of deaths in the cohort by major cause of death by using cause specific death rates by age group, sex, and time period for Western Australia obtained from the Australian Bureau of Statistics.33 We applied these rates to the total person years in the cohort of psychiatric patients. We then calculated excess mortality by cause as the difference between the observed number of deaths and the expected number of deaths.
+All analysis was undertaken using SAS software.34
+Results
+Overall, 292 585 people were in contact with mental health services in Western Australia between 1983 and 2007, of whom 47 669 died in the same period. Table 1^ shows the numbers of active cases and deaths in the first and last cohorts included in this study, those of 1983-87 and 2003-07. From the person years contributed by the cohort we calculated the age standardised prevalence of each mental health condition, as defined by contact with mental health services, and we estimated resident population counts for Western Australia from the Australian Bureau of Statistics.17 The active prevalence of disorder (proportion of the population in contact with mental health services in the preceding five years) increased over time for some disorders (fig 1^), in particular affective psychoses and stress or adjustment reactions. The mental health information system is a population based case register of service contacts so these prevalence rates represent the active prevalence of having been treated for a psychiatric condition by mental health services in the previous five years. These increases in active prevalence may reflect changes in diagnostic practices or a greater focus on community based treatment options, which may result in larger proportions of the population coming into contact with services.
+In the general population, life expectancy for males increased from 73.1 years in 1985 to 79.1 years in 2005 and for females from 79.3 years to 83.8 years (table 2^). Among psychiatric patients, males and females with alcohol or drug disorders had the lowest life expectancy in 1985, and the gap in life expectancy exceeded 20 years at each time point. With the exception of females with stress or adjustment reactions, all disorders were associated with a significant gap in life expectancy throughout the study. For all mental disorders combined, the gap in life expectancy for males increased from 13.5 years in 1985 to 15.9 years in 2005 and for females from 10.4 years in 1985 to 12.0 years in 2005.
+In terms of individual diagnoses, the gap increased for both males and females with adjustment reaction, affective and other psychoses, and depression (figures 2^ and 3-U). The largest increases in the life expectancy gap were seen for people with other psychoses (14.8 to 22.7 years for males and 14.1 to 22.6 years for females) and stress or adjustment reactions (7.3 to 13.2 years for males and -0.2 to 9.3 years for females, table 2).
+Overall, most excess deaths were due to physical health conditions, with cardiovascular disease (including stroke) the main cause of 26.2% of excess deaths in male psychiatric patients and 35.3% in female psychiatric patients, and cancer the main cause of 13.6% of excess deaths in males and 13.3% in females (table 3-U). Suicide was the cause of 16.6% of excess deaths in males and 10.1% in females. Other accidents and injuries accounted for another 8.1% of excess deaths in males and 7.0% in females.
+RESEARCH
+Although suicides represented a larger proportion of excess deaths for patients with affective psychoses (46.4% of males and 27.4% of females), stress or adjustment reactions (53.3% of males and 33.5% of females), and other mental disorders (33.4% of males and 14.8% of females), physical conditions represent the majority of excess deaths for all psychiatric disorders. Cardiovascular disease was the main cause of a substantial proportion of excess deaths for all psychiatric conditions, but particularly for schizophrenia (31.8% of males and 46.3% of females), other psychoses (32.5% of males and 40.6% of females), and neurotic disorders (38.3% of males and 37.6% of females).
+Discussion
+Our study shows that the size of the gap in life expectancy for people with psychiatric disorders in Western Australia increased between 1985 and 2005, from 13.5 to 15.9 years for males and from 10.4 to 12.0 years for females. The majority of excess mortality was attributed to physical health conditions, such as cardiovascular disease, respiratory disease, and cancer. Although some studies have shown that the mortality rate has increased over time for people with schizophrenia,1415 only one study, based on data from Denmark, Finland, and Sweden, has examined changes in the life expectancy gap over time for a wider range of psychiatric conditions.12 In contrast with the Nordic study, which showed a slight reduction in the life expectancy gap, we found that the overall gap in life expectancy increased by 2.4 years for males and by 1.6 years for females between 1985 and 2005. Particularly large increases were found for both males and females with stress or adjustment reactions and with other psychoses. Given that the Nordic countries have some of the most comprehensive and equitable healthcare and social welfare in the world, our results may be more typical of the experience in other places. The increased gap is largely driven by increasing life expectancy in the general population rather than a reduction in life expectancy in psychiatric patients. While life expectancy in Western Australia is high among developed countries, the magnitude of increase in life expectancy between 1985 and 2005 in Western Australia is consistent with that seen in the United Kingdom, the United States, and many European countries.35 By examining gaps in life expectancy rather than mortality rates and by revealing that these gaps have increased over time, the results of this study are significant in that they show that outcomes for people with mental illness have worsened since the 1990s despite increasing knowledge about the impacts of such illness.
+The widest gap in life expectancy was seen in people with alcohol and drug disorders, and this gap of more than 20 years was maintained throughout the period of study. As we assigned a principal psychiatric diagnosis for each patient in this study, the category for alcohol and drug disorders did not include patients with another psychiatric diagnosis and a comorbid substance use disorder but only those with a primary diagnosis of a substance use disorder. Substance misuse is a well established risk factor for cardiovascular disease and many cancers, so it is not surprising that the majority of excess mortality was attributable to heart disease, cancer, and liver disease.
+Our findings suggest that the gap in life expectancy between psychiatric patients and the general population is worse than that for other disadvantaged groups. For instance, for lifelong smokers—a population that receives considerable public health attention and intervention—life expectancy is around 10 years less than non-lifelong smokers.36 The gap in life expectancy
+between Indigenous (Aboriginal and Torres Strait Islander) and non-indigenous Australians is approximately 12 years formales and 10 years for females.37 While inequalities in the health experiences of Indigenous Australians havejustifiably attracted substantial public investment,38 little attention has focused on the mortality rates of people with mental illness in Australia, and there are few interventions designed to reduce the morbidity and mortality associated with common physical illnesses in people with mental illness.
+Possible causes of poor health outcomes in people with mental illness
+Apart from the increased risk of suicide with mental illness, most mental illnesses, although debilitating, are not direct causes of death. Traditionally, there has been a view that suicide and unintentional deaths were a major cause of excess mortality in people with mental illness, and the bulk of public efforts to reduce mortality in such people have been directed at suicide prevention.39 Our data show that almost 80% of excess deaths in people with mental illness are due to physical health conditions. Important advances have been made in reducing the rate of mortality from common physical health conditions in the general population, such as heart disease, respiratory disease, and some cancers. It seems as if people with mental illness have not benefitted to the same extent from these advances.12 40
+Excess morbidity or mortality associated with mental illness is recognised as a complex multifactorial problem.40 41 Higher rates of substance use have been well documented in people with mental illness, including tobacco, alcohol, and illegal drugs,42-44 as well as a higher prevalence of unhealthy lifestyles, including poorer diets and less exercise.45-47 The side effects of drugs, particularly the metabolic side effects of antipsychotics, have also received considerable attention.48 49 Finally, inequalities in access to, and use of, healthcare are well documented.50 All of these factors may have contributed to the overall substantial and widening gap in life expectancy for people with mental illness in this study. Socioeconomic disadvantage is also more common in people with mental illness and is associated with health risk behaviours and reduced access to healthcare.51 However, studies that adjusted for socioeconomic status still found significantly worse morbidity and mortality for people with mental illness,2 8 showing that social deprivation and disadvantage are not the sole determinants of poor health outcomes in people with mental illness.
+Improving health outcomes for people with mental illness
+As multiple factors contribute to worse health outcomes in people with mental illness, a range of solutions is required both to deal with health risk factors at individual patient level and to ensure equitable access to healthcare. To tackle systemic barriers to healthcare provision, a range of solutions has been proposed, including integrated care models such as co-location of physical and mental health services or the use of case managers or other liaison staff to undertake a coordination role between services.50 52 General practitioners also have an important part to play in managing the overall health needs of people with mental illness. Increasing access to screening and funding models that allow general practitioners to spend more time with patients with more complex problems may be advantageous.53 54 In terms of health risk factors at individual patient level, the use of peer supporters or provision of healthcare skills training may help people with mental illness manage their health.55 56 Improvements in risk factor profiles, such as reducing smoking, improving diets, and increasing physical activity, have
+contributed to improvements in life expectancy in the general population. Adapting population health and health promotion approaches to more specifically target disadvantaged populations, including those with mental illness, could help extend these gains to population groups with multiple problems or disadvantages.50 Major improvements in health outcomes for people with mental illness are unlikely without system wide commitment to achieving equality in health service delivery and access. This may require health systems committing to targets for improvements in outcomes, regular monitoring of progress, equitable allocation of resources based on health need, and the integration of the needs of people with comorbid mental health problems into mainstream healthcare and public health initiatives.57 One example is Queensland’s strategy to improve the physical health of people with severe mental illness (Activate: Mind and Body).58
+One researcher16 argued that the continued gap in life expectancy shows the poor value that society places on people with mental illness and that the lost years of life are viewed by society with “cynical disregard.” Psychiatric patients in this study represented over 5% of the Western Australia population, including a large proportion of people who had only short contacts with services.59 Although the pervasive stigma associated with mental illness may lead some to believe otherwise, many adults in this group have families, are employed, and would be expected to be making a contribution to their families and communities. The opportunity for them to do so is being foreshortened by premature mortality.60 Addressing both the physical and mental health of people with mental illness would allow such people to participate more fully in society, including through increased employment opportunities and reduced absenteeism.
+Limitations of this study
+Limitations of this study include the reliance on administrative data of contacts with services. Not all people with mental illness have contact with services, and these data have to be considered representative of the population of people in contact with services, not all people with mental illness. For instance, the register does not cover people with undiagnosed and untreated disorders and those who are only treated by general practitioners or private psychiatrists and psychologists. People with disorders who are not in contact with services may have different mortality, and possibly worse, outcomes than those in contact with services. Changes in life expectancy over time could be influenced by changes in service delivery and diagnostic practices. As the overall prevalence of contact with mental health services has increased over time, however, and if it is assumed that the most severe cases at any point in time are most likely to receive treatment, the increasing prevalence would be expected to reduce the observed gap in life expectancy, not increase it, assuming that the most severe cases have the worst mortality outcomes. Because the mental health information system started at a fixed time, and to avoid change over time being biased by the accumulation of more and more cases, we used an active case definition—that is, people who had ongoing contact with services or contact within the five years before each reference date. Changes in cause of death coding over time could have affected the estimates of excess death by cause. For instance, HIV has been coded as a cause of death in Australia only since 1996, and this would lead to an underestimation of HIV deaths in our cohort. In addition, ICD-10 replaced ICD-9 for the coding of deaths from 1999 onwards. However, an analysis of time trends in causes of death published by the Australian Bureau of Statistics suggests that the introduction of ICD-10 had little impact on rates.27 Furthermore, changes to
+some ICD codes would not affect our findings that psychiatric patients in general, and in each psychiatric diagnostic group, experienced a large gap in mortality, and that the majority of excess mortality was attributable to physical health conditions, including cancer and cardiovascular disease.
+Conclusions
+In summary, these results show the substantial impact of mental illness on life expectancy in Western Australia. Mental illness is common and associated with large increased risks of morbidity and mortality. While strategies aimed at the prevention of suicides and violent deaths remain an important component of efforts to reduce excess mortality in people with mental illness, our results show that almost 80% of excess deaths are associated with physical health conditions. The most common causes of death in the general population—heart disease, respiratory disease, and cancer—are also the most common causes of excess deaths in people with mental illness. Because of the complex, multifactorial nature of these conditions, multipronged approaches will be required to tackle these inequalities, in the same way that multipronged approaches have been used to reduce the mortality associated with these common conditions in the general population. These strategies should include both individual and population based components. It is more challenging treating people with multiple concurrent problems, but it is likely that treating both physical health problems and associated risk factors in people with mental illness would result in improvements to both physical and mental health.
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+Introduction
+The COVID-19 pandemic is causing widespread societal disruption, morbidity and loss of life globally. By the end of December 2020 over 85 million people had been infected and over 1.8 million had died (Worldometers, 2020). There are concerns about the impact of the pandemic on population mental health (Holmes et al., 2020). These stem from the impact of the virus itself on people infected (Taquet et al., 2021), as well as frontline workers caring for them (Kisely et al., 2020) and increases in bereavement. Other concerns relate to the impact on population mental health of the public health measures that have been implemented to minimise the spread of the virus - in particular physical distancing, leading to social isolation, disruption of businesses, services and education and threats to peoples’ livelihoods. Physical distancing measures and lockdowns have resulted in substantial rises in unemployment, falls in GDP and concerns that many nations will enter a prolonged period of deep economic recession.
+There are concerns that suicide and self-harm rates may rise during and in the aftermath of the pandemic (Gunnell et al., 2020; Reger et al., 2020). Time-series modelling indicated that the 1918-20 Spanish Flu pandemic, which caused well over 20 million deaths worldwide, led to a modest rise in the national suicide rate in the USA (Wasserman, 1992) and Taiwan (Chang et al., 2020). Likewise, there is some evidence that previous epidemics and pandemics were associated with rises in suicide and suicidal behaviour (Zortea et al., 2020). Suicide rates increased briefly amongst people aged over 65 years in
+Hong Kong during the 2003 SARS epidemic, predominantly amongst those with more severe physical illness and physical dependency (Cheung et al., 2008).
+The current context is, however, very different from previous epidemics and pandemics. The 2003 SARS epidemic was restricted to relatively few countries. Furthermore, during the 100-year period since the 1918-20 influenza pandemic, global and national health systems have improved, international travel and the speed of communication of information (and disinformation) have increased, antibiotics are available to treat secondary infection, and national economies have become globally inter-dependent. The availability of the internet and technological advancement has made it far easier for people to communicate and engage in home working and home schooling. However, there are marked social inequalities in relation to access to technology and ability to stay safe and continue to work, within and between countries. Public health policies and responses, and the degree of access to technology to facilitate online clinical assessments and treatments differ greatly between countries.
+Key concerns in relation to suicide prevention during the pandemic include: encouraging help-seeking in those with suicidal thoughts and behaviours e.g. people who have attempted suicide may not attend hospitals because they are worried about contracting COVID-19 or being a burden on the healthcare system at this time; uncertainty regarding how best to assess and support people with suicidal thoughts and behaviours, whilst maintaining physical distancing and addressing any impacts of remote consultation; diminished access to community-based support; exposure to traumatic experiences; long term effect of infection with the virus on mental health (Taquet et al., 2021) and an economic recession may have an adverse impact on suicide rates (Chang et al., 2013; Stuckler et al., 2009). There have been increases in bereavement (with many being unusually complicated during the crisis), sales of alcohol (Finlay & Gilmore, 2020) and domestic violence (Mahase, 2020) - all risk factors for suicide (Turecki et al., 2019); the insensitive or irresponsible media reporting of suicide deaths associated with COVID-19 may be harmful (Hawton et al., 2021); and in some countries access to highly lethal suicide methods such as firearms and pesticides may rise (Anestis et al., 2021; Gunnell et al., 2020). However early findings from high income countries with ‘real-time’ suicide trend data, indicates there was no rise in suicide rates in the early months of the pandemic (John et al., 2020a). Japan is the exception to this rule, falls in Japanese suicide rates in the early months of the pandemic have since been replaced by rises above pre-pandemic levels July/ August 2020 and beyond (John et al., 2020a; Tanaka & Okamoto, 2021; Ueda et al., 2021). The longer-term impact of the pandemic on suicide deaths and suicidal behaviour remains uncertain.
+In the context of the COVID-19 pandemic there is a rapidly expanding evidence base on its impact on suicide rates, and how best to mitigate such effects. It is therefore important that the best available knowledge is made rapidly available to
+policymakers, public health specialists and clinicians. To facilitate this, we are conducting a living systematic review focusing on incidence and prevention of suicide and self-harm in relation to COVID-19. Living systematic reviews are high-quality, up-to-date online summaries of research that are regularly updated, using efficient, often semi-automated, systems of production (Elliott et al., 2014). Our first report covered the period up to the 7th June 2020. This paper reports the second set of findings from the review, based on relevant articles identified up to 19th October 2020.
+Aim
+The overarching aim of the review is to identify and appraise any newly published evidence from around the world that assesses the impact of the COVID-19 pandemic on suicide deaths, suicidal behaviours, self-harm and suicidal thoughts, or that assesses the effectiveness of strategies to reduce the risk of
+suicide deaths, suicidal behaviours, self-harm and suicidal thoughts, associated with the COVID-19 pandemic.
+Methods
+This living systematic review (Figure 1) follows published guidance for such reviews and for how expedited ‘living’ recommendations should be formulated where relevant (Akl et al., 2017; Elliott et al., 2017). The review was prospectively registered (PROSPERO ID CRD42020183326; registered on 1st May 2020). An overview of our living review process is provided in Figure 1. A protocol (John et al., 2020b) was published in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guideline (Moher et al., 2015) along with the first update of our review which summarised articles identified up to 7th June 2020 (John et al., 2020c). Since publication of our protocol we have amended our methodology to: 1) search additionally the PsyArXiv
+and SocArXiv open access paper repositories; 2) include modelling studies within the scope of our review (e.g. to predict the likely impact of the pandemic on suicide rates);
+3) update our research questions to include studying the impact of adult self-neglect and parental neglect and fear of losing livelihood on suicide-related outcomes; 4) update our searches with any new citations from PsycINFO prior to each update;
+5) exclude from data extraction and presentation in results tables single-wave, cross-sectional surveys unless they explicitly make comparisons with appropriate pre-pandemic measures or include comparative data between COVID-19 positive and unaffected individuals for pragmatic reasons, due to the volume of such studes but also issues to do with sampling and generalisability of such studies. Surveys that meet the original inclusion criteria are included as an appendix to the update.
+Eligibility criteria
+Study participants may be adults or children of any ethnicities living in any country. Outcomes of interest are:
+1.	Deaths by suicide
+2.	Self-harm (intentional self-injury or self-poisoning regardless of motivation and intent) or attempted suicide (including hospital attendance and/or admission for these reasons)
+3.	Suicidal thoughts/ideation
+Studies must address one of the following research questions:
+(i)	What is the prevalence/incidence?
+• Prevalence/incidence of each outcome during pandemic (including modelling studies)
+(ii)	What is the comparative prevalence/incidence?
+• Prevalence/incidence of each outcome during pandemic vs not during pandemic
+(iii)	What are the effects of interventions?
+•	Effects of public health measures to combat COVID-19 (including physical distancing, school closures, interventions to address loss of income, interventions to tackle domestic violence) on each outcome
+•	Effects of changed and new approaches to clinical management of (perceived) elevated risk of selfharm or suicide risk on each outcome (any type of intervention is relevant)
+(iv)	What are the effects of other exposures?
+•	Impact of media portrayal on each outcome and misinformation attributed to the pandemic on each outcome
+•	Impact of bereavement from COVID-19 on each outcome
+•	Impact of any COVID-19 related behaviour changes (domestic violence, alcohol, adult self-neglect, parental neglect, cyberbullying, isolation) on each outcome
+•	Impact of COVID-19-related workload on crisis lines on each outcome
+•	Impact of infection with COVID-19 (self or family member) on each outcome
+•	Impact of changes in availability of analgesics, firearms and pesticides on each outcome (method-specific and overall suicide rates)
+•	Impact of COVID-19 related socio-economic exposures (changes in fiscal policy; recession/depression: unemployment, debt, fear of losing livelihood, deprivation at the person-, family- or small-area level) on each outcome
+•	Impact on health and social care professionals: the stigma of working with COVID-19 patients or the (perceived) risk of infection/being a ‘carrier’, as well as work-related stress on each outcome
+•	Impact of changes in/reduced intensity of treatment for patients with mental health conditions, in particular those with severe psychiatric disorders.
+•	Impact of any other relevant exposure on our outcomes of interest.
+Qualitative research
+We included any qualitative research addressing perceptions or experiences around each outcome in relation to the COVID-19 pandemic (e.g. stigma of infection, isolation measures, complicated bereavement, media reporting, experience of delivering or receiving remote methods of self-harm / suicide risk assessment or provision of treatment; experience of seeking help for individuals in suicidal crisis); narratives provided for precipitating factors for each outcome.
+No restrictions were placed on the types of study design eligible for inclusion, except for the exclusion of single-person case reports. Pre-prints will be re-assessed at the time of publication and the most current version included. There was no restriction on language of publication. We drew on a combination of internet-based translation systems and network of colleagues to translate reports in languages other than English.
+Identification of eligible studies
+We searched the following electronic databases: PubMed; Scopus; medRxiv, PsyArXiv; SocArXiv; bioRxiv; the COVID-19 Open Research Dataset (CORD-19) by Semantic Scholar and the Allen Institute for AI, which includes relevant records from Microsoft Academic, Elsevier, arXiv and PMC; and the WHO COVID-19 database. A sample search strategy (for PubMed) appears in Box 1 from 1st January 2020 to 19th October 2020. We have developed a workflow that automates daily searches of these databases, and the code supporting this process can be found at https://github.com/mcguinlu/COVID_suicide_living). Searches are conducted daily via PubMed and Scopus application programme interface and the bioRxiv and medRxiv RSS feeds. Conversion scripts for the daily updated WHO and the weekly updated CORD-19 corpus are used to collect information from the remaining sources. The software includes a systematic
+search function based on regular expressions to search results retrieved from the WHO, CORD-19 and preprint repositories (search strategy available in extended data). Our review is ongoing and we continue to investigate the use of other databases and to capture articles made available prior to peer review and assess eligibility and review internally. For this update we therefore included PsyArXiv and SocArXiv repositories in our search strategy via their own open access platforms as we developed our automated system. PsycINFO searches were carried out retrospectively on 6th January 2021, using a publication date filter for 1st January 2020 to 19th October 2020.
+A two-stage screening process was undertaken to identify studies meeting the eligibility criteria. First, two authors (either CO or EE) assessed citations from the searches and identified potentially relevant titles and abstracts. Second, either DG, AJ or RW assessed the full texts of potentially eligible studies to identify studies to be included in the review. This process was managed via a custom-built online platform (Shiny web app, supported by a MongoDB database). The platform allowed for data extraction via a built-in form.
+Data collection and assessment of risk of bias
+One author (DG, AJ or RW) extracted data from each included study using a piloted data extraction form, and the extracted data were checked by one other author (DG, KH, EA, RC, AJ, or EE where AJ extracted data, AJ where DG extracted data). Disagreements were resolved through discussion, and where this failed, by referral to a third reviewer (KH, NK or PM). Irrespective of study design, data source and outcome measure examined, the following basic information were extracted: citation; study aims and objectives; country/setting; characteristics of participants; methods; outcome measures (related to self-harm / suicidal behaviour and COVID-19); key findings; strengths and limitations; reviewer’s notes. For articles where causal inferences are made - i.e. randomised or non-randomised studies
+examining the effects of interventions or aetiological epidemiological studies of the effects of specific exposures - we plan to use a suitable version of the ROBINS-I or a preliminary similar tool for exposure studies to assess risk of bias as appropriate based on the research question and study design (Morgan et al., 2017; Sterne et al., 2016).
+Data synthesis
+We synthesised studies according to themes based on research questions and study design, using tables and narrative. Results were synthesised separately for studies in the general population, in health and social care staff and other at-risk occupations, and in vulnerable populations (e.g. people of older age or those with underlying conditions that predispose them to becoming severely ill or dying after contracting COVID-19) where relevant. Where multiple studies addressed the same research questions, we assessed whether meta-analysis was appropriate and would conduct it where suitable, following standard guidance available in the Cochrane Handbook (Deeks et al., 2019). The current document is the second iteration of our review. We have not considered it appropriate to combine any results identified so far in a meta-analysis due to quality and heterogeneity.
+Results
+In total, 12,397 citations were identified by 19th October 2020 from all electronic searches, after duplicates were removed (Figure 2). The cumulative numbers of articles over time that were identified by the search and included in the review are shown in Figure 3 and Figure 4. The majority of studies identified in the review (5105; 82%) were sourced from two databases, PubMed and WHO; a further 10% (n=622) were drawn from pre-print sites such as MedRxiv.
+Description of included studies
+We included 78 articles in the review. We have highlighted in Table 1-Table 6 where new citations have updated existing studies. Sixty-four cross sectional surveys are included in Appendix 1. In total, six studies spanned several countries or were worldwide, including one using a Reddit mental health dataset (almost half of users are from the USA); 13 were from the United States; seven from China; nine from India; five from the United Kingdom; four each from Japan and Nepal; and between one and three each from Australia, Bangladesh, Canada, Czech Republic, Denmark, France, Germany, Greece, Iran, Ireland, Israel, Italy, Pakistan, Peru, Poland, Portugal, Spain, Qatar and Switzerland. All articles were based on observational studies: twenty-five were case series with a sample of two or more (although Jefsen et al., 2020a and Rohde et al., 2020 were based on the same case series); thirteen were cross sectional surveys; two were based on social media posts; six were modelling studies; twenty were service utilisation studies; and nine assessed suicide rates. Studies are summarised by these study types in Table 1 through Table 6. Three other relevant articles were identified, two of these described mixed methods studies (Evans et al., 2020; Son et al., 2020) and one a casecontrol study (Cai et al., 2020). Almost half (n=34) of the
+articles did not appear to have been peer- reviewed of which ten were pre-prints and 21 were published as research letters to the Editor.
+Study populations
+Sample sizes ranged from two individuals in a number of case series (Kapilan, 2020; Mamun et al., 2020b; Pirnia et al., 2020; Sahoo et al., 2020) to 60 million Twitter posts (Saha et al., 2020). Most studies included both male and female participants, except the studies reported by Wu et al. (2020a) and Sade et al. (2020) which were conducted in pregnant women.
+Outcomes
+Seven of the 24 case series (described in 25 papers) focused on a mix of outcomes including suicide attempts (n=2), suicide deaths (n=14) and suicidal thoughts (n=1). Of the 15 crosssectional surveys five assessed suicidal thoughts alone, others collected data on various combinations of suicidal/self-harming behaviour or thoughts. A range of validated questionnairres were used to assess suicidal thoughts (Table 2). Five surveys used the single item from PHQ-9 ‘Have you had thoughts that you would be better off dead or of hurting yourself in some way’ over the last 2 weeks. Wang et al. (2020b)
+assessed responses to this question in a symptom network analysis.
+Summary of study findings: Case series
+We identified 24 case series of suicide attempts and suicide deaths (Table 1). Fourteen (58.3%) of these used news reports as their data source (Bhuiyan et al., 2020; Boshra & Islam, 2020; Dsouza et al., 2020; Griffiths & Mamun, 2020; Kapilan, 2020; Kar et al., 2020; Mamun & Ullah, 2020; Mamun et al., 2020a; Mamun et al., 2020b; Rahman & Plummer, 2020; Rajkumar, 2020; Shoib et al., 2020; Syed & Griffiths, 2020; Thakur & Jain, 2020) and are unlikely to be representative of general population suicide rates. Several overlap in terms of the information used, such as two letters to the editor about celebrity suicides in India (Kar et al., 2020; Mamun et al., 2020b), and many lack detailed information about the range of contributing factors. Whilst most case series focused on suicdes in the general population, some focussed on specific groups, such as psychiatric patients (e.g. Jolly et al., 2020), healthcare professions (e.g. Kapilan, 2020), patients with COVID-19 (e.g. Nalleballe et al., 2020), couple suicides (Griffiths & Manun, 2020) and alcohol-related deaths (e.g. Ahmed et al., 2020).
+Many reasons for COVID-19 related suicide or suicide attempts were suggested in the case series with conclusions often derived from a journalist’s report of the death. Contributory factors reported included fear of contracting the disease or of passing it on to others, reactive psychoses, financial or economic issues, loneliness and isolation due to quarantine, stress among health professionals, the uncertainty around when the pandemic would end, misinterpretation of fever as COVID-19, contracting COVID-19, an inability for migrants to return home, frustration and the stigma of a (possibly perceived) positive result, which resulted in harassment or victimisation by others in the community. In the largest case series from India (n=72 suicide deaths), Dsouza et al. (2020) reported that the most commonly occurring antecedents to suicide were fear of infection (n=21) and financial crisis (n=19). Two studies reported specifically on the consequences of alcohol withdrawal due to lockdowns (Ahmed et al., 2020; Syed & Griffiths, 2020).
+In the USA, four case reports described stressors for adolescents which include inability to see friends, arguments with parents, unresolvable misunderstandings over social media, academic stress, and feelings of isolation (Jolly et al., 2020). In a case series of adults across three hospitals in Doha, Qatar, three patients (out of 50 patients with COVID-19 receiving a psychiatric diagnosis) self-harmed as a reaction to the pandemic (Iqbal et al., 2020). A study of TriNetX records of people with COVID-19 (n=40,469) found that 0.2% (62 individuals) had suicidal thoughts recorded, although clinicians may not have systematically asked about suicidality (Nalleballe et al., 2020).
+Summary of study findings: Cross-sectional surveys and cohort study
+There were 13 articles describing cross-sectional surveys / cohort studies of two or more waves or one wave surveys where comparisons were explicitly made with appropriate pre-pandemic
+measures; or included comparative data between COVID-19 positive individuals and unaffected comparison individuals (Table 2). Six studies present repeat survey data, with measures recorded during, as well as before, the pandemic (Hamm et al., 2020; Hamza et al., 2021; Raifman et al., 2020; Winkler et al., 2020; Wu et al., 2020b; Zhang et al., 2020). Raifman et al. (2020) compared two nationally representative samples of US adults (one from 2017/18 and one from 2020 during the COVID-19 pandemic) using different survey methodologies. They found that suicidal ideation had increased more than fourfold in low-income households, particularly in those with difficulty paying rent, job loss and loneliness. Similarly, Winkler et al. (2020) reported on a repeated, robustly-sampled, nationally representative survey in the Czech Republic using baseline data from 2017 and found that suicide risk, as measured by the Mini International Neuropsychiatric Interview, increased from 3.9% in November 2017 to 11.9% in May 2020. Both Raifman et al. (2020) and Winkler et al. (2020) used somewhat different data collection approaches before and during the pandemic. Two other studies from China (Wu et al., 2020b; Zhang et al., 2020) reported increases in relevant outcomes during the pandemic compared with before. The cohort study by Zhang et al. (2020) reported increases seen in nonsuicidal self-injury (NSSI), suicidal thoughts, suicidal plans, and suicide attempts in primary and secondary school children pre- compared with post-pandemic. Neither Hamm et al., 2020 (trial participants with depression aged >60years) nor Hamza et al., 2021 (students) found clear evidence of increased risk of suicidal ideation (older adults) or NSSI (students) during the pandemic.
+Three additional articles, other than Raifman et al., 2020 and Winkler et al., 2020, reported cross-sectional surveys in the general population. Two of these used web based recruitment (Iob et al., 2020; Sueki & Ueda, 2020) with non-probability quota sampling or weighting, and one (Wang et al., 2020b) used a Chinese online platform providing functions similar to Qualtrics. Participants were COVID-19 patients in three studies (Wang et al., 2020a; Wu et al., 2020a; Zhao et al., 2020). Wang et al. (2020a) and Zhao et al. (2020) both reported higher levels of suicide-related outcomes in COVID-19 patients than general population (compared with the general population recruited through social media or from literature reports). In a general population sample that included people who reported having been diagnosed with COVID-19, Iob et al. (2020) found suicide/self-harm thoughts were more common in those with a COVID-19 diagnosis than in those not affected (33% vs 17%); likewise for suicide attempts (14% vs. 5%). Two surveys were conducted in university student populations (Debowska et al., 2020; Hamza et al., 2021) from 11 universities, with predominantly female respondents. No statistical evidence of a rise in suicidal thoughts or self-injury was found over a number of waves of data collection. Surveys were targeted at specific populations in a further three studies (Table 2): depressed patients (Hamm et al., 2020); pregnant women (Wu et al., 2020b); and school children (Zhang et al., 2020).
+Summary of study findings: Social media platform posts Two studies (Table 3) assessed posts on social media platforms, looking at Reddit (Low et al., 2020; 50% USA users) and
+Twitter in the USA (Saha et al., 2020). Both studies show marked increases in the proportion of postings related to suicidal thoughts and behaviours, and Low et al’s analysis of Reddit data identified a new cluster of posts about self-harm during the pandemic.
+Summary of study findings: Modelling studies
+We identified six studies (Table 4) that have used modelling approaches to forecast the potential impact of the pandemic on future suicide rates (Bhatia, 2020a; Bhatia, 2020b; Kawohl & Nordt, 2020; McIntyre & Lee, 2020a; McIntyre & Lee, 2020b; Moser et al., 2020). Three estimated the impact of the pandemic on suicide in the USA (Bhatia, 2020a; Bhatia, 2020b; McIntyre & Lee (2020a), while others assessed the impact on suicide in Canada (McIntyre & Lee, 2020b), Switzerland (Moser et al., 2020) and worldwide (Kawohl & Nordt, 2020).
+The models suggest between a 1% rise (Kawohl & Nordt, 2020, globally) and a 145% rise (Moser et al., 2020, in Switzerland) in suicide deaths. Each was based on different assumptions, but the models largely focused on the well-characterised impact on suicide rates of periods of economic recession and rises in unemployment (Chang et al., 2013; Stuckler et al., 2009). Unemployment rates are predicted to rise as a result of a postpandemic recession, due to measures to control the spread of the virus on the wider economy and loss of work as many businesses have been forced to shut down.
+Only one study modelled the effects of physical distancing measures on suicide rates (Moser et al., 2020); it did this by using suicide rates in prisoners in group or single cells as a model for lockdown in a group or in isolation. The prison population is exposed to multiple other risk factors for suicide (e.g. increased prevalence of mental illness, substance misuse and low socioeconomic position) (Humber et al., 2011; Rivlin et al., 2010), and this, coupled with the distinct differences between prison incarceration and the adoption of home quarantine procedures during the pandemic, means this model is likely to overestimate the potential impact of physical distancing measures on suicide risk in the general population.
+Summary of studies’ findings: Service utilisation studies We identified 20 service utilisation studies. Four of these addressed the impact of COVID-19 on suicidal thoughts only, thirteen included suicide attempts and/or self-harm, one suicidal thoughts, attempts and self-harm (McAndrew et al., 2020), one suicide threats and suicides in progress (Lersch, 2020), while in one the precise nature of the suicidal outcome was unclear (Sheridan et al., 2021) (Table 5). Most studies were conducted in the US (5) and the UK (4), three in Australia, two in Ireland and one study in each of the following countries: France, Greece, Israel, Italy, Portugal, and Spain.
+Across the studies focusing on suicidal thoughts, the methodologies varied from studies of presentations to health/mental health services to callers/visits to a website, with wide-ranging sample sizes, from 1668 (Titov et al., 2020) to 90 (Sade et al., 2020); the latter including a specific sample of pregnant women.
+The studies showed either a reduction (Chen et al., 2020; Hernandez-Calle et al., 2020; Smalley et al., 2021) or no change (Sade et al., 2020; Titov et al., 2020) in presentations to health/ mental health services or self-reported suicidal thoughts, with the majority making comparisons to the same time in 2019. The eleven studies examining the impact of COVID-19 on self-harm/suicide attempts used a variety of methodologies, including accessing data from health/mental health services, trauma registries, community-based services, emergency call services and the prison service. Where reported, the sample sizes ranged from 18,646 (Walker et al., 2020) to 30 (Olding et al., 2021). Eight studies reported a decrease in self-harm/suicide attempts during the first months of the COVID-19 pandemic (Capuzzi et al., 2020; Chen et al., 2020; Goncalves-Pinho et al., 2021; Hewson et al., 2020; McIntyre et al., 2020; Pignon et al., 2020a; Rajput et al., 2020; Walker et al., 2020). In two of these studies - both with somewhat longer post lockdown follow-up periods of 3-5 months (Chen et al., 2020; McIntyre et al., 2020) - presentations had returned to pre-lockdown levels by the end of follow-up. Three studies reported an increase in self-harm / suicide attempts (Karakasi et al., 2020; Olding et al., 2021; Rhodes et al., 2020).
+Pignon et al. (2020a) reported a 54.8% decrease in overall psychiatric emergency consultations and a 42.6% decrease in self-harm/suicide attempts during the first 4 weeks of the lockdown in France compared with the same period in 2019. Likewise, Gon^alves-Pinho et al. (2021) identified a 55.6% decrease in presentations of “suicide and intentional self-inflicted injury” to a metropolitan psychiatric emergency department in Portugal in the period 19th March to 2nd May between 2019 and 2020. McIntyre et al. (2020) reported a 35% reduction in self-harm presentations to a general hospital in March-April 2020 in Ireland compared with the same period in 2017-2019; however presentations returned to pre-pandemic levels by the end of May. Another study in Ireland (Mcandrew et al., 2020) also reported a reduction in psychiatric emergency presentations to the emergency department but no change in the proportion of presentations with suicidal thoughts or self-harm. In a study conducted by Hewson et al. (2020) in 31 prisons in the UK between February and April 2020, self-harm incidents decreased by one third between February and April 2020.
+In contrast, whilst Olding et al. (2021) reported a reduction in the incidence of all types of penetrating trauma presenting to a UK hospital during the early period of lockdown, the number of self-harm presentations increased slightly (albeit on the basis of very low event counts). A similar pattern was identified by Karakasi et al. (2020) in Greece, where between March and May 2020 a significant reduction was observed in individuals presenting as emergencies at hospital for psychiatric examination (the number of presentations for suicide attempts was 7 compared with 5 in the same period in 2019). Capuzzi et al. (2020) reported a rise in self-harm / suicide attempts as a proportion of total emergency department presentations in Italy, but this rise in the proportion of cases was in the context of falls in the absolute numbers of cases, set against reductions in total emergency department attendances.
+A study of emergency police calls in Detroit, USA, (Lersch, 2020) showed that the number of general mental health calls declined after the onset of the pandemic in that city, while calls relating to suicides in progress remained relatively stable over the 4 year period. Calls involving suicide threats declined inversely to the increase in COVID-19 infections, although the authors noted some ‘hotspots’ within the city for both infection rates and suicide threats. A study of 31 prisons in the UK found that after lockdown there were fewer implementations of Assessment, Care in Custody and Teamwork (ACCT) processes to initiate care- plans for prisoners considered at risk of self-harm or suicide (Hewson et al., 2020).
+Summary of study findings: impact of COVID-19 on suicide rates
+Nine reports, based on data from four countries - Greece, Japan, Nepal and Peru - describe changes in suicide rates in relation to the onset of COVID-19 and national lockdowns. A challenge with interpreting all the reports is the uncertainty over the extent to which official recording of suicides may have been affected by disruptions in death investigation and reporting due to COVID-19, although this is more likely to lead to under-estimation than over-estimation of suicide rates. Only one of the studies (Calderon-Anyosa & Kaufman, 2020) used appropriate time series to take account of underlying temporal trends in suicide when comparing the COVID-19 period with earlier years/months.
+The four reports from Nepal (Acharya et al., 2020; Pokhrel et al., 2021; Poudel & Subedi, 2020; Singh et al., 2020) were all based on news reports of police data on suicides, rather than on data obtained directly from Nepalese authorities and did not describe the strengths and weakness of the police data. They report between a 20% (Poudel & Subedi, 2020) and 35% (Acharya et al., 2020) rise in suicide in the first 3 months after lockdown compared with either preceding months or a similar period the previous year. These are marked rises, but without longer time series data it is not possible to determine the extent to which these were COVID-19 related or a possible continuation of pre-existing adverse trends. Three reports, based on Japan’s timely national suicide statistics, describe recent trends in Japanese suicide rates (Tanaka & Okamoto, 2020 pre-print, Tanaka & Okamoto, 2021, final version; Isumi et al., 2020; Ueda et al., 2021). The most recent of these, using data up to October 2020, indicate that 14% falls in Japanese suicides in the early months of the pandemic (Feb-June 2020), were reversed during the second outbreak (July to October, 2020) increasing by 16% (Tanaka & Okamoto, 2021). Increases in suicide rates were higher in females (especially housewives) and children and adolescents. Similarly compared with August in 2017-19, figures for August 2020 were increased by 7.7%, with rises particularly in females and people aged <40 years (Ueda et al., 2021). An early report (data up to May 2020) provided some reassurance about the impact of public health measures/school closures on suicide rates in children (<20 years) in Japan (Isumi et al., 2020). However, more recent data (Ueda et al., 2021) flags a concerning rise amongst students and young (<40 years) people, particularly females. The numbers of
+deaths in the autopsy study from Athens (Sakelliadis et al., 2020) is too small to reach any conclusion about the impact on suicide in Greece. Calderon-Anyosa & Kaufman’s (2020) study of suicide in Peru is reassuring, though details of potential impacts of COVID-19 on death registration in Peru are not provided.
+Other studies
+The three other studies investigated various risk groups, using case control and mixed methods approaches. Son et al. (2020) interviewed students from a single US university about the impacts of the pandemic on their mental health; some students described suicidal thoughts and the challenges they faced, one linked suicidal thoughts to being confined at home with their family and another to study-related difficulties. Cai et al. (2020) compared suicidal thoughts in Chinese medical workers dealing with COVID-19 patients and those not in contact with such patients. They found no evidence of increased levels of suicidal thoughts amongst those in contact with COVID patients. Lastly, Evans et al. (2020) studied the pandemic-related stresses felt by Australian families in free text responses to a questionnaire. One respondent, a father with three children described the extreme financial distress they faced with “our three businesses closing, we are eligible for none of the government support due to a tax debt and are looking at bankruptcy and selling our home as the only option. Both of us have had thoughts of suicide” (Quote from father of 3 children). (Evans et al., 2020)
+Discussion
+Seventy-eight articles were included in this review, 49 more than in our review of studies published up to 7th June 2020. All were based on observational studies. The majority of studies were case series or service utilisation studies from across the world. No studies were based on populations from sub-saharan Africa. Almost half of the articles did not appear to have been peer-reviewed, consisting mainly of pre-prints published before peer review, or research letters that may not have been peer-reviewed. In contrast to the last update (John et al., 2020c) in which no studies reported on the change in incidence of suicide or suicidal behaviour after the onset of the pandemic compared with beforehand, we identified nine papers in this update, presenting data on studies from four countries which investigated the impact of COVID-19 on suicide rates. To date, the highest quality data come from Japan which utilises suicide records covering the entire population; these data indicate that the impact of COVID-19 on suicides rates may change over time and have varying effects on different sections of the population. Analysis of data from Peru used appropriate analytic techniques and reported a fall in suicides following the onset of the pandemic during the months March to September (Calderon-Anyosa & Kaufman, 2020). Methodological limitations and the availability of data for only four countries limit our ability to assess the early impact of COVID-19 on suicide rates in this update.
+Evidence published following our cut-off date for inclusion in this iteration of the review indicates there was no rise in suicide rates in the early months of the pandemic in high income
+countries (John et al., 2020a). Since our 19th October search, a further 13 studies analysing suicide trends in ten countries or regions within countries (Australia, Austria, Germany, Greece, Japan; Korea, Norway, Sweden, Thailand and the USA) have been published (Ando & Furuichi, 2020; Bray et al., 2021: Deisenhammer & Kemmler, 2021; Faust et al., 2020; Karakasi et al., 2021; Ketphan et al., 2020; Kim, 2021; Leske et al., 2021; Mitchell & Li, 2021; Qin & Mehlum, 2021; Radeloff et al., 2020 and Radeloff et al., 2021; Ruck et al., 2020; Vandoros et al., 2020). Four of these use appropriate time-series modelling approaches to control for underlying trends (Leske et al., 2021, Australia; Faust et al., 2020, USA; Vandoros et al., 2020, Greece; Ando & Furuichi, 2020, Japan) - these report either no change or a fall in suicide deaths in the early months of the pandemic, although in keeping with Tanaka & Okamoto (2020); Tanaka & Okamoto (2021) and Ueda et al.’s (2021) analysis for Japan, Ando & Furuichi (2020) report a rise in suicides in Japan since July associated with increased unemployment . In keeping with concerns from Nepal, data from Thailand’s Department of Mental Health indicate suicide numbers have risen during the pandemic (Ketphan et al., 2020). Data from Connecticut, USA on suicides during the 10 weeks of stringent lockdown measures in the state indicate that whilst suicide rates fell during this period, the proportion of suicides amongst minority ethnic groups rose, highlighting the possibility that the pandemic may be having a disproportionately greater adverse impact on minority groups (Mitchell & Li., 2021). A concern supported by a recent analysis from Maryland, USA. (Bray et al., 2021).
+The majority of the 13 included cross-sectional surveys were subject to methodological flaws in sampling methods and use of validated instruments. Nonetheless, there is evidence from at least three countries (China, Czech Republic and USA) of increases in suicidal/self-harm thoughts in the general population during the pandemic compared with pre-pandemic levels. Two robustly sampled general population, nationally representative cross-sectional surveys with pre pandemic baseline data from 2017/18 reported a three to four fold increase in suicide risk (Winkler et al., 2020) and suicidal thoughts in low-income households (Raifman et al., 2020), but differences in data collection approaches (i.e. face-to-face vs. on-line) may bias comparisons. Recent studies, with repeat measures of mental health outcomes since the start of the pandemic, also point to rising levels of suicidal thoughts during the pandemic (O’Connor et al., 2020).
+The review included 20 service utilisation studies (compared with only three in the previous update), the majority of which identified a drop in frequency of emergency department contacts for suicidal thoughts, behaviours and self-harm. An increase in contacts to a mental health digital platform was identified in one study (Titov et al., 2020), but with no changes in contacts for suicidal thoughts. There have been several recently published service utilisation studies (Carr et al., 2021; Hawton et al., 2020a; Jollant et al., 2021) which reiterate and extend these findings. Jollant et al. (2021) report a 8.5% decrease in
+hospitalisation for self-harm, greater in females than males, in France in January to August 2020 compared with the same period in 2019. There was also an increase in use of some more lethal methods (firearms / jumping/ drowning) as well as a rise in in-hospital deaths and ITU admissions. Carr et al. (2021) report a 30% fall in consultations for self-harm in April to June 2020 in primary care and secondary care in the UK, the former a setting not explored in currently included studies. They highlight that the treatment gap for depression and anxiety was greater in working age adults, for practice populations in deprived areas, and for self-harm. A limitation of all studies based on hospital presentations is that they may not reflect community prevalence of suicidal thoughts and behaviours. This may be a particular issue if people were deterred from presenting to hospital because of fears of either overburdening already stretched healthcare systems or of contracting the virus in these settings themselves. That said, those who present to services may be able to give some insight into whether COVID-19-related concerns are important. In one UK study, ‘stay-at-home’ related issues contributed to around half of cases, more so in males than females. The most frequent COVID-related factors were mental health issues, including new and worsening disorders, cessation, reduction or transformation of services (including absence of face-to-face support), isolation and loneliness, reduced contact with key individuals, disruption to normal routine, and entrapment (Hawton et al., 2020b).
+Modelling studies that aimed to predict the impact of the pandemic on national or global suicide rates produced widely differing estimates of the likely impact and most focused on predictions based on previous studies of the impact of changes in unemployment levels on suicide. These differences between model estimates were partly due to differences in modelling assumptions, which are themselves in turn associated with considerable uncertainty. Given the methodological limitations, the uncertainty of assumptions about how the economies of individual countries will be affected, as well as international differences in financial supports given to businesses and people out of work, these predictive exercises can at best only provide a guide as to where action and available suicide prevention strategies should be directed.
+Studies of social media posts potentially provide another insight into the impact of the COVID-19 pandemic on suicide risk and have the potential to provide more-or-less real time assessments of changes in risk. The two studies we identified (Low et al., 2020; Saha et al., 2020) reported heightened levels of suicide-related posting/suicidality. However, there are several limitations to this approach making these studies hard to interpret, including: self-selecting biases in respect of who contributes to these fora (and when); the unit of analysis being posts/tweets rather than individuals so multiple posts may be from the same individual; and the dissemination of misinformation; the demographic and clinical characteristics of the people making the posts are unknown; and whether comments reflect their own distress or more general concerns is uncertain.
+It is also not clear whether mentions of suicide on social media posts map to actual rates of suicidal thoughts in the community and whether this changes in particular contexts and over time. The nature of the relationship (if any) between social media reports and behavioural change in the context of suicide needs to be better understood. Insights derived from such approaches may help deepen our understanding of the mental health challenges of the pandemic and how these may change over time. Future research could usefully try to segment the posts by individuals and sociodemographics to explore changes in sub-groups. Another potentially useful approach to assessing the impact on COVID-19 on population mental health and suicide risk is analysis of Google trends data (Jacobsen et al., 2020; Knipe et al., 2020; Rana, 2020; Sinyor et al., 2020), but we excluded such studies from our review as we think that search data constitute an even weaker proxy for population mental health.
+We identified 25 case series of suicide attempts and suicide deaths, 14 based on news stories in India, Bangladesh and Pakistan. Given the relatively low quality of case series in the hierarchy of evidence, often reflecting small numbers and selection bias, but more importantly the lack of comparator data, drawing any reliable inferences from these studies is inherently flawed. Furthermore, news reports report a nonrepresentative sample of suicide deaths and often derive their information from bystanders and witnesses who are unlikely to know the full circumstances of the death (Khan et al., 2009). However, in parts of the world without reliable suicide incidence data they may be the only source of information (Khan & Hyder, 2006). Nevertheless, these studies highlight circumstances surrounding apparently COVID-19-related suicides and flag the potential importance of factors such as economic difficulties, fear of the disease, alcohol withdrawal and social isolation even in young people and children.
+Only 14% (11/78) included studies specifically focussed on children and young people. An early report (data up to May 2020) provided some reassurance about the impact of public health measures/school closures on suicide rates in children (<20 years) in Japan (Isumi et al., 2020). However, more recent data (Tanaka & Okamoto, 2021; Ueda et al., 2021) flags a concerning rise amongst students and young (<40 years) people, particularly females and children and adolescents during the second wave of the pandemic and school closure. Three were cross-sectional surveys with attendant methodological flaws. Two surveys were conducted in university student populations (Debowska et al., 2020; Hamza et al., 2021) in 11 universities with predominantly female respondents. No statistical evidence of a rise in suicidal thoughts or self-injury was found over a number of waves of data collection. Wang et al’s (2020b) network analysis of symptoms of anxiety and depression in young people highlighted an increasing connection between ‘too much worry’ and suicidal thoughts. It is challenging to assess how generalisable these findings from China are to other countries and other phases of the pandemic. If generalisable, it could point to some treatment targets that are more central to
+suicide risk, but this is not yet clear. Zhang et al’s (2020) cohort study reported pre-pandemic comparison data, with increases seen in NSSI, suicidal thoughts, suicidal plans and suicide attempts in primary and secondary school children post-pandemic. However the sampling frame was poorly reported so representativeness of the sample is challenging to assess. Only one of the service utilisation studies focussed on this age group (Sheridan et al., 2021) but this was based in a single tertiary centre; although another study of a broader age range included them (Walker et al., 2020). There were two case series focussed on children and young people (Jefsen et al., 2020b; Jolly et al., 2020). The stressors identified for adolescents included the inability to see friends, arguments with parents, unresolv-able arguments via social media, academic stress and feelings of isolation (Jolly et al., 2020).
+Only three included studies focussed on frontline healthcare staff. Two were case series (Kapilan, 2020; Rahman & Plummer, 2020) based on news reports of six or eight nurses deaths (i.e. there is potential duplication of reports of the same deaths). Factors reported as associated with deaths included: fear they had become infected; positive test result; being in quarantine; fearful of becoming infected; and “ extreme stress and mental disturbance”. The third, a case control study, reported that the prevalence of suicidal thoughts was no higher in medical staff who were in direct contact with COVID-19 patients, compared to those who had no direct contact (Cai et al., 2020).
+Strengths and limitations
+The literature exploring COVID-19 and suicide deaths, suicidal behaviours, self-harm and suicidal thoughts is expanding rapidly. Since our last review end-date (i.e. between 7th June 2020 to 19th October 2020) we identified a further 4156 potentially eligible studies. While most of the published evidence that we identified in this update had important limitations there was a marked improvement in study quality compared with our last update. Importantly, a large volume of the literature remains not peer reviewed; some reports are pre-prints, so this may change, but a number are research letters. All included studies remain observational in design and thus potentially prone to multiple sources of bias (e.g., recall bias, selection bias, confounding).
+A number of the studies included in this update used nonprobability samples e.g. convenience samples of volunteers recruited via the Internet. Such studies tend to attract volunteers who have access to the internet, are already engaged in research or have an interest in the topic. When assessing suicidal thoughts and behaviours, those in most distress or with co-existing mental illness, as well as older people, may be less likely to participate. Therefore prevalence estimates and associations observed among healthy volunteers may not reflect associations that would be seen in representative samples (Pierce et al., 2020). However, such study designs potentially provide potentially valuable information at the very early stages of a health crisis, where the timeliness of studies to inform policy and practice is important and repeated cross sectional studies provide
+valuable evidence about changing levels of population mental health and risk factors (e.g. O’Connor et al., 2020; Raifman et al., 2020). More consistent reporting of sampling frames, repeat survey and the use of validated measures will ensure they make a more meaningful contribution to the evidence base.
+There is a paucity of research focussing or reporting on ethnic minorities within populations, children and young people, the bereaved and frontline health and social care staff, which needs to be addressed. Synthesis of findings across studies, and both between and within countries, is confounded by the timing of data collection; differences between studies may be due not only to methodological differences, but also differences in the extent and stringency of public health prevention measures (physical distancing), economic disruption and COVID-19 infection rates in the any population at the time data are collected. A final limitation of the review is that, due to resource limitations, we excluded grey literature (e.g. Fancourt & Steptoe, 2020; National Child Mortality Database, 2020)
+Implications
+There is thus far no clear evidence of an increase in suicidal behaviour or self-harm associated with the pandemic, nor with the measures taken to curb the spread of COVID-19, although signals from some repeated population surveys and suicide trend data from Nepal and Japan are concerning. There are suggestions of increased risk in people who have been infected with COVID-19, in line with findings from studies showing increased risk of mental health problems in survivors of COVID-19 (Taquet et al., 2021). Declines in levels of hospital presentation for suicidal behaviour may reflect a real decline in suicidal behaviours early in the pandemic perhaps due to the recognised impact of periods of acute stress / national crisis (e.g. wars) on suicide rates or unmet need in the community, with people cautious about overburdening clinical services or of their own risk of contracting COVID-19 (John et al., 2020a). There is a relative lack of high quality studies to inform prevention in Low and Middle Income Countries and in disadvantaged groups, although studies point to an emerging risk in the latter (Mitchell & Li., 2021). There are, as yet, no studies that assess the effectiveness of strategies to reduce the risk of suicide deaths, suicidal behaviours, self-harm and suicidal thoughts, resulting from the COVID-19 pandemic; such research is urgently required.
+Our living review provides a regular synthesis of the most up-to-date research evidence to guide public health and clinical policy to mitigate the impact of COVID-19 on risk of suicidality. However, the rapid growth of research in this area necessarily makes the reporting of the large volume of included studies brief. Therefore in the future we plan to publish timely updates focussed on specific topics like suicide rates, for instance, or in specific populations such as children and adolescents, those with confirmed COVID-19 or healthcare workers. Our future updates will also focus on studies investigating suicide deaths, suicide attempts and self-harm. We will no longer include studies: with suicidal thoughts and “suicide risk” as outcomes; modelling studies (since these have been
+superseded by studies based on suicide deaths) and those based on social media posts (because of the lack of evidence for diagnoses and self-selecting biases in respect of who contributes to these).
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+INTRODUCTION
+The UK’s Quality and Outcomes Framework. (QOF) is the world’s largest pay-for-performance scheme in primary care. It rewards general practices financially for delivering interventions and achieving patient outcomes using evidence-based indicators developed by the National Institute for Health and Care Excellence (NICE).1 Although the QOF its voluntary, nearly 99% of practices in England participate, on average deriving 10-15% of total practice income from the scheme.2
+The introduction of the QOF in 2004 waa a part of a new national contrad for GPp, driven by the need tn respond to yeers of underinvestment in general practice compared with other parts of the healtii service, low morale among GPs, and variations in the quality of primary medical care.3,4 The QOF was irtended to p^ovide a mechanism tn motivate GPs and to I ncrease funding for their practices, and the vast majority of practices took up the opportun^y for additional income. Evidence from the early years of the scheme suggested k reduced variations between practices in the delivery of incentivised interventions,5 and contributed to progress towards better use of electronic records and nurse-led multidisciplinary care of long-term conditions.3 After the first year of the QOF, most practices achieved near-maximum remuneration from the scheme.2
+Arguably, then, the QOF achieved what ii set out to do. But thiit majy have come at a
+cost. It has been suggested that practices prioritise QOF-related activities at the expense of other aspects of1 care, because of their reliance on QOF income.6,7
+A decade after the introduction of the QOF, NHS strategy, set nut in the 2014 Five Year Forward View? is now focused on other challenges. These include fin<^ii^<g new ways to manage people with longterm conditions, whose care is estimated to consume 70% of health service resources.8 Most clinical QOF indicators relate to the care of long-term conditions and are based on good evidence,4 but tend to be Umiied in scope, focusing on single, biomedical dimensions of care. Appendix 1 provides a brief description of the 68 QOF indicator^^ relating to care of long-term conditions in 2016-2017; the total number of indicators for that year was 77.
+In 2015, the Royal College of General Practitioners called for the replacement of the QOF to allow GPs 'to focus on providing the best possible holistic care'.9 NHS England, in April 2016, undertook to review the QOF, acknowledging that it may have served its purpose' and may be ‘a barrier to holistic management'.10 In early 2017, the British Medical Association called for the QOF to be suspended to reduce bureaucratic pressures and free up clinical time.11 Scotland abolished the QOF in 2016.3
+The Policy Research Unit in Commissioning and the Health Care System was commissioned to undertake a review, led by the Centi'e tor Heellh Seirvcee
+Studies at the University of Keent, to report in September 2016. The authors aimed to examine the evidence that: the QOF has improved care and outcomes for pattenn^ with long-term conditions, including elements of care highlighted as priorities in the Five Year Forward View8 such as coordinated and integrated care, hoListic and personalised care, and self-care.
+METHOD
+The authors searched for reports oo empincal quantitative research examining the effectiveness of the QOF in the manngemenn of long-term conditions, published in peere reviewed journals in Engllsh. They included studies of populations re<gist:ered wiih GPs in the UK, and exduded sSudiee oo LuocLL^u designed and implemented pay-for-performance schemes, and sSudies oo fimiied geographical scope (which were defined an examining data from fewerttnan tou^poimary care trusts in EngOind, or fewer thhn 100 practices in Scotland) because oo likely low generalisability. The authors included studies where the comparator was any ooher method of funding general practice, concurred or historical, and, if there was neo concurred comparator, where the analysis 00^00^ for underlying trends. They set no Liimit^ oo outcomes except that they were measured quantitatively and related to patients with long-term conditions, including:
+•	measures of health or morbidity: biochemical and physiological measures, mortality, hospital admissions;
+•	biomedical aspects of delivery of care: diagnostics, plans, referrals, and ongoing monitoring, clinical interventions (for example, prescriptions, immunisations), consultation rates;
+•	broader aspects of care: coordination,
+continuity or integration of care, holistic care (that is, that considers multiple morbidity and social context, personalised for the patient), self-care; and
+•	patient perspectives: patient experience, quality of life, or sa tisfa ration.
+The authors included randomised controlled trials, longitudinal studies where the analysis attempted to control for underlying trends, controlled before-and-after studies, and systematic reviews of these. They excluded cross-sectional studies examining how outcomes varied according to QOF achievement, because of the lack of suiiable conerots, the neare universal high level of achievement, and the high likelihood of confounding of associations between QOF achievement and outcome by other factors. The authors also excluded studies in which the researchers estimated or modelled outcomes rather than reporting empirical data (more details of the inclusion and exclusion criteria are available from the authors). The authors searched electronic databases (Cochrane Database, Medline, Embnse, and Health Management Information Consortium) for studies published between 200-4 (the year the QOF was introduced) and May 201 <6 (see Box 1 for search terms). They examined references of identified papers to search for further reports and asked experts for references to other relevant research.
+Two of the authors assessed suitability for inclusion for each abstract identified, and, where there was no consensus, asked a third author to adjudicate. Data were extracted independently from all papers by two authors.
+The authors assessed quality of randomised controlled trials using adaptations of the Cochrane Collaboration's tool,12 and longitudinal studies and systematic reviews using tools adapted from those developed by the National Institutes of Health.13,14
+RESULTS
+Identification and description of studies
+Figure 1 shows the process oo ideneitication of studies. The three most recenn systemattc reviews asking the same questions as this review had search dates in 201215,16 and 2015.17 These included 20 studies oo the QOF in total.18-37 The systematic review with the 2015 search date17 identified two studies of the QOF,22,32 both of which had been published in 2011 and had been included in one or other of the two reviews with search dates in 2012.
+In all three reviews, while the authors set
+few limits on outcomes, stud;/ outcomes were solely derived from either QOF indicators themselves or prescribing data, with two exceptions: one study examined adherence to British Thoracic Society spirometry standards36 and the other cardiovascular events in patients with hypertension.32
+The authors found five primary research studies meeting the search criteria published since 20 1 2.38-42 The first examined trends in mortality rates for conditions covered by the QOF, comparing the UK with other high-income countries with no pay-for-performance schemes in primary care.38 The others examined the effect of introducing the QOF on patient management and outcomes, either nationally,39 or in samples of practices participating in the UK General Pnactt^^ Research Database (GPRD) or Clinical Practice Research Datalink (CPRD) (627 UK practices,40 516 UK practices,41 148 English practices42).
+The studies’ outcomes were: mortality from long-term conditions;38 hospital admissions for a range of QOF and non-QOF conditions;39 consultation rates in severe mental illness (SMI);40 prescribing in type 2 diabetes;41 and a composite indicator derived
+from QOF data on processes and outcomes of care in type 2 diabetes;.42 Because of a high degree of study heterogeneity, the authors carried out a narrative synthesis. Table 1 summarises the design and results of primary research studies.
+None of the relevant studies identified by the systematic reviews, or the primary studies published since 2012, examined the effect of the QOF on broader aspects of care or patient perspectives.
+Quality
+The systematic reviews were of good qua^y. Due to the nature of the intervenfion, the primary research studies were all before-and-after studies using interrupted time series or difference-in-differences methods and, as such, were of1 good quafiiy for observational studies. However, because of the study designs, the authors cannot be sure that the QOF was responsible for any change in outcomes.
+Findings
+Systematic reviews. The first systematic review concluded that the QOF had had a limited impact on health outcomes.15 The second systematic review concluded that the effect of pay-for-performance remained uncertain.16 The third systematic review concluded that there was limited evidence of the effects of financial incenfives.17
+Primary research. The study examining trends in mortality in the UKcomparedwiih other countries found no effect of the QOF, although the synthetic control approach43 adopted in the study required the use of conservative tests for statistical inference.38 The study examining emergency admissions before and after the introduction of the QOF found that the trend of increasing emergency hospital admission rates (which increased overall by 3-4% between 200-4 and 2010) was modestly lower for condifions incentivised in the QOF compared with conditions that were not incentivised in the QOF, by 3% in the first year rising to 8% in 2010.39 The difference was mainly driven by relative reductions in emergency admission rates for coronary heart disease.
+The study examining consultation rates found a trend of increasing rates overall during the period, with a small step change in 2004; the rate of increase was greater in people with SMI than overall.40 The face-to-face consultation rate in SMI increased from about nine to 11 per patient per year from 2000 to 2011, and in other people it stayed stable at about five per patient per year over the same period.
+The study of prescribing in type 2 diabetes found a modest increase in prescr^ii^incg of antidiabetic medication (changing the direction of the trend of1 decreasing initiation rates to increasing initiation rates) after the introduction of the QOF.41 The increase was sustained at a similar rate unttl 200f^.
+The study examining effects on a composite indicator of process and outcomes in type 2 diabetes found a modest improvement of 14% over and al^c?\^^ the underlying trend in the first: year affer the introduction of the QOF, declining to 8% in the third year42 Table 1 summarises the results of the studies.
+DISCUSSION
+Summary
+The authors found evidence that the QOF may be associated with a modest reduction in emergency admission rates in long-term conditions, a modest increase in consultation rates in SMI, and modess improvements in certain limited aspects of the care of diabetes. They found no clear evidence that these changes have led to any effect on mortality. Because of1 the design of the studies, it is not possible to be sure that any of the positive effects seen are causally related to the QOF.
+The authors found no evidence to suggess that the QOF influences, positively or negatively, other aspects of care, such as integration or coordination of care, holistic or personalised care, or self-care, nor any evidence of its effects on patter^tzs' qual^y oo life, experience, or satisfaction.
+The QOF is unlikely to advance progress towards the vision of the Five Year Forward View for the care oo1 long-term co nd d^ons. To deliver the aims oo1 the Five Year Forward View, the NHS should consider more broadly — beyond what is measured by the QOF — what constitutes high-quality primary care for people with long-term conditions, and consider managing performance on this basis. In the context of a demoralised primary care workforce, it is important also to consider ways other than financial incentives to motivate primary care teams to deliver high-quality care.
+Strengths and limitations
+To the authors’ knowledge, this review is the first to have specifically addressed the effect of the QOF on those aspects of care for long-term conditions that are prioritised by national policy As wiizln any systematic review, the authors’ conclusions are constrained by the limited quantity and quality of the primary research publshed to date. Although the search for quantitative
+research was comprehensive, the authors did not include qualitative research, which may provide other insights.
+Research to date has not attempted to identify the effects of the QOF on any of the broader aspects of care for long-term conditions, having examined effects only on more easily measurable outcomes, for example, those collected as part of the QOF, or routinely available data on mortality, emergency admissions, consultation rates, and prescribing. The authors found no evidence of attempts to evaluate the QOF using validated measures of quality of care in general practice. Perhaps this is because defining and measuring quality of general practice is complex.44
+The lack of effect of the QOF on mortality is surprising, given that the indicators are based on high-quality evidence of effectiveness of interventions. Why this i s the case is not clear The wider dete^minants oo population health (including low income, experience of inequality or discrimination, or quality of air, educatzton, housing, orwork conditions45) may be much more importann than the quality of care in dete^mining mortality. Also, it is recognised that the effectiveness of interventions demonstrated in randomised controlled trials, which include highly selected study participants, is often diluted in routtne clnical pr'acttce.46 Perhaps non-incentivised activities are more important in determining mortality in the patient population. It is also possible that practices misreport performance so as to exaggerate the quality of care, although there is little evidence that this is a significant problem.47
+The authors found evidence that the QOF was associated with a modess slowing oo the increase in emergency admissions for conditions for which care is incentivised by the QOF, and an increase in primary care attendances for people with serious mental illness. Whether the QOF is responsible for these is unclear; many other factors are likely to have influenced admission and attendance rates over the period, including changes in medical technology or access to other services, or national standards for management of long-term conditions. In any case, among interventions to prevent emergency admissions, pay-for-performance is unlikely to be one of the most effective.48,49
+It could be argued that some QOF indicators — in palliative care, cancer, SMI, dementia, and rheumatoid arthritis — incentivise multidisciplinary meetings, reviews, and care plans, considered necessary elements of holistic and
+integrated care (as set out in recent guidance from NICE).50 However, to achieve the indicators, practices are not: required to demonstrate that their activities ensure that holistic or integrated care has been delivered.
+Implications for research and protcke
+The authors found no convincing evidence that the QOF promotes better care and outcomes for people with long-term conditions. QOF may also have negative effects. If practices have achieved maximum or near maximum points under the scheme (which is true for most practices), they have little motivation to improve achievement further It is likely that: the QOF diverts practices and professionals from ways of providing high-quality primary care that: is not QOF-related. Moreover, the QOF does not incentivise practices to improve care for patients with the most complex needs in primary care, because these are more likely to be excepted from the scheme.51 Raising thresholds for achievement may be counterproductive — there is evidence thatt it leads to increased exception reporting, raising apparent achievement with no real increase in the desired actMty.52
+The Chief Executive of the NHS
+announced in October 2016 that the QOF would be phased out:.53 What would happen to the quality of primary care if the QOF is completely abolished is not clear, although it seems unlikely that standards would drop significantly, because the activities rewarded in the QOF are now firmly embedded in practice. There is some limited evidence to suggest that performance did not fall following the withdrawal of certain individual indicators from the scheme.54 Abolishing the QOF may also allow practices to prioritise other activities, which could lead to better care.
+The QOF provides a mapr componenn of practice income; if it were abolished, practices would need to be assured of a stable income. Losing this is likely to have detrimental effects on patient care and further worsen recruitment and retention in primary care, which is once again in a precarious position.55
+Alternative methods of rewarding good practice are being considered for new models of primary care.56 Any replacement for the QOF needs to consider the evidence of effectiveness of pay-for-performance in primary care, and the evidence of what motivates primary care professionals to provide high-quality care.57
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+1.	Introduction
+Close to 800,000 people die by suicide annually, and for each suicide, there are approximately more than 20 suicide attempts [1]. Consequently, suicide has become an urgent public health problem worldwide and reduction of the suicide mortality rate has been prioritized by the World Health Organization (WHO). Simultaneously, while suicides are influenced by many factors ranging from mental health problems to social factors, one of the research areas on the risk factors is the possible influence of media stories on subsequent suicidal behavior [2]. Interestingly, while in medical literature the idea that media reporting on suicides may lead to suicide contagion dates back to the 19th century [3], the debate itself began in the late 18th century, when Goethe's novel The Sorrows of Young Werther (1774) was followed by numerous suicides across Europe [4]. Nevertheless, although in his classic work Suicide (1897) Emile Durkheim argued that some individuals may be prone to suggestion, he did not believe that imitation occurs often enough to affect the suicide rates in society [5]. Consequently, the French sociologist believed that prohibiting newspapers from publishing suicide stories is useless. Thus, until the 1960s, social debate on the copycat effect was based on impressions and it is only in the last 50 years that the effect of media stories on imitative suicides has been recognized as a public health issue and has become a topic of scientific research.
+The first evidence on the imitation effect following broad publicization of suicide stories in the media was presented in 1974 by the American sociologist David Phillips, who noted that after stories about suicides had been published in the New York Times, their number rose significantly [2]. To indicate the negative influence of media portrayal of suicides, Phillips coined the term the ‘Werther effect'. However, it has been also suggested
+that under certain conditions, exposure to accounts of suicidal behavior in the media is associated with a lower risk of suicide attempts and can have a more positive, i.e., educative or preventive, effect, especially when the media present constructive coping strategies with suicidal ideations or emphasize other solutions to adverse life circumstances. Indeed, quite recently, to indicate a positive preventive effect of media reporting on suicidal behaviors, Niederkrotenthaler and colleagues [6] introduced the expression the ‘Papageno effect'. The term itself is a tribute to the character from Mozart's opera The Magic Flute (1791), who overcame his suicidal crisis after a talk with three boys. Thus, although the evidence of the impact of media coverage on suicide continues to mount, modern research has been marked by conflicting results [7-16].
+It is of special importance in the context of the increasing prevalence of nonsuicidal self-injurious behavior (NSSI), which is particularly frequent among adolescents and young adults, who are also more prone to imitation. Additionally, even though some perceive NSSI behaviors as an adaptation strategy to a crisis situation or emotion dysregulation [17], it has been well documented that when NSSI behaviors become chronic, they may evolve toward attempted suicides. Moreover, as it has been proven that the relationship between nonsuicidal self-injurious behaviors, suicide attempts, and actual suicides is causal and real [18-20], it has also been shown that media portrayals of NSSI may lead to increases in such behaviors [21,22].
+Most frequently, the impact of the media on subsequent suicide has been explained either by the social learning theory or by the identification theory [23,24]. The former emphasizes the fact that because most human behaviors are learned observationally through modelling, some vulnerable individuals (for example, patients with depressive disorder, persons with previous suicidal attempts, or those experiencing a psycho-social crisis) may learn from the media that personal problems may be solved by suicide and may adopt suicidal behavior. The latter stresses that because individuals tend to identify with persons who are similar to themselves, persons who face similar emotional states or experience problems or crisis similar to those as suicidal victims presented in the media, may develop a sort of attachment that encourages them to imitate suicidal behavior. At the same time, while some stress the importance of the “horizontal identification", which indicates that individuals identify more strongly with persons who share their demographic characteristics, such as gender and age [13,14,23,25,26], others refer to “vertical identification" and argue that individuals mimic the behaviors of persons who are famous and popular, admired, or perceived as socially superior, i.e., celebrities [13,26-28]. All in all, it is argued that as individuals learn through the media that suicide is an appropriate solution to personal life's problems, imitation plays an important role in modelling suicides.
+Thus, this paper aims to provide an overview of the research referring to the association between media reporting on suicides and the subsequent number of suicidal behaviors in the general population. While previous reviews often focused on a particular type of media (newspaper and television news, television dramas, soap operas, cinema), on the type of suicide (fictional vs. real deaths) or suicide method (i.e., jumping, shooting, railway or subway suicide or paracetamol overdose), on the media effect (the negative vs. the protective effect), or on the social group affected (i.e., patients with depressive disorders, persons with previous suicidal attempts or various demographic groups), this review explores the influence of suicide reporting in various types of media and considers both fictional and real deaths, including celebrity and noncelebrity suicides. It also evaluates the research on both the negative and the protective effect and examines who is more prone to the modelling effect. Thus, it intends to investigate what type of media and what type of suicide reported have the largest influence on imitation suicides.
+2.	Methods
+2.1.	Search Strategy
+The review was conducted in accord with the principles of the PRISMA statement [29], and the results of the identification, screening, eligibility assessment, and final inclusion are
+presented in the PRISMA 2009 flow diagram with some minor modifications (Figure 1). The literature search was conducted in two orfrne WbUograpWe sources: Medline (PubMed) and PsycINFO in October 2020. For the search criteria, a combination of key words was used: ‘suicide’ ('suicidal behaviors’, ‘suicidality’) and ‘media’ (‘newspaper’, ‘television’, ‘film’, ‘movie’) either in the title, abstract, or keywords of the pubUcation.
+Figure 1. PRISMA 2009 flow diagram for the review and selection process of the studies included.
+2.2.	Inclusion and Exclusion Criteria
+Articles were included if they reported empirical studies on the impact of media coverage on suicide, were written in English, and were published in peer-reviewed journals. Research on suicide clusters without the media effect, those on physiological and attitudinal responses to suicide films and other media stimuli, and descriptive studies that provided no data on the copycat effect were excluded. Research that focused on the impact of media awareness campaigns on stigma reduction and help-seeking was also rejected. Finally, as this review focused on the traditional media, studies on the impact of the Internet and social media were also excluded.
+2.3.	Study Selection
+The initial search identified 1617 publications, which were selected based on their titles and abstracts. After removing duplicates, 1025 records were left for screening. The first stage of the screening excluded all non-English articles (n = 113). Next, all other
+articles were screened on the grounds of their titles and abstracts in order to assess the relevance of the subject and eligibility. Thus, of the remaining 912 publications, 779 did not meet the eligibility criteria: 71 were books or book chapters and 42 were editorials, commentaries, communications, or letters. The additional 109 articles focused on suicide clusters without the media effect, 56 on physiological and attitudinal responses to suicide films or other media stimuli, 87 described media reporting on suicide without the copycat effect, 31 reflected on the impact of media awareness campaigns on stigma reduction and increasing help-seeking, and 383 focused on social media. Thus, of all the studies that met the inclusion criteria, 108 were reviewed and analyzed: 98 research articles, 7 systematic reviews, and 3 meta-analyses.
+Such a broad scope of the review has resulted from the research questions posed in the study. As it intended to determine which type of the media and what type of suicide (method) is especially prone to influence the subsequent suicides and whether the media serve as a risk or as a protective factor, research employing a variety of methods was included into the review. However, because of the heterogeneity of the search, the results and the variety of methods used in the studies limited the possibility of a quantitative synthesis of the articles, and both the qualitative and the quantitative findings were integrated and an overall qualitative synthesis was performed and key findings presented.
+To assess the papers, the study findings were read and analyzed according to the review questions regarding the role of the media in suicide contagion, the type of the media, and the type of the suicide method reported in provoking subsequent suicides. The qualified articles were assessed using a thematic analysis [30]. To identify the main themes, the study questions were used. The results were selected by employing a deductive approach and classified as thematic categories, representing the relationship between media stories and suicides. Three main themes were identified: (1) the positive association between media portrayal of suicide and imitation acts, (2) the preventive effect of media reporting on suicidal behaviors, and (3) no copycat effect. Nevertheless, not all the articles dealt with every one of these themes.
+3.	Results
+The main characteristics and findings of the studies included in the qualitative synthesis are presented in Table 1. Most of the studies (n = 45) came from North America, including 41 from the US [2,27,28,31-68] and 4 from Canada [69-72]. In total, 25 studies were conducted in Asia, including 6 in Taiwan [73-78], 8 in South Korea [79-86], 4 in Hong Kong [87-90], 3 in Japan [91-93], 2 in Israel [94,95], one in India [96], and one study was conducted in Hong Kong, Taiwan, and South Korea [97]. Another 23 studies came from different European countries: 10 from the United Kingdom [98-107], 7 from Austria [6,26,108-112], 4 from Germany [25,113-115], and 2 from France [116,117]. Finally, five studies were conducted in Australia [118-122].
+Table 1. Studies examining the impact of the media on imitative suicides.
+In total, 65 studies focused on the impact of newspapers and television news [2,6,26-28, 31-37,39,41,43,45,46,49,51,54-57,59,60,62,63,65,68-72,74-79,81,83-93,95-97,100-102,108-113, 118,119]; 21 examined the copycat effect following television dramas, soap operas, and other types of movies [25,38,42,44,47,48,50,52,53,58,61,67,94,98,99,103-107,116]; and 12 analyzed the influence of mixed media [40,64,66,73,80,82,114,115,117,120-122].
+In total, 20 studies investigated the impact of fictional stories on suicides [25,38,42, 44,47,48,50,52,53,58,61,67,98,99,103-107,116]; and 78 examined the effect of non-fictional suicides, including those committed by celebrities [2,6,26-28,31-37,39-41,43,45,46,49,51,54-57,59,60,62-66,68-97,100-102,108-115,117-122].
+In total, 69 reported the negative impact of the media on the actual suicide rates [2,25,26,31-39,43,44,46-49,51,56,57,59-63,67,68,70-93,96,97,100-104,107,111,113-116,118,119,121,122], and 16 found no imitative effect [31,40-42,45,50,53-55,58,64,69,95,105,106,120] while only 4 reported the protective effect, which followed either the cessation of stories during newspaper strikes or the implementation of media guidelines for suicide reporting [108-110,112]. Additionally, nine studies found mixed or conflicting results [6,27,28,65,66,94,98,99,117].
+3.1.	The Werther Effect
+The first research indicating a positive correlation between media stories about suicides and subsequent deaths was described by Phillips in 1974, who showed that suicide stories published in the New York Times influenced the actual number of suicides [2]. In later studies, Phillips reported that imitational suicides may also be hidden as motor vehicle fatalities [34,36,37] or airplane accidents [35] and that their number increased after suicide stories appeared in newspapers and on television. This debate was further advanced when Bollen and Phillips [39] and Phillips and Carstensen [46,51] found a significant increase in suicides among American teenagers during the week following the broadcasting of suicide
+stories on network television evening news programs between different time periods (1972-1976,1973-1979, and 1968-1985, respectively) [38,43].
+Similarly, the vast majority of research (n = 69) included in this review shows a positive association between media coverage of suicidal behaviors and actual suicidality. At the same time, although researchers have examined the modelling effect following the various types of media, including newspaper and television news, television dramas, soap operas, and cinema, reviews by Stack [13,123] suggested that televised stories were 82% less apt to find the imitation effect than the research based on newspapers. On the other hand, while major television networks had a larger influence on imitation suicides than those covered by other types of news media, such a probability was even higher when suicides were reported by multiple media. Moreover, stories on non-fictional suicides, i.e., those committed by real people, and celebrities in particular, were found to be more likely to influence suicidal behavior than fictional suicides depicted in movies, TV dramas, or weekly soap operas (see below) [7-16]. For example, Stack [123] reported that non-fictional stories were 4.03 times more likely to predict the copycat effect than fictional stories and that stories about celebrity suicides were 14.3 times more likely to provoke the Werther effect. However, some studies reported increased rates in suicides following the exposure to fictional dramatizations of suicides in movies [8,11,25,38,44,47,48,52,61,67,103,104,107,116].
+Because the impact of non-fictional suicides is discussed in the next paragraph, let us now present only some general findings on fictional portrayals of suicide in movies. Thus, in the 1980s, Ostroff et al. [44,48] reported an increased number of young people who attempted suicide after the airing of a TV drama Surviving showing a teenage couple who committed suicide by overdose. Additionally, all respondents they interviewed reported having seen the program. Schmidtke and Hafner [25] also reported the Werther effect after the broadcast of a German fictional weekly TV series featuring the railway suicide of a 19-year-old male. Moreover, they reported both increased suicide rates and the number of suicides committed by the same method. The same effect was observed when the series was repeated years later.
+Biblarz et al. [61] demonstrated that college students exposed to movies about suicide showed significant emotional arousal and changes in the attitude toward suicide. Hawton et al. [107] reported that a TV broadcast Casualty depicting a deliberate paracetamol overdose influenced young people’s self-poisoning, which increased by 17% in the week after the airing of the episode. Moreover, 20% of patients declared that the program had influenced their decision to take an overdose and 17% said it had influenced their choice of the drug. All in all, while it is often suggested that non-fictional suicide stories in newspaper or television news are more likely to provoke the copycat effect, fictional suicides depicted in movies may also provoke imitative behaviors. At the same time, it has been shown that there are some conditions that enhance the probability of the imitation effect: the celebrity status of the suicide victim, similar demographic characteristics of the victim and the media audience, and media reports on a new suicide method.
+As it has been found that in order to influence the public, suicide stories must be about real and not fictional people, research also suggests that it is famous and socially admired individuals who are particularly strong role models and give some sense of legitimacy to the suicidal act [2,27,28,49,57,92]. For example, Jonas [113] reported that each suicide committed by a prominent individual publicized in major German newspapers was associated with additional suicides during the week after the report. Similarly, both Fu and Yip [97] and Park et al. [85] showed that celebrity suicides constitute an independent risk that may trigger suicidal ideation not only among individuals experiencing depression or negative life events but in anyone. A review conducted by Niederkrotenthaler et al. [26] found that the celebrity status of the reported suicide was the major predictor of a postreport increase in total suicides. Additionally, a more recent meta-analysis revealed an association between media stories of a celebrity suicide and the subsequent suicides, which increased on average by 2.6 suicides per 100.000 people within the month following such reports [15,16].
+Thus, especially during the last 20 years, news reporting about celebrity suicides has been at the core of scientific interest in the media impact on the copycat effect [8,10,11,15,16, 26,70-74,79-86,88,91,96,97,111,114,117,120-123]. For example, Ladwig etal. [115] showed that after the railway suicide by a famous German national goalkeeper Robert Enke, the overall percentage of suicides increased by 117.2%. Ueda et al. [93], who investigated the impact of media reporting on 109 celebrity suicides in Japan from 1989 through 2010, found that such reports resulted in an immediate increase in the number of suicides in the general population by 4.7%. Additionally, a recent review by Niederkrotenthaler et al. [16] reported that media stories about celebrity suicides have a strong impact on the total number of suicides in the general population.
+However, research suggests that the imitation effect holds mainly for two types of celebrities: entertainers (i.e., music and movie stars) and political figures, while famous villains and the economic elite do not trigger imitative suicides [8,13,15,28,49,70,72,73,75, 79,80,85,88,93,97,117,123]. For example, Stack [8] reported that the impact of entertainment or political celebrity suicide reports was 14.3 times more likely to trigger the copycat effect than other stories. Additionally, his other meta-analysis found that such suicides were 55.27 times more likely to produce the imitation effect [13]. A similar observation was reported by Niederkrotenthaler et al. [15]. These general findings were supported by Queinec et al. [117], who investigated the impact of media coverage of six famous celebrity suicides between 1979 and 2006 in France and found the modelling effect after the suicides of the famous singer Kurt Cobain and the politician Pierre Beregovoy, but not in the other four. The suicide committed by the famous actor Robin Williams was followed by a 10% increase in US suicide rates, especially among males aged 45-64 years, and many individuals used the same method [71,122]. Menon et al. [96] reported that more than 5% of the total suicides in India were linked to the widely reported death of the celebrity actor Sushant Singh Rajput.
+Nevertheless, some research has shown that not all entertainment celebrity suicides reported by the media increased the actual suicides rates. For example, although in Australia the death of the famous singer Kurt Cobain received wide media attention, owing to its cautious reporting style, the Werther effect did not occur [64,120]. Additionally, Fu and Chan [80] found that apart from intensive media coverage, only 3 of 11 Korean celebrity suicides had a significant impact on the overall suicide rates.
+Yet another study suggests that noncelebrity suicides, too, may trigger the imitation effect as people who commit suicide can also identify themselves with ordinary people, i.e., persons of a similar background or social status [13,14,23,25,26,49,56,90]. For example, Kunrath et al. [114] reported a 44% daily increase in railway suicides after media coverage of a non-prominent railway death. This supports Stack's [56] and Gundlach and Stack's [60] observation that if a story about noncelebrity suicide receives enough publicity, it can also provoke imitation. Thus, arguably, it is not the unique quality of the celebrities' status per se that leads to imitation but rather high publicization of such a suicide that assures the celebrity status of a suicidal person.
+At the same time, most studies report that especially persons who experience emotional or mental problems and share the suicide victim's demographic characteristics or are in similar life situations are more likely to imitate suicidal behavior. It is argued that particularly vulnerable individuals, i.e., those with pre-existing psychopathology or in a psycho-social crisis (suffering from depressive disorder, with previous suicidal ideations or previous suicidal attempts), are at an increased risk of the modelling effect [8,9,13,14,16,25,26,59,68,79,123]. For example, Fu and Yip [97] and Park et al. [85] reported that individuals experiencing depression or negative life events are more likely to imitate celebrity suicides publicized in the media.
+Furthermore, the copycat effect was more notable in the groups whose age, gender, and nationality were similar to those of the victim. For example, as domestic celebrities were found to have a greater potential to provoke the imitation effect than foreign celebrities [8,15,28,123], both Stack [92] and Choi and Oh [84] reported that while Japanese and
+Korean victims triggered subsequent suicides in the general population, non-Japanese and non-Korean suicides did not.
+Furthermore, Fu and Yip [97] reported an elevated risk in terms of the same age (49%), gender (40%), and method (63%). However, although some argue that all age groups are prone to the copycat effect [79,85], both Stack [13] and Chen et al. [75] reported a significantly higher risk among the middle-aged, while Stack [8,57,123] and Sinyor et al. [72] found evidence that media stories about elderly suicides were followed by individuals aged 65 and over. They stressed that it may have resulted from the suicidogenic life circumstances of the elderly, i.e., a high degree of physical illness, deaths of spouses and friends, social isolation, and low income. Similar observations were made in Taiwan [75].
+Nevertheless, it is younger people in particular, including adolescents, who were found to be the most vulnerable to imitative influences [10,12,13,25,26,44,46-48,51,54,55, 66,68,71,72,75,79,80,86-88,96,97,120]. For example, Hawton et al. [107] found out that TV broadcasts picturing a deliberate paracetamol overdose increased young people's selfpoisoning by 17%. Additionally, Gould et al. [12,68] showed that the suicide risk among 15-19-year-olds exposed to suicide reports was 2 to 4 times higher than among other age groups. According to Stack [13], research results based on young persons were 54% more likely to find the copycat effect.
+Although some researchers have found no gender-specific association [88], and there is a body of evidence showing that the imitative effect is more prominent among persons of the same gender as the suicide victim [75,79,88,97]. At the same time, while some studies report that males are more susceptible [71,73,88,97], other suggest a much greater imitation effect among females [2,16,26,73,75,79,80,88,96,97]. For example, as early as in the 1970s, Barraclough, Shepherd, and Jennings [100] reported an increased risk of imitation in men under 45. A similar observation was made in Australia [118]. Additionally, Fink et al. [71] reported a 10% increase in US suicides, especially among men aged 45-64 years, in the months following the suicide of Robin Williams [122]. On the other hand, Phillips and Carstensen [46,51] found a significant increase in suicides among American female teenagers following the airing of suicide stories on television evening news, and, according to Stack [13], studies were 4.89 times more likely to find the copycat effect among women. A recent study in India reports that young females are more susceptible to the imitation effect [96].
+Finally, research has shown that, as the media influences the increase in the total number of suicides, they also boost the probability of using the same method as the victim. Such a probability is especially high when the suicide method was used by a celebrity, reported in detail and as novel or painless [15,75,80,81,97,123]. Some researchers even suggest that after media reports on novel suicide methods, the number of those employing conventional methods dropped [76,78]. Indeed, a recent review of 31 studies published between 1974 and 2019 found that media descriptions increase the cognitive availability of a suicide method used by a celebrity, and this, in turn, results in a 30% increase in deaths by the same method [16]. Thus, while Fu and Yip [97] reported an elevation in the risk of suicide in terms of the same method by 63%, many researchers have found that media stories were associated with an increased number of suicides employing methods, such as burning [101,102], charcoal burning [75-78,87,89,90], hanging [16,70,73,79,96], shooting [111,120], plastic bag asphyxia [116], jumping [79,88,94], railway suicide [25,114,115], subway suicide [108-110], suffocation [71], and paracetamol overdose [44,48,103-105,107]. For example, Ostroff et al. [44] and Ostroff and Boyd [48] observed an increased number of young people admitted to a Connecticut hospital who attempted suicide by overdose, the method used by a teenage couple in a TV drama Surviving. An interview-based study from Taiwan reported that 87% of individuals who survived a suicide attempt by charcoal burning admitted that their choice of the burning method was influenced by the media [75,76,87]. A similar observation was made in Hong Kong [89,91] and in South Korea, where the suicide rates by hanging and jumping increased by 31.80% and 60.71%, respectively, after the media reported about the actress Jin-Sil Choi and former President
+Moo-Hyun Roh of the Republic of Korea who used those two suicide methods [79]. Finally, both Fink et al. [71] and Pirkis et al. [122] found that the suicide rates by hanging increased significantly after the death of Robin Williams.
+At the same time, while some researchers have shown that imitation exerts rather a short-term effect [2,25,39,46,74,83,98,99,111,113], others suggest that it may also trigger a long-term effect [15,70,71,77,79,80,91,97,114,115]. For example, while Bollen and Phillips [39] found two significant post-report peaks in suicides, one immediately after the media reports and another after 10 days, other researchers have established that the media effect can persist for 2 [44,48,75,93], 4 [2,73,77,81,97,115], 6 [79], 8 [94,114], or even 9 weeks [80].
+3.2.	The Papageno Effect
+Even though earlier researchers were skeptical of the possible preventive effect of media reporting on suicide [31], it was Jerome Motto who first demonstrated that suicide rates may be reduced either by a newspaper blackout, lowering of the quantity of reporting, or by changing the quality of the media reporting style, i.e., by elimination of the emotional and sensational details of suicidal behaviors. He found that the 286-day period of a newspaper blackout in Detroit resulted in a reduction of suicide rates among young females [32]. A similar observation was made by Blumenthal and Bergner [33], who reported a decrease in suicides following the cessation of news stories that occurred during newspaper strikes. Finally, early studies by Holding [98,99] showed no increase in suicide attempts following an 11-part series on a suicidal individual who was helped by the Samaritans, the prevention service in Edinburgh. Moreover, the series increased the audience's knowledge on the suicide prevention center and the number of its clients. Consequently, it has been argued that when the media present constructive coping strategies with suicidal ideation or emphasize other solutions to adverse life circumstances they can have a preventive effect [6,14,16,108-110,112].
+Simultaneously, while some researchers suggest that the media support suicide preventive efforts by educating the public about the treatment of mental illness [124], others stress that it is rather negative media coverage of suicides, i.e., the ‘non-attractiveness' of the suicide victim or the circumstances of the suicidal act that decrease the probability of the imitative effect [36,55]. Indeed, researchers have found that when the media describe suicide via unfavorable characteristics, i.e., as a wrong, horrific, painful, or disfiguring act, or present suicidal individuals as deviants, it may weaken the probability of imitation [9,13,26,68,73]. For example, negative media coverage of the Jonestown mass suicide in 1978 [40] and Kurt Cobain's death in 1994 [64,120] was linked to the absence of the copycat effect. Additionally, Romer, Jamieson, and Jamieson [66] observed that although newspaper reports and television news were associated with an aggregate increase in suicide deaths, especially among younger individuals, in persons aged 25-44 they had a protective effect. Finally, Niederkrotenthaler et al. [6] found that reports on individuals who contemplated suicide but later coped with the adverse circumstances in a constructive way were associated with a short-term decrease in suicide rates.
+However, a systematic review 56 studies on the role of the media in suicide prevention by Sisask and Varnik did not confirm the protective effect [14]. Additionally, Sinyor et al. [72] reported that the most putative protective factors, such as unfavorable characteristics of the suicidal act or spreading messages that suicidal thoughts can be overcome, have low base rates in the media and are not associated with a lower number of suicides.
+3.3.	The 'No Effect'
+Although the majority of researchers have found the Werther effect following media reporting, a considerable number of 26 studies have noted either conflicting results [6,27,28, 65,66,94,98,99,117] or no copycat effect at all [31,40-42,45,50,53-55,58,64,69,95,105,106,120]. Likewise, Stack reported that of 42 research findings, less than half were significant and that
+highly publicized stories increased the national suicide rate only by 2.51% [8]. Additionally, his other meta-analysis showed that out of 419 findings, only 35.8% gave evidence on the imitative effects following media reports on suicide [13].
+Earlier studies, in particular, failed to demonstrate the convincing effect of media reports on real suicides. For example, after replicating Phillips’ [38] research, Kessler and Stipp [42] were unable to discover any evidence on the impact of soap opera stories on the actual amount of suicides. In the same way, Wasserman [27], who re-examined Phillips’s [2] data, found no significant linkage between the New York Times’ front page stories on suicide and the subsequent number of suicides, except for celebrity suicides. Additionally, Hittner [65] obtained little or no evidence to support the Werther effect reported by Phillips and Carstensen [46,51]. Finally, Stack [40] did not observe any relationship between media reporting of the mass suicide in Jonestown, Guyana, and the national suicide rates. Moreover, some authors suggest that the increased suicide rates are better explained by an economic rather than imitation paradigm [28,41,63].
+At the same time, while most of those suicides were committed before 1990, some more recent investigations have also found no such association [80,117]. For example, Simkin et al. [106] did not find evidence of the impact of a TV drama Casualty showing a teenage girl’s overdose with paracetamol either on the number of patients’ decisions to take an overdose or their choice of drug. Bakst et al. [95] also did not observe any impact following media reporting about 13 deaths on the subsequent number of suicides in Israel.
+4.	Discussion
+This review confirms that there is a growing amount of evidence suggesting a positive association between media coverage of suicidal behaviors and the actual suicidality [7-16]. Although a considerable number of negative findings has cast some doubt on the existence of a media-induced imitative effect [40-42,45,50,53-55,58,64,69,95,105,106,120], 69 studies included in this review report that media stories on suicide are contagious and may influence the actual suicide rates. Significantly, while before the 1990s most of the studies were conducted in the US, after 2000, the number of investigations from other countries has increased rapidly. It is important because the quality of media reporting on suicide varies from country to country and reflects the differences in social attitudes and ideas about life, death, and suicide embedded in culture. For example, Fekete et al. [125] found that negative evaluations of suicide are much more common in American, Finnish, and German newspapers, which tend to depict suicides as criminal or psychiatric in nature and describe the more negative consequences of suicidal behaviors. Meanwhile, Hungarian and Japanese press characterizes suicides in a more romantic manner. Thus, while the copycat effect is observed in various countries worldwide, it has been shown to be dependent on the country where the study was conducted [15,16,87].
+Although the impact of various kinds of media has been examined, it seems that newspaper stories have the largest influence. Simultaneously, stories presented visually may also influence suicidal ideations and behaviors [121]. However, the copycat effect is even bigger when suicides are reported by multiple media [13,123].
+Another important issue is that presentations of real-life suicides and those committed by famous and socially admired people, in particular, are more likely to trigger additional suicides than fictional suicides depicted in television dramas, soap operas, and the cinema [16,72,84-86,91,96,122,126]. Nevertheless, because, under certain conditions, non-celebrities may also become role models for imitation, it has been suggested that broad publicization and the quality of media reporting are more important for provoking the copycat effect. Thus, the Werther effect depends more on the duration, amount, and prominence of media coverage [13,73,77,83,97,121].
+It has also been observed that a strong modelling effect is based on nationality, age, and gender. Thus, young persons and individuals with previous suicidal ideations or attempts are especially more vulnerable to the imitation effect [8,15,16,26,28,68,79,85,86,123]. Moreover, while some studies have reported a short-term effect, lasting from a couple of
+days to up to two weeks [2,39,44,46,48,74,75,83,98,99,111,113], the media can also trigger a long-term effect, which can persist for eight to nine weeks [15,77,79,80,91,114,115].
+Many researchers emphasize that although suicide is a complex phenomenon, media reports on suicide are selective and oversimplify the causes of suicide. While the factors affecting suicide range from physical and mental health problems, financial problems, broken (family) relationships, failure in school, substance abuse, and peer influence, to stressful life events, the media tend to attribute the act to a single factor. Moreover, they often underreport the relationship between suicide and mental illness, which is the most common factor leading to suicide [6,125]. For example, Gould [9] reported that many Hungarian newspapers characterized suicides in a romantic manner, which resulted in higher rates of suicidal acts in Hungary. Similarly, after analyzing 1377 cinematic suicides appearing in 1158 American movies released between 1900 and 2009, Stack and Bowman [67] found that in contrast to the scientific literature, which attributes suicide mainly to mental health issues, the cinema provides a highly sociological definition of suicide: it is attributed to economic or social strain, rejection in love, relationships falling apart, or shame. Other media also suggest that suicides are caused by societal problems [6].
+Moreover, the media reporting style was described as sensational and characterized by dramatic coverage, glorification, or glamorization of the deceased. For example, an analysis of Swiss press shows that 40-45% reports described suicides irresponsibly: 47% used sensational headings (large print, located on the front page, and sensational content), 39.1% presented at least one picture of the victim, the text of 35.8% articles was sensational, and 8.6% were front page articles [127,128]. A similar observation has been made in other countries, where the media tend to report suicides in a romanticized, glorifying, or thrilling manner. Moreover, the media often depicted the victim as an extraordinary or popular personality and suicide itself was framed as an inevitable or honorary act of bravery. The positive image of suicide was further strengthened by rationalization or romanticization of the victim's motives, for example, by describing it as a result of a terminal or painful illness, financial crisis, job loss, or heartbreak, which in the eyes of the public can legitimatize suicide [68,72,111,124].
+Finally, most studies report that media stories on suicide do not reflect the real incidence of suicidal acts as they often focus on the more violent modes of suicide that are presented in a more dramatic fashion [125,129-131]. Consequently, there is an overrepresentation of some kinds of suicides in the media. Pirkis et al. [130] find that only 1% of Australian suicides were newsworthy and became public via the media. Thus, they noticed an over-reporting of suicides by drowning (6% vs. 2% of all actual suicides), gunshot (18% vs. 10%), and high-impact methods (12% vs. 7%). Moreover, while suicides committed by the elderly are also over-reported (20% vs. 13%), those committed by the young are under-reported (26% vs. 21%). Fu, Chan, and Yip [132] reported that Chinese newspapers over-publicize suicides committed by persons aged under 25 and underplay those by persons aged 60 or over. More importantly, most studies stress over-reporting of suicides committed by celebrities [127,128,130,132]. Thus, there is also a growing amount of evidence showing that extensive media reporting contributes to the ‘advertising' of the unusual, novel, or dramatic and highly lethal suicide methods, which are rare in real life, such as burning, plastic bag asphyxia, or suffocation [16].
+At the same time, especially since the 1990s, researchers have suggested that, under certain conditions, the media may also play a positive, i.e., educative and preventive role [6,14,16,108-110,112]. There is also some evidence that the negative influence of the media may be modifiable and that improving the quality of suicide reporting reduces the risk of contagion. In fact, ever since WHO developed the media recommendations for suicide reporting in 2000, they have been implemented in several countries, including Australia, New Zealand, the United States, Canada, Germany, Austria, Switzerland, the United Kingdom, Japan, Hong Kong, and China [133]. Moreover, while such guidelines have positively influenced media reporting on suicidal behavior [134,135], it has also been proven that modification of the media reporting style may reduce the risk of the Werther
+effect [6,26,64,132-140]. For example, after implementation of the media recommendations in Austria, the suicide rates in the Viennese subway declined by 75% and remained low for more than 5 years [108-110]. In Australia, it resulted in a reduction of 81 suicides [112]. Thus, it seems that the role of the media is more complex, as it can be both a risk and a protective factor. However, the media often do not follow these recommendations: suicide stories frequently appear on the front page, include inappropriate imagery, mention suicide in the headline, describe the suicide method and the location, fail to link the death to broader social issues, and do not provide information about the warning signs, risk factors, and prevention resources [122,124-140].
+Although this review brings some new insight into the impact of media reporting on the subsequent suicidal behavior, it also has some limitations. First, the investigation was limited to two databases and some studies could not be identified. Second, it only analyzed articles written in English and published in peer-reviewed journals. Thus, in the future, it would be advisable to also include some non-English research and those materials published as books or book chapters. However, owing to the large amount of research included, this limitation should not change the general view and conclusions. Another limitation stems from the fact that the studies selected for the review referred to different kinds of media and/or were conducted on different populations. Thus, it is hard to make quantitative comparisons and draw conclusions.
+Finally, as only one coder screened the papers and conducted the analysis, it may have affected the reliability of the coding process. However, despite these limitations, a qualitative analysis was still possible and justified. Yet another limitation stems from the reviewed research itself. Because many studies were designed as ecological research, they failed to provide clear evidence that individuals who engaged in suicidal behavior following an event being reported in the media had actually been exposed to it. Thus, future research, both on the negative and the positive impact of the media, should try to determine whether suicidal individuals actually had access to the stimulus implied. Finally, as much more research has been done on the ‘Werther effect' than on the ‘Papageno effect', there is a risk of a reporting bias.
+5.	Conclusions
+While the media can be a double-edged sword and serve both as a risk and a protective factor, there is a growing amount of evidence showing that the relationship between media reporting and the actual suicide rates is causal and real. Consequently, the influence of media reporting on suicide is defined as an important risk factor affecting suicidality in the general population. At the same time, researchers suggest that both the quantity and the quality of media reporting may trigger additional suicides. Thus, suicide contagion is more likely to occur after extensive media coverage with a content rich in positive definitions of suicide. Moreover, especially non-fictional presentations of celebrity suicides can strongly influence the subsequent suicidal behavior. However, it should be acknowledged that because the negative reporting style can be modifiable and improved, the media can also have an educative or preventive effect and can reduce the risk of contagion. Consequently, it is important to monitor the implementation of the media recommendations for the reporting of suicide, and continual education of reporters is needed.
+Int. J. Environ. Res. Public Health 2021,18, 2396
+16 of 20
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+136.	Michel, K.; Frey, C.; Wyss, K.; Valach, L. An exercise in improving suicide reporting in print media. Crisis 2000, 21,1-10. [CrossRef] [PubMed]
+137.	Jamieson, P.; Jamieson, K.H.; Romer, D. The responsible reporting of suicide in print journalism. Am. Behav. Sci. 2003, 46, 1643-1660. [CrossRef]
+138.	Tatum, P.T.; Canetto, S.S.; Slater, M.D. Suicide coverage in U.S. newspapers following the publication of the media guidelines. Suicide Life Threat Behav. 2010, 40, 524-534. [CrossRef] [PubMed]
+139.	Creed, M.; Whitley, R. Assessing fidelity to suicide reporting guidelines in Canadian news media: The death of Robin Williams.
+Can. J. Psychiatry 2017, 62, 313-317. [CrossRef]
+140.	Stack, S. Media guidelines and suicide: A critical review. Soc. Sci. Med. 2020, 262, 112690. [CrossRef]
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+Introduction
+Suicide is a prominent public health concern, which accounts for almost one million deaths per year worldwide and has devastating impacts on individuals, families and communities [t]. Suicide attempts constitute a major risk factor for completed suicide [2], however, research shows that help-seeking for suicidal ideation is low [3] and suicide prevention services are underutilised [4]. Although limited, prior research shows that negative attitudes and stigma relating to suicide and help-seeking behaviour result in lower intentions to seek help [3].
+Suicide refers to the act of deliberately killing oneself [t]. A suicide attempt refers to the non-fatal attempt to inflict self-harm with the intent to die [5]. Suicidal ideation is a term used to refer to experiencing thoughts about suicide, which can be fleeting, can involve detailed planning, self-harm, and suicide attempts. Suicidal ideation can be assessed by determining frequency, intensity and duration of these suicidal thoughts [5]. Suicidal ideation is generally associated with depression; however, associations have been reported with many other
+psychiatric disorders, life events, and family events, all of which may increase the risk of suicidal ideation [5].
+Comorbid conditions, such as mental health and substance disorders, may present additional complexities and challenges to help-seeking by people experiencing suicidal ideation [6]. In particular, comorbid mood and substance disorders have been found to decrease helpseeking behaviours [6], suggesting that people with these comorbidities may require additional support when experiencing suicidal ideation. The urgency of this issue becomes even more apparent in light of the finding that depression and alcohol use disorders are the most common diagnoses in people with suicidal ideation, and that the risk of suicide increases exponentially when these disorders co-occur [7]; [8].
+Suicide is often conceived of as a “funnel process” [9]. Individuals tend to first experience suicidal ideation and may then engage in planning ways to act on their ideation, leading to an attempt and in some cases suicide. However, contrary to the idea of suicidal thoughts and behaviours being on a continuum, research has shown that suicidal behaviours can be sporadic, and do not always occur in a progressive sequence. De Leo, Cerin, Spathonis and [10] used telephone interviews followed by a postal survey of 1,311 participants to determine the lifetime prevalence of suicidal ideation and attempts, and the possible development of suicidal behaviours on a continuum. They found that 57.1% (n = 190) of participants identified that their suicidal thoughts fluctuated irregularly before they attempted suicide and that this was affected by co-morbid depression and alcohol use. These results indicate that, at least for this population, suicidal thoughts did not occur on a continuum of exacerbation, but that there are individuals who will continue to use suicide behaviour as a method for managing stressful life events. Further research examining the suicidal process, reckless behaviour, and help-seeking attitudes are therefore valuable to assist in the development of long-term prevention strategies and programs, as preventive interventions are possible at a number of points prior to suicide completion [9].
+The interplay of broader social and environmental factors that impact on people’s beliefs, attitudes and behaviours regarding help-seeking for suicidal ideation is complex. While the importance of multilevel, multifactorial systemic approaches to reducing suicide risk is beginning to be recognised in Australia [11]; [12], systems approaches to understanding and addressing suicide are in their infancy [13]. Accordingly, most studies have explored specific components of the help-seeking process, but very few have acknowledged or applied ecological approaches to a comprehensive exploration of the environmental influences (e.g., family, friends, and broader social networks) that appear critical in helping or hindering an individual’s decision to seek help [14].
+Bronfenbrenner’s (1979)[15] ecological systems theory suggests that individuals are best understood within the context of their environment [16]. This theory provides a useful lens for understanding the multiple socio-cultural and political systems that surround and influence an individual. It also recognises the potential for individuals to influence their environment [17]. According to Bronfenbrenner (1979) [15], interaction between an individual and their environment occurs at multiple, interconnected levels, including in the microsystem, mesosystem, exosystem, macrosystem, and chronosystem (see Table 1). He argued that people are embedded within these multilayered systems, and that their development, behaviours and experiences ultimately result from their complex interactions with and between these systems.
+While the ecological approach positions human development as intimately connected with, and dependent on, the multilayered contexts that surround them, it is important to acknowledge individuals’ own agency within these systems. Early literature on this theoretical approach tended to overemphasise systemic factors, and somewhat neglected the role of individual difference [16]. Consequently, the theory was revised by Bronfenbrenner, introducing
+the term ‘bio-ecological’[18], which acknowledges the influence of individual-alongside systemic-factors on a person’s development and experiences.
+The current paper reports on the findings of a qualitative study, which explored the experiences of people with comorbid depression and alcohol use disorders who had previously attempted suicide, and in particular, the help they sought and received prior to and following the attempt. The central aim of the study was to identify opportunities to encourage help-seeking by this population prior to potential suicide attempts. Applying an ecological systems theory lens to the discussion of findings, this paper also aims to present a deeper, more interconnected understanding of the multiple and broader systemic factors that may influence help-seeking behaviours for suicidality in situations where people are already experiencing complex life challenges.
+Methods
+Participants for the current study were recruited by recontacting original participants from the Self-Help for Alcohol and other drug and Depression study (SHADE—[19]) to establish their history of suicide behaviour and help-seeking behaviour. As part of their participation in the original SHADE study, participants (N = 274) gave extended consent to be contacted for further research projects (the SHADE project, Hunter Area Health Service HREC: 03/12/10/3.17, University of Newcastle HREC H-750-0204, Mid-Western Area Health Service HREC 2004/ 04, Central Coast Area Health Service HREC 04/30). Those who provided this extended consent formed the eligible pool for the current study, which received ethical approval from University of Newcastle (HREC reference H-2011-0335).
+Participants
+Participants for the current study were recruited by recontacting original participants (N = 274) from the SHADE study (SHADE-[19], which tested a computerised treatment program for depression and alcohol/other drug use. Eligible participants in the original SHADE study were adults over 18 years of age who reported elevated depression symptoms (a score greater than 17 on the Beck Depression Inventory II [20] and concurrent use of alcohol in excess of national guidelines for low-risk consumption in place at the time of the study (four standard [10g ethanol] drinks per day for men or two for women) and/or at least weekly use of cannabis for the month prior to baseline. Exclusion criteria were active psychosis, inability to comprehend English sufficiently to understand the study interventions, and history of
+traumatic brain injury severe enough to impair capacity to consent and participate in the study interventions. As part of the original SHADE study, all 274 participants were recontacted via mail after 5 years and asked to participate in a follow-up assessment (five-years following the original SHADE baseline). Participants received an information sheet, consent form, and an invitation to participate in the follow-up assessment. They were also advised that the follow-up assessment would include specific questions about previous suicidal thoughts and behaviours they may have experienced and that, if disclosed, they would receive an invitation to participate in an additional sub-study (the present study) about previous suicide attempts and help-seeking around those attempts. For the current study, eligible participants were those who provided consent to participate in the 5-year follow-up assessment for SHADE study participants, and who, during this assessment, indicated at least one previous suicide attempt. Participation in this sub-study was offered until it was determined that no new themes were emerging from the interviews.
+Procedure
+Following provision of informed consent to participate in the qualitative sub-study a 30-min-ute semi-structured telephone interview occurred via the telephone, either at the time consent was provided, or at a subsequent time that was suitable for the participant. Participants were reimbursed $20AUD for their time and contribution to the study. The interview commenced with an open question about the participant’s specific experience of their suicide attempt, and then probed for specific details around help sought and received at the time of the suicide attempt, allowing participants to initiate and discuss those aspects of their suicide attempt and associated help-seeking that were most salient to them (as per Braun and Clarke, 2006[21]). All interviews were audio recorded and transcribed verbatim by the interviewer (VH) immediately following the interview.
+Interviews were conducted until it was determined that no new information or themes were observed in the data [22]. In order to reach this point in the study, each interview was reviewed for emerging themes by VH and TEH independently following each interview and before the next interview was scheduled. Once this was completed, VH and TEH met to discuss identified themes. This assisted in identifying new themes and determining when no new themes emerged. As an added measure of reliability in interview analysis, FKL was involved in review and interview discussions following three interviews (1st, 3rd and 6th). As a result of this process, a sample size of six was the point at which no new themes were emerging from the interviews.
+Measures
+Of relevance to the current study are the following assessment measures that were collected for the 5-year follow-up assessment:
+a.	Beck Depression Inventory Fast Screen [23]: The BDI-FS is a 7-item self-report questionnaire used to screen for the presence of depressive symptoms. It is an affective measure of depression, while excluding symptoms potentially related to medical complications.
+Authors reported that scores 0-3 indicate minimal depression; 4-6 indicate mild depression; 7-9 indicate moderate depression; and 10-21 indicate severe depression.
+b.	Opiate Treatment Index [24]: The OTI was used in this study to measure the quantity and frequency of Alcohol, Cannabis and Tobacco use. Each subtype is assessed in terms of both quantity and frequency of use in the prior month to assessment. An average use quotient is calculated for the prior month, such that a sore of 1 equates to once daily use per day for the month prior to assessment.
+c.	General Help-Seeking Questionnaire [25]: The GHSQ was developed to assess intentions to seek help from different sources and for different problems. It uses a matrix format that can be modified according to purpose and need, therefore help sources and problem-types can be modified to meet sample characteristics and study requirements.
+d.	Suicide Behaviours Questionnaire-Revised [26]: The SBQR is a four-item assessment tool which assesses four domains of suicidality: lifetime attempts and ideation, suicide ideation in the last 12 months, the disclosure of suicidal behaviour and the self-reported likely hood of suicide behaviour.
+Analysis
+Braun and Clarke’s (2006)[21] six-phase model of thematic analysis was used in this study due to its accessible, theoretically flexible approach and potential to yield a ‘rich and detailed, yet complex account of data’ (p. 5). Thematic analysis was considered particularly useful for application in this study due to its ‘theoretical freedom’ [21]; [27], which made it suitable for use within the ecological systems theory framework that formed the basis of this study.
+Qualitative data arising from the interviews were analysed using a combination of manual methods and NVivo 12. Using the best features of manual and electronic methods of analysis has been found to yield the best results in qualitative research [28]. Audio files were retained so the researcher could return to the recordings for a nuanced verification or clarification of content or meaning.
+Initially, a manual thematic analysis was conducted independently by the student researcher (VH) and her two supervisors (TEH, FKL), through a brief reading of the six transcripts to identify recurrent patterns or themes within the data. This method was consistent with Braun and Clarke’s (2006)[21] recommendation that “it is ideal to read through the entire data set at least once before you begin your coding, as ideas and identification of possible patterns will be shaped during the read through” (p. 87). In this study, the significance of a theme was determined not by quantifiable measures but rather by whether it captured something important in relation to the overall research question.
+Following initial coding, each transcript was re-read and coded according to the preliminary codes established. Annotations were also made about possible connections between themes and additional themes. This stage involved the cross checking of coding strategies and interpretation of data by the student researcher and her two supervisors independently. The final stage of the analysis was to identify the ‘story’ that each theme tells and how this linked with the overall ‘story’ about the data [21]. This final stage was carried out by MH and DS.
+Results
+There were six participants in this study (3 females and 3 males), and their background characteristics, based on their assessment from Part One of the study, are presented in Table 2.
+As indicated in Table 2, participants ranged in age from 30-62 years, and reported depression scores between 0.00 (no current depressive symptoms) through to 8-9 (moderate depressive symptoms) through to 10 (severe current depressive symptoms). Only one participant (P4) indicated use of cannabis in the past month prior to the 5-year assessment (8 use occasions per day for the prior month), and three participants (P2, P3 and P6) indicated daily alcohol consumption in the previous month of between 8-14 standard drinks per day. Tobacco use ranged from minimal (P1, P3, P4 and P5) through to daily use in the prior month, of between 5-25 cigarettes per day.
+*BDI FS = Beck Depression Inventory-Fast Screen score
+**As measured by the Opiate Treatment Index. Scores indicate average use of each drug type in previous month to assessment. A score of 0.14 equates to once weekly use for the prior month; 1 is one use occasion per day for the previous month, 2 is two use occasions per day for the previous month, and so on.
+https://doi.org/10.1371/journal.pone.0231647.t002
+All participants reported at least one previous suicide attempt as part of their eligibility for the current study. Details relating to these attempts are displayed in Table 3.
+As indicated in Table 3, participants reported between 1 and 3 (P5) previous attempts, and none had previously told another person (friend, family member or health professional) of the impending attempt. Two participants reported suicidal thoughts in the past 12 months, ranging from once only (P5) to 3-4 times (P1).
+Table 4 displays the self-reported help-seeking intentions of the participants from a range of professional and non-professional sources in relation to thoughts about suicide.
+As Table 4 indicates, likelihood of help-seeking for suicidal thoughts was generally higher from professional than non-professional sources, with only one participant (P3) indicating he would be extremely unlikely to seek help at all for suicidal thoughts. Support from family/ friends for suicidal thoughts ranged between extremely unlikely (P3) through to likely (P2), with no participant indicating this would be extremely likely for them. One participant (P2) indicated he would be extremely likely to seek support from a Minister or Religious Leader. Three participants reported they were extremely likely to seek support from a helpline for their suicidal thoughts (P2, P5, P6), however two also indicated they would be extremely unlikely to seek support from said helpline (P3, P4).
+Thematic analysis: Overview of themes
+The participants willingly and openly described their suicidal experiences and their ability to seek help at the time of their attempt. Overall it was evident that participants were largely aware of the help available to them both through informal social networks and formal services. What was also very clear was a reluctance to access these supports in times of need for varying reasons including; affordability, access to, and awareness of services; compounding life events; coping mechanisms; and challenges of service engagement. Within these, sub-themes were identified, which were significant in describing the meaning associated with each key theme.
+‘Participants rated each source of help on the same scale from 1 (extremely unlikely), 3 (unlikely), 5 (likely) to 7 (extremely likely).
+**Scores averaged for each participant across: intimate partner, friend, parent, other relative.
+https://doi.org/10.1371/journal.pone.0231647.t004
+Reluctance to seek help. A key theme identified in this study was participants’ reluctance to seek help for suicidal thoughts and behaviours from both family and friends and from community-based services. There was an overarching sense that participants gained little benefit from seeking help within their immediate social network of family, friends, or close relatives. Two key reasons were identified for this. Firstly, participants indicated that their social supports were judgmental, unsupportive, and unresponsive to cries for help:
+“I was just attention seeking as far as they were concerned... something happened and I ended up at the front of my place in a crumbled heap on the ground out of frustration, my two sisters and brother, were there and they just looked at me and laughed and drove off.” (P1, female, age 36 years).
+For some, this lack of support was connected to the stigma of mental health:
+“Well it’s the way people treated you... what would have helped? Not having [suicidal thoughts] and ... not having the stigma that people put on having mental problem.” (P1, female, age 36 years)
+Participants also expressed concern that a request for help may place an undue burden on family and friends, who may not be emotionally equipped or prepared for such a role:
+“The burden of ‘I’m going to finish it’ is a bit too much of a burden to lay on a friend or intimate partner. Especially an intimate partner, I mean I can share things of life with my partner but the deep seeded emotions. . . no. You can’t be openly transparent even with the closest.. .person you are with” (P2, male, age 44 years).
+Several participants who did seek help from a friend felt that the high level of distress this caused placed pressure on the relationship, which in turn, decreased the likelihood that they would share these thoughts with friends or family in the future.
+“Most probably not [seek help from a friend again] because it freaked her totally out, and it’s not a nice thing to put on someone, I don’t think.” (P4, female, age 29 years).
+One participant (P5) spoke highly of the support he received from his friend after his suicide attempt. This participant expressed gratitude that his friend persisted with her attempts to engage him with professional assistance at the peak of his distress:
+“He [Psychiatrist] had the order from the Court to say that I had to be released [from inpatient unit] into her [friend] care ... she was there to sort of manage me, if you like ... and she sort of.. .she’s put me on the right track” (P5, male, age 60 years)
+Participants’ responses reflected the complexity of their decision making in relation to who they would seek help from. While some recognised the value of talking with a trained professional who is less involved, they also indicated that they would find it easier to confide in someone they feel close to:
+“Maybe a trained professional is better... someone that’s not so close.. .but I find it easier talking to a close friend.” (P6, female, age 39 years)
+In relation to engagement with community support services, no participants had engaged with a service as their first point of contact for help for suicidal thoughts, although some participants were linked into mental health services at the time. Of these participants, none discussed their suicidal thoughts directly with their treating professionals, with one participant choosing to communicate via his friend instead:
+“I spoke to my friend about it, and not with any of the professionals or anything, and my friend went around and spoke to the psychiatrist.” (P5, male, age 60 years).
+One reason cited for the lack of communication with service providers was an impression that available providers did not offer adequate, proactive support, particularly once people had been discharged into the community:
+“We looked after my dad, he had a stroke, and when he died apparently I had a nervous breakdown. Then they put me in a mental home and then I came up here [moved house]. The mental health [service] came and assessed me, said they’d be back and I never ever saw then again or heard from them or nothing.” (P4, female, age 29 years).
+One participant noted that she had intended to disclose her suicidal thoughts to her doctor but did not feel safe to do this in the presence of his medical students:
+“I had to go for my appointment with [service provider] and he had students in the room and I didn’t want to see him with students .. .didn’t want to talk about it in front of them.. .I left and went home” (P1, female, age 36 years).
+Importantly, there were cases where participants were linked into public community-based services post-attempt, and these participants were more likely to engage in help-seeking for future suicidal crises:
+“Yeah... I really hooked into mental health.. .they have saved my life” (P3, male, age 62 years).
+An interesting finding was that help-seeking was associated with a lack of intent to commit suicide, with one participant noting that if he really wanted to commit suicide, he would not tell anybody:
+“If I was going to attempt suicide right now, I wouldn’t tell anybody because that to me means that I don’t really want to commit suicide.” (P3, male, age 62 years).
+Affordability, access and awareness of services. Affordability, access to, and awareness of, services were identified as barriers to help-seeking. For example, one participant described living alone in an unfamiliar location where help was difficult to access:
+“I was living on my own in a strange town... [help was] an hour away” (P4, female, age 29 years).
+Another participant highlighted the perceived cost of therapy as an inhibiting factor to help-seeking, combined with a sense that support was not available anyway:
+“Couldn’t afford it. And there wasn’t any really here. You know like there was no help... and it’s still the same.” (P4, female, age 29 years).
+Lack of awareness of available support was also related to age, with one participant noting that he was young and did not know about existing support options:
+“Well maybe they were in place... but I was pretty young, and I was unaware of other help” (P2, male, age 44 years).
+Availability of services did not always enhance help-seeking, with one participant reporting that living in a small rural town prevented the anonymity she needed to access available help:
+“I mean... just if there was a psychiatrist in [town] ... but then everybody would have known about.. .it’s one of those little towns that everyone knows everyone” (P6, female, age 39 years).
+Compounding life events and coping mechanisms. Participants all reported experiencing adverse and compounding life events immediately prior to their suicide attempt, which placed a high level of stress on them, leading them to contemplate suicide as an escape route:
+“Yeah I had three young children and was bringing them up on my own. The situation had arose, I had no family to turn to, my youngest sister accused my ex-husband to have interfered with her as a child . . . I had only just really recovered from a bad marriage, and severe chronic back pain and surgery on my back. I was trying to cope with all of that and still work and look after my children. . . so it was like I don’t want to be here right now without thinking of the consequence.” (P1, female, age 36 years).
+Participants’ decision to attempt suicide was often influenced by a sense that there was no solution to, or way of escaping, their current situation. For example, one participant described his suicide as a response to his fear of the future as a young man:
+“You fear leaving your job but you also fear going forward.. .the story goes.. .overdose of pills and I drove my car into the bush until I crashed and went to sleep in the back seat and two hours later the sun was blaring in my face and I went oh that didn’t work.... I went
+home and told my parents this is what I did. . . I tried to commit suicide and they went yeah right you stupid young man. . . This is what I went through with my counsellor as well, in an odd way I still probably had pent up feelings about that until probably my 40’s.” (P2, male, aged 44 years).
+Some participants reported using alcohol as a coping mechanism at times of significant stress, noting that this hindered them from addressing the difficulties they were experiencing:
+“I didn’t know what was going on with me. And I was using alcohol as an excuse to, you know, just drown everything out, which was bad.” (P4, female, age 29 years).
+Need for proactive, flexible support. While a number of participants demonstrated good knowledge of available services, many expressed ambivalence about engaging with services due to prior negative experiences with service providers, or fears about how a service provider might respond to a disclosure of suicidal ideation. However, participants often reported that, while they would not have sought help themselves, they would have accepted it had it been offered. They emphasised the importance of proactive, sensitive service engagement during a suicidal crisis. However, they also expressed a need to be self-reliant once the immediate crisis had passed.
+Initial support followed by self-reliance. Participants who received initial support during a suicidal crisis often reported withdrawing from this support once the crisis had passed. These participants continued to make use of the materials and skills provided to them, suggesting that brief interventions and information provision might be important modes of support for people experiencing suicidal crisis:
+“I ended up, I did, you know the Cognitive [Behaviour] Therapy. Well you have the paperwork, you can always read back over it, or you know stuff like that. ... And that was, that was good. Well I was seeing like a counsellor, you know, where she sits and talks to you and gives you other opinions and ideas. You know how to go about things different ways.......
+And then I was supposed to go back and see her because I was a bit all over the place, and I just couldn’t have been bothered. My doctor said like why haven’t you gone back? I said “Oh I couldn’t have been bothered, was over it.” He said, “Oh okay.”. . . But she’s there you know if I needed to go and see and talk to her. I try and just use what she taught me, work it out, sometimes it doesn’t” (P4, female, age 29 years).
+The importance of the initial response was highlighted by participants who described a single comment or insight that had helped them to move forward after a suicide attempt. For example, one participant reflected on a conversation with her son in which she was reminded of the impact a suicide would have on her family and grandchild:
+“I was happy they [Emergency Department] didn’t put me in the psych unit. ... No [I didn’t seek help]. I just woke up to myself when my son said I had the photo of my granddaughter with me when I tried it [suicide] and I thought well, you know this is just bloody selfish, I can’t do it.” (P6, female, age 39 years).
+Some participants did report receiving a more extended response from mental health professionals. However, this support was generally associated with higher levels of crisis, while initial help-seeking tended to be directed towards friends and regular service providers like the GP:
+“Well I thought it was a bit funny at first because I’d walk in and sit down with the guy [psychologist] and he’d be, ‘how’d you go, what have you been doing, catching any fish’ or whatever. I didn’t realise at the time he was just getting me relaxed and then he’d say ‘you
+haven’t felt like this have you’.... I never realised that I’d given him the answer and then he’d say, ‘well last time when I asked you about that you said so and so, this time you’ve said, so and so, that’s an improvement on this’ you know. Yeah, that’s right he set me up really well... .Oh yeah, yeah, if I ever got down I’d go and see him but, you know, my first port of call, other than my two friends is my doctor...’ cause he’s really taken me under wing” (P5, male, age 60 years).
+Need for proactive service engagement. Several participants highlighted that their most critical need for service engagement and support was during the initial crisis stage, and that they felt services did “not try hard enough” (P4) to engage or intervene with them at this time. Where support was not offered during a crisis, participants reported experiencing adverse consequences:
+“The first time I was seeking help I had an appointment to see a counsellor, I think it was, I was very distraught, I had held myself together until I got to that appointment. I turned up and I was a day early. And I was so stressed and I said ‘but I need to see someone now’. . . but they said no not until tomorrow. I went home and said how am I going to get through ‘til tomorrow? I took a couple of valium to help me get to sleep and before I knew it I had taken the whole packet in that state.” (P1, female, age 36 years).
+Some participants perceived a lack of service response during a crisis as a clear indication that the service did not care, and this discouraged any further attempt to seek help:
+“Because when I rang up like and said to them you know “you came out and assessed me. You said you’d be back. You haven’t come back. You have given me nothing. You’ve just got me hanging, and I don’t know what’s wrong with me”. And they [service providers] said, “Oh that’s not our problem, we’re booked out.” .. .they didn’t give a sXXX really .. .I won’t do that again” (P4, female, age 29 years)
+Several participants emphasised the importance of receiving an offer of help at times when they were not able to ask for support:
+“... there is no one I would have asked.. .it was easier to end it then tell people because you felt stigmatised .. .and they [services] didn’t offer either.. .really other people should have seen the writing on the wall but didn’t.” (P3, male, age 62 years).
+Participants often reported that while they would not have sought help themselves, they would have accepted it had it been offered:
+“I just didn’t think of it, I didn’t want to go to the doctors. And really others should have seen the writing on the wall, but I didn’t.. .If someone had offered a hand to me, I would have taken it.” (P3, male, age 62 years)
+Discussion
+This study aimed to explore the help-seeking experiences and attitudes of individuals with comorbid depression and alcohol use who had a suicide attempt in their lifetime. Findings show that the relationship between help-seeking behaviours and suicidal thoughts and attempts is a complex one, involving internal conflict between an individual’s perception that
+they “should” manage these thoughts and behaviours on their own without “burdening” family and close friends, while at the same time wanting family and friends to understand them, and to “offer” support. This complexity was also apparent in relation to service engagement, with many participants expressing a belief that professionals and services have a significant role to play in preventing a suicide attempt, while also emphasising a need for self-sufficiency and independence once the immediate crisis had passed. These findings suggest that individuals at risk of suicide require support that is both flexible and occurs at multiple systemic levels.
+In particular, findings reveal the importance of communication between, and amongst, the people and services who inhabit the various systems surrounding an individual at risk of sui-cide-their mesosystem. For example, one participant reported a preference for communicating with treating professionals via his friend. Others noted that they would seek help from friends and family in the first instance, suggesting the microsystem has a crucial role to play in linking individuals experiencing suicidal ideation with support services. Participants who were encouraged to access services in this way, were more likely to engage in help-seeking for future suicidal crises. These findings reflect the need for a ‘circle of support’, in which family, friends and professionals work together to offset a suicide attempt. The notion of a circle of support confirms previous research findings, which identified the importance of social connectedness and support networks for reducing suicide risk [29].
+The finding that individuals experiencing a suicidal crisis are more likely to seek support from family and friends in the first instance, is consistent with ecosystems theory, which emphasises the primacy of a person’s microsystemic interactions [30]. According to Rogoff (2003)[31] the microsystem exerts a more powerful influence on individuals than any other contextual factors. Interactions that occur at this level have the power to be either extremely beneficial, or detrimental, to a person’s development and wellbeing. This perspective usefully illuminates participants’ ambivalence about seeking support from family, friends and support services, in an effort to avoid the damaging personal consequences of a judgemental, unsup-portive response, at a time when they are arguably at their most vulnerable. Conversely, participants who encountered positive responses within their circle of support indicated that this had a crucial impact on their decision not to attempt suicide in the future.
+Participants in this study expressed a need for proactive service engagement during a suicidal crisis. However, many individuals did not disclose their suicidal thoughts to health professionals they were in contact with prior to their suicide attempt. A central reason for this was a belief that services either did not recognise the seriousness of the situation or did not care enough to offer support. This suggests that it might be important for professionals to consider routinely asking suicide risk assessment questions of all clients who are engaged with health services (acute, community), irrespective of perceived risk, as this may have important preventative implications. Privacy was revealed as an important consideration in this context, with one participant noting she had planned to disclose to her doctor but decided not to do so in front of medical students. This highlights a need for service providers to ensure that their provision of professional development opportunities does not impact on service-user wellbeing, and to employ a high level of sensitivity when including trainees in routine service provision.
+A further finding was that adverse and compounding life events significantly impacted participants’ level of stress. For some, this led to an increase in unhealthy coping mechanisms, such as alcohol use, which presented a further barrier to addressing challenges or accessing support. This supports findings from previous research that comorbid conditions such as substance disorders present additional complexities and barriers to help-seeking for people experiencing suicidal ideation [6]. From an ecological systems perspective, the influence of an individual’s microsystem extends beyond their interactions with other people, to their engagement with objects and symbols [32]. It is therefore possible to infer that, in the absence of
+support from significant others, an individual may seek comfort in non-human objects such as drugs and alcohol.
+This study revealed the ambivalence that people who are acutely suicidal can experience when engaging with different systems of support, with several participants expressing fears of negative, stigmatising reactions by family members or formal providers to a disclosure of suicidal ideation. This supports Calear et al.’s (2014)[3] finding that perceived negative attitudes and stigma relating to suicide and mental illness can lead to a reduction in help seeking intentions and behaviours. An ecological systems perspective usefully illuminates the critical role of broader social and cultural—macrosystemic—factors in shaping people’s attitudes and behaviours to suicidality. The macro-system constitutes the shared belief systems and values of a cultural group [32]. This system has a cascading influence on interactions at all other systemic levels [33]. Employing this perspective, it is possible to observe the continuing legacy of the historical stigmatisation of suicide as a ‘taboo’ subject [34] and a ‘sinful’ act [35] at the individual level. In this way, an ecological systems frame may enhance our critical awareness of the wider socio-political context that shapes people’s experiences of, and responses to, suicidal ideation [17]; [16].
+Limitations
+The present study has several limitations that should be considered when evaluating the presented conclusions and implications. The authors acknowledge the difficulties in determining true data saturation [36] particularly when working with such small sample sizes [37] as in the current study. It is uncertain if other participants might have provided differing themes and experiences. It is a limitation that the exclusion criteria extended to people who could not speak or understand English sufficiently to engage in the study, highlighting further the potential issues with generalising these results to diverse groups in the community. Even though a randomised approach to the interview schedule was applied it is uncertain if there could be a bias in the sample pool. In addition, although rigorous standards for interpreting and analysing the qualitative data were applied, these are still subject to the researchers’ experience and subjectivity may have affected the results.
+Conclusion
+This study revealed the complex challenges people who are acutely suicidal experience when they engage with different systems of support. Findings highlight important considerations for friends and family members, who are often the first point of call for a person seeking support. They also emphasise the need for sensitive and proactive service engagement with individuals experiencing a suicidal crisis, to avoid the damaging impacts of inaction, stigma and judgement. The qualitative data revealed themes that illustrated many challenges of engaging with different support systems which lends itself well to an ecological systems theory perspective when considering interventions and approaches for this complex population.
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+Introduction
+The prevalence of mental disorders such as depression has been increasing worldwide in the past decades.1 Around the world, more than 650 million people live with diagnosable mental disorders.2 * As a result, many studies have paid attention to the social consequences of the rising prevalence of mental illnesses, attempting to estimate the magnitude and the channels of such impacts. A general conclusion drawn from the existing studies is that mental disorders impose considerable costs (both direct and indirect costs) on the individuals, their families, and the society. In developed countries like the United States, estimation has shown that the economic burden of depression was as high as $210.5 billion in 20 1 03; In developing countries such as China, it has been found that the costs of medical expenditures due to depressive mental illnesses account for 14.7% of China’s total medical spending in 2012.4 The deterioration of mental health has a significant negative impact on the patient’s labor force participation, which in turn reflects the opportunity (indirect) costs of depression in labor markets. Moreover, the socioeconomic costs of mental disorders may go well beyond the traditional boundary of disease burden estimation, presenting implications on the reduction of social trust, self-esteem, and life satisfaction.5 *
+Compared to the rich evidence on the social consequences of mental disorders, there are relatively few studies that investigate the issue of low treatment rate in mental illness. In comparison to other non-communicable diseases (NCDs) with high disease burden and prevalence rate (such as hypertension, diabetes, etc.), the rate of treatment for mental disorders are quite low, indicating a large treatment gap, measured by the difference between the need for and the actual provision of treatment among patients with mental illness. It was estimated that at least 10% of the global population is affected by 1 or more mental disorders; however, according to the estimation by the World Health Organization, more than 3 quarters of people with severe mental disorders in low- and middle-income countries (LMICs) receive no medical treatment. Although the situation in high-income countries is better, there is still a high proportion, ranging from 35% to 50%, of people with mental illness who go untreated.
+The questions that this paper tries to answer are (1) why so many people with mental illness go untreated and (2) how to bridge the treatment gap for them. We first provide economic perspectives to explain the potential causes of treatment gap in mental healthcare generally. Specifically, we propose a testable
+hypothesis that patients with mental illness face 4 major hurdles in accessing appropriate care, including stigma, high out-of-pocket payment, low availability of mental health resources, and the slow diffusion of new medical knowledge and technology. We then use China as a case study to show the evidence in support of this 4-hurdle hypothesis, and we propose 4 policy options to bridge the treatment gap in mental healthcare. Although our empirical evidence and policy discussion lie in the context of China’s health system, our findings also have important implications for other low- and middle-income countries (LMICs) with similar development experience and challenges in the healthcare sector.
+Our work contributes to the growing body of research on the mental health policy designs in LMICs. Many studies have demonstrated a substantial treatment gap in mental health ser-vices,6-8 however, little research has investigated the potential determinants of the treatment gap in a systematic way. Several international agencies have identified major roadblocks to receiving treatment in mental health, including stigma, inadequate funding, and poor design of health system.9 However, there is limited research that unpacks the key factors that shape such roadblocks, especially in LMICs. Our research aims to provide a synthesis for the various academic endeavors and policy discussions on how to help people with mental illness get out of the shadow and receive appropriate diagnosis and treatment. It provides a relatively general framework by integrating institutional analysis with economic analysis of healthcare-seeking behavior to achieve a better understanding on the potential causes of the treatment gap. Based on this analytical framework, we then propose several policy options on bridging the treatment gap in the mental health sector.
+The rest of the paper is organized as following. Section “Conceptual Framework” provides a conceptual framework to develop our testable 4-hurdle hypotheses. Section “Empirical Evidence From China” presents empirical evidence to support the existence of the 4 hurdles in accessing mental healthcare, using the current available data in China. Section “Policy Options for Bridging the Treatment Gap in Mental Healthcare” discusses 4 policy options for bridging the treatment gap in mental healthcare. The last section concludes the paper and discusses the implications of the findings.
+Conceptual Framework
+As first conceptualized by Michael Grossman, the demand for personal healthcare services is derived from the person’s demand for health, which in turn is determined by the
+optimal health capital stock at which the marginal cost of holding health capital equals the marginal efficiency (benefit) of health capital.10 Following this framework, the economic theory of healthcare-seeking behavior predicts that individuals will seek healthcare for their mental illness as long as the expected benefit of doing so exceeds its expected cost. Based on this framework, the low treatment rate of mental illness can be explained by 2 potential reasons: the cost is too high and/or the benefit is too low. A closer look of mental healthcare delivery indicates that the costs of seeking treatment include both monetary and nonmonetary costs. The nonmonetary cost is to a large extent due to the stigma associated with mental illness. The full monetary costs that the patients pay for receiving treatment can be further divided into 2 parts: (1) the financial prices of mental healthcare as reflected by the patient out-of-pocket payment; and (2) the time prices of seeking mental healthcare as reflected by the opportunity cost of a patient’s time allocated to travelling, waiting, and receiving treatment.11
+Compared to physical illness, a most distinctive feature associated with mental illness is stigma .12 Stigma indicates the co-occurrences of the following 5 components: labeling, stereotyping, separation, status loss, and discrimination.13 In the first component, people distinguish and label the differences of persons with mental illness. In the second, negative stereotyping, such as unpredictable, unstable, dangerous, violent, and socially worthless, often surrounds the images of people with mental illness.14 As a result, the stigma of mental illness creates a position of social distance or rejec-tion.15 Specifically, negative consequences of stigma include a decrease in the opportunity of seeking employment and housing, an increase in family stress, and the lower quality of life. The fear of status loss and discrimination in turn becomes the internal cost and a major barrier for people with mental illness to overcome when they seek medical treatment. This implies that the non-monetary cost imposed by social stigma is the first hurdle in the access to mental healthcare.
+The second hurdle in seeking mental healthcare is the money price in the form of patient out-of-pocket payments for mental healthcare utilization. As shown in the WHO report, many countries (especially LMICs) suffer from the under-funding problem in the sense that the public sector allocates an extremely low share of health budgets into the mental health sector.9 According to a recent survey conducted by WHO, governments spend on average 3% of their health budgets on mental health (a figure much lower than the non-mental health sector such as hypertension and diabetes), with a distribution from 0.5% in the low-income countries to 5.1% in the high-income countries.16 A natural consequence of the under-funding problem is that patients with mental illness typically need to pay a higher out-ofpocket amount to finance their treatment compared to their counterparts with physical illness.
+Thirdly, many countries do not have sufficient healthcare resources (including mental health personnel and facilities)
+to deliver the appropriate care to people with mental illness. The low availability of mental health resources is mainly reflected in 2 dimensions: (1) inadequate capacity building in the training of mental health professionals, which leads to the overall insufficient supply in mental healthcare; (2) the limited resources for mental healthcare, including both professionals and facilities, are usually concentrated in the densely-populated urban areas within a country, indicating an uneven geographic distribution of mental healthcare resources. One of the significant consequences of the insufficiency and maldistribution of healthcare resources is the increase in the time cost for seeking mental healthcare, which in turn becomes the third hurdle for people suffering from mental health conditions, such as depression.
+Finally, the fourth hurdle in accessing mental healthcare is the low expected benefits of treatment arising from the technology gaps between the frontier of new knowledge in treatment procedures and the clinical practice available to patients. Although there has been a rapid development in medical knowledge and technology for mental healthcare in recent years, whether the frontier of these new technology and knowledge can be transmitted to become a prevailing local practice standard depends on the speed of knowledge diffusion and technology adoption. Many studies have shown the evidence that the incentives for innovation in general and the technology diffusion in particular are positively correlated with the market size.17,18 As mentioned, many countries face common challenge of inadequate funding in their mental health sectors, indicating that mental healthcare has a relatively smaller market size compared to that of the general healthcare. As a result, mental health sector is in disadvantage in facilitating technology diffusion such as the launch of new prescription drugs and the provision of psychological treatments for mental illness (e.g., cognitive-behavioral therapy for mild depression). This in turn enlarges the gap between the frontier of treatment know-how and the local practice standards in those countries. The existence of such a knowledge gap and outdated clinical practice may reduce the potential benefits of mental health treatment, which in turn further decreases the incentive for the patients to seek medical assistance when in need.
+In summary, the above analyses indicate that people with mental illness face higher marginal costs of accessing mental healthcare than other patients, including the psychological cost imposed by social stigma, the out-of-pocket cost arising from the low public funding, the time cost due to the poor availability of mental healthcare resources. In addition, the perceived benefits of medical treatment may be lower due to the slow diffusion of new medical knowledge and technology. We hypothesize that these 4 hurdles largely explain why many people with mental illnesses tend to delay the treatment or go completely undiagnosed, a stylized fact in epidemiological studies in many countries.19 In the next section, we use China as an example to show the evidence for this 4-hurdle hypothesis.
+Empirical Evidence From China
+Stigma
+WHO has pointed out the long-term negative effects of stigma, highlighting that stigma, as a major source of discrimination and exclusion, can damage people’s self-esteem, disrupt their family relationships, and consequently limit their ability and willingness to socialize, obtain housing and seek employment. Table 1 presents the comparative statistics among adult groups of different mental health status in China based on data from the China Family Panel Studies (2012), a nationally representative household survey. We classify the respondents’ mental health status based on their CES-D (Center for Epidemiologic Studies Depression Scale) scores. The table shows a significant correlation between the respondents’ mental health status and their ideology and social economic status. People with mental depression (CES-D at 28 or higher) or depressive symptoms (CES-D between 16 and 27) are shown to have significantly lower life satisfaction on their family and themselves, less self-confidence and lower trust towards family members and other social groups. About 58.4% of the mentally healthy people (CES-D at 15 or lower), compared to only 37.5% of people with depression, tend to believe that most people are trustworthy.
+In fact, stigma has been reported to hamper the prevention and treatment of mental health disorders and the promotion of mental well-being,20 which in turn results in poorer physical health, suicidality, and higher mortality rates.21 Stigma can increase the feelings of worthlessness and despondency that
+increase the risk of depression and suicide, and potentially more damaging than the mental illness itself.22 For example, based on interviews with close associates of people who committed suicide and of people who died from other injuries in China, it has been found that a high depression symptom score remains a significant predictor for suicidality after adjusting for sex, age, residential location, and other factors.23 What’s more, the negative impacts of stigma are likely to extend from the daily life of patients to that of their family members, friends and even mental health provider groups, implying a negative spill-over effect of social stigma.24
+The long-term consequences of stigma among the mentally ill may also extend from health outcomes to labor market outcomes such as employment and income. Based on the National Co-morbidity Survey-Replicate (NCS-R) data, evaluations have shown that psychiatric disorders are associated with reductions of 9% and 14% of the labor force participation rate and the employment rate for male.25 It has also been found that depression leads to an annual work loss of about 1.4 days (accounting for 33% of total health-related workday loss).26 The CFPS 2012 dataset provides additional evidence of stigma in China’s labor market: for example, the years of education and the levels of personal income are shown to be significantly lower for people with mental depression or depressive symptoms compared to the mentally healthy respondents. On average, individuals without mental illness acquire 7.6 years of formal schooling, which is almost twice as much as the average education years of people with depression. The annual income of individuals without mental illness is 50% higher
+than those with depressive symptoms and triple that of people with depression (see Table 1).
+Out-of-Pocket Costs of Mental Healthcare
+Although China has made significant progress in achieving the goal of universal healthcare coverage, the current system contains more than 3000 local health insurance plans that vary substantially in eligibility criteria, insurance benefits and co-payment schemes.27 More specifically, different health insurance plans, such as the New Rural Cooperative Medical Scheme (NCMS, a government subsidized plan covering all rural families), the Urban Employee Basic Medical Insurance (UEBMI, a social insurance program financed by employers and employees covering urban workers in the formal sectors) and the Urban Resident Basic Medical Insurance (URBMI, an urban health insurance scheme covering informal sector workers and people without employment), differ in their enrollment criteria and co-insur-ance rates. Within each plan, coinsurance and copayment rates also differ by regions and types of treatment. Generally speaking, the copayment rates for outpatient visits are higher than those for inpatient admissions in China, especially for rural health insurance programs.
+Before 2012, the insurance coverage and reimbursement for mental healthcare are usually limited and dependent on the provincial government’s financial capacity. In 2012, the Chinese central government announced a decision to expand the coverage of the country’s health insurance system to include the treatment of critical illnesses including major mental diseases. Meanwhile, the Mental Health Law of China was launched in 2013, which formalizes the legal protection and treatment of people with mental disorders.28 After these milestone steps in strengthening mental healthcare, a significant portion of mental health outpatient and inpatient medical expenses were able to be covered by the national health insurance system. For example, Beijing covered 6 types of major mental illnesses (e.g., schizophrenia, bipolar disorder) in its insurance plan in 2014 and increased the reimbursement rates for the inpatient and outpatient healthcare for these conditions from 60% to 70% with no maximum payment limits. In addition, the essential drugs for the treatment of these major mental diseases are also made free to outpatients, which benefited more than 12,000 mental health patients by 2016. Shanghai included 4 mental disease into its Critical Disease Insurance Plan in 2015, which provides a 50% reimbursement rate (increased to 55% in 2017) in supplement to the basic health insurance coverage. Several cities in China’s eastern coastal provinces, including Jinan, Zhanjiang, Foshan, and Dongguan also added mental diseases into their health insurance coverage in 2015. The city of Shenzhen covered 6 mental diseases in 2016 with maximum reimbursement rates of up to 90%. In the rural sector, NCMS started to launch pilot programs to cover mental diseases and other critical illnesses in 2013. Meanwhile, the
+average government financial support for NCMS increased from 320 RMB (about $53) per person in 2013 to 450 RMB (about $75) per person in 2017. However, there remains substantial variation in the reimbursement rates for mental illnesses across regions within NCMS.
+Despite the above-mentioned progress in extending health insurance coverage for mental health patients, China still suffers from a serious disparity in the coverage and reimbursement rates for mental diseases. High-income areas such as the eastern coastal regions and major urban cities usually have better coverage as well as higher reimbursement rates. For example, based on the data from 1989 to 2011 China Health and Nutrition Survey (CHNS), we estimate the effective reimbursement rates (total medical expenditure less the patient out-of-pocket payment) for mental healthcare and non-mental healthcare in China’s eleven provinces (see Figure 1). As indicated, patients with mental health problems in the eastern provinces have a significantly higher effective reimbursement rate compared to their counterparts in the Northeast, Central and West. In addition, compared with the reimbursement rates of physical conditions such as heart disease, tumor and respiratory diseases, the mental illness patients that are surveyed in the 1989 to 2011 CHNS receive a lower average reimbursement rate (10.46% vs 16.61%). Therefore, there is substantial variation not only in insurance coverage across regions, but also in the depth of benefits between the general healthcare and mental healthcare. Given such disparity of financial support from health insurance
+plans, people with mental health problems in lower-income areas would face higher out-of-pocket burden, which in turn deters the proper use of treatment.29
+Mental Healthcare Resources
+Much of the access barriers for mental healthcare in China is due to the limited supply and unequal distribution of professional mental healthcare resources. For example, China only had 1.46 psychiatrists per 100,000 population in 2010, which was substantially below the global average mental health workforce (4.15 psychiatrists per 100,000 population).30,31 The lack of qualified mental health professionals may be partially due to the government control of medical education and accreditation, and it may also be attributable to the severe under-diagnosis of mental illnesses that results in the mismatch between supply and potential demand of mental healthcare. In China, over 100 million Chinese experience different kinds of mental disorders during a year, and these mental diseases account for over 20% of the total burden of diseases.32 Given the high prevalence rate of depression (4.08%) in China, the medical resource of mental health care is relatively scarce compared to the general health care.28 In addition to the overall undersupply of manpower, geographic mal-distribution of available mental health resources in China and the concentration of qualified personnel in the urban-based psychiatric hospitals indicate that mental health services are quite limited in rural areas.33
+To illustrate the above points, Table 2 compares the mental healthcare resources and general healthcare resources between 2010 and 2015 in China. A cross-sectional comparison indicates that in 2015, the number of licensed doctors in the mental healthcare sector contributes to only 0.9% of the total supply of licensed doctors, and the number of hospital beds in the mental health sector accounts for only 1.1% of total hospital bed capacity in China. A time series comparison indicates that while the physician density of general healthcare has increased from 18.0 per 10,000 population in 2010 to 22.2 in 2015, the density of licensed mental healthcare physicians
+decreased from 0.234 per 10,000 population in 2010 to 0.199 in 2015. In contrast with the stable growth in the density of general healthcare doctors, the growth rate of licensed doctors in the mental health sector has fluctuated between -21.79% to 6.32% in recent years. The annual growth rate of hospital beds in mental health is also significantly lower than that in the general health care until 2014.
+One of the plausible reasons for the undersupply of mental healthcare manpower is that the profession offers less attractive wage payment and working environment compared to other specialties of medical care. In china, healthcare price is rigidly regulated at a very low level, and for a long time, doctors must rely on the kickbacks from drug prescriptions to compensate for their low earnings. In such a setting, certain specialists such as psychiatrists and pediatrician are at disadvantage, as they have very small room for drug overprescription due to the nature of the specialties.34,35 Thus, mental health professionals in China commonly earns less than their counterparts in other specialties. Table 3 presents the service revenue, service costs and the implied gross profit rates of different specialty hospitals in China based on the public data in the national health statistical yearbook of 2016. Compared to the profit-generating specialties such as plastic surgery (83.3%), ophthalmology (52%) and rehabilitation (42.4%), psychiatric hospitals (16.8%) rank comparatively low in the profit rates in year 2015, despite their relatively high annual revenue of 29.6 million Yuan per hospital. (A plausible reason why the profit rate of psychiatric hospital is lower than other specialty hospitals is that almost all psychiatric hospital in China are not-for profit and run by the government, while private and for-profit hospitals are more common for other specialties, such as plastic surgery, ophthalmology, and rehabilitation.) Given that most hospitals in China rely on their own profits for daily operation and physician employment, the above comparison indicates that the prospective income is lower for mental health doctors compared to doctors in other specialties, which suggests that the mental health profession may fail to attract sufficient personnel in the long term.
+In addition to the insufficiency of overall mental healthcare capacity, the geographic mal-distribution of available mental health resources in China is also pronounced. Figure 2 maps the provincial density of hospital beds in psychiatric services in 2015. The figure indicates a dramatic disparity in mental healthcare resources across the country: the economically developed eastern provinces such as Shanghai and Zhejiang enjoy higher densities of psychiatric hospital beds, while the economically less developed inland regions in Central and Western China are in dire need of mental healthcare resources. The most underdeveloped provinces such as Qinghai, Gansu, Ningxia and Guizhou have extremely low densities of hospital beds for professional mental health treatment. Given that the prevalence rates of depression and depressive symptoms are also higher in central and western provinces,28 the above findings indicate that the inland regions suffer from the most severe problem of unmet mental healthcare needs.
+Diffusion of New Medical Knowledge and Technology in Mental Healthcare
+Under the current practices in China’s healthcare sector, 2 institutional features may enlarge the technology gaps in the field of mental healthcare. First, due to the lack of
+government subsidy for low service fees charged by public hospitals, healthcare providers in China rely heavily on profits obtained from prescription drugs as their major sources of revenue, indicating that physicians may choose to prescribe drugs based not on efficacy, safety or cost, but solely on the extent of the profit margins that they or their institutions obtain.36 Second, given the Essential Drug Policy and the regulated insurance reimbursement schedule, there may be a long delay in the launch of new mental healthcare drugs or treatment procedures in China; as a result, physicians may not be able to prescribe what proves to be the most effective treatment regimes, and this translates to another policy-induced barrier for the mental illness patients in China.
+In China, the market access process for pharmaceutical products (patented or differentiated generic drugs) is com-plex,37 and involves the following steps: registration and approval of new drugs, pricing and bidding, reimbursement listing at the local and national level, and at last hospital listing. More specifically, provincial bidding is held every 2 years or so; national reimbursement listing takes place every 4 to 5 years; another 2 years’ time is needed for the hospital listing process. As such, for a domestic or multinational pharmaceutical company to launch a new drug in China, it has to wait 7 years on average for drug approval, launching and listing in the target hospitals. Companies are not allowed
+to sell new drugs on the market until the above process is fully completed. This results in a wide gap in the launch of new and innovative drugs between China and high-income countries such as U.S., Japan and U.K. This is illustrated in Figure 3, which shows that the initial market share of new drugs in China (2.5%) is considerably lower than that in U.S. (56.3%), Japan (12.6%), U.K. (7.7%), Germany (6.5%), and Korea (3.1%) in year 2015. Given that the knowledge and technology frontier in the mental health treatment witnesses fast expansion in recent years, the above statistics suggest that the mental illness patients in China are less likely to benefit from the most innovative drugs and treatment options compared to their counterparts in the above mentioned countries. As a result, this system may produce lower expected value for its patients, which in turn reduces the incentives for people with mental health conditions to utilize the system.
+There is ample evidence to illustrate the slow adoption of mental health drugs in China compared with the high-income countries, taking the United States as an example. First, as Table 4 illustrates, among 12 new molecular entities for Central Nervous System (CNS) diseases (the therapeutic
+category for mental illness) available in the global market, only 1 was launched in China. By contrast, 8 drugs are adopted in the United States. This indicates a difference of 0.583 (8/12 minus 1/12) in the adoption rate between U.S. and China. Furthermore, the difference in adoption rates between CNS drugs and drugs for other NCDs in China is 0.094, which is higher than that of U.S. (0.01). Therefore, not only China has a slow adoption of new drugs, but its adoption of drugs for mental illnesses are even slower than that for other non-communicable diseases. Second, Table 5 takes 14 atypical antipsychotic (AAP) drugs (for the treatment of schizophrenia) as examples, and shows the years in which these mental health drugs were approved by the U.S. Food and Drug Administration (USFDA) and China Food and Drug Administration (CFDA). As indicated, there is a significant time lag (an average of 6 years) between the approvals in the 2 countries, which suggests a long delay in the launch of new pharmaceutical products in China’s mental health sector.
+Another piece of evidence for the knowledge gap comes from the comparison of clinical guidelines for the first-line
+drug prescription on mental health conditions between China and the developed countries (see Table 6). Clinical guidelines for the treatment of mental depression started to be published in China from 2006, the first edition of which is only 5 pages long.38 The guideline was still officially recommending the use of TCAs (a category of first-generation antidepressant with considerable adverse drug reaction), while at the same time second-generation antidepressants such as SSRIs and SNRIs had been widely recommended in U.S. and other developed countries for more than a decade due to their effective treatment and less side effects.39 This represents a lag in guideline development between China and developed countries as well as a technology gap in the pharmaceutical industry. The second edition of official guidelines for the treatment of depressive disorders was published in 2015, with much more detailed and up-to-date content, recommending SSRIs, SNRIs and NaSSAs as first-line treatment options for mental depression.40 However, there still exists a large know-do gap between the official recommendations and the field practices in China, and first-generation therapies such as TCAs and TeCAs were still commonly prescribed by mental health doctors in various regions of China.
+Other than the regulation-induced barrier to the diffusion of medical knowledge and technology, the persistent under-funding problem of the mental health sector also enlarges the gap between the technology frontier and the local clinical practices in China. Figures 4 and 5 present
+the market shares (measured as the number of outpatient visits or inpatient discharges for a specific service type as a percentage of total number of outpatient visits or inpatient discharges) of various types of diseases among China’s medical institutions in 2015. As indicated, both outpatient and inpatient shares of psychiatry (mental health department) account for merely 1% among all types of healthcare services, suggesting that the mental healthcare sector accounts for a very small size in the overall healthcare market in terms of patient volumes and service
+revenues. Given that the public and private funds tend to flow into major sectors with large market sizes (such as internal medicine and traditional Chinese medicine), the under-funding problem is expected to plague China’s mental health sector in the foreseeable future and in turn reduce the speed of technology adoption in the field. The vicious cycle of under-funding and under-treatment is thus exacerbated by the gap, leading to further reduction in the effectiveness of mental healthcare services in China.
+Policy Options for Bridging the Treatment Gap in Mental Healthcare
+Given the evidence that high costs and low benefits are 2 main causes of under-treatment in mental healthcare, we offer 2 approaches to bridge the treatment gap: the “push incentives” and the “pull incentives,” which are designed to reduce the costs of treatment and to increase the benefits of treatment, respectively. For push incentives, we suggest 3
+policy options to reduce the nonmonetary cost, out-of-pocket cost and time cost in the mental healthcare seeking process. For pull incentives, we suggest using the information and communication technology (ICT) to speed up the technology diffusion and hence to increase the quality (benefit) of the treatment. We discuss all these policy options in the following subsections.
+Out of the Shadow: Information Campaign for the Awareness of Mental Illnesses
+Given the high prevalence rates of mental disorders in China, it is important to educate the public through information campaigns to increase the awareness of mental illnesses. In addition, an anti-stigma campaign would be beneficial to reduce the nonmonetary cost of seeking mental healthcare. In the Chinese traditional culture, some forms of the stigma associated with mental disorders arise from the names of mental illnesses per se. Thus, an effective approach to mitigate the stigma is to rename the diseases to eliminate the negative bias inherent to the name tags and to give the medical condition a neutral image. This could be done in both psychiatric textbooks and popular culture, and hence change how doctors and the general public think about mental illnesses. Table 7 lists the traditional names of mental illnesses in the Chinese language (Mandarin) that contain a strong stigma as well as the suggested new names that may substantially reduce the stigma associated with the medical condition.
+International experiences also suggest that mass media campaigns made by trusted sources (such as professional medical associations) can also contribute to reduce the social stigma and encourage patients with mental diseases to seek
+proper healthcare. For example, an advertisement campaign in Germany made by Phychenet features a patient suffering from mental illness, which demonstrates and explains the symptoms and prevalence of mental diseases with warm-hearted encouragement for people with such symptoms to seek help. This campaign has successfully raised the public awareness of mental diseases and let patients know that many other people are suffering from the same health conditions, which in turn helped to reduce the self-perceived stigma among these patients. Another example is the Canadian “Bell Let’s Talk” campaign that encourages discussions about mental health and raises funds (https://letstalk.bell.ca/en/our-initiatives). Similar mass media campaigns have been experimented in various parts of China, with government-financed advertisement displayed on TV, on large advertisement boards in densely populated areas (such as subway stations) and within hospitals.
+Increasing the Public Investment in Mental Healthcare
+Currently, the public funds allocated to the mental health sector only accounts for less than 1% of total health expenditure in China. Thus, China still has ample room for increasing the public investment in mental health resources, which can be achieved through 2 main channels: one is to use the general tax revenues to directly subsidize the mental healthcare institutions; the other is through an earmarked tax in the existing health insurance programs by specifically enhancing the mental health benefit and financing levels.
+The advantage for the direct public subsidy to mental health facilities is to mitigate the price distortion and the related profit-seeking behaviors by physicians and hospitals.
+For example, the essential psychotropic medications for the treatment of mental illnesses are relatively inexpensive in LMICs, because many of them are already off patent and can thus be produced by local pharmaceutical firms. However, this does not mean that physicians in these countries have incentives to prescribe these cost-effective medicines under a profit-centered health system such as China’s, as the hospitals still rely on the profit of higher-priced prescriptions to resolve their funding gaps. The increase in public investment through direct government subsidy may thus help to reduce such behavioral distortion and hence increase the efficiency of mental healthcare. The advantage of the second financing channel (through an earmarked tax) is to reduce the out-ofpocket payment for the patients with mental illnesses, which in turn provides push incentives for reducing the undertreatment gap in mental healthcare.
+A recent study suggests that increasing public funds provides a strong return on investment, ranging from 2.3 to 5.7 USD per dollar invested.41 Although the argument is clear, the government needs to have a strong political willingness to take action. From a theoretical point of view, the need for public investment in the mental health sector arises from the “semi-public good” nature of mental healthcare, which contains an important component of public health with large social benefits.31 This change of mentality and alignment of social awareness are necessary, and they can provide a justification for the government to increase the public investment in the mental health sector.
+Integrated People-Centered Health System
+Many studies have shown that the current hospital-centered health system in China is not an efficient approach to bridge the treatment gap in mental healthcare. Rather, an effective intervention and treatment model is to deliver the mental healthcare at the primary and community level.42-44 There are at least 3 arguments to support the urgent need to restructure the current delivery system for mental health services. First,
+hospital-centered health system is more likely to be constrained by the maldistribution of healthcare resources across regions. By contrast, primary care facilities are relatively easy to access at the community level. As a result, a natural consequence of a shift from the hospital-centered to the primary-care-oriented system is a reduction in the time cost of diagnosis and treatment, which in turn provides strong push incentives to bridge the treatment gap in mental healthcare. Second, a people-centered system, which integrate primary, maternal, and the care for other NCDs together, is in a better position to address the co-morbidities of mental illnesses and the common co-existence of risk factors such as hypertension and obesity. Third, mental health is also strongly correlated to economic poverty and poor lifestyle choices (such as malnutrition and physical inactivity), an integrated system is beneficial in the sense that it provides an effective treatment by integrating mental healthcare with anti-poverty policies and other disease management programs. In sum, an integrated people-centered delivery model can be a viable choice to break the vicious cycle of economic poverty, undertreatment of mental illnesses and the co-morbidity with other NCDs.
+In fact, strengthening primary healthcare has been put on China’s healthcare reform agenda since 2009.45 However, little progress has been made for the transformation of healthcare system from a hospital-centered to a peoplecentered delivery mode.34,35,46 According to the recent reviews, there are 2 main factors that block the progress of this policy. First, China’s healthcare delivery system is still dominated by government-run hospitals.35 Second, due to the existence of multiple-tier medical education system, the quality of physicians is heterogeneous across hospitals. Good doctors with high-quality training are locked in larger urban hospitals, and hence primary care has long been perceived as low-quality and attracts few patients.47 As a result, the free mobility of physicians across healthcare institutions, or the “multiple-point practice” model, may be a necessary step in setting up the integrated people-centered healthcare system.
+e-Health System
+As mentioned, one major barrier for developing the integrated people-centered primary care system in China is that the primary care is often perceived as low-quality care.34 An effective policy option for breaking this perception is to develop an ICT-based platform, or the e-health system, to inform the public about mental healthcare options and to facilitate the remote and data-based healthcare practices. Properly managed, these ICT-based platforms can potentially lead to quality improvement and cost reduction in mental healthcare, with at least the following identifiable benefits. First, digital healthcare can be an effective approach to reducing the regional inequality in the accessibility of mental healthcare resources, especially in mitigating the quality and technological gaps between the urban and rural areas as well as between large hospitals and primary care institutions. Second, ICT offers alternative models of delivering mental healthcare by eliminating many access barriers in the current system, including the transportation barriers, the perceived stigma associated with visiting mental health clinics, clinician shortages, and the slow diffusion of medical technology from urban to rural areas. Third, ICT has a potential to bridge the treatment gap in mental healthcare by providing remote screening, diagnosis, monitoring, treatment and even remote training for non-specialist healthcare workers.
+Conclusion
+One of the common problems that plague the mental healthcare sectors in many developing countries is the substantial unmet healthcare needs, or the large gap between the need for and the provision of mental healthcare treatment. This paper contributes to increasing our understanding on the potential causes of the treatment gap from the perspectives of economics. We hypothesize that mental health services face more access barriers than the general healthcare. Based on the institutional features in China’s health system, we find evidence to support our hypothesis on the 4 major hurdles in accessing mental healthcare, namely, the nonmonetary costs associated with stigma, the monetary costs due to the limited insurance coverage and reimbursement, the time costs that result from the geographic maldistribution of healthcare resources, and the poor healthcare quality due to the slow diffusion of knowledge and technology. (Admittedly, the evidence provided here is not sufficient for making causal claims on the 4 major hurdles of mental healthcare, and future studies based on micro-level datasets are invited to empirically assess the relative importance of these factors in causing the treatment gap in mental healthcare.)
+An important implication of our study is that removing access barriers to mental healthcare is a multi-dimensional task that requires coordination from mental health institutions, the healthcare planning and financing authorities, the patients and the society in general. Previous policy efforts to remove
+access barriers have been focused on reducing the monetary cost of mental healthcare through expanding health insurance coverage and on reducing the time costs through a redistribution of health care resources. This approach that relies on a single policy tool proves to be insufficient to mitigate the treatment gap in mental healthcare. Our analysis indicates that more policy tools and further actions are needed. Specifically, we propose an information campaign for mental health awareness and we suggest properly renaming the mental health conditions in the Chinese language, both of which aim to reduce the social stigma in public perception and to reduce the nonmonetary costs of seeking mental healthcare. In addition, we also call for more policy efforts to accelerate the process of new drug launch and the adoption of new medical technology in the treatment of mental illnesses, which helps to improve the value and to close the treatment gap of mental healthcare.
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+BACKGROUND
+There is a well-reported, very sizeable global gap between the need and provision of services to prevent, identify and treat mental, neurological and substance use (MNS) disorders. To expedite care as efficiently as possible, the WHO’s World Mental Health Report1 recommended the assessment and management of MNS disorders in
+primary healthcare (PHC) and community settings, as outlined in the Mental Health Action Plan.2 To deliver community mental healthcare, PHC staff require training in the assessment, diagnosis and management of MNS disorders, alongside other key components, including regular supervision and support. Central to this agenda is the WHO Mental Health Gap Action Programme3 (mhGAP) and intervention guide (IG).4
+The mhGAP-IG is an evidence-based tool for the assessment and treatment of priority MNS disorders, featuring guidelines for clinical decision making. Aimed at non-specialist PHC staff in low-income and middle-income countries (LMICs), the mhGAP-IG is also used by governments, nongovernmental organisations and researchers. An mhGAP Humanitarian Intervention Guide (HIG),5 for settings characterised by widespread psychological trauma and even less access to mental health specialists, has also been published,5 alongside an mhGAP-IG mobile device app.6
+Our previous systematic review7 identified 33 published peer-reviewed studies and protocols which used the mhGAP-IG. Given that another review found only six published studies of experimental non-communicable disease clinical guideline implementation in LMICs,8 the mhGAP-IG literature was relatively substantial in 2017. The mhGAP-IG had been adopted by clinicians, government ministries, trainers, educators and academics in diverse LMICs. Applications ranged from local adaptation, training and clinical practice, to mobile applications for isolated PHC staff, tablet-based avatar-assisted family training, economic modelling, novel rating scales and comparison interventions in randomised controlled trials (RCTs). The literature was, however, dominated by studies from a subset of countries, suggesting that much implementation was not evaluated, or that evaluations were not widely disseminated. Reliance on relatively limited pre-mhGAP and post-mhGAP training knowledge assessments meant that opportunities may have been missed at times, to describe real-world contextual challenges to widespread uptake and scale-up.
+OBJECTIVE
+In this study, we sought to identify evidence generated since the publication of our previous review for the practical implementation of the WHO mhGAP-IG and associated tools, in terms of how they have been used, evaluated and reported.
+124
+Keynejad R, et al. Evid Based Ment Health 2021;24:124-130. doi:10.1136/ebmental-2021-300254
+BMJ
+STUDY SELECTION AND ANALYSIS
+We included any type of study design, review or report of evidence, experience or evaluation of using the mhGAP-IG in LMICs, from any country, in any language. In order to identify as many potentially relevant records as possible, we searched the following databases on 24 November 2020: 3ie, Cochrane Library, CINAHL, EMBASE, LILACS, Medline, PsycINFO, PubMed, SciELO, Scopus, Web of Knowledge. The only search terms used were ‘mental health gap action programme’ OR ‘mental health gap action program’ OR ‘mhGAP’. The term ‘intervention guide’ was not included, due to its variable use in the literature and in practice. Searches were conducted in English, but studies written in other languages were eligible for inclusion. In addition to database searches, the reference lists of relevant excluded papers were searched for eligible studies. Grey literature, including book chapters and conference presentations, were identified by repeating the search on Google Scholar. We used forward citation tracking of our 2017 systematic review to identify additional records.7 This work is registered on the International Prospective Register of Systematic Reviews (registration number: CRD42017068459).
+FINDINGS
+Figure 1 shows the flow of studies from identification to screening, eligibility and inclusion. The titles and abstracts of the 2002 non-duplicated papers were screened by RK excluding 1807, which did not review or report evidence, experience or evaluation the mhGAP-IG, app or HIG. The details of a subset of excluded studies reporting non-mhGAP-IG-related integrations of mental healthcare into PHC were recorded for a separate review. The remaining 195 full-text articles were screened again for eligibility by JS and categorised into study types. Where studies could be categorised into more than one type (e.g., both clinical application and research), a judgement was made about the study’s primary focus. No papers were excluded based on language, and no relevant papers from high-income settings were identified.
+Table 1 shows the distribution of study types across geographical regions. Most studies (40%, n=62) were conducted in the African region and South-East Asia (25%, n=38). Notably few studies were conducted in the Eastern Mediterranean (7%, n=10), region of the Americas (4%, n=6) and the European region (1%, n=2). Ten per cent of studies (n = 15) reported results from at least two countries on different continents. Most
+Keynejad R, et al. Evid Based Ment Health 2021;24:124-130. doi:10.1136/ebmental-2021-300254
+125
+included studies described uses of the mhGAP-IG, app or HIG for training (38%, n=58), clinical use (30%, n=44) and research (16%, n=25).
+Table 2 shows the study design methods used across different study types. RCTs comprised 15% (n=23) of studies and RCT protocols or trial registrations, 12% (n = 19). Uncontrolled studies comprised 23% of studies (n=36), descriptive accounts 18% of studies (n=28), qualitative studies 8% (n = 13) and other non-randomised designs 7% (n = 10).
+Of the 193 eligible studies, data were extracted by JS and RK from the 162 papers (describing 155 studies) not included in the 2017 systematic review (n=33). The heterogeneity of mhGAP-IG, app and HIG uses, outcome measures, and evaluations precluded meta-analysis. Due to the heterogeneity of designs, including a large proportion of non-randomised designs and variable reporting methods, we did not assess risk of bias across included studies.9 Extracted data included country involved, participants, sample, nature of use, evaluation conducted and summary of findings, where relevant. Below, we summarise the findings reported by studies categorised into six different types: use in training (online supplemental material table 4), use in clinical practice (online supplemental material table 5), use in research (online supplemental material table 6), local adaptation or contextualisation (online supplemental material table 7), economic evaluation (online supplemental material table 8) and other educational purposes (online supplemental material table 9).
+Use in training
+Of the 162 included papers, 58 studies (59 references) reported the use of the mhGAP-IG or HIG in training courses, trained PHC and other community-based staff (health extension workers, community health workers (CHWs), nurses, midwives, pharmacists, physician assistants, clinical officers, general practitioners, social workers), students (nursing and medical undergraduates, master’s students), research assistants, clinical educators, decision-makers, caregivers, traditional and faith healers, and mental health service users.
+The impact of mhGAP-IG-based training was evaluated using cross-sectional designs, cohort studies, pretest/post-test studies, retrospective medical records reviews and RCTs. Overall, post-mhGAP-IG training, PHC and community-based staff showed increased mental health knowledge and awareness, improved attitudes towards mental ill-health and people living with mental health problems, improved attitudes towards psychiatry, more confidence in managing mental health problems in PHC, increased job satisfaction and interest in mental health training. Evaluations of mhGAP-IG-based training highlighted trainees’ satisfaction with the programme, which was generally considered useful, relevant and valuable.
+Aspects of trainees’ practice also improved. Studies reported improved diagnostic skills and diagnostic agreement, increased mental health service efficiency, and greater family involvement in care. Studies also reported benefits for patients following mhGAP-IG-based staff training. These included increased mental
+health service utilisation, clinical improvement, returning to work, fewer discriminatory experiences and greater satisfaction with services.
+Descriptive accounts and qualitative studies of mhGAP-IG-based training highlighted barriers to training PHC and community-based staff. These included limited funding or institutional support for training, difficulty establishing supervision mechanisms, cultural differences in conceptualisations of mental ill-health, the need for further training and time constraints. Descriptive accounts and qualitative studies also emphasised the importance of continued supervision, medication supplies, coordination of services, political commitment to mental healthcare, planning and leadership to the success of mhGAP-IG-based training.
+Four records describing training courses were protocols or trial registrations, from Iran, Sri Lanka India and Nepal. These studies aimed to use mhGAP-IG-based training to improve youth and parent functioning, reduce mental health symptoms, improve clinical skills and reduce stigma among health workers.
+Use in clinical practice
+Forty-six studies (49 references) reported clinical outcomes of applications of the mhGAP-IG or HIG in practice(online supplemental material table 5). Target patient groups included adults with mood or anxiety disorders, adults with severe mental illness (SMI), pregnant women with mood disorders, parents of primary school-aged children and people living with HIV.
+Studies assessed the mhGAP-IG and HIG’s impact in clinical practice through retrospective medical records reviews, uncontrolled studies, cross-sectional studies, case-control studies and RCTs. They reported positive impacts of the mhGAP-IG on mental health awareness, clinician-confirmed case identification, mental health symptoms, clinical recovery, experiences of discrimination, food security, quality of life, contact coverage and access to care, for example, clinical consultation numbers, intervention adherence, retention in treatment and numbers of facilities treating chronic illness. Studies explored factors influencing the mhGAP-IG and HIG’s clinical uptake. These included confidential consultation space, patient trust in health workers, mental health stigma, medication supply, staff (including psychiatrist) availability, staff turnover, supervision, support and health worker motivation.
+Some studies used RCT designs to evaluate clinical use of the mhGAP-IG alone, while others combined the mhGAP-IG with problem-solving therapy, behavioural activation, interpersonal therapy, parent skills training, communication skills and counselling. In some cases, the mhGAP-IG was provided to both intervention and control groups as enhanced usual care.
+Ten records evaluating clinical use of the mhGAP-IG, app or HIG were protocols and three were trial registrations, from Nigeria, Ghana, Kenya, Uganda Rwanda, South Africa, Ethiopia, India, Nepal and Pakistan. These studies aimed to improve patient symptoms or functioning, parent-child communication and interaction, parent quality of life and the feasibility of combining mhGAP-IG-informed care with interpersonal therapy. One trial registration will compare the electronic mhGAP-IG with the paper version.
+Use in research
+Most of the studies included in this review employed research methods in some form. We categorised 25 studies (27 references) as reporting uses of the mhGAP-IG for research where the primary purpose of the work was a research endeavour,
+whose conduct was facilitated by the mhGAP-IG as a tool(on-line supplemental material table 6). Ten studies (seven RCTs, two feasibility studies and one non-randomised trial) used the mhGAP-IG to standardise the mental healthcare received by participants in both intervention and control arms. In India, Counselling for Alcohol Problems was associated with significantly more remission and abstinence from alcohol use disorder at 3 and 12 months' follow-up than mhGAP-IG-enhanced usual care. Also in India, the Healthy Activity Programme behavioural activation intervention was associated with significantly lower depressive symptoms and increased remission at 3 and 12 months' follow-up than mhGAP-IG-enhanced usual care. In Pakistan, there was no difference between the impact of mhGAP-IG-enhanced usual care and an intensive group-based psychosocial intervention on depressive symptoms and remission at 6 and 36 months' follow-up. Also in Pakistan, there was no difference between electronic health records and decision support for integrated management of chronic conditions (including depression, using mhGAP-IG content) and non-electronic training. In Nepal, a feasibility study found that group problem management plus (PM + ) was acceptable and feasible to compare with mhGAP-IG-enhanced usual care. In Somaliland, a non-randomised study found that mhGAP-IG-informed community-based relapse prevention was more effective for patients commencing the programme in remission than those requiring intensive homebased care first.
+Two RCTs used the mhGAP-IG to inform the content of a novel intervention in Pakistan and China and one used it to screen for psychosis and assess suicide risk during a trial in Malaysia. In Pakistan, more pregnant women sought help for distress from CHWs following mhGAP-IG-informed psychoeducation than women in the control arm. In China, an mhGAP-IG-informed intervention was associated with greater reductions in anticipated discrimination, negative symptoms and functioning scores than a community psychiatrist interview. In Malaysia, common mental disorder symptoms improved more in participants receiving integrative adapt therapy than those receiving cognitive behavioural therapy.
+One validation study used the mhGAP-IG to provide treatment for adolescents diagnosed with a mental disorder using a newly adapted screening tool in Ethiopia. The adapted Amharic Youth Self Report was a reliable and valid screen for anxiety, depression and social problems in female adolescents and attention problems in young men. One study developed a scale to categorise free Arabic-language anxiety and depression apps using the mhGAP-IG. One descriptive account incorporated the mhGAP-IG into a novel counselling aid (FELICIA) based on the Thinking Healthy Programme, for couples experiencing infertility in Nigeria; a pilot study is underway.
+Nine studies using the mhGAP-IG for research were protocols: six RCTs, a feasibility study, an implementation study and a cohort study. One prospective cohort study will evaluate the impact on tuberculosis outcomes of mhGAP-IG-based depression treatment. Five RCTs will use the mhGAP-IG to standardise the mental healthcare received by participants in both intervention and control arms, in Ethiopia, Tanzania, Pakistan and Nepal. A sixth RCT protocol aims to compare the reach, costs and clinical effectiveness of mhGAP-IG-based care delivered by specialist mental health professionals, PHC staff and CHWs in Mozambique. The implementation study protocol aims to evaluate implementation strategies for cognitive stimulation therapy in Brazil, India and Tanzania.
+Local adaptation or contextualisation
+Twelve studies (13 references) reported local adaptation or contextualisation of the mhGAP-IG(online supplemental material table 7). Qualitative studies, case studies and descriptive accounts included reflections and experiences of adapting or contextualising the mhGAP-IG. Methods included ethnographic research, in-depth stakeholder interviews and focus groups, theory of change workshops, literature reviews, local epidemiological evidence reviews, situational analyses and asset mapping of community resources.
+Table 3 summarises facilitators and barriers to mhGAP-IG implementation identified by qualitative studies, at service user, staff, service and leadership levels. Consistent themes included integrating the biomedical model with cultural perspectives, addressing stigma, adjusting staff workloads, providing supervision, creating referral pathways and obtaining institutional and political support.
+Recommended considerations when adapting or contextualising the mhGAP-IG included cultural differences in explanatory models and attitudes towards mental disorders, the structure of the local health system, the availability of supervision and support post-training, trainees’ prior education, knowledge and skills, trainee recruitment processes and the wider sociopolitical context.10 Authors also recommended considering the availability of specialists for referral, medication supplies, translation of the IG into local languages and organisational roles.11 12
+One study developed a framework to integrate cultural knowledge, structural competence and ethics into mhGAP-IG planning, adaptation, training and implementation. Domains included examining concepts of wellness and illness, exploring systems of care, recognising formal and informal care systems, and considering the ethical space of power dynamics and decision making.13
+Economic evaluation
+Seven studies (seven references) used the mhGAP-IG to conduct economic evaluations(online supplemental material table 8). Three studies used the mhGAP costing tool to calculate human resource requirements from the prevalence of depression in Syria, MNS disorders in Pacific Island and 48 sub-Saharan African countries. Two studies performed cost-benefit analyses of the scale-up of effective treatments for depression and anxiety in 36 countries, and for psychosis, depression and epilepsy in Ethiopia, India, Nepal and Uganda. Two studies assessed household out-of-pocket health expenditures, finding that having a relative with SMI was associated with catastrophic health expenditure,
+financial dissatisfaction and cost-cutting actions, such as withdrawing children from school. Functional impairment was associated with higher treatment costs in Ethiopia and India, and out-of-pocket expenditure in Uganda and India.
+Other educational purposes
+The final seven studies reported uses of the mhGAP-IG for a range of healthcare education-focused purposes(online supplemental material table 9). Two described the development of a community informant detection tool in Nepal based on mhGAP-IG modules and its cluster RCT evaluation, which found that it significantly increased diagnoses. One study developed an mhGAP-IG-based electronic decision support system in India, where it was acceptable but required modification. An Indonesian study used the mhGAP-IG to code interview responses, identifying greater mental health knowledge and lower stigma among specialist health workers, compared with non-specialists. In Ukraine, the incorporation of the mhGAP-IG into the undergraduate medical curriculum was associated with sustainability, cost-effectiveness, educational and care quality benefits. One study designed mhGAP-IG-based clinician competencies to inform training and assessment. A qualitative study used participant observations of the mhGAP-IG’s use to analyse and critique its algorithmic approach.
+CONCLUSIONS AND CLINICAL IMPLICATIONS
+This updated systematic review of the literature on the mhGAP-IG, app and HIG demonstrates a dramatic increase in published studies of its use and evaluation since 2017.7 Most of the literature comes from the African region and South-East Asia, with notable evidence gaps including the region of the Americas and the Eastern Mediterranean. Countries participating in the programme for improving mental healthcare (PRIME) study (Ethiopia, India, Nepal, South Africa and Uganda) are well represented, demonstrating the benefits to mental health research and evaluation of investment, shared training and methodologies. PRIME identified barriers to integrating mental health into PHC, including limited funding, insufficient specialists to supervise non-specialist workers, inadequate health system structures to support roll-out of task-shared interventions, low community awareness of mental health and high levels of stigma.14 Building on PRIME, the AFFIRM study is conducting two RCTs in South Africa and Ethiopia of task-shared psychological interventions, building capacity through fellowships and mentorships for early career researchers from Ethiopia, Ghana, Malawi, Uganda and
+Zimbabwe, and building a network for interdisciplinary collaboration.15 Both projects demonstrate ways of enhancing the impact and sustainability of mental health research, through long-term collaborations between LMIC and HIC institutions, funded to design and evaluate new interventions from piloting to scale-up, and develop the next generation of global mental health researchers in LMICs.
+Similar study types to those identified in our 2017 review7 have been published since, with the highest proportions of studies reporting on mhGAP-IG training courses, clinical and research applications. The surge in literature also includes contextual adaptations, economic analyses and novel applications to health education. Studies demonstrated the benefits of the mhGAP-IG’s use in training for improving the knowledge, attitudes and confidence of health workers in relation to mental health disorders. Clinical studies identified improvements in patient symptoms, quality of life, access to and engagement with mental healthcare associated with mhGAP-IG implementation.
+The published evidence comprises a breadth of research methods, with high proportions of RCTs and protocols for RCTs, and smaller proportions of related feasibility studies, implementation studies and process evaluations. The publication of uncontrolled, non-randomised, descriptive and case studies contributes a valuable literature on the mhGAP-IG from settings where more extensive research studies may not be feasible. Adaptation studies recommended a range of contextual considerations to optimise the success of mhGAP-IG implementation.
+Many research uses of the mhGAP-IG aimed to adapt and evaluate brief psychological interventions for settings where specialist mental healthcare is limited or unavailable. Often, such interventions were implemented alongside mhGAP-IG training (as ‘enhanced usual care’) or as a next step after successful mhGAP-IG training for clinical use. Literature on the Thinking Healthy Programme, Healthy Activity Programme, Counselling for Alcohol Problems and PM+, largely from South Asia, demonstrates the broader role of the mhGAP-IG in encouraging the development and rigorous evaluation of acceptable, feasible and scalable talking therapies in LMICs. Sharing of intervention manuals via the WHO website should facilitate the adaptation and evaluation of these interventions for other countries, cultures and contexts. SMIs such as schizophrenia, severe depression and bipolar affective disorder in PHC were less frequently targeted by intervention studies, the majority of which focused on mild to moderate depression. Lessons learned from the use of the mhGAP-IG for common mental disorder research could now be applied to research and intervention development for people with SMI.
+Recognition has grown of the need to improve the quality of inpatient and community mental healthcare, attend to the human rights of people with mental disorders and promote recovery.16 The fact that most mhGAP-IG, app and HIG training combined lectures and small-group tasks with role play interactions suggests that scaling up the mhGAP-IG could have wider benefits, improving health worker communication skills and the compassion and respect with which care is delivered. Twenty-one included studies addressed or measured stigma, or planned to. Of 15 completed studies, nine reported reductions in one or more measures of stigma, even where health worker knowledge or skills were unchanged.17-25 Economic studies identified the scale of the mental health gap, its financial26 and disability impacts,27 and calculated the workforce requirements28-30 and costs of treatment.
+The principal limitation of this study is the likelihood of publication bias, with studies finding no significant or negative
+results less likely to have been published. A substantial number of reports is likely to remain in the ‘grey literature’ outside peer reviewed journals. A further limitation is that we conducted single-author screening of potentially eligible studies, raising the risk that some eligible studies could have been missed.
+Since their release, the WHO mhGAP-IG, app and HIG have made a remarkable impact on global mental health education and training, clinical practice, research and policy. Included studies used a range of methods: a testament to practitioners’ commitment to mhGAP-IG monitoring, evaluation and implementation research. Promising approaches to strengthen the evidence base include consortia bringing researchers together from a range of LMICs and HICs for collaborative, interdisciplinary study, benefiting from shared learning and pooled resources. The mhGAP-IG has created a dynamic field in which practitioners from diverse international contexts learn from each other’s experiences and adapt study designs and intervention models for their own context. The next step must be to fill gaps in the evidence base for under-studied regions and disorders, and to investigate the scale-up and sustainable integration of isolated interventions into long-term clinical practice.
+Systematic review
+and indication of whether changes were made. See: https://creativecommons.org/ licenses/by/4.0/.
+ORCID iD
+Roxanne Keynejad http://orcid.org/0000-0003-4434-3526
+REFERENCES
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+4	WHO. mhGAP Intervention Guide - Version 2.0, 2016. Available: https://www.who.int/ publications/i/item/mhgap-intervention-guide-version-2.0
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+11	Doherty S, Dass G, Edward A, et al. Challenges and lessons learned in re-filming the WHO mhGAP training videos for Sri Lankan context - a qualitative study. Confl Health 2020;14:7.
+12	Bitta MA, Kariuki SM, Omar A, et al. Contextualizing and pilot testing the mental health gap action programme intervention guide (mhGAP-IG) to primary healthcare workers in Kilifi, Kenya. Glob Ment Health 2020;7:e11.
+13	Gomez-Carrillo A, Lencucha R, Faregh N, et al. Engaging culture and context in mhGAP implementation: fostering reflexive deliberation in practice. BMJ Glob Health 2020;5.
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+15	Lund C, Alem A, Schneider M, et al. Generating evidence to narrow the treatment gap for mental disorders in sub-Saharan Africa: rationale, overview and methods of affirm. Epidemiol Psychiatr Sci 2015;24:233-40.
+16	WHO. Who QualityRights initiative - improving quality, promoting human rights, 2019. Available: https://www.who.int/mental_health/policy/quality_rights/en/
+17	Kohrt BA, Mutamba BB, Luitel NP, et al. How competent are non-specialists trained to integrate mental health services in primary care? global health perspectives from Uganda, Liberia, and Nepal. Int Rev Psychiatry 2018;30:182-98.
+18	Kohrt BA, Turner EL, Rai S, et al. Reducing mental illness stigma in healthcare settings: proof of concept for a social contact intervention to address what matters most for primary care providers. Soc Sci Med 2020;250:1 12852.
+19	Li J, Fan Y, Zhong H-Q, et al. Effectiveness of an anti-stigma training on improving attitudes and decreasing discrimination towards people with mental disorders among care assistant workers in Guangzhou, China. Int J Ment Health Syst 2019;13:1.
+20	Li J, Huang Y-G, Ran M-S, et al. Community-Based comprehensive intervention for people with schizophrenia in Guangzhou, China: effects on clinical symptoms, social functioning, internalized stigma and discrimination. Asian J Psychiatr 2018;34:21-30.
+21	Li J, Li J, Thornicroft G, et al. Training community mental health staff in Guangzhou, China: evaluation of the effect of a new training model. BMC Psychiatry 2015;15.
+22	Maulik PK, Kallakuri S, Devarapalli S, et al. Increasing use of mental health services in remote areas using mobile technology: a pre-post evaluation of the smart mental health project in rural India. J Glob Health 2017;7:010408.
+23	Iheanacho T, Marienfeld C, Stefanovics E, et al.Attitudes toward mental illness and changes associated with a brief educational intervention for medical and nursing students in Nigeria. Acad Psychiatry 2014;38:320-4.
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+Praharso NF, Pols H, Tiliopoulos N. Mental health literacy of Indonesian health practitioners and implications for mental health system development. Asian J Psychiatr 2020;54:102168.
+Mutiso VN, Pike K, Musyimi CW, et al. Feasibility of who mhGAP-intervention guide in reducing experienced discrimination in people with mental disorders: a pilot study in a rural Kenyan setting. EpidemiolPsychiatr Sci 2019;28:156-67.
+Hailemichael Y, Hailemariam D, Tirfessa K, et al. Catastrophic out-of-pocket payments for households of people with severe mental disorder: a comparative study in rural Ethiopia. Int J Ment Health Syst 2019;13:39.
+Chisholm D, Garman E, Breuer E, et al. Health service costs and their association with functional impairment among adults receiving integrated mental health care in five low- and middle-income countries: the prime cohort study. Health Policy Plan 2020;35:567-76.
+Charlson FJ, Diminic S, Lund C, et al. Mental and substance use disorders in subSaharan Africa: predictions of epidemiological changes and mental health workforce requirements for the next 40 years. PLoS One 2014;9:e110208.
+Charlson FJ, Diminic S, Whiteford HA. The rising tide of mental disorders in the Pacific region: forecasts of disease burden and service requirements from 2010 to 2050. Asia Pac Policy Stud 2015;2:280-92.
+Charlson FJ, Lee YY, Diminic S, et al. Applications of the epidemiological modelling outputs for targeted mental health planning in conflict-affected populations: the Syria case-study. Glob Ment Health 2016;3:e8.
+Catalao R, Eshetu T, Tsigebrhan R, et al. Implementing integrated services for people with epilepsy in primary care in Ethiopia: a qualitative study. BMC Health Serv Res 2018;18:372.
+Breuer E, Subba P, Luitel N, et al. Using qualitative comparative analysis and theory of change to unravel the effects of a mental health intervention on service utilisation in Nepal. BMJ Glob Health 2018;3:e001023.
+Spagnolo J, Champagne F, Leduc N, et al. "We find what we look for, and we look for what we know": factors interacting with a mental health training program to influence its expected outcomes in Tunisia. BMC Public Health 2018;18:1398.
+Kerbage H, Marranconi F, Chamoun Y, et al. Mental health services for Syrian refugees in Lebanon: perceptions and experiences of professionals and refugees. Qual Health Res 2020;30:849-64.
+Miguel-Esponda G, Bohm-Levine N, Rodriguez-Cuevas FG, et al. Implementation process and outcomes of a mental health programme integrated in primary care clinics in rural Mexico: a mixed-methods study. Int J Ment Health Syst 2020;14:21. Miguel Esponda G. What enables and hinders implementation? A mixed-methods case study of a mental health programme implemented in primary care clinics in rural Mexico: London School of Hygiene & Tropical Medicine, 2020.
+Hailemariam M, Fekadu A, Medhin G, et al. Equitable access to mental healthcare integrated in primary care for people with severe mental disorders in rural Ethiopia: a community-based cross-sectional study. Int JMent Health Syst 2019;13:78.
+Musyimi CW, Mutiso VN, Ndetei DM, et al. Mental health treatment in Kenya: tasksharing challenges and opportunities among informal health providers. Int J Ment Health Syst 2017;11:45.
+Luitel NP, Breuer E, Adhikari A, et al. Process evaluation of a district mental healthcare plan in Nepal: a mixed-methods case study. BJPsych Open 2020;6:e77.
+Spagnolo J, Champagne F, Leduc N, et al. A program to further integrate mental health into primary care: lessons learned from a pilot trial in Tunisia. J Glob Health Rep 2019;3.
+Chaulagain A, Pacione L, Abdulmalik J, et al. Who mental health gap action programme intervention guide (mhGAP-IG): the first pre-service training study. Int J Ment Health Syst 2020;14:47.
+Diez-Canseco F, Toyama M, Ipince A, et al. Integration of a technology-based mental health screening program into routine practices of primary health care services in Peru (the Allillanchu project): development and implementation. J Med Internet Res 2018;20:e100.
+Rai S, Gurung D, Kaiser BN, et al. A service user co-facilitated intervention to reduce mental illness stigma among primary healthcare workers: utilizing perspectives of family members and caregivers. Fam Syst Health 2018;36:198-209.
+Hughes P, Thomson S. mhGAP - the global scenario. Prog Neurol Psychiatry 2019;23:4-6.
+Echeverri C, Le Roy J, Worku B, et al. Mental health capacity building in refugee primary health care settings in sub-Saharan Africa: impact, challenges and gaps. Glob Ment Health 2018;5.
+Evid Based Mental Health: first published as 10.1136/ebmental-2021-300254 on 26 April 2021. Downloaded from http://ebmh.bmj.com/ on December 23, 2022 by guest. Protected by copyright.
+130
+Keynejad R, et al. Evid Based Ment Health 2021;24:124-130. doi:10.1136/ebmental-2021-300254
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+Globally, the vast majority of people with mental health conditions do not receive effective care. Among people living with depression, only 1 in 5 persons in high-income countries and 1 in 27 in lower-middle income countries receive minimally adequate treatment1. There is a dearth of health workers trained in mental health care, with only one trained provider per 10,000 people in most countries2. One key action to improve access to mental health care is to expand psychological and psychosocial support services delivered by diverse cadres across settings3.
+There is now good evidence that persons who are not specialists in mental health can effectively deliver psychological interventions, but they must be adequately trained and supervised4. Non-specialist providers include primary care workers, community workers, psychosocial workers, teachers, family members and peers. However, unlike licensed professionals for whom
+there are professional associations to assure standards, there are usually no systems or mechanisms in place that check whether non-specialist providers have sufficient training and supervision to achieve minimum competency to effectively and safely deliver interventions. This raises the question: how can governments and the general population be assured that non-specialists provide quality care?
+One way to address this challenge is to establish competencybased training approaches and competency assessment measures that governments, non-governmental organizations and other institutions can use to benchmark skills for safe and effective care. Competency measures can be used to determine who is or is not competent as well as to tailor supervision and supplementary training to address gaps in skills. Having competency targets in mind can also inform training duration and content
+that may need to vary across sites or cadres. Competency-based training approaches have already demonstrated success in diverse areas of health care in low resource settings, including surgery and obstetric care5,6.
+To facilitate competency-based training in psychosocial support, psychological treatments, and foundational helping skills, the World Health Organization (WHO) is developing the Ensuring Quality in Psychological Support (EQUIP) platform (https:// www.who.int/mental health/emergencies/equip/en/).
+The EQUIP platform aligns with WHO's work on universal health coverage, that is establishing competency frameworks across fields of health care. EQUIP will be an online resource to help program managers and trainers utilize competency assessments to evaluate trainings and to feedback those competency results to support trainee development and modify curricula.
+The full suite will comprise tutorials on implementing competency assessments, including how to achieve interrater reliability with global rating standards and how to use role plays to assess competency. It will include guidance for trainers on delivering competency-based training programs, and for implementation and adaptation of psychological interventions. In addition, the EQUIP platform will offer training modules on common factors that can be selected based on competency assessment outcomes. Common factors are general elements of psychosocial support and psychological care - such as communication skills, empathy, collaboration, and helper-client alliance - that are vital ingredients for any intervention to be effective7.
+Contents of the EQUIP platform have been informed by a theory of change workshop attended by mental health and psychosocial service stakeholders with different practice experiences from diverse global settings. The EQUIP team has reviewed manuals and training materials for interventions delivered by non-specialists with effectiveness demonstrated in randomized controlled trials. This has led to the identification of competencies for both common factors and specific classes of psychological interventions (e.g., cognitive, interpersonal, problem solving, behavioral and trauma-focused techniques).
+EQUIP will encompass a competency evaluation tool, the Enhancing Assessment of Common Therapeutic Factors (ENACT), that has been developed for role-play based assessment of mental health and psychosocial support skills for non-specialist and specialist providers across cultures, context and types of interventions8,9. In addition, a suite of competency assessment tools based on ENACT is being developed and tested. Below we briefly outline who, how, where and when EQUIP can be used.
+Who can use EQUIP? EQUIP is intended for trainers, supervisors and project managers implementing psychosocial support and psychological interventions.
+How can EQUIP be used? EQUIP can be used to improve implementation plans, competency assessments of trainees, and training and supervision curricula in common factors to accompany manualized interventions. Competency assessments may also be used to aid selection of trainees and to guide institutional certification after achieving minimum skill targets.
+Where can EQUIP be used? EQUIP will be an online platform of resources with offline formats.
+When can EQUIP be used? To refine the platform and its materials, EQUIP is being developed using a human-centered design approach to enhance usability and engagement, and piloted in multiple countries. After piloting, materials will be available in English, Arabic and Spanish.
+Ultimately, EQUIP is intended to be a resource that will undergo iterative transformation based on feedback from the global practitioner community. Addressing mental health and psychosocial needs requires radical growth in the global workforce to ensure safe and effective delivery of psychosocial support and evidence-based psychological interventions. The EQUIP platform will make competency-based training and assessment resources widely available and adaptable to the contexts and needs of local organizations and practitioners.
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+The WHO EQUIP Foundational Helping Skills Trainer’s Curriculum
+Foundational helping skills are the provider’s competencies munication, demonstrating empathy, rapport building, and proneeded to build a warm and trustworthy relationship with a moting hope and expectancy of change1.
+client. Examples include effective verbal and non-verbal com-	These skills have been widely established as an essential and
+universal prerequisite for the delivery of any effective psychosocial or psychological care1, and identified as core competencies required for all health workers in the forthcoming World Health Organization (WHO)’s Global Competency Framework for Universal Health Coverage2.
+Competent use of these skills by providers improves treatment outcomes for people accessing the whole range of health services, from surgery to mental health services1,3, and use of these skills has been shown to support greater treatment compliance also outside the mental health field - for example, HIV treatment adherence4.
+The recent global experience of the COVID-19 pandemic has demonstrated that mental health and psychosocial support skills cannot be limited to mental health specialists only. Health systems will be able to better respond to public health emergencies as well as provide superior routine care if all health care providers are competent in foundational helping skills. Yet, in many health training programs, the attention to these skills and their evaluation is limited5.
+The WHO developed the Ensuring Quality in Psychological Support (EQUIP) project, which aims to strengthen quality in the delivery of psychosocial support and psychological training within the Universal Health Coverage agenda. The EQUIP platform will offer materials for trainers, supervisors, and program managers on competency-based training and assessment6. One such resource for trainers is the competency-based Foundational Helping Skills Trainer's Curriculum.
+The formative process to develop this training package included a narrative review, identification of empirically supported common factors used across effective interventions7, human centered design inputs, and extensive expert consultation, including experts from field sites, programme managers, and academics. Based on these contributions, a range of skills were identified. Examples include verbal and non-verbal communication skills, using culturally and age-appropriate terminology and concepts for distress, confidentiality, normalization of feelings, expression of empathy, promoting hope, and suicide risk assessment. In addition, based on the importance of attitudes in motivating caring behaviours8, a module on attitudes toward helping others was included.
+The training curriculum is in a modular format, to allow trainers to fit it to the trainees' needs based on brief competency assessments conducted throughout the training programme. The curriculum includes didactics, participatory group activities, and
+450
+World Psychiatry 20:3 - October 2021
+skill remediation techniques, which can be delivered online, face-to-face, or in a combined approach. Role-play based competency assessments9 are conducted throughout the training to monitor progress, to determine minimum competency, and to ensure that the trainee does not engage in harmful behaviours (e.g., being dismissive or judgmental, ignoring or minimizing suicide warning signs)9.
+The EQUIP Foundational Helping Skills Trainer's Curriculum is intended to be a brief course: approximately 20 content hours, with flexibility based on the prior skill level of trainees. It is designed for implementation across a wide variety of government and non-government organization sectors, such as public health, family and community services, education, and law enforcement, with trainees such as professionals and para-professionals without prior training in mental health and psychosocial support skills.
+Pilot testing of the training package is currently underway in Uganda, Nepal and Peru, assessing its feasibility, acceptability, and perceived benefit for remote and in-person delivery.
+The EQUIP Foundational Helping Skills Trainer's Curriculum aims to meet an indispensable need by ensuring that the growing workforce of health care professionals and non-specialist providers are competent in foundational helping skills. This, alongside other activities, will hopefully lead to improved quality of care and will be one step closer to achieving the goal of a competent health workforce for Universal Health Coverage.
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+The initial five years after the onset of a psychotic disorder have been identified as a ‘critical period’ (1), during which most of the clinical and psychosocial deterioration occurs (2, 3). Providing appropriate treatment during this critical period is more likely to achieve good outcomes, both in terms of symptoms and of prevention or reduction of disability (4, 5). In the last two decades, Early Intervention in Psychosis (EIP) teams have been established across several countries, supported by robust evidence of their clinical and cost effectiveness (6).
+Established interventions provided by EIP teams include family interventions, psychoeducation, cognitive-behavioural therapy, and vocational interventions (7). This review summarises recent evidence for these ‘traditional’ EIP interventions along with emerging evidence on other psychosocial interventions, which might in the future become part of routine EIP care provision.
+2.	Established EIP interventions
+Assessing individual evidence for established psychosocial interventions for the treatment of FEP has become increasingly difficult, because these interventions are routinely provided all together as part of EIP usual care. In general, and unsurprisingly, the positive effects of individual interventions, when tested in isolation, are attenuated. However, it is interesting to note that the different interventions show a distinct profile of effectiveness on specific outcomes.
+2.1	Family interventions
+Family interventions (FI) within EIP teams are usually based on the behavioural family therapy (BFT) model, including psychoeducation, communication skills and problemsolving modules (8). The effectiveness of FI in reducing relapse and hospitalisation rates has been supported by systematic reviews (9). This is likely mediated, at least in part, by improvement in medication adherence and engagement with services (10).
+However, it was found that, to reduce relapse, FI need to have relapse reduction as an explicit objective (11).
+Recent studies have tested different models of provision of FI. No differences were identified between FI including different patients and families (multifamily interventions) and FI focused on individual families (single-family interventions) (12). Interventions which only involve family members and/or significant others (also named carers), and not the patients, have shown benefits in terms of reducing carers’ burden (13). In general, the role of family involvement in treatment appears to be key in improving outcomes of FEP. Notably, in cultures and countries where family involvement in treatment is more regular, more positive treatment outcomes were found and a dose-response effect between family involvement and positive outcomes was proposed (14).
+2.2	Cognitive-behavioural therapy (CBT)
+Several studies have shown efficacy of CBT compared with treatment as usual in reducing symptoms of FEP patients (9, 15), but not in reducing relapse (15). The effect size of symptom improvement is moderate (9, 15) for overall and positive symptoms, and low to uncertain for negative symptoms (9, 16, 17).
+The heterogeneity in studies testing CBT in people with FEP was identified as high (16, 18) which might underlie a different response to CBT of different patients (19). A recent review shed some light on this, identifying that 44.5% of the patients reach a 20% (minimally improved) and 13.2% reach a 50% (much improved) reduction of overall symptoms (18).
+More recent research has tested the effect of CBT as an alternative treatment to antipsychotic medications. A triple-blind non-inferiority RCT found that intensive psychosocial intervention (cognitive-behavioural case management) without
+antipsychotic treatment was not inferior to the combination of antipsychotic medication
+and CBT-based case management over six months (20). However, for safety reasons, participants were selected as those having low levels of suicidality and aggression, a
+duration of untreated psychosis (DUP) of less than 6 months, and living in stable accommodation with social support, hence the results cannot be necessarily generalised to many FEP patients (20). A similar study was carried out with adolescents with FEP, identifying a satisfactory safety of the three treatment strategies (21). The feasibility of a larger trial was, however, deemed to be unclear due to recruitment challenges.
+Hence, robust evidence as to whether CBT can be used as an alternative rather than in combination with pharmacological treatment is still lacking.
+2.3	Vocational Interventions
+FEP is often associated with decline in education and poor employment outcomes (22, 23). Research studies reported that a high proportion of young patients with FEP want to work, but fail to gain employment (22). In this context, vocational interventions have been widely advocated to be part of EIP treatment packages (23, 24).
+Several RCTs and systematic reviews have consistently established the efficacy of vocational rehabilitation programmes in improving vocational outcomes in patients with psychosis (25-29). In patients with FEP, the evidence is still limited and preliminary, although currently suggesting a good success rate for vocational interventions, in terms of both education and employment (between 29% and 83%) (22). The Individual Placement of Support (IPS) model has been indicated as a particularly promising model for people with FEP. IPS has competitive employment as its goal, and encompasses rapid job search, integration of rehabilitation and mental health, attention to consumer preferences, continuous and comprehensive assessment, and time-unlimited support (25). The promising results of IPS in small scale studies (30) have been only partially confirmed by the largest and most recent
+trial (31). In this trial the IPS group achieved a very high employment rate during the six months of the intervention but the advantage of IPS over usual treatment was not maintained in the long term (at 12 and 18 months).
+On balance, the evidence points towards an important role of vocational interventions within EIP. However, further research should comprehensively identify the vocational needs of young people with FEP, the important predictors and indicators of vocational success and the cost-benefit profile of IPS over less group-targeted and intensive vocational interventions.
+2.4	Interventions for people at risk of psychosis
+EIP treatment programmes traditionally include offering treatment, which is frequently of a psychosocial nature, to people who are deemed to be at high risk of psychosis (96). The identification of eligible people is based on family history and on the presence of brief or attenuated psychotic symptoms and social dysfunction, but the specific criteria vary among services on an international level (32-34).
+Davies et. al, in large network meta-analyses of RCTs of pharmacological and/or non-pharmacological interventions for CHR-P individuals, reported no superiority of any particular intervention on outcomes such as rates of transition to psychosis, acceptability (35) or attenuated positive psychotic symptoms (96). There are still questions on the effectiveness of CBT which is probably the intervention most often provided to people deemed to be at high risk of psychosis (36). Large trials have failed to show significant effects in reducing transition (35, 37, 38) and the methodological rigour of some of the more recent trials in this area, which showed promising effects, has been thoroughly questioned (39).
+In summary, whilst it is possible that some interventions could prevent development of psychosis in people at-risk, the evidence is still unclear in that regard, and there is no specific support for the use of any particular psychosocial (or pharmacological)
+intervention. Some scepticism has developed in the scientific community about whether providing interventions for people at risk of psychosis should be the preferred strategy or we should rather focus on reducing substance use or other risk factors in the general population (40). A better understanding of the characteristics of people who actually are at risk of psychosis and of key predictors of transition seems to be required in order to identify and test potentially helpful interventions (41).
+3.	Interventions with emerging evidence and novel interventions
+3.1	Interventions to reduce substance misuse
+Substance misuse is common among patients with FEP (42, 43) and a negative prognostic factor for engagement and for the long-term outcome of psychosis (44, 45). And yet, only few studies have evaluated the effectiveness of psychosocial interventions in reducing substance misuse in people with FEP. The Danish OPUS trial, which tested a comprehensive EIP treatment package without specific interventions for substance misuse, reported a reduction in substance misuse in the experimental group (46). Specialised psychosocial interventions to reduce substance misuse within EIP services have been based on a mix of motivational interviewing and CBT principles and techniques and focused particularly on cannabis misuse. However, all the RCTs carried out so far reported a lack of effectiveness of specialised interventions for substance misuse (47-49). The NAVIGATE programme, which integrated a substance misuse treatment component with EIP regular provision of care did not found that the experimental programme of treatment was superior to standard care in reducing substance abuse (50). A low take-up (<25%) of interventions to reduce substance misuse, as found in this trial, may be one of the causes for the lack of success of these interventions so far (50).
+3.2	Interventions to reduce physical health comorbidities and lifestyle interventions
+People with psychotic disorders have a 15- to 20-year shorter life expectancy (51) due to physical health comorbidities, particularly cardiovascular disease (CVD) (52). The reasons for this are multifactorial. People with psychotic disorders have poor physical activity levels (53) (54), increased prevalence of tobacco use (55), limited access to physical health monitoring, care and intervention (56, 57). This is in addition to the adverse cardiometabolic side effects of antipsychotics (58).
+In a recent large meta-review of meta-analyses (27 meta-analyses, representing 128 trials; N=47,231), individual lifestyle counselling showed large effect on reducing body weight; dietary interventions reported medium effect on body weight, small effect on diastolic blood pressure and glycaemic indices; exercise had large effect on reducing body weight and functional exercise capacity; individualized lifestyle interventions were superior to group-based approaches; and CBT focused on weight loss and psychoeducation only had small-to-medium effects on weight reduction (59). A more recent systematic review and meta-analysis (60) of RCTs testing interventions to improve diet and physical activity in people with non-affective psychosis, including first-episode psychosis, identified fifty-nine studies. A larger improvement in physical health parameters was found for interventions including active exercise, especially moderate/vigorous aerobic exercise, but engagement over time was greater for interventions which privileged a psychotherapeutic component.
+In summary, whilst the data on psychosocial intervention to promote physical health in people with FEP is not yet adequate to suggest any far-reaching changes to clinical guidelines and procedures, it provides important directions. Strategies to increase physical exercise and dietary interventions are promising and they should be informed by psychotherapeutic principles to foster engagement of patients with FEP.
+3.3	Interventions to increase social integration and functioning
+People with FEP show poor social functioning and are particularly vulnerable to social isolation (61,62). Evidence from systematic reviews and meta-analyses have reported a significant positive correlation of social isolation with both severity of psychotic symptoms and treatment outcomes (63, 64).
+Social skills training (SST) was part of the EIP treatment programme in the OPUS study (50). Whilst it could be reasonably expected that SST contributed to the effectiveness of the treatment programme in improving global functioning, it is uncertain what the specific added value of social skills training was (50). Other trials of SST have focused on the larger population of all people with psychotic disorders (65-67). A	recent review found positive effects	of SST on	positive and	negative
+symptoms, but was inconclusive on the effect of SST	on	“social
+performance/competence” (68).
+Addressing cognitive impairments through cognitive training and remediation is seen as a promising avenue to increase social functioning, although so far trials have identified stronger effects in improving measures of cognitive functioning rather than real-life social participation (17, 68, 69).
+A more recent development is social recovery therapy, which in a single RCT in EIP services was found to be useful in improving the extent to which people with FEP engaged in social activities (70).
+Further areas for development in this area include: a) strategies to ensure that social isolation is consistently assessed in EIP services, as this has been identified as a practice gap (63); b) the development of interventions to increase not only social activities, but also social contacts and networks to achieve a more profound effect on reducing loneliness and improving social participation and functioning.
+3.4	Peer support programmes
+Peer support harnesses the experiential knowledge of service users and carers to enable emotional and practical support (71). There is a high interest in integrating peer support programmes in EIP services, both for service users and their family members (72-74). Recent meta-analyses of peer support (one-to-one) intervention for individuals using mental health services found evidence of improvement in selfrecovery and empowerment perceptions, but not in standard clinical outcomes (75, 76). The evidence on clinical outcomes is, however, still scarce and mostly includes low-quality studies (75, 76). On the other hand, people with FEP appear to highly value peer support programmes (77) and there are questions as to whether standard RCTs are the most indicated methods to capture the benefits of peer support (71).
+3.5	Digital and technology-based interventions
+Major advances in digital and technology-based interventions offer opportunities to enhance engagement of service users with EIP services and to provide innovative and attractive interventions (78, 79). In particular, the rates of use of online social media of people with psychosis for social participation is high and similar to general population use (80, 81), which is promising , as usually people with psychosis tend to experience higher level of difficulties in ‘offline’ social interactions (61,62).
+Mobile-based interventions, either as a combination of mobile app and face-to-face components or traditional face-to-face therapy facilitated through mobile apps are increasingly being developed.
+Studies on mobile-based interventions for FEP have mainly been small scale pilot trials so far, but have consistently shown feasibility, acceptability and beneficial effects in terms of symptoms and social connectedness in people with FEP (82-84) and in people deemed to be at risk of psychosis (85).
+A particularly interesting development are Virtual-Reality based therapies and among them, the “Avatar therapy”. These therapies focus on helping people to manage
+distressing persecutory delusions and hallucinations. The preliminary evidence is promising but still mixed with regard to effectiveness, and biases of available studies were identified (86, 87).
+In summary, so far there is encouraging evidence on acceptability, feasibility and promise of digital and technology-based interventions for people with FEP. However, validation of clinical effectiveness and of other benefits in larger-scale studies is required.
+4.	Discussion
+Different psychosocial interventions have an established or emerging evidence of benefit for people with FEP. Decisions on which interventions to include within the EIP treatment packages will need to be informed not only by the available evidence, but also consider available funding and professional expertise, as well as contextual variables, i.e. local users’ preferences and priorities and geographical areas. Mobilebased interventions, for example, might be particularly helpful in areas where geographical distance from EIP facilities are higher (88).
+When considering the available evidence, one needs to be aware that EIP teams offer clusters of interventions rather than isolated individual psychosocial interventions (89). This is positive and beneficial as different interventions might augment the effect of each other. However, it makes it difficult to evaluate the contribution to clinical benefits of individual interventions and orient decisions as to whether interventions should be retained or replaced with other interventions. Indeed, some of the heterogeneity found in meta-analyses may result from differences in the overall care packages offered (34,44). Factorial trial designs might offer a way into understanding the complex interactions of different interventions (90). Implementation studies will allow a better understanding of whether and how are the different psychosocial interventions put into practice, which is known to be highly variable (7).
+Another problem with evaluating effectiveness of psychosocial interventions is that engagement with care of people with FEP is a major challenge (70, 91). Some potentially effective interventions may simply have to improve in the acceptability and engagement strategies to fully display their clinical benefit. Strategies to foster engagement should consider in particular groups at risk of disengagement such as people from minority ethnic groups, migrants, people with low educational and sociodemographic background and people who use substances (92).
+The prevention “function” of EIP services is a particularly fascinating, yet controversial one. There is currently a debate as to whether prevention should focus on risk factors in the general population (40) or focus, as EIP services are currently doing, on people who are deemed to be at high risk of developing a psychotic disorder (36). Given the high personal and societal cost of psychotic disorders (93), it is hard to argue against either strategy. The value of prevention strategies within EIP teams is, however, likely to be increased by more conceptually refined and consistent target at-risk groups and more tailored interventions (41).
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+In 2016, nearly 45,000 suicides occurred in the US [45]. For each of these, an estimated 30 others attempted suicide [47]. Suicide is the tenth leading cause of death in the US and is one of only three causes that continues to rise [45]. Beyond the number of direct casualties, suicide has a tremendous negative impact on communities and the national economy [47]. These include emotional suffering of families and friends, and economic costs associated with medical care, funeral expenses, and lost productivity [47]. Because of the myriad consequences, suicide is a national concern that must be addressed with urgency and understanding. This is especially true for New Mexico, whose suicide rate has consistently been 50% higher than the US national average and was fourth highest in the country in 2015 [32].
+Here, we explore a suicide prevention framework based in social justice. The integration of a social justice context to supplement current suicide prevention efforts in the US can help efforts more fully address suicide-related factors as identified by the socioeecological model (SEM) currently used by the US Surgeon General.
+* Correspondence: eenglandkennedy@ric.edu; liz_ek@hotmail.com; elizabeth.englandkennedy@gmail.com
+department of Health & PhysicalEducation, Rhode Island College, 138 Murray Center, 600 Mt. Pleasant Ave, Providence, RI02908, USA Full list of author information is available at the end of the article
+A social justice perspective has the potential to improve suicide prevention efforts by reducing stigma, improving access to appropriate healthcare and other services, and creating a supportive, less judgmental, and more inclusive community.
+The Socioecological model
+Official suicide prevention efforts in the US began in the late 1950s and have since grown to include various national organizations and task forces, including the National Strategy for Suicide Prevention Task Force, which led to the creation of the 2001 National Strategy for Suicide Prevention, updated by the US Surgeon General in conjunction with the National Action Alliance for Suicide Prevention [47]. Despite these endeavors, suicide rates have remained mostly unaffected for decades and are on the rise in some regions of the country and specific population groups [45]. For example, from 1999 to 2016, suicide rates in the US had increased by almost 30% in the western region and had increased among American Indians and Alaskan Natives [45].
+Suicide is often tied to mental health conditions [47]. As a result, suicidology, the study of suicidal behavior and prevention, has been dominated by a clinical-psychiatric approach [4]. However, in 2015, 54 % of
+suicide victims in 27 states, including New Mexico, did not have a known mental health condition [45]. In fact, mental health status is only one of many factors associated with suicide [47]. Other factors are classified according to the socioecological model outlined in the 2012 National Strategy for Suicide Prevention.
+The four levels of influence
+Suicide is an outcome of a complex interaction of multiple components ranging from individual characteristics to environmental influences [47]. These can be found at the four levels established in the socioecological model used by the US Surgeon General: individual, relationship, community, and societal [47]. The individual level of influence includes genetic predispositions, personality traits, and personal experiences, attitudes, perceptions, and views [47]. The relationship level includes family history and interpersonal relationships [47]. The community level takes into consideration interactions with neighborhoods, educational institutions, workplaces, and other community environments, including the healthcare system [47]. The societal level considers media influences and the impact of governmental policies and decisions, including policies related to access to lethal means [47]. Within each of these are embedded factors that can either increase or reduce a person’s susceptibility to suicide, known as risk and protective factors [47].
+Risk and protective factors
+Risk factors are characteristics that increase an individual’s potential to develop suicidal ideation and engage in suicidal behaviors [30]. These can be shortterm crises or long-term sources of stress [30]. They exist within all four levels of influence and can include substance abuse, family history of suicide, barriers to healthcare, and problematic media portrayals of suicide [47]. Protective factors are characteristics that make development of suicidal ideation and behaviors less likely [30, 38]. They provide sources of support and promote strong connections between individuals and the world around them [30].
+Protective factors also exist at every level and include healthy coping skills, supportive social interactions, safe community environments, and affordable healthcare [38, 47]. According to the 2012 National Strategy for Suicide Prevention, suicide is less likely to occur when risk factors are minimized and protective factors are maximized, Suicide prevention strategies need to incorporate multiple perspectives to address as many of these factors as possible [47].
+Although suicide can affect all, certain groups have been impacted more than others. These include American Indians and Alaska Natives (AIAN), members of the military, including veterans, men in their midlife, and
+older white men, as well as LGBTQ individuals, people who have lost loved ones to suicide, are in justice and/or child welfare systems, engage in non-suicidal self-injury, attempted suicide in the past, have chronic medical conditions, are in grief or enduring severe pain, and/or have mental and/or substance abuse disorders [47]. Each of these groups has a wide range of specific risk factors; however, here we focus on factors that are impacted by unjust socioeconomic conditions, negative attitudes, and stigmas concerning suicide. Many are interrelated and can be experienced concurrently, which can make the ability to combat suicidality on an individual level more difficult and even impossible.
+Risk factors experienced by AIAN individuals, as well as those in other ethnic and minority communities, include discrimination, limited culturally-appropriate mental health service access, and historical trauma [47]. Discrimination can include individual- and institutional-level discrimination. Institutional discrimination can come in the form of laws and public policies that create inequalities or omissions from benefits or protections [33]. This type of discrimination can lead to inadequate and inappropriate care of vulnerable populations [33]. For example, widely-used preventive interventions and currently-identified suicide-related risk (and protective) factors may not necessarily be applicable to people of color, because they were tested in White-majority communities [52]. Institutional discrimination can therefore be a reason why AIAN and other minorities have limited access to culturally-appropriate and compassionate mental and physical health services that could help them tackle issues like historical trauma, a known threat to physical and mental health [38]. Individuals with mental and/or substance abuse disorders, people in justice and/ or child welfare settings, and LGBTQ persons can also suffer from a lack of appropriate mental health services due to institutional discrimination [33, 47].
+Members of sex and gender minorities are more likely to encounter healthcare providers who either do not have the proper training to address specialized health needs or have personal prejudices and biases that interfere with their ability to deliver timely and non-judgmental care [33]. LGBTQ individuals are also at risk for suicide due to minority stress, which stems from cultural and social prejudices they face daily because of their minority sexual orientation and gender identity status [47]. This also contributes to their susceptibility to suicide contagion, due to media coverage that presents suicidal behavior as a normative reaction to minority stress [47].
+Social disconnection and reluctance to seek help are risk factors experienced by men in their midlife, who account for the majority of suicides in the US, and older white men, whose suicide rate is almost three times
+higher than the rate of the general population [47]. Although white men are not usually thought to suffer consequences of social injustice, studies have proposed that social pressures associated with maintaining one’s reputation and conforming to cultural ideals of masculinity play a part in increased risk of suicide for this demographic [10].
+Unequal economic conditions can also exacerbate risk factors and limit development of protective factors, especially for people who experience many of the abovementioned conditions concurrently. For example, poverty has both short- and long-term effects on health and sense of security because income is necessary for acquiring and maintaining resources like food, shelter, and healthcare [35]. People with serious and acute emotional and psychological distress are less likely to have health insurance because of an inability to afford and/or acquire insurance [8]. Even people with insurance often find they do not have adequate coverage for psychiatric treatment and/or therapeutic services, either because they cannot find professionals who accept their insurance, or because their insurance has high copayments and deductibles [8]. Finally, inability to provide for family or themselves can also lead to feelings of hopelessness and negativity, particularly for men [35].
+A different perspective
+A clinical-psychiatric framework works well in addressing factors within the individual level of influence. Therapeutic interventions based on this framework include psychotherapy and medication, which address risk factors like mental health disorders, substance abuse, and aggression, while also promoting protective factors like healthy coping skills and open communication [31]. However, limitations exist because counselors, therapists, and psychiatrists can only help at-risk individuals if these individuals are able to afford and consistently access adequate physical and mental health care [44]. Additionally, current mainstream studies and practices of suicide prevention tend to privatize pain and individualize suicide by focusing on risk factors like short-term crises, family history of suicide, mental health disorders, and substance abuse. This can propagate stigma and discrimination and therefore make it less likely for at-risk individuals to seek help, even if they can afford it [39]. Furthermore, because current approaches to suicide prevention are often based on psychiatric and individual dynamics, they do not adequately acknowledge or address other risk factors discussed previously [28]. Although therapeutic interventions can help individuals deal with how they feel about their unjust circumstances, a clinical-psychiatric approach alone cannot adequately address social injustice.
+Nations, including the US, have an implicit obligation to improve environmental conditions that correlate with higher rates of suicide because one of the primary goals of a political state is to protect its citizens from violence and death [4]. A social justice framework brings this obligation to the forefront by making community members aware of the societal status quo, the harmful effects of social injustice on at-risk individuals, their own direct or indirect role in maintaining these dangerous inequities, and their ability to change so their fellow citizens can have equal opportunity to dignified lives [4]. By encouraging a critical analysis of the social, economic, relational, and cultural contexts within which suicide occurs, a social justice framework can minimize risk factors and increase protective factors at the community and societal levels of influence, thereby allowing suicide prevention efforts to be more comprehensive, effective, and long-lasting.
+A social justice framework
+A social justice framework, in general, is composed of three main concepts: (1) acknowledgment of inequality and oppression, (2) assumption of involvement, and (3) obligatory addressal of the issues through responsible action [43]. People seeking to implement a social justice framework must first acknowledge that there is an inequitable distribution of power, resources, and access within society [43]. Beyond acknowledging that social injustice exists, people must also recognize that everyone and everything within the system participates in maintaining the societal status quo, however unintentionally [43]. Finally, action should be taken to mitigate negative effects of social inequality and promote social, political, and economic parity [43]. Though a social justice framework can be applied to multiple social problems, it is particularly suited to and important for suicide prevention efforts because of its multifaceted nature. It is also based on the recognition that inequality and oppression contribute to the prevalence of suicide and on the understanding that we as individuals and communities have the power to advocate for and create sustainable change that addresses suicide in thoughtful and compassionate ways.
+If we follow the general social justice framework presented by Smith et al. [43], the first aspect of a social justice-based framework for suicide prevention should be the acknowledgment of the insidious effect of social injustice on suicidal behavior [43]. Socioeconomic inequality cultivates social injustices, including poverty, homelessness, racism, sexism, homophobia, ageism, refugee status, and historical trauma that inordinately affect minority and marginalized groups and communities and contribute to suicidality, especially in already-vulnerable people [11, 13-15, 18, 22, 29, 51, 53]. In some cultural
+settings, such as Maori culture, the power of current sociocultural forces disrupts traditional understandings and social modelings and increases the risk of suicidality [16].
+Despite these socioeconomic risk factors, suicide is primarily viewed as a moral failure of individuals, e.g., as a sign of “weakness,” “cowardice,” “badness,” “mental illness,” or “admission of failure” [12, 24, 40], This perspective has become normalized in many societies, and reflects discomfort with and avoidance or rejection of the topic [24, 40]. This view of suicide has been documented at all socioecological levels, including individuals who self-stigmatize [24, 40, 41]; families, peers, and other social groups [9, 24]; community [1]; policy (e.g., insurance companies, policies, and personal attitudes affecting coroners’ official declarations of cause of death; Noble, 2010 [46];); and media messages, including mass and social media [21, 25, 26].
+This perspective is structurally reinforced. For example, insurance companies may legally deny coverage for death resulting from suicide [34]. Commonly-offered suicide prevention and intervention trainings such as Applied Suicide Intervention Skills Training (ASIST) typically focus on individuals rather than systemic or contextual factors (e.g., Evans & Price, 2012 [17]; Shannonhouse, et al., 2017 [42];). In some cases, institutionalized discomfort and/or lack of familiarity with the topic of suicide has led to avoidance of the topic by schoolbased counselors in conversations with students, potentially due to assumptions made in professional education curricula [3, 23].
+Because suicide is still largely seen as an entirely personal matter, everyone - from healthcare providers to politicians to educators - must become more aware of how suicide can be a response to external conditions that are outside of an individual’s immediate control. We need public health strategies based on a social justice framework that allow for a deeper and non-judgmental understanding of the underlying reasons for suicide prevalence in specific communities and certain regions of the country.
+A contemporary way of raising consciousness is through use of popular cultural media. As previously mentioned, media can serve as a major factor that can increase the risk of contagion through sensationalization, romanticization, and/or normalization of suicide, especially when reporting on suicide completion by celebrities [5, 21, 26, 53, 54]. However, it can also be a powerful tool that provides information on how and where to seek help, and highlights ways to cope with life stressors and what can be done collectively and intersec-tionally to improve suicide prevention efforts. This is especially true when suicide-reporting guidelines are closely followed, suicide literacy is promoted, and/or
+limitations on sales of suicide-related products are legally instituted to support prevention efforts [5, 19, 50, 54]. If not, imitative suicide rates can be high enough to alter long-standing epidemiological patterns [2, 6].
+Although media can be helpful in dispensing basic information and context to the general public, more specific tools should also be utilized. For example, in order to more effectively raise consciousness and understanding of suicide, education tools should also be culturally appropriate when possible. This can include memoirs and fotonovelas, booklets with posed photographs and easy-to-understand text bubbles that have been used to provide cost-effective health education to Hispanic populations and underserved populations with limited health literacy [20, 37]. These have been used to improve knowledge about depression and have been proposed as a channel to help combat mental health stigma on college campuses [37]. Since fotonovelas can be adopted to any culture, and have already been used to address suicide risk factors like mental health disorders, they are an ideal channel to disseminate information and explain how social injustice may contribute to suicide prevalence [37]. By raising awareness and educating people about how social injustice can worsen suicidal ideation, attempts, and completions, at-risk individuals can be helped to realize that they are not alone and that they are not at fault.
+The second concept is recognition of personal and collective involvement in the propagation of socially unjust conditions. This can be achieved by promoting community engagement, social activism, and advocacy because they will help people realize they have the capacity and power to combat and change social injustice and pervasive outcomes such as suicide. People who benefit from current societal attitudes, economic conditions, and public policies often fail to see how they are protected by society, just as they fail to realize how their neighbors and friends are harmed by it [35]. Therefore, it is important to actively mobilize bystanders and to connect people and communities so the unaffected can learn and help contribute to humanistic change [44].
+The third and final concept of the social justice framework presented by Smith and colleagues is action. Action should be taken to mitigate negative effects of injustice and inequality; promote social, political, and economic equity and personal agency; and promote a sense of collective and communal responsibility [43]. We recommend a broadened scope of research to explore the implementation of a social justice framework, evaluate its effectiveness. and provide directions for suicide prevention program designers and policy-makers. College campuses are well-suited for this investigation because of the diversity of their student populations, which include students with disabilities, ethnic and/or sexual minority students,
+and those who are of undocumented immigrant status [37]. Finally political action must be taken to ensure implementation of policy and legislative changes that actively reduce resource gaps and highlight the humanity of those experiencing suicidality [36].
+Currently, New Mexico has one of the highest suicide rates in the country [32]. The state is predominantly rural, culturally diverse, tribal, and economically disadvantaged, and shares a border with Mexico. Existing socioeconomic hardships, limited access to healthcare resources, discrimination, oppression, and disconnectedness all contribute to the high rate of suicide [49]. For these reasons, states such as New Mexico can be ideal for implementing a social justice framework.
+A complex interaction of political, social, and economic disparities contribute to and maintain unjust conditions. In New Mexico, these disparities include lack of firearm education and regulations, and stigmas surrounding people who are poor, homeless, and unemployed [35]. The combination of these disparities, conditions, and circumstances may be a critical reason why New Mexico has experienced increased suicide rates, which makes the implementation of a social justice framework essential. This framework has potential to intentionally raise awareness and educate people and communities to become aware of underlying and upstream contributors to suicide, to engage in activism and advocacy, to become empowered change agents, and to push for micro/individual-level changes and/or policy and legislative changes that are rooted in human dignity and compassion for those who are feeling the burden of suicide.
+The efforts of an Athabaskan tribe in New Mexico are an example of how a social justice framework that integrates greater understanding of socioeconomic conditions can be effective. Community engagement that addresses socioeconomic problems within the tribe enhances collaborations between the tribal council, tribal leaders, citizens, and institutions such as Indian Health Service (IHS), which in turn increases collective and individual awareness of suicidal behaviors and efficacy. The program was initiated through collaboration by the tribal council, community, and IHS in 1990 following an increase in suicides by tribal adolescents and young adults [28]. The tribe at the center of the study was located in a rural, isolated, impoverished area. Eighty percent of the tribe who were aged sixteen and older were either wholly unemployed or had limited seasonal employment; most were unemployed. The program’s goal was to reduce the incidence of adolescent and young adult suicidal behavior through community education and awareness about suicides and related behavioral issues [28].
+The program included multiple community-based interactive workshop sessions that examined questions
+about problems and issues facing the community, barriers to resolving these issues, and possible solutions. Problems included alcoholism, other substance abuse, domestic violence, child abuse, and unemployment. Members of the community were educated on risk factors for suicide and high-risk individuals were linked to services and community-based prevention-focused activities, using a systems-based approach. Workshop sessions were followed by screening and clinical interventions, community events, social services, and school-based prevention programsthat involved stakeholders such as tribal leaders and elders, family members, and health professionals. Additional program components included local surveillance and outreach, diligent record-keeping, consistent evaluation, community-based education, integration of appropriate violence and substance abuse prevention services, a team-based approach, a behavioral health base, cultural relevance, and integration into the tribal infrastructure. Community members were involved at all levels of the program, from planning through implementation. All community members were involved in the program in some capacity, whether as part of peer training, as informal counselors, or as program advocates. During the planning process, community members indicated that suicidality could not be addressed as a problem of individuals, as multiple social risk factors contributed to the problem [28].
+According to the outcome evaluation, the total number of self-destructive acts declined within the targeted age group by 73% across the program’s span. The clearest drop in frequency was of suicidal gestures, although suicide attempts were also significantly lowered [28]. By acknowledging and addressing socioeconomic conditions that contributed to tribal youths’ suicidal behaviors, the program fostered active community involvement, which in turn promoted the de-individualization and destigmatization of suicide. The evaluation suggested that suicide prevention programs need to identify and mitigate underlying social, psychological, and developmental issues. The authors also highlighted the importance of active and ongoing community involvement and participation in the development and implementation of such programs. These recommendations reflect the principles of a social justice framework: Acknowledgment of the insidious effect of social injustice on suicidal behavior, the relevance of involvement, and use of thoughtful, community-driven actions to address issues. This program can therefore serve as a template to craft socialjustice-based approaches to suicide prevention, especially because of its promising results.
+Other suicide prevention projects have also indicated a need to address historic and multiple social factors in suicide prevention. Walls, Hautala, and Hurley [48]
+found that Aboriginal First Nation community members in multiple sites identified historical trauma and normalization of suicide as key risk factors. Loss of a sense of tribal identity and communication barriers leading to social disconnection were identified as underlying causes of this normalization, and a need to return to pre-colonial forms and levels of connectedness was highlighted as a means of lowering suicide risk.
+Conclusions
+Unjust and discriminatory societal conditions and attitudes often contribute to suicides. Some groups and/or communities are disproportionately affected by these societal conditions, particularly those that have experienced historical trauma, such as tribal and other marginalized communities. Generally, current suicide prevention efforts do not incorporate ways to adequately acknowledge or address these disparities or discriminatory practices. Therefore, we posit that a social justice practice framework is needed, especially in states like New Mexico and in tribal and aboriginal communities, where at-risk individuals face a host of interrelated socioeconomic and other issues that increase vulnerability to suicide.
+By highlighting injustices, this framework pushes people to question and examine the historical, socioeconomic, cultural, and other factors that contribute to suicide, which can in turn help promote protective factors and reduce risk factors identified by the socioecological model currently utilized by the United States Surgeon General [47] and the WHO (2020) in its Global Campaign for Violence Prevention. A social justice framework not only acknowledges that a variety of external social and environmental influences can detrimentally affect individual suicidality, but also proposes that these exogenous factors can be altered [4]. In doing so, this framework promotes community education and engagement, which in turn helps combat the privatization of pain and the individualism of suicide [39]. It also has the capacity to reduce stigmatization of and negative judgments towards suicide, which can then create spaces for disclosure, dialogue, and help-seeking behaviors [44]. Although not all instances of suicide are responses to social injustice, incorporating this framework into suicide prevention strategies and programs can foster a more nuanced understanding of the problem and enhance the effectiveness of prevention efforts [39].
+Although a social justice framework has the theoretical and intuitive potential to improve suicide prevention efforts, rigorous evaluations are necessary to document its efficacy. Previous systematic reviews of suicide prevention strategies and interventions stress the need for increases in the number and quality of evaluations to optimize the use of limited resources [7, 27].
+Currently, only education of physicians in identifying at-risk individuals and restricting access to lethal methods has been shown to effectively reduce suicide rates [27]. Other social justice-based interventions, like stigma reduction campaigns, public education efforts, awareness campaigns, inclusion/connectedness strategies, and others need further testing to evaluate their efficacy [27].
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+A number of reviews and studies have shown that people with severe mental illness (SMI), including schizophrenia, bipolar disorder, schizoaffective disorder and major depressive disorder, have an excess mortality, being two or three times as high as that in the general population (1-21). This mortality gap, which translates to a 13-30 year shortened life expectancy in SMI patients (4,5,22-27), has widened in recent decades (11,28-30), even in countries where the quality of the health care system is generally acknowledged to be good (11). About 60% of this excess mortality is due to physical illness (27,31).
+Individuals with SMI are prone to many different physical health problems (Table 1). While these diseases are also prevalent in the general population, their impact on individuals with SMI is significantly greater (31,32).
+Although many factors contribute to the poor physical health of people with SMI (33), the increased morbidity and mortality seen in this population are largely due to a higher prevalence of modifiable risk factors, many of which are related to individual lifestyle choices (31). However, this is not the whole story. It seems that the somatic well being of people with a (severe) mental illness has been neglected for decades (15), and still is today (7,34-39,40,41). There is increasing evidence that disparities not only in health care
+52
+access and utilization, but also in health care provision contribute to these poor physical health outcomes (33-39). A confluence of patient, provider, and system factors has created a situation in which access to and quality of health care is problematic for individuals with SMI (31). This is not totally surprising as we are today in a situation in which the gaps, within and between countries, in access to care are greater than at any time in recent history (42). Therefore, this growing problem of medical comorbidities and premature death in people with SMI needs an urgent call to action.
+This paper highlights the prevalence of physical health problems in individuals with SMI. Furthermore, contributing factors are considered that impact on the physical health of these people, such as psychotropic medications (antipsychotics, antidepressants and mood stabilizers), individual lifestyle choices (e.g., smoking, diet, exercise), psychiatric symptoms, as well as disparities in the health care. This is a selective, rather than a systematic review of clinical data on physical health problems in people with SMI, as we did not include all physical diseases. We searched MEDLINE (1966 - August 2010) for epidemiological, morbidity and mortality data on the association between physical illnesses and schizophrenia, bipolar disorder and major depressive disorder. We com
+World Psychiatry 10:1 February 2011
+bined the MeSH terms of these psychiatric disorders with the different MeSH terms of major general physical disease categories. We included pertinent reviews to identify prevalence figures and factors contributing to the excess morbidity and mortality rates. Reference lists of reviews were searched for additional relevant studies. Moreover, if necessary to obtain more specific information, for some of the general physical disease categories (e.g., respiratory diseases), we also used specific physical illnesses as a search term.
+PHYSICAL DISEASES LINKED TO SMI AND/OR PSYCHOTROPIC TREATMENT
+Obesity
+Obesity is becoming a significant and growing health crisis, affecting both developed and developing countries (43,44). People with obesity have shorter life spans and are at increased risk for a number of general medical conditions, including type 2 diabetes mellitus, DM (relative risk, RR >3), cardiovascular disease, CVD (RR >2-3), dyslipidemia (RR >3), hypertension (RR >2-3), respiratory difficulties (RR >3), reproductive hormone abnormalities (RR >1-2) and certain cancers (e.g., colon) (RR >1-2) (22,45-49,50).
+Several methods are available to assess overweight and obesity. Body mass index (BMI) is a direct calculation based on height and weight (kg/m2). A BMI >25 kg/m2 corresponds to overweight, a BMI >30 kg/m2 to obesity (31). BMIs >30kg/m2 are known to shorten life expectancy (48,51). However, based on evidence for higher morbidity and mortality risk at BMIs below 30 Kg/m2 in Asian popu
+lations, the threshold for the definition of overweight in these populations is modified to a BMI >23 Kg/m2 and the threshold for obesity to a BMI >25 Kg/m2. Waist circumference (WC), measuring abdominal or central adiposity, is emerging as a potentially more valid and reliable predictor of risk for CVD, type 2 DM, and other metabolic risk-related conditions, compared with BMI (31). Accumulating evidence argues that lower cutoff points for WC should be used for Asians, as this population is prone to obesity-related morbidity and mortality at shorter WCs (52-56). The International Diabetes Federation (IDF) provides sex-and race-specific criteria in defining WC to identify people with central obesity, thus adjusting this criterion to make it also useful in non-Caucasian populations (Table 2). However, long-term prospective studies are still required to identify more reliable WC cut points for different ethnic groups, particularly for women (57).
+Obesity in SMI patients
+SMI and obesity overlap to a clinically significant extent (45). Increasing evidence suggests that persons with SMI are, compared to the general population, at increased risk for overweight (i.e., BMI =25-29.9, unless Asian: BMI =23-24.9), obesity (i.e., BMI >30, unless Asian: BMI >25) and abdominal obesity (see Table 2) (63-75), even in early illness phase and/or without medication (76-78). The risk of obesity in persons with SMI, however, varies by diagnosis. People with schizophrenia have a 2.8 to 3.5 increased likelihood of being obese (79). Several Canadian and US studies reported rates of obesity (BMI >30) in patients with schizophrenia of 4260% (63,79,80). On the other hand, those with major depres
+53
+sion or bipolar disorder have a 1.2 to 1.5 increased likelihood of being obese (BMI >30) (44,69,70,81,82). Clinical research has suggested that up to 68% of treatment-seeking bipolar disorder patients are overweight or obese (83). One study found an obesity rate (BMI >30) of 57.8% among those with severe depression (84).
+In patients with SMI, as in the general population, obesity is associated with lifestyle factors (e.g., lack of exercise, poor diet), but also with illness-related (negative, disorganized and depressive symptoms) and treatment-related factors, including weight liability of certain psychotropic agents. Adverse effects, such as sedation, should also be considered as potential contributors to weight gain in addition to, still not fully elucidated, medication induced effects on appetite and food intake (45,73,50,85-87).
+Obesity and psychotropics
+Weight gain during acute and maintenance treatment of patients with schizophrenia is a well established side effect of antipsychotics (AP), affecting between 15 and 72% of patients (26,50,77,88-98). There is growing evidence for similar effects in patients with bipolar disorder (65,83,99). There is a hierarchy for risk of weight gain with AP that has been confirmed in different studies and meta-analyses (88,92,100-106). Weight gain is greatest with clozapine and olanzapine (107,108), while quetiapine and risperidone have an intermediate risk. Aripiprazole, asenapine, amisulpride and ziprasidone have little effect on weight. A recent systematic review of randomized, placebo controlled trials of novel AP in children and
+adolescents (<18 years old) identified the same hierarchy for risk of weight gain for this vulnerable population (109). Among the conventional AP, so-called low-potency agents, such as chlorpromazine and thioridazine, have a higher risk than high-potency drugs, such as haloperidol (110-112). No agent, however, should be considered as truly weight-neutral, as the proportion of individuals experiencing >7% weight gain is greater with any atypical AP than with placebo (92), and all AP have been found to cause significant weight gain in AP-naive or first-episode patients (113-115). Even amisulpride, ziprasidone and low-dose haloperidol demonstrated notable weight gain of 9.7 kg, 4.8 kg and 6.3 kg respectively at endpoint in a 12-month trial of AP in first-episode patients (102). Equally, antidepressants (AD) such as paroxetine (116), and mood stabilizers, such as lithium and valproate (117-119), have been associated with weight gain (Table 3).
+The high interindividual variability in medication-induced weight gain suggests that genetic factors influence the risk to gain weight (50,122). Studies of genetic predictors of weight gain under AP therapy have mainly but not exclusively (131) focused on HTR2C (132-135) and LEPR (135,136) gene polymorphisms. Although the results are promising, the role of genetic factors in predicting this severe side effect remains an option for the future.
+Metabolic syndrome
+Obesity is also associated with the metabolic syndrome (MetS), a clustering of abnormalities that confers a 5-6-fold
+increased risk of developing type 2 DM and a 3-6 fold increased risk of mortality due to coronary heart disease (137144).
+There is also evidence supporting the hypothesis that the MetS or components of the MetS may be important etiologic factors for certain cancers (e.g., colon cancer) (145,146).
+Although some controversy exists whether the MetS is a true syndrome (57,147-149), and despite differences in specific criteria among the definitions (Table 4), there is agreement that the major characteristics of the syndrome include central obesity, hypertension, dyslipidemia, glucose intolerance or insulin resistance (45,137,150). Studies show large variations in prevalence estimates of the MetS across definitions, countries or regions, gender, ethnicity, and age groups (137). Countries in North and South America (151-154) reported a relatively higher prevalence than other countries or regions in the world (137).
+MetS in SMI patients
+The MetS is highly prevalent among treated patients with schizophrenia. Depending on used MetS criteria, gender, ethnicity, country, age groups and AP treatment, percentages vary considerably (between 19.4% and 68%) (155-167). However, there is little debate that people with schizophrenia exhibit a higher MetS prevalence than their peers in the general population across the world (168). MetS rates in patients with bipolar disorder and schizoaffective disorder have been reported to be 22-30% (143,169,170) and 42% (171), respectively.
+Table 5 summarizes the potential of various AP medication to cause or exacerbate the metabolic syndrome. Nevertheless, lifestyle and behavioral patterns (smoking, physical inactivity, dietary habits) also play important roles in the prevalence of the MetS in SMI populations (118,168,176).
+Disparities in health care
+The proportion of SMI patients not receiving tests for assessing metabolic risk factors, even for factors relatively simple and easy to measure, such as obesity and blood pressure, is high (141,177-181). At present, neither psychiatrists nor primary care physicians carefully screen or monitor patients receiving AP medication for metabolic risk factors (173). Even after FDA (Food and Drug Administration) and ADA (American Diabetes Association)/APA (American Psychiatric Association) recommendations for novel AP, the frequency of baseline glucose and lipid testing showed little change. Several large-scale pharmacoepidemiologic studies of individuals initiating a novel AP (with non-psychiatric large control groups) reported low mean baseline metabolic testing rates, varying between 8% and less than 30% (181183) and follow-up assessments done in only 8.8% of patients. Likewise, most children starting treatment with novel
+AP do not receive recommended glucose and lipid screening. In a related study in children receiving AP treatment, similarly low metabolic monitoring rates were found (184). The MetS remains, thus, widely underdiagnosed and undertreated among patients with SMI.
+Diabetes mellitus
+Three to four percent of the world’s population have DM, which leads to a markedly increased risk of blindness, renal failure, amputation and cardiovascular disease, and reduces life expectancy by 10 or more years. Currently, 70% of people with DM live in developing countries, and while DM is increasing across the world, its greatest increase will be in these countries. By 2030 more than 80% of people with DM will live in developing countries (195).
+There are well-defined biological and behavioral risk factors for type 2 DM (195). The most important of these are overweight and obesity (RR: 4.10-17.5)(196), particularly abdominal obesity, and physical inactivity (RR: 1.12-2.18) (196-205). Other behavioral risk factors include certain dietary patterns (over and above any effect on obesity), such as diets low in whole grains and other sources of fibre, as well as smoking (206).
+Identifying people at high risk of DM is important because it has been demonstrated that intensive interventions in this group can reduce the incidence of DM. In individuals at high risk, a combination of moderate weight loss, increased physical activity and dietary advice can lead to a 60% reduction in DM incidence (207,208).
+DM in SMI patients
+Evidence suggests that the prevalence of DM in people with schizophrenia as well as in people with bipolar disorder and schizoaffective disorder is 2-3 fold higher compared with the general population (103,209-216). The risk of DM in people with depression or depressive symptoms is 1.2-2.6 times higher compared to people without depression (217225).
+The reason for the increased risk of DM in SMI patients is multifactorial and includes genetic and lifestyle factors as well as disease and treatment specific effects. An increase in well-established DM risk factors in these patients partially accounts for much of the increased risk (16,226). However, additional factors (disease, treatment) are important as well, and research suggests that, compared to the general population, the prevalence of DM in schizophrenia patients is 4 to 5 times higher in different age groups (15-25: 2% vs. 0.4%; 25-35: 3.2% vs. 0.9%; 35-45: 6.1% vs. 1.1%; 45-55: 12.7% vs. 2.4%; 44-65: 25% vs. 5.8%) (227).
+DM and psychotropic medications
+Atypical AP seem to have a stronger diabetogenic risk than conventional AP (96,228,229), the risk being 1.3 fold higher in people with schizophrenia taking atypical AP compared with those receiving conventional AP (230). However, the risk of DM-related adverse events differs between atypi
+56
+cal AP. Of the atypical AP, specifically olanzapine (231-234) and clozapine (232,234,235) and, to a lesser extent, quetiapine (236) and risperidone (237), are associated with an increased risk of DM (80) in people who have schizophrenia or bipolar disorder (238,239). A recent large-scale pharma-coepidemiologic study (including 345,937 patients who purchased antipsychotics and 1,426,488 unexposed individuals)
+World Psychiatry 10:1 February 2011
+found low to moderate, but significantly increased rates of incident DM compared with the general population for clozapine (RR=1.45), olanzapine (RR=1.29) and risperidone (RR=1.23). Rates increased two or more times with ziprasi-done and sertindole. Aripiprazole, amisulpride and quetiapine did not have a significantly increased rate (240).
+In the only study to date in first-episode patients, DM development was promoted in patients with schizophrenia by initial treatment with olanzapine (hazard ratio, HR=1.41) and mid-potency conventional AP (HR=1.60), as well as by current treatment with low-potency conventional AP (odds ratio, OR=1.52), olanzapine (OR= 1.44) and clozapine (OR=1.67). Current aripiprazole treatment reduced DM risk (OR= 0.51) (241). An analysis of the FDA’s DM-related adverse events database (ranging from new-onset hyperglycemia to life-threatening ketoacidosis), found the following adjusted reporting ratios for DM relative to all drugs and events: olanzapine 9.6 (9.2-10.0); risperidone 3.8 (3.5-4.1); quetiapine 3.5 (3.2-3.9); clozapine 3.1 (2.9-3.3); ziprasidone 2.4 (2.0-2.9); aripiprazole 2.4 (1.9-2.9); haloperidol 2.0 (1.72.3) (242). However, a systematic review of 22 prospective, randomized, controlled trials found no difference in the incidence of glycaemic abnormalities between placebo cohorts and AP medication cohorts, as well as no significant difference between any of the AP medications studied in terms of their association with glycaemic abnormalities (243). Although the latter analysis was restricted to mostly short-term trials, this inconsistency of findings suggests that medication effects interact with patient, illness, cohort and study-specific factors.
+AD may also increase the risk of DM, probably partly due to side effects such as sedation, increased appetite, and weight gain (244-248). However, although increasing, specific data on the risk of DM associated with the use of AD are sparse. Given the heterogeneity and small sample sizes of the few currently available studies, it is unclear whether or not specific AD themselves may increase the risk of DM. Nevertheless, it seems that an increased risk of DM is associated with the concurrent use of tricyclic AD and serotonin
+reuptake inhibitors (SSRIs) (OR=1.89) (249), the long-term use of both tricyclic AD (incidence rate ratio, IRR=1.77) and SSRIs (IRR=2.06) in at least moderate daily doses (250), as well as the use of AD medication in high-risk patients (251).
+Furthermore, although understudied, certain mood stabilizers, especially valproate, have been associated with an elevated risk for the development of insulin resistance (252,253), conferring a risk for DM, which is possibly related to weight gain (254), and/or fatty liver infiltration (255), but also to valproate itself (256).
+Disparities in health care
+There is evidence that diabetes patients with mental health conditions are less likely to receive standard levels of diabetes care (35,257,258). In the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia study, non-treatment rate for DM was 45.3% (35). One study (n=76,799), examining the impact of mental illness on DM management, found the unadjusted OR to be 1.24 (1.221.27) for no hemoglobin A(1c) testing, 1.25 (1.23-1.28) for no low-density lipoprotein cholesterol testing, 1.05 (1.03-1.07) for no eye examination, 1.32 (1.30-1.35) for poor glycemic control, and 1.17 (1.15-1.20) for poor lipaemic control (257). Despite clear guidance and a high prevalence of undiagnosed DM, screening rates for metabolic abnormalities in people with SMI remain low, which may lead to prolonged periods of poor glycaemic control (259-263). Delayed diagnosis results in prolonged exposure to raised blood glucose levels, which can, among other things, cause visual impairment and blindness, damage to kidneys with the potential consequence of renal failure, and nerve damage (264).
+Diabetic ketoacidosis
+Although diabetic ketoacidosis (DKA), a potentially fatal condition related to infection, trauma, myocardial infarction or stroke (265), occurs most often in patients with type 1 DM, it may be the first obvious manifestation of type 2 DM. Symptoms include: increased thirst and urination, nausea and vomiting, abdominal pain, poor appetite, unintended weight loss, lethargy, confusion and coma.
+The incidence of DKA is nearly (266) or more (267) than 10-fold greater in those with schizophrenia compared to the general population. Cases of DKA have been reported with the atypical AP clozapine (235,268), olanzapine (233,269), quetiapine (236), risperidone (237), aripiprazole (270-272) and ziprasidone (242). However, not all atypical AP appear to have the same propensity to cause this complication (273). The incidence of DKA for each atypical AP over a 7-year period was as follows: clozapine, 2.2%; olanzapine, 0.8%; and risperidone, 0.2% (267). However, higher incidence rates for clozapine and olanzapine can be due to reporting and detection biases (more DKA cases may be re
+57
+ported for these agents since doctors in general are more careful about clozapine and olanzapine and therefore detect and report such cases with these agents more frequently). Within the class of conventional AP, cases of DKA have been reported with chlorpromazine (274,275), but no such cases have been reported for other conventional AP. The mortality of reported cases of DKA varies between 15.4% and 48% (233,235-237), which is up to ten times higher than the 4% rate in the general population (276).
+Cardiovascular diseases
+The term cardiovascular diseases (CVD) refers to any disease that affects the cardiovascular system. Coronary heart disease and cerebrovascular disease are the principal components of CVD and make the largest contribution to its global burden (277,278). CVD accounts for 17.1 million or 29% of total worldwide deaths (279). While there are downward trends in CVD mortality in most developed countries due to successful secondary prevention, the mortality rates in developing countries are rising (280). A staggering 82% of worldwide CVD deaths take place in developing countries (279). Global trade and food market globalization have led to a transition toward a diet that is energy dense and nutrient poor. The resultant increases in obesity are accompanied by physical inactivity. In addition, tobacco consumption is increasing at alarming rates in developing countries (281). Finally, people in developing countries have less access to effective and equitable health care services which respond to their needs (279).
+The conventional risk factors for CVD are smoking, obesity, hypertension, raised blood cholesterol and DM. Many other factors increase the risk of CVD, including unhealthy diet, physical inactivity and low socioeconomic status (282, 283). Table 6 shows the summary prevalence of CVD risk factors in developed and developing countries, based on the World Health Organization (WHO) comparative risk factor survey data. The risk of late detection of CVD risk factors and consequent worse health outcomes is higher among people from low socioeconomic groups due to poor access
+to health care. This gradient exists in both rich and poor countries (284,285).
+CVD in SMI patients
+The preponderance of evidence suggests that patients with major depression, bipolar disorder and schizophrenia are at significantly higher risk for cardiovascular morbidity and mortality than are their counterparts in the general population (2,9,11,23,28,29,287-295). Moreover, in SMI patients, CVD is the commonest cause of death (2,25,33, 218,289,290,296-300).
+The prevalence of CVD in people with schizophrenia and bipolar disorder is approximately 2- to 3-fold increased, particularly in younger individuals (5,16,25,29,297,299,301,302). A recent review of all published larger (>100 patients) studies between 1959 and 2007 found the mortality risk for CVD to be 35% to 250% higher among persons with bipolar spectrum disorders compared to the general population (6). People with depression have a 50% greater risk of CVD (22). Besides the fact that depression is an independent risk factor for aggravating morbidity and mortality in coronary heart disease (303), the main factor mediating the link between depression and coronary events seems to be lack of physical activity (304).
+The aetiology of this excess CVD is multifactorial and likely includes genetic and lifestyle factors as well as disease specific and treatment effects (16). People with SMI have significantly higher rates of several of the modifiable risk factors compared with controls. They are more likely to be overweight or obese, to have DM, hypertension, or dyslipidemia and to smoke (25,95,229,178,305-308). The excess CVD mortality associated with schizophrenia and bipolar disorder is widely attributed to the 1-5 fold RR of the modifiable CVD risk factors in this group of patients compared with the general population (Table 7).
+Coronary heart disease in SMI patients
+Coronary heart disease refers to the failure of coronary
+circulation to supply adequate circulation to cardiac muscle and surrounding tissue, a phenomenon that can result in a myocardial infarction. During the 21st century, coronary heart disease will remain the leading cause of death in developed countries, will become the leading cause of death in developing countries, and therefore, will emerge as the leading cause of death in the world (25). The risk of coronary heart disease seems to be 2-3.6-fold higher in patients with schizophrenia (25,299). One large study found that the ten-year coronary heart disease risk was significantly elevated in male (9.4% vs. 7.0%) and female (6.3% vs. 4.2%) patients who have schizophrenia compared to controls (p=0.0001) (101). People with bipolar disorder have a 2.1 fold higher risk (299). The RR of myocardial infarction in people with major affective disorder was found to be 1.7 to 4.5 (310-313). Depression is an even stronger risk factor for cardiac events in patients with established coronary heart disease: prospective studies have shown that depression increases the risk of death or nonfatal cardiac events approximately 2.5-fold in patients with coronary heart disease (314).
+Cerebrovascular disease in SMI patients
+Cerebrovascular disease is a group of brain dysfunctions related to disease of the blood vessels supplying the brain, and can result in a cerebrovascular accident or stroke. The risk of cerebrovascular accident seems to be 1.5 to 2.9 fold higher in patients with schizophrenia (40,41,299,302,315, 316) and 2.1 to 3.3 fold higher in patients with bipolar disorder (299,317). The RR of developing cerebrovascular accident for patients with major affective disorder was found to be 1.22 to 2.6 (318,319). Obesity, DM, CVD as well as depressive symptoms are recognized as risk factors for cerebrovascular accident (317,320).
+CVD and psychotropics
+In addition to weight gain and obesity related mechanisms, there appears to be a direct effect of AP that contributes to the worsening of CVD risk (96,97,121,321). A recent publication demonstrated that atypical AP D2 antagonism
+could have a direct effect on the development of insulin resistance (322). Evidence was found that higher AP doses predicted greater risk of mortality from coronary heart disease and cerebrovascular accident (299).
+Overall, SSRIs appear safe in cardiac populations, with few cardiac side effects (287,311), while studies have found an increased risk of adverse cardiac events in patients using tricyclic AD (311,323,324). Tricyclic AD commonly increase heart rate by over 10%, induce orthostatic hypotension, slow cardiac conduction, and increase the risk of arrhythmias. Although it can have some cardiac conduction effects, in general, lithium can be safely used in cardiac patients (287).
+Sudden cardiac death and psychotropics
+Patients with schizophrenia have been reported to be three times as likely to experience sudden cardiac death as individuals from the general population (325,326). In patients with AP monotherapy, a similar dose-related increased risk of sudden cardiac death was found for both conventional and atypical AP, with adjusted RRs of 1.31 vs. 1.59 (low dose, chlorpromazine equivalents <100mg), 2.01 vs. 2.13 (moderate dose, chlorpromazine equivalents 100-299mg) and 2.42 vs. 2.86 (high dose, chlorpromazine equivalents >300mg), respectively (327). In large epidemiological studies, a dose dependent increased risk of sudden cardiac death has been identified in current users of tricyclic AD (328).
+There is a consensus that QTc values >500 msec, or an absolute increase of 60 msec compared with drug-free baseline, puts a patient at significant risk of torsade de pointes, ventricular fibrillation and sudden cardiac death (94,329, 330). Most AP and some AD may be associated with QTc prolongation (331). Patients using AP have higher rates of cardiac arrest or ventricular arrhythmias than controls, with ratios ranging from 1.7 to 5.3 (332-335). AP associated with a greater risk of QTc prolongation include pimozide, thioridazine and mesoridazine among the conventional AP (94,335,336) and sertindole and ziprasidone among the atypical AP (94,337). However, the largest randomized study to date (n=18,154) did not find a statistically significant difference in the risk of sudden cardiac death between ziprasi-done and olanzapine treated patients with schizophrenia
+59
+(338,339). Similarly, another large randomized study (n=9,858) observed no significant differences between sertindole and risperidone recipients in cardiac events, including arrhythmias, requiring hospitalization. However, cardiac mortality in general was higher with sertindole (337). These large randomized studies, which focused on a low incidence serious side effect, suffer from the problem that they did not enrich samples for cardiac risk, so that they lack power and, possibly, generalizability. Cases of torsade de pointes have been reported with thioridazine, haloperidol, ziprasidone, olanzapine, and tricyclic AD. Although SSRIs have been associated with QTc prolongation, no cases of torsade de pointes have been reported with the use of these agents. There are no reported cases of lithium-induced torsade de pointes (328).
+Disparities in health care
+SMI patients have the highest CVD mortality but the least chance of receiving many specialized interventions or circulatory medications. Evidence suggests that people with schizophrenia are not being adequately screened and treated for dyslipidemia (up to 88% untreated) and hypertension (up to 62% untreated) (35,306,340-343). The care of these patients shows a significant deficit in the monitoring of cholesterol values and the prescription of statins (25,35,40,344). They also have low rates of surgical interventions, such as stenting and coronary artery bypass grafting (40,41,291, 297,345). A poorer quality of medical care contributes to excess mortality in older people with mental disorders after heart failure (346). Another important barrier is the lack of seeking medical care by SMI patients themselves, even during acute cardiovascular syndromes (25).
+Viral diseases
+Patients with SMI are at increased risk for a variety of chronic viral infections, of which the most serious are the diseases associated with human immunodeficiency virus (HIV) and hepatitis C virus.
+HIV positivity
+The prevalence of HIV positivity in people with SMI is generally higher than in the general population, but varies substantially (1.3-23.9%) (347-370). The high frequency of substance abuse, sexual risk behaviors (e.g., sex without a condom, trading sex for money and drugs), and a reduced knowledge about HIV-related issues contribute to this high HIV prevalence (364,371-376). Therefore, it is important that patients with SMI are tested for HIV (377). However, studies investigating HIV testing rates among individuals with a SMI indicate that fewer than half of these patients (percentages ranging from 17% to 47%) have been tested in the past year (378-394).
+60
+Since many patients with SMI are exposed to atypical AP, which have been associated with metabolic abnormalities, and since patients infected with HIV and on highly active antiretroviral therapy may also develop metabolic abnormalities, this group of patients is at particularly high risk for developing MetS and ultimately CVD (395).
+Hepatitis
+Across different continents, markedly elevated rates of hepatitis virus infection have been reported in persons with SMI compared to the general population (364,396-403). The largest study to date found prevalence rates of hepatitis B virus (23.4%) and hepatitis C virus (19.6%) in SMI patients to be approximately 5 and 11 times the overall estimated population rates for these infections. Overall, an estimated 20-25% of persons with SMI are infected with hepatitis C virus (360,404-407).
+The most common transmission routes for persons with SMI are drug-use behaviors and sexual behaviors related to drug use (404-406). Therefore, especially patients with SMI and substance use disorders (including dependency) should have routine screening and treatment for hepatitis C virus infection to prevent associated morbidity and mortality (400,407,408). Interventions exist that are specifically designed to facilitate integrated infectious disease programming in mental health settings for people with SMI and to overcome provider- and consumer-level barriers at a modest and specified cost (409). A recent study showed that the assignment of people with SMI to the “STIRR” (Screening, Testing, Immunization, Risk reduction counseling, medical treatment Referral) intervention had high levels (over 80%) of participation and acceptance of core services (testing for hepatitis C, immunization against hepatitis, knowledge about hepatitis) (407).
+Respiratory tract diseases
+Up until 50 years ago, respiratory diseases, such as pneumonia and tuberculosis, accounted for the majority of deaths amongst people with SMI who lived in institutions (2). Today, respiratory diseases are still more prevalent in people with SMI (8,410-417).
+Tuberculosis
+Studies consistently show a higher incidence of tuberculosis among patients with schizophrenia compared with the general population (422-426). In some countries, tuberculosis still occurs so frequently that mental hospitals have special wards for people with both tuberculosis and schizophrenia (15). If untreated, up to 65% of people with active tuberculosis will die of the disease. However, chemotherapy is
+World Psychiatry 10:1 February 2011
+effective and the vast majority of people with drug-susceptible forms of tuberculosis are cured if properly treated (427).
+Pneumonia
+A nationwide, population-based study found schizophrenia to be associated with a 1.37 times greater risk of acute respiratory failure and a 1.34-fold greater risk of mechanical ventilation (428). Filik et al (414) found that people with SMI have a higher prevalence of angina and respiratory symptoms and impaired lung function when compared with the general population. Significant barriers to prompt and appropriate medical care for pneumonia still persist for patients who have schizophrenia (428).
+Chronic obstructive pulmonary disease
+The prevalence of chronic obstructive pulmonary disease, i.e. chronic bronchitis and emphysema, is significantly higher among those with SMI than comparison subjects (429-433). In a study of 200 outpatients in the US, 15% of those with schizophrenia and 25% of those with bipolar disorder had chronic bronchitis, and 16% of people with schizophrenia and 19% of people with bipolar disorder had asthma. These rates were significantly higher than those of the matched controls from the general population. The authors also found that, even when smoking was controlled for as a confounder, both people with schizophrenia and bipolar disorder were more likely to suffer from emphysema (430). Although the association remains unclear, a higher incidence of chronic obstructive pulmonary disease in the past two decades has been associated with the side effects of phenothiazine conventional AP (434).
+Cancer
+Cancer risk in SMI patients
+Given that obesity and unhealthy lifestyle behaviors are known risk factors for a number of cancer types (149,435438), one would expect to see higher cancer rates in patients with SMI. However, studies exploring the relationship between SMI and all cancer types together have shown conflicting results (30,439). Some studies have demonstrated a decreased cancer risk in schizophrenia (440-448). On the other hand, other studies found an increased (9,21,28,449451) or no different (292,419,452,453) overall risk of cancer in patients with schizophrenia compared to the general population. In the population of bipolar spectrum disorders, deaths from cancer are not higher (8,288,416,417,454-456) or only slightly elevated (417,418,456) compared with the general population, despite the higher number of risk factors for cancer (such as obesity) in this population. This discrep
+ancy of results may be a result of various confounding factors that could artificially lower the rates of diagnosed and reported cancer in SMI populations. For example, people with SMI are less likely to receive routine cancer screening (457-460). Furthermore, patients with SMI have a shorter life expectancy, so they may die from cardiovascular reasons before reaching the expected age of death from cancer (30). Another tentative hypothesis is that AP have antitumour properties (448) or that the disease itself has a possible protective effect, including a tumor suppressor gene or enhanced natural killer cell activity (461,462). Nevertheless, a problem with most of the existing data base analyses is that etiologically disparate cancer types were lumped together. An important analysis of cause-specific excess deaths associated with underweight, overweight, and obesity in the general population found that obesity was associated with an increased mortality from cancers considered obesity-related but not with mortality from other cancers (463).
+Cancer risk and psychotropics
+Because of the possible, but still controversial, role of prolactin in breast cancer, the assumption has been made that exposure to prolactin-raising dopamine antagonists could result in breast cancer. The current study database on AP and breast cancer risk is very limited (464). The majority of the studies in which the risk of breast cancer has been investigated in patients treated with conventional AP (465-468) did not uncover an increased risk of breast cancer, an exception being the cohort study by Wang et al (469).
+Musculoskeletal diseases
+Osteoporosis in SMI patients
+Schizophrenia, schizoaffective states, major depression and bipolar disorder are known to be associated with low bone mineral density (BMD) (470). In comparison with the general population, untreated patients with schizophrenia appear to have an increased risk of developing osteoporosis. On the one hand, this is because of the disease itself, on the other hand, because of risk factors related to their lifestyle (e.g., smoking, reduced physical activity, alcohol abuse, vitamin D and calcium deficiency, polydipsia) (470-476). Although the association between depression and loss of BMD has been reported inconsistently, most studies have found low BMD in patients with depressive symptoms or major depressive disorder (477-483). Two recent meta-analyses confirmed that depression is associated with low BMD and should be considered as an important risk factor for osteoporosis, although this increased risk may be mediated by AD (484,485). However, physiologic changes and the adoption of poor health behaviors are two prominent ways in which depression is hypothesized to directly affect BMD (486).
+61
+Osteoporosis and psychotropics
+Although it has been suggested that raised prolactin levels provoked by AP medication can lead to an increased risk of osteoporosis in patients with schizophrenia (471, 487), clinical data implicating AP-induced hyperprolactinemia as a possible major risk factor for bone loss are limited and contradictory (488,489). Some studies (490493) found a relationship between the use of prolactin-raising medication and low BMD in patients with chronic schizophrenia, while others (474,489,494-498) failed to find a relationship between prolactin, AP and osteoporosis. Nevertheless, the available data seem to indicate that hyperprolactinemia with associated hypogonadism may be a risk factor (488), leading to bone mineral loss in women as well as men (499).
+The majority of studies directly examining the relationship between AD and BMD in humans report that the use of these medications is associated with low BMD (486). However, this finding seems to be restricted to the SSRI class of AD (500-502).
+Data describing the epidemiology of osteoporotic fracture and psychotropics in patients with SMI are limited. Regarding AP, conflicting results exist (503). Some of these studies have reported higher prevalence rates of osteoporotic fractures in patients with chronic schizophrenia, entirely or partly independent of the use of AP (504,505). Other studies (506-510) have found significant increases (OR=1.2-2.6) in the risk of fractures associated with AP. For AD, a dose-response relationship was observed for fracture risk (504,508). SSRIs seem to be associated with the highest adjusted odds of osteoporotic fractures (OR=1.5) (504,505, 508). A metaanalysis showed a 33% increased risk of fractures with SSRIs compared to non-SSRI AD. The RR of fractures in this metaanalysis was 1.60 for AD and 1.59 for AP (511). Although lithium has a potentially negative impact on bone metabolism (470), it is associated with lower fracture risk (OR=0.6) and, thus, seems to be protective against fractures (504,505).
+Urological, male/female genital diseases and pregnancy complications
+Sexual dysfunction in SMI patients
+Sexual dysfunction in SMI patients has received little attention from clinicians (512,513). This low awareness has a significant negative impact on patients’ satisfaction with treatment, adherence, quality of life and partner relationships (450). Although there are relatively few systematic investigations concerning sexual disorders in schizophrenia (514), sexual dysfunction in schizophrenia is, compared to normal controls, estimated to be more frequent (515-519) and to affect 30-80% of women and 45-80% of men (512,515, 520-523). This dysfunction can be secondary to the disease itself and to comorbid physical disorders, or be an adverse
+62
+event of AP (520,524,525). Sexual dysfunction is also a common symptom of depression (526-530). Up to 70% of patients with depression may have sexual dysfunction (466). Approximately 25% of patients with major depression may experience problems with erection or lubrication (531).
+Patients with SMI are likely to engage in high-risk sexual behavior, putting them at risk of sexually transmitted diseases. However, findings suggest that sexual health education for these people tends to produce a reduction in sexual risk behavior (532).
+Sexual dysfunction and psychotropics
+Psychotropic drugs are associated with sexual dysfunction (514). To date, only few studies (534-547) have directly compared the sexual functioning associated with different atypical AP. These studies suggest that the relative impact of AP on sexual dysfunction can be summarized as: paliperi-done = risperidone > haloperidol > olanzapine > ziprasi-done > clozapine > quetiapine > aripiprazole (503,520,536). Conventional AP cause less sexual dysfunction than risperidone but more than the other novel AP (520,522).
+AD therapy (except for mirtazapine, nefazodone and bupropion) frequently induces or exacerbates sexual dysfunction, which occurs in approximately 50% of patients (548). Although sexual dysfunction has been reported with all classes of AD (549), SSRIs are associated with higher rates (550-552). Published studies suggest that between 30% and 60% of SSRI-treated patients may experience some form of treatment-induced sexual dysfunction (553,554).
+Pregnancy complications, SMI and psychotropics
+There is an extensive literature reporting an increased occurrence of obstetric complications among women who have schizophrenia (15). During pregnancy, it is important to evaluate the safety of psychotropic drugs. Most women with a SMI cannot stop taking their medication, as this would interfere with their activities of daily living, especially taking care of an infant (555). There is a paucity of information, with a lack of large, well designed, prospective comparative studies during pregnancy. However, no definitive association has been found up to now between the use of AP during pregnancy and an increased risk of birth defects or other adverse outcomes (555,556). Among AD, SSRIs and, possibly, serotonin and noradrenaline reuptake inhibitors (SNRIs) have been associated with preterm labor, respiratory distress, serotonin rebound syndrome, pulmonary hypertension and feeding problems in the neonate (557-559). Furthermore, a number of mood stabilizers have been associated with fetal malformations, including carbamazepine and valproate (560,561). Current evidence seems to suggest that Fallot’s tetralogy is not considerably elevated with lithium compared to the rate in the general population (560).
+World Psychiatry 10:1 February 2011
+Stomatognathic diseases
+Oral health in SMI patients
+Dental health has been consistently found to be poor in people with SMI (562-573). A study using an overall dental status index (DMF-T) in chronically hospitalized patients with mental disorders (mostly schizophrenia) found a mean score of 26.74 (out of a possible 32), one of the highest reported in the literature (571). According to another study, only 42% of patients with schizophrenia brush their teeth regularly (at least twice a day) (573). This poor dental health leads to functional difficulties. In one large study (n=4,769), 34.1% of the patients with SMI reported that oral health problems made it difficult for them to eat (572).
+Factors which influence oral health include: type, severity, and stage of mental illness; mood, motivation and selfesteem; lack of perception of oral health problems; habits, lifestyle (e.g., smoking), and ability to sustain self-care and dental attendance; socio-economic factors; effects of medication (dry mouth, carbohydrate craving); and attitudes and knowledge of dental health teams concerning mental health problems (569,574).
+Oral health and psychotropics
+AP, AD and mood stabilizers all cause xerostomia (575). This reduction in salivary flow changes the oral environment and leads to caries, gingivitis and periodontal disease (576).
+Disparities in health care
+Oral health status is a frequently disregarded health issue among SMI patients (498), with low rates of dental examination within the past 12 months (569,577-579). In one study of a mixed psychiatric population, 15% had not been to a dentist in the last 2 years (579), while in another only 31% of schizophrenia patients had visited a dentist during a three year period (577). In the latter study, non-adherence to annual dental visits was predicted by substance abuse diagnosis, involuntary legal status, living in an institution, admission to a psychiatric facility for a minimum of 30 days, and male gender, whereas clozapine treatment, novel AP treatment, at least monthly outpatient visits, and age > 50 years were associated with a lower risk for inappropriate dental care.
+Taken together, these findings confirm the urgent need for an intervention program to improve oral health outcomes among patients with SMI, by facilitating access to dental care and addressing modifiable factors such as smoking and medication side effects (571,572), especially because oral diseases are preventable and social inequity in oral health avoidable (580). Moreover, improving dental health status and care are relevant, as poor dental status is associated with endocarditis and reduces social and work opportunities.
+Other physical health conditions in people with SMI
+This review is by no means exhaustive. We speculate that perhaps most medical illnesses occur with greater frequency in SMI, which in itself serves as a vulnerability factor (587).
+Haematological diseases, which may in themselves be primary problems in patients with SMI, have frequently been described in the literature as potential serious complications of psychotropic medications. AP (e.g., clozapine, haloperidol, olanzapine, phenothiazines, quetiapine, risperidone, ziprasidone), AD (e.g., amitriptyline, clomipramine, imipramine) as well as lithium are associated with blood dyscrasias. Clozapine (approximately 0.8%) and phenothiazines (chlorpromazine approximately 0.13%) are the most common causes of drug-related neutropenia/agranu-locytosis. AD are rarely associated with agranulocytosis. With appropriate management, the mortality from drug-induced agranulocytosis in Western countries is 5-10% (before the use of antibiotics this percentage was 80%) (582).
+Some physical conditions, although important, are rarely studied, underreported and not systematically assessed. Although a common side effect of AP that can be severe and lead to serious consequences and even death, constipation has been given relatively little attention. The most reported complications of this physical condition are paralytic ileus, faecal impaction, bowel obstruction and intestine/bowel perforations. Constipation has most widely been reported for clozapine, although it can be associated with other AP as well. Prevalence of constipation in randomized controlled trials for different AP is: zotepine 39.6%, clozapine 21.3%, haloperidol 14.6% and risperidone 12% (583). Next to medication effects, lifestyle and diet factors can contribute to the occurrence of constipation in people with SMI (sedentary life, low physical activity, diet low in fibre, limited fluid intake) (584). Clinicians should actively and systematically screen and monitor symptoms and possible complications of constipation (585-588).
+CONCLUSIONS
+In summary, many physical disorders have been identified that are more prevalent in individuals with SMI. In addition to modifiable lifestyle factors and psychotropic medication side effects, poorer access to and quality of received health care remain addressable problems for patients with SMI. Greater individual and system level attention to these physical disorders that can worsen psychiatric stability, treatment adherence, and life expectancy as well as quality of life will improve outcomes of these generally disadvantaged populations worldwide. The barriers to somatic monitoring and interventions in persons with SMI will be summarized in the second part of this educational module, where monitoring and treatment guidelines as well as recommendations at the system level (state and health care institutions) and individual level (clinicians, patients, family) will be provided.
+63
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+Higher mortality risks in many mental disorders are well recognized and may be worsening over time (1,2). Data from the Global Burden of Disease (GBD) study suggested that mental and behavioural disorders account for 8.6 million, or 0.5%, of all years of life lost to premature mortality (3). This is equivalent to 232,000 deaths in 2010, an increase from 1990, when there were 138,000 premature deaths secondary to mental disorders (4). More than three-quarters of these deaths were attributed to substance use disorders. However, substance use and mental illness are commonly comorbid and mutually amplify the risk to premature death, often by suicide.
+The GBD study also reported that suicide was the 13th leading cause of death globally, and was more prevalent in regions with advanced health care systems (4). Suicide accounted for 5% of female and 6% of male deaths in persons aged 15-49 years old, and 884,000 deaths across all ages.
+These stark mortality figures highlight an obvious challenge to preventive medicine, because mental disorders and substance use have evidence-based treatments. Delivering such treatments effectively should reduce the risks of premature death for individual patients, particularly from suicide.
+Clarifying the pattern of risks across mental disorders is a necessary step to identify where resources can be most effectively targeted and interventions prioritized. However, syntheses of mortality risks associated with different diagnoses have not been attempted since the 1998 publication of the highly influential meta-analysis by Harris and Barraclough (5). This is despite the exponential growth in the literature over recent decades and contrasting estimates in subsequent studies for mortality in individual conditions. For example, a 2007 systematic review (1) suggested that the standardized mortality ratio (SMR) for patients with schizophrenia is 2.5, while Harris and Barraclough’s estimate was 1.6. Another recent review (6) provided an SMR for opioid use of 14.7,
+more than twice that reported in the Harris and Barra-clough review (6.4). In addition, there is now a much greater awareness of the contribution of treatable physical ill health to premature death in psychiatric patients. An understanding of the comparative data for exposure to known physical risk factors, like tobacco smoking, is also currently lacking.
+With the increase in evidence over recent decades and contrasting estimates in meta-analyses, an updated review is required. This will enable clinicians to prioritize interventions based on the comparative risks of mortality across disorders, researchers to identify where gaps exist in the literature, and commissioners and policy makers to target resources more effectively. We have therefore conducted a meta-review, or a review of systematic reviews, of all-cause and suicide mortality in all major mental disorders.
+methods
+Using the Google Scholar database, a systematic search was conducted to identify systematic reviews and metaanalyses that reported on risks for all-cause and suicide mortality for unipolar depressive disorders, anxiety disorders, bipolar disorder, schizophrenia spectrum disorders, eating disorders, learning disability and autistic spectrum disorders, childhood behavioural disorders (including conduct disorder and oppositional defiant disorder), personality disorders, dementia, substance use disorders, alcohol use disorder and smoking.
+We used the following search terms: ‘allintitle: mortality OR death OR suicide OR suicidal OR suicidality, review OR meta-analysis OR meta-analytic, psychiatry OR psychiatric OR mental OR mood OR affective OR depression OR depressive OR dysthymia OR cyclothymia OR adjustment OR anxiety OR anxious OR “obsessive compulsive” OR
+153
+OCD OR panic OR “post-traumatic” OR posttraumatic OR PTSD OR neurosis OR neuroses OR bipolar OR manic OR schizophrenia OR psychotic OR psychosis OR psychoses OR dementia OR demented OR Alzheimer OR “learning disability” OR “learning disabilities” OR IQ OR “mental retardation” OR autism OR autistic OR Asperger OR “attention deficit” OR ADHD OR hyperactivity OR hyperkinetic OR conduct OR disruptive OR personality OR personalities OR borderline OR antisocial OR psychopathic OR dissocial OR forensic OR narcissistic OR schizoid OR schizotypal OR paranoid OR dependent OR avoidant OR “emotionally unstable” OR eating OR anorexia OR bulimia OR EDNOS OR heroin OR opioid OR opioids OR cocaine OR cannabis OR marijuana OR alcohol OR alcoholism OR benzodiazepine OR benzodiazepines OR hypnotic OR hypnotics OR amphetamine OR amphetamines OR barbiturate OR barbiturates OR smoking OR smokers OR cigarette OR cigarettes’. We targeted articles published between January 1,1998 and February 19,2014.
+154
+Papers were excluded if they did not report a pooled allcause mortality or completed suicide statistic. Genderspecific estimates were reported if available. If a review was superseded by a more recently published review, only the more recent paper was included. For diagnoses where no pooled mortality statistic was found, we identified the most recent large (N>1000) single study providing data on mortality by conducting further database and citation searches.
+A second search was carried out to identify systematic reviews and studies on life expectancy in mental disorders. Using the Google Scholar database, the following search terms were used: ‘allintitle: review, life expectancy’ and ‘allintitle: life expectancy, mental OR psychiatric OR psychiatry OR mood OR depression OR bipolar OR schizophrenia OR personality OR anxiety OR smoking OR substance OR opioid OR alcohol OR anorexia OR eating’. We targeted articles published between January 1, 1998 and February 19, 2014. Supplementary citation searches were used to identify additional studies.
+World Psychiatry 13:2 - June 2014
+The majority of reviews reported SMRs. An SMR compares the gender and age standardized mortality of a sample (i.e., people with a mental disorder) to the whole population. Some studies instead reported relative risks (RRs) or odds ratios (ORs). A RR is defined by the incidence in the exposed divided by the incidence in the unexposed. The OR is defined by odds of an event (i.e., death or suicide) in the exposed divided by the odds of such an event in the unexposed (7). The OR and RR tend to report a larger effect than SMR, because the denominator in the SMR includes those with mental illness (whereas these individuals are excluded in the denominator for an OR or RR). Typically, the OR is similar to the RR when events are rare, as is the case for death and suicide (8).
+One of the authors (EC) extracted mortality statistics with their 95% confidence intervals. Another researcher reassessed the data extraction - no discrepancies were identified. If reported, RRs/ORs adjusted by age and gender were included. We chose random effects estimates if reported, as heterogeneity in individual reviews was high.
+Each review was rated using the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) (9), an empirically developed scoring system to assess the quality of systematic reviews, made up of the following eleven criteria scored from 0 to 1: Was an “a priori” design provided? Was there duplicate study selection and data extraction? Was a comprehensive literature search performed? Was the status of publication (i.e., grey literature) used as an inclusion criterion? Was a list of studies (included and excluded) provided? Were the characteristics of the included studies provided? Was the scientific quality of the included studies assessed and documented? Was the scientific quality of the included studies used appropriately in formulating conclusions? Were the
+156
+methods used to combine the findings of studies appropriate? Was the likelihood of publication bias assessed? Was the conflict of interest stated? Scores of 0 to 3 are considered low, 4 to 7 medium, and 8 to 11 high (10).
+We excluded a review of mortality in benzodiazepine use (11) as it only provided risk data for prescribed use, not misuse. In addition, a review of suicide in bipolar disorder (12) was excluded as it did not provide a pooled mortality statistic. Finally, a review of suicide in attention-deficit/hyperac-tivity disorder (ADHD) (13) was excluded as it did not use comparative population data.
+RESULTS
+The search for reviews on all-cause mortality identified 406 citations, and additional citation searches identified one review. After removing duplicates, and screening titles and abstracts, 96 reviews were identified. After exclusions, a final sample of 20 systematic reviews and meta-analyses (1,6,14-31) were included (Figure 1, Table 1). Excluding the smoking review, these reviews included over 1.7 million individuals with mental disorders and investigated over a quarter of a million deaths. We identified a further 12 mortality estimates from the largest single studies for disorders where there were no systematic reviews (32-42) (Table 2).
+The search for reviews on life expectancy yielded 28 papers, none of which was relevant. The second search, for single studies, identified 123 relevant papers, of which 8 were included. Further citation searches, and the results from the above all-cause and suicide mortality search, identified one further systematic review and five relevant
+World Psychiatry 13:2 - June 2014
+primary studies. Therefore, we identified 14 relevant publications (37,40,43-54) (Table 3).
+All mental disorders had higher mortality risks than general population samples, but there was a considerable range from dysthymia, with an RR of 1.4, to opiate use disorders, with an SMR of 14.7. Substance use disorders and anorexia nervosa had the highest mortality risks (Table 1). For schizophrenia and autism, mortality risks were at least as high as heavy smoking (Table 4).
+The pattern of suicide estimates was different from allcause mortality (Table 1). Borderline personality disorder, depression, bipolar disorder, opioid use and schizophrenia,
+as well as anorexia nervosa and alcohol use disorder in women, had higher suicide risks than most other disorders. A sample of anorexic patients presenting to specialist care (outpatients and inpatients) was an outlier for suicide (SMR=31.0,95% CI: 21.0-44.0) (18). As a review on suicide in borderline personality disorder (29) did not provide a pooled mortality statistic, we combined data to provide a median SMR (SMR=45.1,95% CI: 29.0-61.3). Again, these patients were mostly hospitalized and thus represent the severe end of the diagnostic spectrum.
+Two reviews (23,27) reported data on all-cause mortality relevant to older adults. Increases were seen in depression
+157
+(RR=1.6, 95% CI: 1.4-1.8), and dementia (RRs of 1.5 to 3.0). These are not dissimilar to risks of death for elderly smokers (RR=1.8,95% CI: 1.7-2.0) (55).
+The reduction in life expectancy associated with moderate to heavy smoking ranged from 8 to 10 years. This range is similar to that reported for a single depressive episode or recurrent depressive disorder (7-11 years), but lower than that associated with substance use (9-24 years), personality disorders (13-22 years), schizophrenia (10-20 years), and bipolar disorder (9-20 years) (Table 3).
+We did not identify any reviews on all-cause mortality for some major diagnostic classes, including bipolar disorder, anxiety disorders, personality disorders, ADHD, obsessivecompulsive disorder and post-traumatic stress disorder. For suicide, we did not identify systematic reviews for cocaine
+158
+and amphetamine use, autism, dementia and ADHD. In addition, we identified no reviews of mortality in people with dual diagnosis.
+DISCUSSION
+To our knowledge, this is the first meta-review of all-cause mortality and suicide risks in mental disorders. We identified 20 systematic reviews and meta-analyses that reported such risks in over 1.7 million individuals with mental disorders and over a quarter of a million deaths. We located a further 14 publications on life expectancy.
+We found that all the reported mental disorders had elevated all-cause mortality risks compared with the general population. Several disorders had higher or equal mortality risks compared to heavy smoking. However, there was considerable variation between diagnoses, for both mortality and completed suicide. Substance use disorders and anorexia nervosa had the highest mortality ratios. The mortality data for schizophrenia (SMR=2.5) and bipolar disorder (adjusted hazard ratio=2.0-2.3) were comparable with that for heavy smoking (RR=2.4-2.7). This result was underscored by the data for life expectancy, where all major mental disorder diagnoses had reductions (7-24 years) similar to, or greater than, heavy smoking (8-10 years).
+For suicide mortality, borderline personality disorder, depression, bipolar disorder, opioid use and schizophrenia, as well as anorexia nervosa and alcohol use disorder in women, had substantially increased rates (greater then 10 times) compared with the general population. There was also an increased risk for suicide in moderate smokers, but this was lower than the mental disorder diagnoses investigated.
+A limitation of this meta-review is the varied quality of the reviews (7 out of 20 had a low AMSTAR quality rating score, a further 12 had a medium score, and only one review could be considered of a high quality). Common omissions in the reviews were lack of testing for publication bias, not searching grey literature, and not having two data extractors. An additional limitation is the underreporting of suicide statistics which is common in some countries (56) and the potential contribution of psychiatric comorbidity to mortality.
+Few reviews specified or commented on the samples included in their meta-analyses. The common use of solely inpatient data may overestimate risks for disorders mild enough to be managed in primary care (57). This may not be the case for schizophrenia, in which mortality was compared between inpatient and outpatient populations and no difference was found (1). Furthermore, the review on depression (25) had similar mortality risks for all patients compared with depressed persons identified solely using community based data. The reviews on dysthymia and depression in the elderly (27) and dementia (23) also used community and outpatient clinic data only. On the other hand, the review on
+World Psychiatry 13:2 - June 2014
+amphetamine use only reported one SMR, which was taken from an inpatient sample (15). Furthermore, the two outliers for suicide risk - anorexia nervosa (18) and borderline personality disorder (29) - were taken from samples with high proportions of inpatients, and thus represent the most severe cases of these disorders.
+Most of the primary studies making up the reviews made use of large administrative data sets. As recently highlighted by loannidis (58), these data sets have their weaknesses, despite the precision associated with their large sample sizes. Analyses using such data are typically overpowered, so that statistically significant results can be obtained despite very small differences in mortality. As a result, it is not only the statistical significance that is important, but the size of the difference in mortality risk needs to be considered and balanced against the relative risk. Further, data were not collected for research purposes and hence diagnoses may be subject to substantial noise. Sensitivity analysis should be undertaken to examine diagnoses that have been made in different ways and also the degree of miscoding. Lack of, and error in the measurement of covariates may lead to inadequate adjustment, as can differences in the coding and treatment between hospitals and other health care settings (23).
+Smoking has been an important target for prevention because it is so common and perceived to be so dangerous. Mental disorders are also relatively common when considered together, but the risk to life is not perceived in the same way. From a public health perspective, patients with serious mental illness should be designated as a high risk population for physical illness, given the substantial health disparities compared with the general population. National strategies could and should target improving access to physical healthcare (59).
+In conclusion, the impact on mortality and suicide of mental disorders is substantial, and probably poorly appreciated as a public health problem. The scale of the unmet needs complements the social burden and costs of mental disorders (60). These findings must justify a higher priority for research, prevention and treatment of the determinants of premature death in psychiatric patients.
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+People with severe mental illness (SMI), particularly schizophrenia, bipolar disorder and major depressive disorder, have an average mortality rate that is 2-3 times higher than the general population (1-3), corresponding to a 10-25 year shortened life expectancy (2-9). The most common causes of death in people with SMI are physical diseases (10).
+Mental illness-related factors, unhealthy lifestyle choices, as well as disparities, not only in health care access and utilization, but also in health care provision, contribute to the poorer physical health outcomes in people with SMI (11). However, the use of psychotropic medications can further increase the risk of physical complications/disorders.
+Thorough knowledge about the effects of frequently used psychotropic medications - antipsychotics, antidepressants and mood stabilizers - on physical health in people with SMI can inform better treatment choices and/or strategies.
+Updating and expanding our prior review published in this journal (11), we systematically searched MEDLINE (November 2009 - November 2014) for epidemiological, morbidity and mortality data combining the MeSH terms of major physical disease categories and/or relevant diseases within these categories (Table 1) with schizophrenia, major depressive disorder and bipolar disorder, and the three major psychotropic classes which received regulatory approval for these psychiatric disorders, i.e., antipsychotics,
+antidepressants and mood stabilizers. While psychotropic medications can potentially increase the risk of many physical diseases, we focused on a selected number of diseases. We further restricted our search to pertinent English-language (systematic) reviews and meta-analyses, although for certain physical diseases relevant individual studies were selected.
+The MEDLINE searches yielded 13,477 hits (Table 1). Below, we summarize the findings concerning the relationship of antipsychotics, antidepressants and mood stabilizers to each physical illness/domain.
+nutritional and metabolic diseases
+Obesity
+People with SMI are, compared to the general population, at increased risk for being overweight and obese (1215). The likelihood of being obese is increased 2.8-4.4 fold in patients with schizophrenia and 1.2-1.7 fold in those with major depression or bipolar disorder (16-22).
+Weight gain - commonly assessed as body weight change, change in body mass index, or clinically relevant (>7%) weight change from baseline (23,24) - is a well-established side effect of antipsychotics during the acute and maintenance
+119
+treatment of patients with schizophrenia, affecting 15 to 72% of these patients (11).
+There is, however, a hierarchy for risk of weight gain among antipsychotics, that has been confirmed in different studies and meta-analyses (11,23-33). Weight gain is greatest with the second-generation antipsychotics (SGAs) clozapine and olanzapine, while quetiapine, risperidone, paliperidone and iloperidone have an intermediate risk. Ari-piprazole, amisulpride, ziprasidone, asenapine and lurasi-done have less or little effect on body weight (11) (Table 2), although observed effects depend on the degree of prior treatment exposure (30). In children and adolescents (<18 years old), roughly the same hierarchy for risk of weight gain with these agents has been identified (23,34-36), yet at a higher level, likely due to less prior antipsychotic exposure (30). Among the first-generation antipsychotics (FGAs), the so-called low-potency agents, such as chlorpromazine and thioridazine, have higher weight gain potential than the high-potency drugs, such as haloperidol (11,30).
+No antipsychotic, however, should be considered truly weight neutral, as the proportion of individuals experiencing significant weight gain is greater with any SGA than
+120
+with placebo (11,31). Antipsychotic-naive or first-episode patients are more vulnerable to weight gain, as all antipsychotics have been found to cause significant weight gain in these patients (24). Moreover, antipsychotics have been found to produce more severe weight gain in these patients compared to those with chronic schizophrenia (37).
+Generally, weight gain with antipsychotics is rapid during the first few weeks, slows gradually, and often reaches a plateau within one year (23). Results indicate that the first year of antipsychotic treatment is a critical period for weight gain and metabolic abnormalities (38), as initial rapid weight gain is a good indicator for long-term weight gain and obesity (23,39). According to a recent meta-analysis (24), almost all antipsychotics show a degree of weight gain after prolonged use, except for amisulpride, aripiprazole and ziprasi-done. This meta-analysis also documented that switching subjects to metabolically more neutral compounds may not result in weight loss in all cases.
+Antidepressants, such as amitriptyline and mirtazapine, and mood stabilizers, such as lithium and valproate, have also been associated with weight gain (23,40,41) (Table 2). However, weight gain is generally more modest or mild with antidepressants and mood stabilizers, and differences between antidepressants are modest (40).
+Clinical and animal study data suggest that increasing appetite and food intake, as well as delayed satiety signaling, are key behavioral changes to antipsychotic-induced weight gain/obesity (24,39,42). Antagonism at 5-HT2C and H1 receptors seems involved in antipsychotic-induced weight gain. Among antipsychotics, clozapine and olanzapine, which have the highest weight gain/obesity risk, also have the highest affinities for 5-HT2C and H1 receptors (39).
+There are marked individual variations in weight gain, irrespective of prescribed antipsychotic (39): some subjects lose weight, others maintain or gain weight with the same agent (24). Although (partial) non-adherence can be a con-founder, this observation, together with the results from monozygotic twin and sibling studies, suggests that genetic factors play an important role in medication-induced weight gain (43-45), with estimates as high as 60-80% for antipsychotic-related weight gain (46).
+Dyslipidemia
+Antipsychotics have been associated with lipid abnormalities to relevant degrees (11,30) (Table 2). Adverse effects on triglycerides and cholesterol occur early and may even precede weight gain, pointing to weight-independent, molecular effects in addition to weight-related ones (30).
+Compared to age- and sex-matched general population cohorts, metabolic syndrome criteria for elevated triglycerides (OR = 2.73, 95% CI: 1.95-3.83) and decreased high-density lipoprotein (HDL) cholesterol (OR = 2.35, 95% CI: 1.78-3.10) were more commonly met in patients with schizophrenia (17). Moreover, in chronic antipsychotic
+World Psychiatry 14:2 - June 2015
+treated patients, compared to first-episode or untreated patients with schizophrenia, metabolic syndrome criteria were more commonly met for elevated triglycerides (19.6%
+and 16.9% vs. 41.1%) and low HDL cholesterol (21.9% and 20.4% vs. 44.7%) (17). An elevated risk for meeting the triglyceride and HDL cholesterol criteria for metabolic
+syndrome was also found in patients with depression (22) and bipolar disorder (47), with higher metabolic syndrome risk in populations receiving antipsychotics.
+Although some antidepressants have been associated with weight gain (23,41), which is a risk factor for lipid abnormalities, data on adverse lipid effects of these medications remain scarce, and most antidepressants have not been associated with dyslipidemia (48) (Table 2).
+Among mood stabilizers, lithium has not been associated with relevant lipid abnormalities (49), although lithium-induced hypothyroidism can lead to weight gain and changes in lipid profile (50). Valproate has been associated with reductions in total and low-density lipoprotein (LDL) cholesterol in patients with schizophrenia (51) and bipolar disorder (52), despite its association with weight gain, increased triglycerides and glucose, and insulin abnormalities (53) (Table 2).
+ENDOCRINE SYSTEM DISEASES
+Diabetes mellitus
+Evidence suggests that the prevalence of type 2 diabetes mellitus (DM) in people with schizophrenia, bipolar disorder and schizoaffective disorder is 2-3 fold higher than in the general population (9,16,25,39,54,55). Two meta-ana-lyses found overall prevalences of DM in people with multiepisode psychosis to be 9.5% (N = 116,751) (16) and 12.8% (N = 2,098) (56), respectively, nearly twice as high as in the general population (9). The risk of type 2 DM in people with (major) depression or depressive symptoms is 1.2-2.6 times higher than in those without depression (11,57). The age of onset of DM in individuals with a SMI seems to be about 10-20 years earlier than in the general population (58,59).
+An association, albeit of uncertain magnitude, seems to exist between antipsychotics and DM, affecting about 12% of people receiving these medications (9). Recently, metaanalyses (16,56) showed that the prevalence of DM is not appreciably increased in drug-naive patients during the first episode of psychosis once control groups are age-matched. The majority of studies suggest that metabolic abnormalities accumulate rapidly after the initiation of treatment (9,30).
+Although a former meta-analysis showed that SGAs seem to have a stronger diabetogenic risk than FGAs, the risk being 1.3 fold higher in people with schizophrenia taking the former compared with those receiving the latter medications (60), a more recent meta-analysis indicated that, at the moment, evidence is still insufficient to draw firm conclusions about the relative risk of SGAs and FGAs (61). However, this uncertainty may well be due to the fact that neither of the two classes is homogeneous regarding cardiometabolic risk. Several studies suggest that the differing weight gain liability of SGAs contribute to the differing relative risks (RRs) of DM with these agents: specifically,
+122
+olanzapine and clozapine, and to a lesser extent quetiapine and risperidone, were shown to be associated with an increased risk of glucose dysregulation or DM in people who have schizophrenia or bipolar disorder (28,62) (Table 2).
+Nielsen et al (63) showed DM development in first-episode schizophrenia patients initially treated with olanzapine (hazard ratio, HR = 1.41) and mid-potency FGAs (HR = 1.60). During longer-term treatment and adjusting for follow-up duration, DM was associated with low-potency FGAs (OR = 1.45), olanzapine (OR = 1.57) and clozapine (OR = 2.31). Fleischhacker et al (64) found, in first-episode schizophrenia patients, newly diagnosed cases of DM with olanzapine and amisulpride during a 52-week treatment period.
+Antipsychotics should be used with caution in children and youth (65). A recent study (66) found a 3-fold increased risk of DM in children and youth (the most frequently recorded psychiatric diagnoses were mood disorders, atten-tion-deficit/hyperactivity disorder and conduct disorder) who had recently initiated antipsychotic treatment (HR = 3.03,95% CI: 1.73-5.32), compared to those receiving other psychotropic medications. The risk was already increased within the first treatment year (HR = 2.49, 95% CI: 1.274.88), increased further with cumulative dose, and remained elevated one year after antipsychotic discontinuation (HR = 2.57; 95% CI: 1.34-4.91) (67).
+Antipsychotics may induce DM independent of weight gain and adiposity (39,42). Thus, a model in which antipsychotic-induced DM is solely due to obesogenic effects is an oversimplification (42). These medications appear to contribute to DM both indirectly, by inducing weight gain, and directly, by promoting insulin resistance. M3 receptors play a crucial role in the regulation of insulin secretion through both peripheral and central cholinergic pathways (39). Therefore, DM induced by SGAs may be partly due to the blockade of central and peripheral M3 receptors. Olanzapine and clozapine, the SGAs with the highest risk to induce DM, also possess the highest M3 receptor-binding affinity (40). M3 blockade may lead to an initial disruption of insulin secretion and glucose homeostasis that can progressively lead to insulin resistance and DM during chronic treatment (67).
+The literature is still inconclusive on a possible association between antidepressants and DM (23,68-71). Several reports (68,72-75) suggest that the (concurrent) use of (certain) antidepressants is associated with an increased risk of glucose dysregulation or DM; others do not (76,77) (Table 2).
+A recent meta-analysis (78) found that antidepressants increased the likelihood of new-onset DM (OR = 1.50,95% CI: 1.08-2.10; HR = 1.19, 95% CI: 1.08-1.32). However, because only observational studies were included in this analysis, a causal relationship could not be established (71,78). Long-term randomized controlled and prospective studies are needed to confirm a possible cause-effect relationship. Another problem is that (major) depression may
+World Psychiatry 14:2 - June 2015
+act as a confounding variable in the relationship between antidepressants and DM risk: antidepressants may have an impact on mental illness-related factors relevant to the risk for DM, such as physical activity and diet (71,79).
+Nevertheless, there are indications that an increased risk of DM is associated with the concurrent use of tricyclic antidepressants and selective serotonin reuptake inhibitors (SSRIs) (OR = 1.89) (80), the long-term use of either tricyclic antidepressants (incidence rate ratio, IRR = 1.77) or SSRIs (IRR = 2.06) in at least moderate daily doses (81), and the use of antidepressants in high-risk patients (82).
+Some reports (57) also indicated that the use of mood stabilizers is associated with DM (OR = 1.64) in patients with major depression. Indeed, certain mood stabilizers, especially valproate, have been associated with an elevated risk for the development of insulin resistance (83) (Table 2).
+Diabetic ketoacidosis
+Although diabetic ketoacidosis (DKA), a potentially fatal condition (84), occurs most often in patients with type 1 DM (85), it may be the first obvious manifestation of type 2 DM. Physical symptoms include: increased thirst (polydipsia) and urination (polyuria), excessive appetite (polyphagia), nausea, abdominal pain and vomiting, dehydration, Kussmaul breathing, acetone (“fruity apple-like”) breath, weakness or lethargy, confusion and alerted consciousness (85).
+The incidence of DM presenting as DKA is, compared to the general population, nearly 10-fold higher in patients with schizophrenia (86). Cases of DKA in patients not previously known to be diabetic, including several fatalities, have been associated with SGA treatment initiation (54,86). While the underlying mechanisms are not well understood, antipsychotic-related DKA can occur soon after treatment onset and in the absence of weight gain (over one third of cases presented with either no weight gain or even weight loss) (85).
+DKA can occur with almost all SGAs. However, at least half of the reports involve individuals on polypharmacy, complicating the risk attribution to a specific antipsychotic (85). The greatest number of DKA cases has been observed with clozapine and olanzapine. However, cases have also been reported with quetiapine, risperidone, and even with aripiprazole and ziprasidone (85,87), although order or channelling effects (i.e., shifting high-risk patients to lower risk agents) cannot be excluded.
+Although DKA remains a rare adverse effect of SGAs, clinicians must remain vigilant, given its acute onset and potential lethality (85).
+Hypothyroidism and hyperparathyroidism
+Hypothyroidism is a common adverse effect of lithium, warranting continued monitoring (Table 2). A recent sys
+tematic review (40) concluded that, compared to placebo, lithium is associated with increased thyroid stimulating hormone (TSH) levels (+4.00 iU/mL, 95% CI: 3.9-4.1) and clinical hypothyroidism (OR = 5.78,95% CI: 2.00-16.67).
+In addition, lithium can produce adverse effects on the parathyroid gland. Compared with placebo, lithium was associated with increased parathyroid hormone (+7.32 pg/ mL, 95% CI: 3.42-11.23) and blood calcium ( + 0-09 mmol/ L, 95% CI: 0.02-0.17), but effects are generally mild (40).
+Although quetiapine has been associated with mild T4 elevations, TSH levels were within normal limits and patients remained euthyroid (88).
+Other antipsychotics, antidepressants, valproate and carbamazepine do not seem to affect thyroid or parathyroid functioning (Table 2).
+Syndrome of inappropriate antidiuretic hormone secretion and hyponatremia
+Antipsychotics appear to be associated with an increased prevalence of hyponatremia (89,90), which is often associated with polydipsia (Table 2).
+Antidepressants, especially SSRIs, have been associated with the syndrome of inappropriate antidiuretic hormone secretion and with hyponatremia (91) (Table 2). In a recent systematic review (92), that was limited by variations in study designs, populations, and utilized thresholds, the incidence of hyponatremia diverged between 0.06% and 40% for SSRIs and between 0.08% and 70% for venlafaxine. Incidences for mirtazapine and tricyclic antidepressants were lower, and ORs for SSRIs (1.5-21.6) were consistently higher than for tricyclic antidepressants (1.1-4.9), but much less evidence was available for non-SSRI antidepressants. Identified patient risk factors included older age (OR = 6.3) and concomitant use of (thiazide) diuretics (OR = 11.213.5) (92). Carbamazepine and valproate have been associated with hyponatremia in case reports.
+Based on the above, electrolytes should be checked in patients on antipsychotics, antidepressants and/or mood stabilizers with otherwise unexplained physical or mental state deterioration (Table 2).
+CARDIOVASCULAR DISEASES
+Hypertension
+Although antipsychotics increase body weight and are associated with obesity, their effect on blood pressure is less pronounced than expected. This has likely to do with their alpha-1 blocking effects (93), which can lower blood pressure. Nevertheless, hypertension criteria for metabolic syndrome are more commonly met in patients with schizophrenia than in the general population (OR = 1.36, 95% CI: 1.21-1.53) (16), as well as in chronic patients with schizo
+123
+phrenia receiving antipsychotics (39.7%) than in first-episode (30.4%) or untreated patients (24.3%) (56). Elevated risk for meeting the hypertension criterion for metabolic syndrome was also found in patients with depression (22) and bipolar disorder (47), with higher metabolic syndrome risk in populations receiving antipsychotics (Table 2).
+Among antidepressants, venlafaxine is the one most frequently associated with increase in blood pressure (94), while mirtazapine has been found to be associated with hypertension less than tricyclic antidepressants (95) (Table 2). Generally, mood stabilizers do not affect blood pressure, unless chronic renal failure induced by lithium affects volume distribution (Table 2).
+Coronary heart disease and stroke
+The preponderance of evidence suggests that patients with schizophrenia, bipolar disorder and major depression are at significantly higher risk for cardiovascular morbidity and mortality than their counterparts in the general population (1,5,6,11,96-99). The risk is approximately 1.5 to 3-fold increased in patients with schizophrenia and bipolar disorder, and on average 1.5-fold increased in those with major depression. Moreover, cardiovascular diseases are the commonest cause of death in patients with SMI (100,101), with risks 10-fold higher than suicide (102). The literature on antipsychotic-related cardiovascular outcomes in patients with a SMI is sparse. Moreover, data are conflicting.
+Although some studies (103,104) reported a higher risk of cerebrovascular diseases in patients using antipsychotics, others (105) did not (Table 2). In case-control studies with elderly patients, the probability of cerebrovascular accidents in antipsychotic users, compared with non-users, was approximately 1.3- to 2-fold greater (106). The risk of stroke is highest during the first weeks of treatment (104,106). A recent meta-analysis of 20 observational cohort studies found that older adults (> 65 years) using FGAs were not at a statistically significantly increased higher risk (RR = 1.4; 95% CI: 0.81-1.91) for stroke, as compared to SGA users (107).
+Few studies have looked at the association between antipsychotics and myocardial infarction, which remains controversial because of heterogeneous clinical settings and methodological approaches (108,109). Some found an increased risk of myocardial infarction in older patients (> 66 years) with or without dementia (110,111) or patients with SMI (109,112) using antipsychotics compared to control subjects (RR = 1.15-6.2) (110,111). In the study by Lin etal (109), carried out in a large sample of patients with schizophrenia, mood disorders or dementia, the adjusted OR of acute myocardial infarction risk was 2.52 (95% CI: 2.372.68) for any antipsychotic, 2.32 (95% CI: 2.17-2.47) for FGAs, and 2.74 (95% CI: 2.49-3.02) for SGAs. A recent meta-analysis found that older adults (>65 years) using FGAs were at higher risk for myocardial infarction (RR =
+124
+1.2; 95% CI: 1.16-1.23) compared to SGA users (107). However, several other studies (e.g., 112) found no significant association between the risk of myocardial infarction and antipsychotic exposure.
+The risk for cardiovascular events varies with the individual SGAs. This risk seems to be lowest with aripiprazole and ziprasidone (113-115). Considering FGAs, a nationwide, register-based, five-year follow-up study of all patients presenting with first onset of schizophrenia found an increased likelihood for cardiovascular deaths among users of levome-promazine (OR = 2.68; 95% CI:1.37-5.25, p = 0.004) (116).
+Data on the comparative acute cardiovascular safety of SGAs in younger adults are limited. In a recent large nationwide register-based cohort study (N = 48,625) (117), the risk of major cardiovascular events (cardiovascular mortality, acute coronary syndrome or ischemic stroke) in non-elderly (18-64 years) psychiatric outpatients was similar with risperidone, olanzapine and quetiapine within one year of treatment initiation. In another recent commercial U.S. claims database inception cohort study of 284,234 non-elderly adults aged 18-65 years (118), individuals within one year of exposure to SGAs showed a higher risk of essential hypertension (adjusted HR, AHR = 1.16, 95% CI: 1.12-1.21), DM (AHR = 1.43, CI: 1.33-1.53), hypertensive heart disease (AHR = 1.34, CI: 1.10-1.63), stroke (AHR = 1.46, CI: 1.221.75), coronary artery disease (AHR = 1.17, CI: 1.05-1.30), and hyperlipidemia (AHR = 1.12, CI: 1.07-1.17) than those exposed to antidepressants.
+Compared to obese individuals without a SMI, obese patients with SMI have a significantly higher cardiovascular risk (119). This raises the possibility that, in addition to weight gain and obesity-related mechanisms, a direct effect of antipsychotics on cardiovascular risk may exist. For example, autonomic nervous system dysfunction triggered by schizophrenia may be exacerbated by antipsychotic treatment through blockade of peripheral dopamine receptors, increasing sympathetic activity (120). A direct effect of antipsychotics on insulin resistance, causing glucose intolerance, is another possible mechanism contributing to the increased risk of cardiovascular diseases (121).
+The potential cardiovascular effects of tricyclic antidepressants are well known. They can cause orthostatic hypotension, slowed cardiac conduction, and increased heart rate, and are therefore best avoided in patients with pre-existing cardiovascular disease (122). SSRIs appear to have a better cardiovascular safety (123,124) (Table 2). Nevertheless, in patients with high risk factors, SSRIs (i.e., citalopram) (125,126) may be associated with (modest) QTc prolongation. Serotonin-norepinephrine reuptake inhibitors (SNRIs) are associated with a small, but increased incidence of cardiovascular adverse events (hypertension, tachycardia and orthostatic hypotension), while at therapeutic doses they do not seem to cause QTc prolongation (122). Although lithium can have some cardiac conduction effects, in general, it can be used safely in patients with cardiac disease (123) (Table 2).
+World Psychiatry 14:2 - June 2015
+Myocarditis
+Myocarditis is a potential risk of clozapine treatment, occurring often early in treatment and in young male patients (127). Therefore, routine electrocardiographic monitoring for the first four weeks, and discontinuation of clozapine in the presence of myocarditis, may assist to prevent fatalities (128). However, case reports suggest that rechallenge with clozapine using slow titration may be successful in the majority of reported cases (129).
+Sudden cardiac death
+Patients with schizophrenia have been reported to be 2-4 times more likely to experience sudden cardiac death (SCD) than the general population (130,131). Although reasons for this increased risk remain unclear, individual susceptibility (e.g., underlying coronary artery disease (101) and higher prevalence of Brugada electrocardiographic abnormalities (131)) seems to be a relevant factor. Additional important risk factors include unhealthy lifestyle factors and psychotropic medications.
+The association between SCD and specific psychotropic drugs has been explained by a lengthening of ventricular repolarization (QTc prolongation), predisposing the patient to life-threatening ventricular tachyarrhythmias (i.e., torsades de pointes, TdPs) (132). There is a consensus that QTc values >500 msec, or an absolute increase of >60 msec compared with drug-free baseline, puts patients at significant risk of TdPs and SCD (126,133). However, although a link exists between QTc and TdPs, this is neither linear, nor straightforward (126). Indeed, TdPs can occur at therapeutic doses of antipsychotics or antidepressants with a QTc interval <500 ms (134).
+Patients using FGAs or SGAs have an increased risk of SCD compared to non-users with or without a psychiatric illness, with ratios ranging from 1.5 to 5.8, depending on the type of antipsychotic and restrictiveness of the SCD definition (11,131,135,136). The largest study to date (459,614 incident antipsychotic users) reported a SCD incidence of 3.4 per 1,000 person-years (137). Antipsychotics with a greater risk of QTc prolongation include thioridazine (greatest risk), pimozide, droperidol, mesoridazine, and i.v. haloperidol (total cumulative dose > 2mg) among FGAs (126,133), and sertindole, amisulpride and ziprasidone among SGAs (32,133). QTc prolongation with lurasidone and aripiprazole is judged to be clinically insignificant (32,133). QTc prolongation associated with asenapine and iloperidone is comparable to that associated with risperidone, olanzapine and quetiapine (32,133) (Table 2).
+According to a recent meta-analysis, SSRIs are, compared to placebo, associated with a statistically significant (but clinically insignificant for most patients) dose-dependent increase in the QTc interval (+6.10 milliseconds; 95% CI: 3.47-8.73, p<0.001) (125). The highest effect seems to
+be associated with citalopram (138). Tricyclic antidepressants prolong the QTc interval to a greater extent than SSRIs by a factor of more than 2 (125).
+Studies linking antipsychotics and antidepressants with an increased SCD risk suggest a dose-dependent relationship (135,139).
+Cases of TdPs have been reported with antipsychotics, tricyclic antidepressants and SSRIs. Using the FDA Adverse Event Reporting System (FAERS) data, the Arrhythmogenic Potential of Drugs (ARITMO) project (140,141) classified, next to ziprasidone, five other SGAs (amisulpride, clozapine, olanzapine, quetiapine and risperidone) as having a very strong torsadogenic signal. However, these antipsychotics (with the exception of amisulpride and possibly quetiapine) have, in general, been associated with a QTc prolongation potential of questionable clinical concern (133). SSRI-associated TdP is a very rare event: only very few cases have been reported (142). However, adding SSRIs to SGAs may, although also very rarely, contribute to TdPs (143). There are no reported cases of lithium-induced TdPs (139).
+Notably, coronary heart disease underlies the majority of SCD (144). A recent recommendation concludes that it is not mandatory to perform electrocardiogram monitoring as a prerequisite to initiating antipsychotic treatment in the absence of cardiac risk factors, unless the prescribed antipsychotic has been established to have an increased risk of TdP and SCD (133) (Table 2).
+RESPIRATORY TRACT DISEASES
+Pneumonia
+One century ago, respiratory diseases, such as pneumonia and tuberculosis, accounted for the majority of deaths amongst people with SMI who lived in institutions (145). Today, they are still more prevalent in these individuals compared to the general population, and among the most common causes of death (146-149).
+Not only having a SMI, but also the use of psychotropics is a risk factor for respiratory tract diseases. A dosedependent increased risk for pneumonia is associated with current use of SGAs in patients with schizophrenia (adjusted RR, ARR = 1.69, 95% CI: 1.43-2.01 vs. non-users) (150,151) and bipolar disorder (ARR = 2.07, 95% CI: 1.582.71 vs. non-users) (152). Similarly, the current use of SGAs and FGAs in elderly patients without a SMI seems to be associated with a dose-dependent increase in the risk for pneumonia (153-157).
+In patients with schizophrenia, particularly the current use of clozapine is associated with an elevated and dosedependent risk of pneumonia (ARR = 3.18, 95% CI: 2.623.86, p<0.001), while this risk is moderate for olanzapine, quetiapine and risperidone (ARR between 1.32 and 1.83, p<0.001), compared to patients not currently using anti
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+psychotics (150) (Table 2). In patients with bipolar disorder, the current use of clozapine (ARR = 2.59, p<0.01), as well as the current use of olanzapine and haloperidol, were associated with dose-dependent risk ratios for pneumonia greater than 2.50. Furthermore, pneumonia had a longer duration in these patients during the period of exposure to each of these drugs (151).
+With antidepressants, no increased risk of pneumonia has been found in most studies (e.g., 158) (Table 2).
+There seems to be no significant association between mood stabilizers and pneumonia, and lithium even has a dose-dependent protective effect (152) (Table 2). However, the combination of mood stabilizers and SGAs or FGAs was associated with an increased risk. Among drug combinations, olanzapine plus carbamazepine had the highest risk (ARR = 11.88, p<0.01), followed by clozapine plus valproic acid (RR = 4.80, p<0.001) (152).
+Although the possible mechanisms of drug-induced pneumonia remain speculative (153), H1 antagonism by clozapine and olanzapine, inducing sedation, and M1 antagonism, inducing dryness of the mouth, esophageal dilatation and hypomotility, may be involved, as well as the additive sedating effect by carbamazepine or valproic acid (152).
+GASTROINTESTINAL DISEASES
+Liver diseases
+Liver function test abnormalities in patients receiving antipsychotics are common, but often mild and transient. According to a systematic review (159), the median percentage of patients with any abnormal liver function test on any antipsychotic was 32% (range: 5-78%). However, the median percentage of patients with clinically significant elevations (i.e., > 3-fold above the upper limit of normal for alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transpeptidase or > 2-fold the upper limit of normal for alkaline phosphatase) was 4% (range: 0-15%) (Table 2). Abnormalities were generally asymptomatic, arose within 6 weeks of starting an antipsychotic, and did not worsen or resolved with continued treatment. The most commonly abnormal liver function test involved transaminases, and there was no clear difference between FGAs and SGAs.
+Rarely, antipsychotics have been associated with severe or fatal hepatic injury. The FGA chlorpromazine has been most widely implicated with severe cholestatic hepatic injury. There are three main mechanisms by which antipsychotics can be associated with liver injury: by impairing bile secretion, leading to cholestasis; by exerting a direct toxic effect on hepatocytes; and by affecting the liver indirectly via obesity leading to non-alcoholic fatty liver disease (159).
+Between 0.5 and 3% of patients receiving antidepressants may develop asymptomatic mild elevation of serum aminotransferase levels. However, all antidepressants can induce hepatotoxicity, especially in elderly patients and those on
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+polypharmacy (Table 2). The antidepressants with the highest risk of hepatotoxicity include iproniazid, nefazodone, phenelzine, venlafaxine, imipramine, amitriptyline, duloxe-tine, bupropion, trazodone, tianeptine and agomelatine (160-162). Those with the least potential include citalopram, escitalopram, paroxetine and fluvoxamine (162). Monitoring of liver function tests and immediate discontinuation upon emergence of abnormal laboratory findings or signs/symptoms of liver dysfunction are crucial, since most cases of hepatic damage are reversible when detected early (162).
+Among mood stabilizers, carbamazepine and valproate have been associated with liver dysfunction and should be avoided in patients with pre-existing liver disease (163) (Table 2).
+Constipation
+Severe constipation leading to serious consequences and even death can occur with certain antipsychotics. The most reported complications are paralytic ileus, faecal impaction, bowel obstruction and intestine/bowel perforations (164).
+Constipation has been most widely reported with clozapine (123,164), although it can be associated with other antipsychotics as well (165) (Table 2). The prevalence of constipation in randomized controlled trials is 39.6% with zotepine, 21.3% with clozapine, 14.6% with haloperidol and 12% with risperidone (166).
+Constipation is a common side effect of tricyclic antidepressants, while it is not particularly associated with exposure to mood stabilizers (Table 2).
+HAEMATOLOGIC DISEASES
+Leucocytopenia and agranulocytosis
+Antipsychotics (especially but not only clozapine), antidepressants (e.g., clomipramine and imipramine) and mood stabilizers (especially carbamazepine) have been associated with leucocytopenia and agranulocytosis (167-170) (Table 2).
+Clozapine (particularly in the first three months of treatment) and phenothiazines are the most common causes of drug-related neutropenia/agranulocytosis (169). The risk for neutropenia and agranulocytosis with clozapine is approximately 3% and 1%, respectively, with older patients being at higher risk (167,168). Carbamazepine should not be used in combination with clozapine, due to its potentiation of neutropenia and agranulocytosis (169,170). Anti-infective agents, proton pump inhibitors and other gastrointestinal agents have also been associated with haematological adverse effects when co-prescribed with clozapine (171). Non-tricyclic antidepressants are rarely associated with agranulocytosis.
+World Psychiatry 14:2 - June 2015
+With appropriate management, the mortality from drug-induced agranulocytosis in Western countries is currently approximately 5% (decreasing from 10-16% over the past two decades) (167).
+Thrombocytopenia
+Among the reviewed psychotropic drug classes, only valproate has been associated with thrombocytopenia to a relevant degree (Table 2). The incidence may be around 5%, and more likely at valproate serum levels above 80 mcg/ml, especially in females and older people (172).
+NEOPLASMS
+Breast cancer
+Generally, patients with SMI, especially schizophrenia, have lower cancer rates than the general population (173), despite unhealthy lifestyle and a higher likelihood of obesity. However, this comparison is complicated by the fact that most cancers accumulate with age and that people with SMI die on average 15-25 years earlier than the general population (11).
+Breast cancer is one of the most commonly diagnosed cancers worldwide (one in eight women will be diagnosed with this cancer during their lifetime), is the leading cause of cancer death among females, and starts occurring in early adulthood (174-176). Given that women with schizophrenia have lower parity (177,178) and higher frequencies of other known breast cancer risk factors (obesity, DM, unhealthy lifestyle behaviors, including alcohol dependence and smoking), one would anticipate higher breast cancer rates in this population. However, data are conflicting. Although several studies have shown an increased breast cancer risk and mortality rate among women with schizophrenia (e.g., 173,179-182), other studies have found a decreased or a statistically non-significantly increased risk (e.g., 183,184) (Table 2). A recent meta-analysis of observational studies in people with central nervous system disorders found that patients with schizophrenia showed a higher co-occurrence of breast cancer (effect size = 1.25; 95% CI: 1.10-1.42) (185).
+Increasing experimental and epidemiological data point to the influence of prolactin in mammary carcinogenesis (186), raising questions about the possible relationship between prolactin-raising antipsychotics and breast cancer risk. The current evidence base, however, is very limited. The majority of studies focused on patients treated with FGAs (186), not finding an increased breast cancer risk. An exception is the cohort study by Wang et al (187), in which 52,819 women on antipsychotic dopamine antagonists were compared with 55,289 women who were not on antipsychotics. The authors found that, compared with
+non-users, women who used antipsychotic dopamine antagonists had a 16% greater risk (AHR = 1.16, 95% CI: 1.07-1.26) of developing breast cancer, with a direct doseresponse relationship. As stated by the authors, the magnitude of the observed risk, although statistically significant, was small in absolute terms (1,239 cases of breast cancer in the user group versus 1,228 cases in the non-user group). Furthermore, it was estimated that there is less than a 14% chance that a dopamine antagonist user who develops breast cancer does so on the basis of her antipsychotic use. The authors therefore concluded that their findings “do not warrant changes in patients’ antipsychotic medication regimens” (187, p. 1153).
+Among SGAs, there has been concern that risperidone, amisulpride and paliperidone, which have been associated with hyperprolactinemia (188), may increase the risk of breast cancer. However, so far, results indicate that these compounds do not seem to increase this risk (189).
+A systematic review (190), including 93 studies (in vitro, animal and human studies) considering the effects of antipsychotics (FGAs + SGAs) on cancer development, found that these medications as a class cannot be considered as a risk factor for breast cancer in humans. Moreover, some reports describe mechanisms of cancer protection with antipsychotics (or antidepressants) (186,191,192). For example, it has been shown in vitro that (prolactin-elevating) phenothiazines may enhance the effect of tamoxifen, a first-line adjuvant treatment for breast cancer patients, in human breast cancer cells (193,194). Thus, to date, no robust evidence exists for an increased risk of breast cancer due to antipsychotic-induced hyperprolactinemia (186,195).
+As evidence suggest that SSRIs can increase circulating prolactin above the accepted normal range (20ng/ml in men and 25ng/ml in women) (196-198) via prolactin releasing factors, such as vasoactive intestinal peptide and oxytocin (188), antidepressant-related breast cancer risk has been questioned. Overall, however, results do not support the serotonin-mediated pathway for the prolactinbreast cancer hypothesis, irrespective of the type of antidepressant (198-200) (Table 2).
+Prolactinoma
+Although a pharmacovigilance study raised concern about a possible association between prolactin-raising antipsychotics and prolactinomas (201), evidence for a causal relationship is missing (Table 2). Potential reasons for the observational association include a background rate of 510% of silent prolactinomas that are more likely detected when elevated prolactin levels prompt brain imaging studies, and the potential misclassification of pituitary hypertrophy related to prolactin elevation due to antipsychotics as prolactinoma (188). In patients with secreting prolactinomas and psychosis, the partial D2 agonist aripiprazole may be particularly useful (202).
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+MUSCULOSKELETAL DISEASES
+Osteoporosis
+Schizophrenia and bipolar disorder are associated with lower bone mineral density (BMD) and higher prevalence of osteoporosis compared to the general population (203206). The etiology of BMD loss in these patients is complicated (204,207). Risk factors related to patients’ lifestyle (e.g., smoking, reduced physical activity, alcohol abuse, vitamin D and calcium deficiency, polydipsia) (203), as well as use of antipsychotics (207,208), are likely to be involved. Several reviews and meta-analyses (209-213) have reported that (major) depression is also associated with low BMD and increased fracture risk.
+Although raised prolactin levels induced by antipsychotics have been associated with an increased risk of osteoporosis (214) (Table 2), clinical data implicating antipsychotic-induced hyperprolactinemia as a possible major risk factor for bone loss remain limited and contradictory (203,204). One review (203) showed that 60% of the studies examining the relationship between antipsychotic-induced hyperprolactinemia and BMD loss found some effects of hyperprolactinemia. However, samples and effects were small, and only few studies were prospective. The increased risk for bone loss induced by hyperprolactinemia is believed to be mediated by hypogonadism (215), leading to abnormally low sex hormone levels, although some evidence suggests direct effects of prolactin on human osteoblasts (216).
+Most studies and reviews (157,203,217-223) found significant increases in the risk of fractures (ORs between 1.2 and 2.6) associated with antipsychotics. Compared with SGAs, a higher fracture risk was found for FGAs in some studies (158,220,224), possibly due to extrapyramidal symptoms causing gait disturbances and impairing mobility and balance, which are risk factors for falls (and, thus, fractures) in older adults (107). However, other studies (105,219,225) found no differences between FGAs and SGAs. Moreover, it is also unclear whether individual SGAs differ in the risk of falls/fractures (105,218,226).
+Longitudinal, cross-sectional and prospective cohort studies, as well as meta-analyses, suggest that antidepressants, particularly SSRIs, at therapeutic doses are associated with decreased BMD and increased fracture risk, especially in older adults (218,220-222,227-249) (Table 2). Reduced BMD has also been found in young adults and children prescribed SSRIs (250,251). For SSRIs and tricyclic antidepressants, a growing excess risk of fractures has been reported with increasing dose (227,233,235), although this effect does not appear to be homogeneous across the whole class of drugs (227). The increase in risk is highest during the early stages of treatment, with a dramatic increase after initiation, reaching a peak within one month for tricyclics and eight months for SSRIs (228), decreasing towards baseline following discontinuation (227,228).
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+The most recent meta-analysis (237), which pooled results from 13 qualifying cohort and case-control studies, found that SSRIs were associated with a significantly increased risk of fractures (RR = 1.72, 95% CI: 1.51-1.95, p<0.001). This increased risk was also observed in studies that adjusted for depression (RR = 1.74,95% CI: 1.28-2.36, p<0.001) and for BMD (RR = 1.70, 95% CI: 1.28-2.25, p<0.001). Treatment with SSRIs seems also to be associated with an increased failure of bone implants, which suggests the need for careful surgical treatment planning in SSRI users (252). The effect of SSRIs on bone formation and resorption appears to be governed by the activation of a number of 5-HT receptors on osteoblasts and osteoclasts via endocrine, autocrine/para-crine and neuronal pathways (241,253,254).
+Lithium is possibly associated with a reduced fracture risk (255,256) (Table 2). Long-term treatment with valproate combined with low-dose SGAs may adversely affect BMD in premenopausal women with bipolar disorder (257). Finally, concomitant use of an opioid with one or several antipsychotics may also increase fracture risk in elderly patients (258,259).
+OTHER PHYSICAL DISEASES
+Kidney diseases
+Nephrotoxicity is a well-known side effect of lithium (260) (Table 2). Acute renal failure has been described in lithium intoxication (261), but the greatest concern is the possible progression to end-stage renal disease during longterm use (262).
+However, conflicting evidence concerning lithium’s effect on renal function exists. A systematic review (263), investigating the effects of lithium on renal function in older adults, and the largest and most recent meta-analysis to date (40), screening nearly 6,000 publications on various aspects of potential lithium toxicity in patients with depression or bipolar disorder, both concluded that there is little evidence for a clinically significant reduction in renal function in most patients, and that the risk of end-stage renal failure is low. These results are consistent with a former metaanalysis (264). Nevertheless, end-stage renal failure only starts appearing in some patients after continuous treatment for more than 15-20 years, whereas meta-analyses include numerous patients treated for shorter periods (265). Moreover, several studies (266,267) on prolonged lithium treatment have suggested that the risk of renal end-stage failure might not be that low.
+According to the International Group for the Study of Lithium-Treated Patients, approximately 25% of patients on medium-term lithium therapy (<15 years), as well as most patients on long-term lithium treatment (>15 years), develop some form of chronic lithium nephropathy (268). However, this condition manifests primarily as impaired urinary concentration with or without polyuria, which generally has
+World Psychiatry 14:2 - June 2015
+little clinical relevance. In contrast, patients with severe polyuria are at increased risk for lithium intoxication due to fluctuations in sodium levels. Effects of lithium intoxication range from minor tubular changes to acute tubular necrosis, which generally is reversible upon removal of excess amounts of lithium. Recurrent lithium intoxication, however, is thought to promote progressive lithium nephropathy (269). Thus, regular lithium level monitoring may protect against acute and chronic renal failure, and should be mandatory in long-term lithium-treated patients (269).
+Movement disorders
+In susceptible patients, chronic treatment with antipsychotics can lead to movement disorders, including tardive dyskinesia, tardive dystonia and tardive akathisia (270) (Table 2). Although SGAs seem to have a 5- to 6-fold reduced risk for tardive dyskinesia compared to FGAs (271,272), the risk is not zero. Moreover, older people (273) and those with extrapyramidal symptoms or anticholinergic use (271) are at elevated risk for tardive dyskinesia.
+Antidepressants, lithium and valproate are generally not associated with tardive dyskinesia (Table 2). Nevertheless, tremor and myoclonus, which can occur with lithium and valproate, respectively, can be mistaken for tardive dyskinesia. Moreover, movement disorders can also occur endogenously in patients with schizophrenia (274), and other medications, such as metoclopramide (275), also carry a risk for tardive dyskinesia.
+Seizure disorders
+All antipsychotics, especially clozapine, have the potential to reduce the seizure threshold (276,277). This effect is generally not clinically relevant, but is dose-dependent and rises sharply at clozapine doses of 500-600 mg/day, while relationships with clozapine blood levels are less clear (278). When seizures occur, this is not a reason to discontinue clozapine; rather, valproate should be added for seizure prophylaxis (adjusting the clozapine dose as needed) (127).
+Antidepressants can also lower the seizure risk threshold (91,276), with intermediate epileptogenic potential for tricyclic antidepressants and lower epileptogenic potential for bupropion (277), which is still contraindicated in people with seizure disorders (Table 2). Being antiepileptic medications, valproate and carbamazepine reduce seizure risk, while lithium, which at lower doses may even be protective (279), can lead to seizures when reaching toxic levels.
+CONCLUSIONS
+Patients with SMI are at increased risk for physical diseases and related earlier mortality (11,280). Besides mental
+illness-related factors, disparities in health care access and utilization, and unhealthy lifestyle, psychotropic medications can contribute to the emergence or aggravation of physical diseases. This review summarized recent evidence for the effect of antipsychotics, antidepressants and mood stabilizers on physical health/illness in patients with schizophrenia, major depression and bipolar disorder.
+In general, adverse effects on physical health are greatest with antipsychotics, followed by mood stabilizers, tricyclic antidepressants and newer antidepressants. However, effects vary greatly among individual agents, and interactions with underlying host factors are relevant. Higher dosages, polypharmacy, and the treatment of vulnerable (e.g., old or young) people seems to be associated with a greater effect on most physical diseases.
+Although antipsychotics have the greatest potential to adversely affect physical health, it is important to note that several large, nationwide studies providing generalizable data have suggested that all-cause mortality is higher in patients with schizophrenia not receiving antipsychotics (4,281). Furthermore, clozapine (4), antidepressants (282), and lithium (283), as well as antiepileptics (284), are associated with reduced mortality from suicide. Thus, the potential risks of antipsychotics, antidepressants and mood stabilizers need to be weighed against the risk of the psychiatric disorders for which they are used and the lasting potential benefits that these medications can produce.
+Nevertheless, greater attention to the possible impact of psychotropic medications on the physical health of people with SMI can aid clinicians in selecting appropriate treatments for individual patients whose medication-independent risk factors for specific disorders require special consideration. Moreover, knowledge about specific medication effects can help implementing appropriate monitoring and management strategies aimed at improving physical and well as mental health outcomes of these generally disadvantaged populations.
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+DOI 10.1002/wps.20204
+20515545, 2015, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/wps.20204 by CAPES, Wiley Online Library on [25/12/2022]. See the Terms and Conditions (https://onhnelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+136
+World Psychiatry 14:2 - June 2015
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diff --git a/World Psychiatry - 2017 - Liu - Excess mortality in persons with severe mental disorders a multilevel intervention.txt b/World Psychiatry - 2017 - Liu - Excess mortality in persons with severe mental disorders a multilevel intervention.txt
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@@ -0,0 +1,109 @@
+Persons with severe mental disorders (SMD) - i.e., schizophrenia and other psychotic disorders, bipolar disorder, and moderate-to-severe depression - die 10 to 20 years earlier than the general population. This premature mortality has been well documented in meta-analyses and systematic reviews1-7. Additionally, recent studies, commentaries and editorials have brought heightened awareness and attention to the topic8-12. Despite this, little to no progress has been made - in fact, evidence suggests that the gap may be increasing over time4, and recently published studies show standardized mortality ratios that are higher than those previously reported13.
+The majority of deaths in persons with SMD are due to preventable physical diseases, especially cardiovascular disease, respiratory disease, and infec-tions14-16. These persons have a 2 to 3 times higher risk of dying from cardiovascular diseases than the general popu
+lation17,18. Up to 75% of persons with schizophrenia (compared to 33% of the general population) die of coronary heart disease19. Persons with SMD die of respiratory diseases at 2 to 6 times the rate of the general population5’15’20, even after controlling for tobacco smoking and substance abuse, and die of infectious diseases at about 2 to 4 times the rate of the general population4. These persons are also more likely to die of diabetes mellitus15 and cancers21. In low- and middle-income countries (LMICs), available studies suggest that excess mortality is similar, if not worse - with the large majority dying of physical diseases, especially infectious diseases16.
+The remaining deaths in persons with SMD are due to unnatural causes, including suicide, homicide and accidents. Suicide continues to be an important cause of death, especially in the first year following discharge from an inpatient unit22. Compared with the general population, persons
+with SMD are about 2-3 times more likely to die by accidental death, which appears more common than suicide in this pop-ulation18’23. Furthermore, persons with SMD appear to be overrepresented among homicide victims and are 2-4 times more likely to die by homicide or violent deaths than the general population7’24.
+Overall patterns of mortality appear similar across countries, but there are likely differences in which solutions are needed. In the following sections, we present: a) a multilevel model of risk for excess mortality; b) a comprehensive framework, informed by the multilevel model of risk, to guide the development and implementation of effective interventions that offer the promise of reducing excess mortality in persons with SMD25,26; c) a set of priorities for clinical practice, policy and research agendas in this area. The aims of this paper are in line with the vision statement of the World Health Organization (WHO) Comprehensive Mental
+30
+World Psychiatry 16:1 - February 2017
+Health Action Plan 2013-202027, which underscores the importance of persons with mental disorders to be able to enjoy the full range of human rights and to access high-quality, culturally-appropriate health and social care in a timely way to promote recovery.
+MULTILEVEL MODEL OF RISK FOR EXCESS MORTALITY
+The multilevel model of risk (Table 1) highlights risk factors for excess mortality in persons with SMD at the individual, health system and socio-environmental levels.
+Risk factors at the individual level include characteristics inherent to SMD or an individual’s health-related behaviours. These can be related to the severity of the SMD (e.g., symptoms, hospitalizations, impulsivity, physiological and emotional dysregulation); affect the engagement or interaction of the person with the health care system (e.g., cognitive deficits, social skills deficits, low motivation or mistrust
+of providers); or include behaviours that lead to or exacerbate health problems. Importantly, about 50-60% of persons with SMD smoke, one of the leading preventable causes of death28. Moreover, persons with SMD tend to have poorer diets29 and more sedentary lifestyles30 than the general population.
+Health system factors include treatments, delivery of services, and organizational characteristics such as the workforce or information systems infrastructure. These often vary across different settings. As an example, a mainstay of treatment for many persons with SMD is antipsychotic medications, which are associated with well-known side effects that can contribute to obesity, glucose intolerance and dyslipidemia31,32. Depending on the setting, both a lack of antipsychotic medication16 and excess dosing of this medication33,34 appear to be risk factors for elevated mortality. Once antipsychotic medications are prescribed, monitoring for potential side effects is important and requires knowledge and communication between providers10.
+Persons with SMD often receive poor quality of physical health care, spanning from health promotion and disease prevention to intervention. Although they have two times as many health care contacts, they receive less physical checkups and screenings, less prescriptions and procedures35,36, and less cardiovascular and cancer diagnoses, even though they have a higher risk of dying from these conditions15,35,37. For example, in a study from Western Australia, although persons with SMD had the same cancer incidence as the general population, they were more likely to die from can-cer22. Even under universal health care, persons with SMD do not receive adequate treatment for cardiovascular problems, such as a coronary artery by-pass, prescriptions of beta-blockers and statins, admissions for stroke, and revascularization procedures36,37.
+When hospitalized for medical care, persons with SMD often have poor outcomes, including more adverse events, more days in an intensive care unit and more complications than those without
+SMD38,39. There is also evidence for a time dimension to appropriate care: many studies highlight a peak in excess mortality for both natural and unnatural causes during the first year after discharge from hospital16'18, suggesting a systematic failure of the health care system to prevent, identify and treat physical diseases during hospitalization for a mental disorder.
+Some authors suggest that the poorer health outcomes could be related to providers’ negative beliefs and attitudes towards persons with SMD, including beliefs about the causes of illnesses, ability of persons with SMD to maintain an active and healthy lifestyle, or other beliefs about functioning40. Mental health and primary care providers’ attitudes towards patients with SMD appear related to treatment intentions, including their likelihood of referring patients to a specialist or refilling their prescription41. There is evidence of variation in the quality of care depending on the provider, insurer and type of health care system42.
+Fragmented health care systems (e.g., dichotomized physical and mental health care) present a challenge to meeting the complex physical health needs of persons with SMD43. A component cause may be the limited expertise of mental health
+providers to recognize and address physical health care needs, and of physical health providers to address the full range of health concerns of those with SMD10.
+Social determinants of health include, but are not limited to26, public policies, an individual’s socio-economic position, cultural and societal values, environmental vulnerabilities and social support. Persons with SMD often have limited access to health care either due to cost or denial of insurance coverage44. They are also more likely to be poor and at risk for homelessness. In high-income countries, homelessness and a low socio-economic status confer additional mortality risk to those with SMD45,46. Disability associated with the disorder may contribute towards unemployment, which is a strong independent risk factor for increased mortality15,47.
+Persons with SMD also tend to live in less safe neighbourhoods, have less access to healthy foods, and have less opportunities to be involved in healthy activities, which may contribute to poor lifestyle behaviours. They may be perceived as dangerous by others, which may drive the high rates of homicide victimization. A large majority has limited social support, including never being married (e.g., nearly 75%15) or limited family
+involvement. When family members are involved, they may already be under a heavy caregiver burden, and additional physical health problems may overstretch family support16.
+It is important to emphasize that these factors are intertwined, and interrelationships at multiple levels likely contribute towards excess mortality. No single factor alone causes excess mortality: persons with SMD have high rates of adverse health behaviours, including tobacco smoking, substance use, physical inactivity and poor diet; yet, studies clearly demonstrate the role of factors beyond disorder-specific and lifestyle behaviours in excess mortality. For example, although a large majority of persons with SMD die of cardiovascular diseases, only 25% of them receive a diagnosis for this - after controlling for whether a person had received a diagnosis, the risk due to ischemic heart disease approximates that of the general population15.
+Parceling out the effects of clinical factors, health system factors and socioeconomic factors continues to show that factors at each level are involved48. In general, the more factors included in the model, the more variance is accounted for in excess mortality5.
+MULTILEVEL INTERVENTION FRAMEWORK TO REDUCE EXCESS MORTALITY
+A number of interventions, guidelines and programmes have been developed to address correlates of excess mortality in persons with SMD. These primarily target mental health, lifestyle behavioural risk factors, and screening for and management of physical health conditions. Some interventions have proven to be effective but are not widely disseminated; others have not been rigorously tested; for some the evidence is mixed or inconclusive. For example, care programmes with an emphasis on monitoring and managing the adverse metabolic effects of antipsychotics are being implemented in several contexts, but many have not been well evaluated. Overall, the number and scope of truly tested interventions remain limited, and strategies for implementation and scaling up of programmes with a strong evidence base are scarce. Moreover, the majority of available interventions focus on a single or an otherwise limited number of risk factors.
+Informed by the multilevel risk factor model, we describe here a comprehensive framework that may be useful for designing, implementing and evaluating interventions and programmes to reduce excess mortality in persons with SMD (Figure 1).
+Our first level of interventions is individual-focused, while the second focuses on health systems. We then incorporate socio-environmental interventions emphasizing broader social determinants of health, including social support and stigma reduction. Some programmes address components at multiple levels (e.g., simultaneously targeting individual behaviours and health systems through behavioural weight management plus care coordination); we have categorized them based on the main emphasis of the programme. The assumption of our framework is that an effective approach must comprehensively target individual behaviours, health systems and social determinants of health. However, the effective and scalable combinations of these differ
+ent interventions have yet to be fully evaluated.
+Individual-focused interventions
+Interventions at this level include strategies delivered to the individuals with SMD to target their mental health condition, physical health and lifestyle behaviours. Although individual-focused interventions are described separately, the implementation and impact of these interventions are likely affected by the functioning of the larger health care system.
+Mental health disorder management
+Persons with SMD first of all require an early detection and appropriate treatment of their mental health condition. Especially in LMICs, no access to treatment or a long interval before mental health treatment is started can increase the risk for mortality16,49. A comprehensive tool to address most major mental health conditions, the Mental Health Gap Action Programme (mhGAP) intervention guide50, incorporates evidence-based recommendations for a range of disorders, including SMD. The guide’s innovation is in facilitating the delivery of evidence-based mental health interventions in LMICs through primary health care services, using specific assessments and decision points to reach a comprehensive management plan for each person. Although research on the implementation and impact of the guide is still ongoing, it offers a promising approach to effective and efficient delivery of mental health services.
+Appropriate administration of medications can reduce excess mortality in persons with SMD. Recent studies and evidence summaries highlight the beneficial impact on mortality of continuous medication treatment30,51, proper dosing ranges33 and current and long-term use compared with no medication, particularly in schizophrenia52. Adherence to medication guidelines — such as the American Schizophrenia Patient Outcomes Research Team (PORT) Treatment Recommendations53 — appear to have an
+effect on reducing mortality in schizophrenia. Recovery-oriented programmes, with a focus on psychoeducation and increased awareness of symptoms, coping with stress and problem-solving skills, are also beneficial54, as well as strategies supporting people with SMD and their families around treatment engagement55.
+The risk for suicide is highest within a year following discharge from a psychiatric hospitalization56, with at least one quarter of cases occurring within 30 days of discharge57. Thus, suicide prevention interventions58 need to be an important component in mental health treatment plans for those with SMD, especially early in the course of illness. In addition, persons with SMD are commonly victims of interpersonal violence, with a recent meta-analysis estimating the prevalence of recent violence at 24%59. Few interventions have addressed victimization in persons with SMD, and more studies are needed in this area.
+Physical health treatment
+Medical treatment for hypertension, diabetes mellitus and dyslipidemia should be similar for those with SMD as they are for the general population. However, selfmanagement components (e.g., for diabetes) may require tailoring which accounts for cognitive, functional or motivational deficits.
+Available evidence suggests that interventions to improve screening for obesity, hyperlipidemia and hypertension have been effective at improving the detection of these conditions among persons with SMD42, although much more work is needed in this area.
+Interventions addressing lifestyle behaviours
+Tobacco cessation interventions have proven beneficial in adults with schizophrenia and are recommended at the earliest possible phases of treatment42,53. Combination treatment with counseling and bupropion with or without nicotine replacement therapy or varenicline is efficacious and has benefits on both point abstinence and continuous abstinence
+from tobacco, though relapse is common60. Longer-term studies are needed to better understand optimal treatment duration, and importantly more work is needed to incorporate evidence-based tobacco cessation treatment into regular health care management for persons with SMD who smoke.
+Behavioural weight loss programmes tailored for persons with SMD have been shown in randomized clinical trials to be successful in achieving clinically significant weight loss61-63. Effective interventions are often built on those shown to be successful for improving diet and increasing exercise in the general population, but with adaptations for cognitive needs of those with SMD, such as tailoring content and delivery to address memory and executive function deficits, and emphasizing environmental supports64-66.
+Some questions remain unanswered for optimal implementation and dissemination of these programmes, especially in LMICs. These include: a) what will be the true needed duration and intensity for long-term effectiveness of healthy weight interventions, as they are likely relatively labor intense (e.g., more frequent contacts) in persons with SMD; b) who should deliver these interventions in different types of environments; c) how can lay providers be trained to deliver effective weight management and other healthy lifestyle behaviour change interventions to this population.
+There is a limited evidence base on effectiveness of interventions addressing substance abuse and risky sexual behaviour. The literature on interventions for reducing substance abuse in persons with SMD is large but inconsistent67. Outcomes for these interventions remain limited, especially due to problems with engagement and retention in programmes68. The impact of interventions for reducing risky sexual behaviours is also limited, even though they might be able to increase other health promoting behaviours, such as immunizations. For example, one comprehensive intervention programme delivered in mental health care settings addressed screening, testing, immunization, reducing risky behaviours and medical referrals for HIV and hepatitis, using a
+health promotion empowerment model; however, although participants had a higher prevalence of hepatitis B and C testing, higher immunization for hepatitis A and B, increased hepatitis knowledge and decreased substance use than the control group, risky sexual behaviour did not decrease69.
+Health system-focused interventions
+The next level in the framework encompasses interventions and programmes within health systems targeting health care providers and service delivery components. These will vary across different settings depending upon many parameters, such as the number of specialists versus primary care providers, the different distribution of health risk factors, the presence or absence of universal health care, and the availability of health technologies and medications. Strengthening of the six building blocks of the health systems - service delivery; health workforce; information; medical products, vaccines and technologies; financing; and leadership and governance (stewardship) - would improve outcomes for persons with SMD70.
+Care coordination, collaborative care or integrated care programmes include support to better equip health systems, usually through the provision of additional supportive members who can serve as a liaison between mental health and physical health care systems or through linking of delivery of physical and mental health services. Few randomized trials have tested care coordination programmes for physical health conditions and cardiovascular risk factors in adults with SMD.
+One intervention used a nurse care manager at a community mental health center to help participants become more involved in their own health care, communicate with physical and mental health providers, and assist in minimizing system-level barriers for health care71. At 12 months, nearly 60% of those in the intervention group received recommended preventive services compared to just over 20% in the control group. In addition, the former were more likely to
+have a primary care provider (71.2% vs. 51.9%) and, among the subset with laboratory data, they had lower (better) Framingham cardiovascular risk scores71.
+A recent trial examined a one-year intervention of care coordination alone, lifestyle coaching plus care coordination, or treatment as usual in adults with schizophrenia-spectrum disorders and increased waist circumference, with a primary outcome of cardiovascular risk reduction72. A nurse delivered care coordination, including contacting primary care providers and communicating test results and need for physical health care to participants. Lifestyle coaching provided weekly home visits with cardiovascular risk factor counseling based on individual participant preferences. The study did not find differences in outcomes, which may be due in part to the preexisting high quality of health care delivery. Also, while incorporating participant preferences is an important component of behaviour change, the resultant lifestyle coaching may not have been efficacious enough for change in risk behaviours. As suggested by the authors, environmental change may be a next step to investigate for lifestyle modification in that setting72.
+Guidelines that incorporate combinations of screening for physical health conditions, care coordination among mental health and primary care providers, metabolic monitoring, and delivery of medical services in mental health settings have been implemented in several countries, including the US, the UK and Australia73-78.
+In the US, the Substance Abuse and Mental Health Services Administration funded 187 grants since 2009 for community-based agencies to create or increase the capacity to provide primary care services to persons with SMD at settings where they already receive mental health care79. An evaluation of the first years of the program reported that sites provided a range of integrated behavioural health and primary care services to persons with need for care80. Challenges included lower than estimated consumer engagement, financial sustainment, and organizational culture issues. In addition,
+implementation of lifestyle behavioural interventions for weight management and tobacco smoking was challenging. Several suggestions were put forth for current and future agencies receiving funding, such as incorporating strategies to improve consumer access to services and addressing fidelity to evidence-based wellness interventions.
+In Australia, the Western Australia Department of Health Mental Health Division developed a package of Clinical Guidelines for the Physical Health Screening of Mental Health Consumers and a set of Health Nurse Practitioner protocols81. The package was built up as a preventive, evidence-based framework for mental health services, to facilitate coordination of care between health providers and with mental health consumers, relevant for hospital, clinic or community care settings. A 2010 report76 showed three key areas of concern: standardization across services, fidelity and frequency of use, and sustainability of the guidelines. Recommendations included management plans modified for each setting and coordination between health professionals to prevent failure to screen or redundant screening.
+The set of protocols focuses on nurse practitioners in mental health, and highlights their role as both coordinators and providers, including for: comprehensive physical health evaluation; management and referral; education and support to consumers; enhancing continuity of care for patients; facilitating communication, appropriate access and utilization of hospital services for persons with SMD; collaboration between mental health professionals and primary care, including dieticians and other lifestyle consultants; provision of health promotion; assisting the patient in making appointments or involving the case manager in ensuring the patient is able to attend appointments.
+In New South Wales, Australia, a metabolic monitoring programme82 is used to guide public mental health workers to monitor and manage metabolic syndrome and provide education to clinicians and patients. A study showed that this was implemented with about 60% coverage of monitoring of blood glucose and lipids and 54% of weight measurement. The
+compliance with measurement of waist circumference was lower (7%)83.
+In the UK, the National Institute for Health and Care Excellence (NICE) guidelines on psychosis and schizophrenia74 include direction about providers’ assessment and treatment of physical health conditions, and routine monitoring of the physical health side effects of medication, offering behavioural counseling and linking to other guidelines (e.g., obesity or diabetes) when appropriate. Since 2009, NICE has recommended that mental health care providers routinely monitor weight and cardiovascular and metabolic indicators of morbidity in people with SMD and offer interventions for obesity, lipid modification or preventing type 2 diabetes, as appropriate. In 2014, NICE provided updated guidelines about physical health in persons with SMD, specifically new tobacco cessation recommendations. In addition, the guidelines specifically called for data collection on the prevalence of those with schizophrenia who received combined healthy eating and physical activity interventions and tobacco cessation interventions.
+Most recently, a multi-country effort has encouraged the use of the Lester UK Adaptation of the Australian Positive Car-diometabolic Health Resource, which summarizes safe interventions to help frontline staff make assessments of cardiac and metabolic health in persons with SMD78. Several dissemination efforts include a downloadable poster and forms for clinicians and clinics, service user cards for persons with SMD to approach their general practitioner or mental health provider in order to get additional help, and an action planning toolkit to help with the health care delivery system implementation of the resource.
+This level of the intervention framework also includes health care leaders implementing national and international guidelines for care of persons with SMD in their organization, and aligning financing policy and information systems for the missions of improving and monitoring quality of care63. An important question for organizational leaders is who will deliver an evidence-based preventive health or care coordination intervention to de
+crease premature mortality in SMD. For example, dieticians and exercise leaders may be cost prohibitive, and sustainability may be more likely if mental health employees could deliver a physical health intervention. However, if mental health providers are to implement the intervention, they will likely need specific training and supervision. This is an important area for future research.
+While many components of these health system-focused interventions are evidence based, implementation of these programmes and guidelines on the whole have not been formally evaluated for their success in achieving their intended outcomes. Several doubts remain about sustainability and the most effective and cost-efficient model of care. Furthermore, these programmes are largely based on high-income countries; the degree to which they are feasible in LMICs will be an important area of further study. Meanwhile, as the provision of mental health care grows in LMICs in primary care set-tings49, these settings may provide opportunities to further test and refine effective models of mental health care that can reduce excess mortality.
+Interventions focused on socio-environmental determinants
+The broadest level of the framework incorporates socio-environmental factors and the social determinants of health. This part of the model acknowledges the range of potential interventions originating from the community to address contributors to premature mortality.
+Peer support programmes, family support programmes and mental health consumer groups84 are important potential resources that can implement or assist with health interventions, whether focused on health behaviours, chronic disease self-management, or recovery-based programmes.
+Evidence for peer-led interventions for chronic disease self-management appears promising: a 6-week programme tailors chronic disease self-management interventions for those in the general population to those with SMD, delivered by
+peers with SMD85, and addresses tasks common across chronic health conditions such as action planning and feedback, modeling of behaviours and problemsolving, reinterpretation of symptoms and training in specific disease management techniques. The programme has been shown to improve health status and efficiency of health care utilization. The available evidence shows improvements in quality of life, medication adherence, and a primary care visit86. In a small randomized trial of a different adaptation of the same programme, also using peers with SMD and consisting of 13 weekly group sessions, participants showed improvement in self-management and better use of health care compared to controls85. Both of these studies had relatively short follow-up and used self-report measures for outcomes; however, they support recovery-oriented illness self-management interventions for persons with SMD and a chronic medical condition as well as roles for peers with SMD to deliver these interventions. More work is needed to develop the evidence base for peer-led and peer-supported interventions to improve physical health in persons with SMD.
+Stigma reduction programmes87,88 also appear important for improving the lives of persons with SMD, within and beyond the health care community. A recent review of effective interventions to reduce mental health related stigma and discrimination reported that, for the general population, interventions can improve short-term attitudes, and of these, socialcontact based interventions seem to be the most effective. For those with mental disorders, group-level interventions appear helpful. However, across studies for those with and without SMD, further research is needed with strong designs, longer term follow-up and a focus on mental health consumers’ perceptions of stigma. In addition, studies should examine behavioural and not only attitudinal change as a result of interventions to decrease stigma and discrimination88, as well as effective stigma reduction strategies in LMICs87.
+On a wider scale, policies that have a beneficial effect on all individuals may also be beneficial for those with SMD, or
+policies may need to be shaped specifically to influence health for persons with SMD. For example, public health policies providing mental health parity could greatly improve lives of those with SMD. Employment programs89 and policies to provide stable housing may impact the ability of persons with SMD to fully integrate into society, which should lead to improved physical health. Policylevel interventions that affect screening or management of suicide, HIV or tobacco smoking are especially relevant to those with SMD and may have even greater effects on the health and well-being of this high-risk population. Knowing how policy-level interventions need modifications to best improve and lengthen the lives of persons with SMD will be important for future impact. For example, protection legislation may be in force, but individuals may not seek this protection due to not wanting to be identified as having a mental disorder.
+In the UK, the Health and Social Care Act 2012 established new legal responsibility for the national health system to deliver parity between mental and physical health, i.e., ensuring that there is as much focus on improving mental health as physical health and that persons with mental health problems receive an equal standard of care. Furthermore, the Commissioning for Quality and Innovation Scheme provides additional income for national health system trusts that meet specific indicators for people with mental health problems under that care, including recording relevant data on patient health, completing yearly physical health checks, and encouraging smoking cessation. Critically, the scheme mandates communication with the patient’s general practitioner on discharge from hospital or after review by a community team. Sustainability of such policies will be important in the future.
+In the US, a proposed option is the designation of persons with SMD as a health disparity group by the federal government, which would also require the tracking of vital health statistics separately for this population and make them eligible for more technical assistance opportunities63.
+Importantly, the factors in this part of the model link across to both health system and individual-focused interventions. Public health policies affect health systems, and specific environmental or social support programmes are often implemented through health systems (e.g., peer support programmes). Public health policies such as mental health parity or insurance coverage affect the services that the individual mental health consumer can access and will be critical to their sustainability. However, an evidence base for policies that effectively reduce excess mortality in persons with SMD is still needed.
+PRIORITIES FOR CLINICAL PRACTICE, POLICY AND RESEARCH AGENDAS
+Incorporating lessons learned from the multilevel model of risk for excess mortality and the comprehensive intervention framework, we prioritize the following key action points for clinical practice, policy and research agendas to decrease excess mortality in persons with SMD.
+Clinical practice
+Evidence from current literature combined with principles of health equity provide sufficient rationale to advance certain practice concepts. Individual practitioners can take steps now to provide guideline-consistent care. At minimum, the same guidelines for physical health care as the general population can be offered to persons with SMD. Practitioners should be especially attuned not to overlook somatic concerns and to pay attention to the lifestyle behaviours and physical health of persons with SMD.
+The evidence base and considerations for health equity support the following practices:
+• Coordination of outpatient support efforts is recommended in the first year after discharge from psychiatric hospitalization (e.g., following-up with health care providers, continuity of care) to
+help with reducing suicides57. This may be especially needed among certain age groups of those with SMD who are at a high risk of suicide22.
+•	Patients with SMD should have providers responsible for their mental health and physical health. If these are different providers (e.g., psychiatrist and primary care physician), there should be communication and coordination between them, so that screening, preventive services, and monitoring for antipsychotic side effects (if applicable) are ensured10,76,78.
+•	Patients with SMD should be offered the same basic health screenings90 as the general population (e.g., cardiovascular risk and cancer).
+•	Providers should address tobacco cessation with every patient with SMD. Persons with SMD can quit and many want to quit smoking; however, practitioners often do not address tobacco cessation91-93.
+•	Lifestyle interventions with an evidence base in SMD to address health behaviours, such as diet and physical activity, should be implemented. Behavioural interventions, if not already tailored, will likely need to be modified to account for motivational and cognitive challenges in this population. These may include social support strategies and environmental supports42.
+Persons with SMD should be viewed as a vulnerable population characterized by significant health care disparities. For example, for interventions including smoking cessation, provider training and materials specific to those with SMD may be recommended. Adding environmental supports (i.e., resources or cues in the environment that facilitate functioning, such as smartphone reminders), strategies to adapt for cognitive and motivational deficits (e.g., breaking large tasks or pieces of information into smaller components, repetition, multimodal delivery of information), increased frequency of contact, and social support may help health provider interactions be most effective.
+These clinical practice action points are made with an understanding that implementation will vary based on the
+distribution of specialists, primary care providers and lay health providers in different countries.
+Policy
+At the international level, reducing excess mortality in persons with SMD should be part of the broader health agenda. The WHO Mental Health Action Plan 2013-2020 established mental health as a fundamental component of WHO’s definition of health, with objectives that include comprehensive and integrated mental health care services27. Mental health is now included as a priority in the United Nations Sustainable Development Goals. Reducing the life expectancy gap in those with SMD would also be a major step towards the goals of achieving universal health care coverage, effective treatment of noncommunicable diseases, tobacco cessation, and suicide reduction58. These policies further promote the rights of persons with SMD to attain the highest level of health possible and full participation in society and at work.
+Internationally, top-level integration in the plans and programmes among various efforts (e.g., mental health and substance abuse, non-communicable diseases, tobacco cessation, violence prevention, nutrition and physical exercise) would set a precedent for combining efforts and making strides in addressing complex, multifactorial health problems. This might lead to special considerations specifically for those with SMD across health domains that can help with closing the health equity gap in this vulnerable population. For example, the Package of Essential Noncommunicable (PEN) disease interventions for primary health care in low-resource settings recommends counseling for all health behaviours in the general population94. Persons with SMD may need more resources and more targeted approaches to implement any given guideline than the general population, and special considerations for this population (such as supportive assistance, longer duration and intensity of interventions, and cognitive tailoring) might be included in these documents. Such policies further
+converge with WHO Mental Health Action Plan’s six cross-cutting principles of universal health care coverage, human rights, evidence-based practice, a life course approach, a multisectoral approach, and the empowerment of persons with SMD.
+At the national level, policies should be geared at strengthening existing health care platforms. These will facilitate the delivery and integration of effective interventions into the health system and the community to improve mental health95.
+In addition to specific programmes targeting services for individuals and populations, national policies should enable and provide sufficient resources for routine data collection of key indicators of excess mortality in persons with SMD at local facilities, national and regional databases. Health information and surveillance systems will be needed to monitor mortality records and cite trends. Countrylevel data need to be specific to the needs of their populations, examining the impact of excess mortality in persons with SMD on disabilities and deaths, including prevalence of cardiovascular risks, infectious diseases and other relevant conditions. This will be especially important for LMICs, where trends and needs may be different from high-income countries. Ultimately, this will allow for both intra-country and international comparisons and provide data to inform efforts to close the mortality gap.
+Research
+Scientists working to understand causes of excess mortality and design and test interventions and programmes to decrease contributors to premature death in persons with SMD have made progress in recent years, and this is reflected in the evidence supporting the multilevel model of risk presented in this paper. At the same time, there is a need to delineate specific risk factors more clearly, identify which ones are modifiable, and how these may be different across settings, particularly in LMICs.
+While evidence for mental health treatments is strong, the evidence for effectiveness of interventions in ordinary
+settings to prevent and treat physical conditions in those with SMD is limited. Also missing in the literature is the role of resilience and other factors that may be protective, and a parsing out of the roles of factors that are intrinsic to SMD versus those related to socio-economic and health system variables. This includes the need for a better understanding of attributable risk for excess mortality in those with SMD.
+While evidence exists for the effectiveness of specific behavioural and pharmacological interventions for unhealthy dietary habits, sedentary life style and tobacco smoking cessation, behavioural intervention trials for other risk behaviours are needed, especially for comorbid substance abuse. For current evidencebased interventions, research is needed on optimal length and dose needed to positively affect health, which will also be important for resource allocation. Timing of these behavioural and pharmacological interventions may also instigate health benefits.
+Interventions developed for the general population geared at non-communicable diseases, infectious diseases or other health problems are likely less effective for persons with SMD, given cognitive deficits and special needs of this population. Thus, interventions for SMD require tailoring. However, more work is needed on the degree of tailoring required. Multimodal approaches, which can include behavioural plus pharmacological interventions and include components such as peer support or technology are promising, but have yet to be studied systematically to clarify whether or which multicomponent programs are effective, and which components of the intervention are most beneficial. Recent results suggest that some combined approaches may not be effective or may be dependent on existing health care systems72. We need to consider how structural interventions can facilitate these efforts. Many people with SMD have multiple cardiovascular and other risk behaviours which may be modifiable, and future research studies should test interventions addressing multiple risk factors, as well as those which are directly linked to mortality.
+Research is needed to identify and manage barriers to and facilitators of implementing evidence-based guidance and policy recommendations at all levels (individual, health systems and social determinants) of the intervention framework. We need to understand how to deliver evidence-based interventions successfully in the real world, taking into account training and workforce issues and often-limited resources in local community settings. We need to understand to what extent interventions and programmes could or should be disseminated across countries.
+Another important area of research will be to assess the effects of health system and policy interventions on excess mortality in SMD. We need to understand why those with SMD have not benefitted from trends in the general population towards reduced mortality in some diseases and smoking cessation. Researchers should take advantage of natural experiments and also design studies in health systems and at the population level to evaluate the impact of these programmes.
+Although several guidelines for screening, monitoring and management of mental health and physical conditions have been developed from evidencebased best practices, the implementation of these guidelines has not been studied systematically in order to support their widespread application and impact on risk factors for excess mortality in persons with SMD. Similarly, integrated care programmes will need to be evaluated for their actual effectiveness on risk factors for excess mortality. Care coordination approaches are often elements of these integrated care programmes and have utilized providers, nurses, peers and others to play key roles in facilitating the adequate provision and connection of mental health and physical health care. Questions remain regarding the appropriate elements of care coordination, including tasks, roles and responsibilities of involved persons. Finally, as these are resourceintensive programmes, cost-effectiveness models of different approaches96 in persons with SMD will be important, especially in low-resource settings. This will be particularly needed as we seek to prioritize
+understanding risk factors for premature mortality of persons with SMD in LMICs.
+CONCLUSIONS
+Excess mortality in persons and populations with SMD remains an important global public health problem. Persons with SMD represent a vulnerable group with many and large health care needs. Despite known risk factors for premature mortality, evidence for effective interventions in persons with SMD is limited.
+In this paper we proposed and described models to better understand the complex relationships among risk factors and correlates of mortality, and to conceptualize interventions at the individual, health system and socio-environmental levels. These models guided us to outline key action points for clinical practice, policy and research agendas to move towards health equity for those with SMD.
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+The unequal distribution of income and wealth has been growing steadily over the past three decades to astonishing levels, fuelled by the wide adoption of neo-liberal economic policies and globalization. In 2016, while the bottom half of the global population collectively owned less than one percent of total wealth, the wealthiest top 10 percent owned 89 percent of all global assets1.
+The growth of income and wealth inequality has been observed in countries at all levels of socio-economic development. In the US, one of the richest countries in the world, the top 10 percent of the population now average nearly nine times as much income as the bottom 90 percent2. In India, an exemplar of a low- or middle-income country (LMIC), the richest 1% owned nearly 60% of the total wealth of the country in 2016.
+However, there is a three-fold variation in the range of levels of inequality among countries, with the most equal countries mostly clustered in Western Europe and the most unequal countries comprising LMIC and the US. These variations at the country level, as well as at sub-national levels (i.e., provinces or states) allow the exploration of the association between income inequality and a variety of social outcomes, notably health.
+There is a robust body of evidence linking inequality and health outcomes, ranging from infant mortality and life expectancy to obesity. A compelling case for a causal relationship between inequality and a number of negative health outcomes has been recently presented3.
+Not surprisingly, there is also evidence linking income inequality with mental health outcomes. A significant positive relationship has been reported between the incidence rate of
+schizophrenia and country-level Gini coefficient, a widely used measure of the distribution of income or wealth in a population. A possible mechanism proposed for this association was that inequality impacts negatively on social cohesion and capital, and increases chronic stress, placing individuals at a heightened risk of schizophrenia4.
+A review of studies on the association of income inequality and a range of mental health related outcomes reported heterogeneous findings, with about one third of studies observing a positive association between income inequality and the prevalence or incidence of mental health problems, one third observing mixed results for different subgroups, and one third observing no association5. Depression was one of the mental health outcomes considered in studies showing a positive association with income inequality.
+Although potential mechanisms that underlie the observed association between income inequality and health have been proposed6, little is known about the mechanisms involved in the case of depression. Hence, there is a need for a systematic review focusing on this association which also sets out to identify potential mechanisms and develops a conceptual framework that can further our understanding and set an agenda for future research in the field.
+The present study sought to advance the scientific inquiry of the association between income inequality and mental health in three specific ways. First, we systematically identified and descriptively synthesized the most updated literature on depression and income inequality, with a focus on study characteristics and potential differential impact by gender and level
+of poverty. Second, we quantitatively assessed the strength of the association of income inequality and depression prevalence through a meta-analysis. Finally, we conducted a scoping review of the literature to explore the potential mechanisms, and developed a theoretical framework for this association.
+By focusing on one mental health outcome (depression), we hoped to provide a more in-depth analysis of potential mechanisms than has previously been possible. Our ultimate goal was to elaborate the implications of this body of evidence on policies which influence the distribution of income and wealth, and identify specific gaps in our knowledge which deserve further research investment.
+METHODS
+Systematic review and meta-analysis
+Search strategy
+The search strategy was guided by our protocol (PROSPERO registration: CRD42017072721), which is available on request. In brief, we searched PubMed/Medline, EBSCO and PsycINFO databases. The search string used was “(depress* OR mental) AND (inequal* OR Gini)”. The electronic databases were searched for titles or abstracts containing these terms in all published articles between January 1, 1990 and July 31, 2017. The search was limited to studies published in English and involving human subjects. The reference lists of all included studies were hand-searched for additional relevant reports or key terms. If new key terms were identified (new term included: “mood”), additional searches of the above databases were conducted and relevant papers were added until no further publications were found.
+We included all studies providing primary quantitative data with a measure of depression or depressive symptoms as an outcome and any measure of income inequality at any geographical scale. Exclusion criteria were: unpublished data of any form including conference proceedings, case reports, dissertations; qualitative studies; and publications reporting duplicate data from the same population (in such cases, the report with the larger sample size was included).
+All titles and abstracts identified in the search were screened to exclude those that were obviously irrelevant based on the above exclusion criteria. Full-text versions were obtained for all abstracts remaining after screening. Obtained full-text articles were read and those not satisfying inclusion criteria were subsequently removed. The remaining articles were included in the systematic review (see Figure 1).
+Analyses
+Study data were extracted onto a customized sheet. Quality assessments were independently performed by using the Systematic Appraisal of Quality in Observational Research (SAQOR)
+tool, that comprises six domains (each containing two to five questions): sample, control/comparison group, exposure/out-come measurements, follow-up, confounders, and reporting of data7.
+The SAQOR has been adapted for use in cross-cultural psychiatric epidemiology studies8. In the current study, two domains were omitted (control/comparison group and follow-up), as they were not applicable to any of the papers identified. A summary quality assessment was made by a single rater (JKB) for each of the four domains, and then an overall summary grade was determined based on adequacy in the four domains. The overall quality of each study was graded as high, moderate or low.
+The meta-analysis was conducted using Cochrane Review Manager (RevMan) version 5.39. Data were extracted from studies to calculate risk ratios for the association of income inequality and depression. Studies that included data on depression event rates stratified by income inequality were included in the meta-analysis.
+Compared to risk ratios, odds ratios exaggerate effect sizes, with the distortion most pronounced for outcomes with prevalence greater than 10%10,11. Because depression prevalence may be greater than 10% in a population, risk is more accurately estimated with risk ratios. To calculate risk ratios, income inequality for each study was categorized as binary outcomes (higher vs. lower income inequality in a given population).
+When income inequality was categorized into three or more groups, we re-categorized the groups as follows: in studies with three groups, the lowest income inequality group was used as the reference and the medium and high inequality groups were collapsed; studies with four inequality groups were recategorized grouping the two lower and two higher inequality strata; for quintiles, the two low inequality quintiles were pooled as the reference group to compare with the three high inequality quintiles, which were collapsed into one stratum; finally, in studies with more than five inequality groups, these were recategorized into two strata of roughly equal sample size.
+Unadjusted prevalence rates of depression were used whenever available, given the lack of consistency across studies in variables used for adjusting outcomes. When unadjusted prevalence rates were not available, demographics and other characteristics used for adjustment are reported. A random effects meta-analysis was conducted because of the heterogeneity in design, populations and outcome measures.
+We conducted sensitivity analyses using the leave-one-out approach to test the impact of excluding single studies contributing a disproportionately large effect. A forest plot of the risk ratios with summary statistics (pooled effect sizes) was completed using RevMan. Heterogeneity among trials was calculated using the I2 measure of inconsistency.
+Scoping review of mechanisms
+We searched the Introduction and Discussion sections of studies included in the systematic review, to identify authors’ hypothesized mechanisms of the relationship between inequal-
+ity and depression. We subsequently considered hypothesized mechanisms in a recent review regarding the causal links between inequality and health3. We then compiled a list of hypothesized mechanisms based on their plausibility, specifically the extent to which the purported mechanisms were supported by the data reported in the included studies. We sought to improve the overall coherence by grouping different hypothesized mechanisms into conceptual categories.
+Finally, we supplemented these findings with an analysis of the variability in findings from the studies included in our systematic review. This led to consideration of a number of other factors that might inform the hypothesized mechanisms, namely the geographical unit of analysis, level of national devel
+opment of the study country, effects of income inequality on low- vs. high-income groups, cultural variations across countries, broader political and historical context, life course or developmental stage factors, as well as gender and methodological considerations.
+RESULTS
+Systematic review and meta-analysis
+Searches of the listed databases using the search string as well as hand-searching reference lists identified 1,894 potential
+articles. After screening titles or abstracts, 1,813 were removed as they were irrelevant or clearly did not contain primary data. Full-text reports were retrieved for 81 articles, which were assessed for eligibility against inclusion and exclusion criteria. Of these, 55 were removed as they did not provide primary quantitative data on the relationship between income inequality and depression, contained duplicate data or otherwise met exclusion criteria. Thus, 26 studies were included in the systematic review (Table 1). The selection process for included studies is illustrated in Figure 1.
+Study characteristics
+The majority (N=18) of the 26 studies testing associations between income inequality and depression came from high-income countries, with 15 studies reported from the US. In terms of geographical scale, four were conducted at the country level, 14 at regional level (state, county, district, municipality), and eight at local area or neighbourhood level.
+A number of studies were conducted in specific populations: five in older persons only19,22,27,30,35; four in adolescents only7,23,26,28; one in students aged 17-30 years25; and one in low-income nursing assistants21.
+The most common measure of depression, used in ten studies, was the Centre for Epidemiologic Studies Depression Scale (CES-D), while four studies used the Composite International Diagnostic Interview (CIDI), two the Patient Health Questionnaire (PHQ), and two the Alcohol Use Disorder and Associated Disabilities Interview Schedule - IV (AUDADIS-IV). Each of the remaining eight studies utilized a different instrument. Godoy et al17 investigated 655 adults in villages within the Bolivian Amazon and found a positive relationship between village-level Gini coefficient and experiences of “sadness” over the last seven days.
+Income inequality was most commonly measured using the Gini coefficient (21 studies), with the remainder using a ratio measure (e.g., 20%:20% ratio; P90/P10 ratio). Notably, all country-level studies utilized the Gini coefficient, while ratio measures were more commonly used in local area-level studies (three out of eight) than in regional-level studies (two out of 14).
+Associations between income inequality and depression
+Nearly two-thirds (N=16, 61.5%) of studies found a significant positive relationship between income inequality and risk of depression, while another three (11.5%) reported a positive relationship that was significant in bivariate but not multivariate regression analysis. Six studies (23.1%) found no significant relationship, while only one (3.8%) reported a negative relationship between income inequality and risk of depression (Table 1).
+Nineteen studies did not stratify their analysis by absolute income. Out of the seven studies that stratified analyses by absolute income, two showed a significant effect of income
+inequality on depression only in low-income participants, and two demonstrated that the effect size was the strongest in low-income individuals. Studies documenting greater effects in low-income participants were conducted at either the regional (N=2) or the local level (N=2). The three studies reporting no absolute income effect were conducted at either the regional (N=2) or the country level (N=1).
+Five studies stratified their analyses by gender. Of these, three found an association between income inequality and depression in females only18,23,24, one detected no gender effect4, and one found an association in men only in the bivariate analysis29.
+Although none of the studies stratified the analyses by age group, several were conducted exclusively in adolescent or older adult populations, and some interesting observations can be made here. Of the four studies in adolescents only, three found a significant association between income inequality and depression (two in the regression23,26 and one in bivariate analysis only28). Of the five studies in older adults only, three found an association between income inequality and depression (two in regression19,22 and one in bivariate analysis only27), one found no association30, and one found a negative association35.
+Participant ethnicity was reported in eight studies, with only three conducted in specific ethnic populations: in nearly 9,000 Hispanic adults aged 60 and older in Mexico30; in nearly 6,500 Black and Hispanic adolescents in the US7; and in Tsimane villagers in the Bolivian Amazon17. Notably, two of these latter studies did not find a relationship between income inequality and depression. Of the five studies that stratified their analysis by ethnicity, only one found an ethnicity effect, with the relationship between income inequality and depression most pronounced in middle-class Blacks in a population representative panel in South Africa4.
+Of the 26 studies, only six were longitudinal, allowing for temporal analyses. Of these, five reported a significant positive relationship between income inequality and depression 4,16,17,21,24; and one reported no association31. All except two studies had large sample sizes of over 1,000 participants (ranging from 1,35512 to 293,40515).
+Meta-analysis
+Twelve studies were included in the meta-analysis, based on availability of event rates of depression to calculate risk ratios. Quality ratings of the included studies using SAQOR ranged from high to moderate (see Table 2). The pool of studies included six US studies, three multi-country studies, one UK study, one Brazil study, and one South Africa study. Two of the US studies were limited to older adults. One study only included women18. One multi-country study limited the sample to university students25.
+Four studies employed three strata of inequality13,14,18,35; one study employed four strata12 and two studies employed
+quintiles15'29. All were re-categorized as dichotomous as described earlier. Ladin et al19 divided the sample of ten European countries into five high inequality versus five low inequality countries. We followed a similar procedure for Steptoe et al’s25 study of 23 countries, by creating a group of 11 low inequality countries and 12 high inequality countries.
+For the South Africa data, we extracted information from the two studies that employed the South Africa National Income Dynamics Study4,31. Data were available from Burns et al4 for the depression prevalence by municipality. Adjaye-Gbewonyo et al31 calculated Gini coefficients for each municipality based on the 2011 census. We integrated depression prevalence data and Gini coefficient data by municipality and split the dataset into approximate halves around a Gini coefficient of 0.75.
+Unadjusted data were used for all studies when available. Unadjusted data were not presented in the study by Cifuentes et al14, and rates were adjusted for age, gender and marital status. Fan et al15 only presented adjusted prevalence figures: rates were adjusted for gender, age, race/ethnicity, marital status, education, household income, and chronic medical conditions.
+Based on the 12 studies with dichotomized inequality groupings, the pooled risk ratio was 1.19 (95% CI: 1.07-1.31), demonstrating greater risk of depression in populations with higher income inequality relative to those with lower income inequality (see Figure 2). The heterogeneity was very high, I2=98%, which is likely due in part to the diversity of sample designs, populations, measures used, and adjustments and weighting in analyses. In all sensitivity analyses, the pooled risk ratio was significant for higher income inequality associated with increased risk of depression (p<0.05).
+Multiple studies conducted moderator analyses by stratifying the samples by gender, absolute income, country econom
+ic status, and ethnicity/race. Because of the limited number of studies with outcomes that could be dichotomized by depression and income inequality, we did not create subpools of studies or employ meta-regression to assess these potential moderators.
+Scoping review of mechanisms
+Based on the results of the systematic review, a number of potential mechanisms of the inequality-depression relationship may be hypothesized, operating at different ecological levels, from the individual to the neighbourhood to the regional or national levels.
+At the individual level, the effects of income inequality on general health are likely to be primarily mediated through psychological stress3. This may be regarded as the final mechanism mediating the effects of income inequality on depression in a range of pathways.
+At the neighbourhood levels, two mechanisms are hypothesized. The first is the social comparison or status anxiety hypotheses36, which argue that comparing oneself to those who are better off in a highly unequal context creates feelings of social defeat or status anxiety4,37. In a similar vein, Walker et al38 hypothesized feelings of withdrawal and shame experienced by those in lower social positions. The second neighbourhood mechanism is the social capital hypothesis, which argues that income inequality erodes social capital, including two key components: cognitive social capital (especially social trust)26 and structural social capital (the organizational and structural arrangements which facilitate social interactions and build social trust and cooperation, for example through group mem-bership)39.
+World Psychiatry 17:1- February 2018
+83
+Social capital is critical, because it facilitates social integration (a dynamic process by which members of a social group participate in dialogue or collaborate to achieve a shared social goal). Income inequality therefore undermines social capital and social integration, promoting social isolation, alienation and loneliness. It also undermines perceptions of fairness (a component of trust)37. Ichida et al40 confirmed the social capital hypothesis in Japan, showing that social capital (measured as social trust) mediated the effect of inequality on self-rated health. This is supported by Durkheim’s theory of social integration and social regulation41, the failure of which he linked to suicide.
+Perceptions of fairness and trust are also consistent with Merton’s anomie disjunction between society’s goals and normative structures governing the means to attain that goal42. This is more exaggerated in societies with higher levels of inequality, where the means of attaining upward social mobility are severely constrained, and therefore there is a disjunction between society’s goals or aspirations (for example of acquiring wealth) and the means to attain that goal, which are not accessible to those who are lower on the socio-economic hierarchy.
+Both the above neighbourhood mechanisms may be more pronounced at certain developmental stages, in particular in adolescence, when social trust and group membership are being established, and when most mental health problems emerge. For example, social status was associated with depression among adolescents whose parents had lowest levels of education7. In addition, social comparison may be amplified by other group identities, for example ethnicity or gender.
+At the national or regional levels, the neo-material hypothesis proposes that greater income inequality coexists with a wide range of material deprivations which are relevant to health43. These include lack of investment in housing, education and public transport as well as pollution control, healthy food availability and accessibility of health care. Thus, greater inequality leads to worse physical health (for example due to less public spending on health care in more unequal societies), leading in turn to an increased rate of depression.
+This hypothesis was supported by Muramatsu22, who found that the association between inequality and depression was stronger among those with more illnesses. However, it is worth noting Zimmerman et al’s opposite finding that more unequal states did not in fact spend less on health care34. Also, Fone et al36 argue that it is unlikely that the neo-material hypothesis would apply at small area level (such as neighbourhoods), as resource allocation decisions for major services are not typically made within these areas.
+For all of these potential mechanisms, it is important to consider a range of other factors that may moderate the relationship between income inequality and depression, reflected in the available studies. The first is the geographical unit of analysis. Of the six studies that found no association, five conducted analysis at the district level, and national level effects appeared to be more marked in the studies included in this review. According to Ahern and Galea12, this is likely to be at least partially influenced by the nature of the area demarcation. For example, if a neighbourhood includes strong contrasts of high- versus low-income groups, the effect of income in
+equality is likely to be more pronounced at that neighbourhood level. But frequently neighbourhoods involve homogeneous demarcations, and the effect may then be less pronounced.
+In a similar vein, Fone et al36 found that in Wales income deprivation was more important than income inequality for common mental disorders at the local neighbourhood level, but that the effect of income inequality became more evident at larger regional levels. Furthermore, Chen and Crawford44 reported that, when comparing US counties and states, the income inequality/health relationship was more evident at the state than the county level, (although this was true for health insurance as an outcome but not for self-reported health). Thus, it may be possible to argue that different mechanisms operate at different geographical levels or units of analysis.
+A second important consideration is the level of national development, for example as measured using the human development index (HDI). One study showed a possible interaction with country HDI level, namely that the inequality/depres-sion association was more evident in higher HDI countries14. Income inequality may matter in high-income countries with low levels of poverty, but not in low- or middle-income countries with high levels of poverty, where the effects of material poverty and absolute income may be more significant.
+A third consideration is the effect of income inequality on low- vs. high-income groups. Within countries, the effect of inequality on depression appears to be more pronounced among low-income groups12. This is consistent with the hypothesized role of upward social mobility, the constraints of which are more likely to be experienced by low-income groups. The hypothesis that inequality is deleterious for high-income groups too is proposed by other authors3. Kawachi et al45 argue that the wealthy in highly unequal societies cannot escape the “pathologies of poverty”, including crime, violence and exposure to some infectious diseases.
+A fourth consideration is cultural variation across countries. Although this may be difficult to test empirically, Steptoe et al25 considered the results in a multi-country study with respect to cultural variation along the axis of individualism and collectivism. The likelihood of high levels of depressive symptoms was lower in more individualistic cultures, with 26% reduction in the odds of elevated symptoms with every unit change in individualism-collectivism score.
+A fifth consideration is the broader political and historical context within which depression and inequality are measured. For example, in post-apartheid South Africa, there have been expectations of rapid social improvements, and there is clear evidence of improvements for some people, but for those who remain in poverty there is a sense of frustration, alienation, disappointment and anger, manifest in frequent service delivery protests4. This may well exaggerate the effects of income inequality on depression.
+A sixth consideration is life course or developmental stage. According to one study, childhood social class is more predictive of self-rated health than adult social class16. Prevalence of
+depression varies substantially across the life course46, and early exposure to inequality may well affect later mental health. Most of the studies included in this review lack a life course or developmental framework, even when the effect of inequality on specific age groups was examined, for example in the case of adolescent depression.
+A seventh consideration is gender. In at least one study23, the effect of inequality on depression was found for adolescent girls but not for boys. This was confirmed by Hiilamo47 in a study in Finland, which explored changes in municipalitylevel relative poverty and antidepressant prescriptions from 1995 to 2010, and found a positive association for young adult females.
+A final consideration is the methods employed by the studies themselves. For example, contrary to the finding that the association between inequality and depression was less evident in more local, homogeneous populations, Fiscella and Franks16 did find a positive association at local level. This finding may be attributable to the study design, which employed longitudinal, multi-level methods and collected baseline data on county income inequality, individual income, age, gender, self-rated health, level of depressive symptoms, and severity of biomedical morbidity.
+DISCUSSION
+In this paper we present, to our knowledge, the most comprehensive review of the evidence on the relationship between income inequality and depression. Despite the relatively small evidence base (especially from LMIC) and methodological limitations of the available studies, we report a compelling quantitative association between income inequality and depression. Even though the absolute effect size was relatively small (risk ratio of 1.19), the translation of this risk to population mental health is likely to be very large.
+Further, we note that the primary outcome of the studies we included was a categorical outcome of “case-level” depression. This is a crude indicator of population mental health, and the associations between income inequality and mood are likely to be greater when the latter is treated as a continuous dimension, which could capture dose-effects of the degree of inequality on the distribution of affective symptoms.
+If our findings are indicative of a causal relationship, then we should expect worse mental health globally in the years ahead, as income inequality is continuing to increase in most countries, making the United Nations (UN) Sustainable Development Goal targets for mental health48 even harder to achieve. This is especially likely to be the case for disadvantaged or vulnerable groups in the population that already bear a disproportionate burden of mental health problems, such as women, adolescents, older adults and low-income groups.
+The heterogeneity of the findings of studies across populations and over time is not surprising, given the complexity of
+likely mechanisms and pathways, and their moderation by a range of contextual factors which we have attempted to delineate. These mechanisms operate at different ecological levels, but the final pathways are, as with any mental health problem, uniquely individual, moderated by a range of distal and proximal determinants.
+Although we do strongly endorse the need to “unpack” these mechanisms through carefully designed studies, such research is likely to be complex, time-consuming and costly. Thus, we propose that the evidence which already exists is sufficient to take pre-emptive action to halt the potentially damaging effects of income inequality on the mental health of populations.
+Implications for reducing the global burden of depression
+Our ecological framework offers indications for the kinds of interventions which hold promise (see Figure 3). Obviously, at the national or regional level, economic policies which promote the fair distribution of income, for example through a universal basic income and progressive taxation, are potentially the most tractable49. Additionally, promoting social policies that reduce gender inequities which systematically disadvantage women, and income inequities, such as universal health coverage and expanding opportunities for educational attainment, can reduce the impact of the neo-material effect on low-income populations.
+In addition to structural interventions, the mechanisms we propose suggest attractive opportunities for proximal interventions to mitigate the adverse personal consequences of living in unequal societies. The Disease Control Priorities pro-ject50 has recommended a series of interventions for the prevention, treatment and care of mental health problems, most of which can be delivered through community and routine health care platforms, using task-sharing by non-specialist providers. Particularly relevant examples would include interventions in early life through adolescence to build resilience (for example, parenting interventions and life skills interventions), as well as promoting early detection and self-help for mood and anxiety disorders (for example, through improving access to empirically supported digital apps, especially with guidance)51.
+A recent systematic review has demonstrated the effectiveness of psychological therapies delivered by non-specialists in low-resource settings52. Such therapies may be modified when delivered in the context of high inequality through a focus on mechanisms related to cognitive comparisons leading to social defeat and worthlessness. For example, interventions that focus on demoralization53,54 may be especially important in highly inequitable societies and communities. Third-wave psychological therapies that include components of self-validation may also counter social defeat and worthlessness associated with depression and suicidality55. These therapies are currently being adapted for delivery in settings of extreme poverty56.
+Interventions that harness the power of social networking sites to build social capital also show promise at mobilizing
+specific subgroups and reducing the risk of social isolation. Pilot programs in Mexico and South Africa have shown encouraging results at reducing levels of anxiety, depression and feelings of social isolation in adolescents and pregnant women with HIV/AIDS57-60. Marshall et al35 report that social interactions and networks among subgroups in mixed-income neighbourhoods cushion the impact of income inequality on depression.
+Other research points to the role of social interactions, cultural biases and belief systems in maintaining and perpetuating conditions for income inequality61,62. Thus, it is important that we develop interventions that target social and cultural aspects of inequalities (for example, designing institutional platforms such as schools and health institutions) to enhance social capital, and all interventions must be guided by a strong emphasis on equitable coverage. This is consistent with a shift from cultural competency to “structural competency”, which emphasizes the need for mental health providers to be knowledgeable of context and resources of their patients and actively draw upon resources to mitigate social and structural determinants of mental illness63.
+Limitations of the study
+There are limitations to our study which should be noted. First, publication bias, namely a propensity for journals to publish positive findings, may overestimate the strength or consistency of the association between inequality and depression. Second, there was a heterogeneity of outcome measures for depression, with some studies not utilizing validated assessment instruments, and a diversity in sample size and sampling strategies, all of which impact depression prevalence esti-mates64. Third, the majority of studies failed to stratify their samples by important socio-demographic factors such as gender, age and absolute income, limiting our ability to explore in greater depth the controversial question of whether the negative effects of income inequality are evenly distributed across the population or if certain vulnerable groups are particularly affected65.
+Regarding the meta-analysis, we were unable to use unadjusted data across all studies, and it is likely that the studies that adjusted inequality by outcomes reflect aspects of the association differently than unadjusted studies. In addition, the inequality cut-off for each study was different, based on the relative levels of inequality within the sample. For example, inequality levels within the South Africa dataset were high on average compared to European nations. Therefore, our findings are reflective of regional and national relative income inequality rather than the effect of absolute inequality (e.g., dividing all samples at one specific Gini coefficient cut-off, which would have been arbitrary, given that inequality is, by definition, a relative measure).
+The meta-analysis also demonstrated high heterogeneity. As the pool of studies examining income inequality and men
+tal health grows, it will be possible to perform more subgroup analyses with studies that employ comparable designs and samples in order to reduce heterogeneity.
+Implications for research
+Future research should aim to unpack the mechanisms underlying the association between inequality and depression, in particular to explain the heterogeneity of findings across contexts. This research should involve prospective studies in diverse countries, in particular in a range of LMIC which are witnessing rapid socio-economic changes, such as the BRICS nations. Notably, Brazil and South Africa, which both have high levels of inequality, showed comparable high effects of income inequality on depression in our meta-analysis (risk ratios were 1.38 and 1.33 respectively).
+Future studies should include the effects of changes in income inequality (at different geographical levels and population subgroups of analysis) over time, with embedded assessments of hypothesized individual and area-level mechanisms; and evaluation of the effects of interventions addressing the proposed pathways. Additionally, further exploration of the studies with equivocal findings, such as countries with high levels of income inequality which did not show an increased prevalence of depression, should also be conducted, to understand possible structural differences, policies or socio-cultural factors that mitigate this effect.
+It is important to methodologically take note of the historical, political and cultural forces that may shape the association between income inequality and depression in developing countries. Modelling contextually grounded forces can shed greater light on the precise mechanisms that may be operating in these contexts.
+CONCLUSIONS
+Mental health professionals, regardless of their political persuasion, should carefully assess the evidence presented in this review to shape their position with respect to the ideologically contentious issue of income inequality.
+They should ally with other stakeholders in government and civil society who are arguing for a fairer, more equitable distribution of income, as this is a major social determinant of poor mental health, while also drawing attention to the need for greater investments in proven individual interventions for the prevention and treatment of depression.
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+53.	Noordhof A, Kamphuis JH, Sellbom M et al. Change in self-reported personality during major depressive disorder treatment: a reanalysis of treatment studies from a demoralization perspective. Personal Disord (in press).
+54.	Griffith JL. Hope modules: brief psychotherapeutic interventions to counter demoralization from daily stressors of chronic illness. Acad Psychiatry (in press).
+55.	Hayes SC. Acceptance and commitment therapy, relational frame theory, and the third wave of behavioral and cognitive therapies. Behav Ther 2004;35:639-65.
+56.	Ramaiya MK, Fiorillo D, Regmi U et al. A cultural adaptation of dialectical behavior therapy in Nepal. Cogn Behav Pract 2017;24:428-44.
+57.	McClure CM, McFarland M, Legins KE. Commentary: innovations in programming for HIV among adolescents: towards an AIDS-free generation. JAIDS 2014;66:S224-7.
+58.	The SHM Foundation. Zumbido health. http://shmfoundation.org/.
+59.	The SHM Foundation. Project Kopano. https://shmfoundation.org/.
+60.	The SHM Foundation. Project Khulama. https://shmfoundation.org/.
+61.	Bowles S, Durlauf SN, Hoff K (eds). Poverty traps. Princeton: Princeton University Press, 2006.
+62.	Hoff KR, Pandey P. Belief systems and durable inequalities: an experimental investigation of Indian caste. Washington: World Bank Publications, 2004.
+63.	Metzl JM, Hansen H. Structural competency: theorizing a new medical engagement with stigma and inequality. Soc Sci Med 2014;103:126-33.
+64.	Steel Z, Marnane C, Iranpour C et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. Int J Epidemiol 2014;43:476-93.
+65.	Kawachi I, Subramanian SV, Almeida-Filho N. A glossary for health inequalities. J Epidemiol Community Health 2002;56:647-52.
+DOI:10.1002/wps.20492
+20515545, 2018, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/wps.20492 by CAPES, Wiley Online Library on [25/12/2022]. See the Terms and Conditions (https://onhnelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
+World Psychiatry 17:1- February 2018
+89
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+Stigmatization of people with mental illness can contribute to adverse consequences, including poor access to mental and physical health care; reduced life expectancy; exclusion from higher education and employment; increased risk of contact with the criminal justice system; victimization; poverty and homelessness1.
+The WPA Open the Doors programme was initiated by one of us (NS) in 1999 and included both action and research components in 23 countries worldwide. This work led to the creation of the WPA Stigma Section, and to establishing a series of biannual international stigma related conferences, entitled Together Against Stigma.
+We subsequently created the INDIGO (International Study of Discrimination and Stigma Outcomes) Research Network to undertake research related to stigma and discrimination (http://www.indigo-group.org/). Here we summarize the work of the INDIGO network over the last decade, and set out what we have learned.
+In the first phase, colleagues in 27 countries worldwide agreed to join the network, and we realized that no suitable scales existed to measure mental illness related discrimination. We therefore created the Discrimination and Stigma Scale (DISC), which was found to have strong psychometric properties2. Since 2012, the DISC-12 scale has been accessed by 216 research users in 55 countries worldwide.
+In our first global stigma project, the DISC-12 scale was used to interview 729 people with a clinical diagnosis of schizophrenia across 27 countries. The results showed that over 90% had experienced discrimination because of their mental health status3. Most people (72%) reported a need to conceal their diagnosis. The results confirmed the universality of discrimination adversely affecting people with schizophrenia.
+We next assessed 1,082 people with major depressive disorder in 35 countries, and found that 79% reported experiencing discrimination in at least one life domain. In exploring the data further, we unexpectedly found higher levels of experienced discrimination in high-income compared with middle-and low-income countries (LMIC)4.
+We conceptualized stigma in relation to its three components of knowledge, attitudes and behaviour. We therefore created and psychometrically tested the following toolkit of scales and measures across those domains, to be freely available to researchers worldwide: the Barriers to Access to Care Scale (BACE), formulated following a systematic review of barriers to help-seeking5; the Costs of Discrimination Assessment (CODA), assessing the costs related to mental illness related discrimination6; the short version of the DISC-12 (DISCUS) scale, with strong psychometric properties and comparable reliability and validity to the original scale; the Mental Health Knowledge Schedule (MAKS), assessing factual items
+related to mental health7; the Mental Illness: Clinicians' Attitudes (MICA) scales, evaluating attitudes among health care professionals or medical students towards people with mental illness8; the Questionnaire of Anticipated Discrimination (QUAD), exploring future expectation of discrimination9; and the Reported and Intended Behaviour Scale (RIBS), a short measure of the above domains10.
+These scales have been designed for global and open access use. They can be translated into any language, provided that each translation is copied to the repository at King's College London, to be freely available for other researchers. Up to now, the INDIGO scales have been translated into a total of 31 languages. They have been used in 67 countries during the last five years. The scales are available on request (maria.milenova@kcl.ac.uk).
+Following the toolkit phase of work, we more directly focused upon intervention studies. We produced a narrative and a systematic review of the global literature on interventions to reduce stigma and discrimination1 and a paper on intervention studies in LMICs.
+Taken as a whole, these reviews establish that: a) social contact (i.e., interpersonal contact between people with and without experience of mental illness) is the strongest proven active ingredient to reduce mental illness related stigma and discrimination; b) such social contact is most effective in educational settings for young people; c) there is emerging evidence that virtual/social media contact may be as effective as direct face-to-face contact; and d) there is a research gap on all of these issues in LMICs.
+Since the INDIGO network was established, we have learned the following lessons on how a network may become successful, productive and sustainable:
+•	Clear ground rules are vital in terms of what are the role and responsibilities of all partners.
+•	Establishing a learning collaborative: we actively encourage sites to support each other, particularly in similar language or resource-level settings.
+•	Taking a long-term view for sustainable capacity building: for an international research network to survive, let alone thrive, it is necessary to purposively support early- and midcareer academic staff.
+•	A distributed model of leadership for shared responsibilities and co-operation: we have found it useful to distribute specific roles into discrete work packages, and to establish task teams for each of these tasks.
+•	Freedom within aframework the coordinating centre agrees with project staff what their ultimate products or deliverables will be, when they will be delivered, and the intermediate steps, or milestones that will have to be completed to a given set of time points.
+World Psychiatry 18:2 - June 2019
+•	Multidisciplinary approach to research: the network provides a unique resource for the development of new research in the field of stigma by bringing a variety of inter-disciplinary skills.
+•	Regular communication: it is vital to build a sense of belonging to a valued group of colleagues, and to celebrate intermediate as well as final project successes.
+From our work in the INDIGO network so far, we have learned that stigma and discrimination are universal, that they are reversible, and that there are some variations in their manifestations across cultures. We continue to welcome colleagues who wish to join this network, and we are now considering how the learning generated by the network may be used to counteract stigma in other arenas.
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+Introduction
+The prevalence of mental disorders is high, with estimates indicating that one in three individuals experiences a mental disorder during their lifetime.1-3 Additionally, there is robust evidence indicating that mental disorders are associated with premature mortality.3 Many studies on mental disorders report risk ratios such as standardised mortality ratios and mortality rate ratios (MRRs), which are useful for comparing how different types of disorders affect premature mortality and to identify specific causes of death.3 Health metrics can also provide information on the age of death or
+differential life expectancy associated with mental disorders.4 While previous literature has provided a range of health metrics for people with mental disorders,4-6 there is a need for a harmonised panel of mortality-related health metrics that includes a comprehensive range of mental disorders.
+Life expectancy is a widely used health metric, which is readily understood by experts and non-experts alike, and comparing the difference in average lifespan between people with mental disorders and the general population is an intuitive way to measure premature mortality. The best known metric for premature mortality is years of life
+Research in context
+Evidence before this study
+Systematic reviews have consistently shown that people with mental disorders have an increased risk of premature mortality. T raditionally, this evidence has been based on relative risks (eg, mortality rate ratios [MRRs]) or crude estimates of life expectancy that do not incorporate variation in the age of onset of the disorder. Recently, a new method has been introduced to estimate life-years lost (LYLs), a measure that incorporates the precise age of onset of the disorder. A literature review indicates that this method has not been applied to a comprehensive range of mental disorders, nor contextualised with traditional mortality-related measures such as MRRs.
+Added value of this study
+In this register-based cohort study, we use a variety of mortality-related metrics, presented by age, sex, and cause of death, to investigate mortality associated with different types of mental disorders. Besides MRRs, we report premature mortality based on LYLs. We found that people with mental disorders had higher mortality rates than those without, with the largest difference observed at age 33 years (MRR 7-25, 95% CI 7-06—7-44). When looking at specific types of disorders, being diagnosed with all types was associated with higher mortality rates and shorter life expectancies, both in males and
+females. The largest reduction in remaining life expectancy after diagnosis was observed for substance use disorders. When examining specific causes of death, we found that men with all types of mental disorder had fewer LYLs associated with neoplasm-related deaths compared with the general population, which indicates that the general population loses more years of life due to neoplasms than do those with mental disorders. This finding relates to the ability of the LYL to accommodate competing causes of death. While those with mental disorders had higher rates of dying from neoplasms, they had even higher rates of mortality from other causes, thus reducing their risk of dying from neoplasms. Finally, through an interactive data visualisation website, we allow fine-grained interrogation of our results.
+Implications of all the available evidence
+Mental disorders are associated with both increased mortality rates and reduced life expectancy when assessed with LYLs. Our new methods provide more accurate estimates of premature mortality and reveal previously underappreciated features related to competing risks and specific causes of death. Our findings reinforce the need to optimise the coordinated care of general medical conditions in those with mental disorders.
+lost (YLLs), which is used by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD).7 When considering deaths attributable to specific causes, YLLs estimate the expected remaining life expectancy from age at death on the basis of an ideal standard life expectancy derived from the lowest age-specific mortality rates in any population in the world greater than 5 million. Other studies have used methods based on remaining life expectancy by applying assumptions related to age of onset of the disorder or age of the cohort for follow-up. For example, an Australian study found the life expectancy gap for all mental disorders to be 15-9 years for men and 12-0 years for women.8 A Danish study found similarly large estimates of reduced life expectancy for people with schizophrenia (18-7 years for men and 16-3 years for women).9 However, these studies assumed that all cases of mental disorders had onset at age 15 years.
+Recent advances in health metrics can now take into account the observed age of onset (or age of first administrative onset) of the disorder of interest.4,10 The life-years lost (LYLs) method focuses on the observed age at diagnosis, differing in this way from GBD’s YLLs, which focus on age at death. By using the age at diagnosis, the LYL method allows quantification of the reduction in life expectancy for any type of disease, and not only those defined as potential causes of death, as in the GBD estimations. With LYLs, it has been estimated that the average life expectancy for people with any mental disorder is 10-2 years shorter
+for men and 7 • 3 years shorter for women.4 These findings, and a recent study applying these methods to schizophrenia,11 indicate that past estimates might have overestimated the actual contribution of mental disorders to premature mortality. Another advantage of using the LYL metric is that it allows investigation of the association between particular disorders and premature mortality, regardless of the main underlying cause of death. It also allows the decomposition of the total reduction in life expectancy into specific causes of death. Given that differential life expectancy in people with mental disorders is used as a proxy indicator of the effectiveness of health care, which can influence resource allocation,12 it is essential that valid metrics related to differential life expectancy be available.
+The aim of this study was to investigate mortality associated with different types of mental disorders. We used high-quality Danish registers to provide an integrated atlas of different estimates related to premature mortality, which can be interrogated by age, sex, type of mental disorder, and specific cause of death.
+Methods
+Study population and assessment of mental disorders
+We designed a population-based cohort study including all people younger than 95 years living in Denmark at some point between Jan 1, 1995, and Dec 31, 2015. Since 1968, the Danish Civil Registration System13 has maintained information on all residents, including sex, date of birth, continuously updated information on vital
+status, and a unique personal identification number that can be used to link information from various national registries. Information on mental disorders was obtained from the Danish Psychiatric Central Research Register,14 which contains data on all admissions to psychiatric inpatient facilities since 1969 and visits to outpatient psychiatric departments and emergency departments since 1995. The diagnostic system used was the Danish modification of the International Classification of Diseases, Eighth Revision (ICD-8) from 1969 to 1993, and ICD, Tenth Revision (ICD-10) from 1994 onwards.15 Specific disorders were described with the ten-level subchapter categories used in previous publications based on Danish registers,2,16 as described in ICD-10 (eg, F00-09, F10-19, F20-29, etc) and corresponding diagnoses in ICD-8. Details of the specific diagnoses included in each group of disorders are presented in the appendix (p 3), with some disorder groups only including one specific disorder (eg, developmental disorders only includes autism spectrum disorder). From this point onwards, we use the term disorder to describe each group of specific diagnoses, employing the following short labels: organic disorders, substance use disorders, schizophrenia, mood disorders, neurotic disorders, eating disorders, personality disorders, intellectual disabilities, developmental disorders, and behavioural disorders. For each individual in the study, the date of onset for each disorder was defined as the date of first contact (inpatient, outpatient, or emergency visit).
+The Danish Data Protection Agency and the Danish Health Data Authority approved this study. According to Danish law, informed consent is not required for register-based studies. All data were de-identified and not recognisable at an individual level.
+Mortality
+Information on date and primary cause of death was obtained from the Danish Register of Causes of Death.17 All deaths were categorised in 11 non-overlapping groups according to ICD-10 codes: infectious diseases (A00-B99), neoplasms (C00-D09), diabetes (E10-E14), circulatory system diseases (F03-9, I00-I25, I27, I30-I52, I60-I84, I86-I99, R54), respiratory diseases (J00-J99), digestive diseases (K00-K69, K71-K93), alcohol misuse (F10, I85, K70), suicide (X60-X84, Y87-0), homicide (X85-Y09, Y87-1), accidents (V01-X59, Y10-Y86, Y87-2, Y88-Y89), and all other causes. These causes were further categorised into two traditionally reported categories: natural and unnatural deaths. Unnatural deaths are deaths by external causes, comprising suicide, homicide, and accidents; natural deaths are deaths from diseases and medical conditions, and included all other causes.
+Statistical analysis
+For each specific mental disorder, follow-up started at minimum possible age of onset2 (appendix p 3) or on Jan 1, 1995, whichever came later, and ended on the date
+of the individual’s 95th birthday, their death, their emigration from Denmark, or Dec 31, 2015, whichever came first. All mental disorders were treated as timevarying factors. Thus, any individual was considered unexposed until the first diagnosis of a specific disorder (if it occurred); subsequently, the individual was considered to be exposed to the specific mental disorder. Hence, individuals who were diagnosed before start of follow-up were continually counted as exposed. For individuals who were diagnosed during follow-up, this was dealt with in the analyses by splitting the follow-up for these individuals into an unexposed part and an exposed part, and treating the parts as distinct individuals with variables that did not change with time.18 People with more than one disorder were considered as being exposed to each of the disorders, with different onsets depending on the date of first diagnosis for each disorder. When estimating mortality for one specific disorder, information on all other disorders was not taken into account.
+Mortality rates for the whole population and for those diagnosed with at least one disorder, as well as with each specific disorder, were calculated as the number of deaths divided by the total follow-up time in person-years. Additionally, mortality rates for the whole population were calculated via direct standardisation using the distribution of sex, age (5-year categories), and period (1995-99, 2000-04, 2005-09, 2010-15) of those diagnosed with each disorder and those diagnosed with at least one disorder. These standardised rates can be interpreted as the overall mortality rates in Denmark scaled to the age and sex distribution of those with the disease, therefore avoiding confounding by these factors.18 Finally, MRRs, defined as the ratio of mortality rates among people with and without each specific disorder (or with and without any disorder), and their 95% CIs were estimated for each specific cause of death and for all causes combined using Poisson regression models,18 adjusting for sex, age, and calendar time. Age (in 1-year categories) and calendar time (in 5-year categories) were included in the models using cubic splines with four knots. Sex-specific and agespecific estimates were obtained by including interaction terms in the models.
+Differences in average life expectancy between the group of people with a specific mental disorder and the general population were calculated separately for men and women as excess LYLs. The technical development of this method has been published previously.4,10 In brief, the expected residual lifetime for the group of people with a specific mental disorder after diagnosis and before a specified maximum age—set to 95 years for this study—was estimated as the area under the survival curve, calculated using age-specific mortality rates. LYLs quantify the average number of years lost before the specified maximum age as the difference between 95 and this expected remaining life expectancy. For the general population, this measure quantifies the average number of years lost before age
+95 years for a group of people of the same sex who are alive at ages corresponding to the age-of-onset distribution for those with the mental disorder. The difference in LYLs between the two groups, defined as excess LYLs, can be interpreted as the average number of years that people with a specific mental disorder lose in excess of that found in the general population of the same sex and age. Specifying a maximum age is necessary for the LYL method; additionally, the selection of a point in which the survival probability is as low as possible provides easily interpretable results. In this study, our choice of 95 years is well above the life expectancy for the Danish population,19 thus associated with low survival probability. Finally, excess LYLs were decomposed into specific causes of death using a competing risks model.20 Negative excess LYLs for a specific cause indicate that those with a mental disorder had fewer LYLs compared with the general population or, in other words, the general population lost a larger amount of life due to this specific cause than did those with a mental disorder. 95% CIs for these estimates were obtained using non-parametric bootstrap with 1000 iterations. All analyses were done on the secured platform of Statistics Denmark using R version 3.5.2.
+Role of the funding source
+The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The first and corresponding authors had full access to all data in the study and had final responsibility for the decision to submit for publication.
+Results
+7 369 926 people (3 671 564 males and 3 698 362 females) were followed up for 111 027 388-7 person-years (table).
+Overall, 762 419 (10-3%) people were diagnosed with at least one mental disorder (347 972 [9-5%] males and 414447 [11-2%] females). During the 21-year follow-up period, 1 122 351 (15-2%) people died (564 301 [15-4%] males and 558 050 [15-1%] females). We have included all data on an interactive data visualisation website to facilitate fine-grained interrogation of the data and results.
+Overall, mortality rates were higher among people with a diagnosis of a mental disorder (28-70 deaths [95% CI 28-57-28-82] per 1000 person-years) compared with the general Danish population (12-95 deaths [12-93-12-98]) after standardisation to the same age, sex, and period distributions as among the diagnosed. Consequently, mortality due to all causes was more than two times higher among people diagnosed with a mental disorder compared with those without any diagnosis (adjusted MRR 2-53, 95% CI 2-52-2-54). All types of mental disorders were associated with higher mortality rates, with MRRs ranging from 1-92 (95% CI 1-91-1-94) for mood disorders to 3-91 (3 • 87-3 • 94) for substance use disorders (table). Sex-specific estimates showed similar results for females (all-cause MRR 2-30, 95% CI 2-28-2-32) and males (2-84, 2-82-2-86; appendix p 4). All types of mental disorders were associated with higher mortality rates, both for females and males, with MRRs higher for males for all disorders except for intellectual disabilities and developmental disorders (appendix p 5).
+As expected, mortality rates increased constantly with age; however, the ratio in mortality rates between those with and without a mental disorder was not constant (figure 1). Overall, MRRs peaked at age 33 years (MRR 7-25, 95% CI 7-06-7-44) and then decreased constantly. However, even people in their 80s had MRRs
+of about 2. Sex-specific MRRs for people with any mental disorder showed a similar trend for males and females, although males had higher MRRs than females after age 20 years. MRRs for all types of disorders are available in the appendix (pp 6-15).
+Mortality rates for all specific causes of death were higher for people with any mental disorder compared with those without any diagnosis of a mental disorder, with MRRs ranging from 1-40 (95% CI 1-38-1-41) for neoplasms to 11-82 (11-43-12-22) for suicide (appendix p 16). Cause-specific MRRs were systematically higher among males; however, the association between any mental disorder and suicide was higher for females (MRR 17-52, 16-28-18-86) than for males (10-12, 9-67-10-59; appendix p 16). When considering sexspecific MRRs for specific causes of death for each type of mental disorder, most mental disorders were associated with higher mortality rates for all specific causes of death (figure 2; appendix p 17). For mood disorders, for example, MRRs followed a similar pattern across all causes of death, with the lowest MRR for neoplasms (1-31 [95% CI 1-27-1-35] in males and 1-22 [1-19-1-25] in females) and the highest MRR for suicide (12-49 [11-79-13-22] in males and 18-22 [16-93-19-60] in females). Some cause-specific MRRs were generally higher among males than females (eg, digestive diseases), whereas others such as suicide were higher among females (appendix p 18).
+Excess LYLs for each specific mental disorder are shown in figure 3. On average, remaining life expectancy after diagnosis in those with a mental disorder was 10-06 years (95% CI 9-98-10-13) shorter for males and 7-06 years (6-99-7-12) years shorter for females compared with the general population of the same age. All types of mental disorders were associated with shorter remaining life expectancies. The disorder associated with the most excess LYLs was substance use disorders in males (14-84 years [95% CI 14-70-14-99]) and the disorder associated with the least excess LYLs was organic disorders in females (5-42 years [95% CI 5-36-5-48]). For mood disorders, for example, remaining life expectancy after diagnosis was 6-22 years (6-10-6-36) shorter for females and 7-91 years (7-78-8-04) shorter for males compared with the Danish general population whereas these estimates were 9-37 years (9-15-9-56) for females and 12-06 years (11-83-12-25) for males diagnosed with schizophrenia. Although most LYLs associated with mental disorders are due to natural causes (eg, general medical conditions), deaths by external causes (eg, suicide, homicide, and accidents) also contribute substantial LYLs to most mental disorders.
+Excess LYLs by mental disorder and specific cause of death are shown in figure 4, with results for each mental disorder shown in the appendix (pp 19-24). For example, on average, people with mood disorders had a larger number of LYLs than the general population for all causes of death, except for neoplasms in males,
+for which males with a mood disorder diagnosis had fewer LYLs (excess LYLs -0-66 years, 95% CI -0-78 to -0-54). Interestingly, males with any type of mental disorder had negative excess LYLs due to neoplasms compared with the general population, which indicates that males in the general population lose more years of life due to neoplasms than do males with any type of mental disorder. Finally, we present a visual comparison of cause-specific excess LYLs and MRRs in the appendix (pp 24-29).
+Discussion
+To our knowledge, this study provides the most comprehensive analysis of mortality-related metrics in people with mental disorders, providing estimates for each group of mental disorders separately. The highest MRR was found for substance use disorders; however, all types of mental disorders were associated with higher mortality rates. The point estimates varied by sex, with higher estimates for males than females for all disorders except for developmental disorders and intellectual disabilities. The sex-adjusted MRR for all mental disorders is broadly consistent with estimates from a previous Danish-register study4 that examined excess mortality associated with any mental disorder, although not including organic disorders such as dementia.
+Besides showing age-specific and sex-specific MRRs, we examined differences in life expectancy after disease diagnosis for a broad range of mental disorders using LYLs. All types of disorders were associated with shorter life expectancies compared with the general population; the disorders associated with the largest number of excess LYLs were substance use disorders. As observed in two recent papers that used the same methodology,4,11 these estimates are more conservative compared with previous estimates of reduced life expectancy5,8 because we incorporated variations in age of onset into the health metric. However, LYLs associated with different types of mental disorder remain substantial. In particular, for disorders such as mood, neurotic, and substance use disorders, the total contribution of LYLs at the population level would be substantial, given the high prevalence of these disorders.2
+Our study confirms that people with mental disorders have premature mortality attributable to suicide and other external causes of death; however, most premature mortality is attributable to causes of death related to general medical conditions, including infectious diseases, neoplasms, diabetes, circulatory system diseases, and respiratory diseases. These findings are consistent with the very large literature based on a wide range of methodologies and different countries.21 However, our study confirms an underappreciated feature regarding cancer deaths in those with mental disorders.4,11 Although each type of mental disorder is associated with higher rates of mortality related to neoplasms, we found that men with mental disorders lost fewer years of life related to neoplasms compared with the general population. This finding relates to the ability of LYLs to accommodate competing risks among different causes of death.
+Although men with mental disorders had higher rates of dying from neoplasms, they had even higher rates of dying from non-cancer causes, which precluded their dying from neoplasms. We provide more details of this process in the appendix (p 2).
+The use of LYLs provides important new insights into the nature of premature mortality associated with mental disorders. It is widely appreciated that relative risks such as MRRs can be misunderstood in the absence of complementary data related to absolute risks. Thus, it is important to explore a range of mortality-related health metrics and present them together. The LYL is an appealing metric to use alongside MRRs for a range of reasons. First, the concept of reduced life expectancy is widely understood by the general community, making it easy to communicate these findings. Second, it allows for the evaluation of the impact of particular disorders on premature mortality, regardless of how the individual died. Here, it has advantages compared with widely cited burden of disease studies that do not allow the causes of death to be directly attributed to most mental disorders.22 Third, it allows the total average LYLs to be partitioned into specific types of causes of death. Because only one underlying cause of death is considered, and because different causes of death are mutually exclusive and comprehensively exhaustive, the cause-specific LYLs are additive. Finally, as was shown in this Article, causespecific LYLs reveal previous underappreciated features related to competing risks, and thus can help to identify subtle but important features of the pathways between mental disorders and premature mortality.
+Although this study is based on a large sample from complete Danish registers, it has several limitations. Diagnosed individuals only include those with mental disorders registered in hospital, outpatient, and accident and emergency settings. It does not include the many patients with milder disorders who are only treated by a general practitioner, nor those who do not seek help for their mental disorder. Although it is reasonable to assume that severe disorders will eventually be registered in a hospital setting, the prevalence of untreated mild disorders is unknown. A recent study23 estimated that around a quarter of people who started treatment with antidepressants in Denmark received psychiatric hospital care for depression either initially or within 5 years of their first antidepressant treatment. These factors could lead to larger estimates of MRRs and LYLs associated with mental disorders compared with those based on community-based surveys with longitudinal follow-up. However, because we rely on age at first administrative onset, we would overestimate the actual age of onset of the mental disorder—thus, the LYLs method probably slightly underestimates the actual LYLs. Furthermore, although mental disorders were identified through the psychiatric register from 1969, follow-up for mortality only started in 1995. Thus, for an unknown proportion, we were not able to observe the initial phase after the
+onset of the mental disorder, when MRRs are higher. As a consequence, this issue could lead to an underestimation of MRRs. Additionally, there is no information in the registers regarding remission; the group of individuals with a mental disorder can therefore be interpreted as people who have ever had a diagnosis of a mental disorder, although they might have recovered afterwards. In this study, specific types of mental disorders were considered separately; all people with a diagnosis of a specific disorder were treated similarly, irrespective of whether they had diagnoses for other disorders. While studies have confirmed that register-based diagnoses have good validily, ' not all disorders have been validated and the mental disorders were not confirmed with structured diagnostic interviews. The validity of diagnosis might also vary within the clinical settings (eg, because of differing skills and duration of assessments, diagnoses made in emergency room settings might be less reliable compared with outpatient or specialist inpatient units). We used broad diagnostic categories to allow comparisons with related publications.2,16 Additionally, date and cause of death were also obtained from registers. Date is thought to be reasonably accurate and all-cause mortality is unlikely to be affected by misclassification. However, only 5% of deaths in Denmark are examined by autopsy,28 which could lead to some misclassification of the specific causes of death. Finally, although our estimates might be reflective of high-income nations in the Nordic region, we cannot assume that our findings would generalise to all countries.
+Our findings highlight the need for coordinated care of general medical conditions in those with mental disorders.29,30 For example, clinicians can be encouraged to routinely collect physical health outcomes related to comorbid general medical conditions in this patient population. Funders and providers can set targets to optimise the detection and assertive treatment of comorbid general medical conditions. Additionally, we hope that these estimates can provide a solid foundation for future research related to improving the life expectancy of people with mental disorders. In particular, we hope that the level of detail provided in our analyses, the range of mortality-related health estimates, and provision of an interactive website can together provide a template for the next generation of research on this topic.
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+Introduction
+Background
+Mental illnesses are common, affecting 1 in every 3 people during their lifetime [1]. Globally, mental illnesses are the leading contributor of years lived with a disability [2]. Despite the prevalence and impact of mental illness, a large difference between true and treated prevalence rates of mental disorders, also known as the mental health treatment gap, exists. It is estimated that more than 80% of people with severe mental illness in low- and middle-income countries (LMICs) receive no treatment [3]. Only 16.5% of people with depression living in LMICs have access to minimally adequate treatment [4]. The consequences of this treatment gap include symptom persistence and deterioration, social exclusion, and long-term disability of people who could be economically productive and socially included. Globally, there is growing recognition of the importance of mental health, as evidenced by its incorporation in the United Nations 2030 Agenda for Sustainable Development and extension of the World Health Organization (WHO) Comprehensive Mental Health Action Plan to 2030 by the World Health Assembly [5].
+The limited number of mental health specialists and the concentration of care in hospital settings in urban rather than rural areas limit the availability and accessibility of care [6]. Low treatment rates in LMICs are related to poor demand and supply-side forces. High levels of stigma associated with mental illness manifest in low rates of help seeking among those who would benefit from care [7-10]. The WHO recommends a task-shifting approach to strengthen the generalist workforce and improve access to health care, including mental health care [11]. However, this method requires the availability of
+evidence-based tools and appropriate training, supervision, and support.
+In recent years, there has been an exponential rise in global access to mobile technologies in LMICs. In 2012, there were 287 million unique mobile phone subscribers across sub-Saharan Africa, covering 32% of the population [12]. Moreover, 6 years later, that number rose to 465 million, representing 44% of the population. In Nepal, the number of mobile contracts (27.85 million) surpasses the total population (26.49 million) [13]. The increased application of mobile technology to the health care arena, known as mobile health (mHealth), aims to provide a powerful platform to improve the quality of interventions using a task-shifting approach and reduce the treatment gap. mHealth refers to the use of mobile technology in health interventions and service provision [14]. In a recent WHO survey, 87% of the responding countries reported at least one government-sponsored mHealth program in their country [14]. However, only 14% of countries reported an evaluation of these programs, raising concerns about insufficient evidence of impact.
+A systematic review of smartphone use in clinical decision-making by health care professionals identified 7 randomized controlled trials conducted in high-income settings, which demonstrated improved knowledge, diagnosis, treatment decisions, and documentation using mHealth technology [15]. Studies on mHealth tools in LMICs have yielded mixed results [16]. Qualitative data, however, suggest that the intervention facilitated task shifting and improved health workers’ morale.
+In 2010, the WHO launched the Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) [17], an evidence-based assessment and management guide for mental, neurological, and substance use conditions designed for use by
+primary and community health staff in LMICs [18]. The first edition of the mhGAP-IG (v1.0) has been implemented in over 100 countries. An updated version (v2.0) was launched in 2016, with new sections and updated evidence-based guidance [17], along with a first version of a smartphone app available for both Android and iOS devices in 2017. The mhGAP-IG v2.0 consists of 8 modules addressing priority conditions (ie, depression, psychoses, epilepsy, child and adolescent mental and behavioral disorders, dementia, disorders due to substance use, self-harm or suicide, and other significant mental health complaints that impair daily functioning or lead to help seeking). It provides an overview of common presentations for each condition, followed by detailed guidance for assessment, management (including referral to specialist care), and follow-up.
+The Emilia (E-mhGAP Intervention Guide in Low- and Middle-Income Countries: Proof-of-Concept for Impact and Acceptability) project seeks to readdress the treatment gap by developing a potentially practical way for primary care workers (PCWs) to diagnose and treat people with mental illness according to evidence-based guidelines.
+Aims and Objectives
+Emilia aims to test the feasibility of an updated electronic Mental Health Gap Action Programme Intervention Guide (e-mhGAP-IG) and trial procedures for the future conduct of a large-scale trial, which would evaluate differences in depression detection between facilities using the e-mhGAP-IG versus the paper mhGAP-IG. The objectives of this feasibility study, in preparation for a future trial, include the following:
+1.	To evaluate the feasibility and overall implementability of primary care mental health services using the e-mhGAP-IG for training, supervision, and delivery of care (primary objective).
+2.	To determine recruitment and retention rates of PCWs and patients.
+3.	To establish the acceptability and feasibility of assessing PCW and patient outcomes.
+4.	To assess ethics and safety procedures using adverse event reporting.
+5.	To describe depression detection rates in primary health clinics (PHCs).
+6.	To describe depression treatment outcomes in PHCs.
+Methods
+Settings
+The study will take place within the administrative districts of Nepal (Jhapa administrative district) and Nigeria (Ibadan North, Ibadan North West, Ona Ara, and Akinyele local government areas). In each country, a minimum of 6 PHCs will be recruited for the study to represent a range of urban and rural settings.
+Nepal is classified as a lower-middle-income country, with an estimated population of 28.1 million [19]. In a recent survey, 16.8% of individuals attending primary care facilities met the criteria for depression [20]. However, only 8.1% had sought care for their mental health [21]. Primary care facilities include PHCs, health posts, urban health centers, community health units, and primary health care outreach clinics. In these facilities,
+services are delivered by medical officers, health assistants, staff nurses, auxiliary health workers, and auxiliary nurse midwives. However, medical officers and staff nurses are only available in PHCs [22]. The availability of mental health care in the country is limited to services largely provided through hospitals located in the larger cities [22]. The Jhapa administrative district has a total population of 812,650 people. Medical care is provided through 1 zonal hospital, 6 PHCs, 44 health posts, and 6 urban health centers [23]. Specialist outpatient mental health services are located in 2 private hospitals within the district. The mhGAP-IG was adopted by the government of Nepal and implemented in several districts after the major earthquake in 2015. The government has allocated a budget for district-level mental health care services to include the addition of 6 psychotropic drugs to those already freely available within health facilities and the strengthening of community mental health care services [24].
+Nigeria is classified as a lower-middle-income country. It is home to the largest national population in Africa (195.9 million as of 2018) [25]. Recent research in the country estimated a 5.5% prevalence of depression [26]. However, similar to other LMICs, 85% of individuals living with a mental disorder receive no treatment [27]. Ibadan metropolis has 11 local government areas and a population of approximately 3.5 million people. The mental health services in Ibadan are primarily provided by 2 large general hospitals. There are 186 PHCs, each serving a population of approximately 10,000 people. The study will be conducted in 2 urban and 2 rural local government areas. PHCs in Nigeria are staffed by nonphysician health workers (nurses, community health officers, and community health extension workers) who provide treatment for common disorders (including depression) presenting in primary care. The country adopted the mhGAP-IG as a national program for expanding mental health services in 2013, and PHCs are among those where providers have received training in the use of the mhGAP-IG. In Ibadan, PCWs are provided with unstructured supervision by a supervisory general practitioner who typically oversees a group of 6-8 PHCs within a local government area.
+The use of digital technology in both Nepal and Nigeria has seen exponential growth in recent years, with the trend expected to continue. In Nepal, mobile penetration was 133% in 2018, which is greater than 100% because most Nepalis have multiple mobile phone provider contracts [28]. In the same year, mobile penetration was estimated to be 49% in Nigeria, with a projected increase to 55% by 2025 [29]. A total of 36% of mobile phone connections in Nigeria are linked to a smartphone.
+Technology
+A 2015 WHO consultation on the 5-year impact of the mhGAP-IG v1.0 highlighted the demand for an e-version. Respondents identified increased utility and coverage of an electronic guide as reasons for its development. An e-version also creates new opportunities for quality improvement (eg, in remote supervision). A year later, a privately developed e-version of the mhGAP-IG for use in Afghanistan was used for 3000 screenings and 600 referrals [30]. Community health workers reported good acceptability of the mobile app. An
+e-version of the mhGAP-IG v2.0 was launched by the WHO in October 2017 for Apple and Android smartphones and tablets.
+The Emilia project comprises 3 phases: (1) development of an adapted e-mhGAP-IG, (2) feasibility testing, and (3) knowledge transfer and future work. In phase 1, an updated version of the WHO’s electronic intervention guide was developed using a human-centered design approach. Human-centered design is an approach that actively engages stakeholders in the design process using cutting-edge methods to ensure that interventions are optimized for both front-line use and local and national implementation [31]. The approach includes qualitative research with key stakeholders to identify motivations, an iterative process of intervention development and prototyping, and intervention evaluation. The updating of the e-mhGAP-IG included individual and group interviews with PCWs in Nepal and Nigeria to understand the accessibility of technology by health workers, their use patterns, and their preferences for the design of the e-mhGAP-IG. The findings showed that most health workers had access to a personal smartphone, were familiar with the use of various smartphone apps, and valued the idea of an e-version of the mhGAP-IG. Requested features included decision support functions, ability to be used in offline mode, and an easy-to-use design that limited text entry. The formative work also highlighted the importance of recording patient information for review and use in supervision through a clinical dashboard. The resulting updated app features a reference mode for training and exploration and a patient mode for completing an assessment or management visit with an individual. Health workers can access a brief description of possible conditions through the Master Chart (a feature of mhGAP-IG 2.0) and then select modules for further assessment (Multimedia Appendix 1). The mhGAP-IG algorithms are
+presented in a single-page series of yes or no questions with a proceed button, indicating when the end of an algorithm has been reached. Health workers are then presented with an assessment summary, including any additional information to consider, such as whether the individual belongs to a special population that may affect treatment decisions. Within the app, health workers can complete additional information for a patient’s record, including measures of severity, functioning, and information for follow-up visits. All information is accessible in a clinical dashboard that summarizes key information for each visit as well as aggregate information to help supervisors identify any issues that can be addressed in supervision (eg, overmedication or inaccurate diagnosis). The process of intervention delivery and supervision is described in Multimedia Appendix 2. The e-mhGAP-IG will be available in both English and Nepali. Prototypes of the app have been tested with health workers in Nepal (5 iterations) and Nigeria (4 iterations) who found the app to have an intuitive design that is appropriate and feasible for use in clinical work.
+Study Design
+A feasibility cluster randomized controlled trial (cRCT) will be conducted to evaluate and compare the implementation outcomes [32] and clinical outcomes of the adapted e-mhGAP-IG v2.0 and the paper version of the mhGAP-IG v2.0. A total of 10 PHCs in each country (Nepal and Nigeria) will be randomized to training, supervision, and care delivery using the paper mhGAP-IG (control arm) or the e-mhGAP-IG (experimental arm), and outcomes of PCWs and patients will be collected over a 9-month period (Figure 1). Data collected through the feasibility study will be used to further refine the intervention (eg, acceptability and feasibility for randomization and recruitment) and power a subsequent cRCT.
+Participants
+An estimated 20 PHCs in Nepal and 6 PHCs in Nigeria will be identified by local partners. Clinic managers and PCWs will be approached by local research staff with invitations to participate in the study. On the basis of the staffing of PHCs, we estimate that 2-5 PCWs per PHC will participate, equivalent to 40 PCWs in Nepal and 30 PCWs in Nigeria, approximately 15-20 health workers per arm per country (Figure 2).
+PCWs will be eligible to participate if they are employed by the PHC or government and have roles and responsibilities related to the use of the mhGAP-IG (eg, direct clinical use or supervision). All relevant PCWs, regardless of individual study participation, will receive training in the mhGAP-IG v2.0 (electronic or paper version) and have ongoing remote support and supervision by health care providers with enhanced mental health knowledge.
+Patients presenting to primary care will be enrolled in the study to evaluate their perceptions of services and to obtain descriptive information on detection rates and treatment impact to inform
+a subsequent fully powered cRCT. There will be 2 periods of patient enrollment: pre training, which is before PCWs receive mhGAP-IG training, and post training, which is after PCWs have received mhGAP-IG training. For the 1-month before the mhGAP-IG training, a random selection of adults presenting to primary care will be screened by research assistants to determine depression status, which will be compared with documented diagnoses by the PCWs. Similarly, beginning at 3 months post training, a random selection of patients will be screened by research assistants for depression status. This posttraining patient enrollment will last a minimum of 3 months.
+On the basis of prior data on primary care service use and screening depression rates, we anticipate being able to screen approximately 50% of adult patients presenting to primary care, with a possibility of screening a higher percentage depending on patient flow in the facility [33]. Therefore, we anticipate screening approximately 200 patients per arm per country per month (ie, 400 patients per country in the 1-month pretraining patient enrollment period and 1200 patients per country in the 3-month posttraining patient enrollment period).
+Inclusion criteria for patients to be screened include the following:
+•	Attending PHC for treatment of a new case at recruitment
+•	Reached the age of adulthood (ie, >18 years)
+•	Fluent in Nepali (Nepal only) or English or Yoruba (Nigeria
+only).
+Adult attendees will be deemed ineligible for the study if they are unable to understand or complete study assessment (eg, individuals with severe learning disability or dementia), unable to provide informed consent, or have a medical emergency requiring immediate intervention.
+All patients who meet the eligibility criteria and choose to enroll in the study will then be screened by a research assistant to determine their depression status. Following this screening, patients will be evaluated by a PCW who will make an independent diagnosis without speaking to the research assistant or reviewing the screening results. Administration of screening
+tools may occur before or after the patient meets the health worker based on the workflow of the clinics. After patients meet the PCWs, a research assistant will review the provider’s case notes to document whether or not a depression diagnosis was made.
+Study Arms
+Intervention Condition
+The intervention will consist of (1) availability of the adapted e-mhGAP-IG for use on a clinic tablet, (2) participation in remote supervision and the support module developed in phase 1 for implementation in Nepal and Nigeria, and (3) training to all relevant PCWs and supervisors in the use of the adapted e-mhGAP-IG and clinical dashboard. Health care providers with enhanced mental health knowledge who have attended the training of trainers workshops for the e-mhGAP-IG and remote supervision will act as supervisors for the purpose of the study.
+JMIR RESEARCH PROTOCOLS
+These might include, but are not limited to, psychiatrists, mental health nurses, general physicians, senior PCWs, and counselors.
+Control Condition
+The control condition will consist of (1) availability of the paper version of the mhGAP-IG v2.0 adapted for use in Nepal and Nigeria, (2) participation in remote supervision and the support module developed in phase 1 for implementation in Nepal and Nigeria, and (3) training to all relevant PCWs and supervisors
+Taylor Salisbury et al
+in the use of the paper version of the mhGAP-IG v2.0 and supervision from distance (Table 1). Trained specialists in the research teams within each study site will act as supervisors for the purpose of the study. Supervisors are intended to be health care providers with enhanced mental health knowledge who have attended the training of trainers workshops for the paper mhGAP-IG v2.0. These might include, but are not limited to, psychiatrists, mental health nurses, general physicians, senior PCWs, and counselors.
+Remote Supervision
+In both study arms, remote supervision will comprise an initial face-to-face meeting (where possible) and subsequent contact by voice and text messaging to discuss implementation of the mhGAP-IG (paper or e-version) and any case queries. Supervision format (eg, voice calls, video calls, messaging, group, and individual) will be agreed upon by each supervision dyad. Supervisors will be provided with training on how to set up and run supervision remotely using telephone and other common communication platforms such as WhatsApp groups before the start of the study. Training will cover issues such as building rapport, format for running supervision over a telephone, format for clinically supportive WhatsApp groups, and ensuring patient confidentiality when using remote supervision methods. PCWs will receive training on how to make the most of the remote supervision during the mhGAP-IG training. PCWs will have access to project mobile phones to facilitate remote supervision. Both PCWs and supervisors will be provided with data packages.
+Randomization and Allocation Concealment
+Clinics will be randomized to the control or intervention conditions, with equal number of clinics in either group (1:1). Randomization will be carried out independently (to ensure concealment) by the trial statistician via a computer-generated random sequence before participants are recruited or the intervention is initiated. PCW selection will be performed according to health staffing levels before randomization of clinics to a study arm. Blinding or masking of participants, clinicians, and fieldworkers will not be possible, as it will be clear which conditions clinics or municipalities are assigned to
+during implementation and data collection. However, the senior and junior trial statisticians who carry out the randomization will not know the characteristics of the clinics being randomized, and the primary statistical analysis will also be blinded to allocation status.
+Statistical Power and Sample Size Calculation
+The study will take place in 1 government administrative region in Nepal and 4 in Nigeria. We will identify approximately 20 PHCs in Nepal and 6 in Nigeria. In each country, half of the PHCs will be randomly allocated to either the e-version or the paper version of the mhGAP-IG. As the clinics vary in size, we will assess at least two staff members in each clinic, with an estimated 2-5 health workers per facility and an overall enrollment of 40 PCWs in Nepal and 30 PCWs in Nigeria. Regarding patient numbers, on the basis of the sample size for the study by Sangha et al [34] on smartphone app for improving cognition to improve case detection, we will conduct within-arm comparison detection rates if at least 40 patients screen positive on the Patient Health Questionnaire (PHQ-9) in the research assistant interview in the 1-month pretraining period and in the first month of the posttraining patient enrollment period, which begins after 3 months of intensive supervision. This will allow us to detect a within-arm increase in the clinical case identification rate of 43% for the e-mhGAP-IG after the implementation of the e-version with 90% power at the 5% level of significance, assuming an intraclass correlation coefficient of 0.02. For example, with a 10% detection rate pretraining, 2 patients would receive a health worker depression diagnosis out of 20 patients screening positive. After training, the detection rate would increase to 53%, which equates to 11
+RenderX
+patients diagnosed by a health worker out of 20 patients screening positive.
+Data Collection
+Overview
+In line with the current best practice in implementation research [32], the research team will evaluate implementation processes and outcomes (eg, acceptability and feasibility) and factors that influence effectiveness implementation (eg, organizational readiness to change) across multiple stakeholder groups, including patients, PCWs, and supervisors, and at different stages of implementation [32,35]. This offers a 360-degree implementation evaluation, which considers the needs and perspectives that typically differ between stakeholder groups and can vary over time. Data collection procedures are outlined in Figure 1 and presented in Multimedia Appendix 3.
+Implementation Data
+PCWs Data
+PCWs will be interviewed by a member of the research team during a 1-week period at each PHC clinic at 4 time points: (1) before mhGAP-IG training, (2) immediately post training, (3) 3 months post training, and (4) 8 months post training (Multimedia Appendix 3). During the research interviews, all PCWs will complete quantitative assessments, assessing 6 variables related to the implementation of the intervention:
+1.	Implementation readiness: Health workers’ resolve and capability to implement the e-mhGAP-IG (ie, readiness to change) will be assessed using the Organizational Readiness for Implementing Change (ORIC) scale [36]. ORIC is a 12-item, theory-based measure assessing health workers’ commitment toward and ability to implement change.
+2.	Acceptability, appropriateness, and feasibility: Acceptability, appropriateness, and feasibility of the mhGAP-IG and remote supervision will be assessed using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM), respectively [37]. These brief, 4-item instruments have been developed by implementation scientists and mental health professionals and display good psychometric properties. Cultural and linguistic adaptation and further psychometric evaluation will be undertaken before the deployment of the measures in this study. The appropriateness, feasibility, and acceptability of the mhGAP-IG and remote supervision will also be assessed through qualitative interviews with health workers at the last data collection time point (PCW3).
+3.	Fidelity: Patient records and app use, as recorded electronically through the e-mhGAP-IG v2.0, or patient records, as recorded on paper in clinics randomized to provide the paper version mhGAP-IG v2.0 and supervision notes, will be assessed by members of the research team to determine fidelity to the training manuals for the paper mhGAP-IG v2.0 and the adapted e-mhGAP-IG v2.0.
+4.	Adoption: Health workers’ intention to adopt mhGAP-IG will be assessed by using 2 study-specific questions regarding provider-intended use within the study and after the study has ended. Intention to adopt at the provider level
+will also be assessed through qualitative interviews with health workers.
+5.	Integration and sustainability: The potential for long-term integration of the mhGAP-IG v2.0 within standard health services will be assessed using the NoMAD (Normalization Measure Development) scale [38,39]. NoMAD is a 23-item instrument that assesses staff perceptions of factors relevant to embedding interventions in health care. NoMAD consists of 4 theoretical constructs: (1) coherence, (2) collective action, (3) cognitive participation, and (4) reflexive monitoring.
+6.	Operating costs: The time taken to be trained and to use the paper and electronic tools and for remote supervision will be estimated from information collected from staff, and this, combined with information on staff wages, will be used to derive operating costs for the economic modeling.
+During the last data collection time point (PCW3), 15 PCWs in each country will be randomly selected and invited to participate in an additional qualitative interview with a member of the research team to gain further insight into their views on the implementation of the mhGAP-IG v2.0 and remote supervision. We will aim to conduct focus group discussions (FGDs) in the first instance. However, where this is not possible, individual interviews will be conducted. Individual interviews and FGDs will be audio recorded and last no longer than 30 and 60 minutes, respectively. The interviews will be transcribed and anonymized before analysis.
+Supervisor Data
+mhGAP-IG supervisors will be interviewed by a member of the research team during a 1-week period at 2 time points: (1) immediately post training and (2) 8 months post training (Figure 1). Interviews will focus on perceived acceptability, appropriateness, and feasibility of remote supervision using the quantitative assessments described above (ie, AIM, IAM, and FIM; Multimedia Appendix 3). All supervisors will be invited to participate in a qualitative interview 8 months post training to gain further insight into their experiences of remote supervision. FGDs will be conducted where possible, with the remaining selected participants completing individual interviews. All qualitative interviews will be audio recorded and last between 30 minutes for individual interviews and 60 minutes for FGDs. The interviews will be transcribed and anonymized before analysis.
+Patient Data
+We estimate screening a minimum of 400 patients at each site during the pretraining enrollment period and 1200 patients in the posttraining enrollment period (starting 3 months after the mhGAP-IG training). In the posttraining period, we will include a subset of patients who have received a health worker depression diagnosis to follow-up for treatment outcomes (Multimedia Appendix 3). We will follow up with them at approximately 3 months after they are screened to measure their treatment outcomes. A subset of patients will also participate in FGDs to assess their perspectives on the acceptability (AIM) and appropriateness (IAM) of the intervention 3 months after their baseline health care appointment.
+RenderX
+Outcome Data
+PCWs Data
+PCWs will be interviewed by a member of the research team during a 1-week period at each PHC clinic at 4 time points: (1) before mhGAP-IG training, (2) immediately post training, (3) 3 months post training, and (4) 8 months post training (Multimedia Appendix 3). During the research interviews, all PCWs will complete quantitative assessments, assessing 5 variables related to the PCW outcomes:
+1.	mhGAP-IG Knowledge Scale: In the mhGAP-IG training, knowledge is assessed by a standardized set of 30 questions in the multiple-choice question format, the mhGAP-IG Knowledge Scale. These are administered before and after training to measure the change in knowledge.
+2.	Revised-Depression Attitude Questionnaire (R-DAQ): Previously used in both Nepal and Nigeria, the R-DAQ [40] assesses clinicians’ views and understanding of depression. The R-DAQ 22-item scale asks clinicians to rate each item as strongly disagree, disagree, neither disagree nor agree, agree, or strongly agree. Examples of items include “depression is a disease like any other (eg, asthma, diabetes),” “psychological therapy tends to be unsuccessful with people who are depressed,” and “becoming depressed is a natural part of being old.”
+3.	Social Distance Scale (SDS): The SDS was designed by Bogardus [41] to measure the level of acceptability of various types of social relationships between Americans and members of common ethnic groups [42,43]. The modified SDS has been widely used to measure mental health-related stigma and to understand the importance of labels attached to people with former mental illnesses [42,44]. The modified version consists of 12 items that represent social contact with different degrees of distance. The SDS measures the acceptability of different degrees of social distance and thus, by inference, the attitude of the respondent to the person with the condition [45]. The SDS sum score represents the attitude of the respondent toward the condition. The SDS has been used in global mental health research. Among stigma measures, it has been shown to be most strongly associated with health worker competence [46].
+4.	The Enhancing Assessment of Common Therapeutic Factors (ENACT)-clinician version: The ENACT-clinician version [47] is a measure of therapist competence that has been developed for use in training and supervision across settings varied by culture and access to mental health resources. A version adapted for mhGAP-IG trainings focusing on depression was used, with competencies rated on a 4-point scale from potentially harmful to done well, with good reliability (a=.89). Examples of items include “non-verbal communication, active listening,” “assessment of functioning & impact on life,” and “explanation and promotion of confidentiality,” among others.
+5.	Perceptions of Supervisory Support Scale (PSS): Supervision quality will be assessed using the PSS [48]. The PSS is a 19-item scale that assesses perceived support. Subscales include emotional support, support for client goal achievement, and professional development support. Each
+item is rated using a 5-point Likert scale. Additional information about supervision quality will be collected through qualitative interviews with health workers at the last data collection time point.
+Patient Data
+In the month before mhGAP-IG training, the research team will collect depression diagnosis and treatment initiation data from patient baseline interviews and clinical notes at participating clinics (Multimedia Appendix 3). These data will be used as a baseline assessment of the accuracy of diagnosis and the adequacy of treatment initiation. Patients attending PHC 3-8 months following PCW mhGAP-IG training will be interviewed by a trained member of the research team either immediately before or after their clinic appointment. Quantitative measures will be used to assess depression, disability, intervention acceptability, and therapeutic alliance during individual interviews with a member of the research team as follows:
+1.	Depression: The PHQ-9 [49] is a self-administered diagnostic instrument for depressive disorders. The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for depression are scored as 0 (not at all) to 3 (nearly every day). A PHQ-9 score >10 had a sensitivity of 94% and 85% and a specificity of 80% and 99% for major depression in Nepal [50] and Nigeria [51], respectively.
+2.	Disability: Data on sociodemographic information (sex, age, education, marital status, and work status) will be collected through questions A1-A5 of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) [52]. The WHODAS 2.0 is a generic assessment instrument assessing health and disability across 6 domains (cognition, mobility, self-care, getting along, life activities, and participation). The research team will use the 12-item interviewer-administered version. The WHODAS 2.0 has been adapted and validated for use in Nepal [53-55] and Nigeria [56].
+3.	Treatment initiation: Treatment details will be extracted by the research team from patient records to document how diagnosis matches up with treatment and treatment modifications during care.
+4.	Therapeutic alliance: The ENACT-service user version [47] is a 15-item measure of patient experience of care and perception of therapeutic engagement. Example items include clear explanations, names for health problems, understanding and empathy, and expectations for recovery.
+5.	Suicidal ideation and behavior: Suicidal ideation and behavior will be assessed, in Nepal only, using suicidality questions adapted from the Composite International Diagnostic Interview suicidality module [57]. This tool has been widely used in Nepal [58]. We will ask participants whether they had thought of taking their own life in the past 12 months. Those who will respond affirmatively to the ideation question will be asked if they had made a plan to take their own life. In Nepal, those who meet the criteria for current suicidal thoughts or those who attempted suicide in the past 3 months will be immediately referred to a trained psychosocial counselor. In Nigeria, patients endorsing the suicidal ideation item from the PHQ-9 will
+receive further assessment of suicide risk in line with local protocols.
+Confirmation of diagnosis and treatment initiation will be collected from the clinical notes by PCWs. Patients interviewed in the first 3 months of data collection and who screen positive for depression will be invited to participate in a follow-up interview 3 months after their first appointment. The interview will consist of quantitative assessments of depression (PHQ-9), disability (WHODAS 2.0), therapeutic alliance (the ENACT-service user version), and suicide ideation and action Composite International Diagnostic Interview. Treatment details will be extracted by the research team from patient records to document treatment retention.
+Measure Translation and Adaptation
+All standardized measures, except for the AIM, IAM, FIM, ORIC, NoMAD, and PSS (Nigeria only), have been translated and culturally adapted in Nepal and Nigeria. Psychometrics for translated and validated (when appropriate) measures are provided in the descriptions above. Within each site, where measures have not yet been adapted and validated, the International Test Commission Guidelines for Translating and Adapting Tests [59] will be followed, including cognitive interviewing [60,61] and the assessment of content validity.
+Analysis
+Implementation Outcome Analysis
+Quantitative data will be assessed using generalized linear mixed models, depending on the distribution of the outcome (continuous, binary, and count). Descriptive statistics of the implementation survey data (FIM, AIM, and IAM) will be provided. The association between the primary outcome (changes in the PHQ-9 detection rate) and implementation survey data will be analyzed using linear mixed models at 3 and 8 months post training. A 2-level hierarchical model will be used, and all time points will be included as repeated measures in the model at baseline and at 3 months post training and 8 months post training to improve power and account for clustering of observations at patient and PCW levels. These models use maximum likelihood estimation and thus allow for missing outcome data under the missing at random assumption. Associations between secondary outcomes (eg, knowledge, attitudes, and competency) and implementation survey data will be assessed with a similar methodology for the primary outcomes, using generalized linear mixed models depending on the type of outcome (eg, normal, binary, and count). All analyses will be conducted using STATA V. 15.1. (StataCorp).
+Qualitative data will be assessed using thematic analyses [62]. Thematic analysis consists of 5 stages: familiarization, generating codes, constructing themes, revising themes, and defining themes [63]. Draft codebooks for each participant group (eg, PCWs, supervisors, and patients) will be developed on an initial subset of transcripts by 2 researchers at each site. Following refinement based on researcher consensus, the final codebooks will be used to code each transcript independently by 2 researchers at each site. Subcategories will be assessed for the number of occurrences across all transcripts and themes and categories relevant to the data identified. The findings will be
+triangulated with quantitative data on implementation and provider and patient outcomes to assess the feasibility and impact of the e-mhGAP-IG.
+PCW and Patient Outcome Analysis
+The primary outcome will be the accuracy of the depression diagnosis. The research team will identify a patient as screening positive based on the PHQ-9 result immediately before or after the patient sees the PCW. Screening positive will be defined as scoring >10 on the PHQ-9. The research team will also assess sensitivity to change, for change in both the PHQ-9 and WHODAS 2.0 scores from baseline and 3 months after initiation of treatment. Positive diagnosis will be defined as a clinical diagnosis of depression documented by the health worker in the clinical notes. Accurate detection will be defined as phq-9>10 and a health worker depression clinical diagnosis. We will also report findings in terms of the sensitivity and specificity of health worker diagnoses. Sensitivity will be defined as the proportion of health worker-diagnosed patients who had PHQ-9 above the cutoff, out of all patients who scored above the PHQ-9 cutoff. Specificity will be defined as the proportion of patients who did not receive a health worker depression diagnosis and scored below the PHQ-9 cutoff, out of all patients scoring below the PHQ-9 cutoff.
+Features of the cRCT design will be accommodated in all analyses, and an intention-to-treat approach will be used. The principles of analysis will be (1) the PHC-level accuracy outcome is binomial (ie, number of accurate diagnoses out of all diagnoses based on patient-level data), and therefore, a generalized linear mixed model will be used (specifically, a log-binomial regression to obtain probability ratios); (2) accuracy is a cumulative measure, and therefore, there are no repeated measures over time; and (3) missing data can occur at either the health worker or patient level.
+Differences in detection rates will be assessed using patient outcome data at 3 months after enrollment, only in depression cases identified in each arm (ie, scoring >10 on the phq-9), and the health worker clinical diagnosis. A 3-level hierarchical model will be used when all time points will be included as repeated measures in the model to improve power and take into account clustering of the observation at patient and PHC levels. The 3-level linear mixed model will be used to estimate a 95% CI for the comparison of clinical diagnosis and PHQ-9 screening positive rates (as well as a subanalysis with PHQ-9 plus WHODAS 2.0 criteria) within electronic and paper mhGAP-IG versions. Secondary outcomes (eg, knowledge and attitudes) will be assessed within each arm with a similar methodology for the primary outcomes, using generalized linear mixed models depending on the type of outcome (normal, binary, and count). Clinical feasibility will also be triangulated using penetration and costing data.
+Health Economic Analysis
+We will develop a simulation model to assess the potential cost-effectiveness of the e-mhGAP-IG compared with the paper-based tool. We are not measuring patient outcomes within the study; hence, costs of services and impacts on patients will be taken from other sources. The model will take the form of a
+JMIR RESEARCH PROTOCOLS
+decision tree that maps out key events following the use of either tool and outcomes that are achieved. A simple form of the model will have cases detected or not as key events and whether or not outcomes improve as a result of detection (the latter information coming from previous research). The cost of providing care for people detected will be included as will the reduction in disability-adjusted life years (DALYs) following treatment. Some of the data will be obtained from within the study (costs of using the tools based on staff time and rate of detection of depression), whereas other data (costs of treatment and DALYs) will be obtained from the wider literature and expert opinion. The model will be subject to sensitivity analyses to address the uncertainty of the model parameters. The model will enable us to generate a cost per DALY avoided by using the tool.
+Feasibility Criteria for Progression to Full Trial
+The primary objective is to evaluate the feasibility and acceptability of the intervention, its implementation, and the trial procedures for the subsequent cRCT. We must establish indicators on what procedures to carry on to the full trial and where modifications should be made to study design or content. The overall feasibility and acceptability will be determined in the intervention arm by the following criteria at the end of the study to determine progression to the full trial:
+•	Identification of qualitative themes reporting that both PCWs and clients perceive group primary care mental health services as being acceptable, feasible, and appropriate
+•	Retention of at least 67% (27/40) of PCWs and patients through end line assessments
+•	Fewer than 15% (15/98) of missing items on outcome measures across all assessments or fewer than 15% (eg, 3/22 items on the R-DAQ) for each questionnaire with more than 10 items) or 50% (eg, 4/9 items on the PHQ-9; for each measure with 10 items or fewer) of missing items on an individual assessment.
+•	Presence of adverse events among fewer than 10% (4/40) of the participants and any serious adverse events.
+In domains where criteria are met, we will retain the procedure for the full trial. In domains where criteria are not met, we will modify the procedures for the full trial, guided by data collected during interviews. The presence of any adverse events and/or serious adverse events will be addressed by the trial team to identify alternative strategies for the full trial and data safety monitoring committee. The number of feasibility and acceptability criteria that are not met will determine the extent of intervention and trial design modification. Ethical approval for this feasibility study was obtained from the following collaborating institutions:
+Taylor Salisbury et al
+•	Psychiatry, Nursing, and Midwifery Research Ethics Committee, King’s College London, United Kingdom (May 2020)
+•	University of Ibadan and University College Hospital Joint Ethics Committee, University of Ibadan, Nigeria (December 2018)
+•	Nepal Health Research Council for the Transcultural Psychosocial Organization Nepal (October 2020)
+•	WHO Ethics Review Committee, WHO, Geneva (May 2020).
+Results
+The Emilia project was funded in July 2018. Formative qualitative studies have been conducted in both countries, with results published from Nepal [64]. Data collection for the pilot feasibility cRCT began in January 2021 and is protected to be completed by March 2022. Activities have been delayed in both countries because of COVID-19.
+Discussion
+The mhGAP-IG enables greater access to evidence-based mental health care by targeting nonmental health specialists (eg, primary care doctors, nurses, and community health workers) as providers. Despite its use in more than 100 countries, a WHO consultation process identified the paper format to be a hindrance to its uptake, due to the burden on PCWs to carry the guide with them during appointments, and limited availability of mental health specialists to provide support and supervision. The Emilia project aims to address these barriers to scale. The existing WHO-developed electronic mhGAP-IG v2.0 has been adapted and refined for use in Nepal and Nigeria. Through a feasibility cRCT, its impact on detection and treatment initiation for depression, one of the most common mental conditions, will be tested along with stakeholders’ perceptions of its implementation and suitability for scale. Although we believe that the availability of the e-mhGAP-IG will improve the demand and usability of the mhGAP-IG, the paper version will still play a vital role in settings where access to electronic technology and/or the internet is limited. The inclusion of a new remote supervision module for the ongoing support of health workers, made available in both trial arms, will allow us to assess its feasibility, appropriateness, and acceptability, in addition to its potential impact on the success of both the paper and electronic mhGAP-IG versions.
+This feasibility trial will provide initial evidence of the utility and impact of the e-mhGAP-IG and remote supervision. The study takes advantage of the availability and potential of electronic technology and will advance work to reduce the mental health treatment gap around the world.
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+1. Introduction
+Technology roadmapping, and its many derivatives, has become one of the most widely used management techniques for supporting innovation and strategy, at firm, sector and national levels. The roadmapping approach, the initial development of which is widely attributed to Motorola more than 25 years ago, has been adopted (and adapted) by many organizations, initially within other large technology-intensive firms in the consumer electronics, aerospace and defense sectors, and then spreading to many other areas [1-6].
+A key benefit of the approach is the communication associated with the development and dissemination of roadmaps, particularly for aligning technology and commercial perspectives, balancing market ‘pull’ and technology ‘push’. Communication roadmaps can take many forms, but the most general and flexible approach for developing roadmaps comprises a visual timebased, multi-layered chart, illustrated in Fig. 1, enabling the various functions and perspectives within an organization to be aligned, and providing a structured framework to address three key questions: Where do we want to go? Where are we now? and How can we get there?
+The form of roadmap illustrated in Fig. 1 is very flexible, and the structure of the roadmap, and the process used to develop it, can be adapted to suit many different strategic and innovation contexts [7]. The roadmap framework can be considered as a dynamic business or systems framework, with the architecture of the roadmap providing a coherent and holistic structure (a common language) within which the development and evolution of the business or system and its components can be explored, mapped and communicated.
+This paper concerns structural aspects of roadmaps, together with the associated process of roadmapping, building on systems thinking. The power of roadmaps as strategic lenses through which organizations and complex systems can be viewed to support strategic planning is discussed, drawing on all key stakeholder perspectives. The principles of roadmap architecture and process
+design are described, and the various concepts are drawn together through consideration of the issue of ‘granularity’ as a key design parameter.
+Despite the widespread use of roadmapping as a management technique, research in this area is relatively sparse. The proposed architectural framework is based principally on more than a decade of applied research and practical experience of the authors in supporting the development of roadmaps in a wide variety of industrial and business contexts, including product, business and sector level applications (for example [7-14]). The variety of objectives and sectors covered (for example, electronics, medical devices, automotive, aerospace, services, software, packing, telecoms and food processing) has enabled the roadmapping approach to be generalized and some underlying principles to be identified. The aim of this paper is to make a contribution to the conceptual foundations of the technique, to stimulate further research.
+2. Roadmaps as strategic lenses
+The condensed visual format of a roadmap is important, providing a ‘one-page’ high-level view of the system in question, incorporating all key perspectives in a form that supports the strategic dialogue necessary for developing consensus and aligning action, and identifying challenges, risks and tensions. This kind of roadmap can be thought of as a general-purpose ‘strategic lens’, through which a complex system (such as a business) can be viewed (Fig. 2). The purpose of this lens is to structure and represent multiple interrelated perspectives on the evolution of the system, providing a framework to support understanding and dialogue. The roadmap lens is comprised of two distinct layers:
+1.	An underlying information-based structure (the roadmap architecture) — how the information contained within the roadmap is organized, which represents the key elements of the system (layers and sub-layers of the roadmap), set against time.
+2.	An overlaying graphical layer, with format, style and color chosen to represent the roadmap structure and its contents for communication purposes. The multi-layered time-based format is posited as the most comprehensive and flexible format for developing roadmaps, although different graphical styles have been developed for summary and communication purposes [8].
+The focus of this paper is on the structure (architecture) of roadmaps, rather than graphical style, where further work is needed to understand which formats are best suited to which purpose.
+Roadmapping is a very flexible approach, which typically needs to be customized to address the specific system or subject in question. Roadmapping can and has been applied to many different subjects, focusing for example on technology, products, programs, industrial sectors and fields of science.
+Roadmaps can cover a tremendous ‘dynamic range’, in terms of scale and complexity of the system. For example, Fig. 3 shows that a sector roadmap can be viewed at the level of a limited set of sector trends (order of magnitude 101), with the goal of relating these trends to relevant mono-disciplinary technology developments. Hundreds of systems may play a role in the sector, with the behavior of a single complex system determined by millions of details (order of magnitude 107). The challenge is to find the most relevant technology details in relation to the sector trend. The scientific foundation of the technologies used in the systems may be orders of magnitude more detailed again. Nevertheless, the purpose of a roadmap is often to align scientific efforts with the sector trends. Roadmaps provide a means for addressing this complexity.
+When designing a roadmap architecture and process, a key initial step is to understand the strategic context, in terms of focus, scope and aims, together with identifying which perspectives are critical for understanding the system dynamics, defining goals, exploring strategic options and implementing change. This initial planning and design work should be done by a small group of people (‘process team’), liaising with other key stakeholders, as appropriate (for example, senior management, steering group, experts). The process team should be perceptive to change and opportunities, ‘out-of-the-box’ thinkers, visionary by nature. During the later phases of the development tensions caused by constraints will be added by broadening the involved stakeholder group.
+The development of good roadmaps requires the involvement of key stakeholders and groups, often representing very different perspectives. Identifying appropriate participants to be involved, particularly in workshops, is a key consideration during the planning phase [15]. Typically, the process benefits from a range and diversity of input, in terms of disciplines, functions, levels within the organization and personalities, including external perspectives where feasible.
+The subject of the roadmap (e.g. a technological product) is researched, explored or realized by organized groups of people, such as engineers or scientists, and then used by other groups of people, such as senior management to make decisions, or customers who purchase products in the market. Subject, creation and usage contexts provide different perspectives for the roadmap (Fig. 4), corresponding to the ‘why?’, ‘what?’ and ‘how?’ questions highlighted in Fig. 1. The stakeholders representing these perspectives have different concerns: for example value and usability for users, skills and competences for creation, and feasibility and challenges for the subject itself (e.g. technical risks to overcome).
+3.	Architecting a roadmapping system
+3.1.	Architecting roadmaps
+As highlighted in Fig. 3, roadmaps can apply at various levels of granularity, focusing on anything from components of complex systems, to entire sectors or fields or science. The architecture (structure) of the roadmap must be configured to suit the focus and scope of the issue being addressed, to provide a framework and common language to support the dialogue necessary to develop and implement the desired innovation, strategy or policy. The roadmap architecture is comprised of two key dimensions:
+1.	Timeframes (typically the horizontal axis), which may include the past, short-, medium- and long-term perspectives, as well as aspirations/vision.
+2.	Layers and sub-layers (typically the vertical axis), represented by a systems-based hierarchical taxonomy, which allows different levels of detail to be addressed, illustrated in Fig. 5. This enables a family of roadmaps to be developed, spanning different levels of granularity. The strategic lens provided by the roadmap can ‘magnify’ and focus on the issues and areas of the system of most importance. The ‘fractal’ nature of the roadmap architecture enables scalability of the approach, which is a key strength of the method, allowing the approach to be customized to suit the particular focus of interest.
+While linkages between specific elements of a roadmap can be readily shown in a roadmap, the fundamental causal relationships between the layers are not easily depicted. ‘Linked analysis grids’ are often used to understand and discuss these relationships, in terms of both market pull and technology push, as shown in Fig. 6. The structure of these grids, which span the broad layers of the roadmap, can be based on the same hierarchical taxonomy as the roadmap itself, and hence the two frameworks fit well together.
+Linked analysis grids can be used in various ways: ranging from supporting identification and discussion of the general requirements and capabilities needed or offered (i.e. market pull vs. technology push), to scoring and prioritizing the relative potential of possible product or technology solutions to address a stated customer requirement or market driver (see for example [16] and the QFD method [17]).
+The following guidelines can be used to support the design of roadmap architectures, illustrated by the example shown in Fig. 7.
+3.1.1.	Timeframes
+The timeframes that should be represented on the roadmap depend on the rate of change to which the business or system is subject. In fast-moving sectors, such as software or electronics, the timeframe will be shorter, while for aerospace systems or infrastructure the time horizon will be much longer. Extremes that have been observed range from 2 to 3 year horizons for software companies, to 100 years for long-range energy futures. However, for many firms a time horizon of about 10 years is appropriate, in
+terms of representing the longer term trends and drivers that affect industry, together with the time that it can take to develop and commercialize new technology. It is recommended that five broad time horizons be included in roadmaps:
+1)	The past (and current situation): often companies are reluctant to emphasize the past, preferring to focus on the future, but it is helpful to include the past in order to draw out key influences and events that have led to the current situation, (‘where are we now?’) and to highlight learning points that will influence the success of future plans.
+2)	Short-term: typically this might be a one-year horizon. This part of the roadmap is the most important output, as it will be transformed into tangible plans and actions that will be committed to if change is to be effected (the only reason for considering the future and past is to agree what needs to be done next). This timeframe can be considered as the ‘budget horizon’, since resources will need to be committed to enable actions to be fulfilled.
+3)	Medium-term: typically this might be a three-year timeframe, linked to the strategic planning horizon, highlighting the broader direction and options that influence the short-term decisions and plans.
+4)	Long-term: typically this might be a ten-year timeframe, providing a bridge between the medium-term strategy and the vision or aspirations of the organization, enabling key uncertainties and scenarios to be articulated, and long-term shifts in the technology, business and market environment to be explored, providing a ‘radar’ to capture and assess longer-term issues that affect current decisions and plans (e.g. research).
+5)	Vision: it is important to know where you are going (one of the three fundamental questions highlighted in Fig. 1). It is useful to set out the long-term aspirations of the organization, including its mission, providing a ‘beacon’ to head towards. Often, these aspirations can then be translated into firmer, quantified goals, targets and milestones in the short-, medium- and long-term, creating ‘stepping stones’ that lead from the current situation to the desired future state.
+3.1.2.	Layers
+At the highest level, roadmaps comprise three broad layers:
+1)	The top layer relates to the trends and drivers that govern the overall goals or purpose associated with the roadmapping activity, including external market and industry trends and drivers (social, technological, environmental, economic, political and infrastructural), and internal business trends and drivers, milestones, objectives and constraints. Collectively, the type of information contained in the top layer can be thought of as representing the ‘know-why’ dimension of knowledge.
+2)	The middle layer generally relates to the tangible systems that need to be developed to respond to the trends and drivers (top) layer. Frequently this relates directly to the evolution of products (functions, features and performance), but the middle layer can also represent the development of services, infrastructure or other mechanisms for integrating technology, capabilities, knowledge and resources in a way that delivers benefits to customers and other stakeholders (and hence value to the business), such as engineering systems and organizational capabilities. Collectively, the type of information contained in the middle layer can be thought of as representing the ‘know-what’ dimension of knowledge.
+3)	The bottom layer relates to the resources that need to be marshaled to develop the required products, services and systems, including knowledge-based resources, such as technology, skills and competences and other resources such as finance, partnerships and facilities. Collectively, the type of information contained in the bottom layer can be thought of as representing the ‘know-how’ dimension of knowledge.
+Usually a hierarchical structure (taxonomy) is appropriate — see Fig. 5. If more than one roadmap is to be developed across an organization, then the architecture needs to reflect this. This will enable roadmaps to be linked and combined (‘rolled up’) to form higher level views (e.g. corporate roadmaps sit above business unit roadmaps), and also for ‘drilling down’ to create more detailed views (e.g. product-level roadmaps sit below business unit roadmaps). Sub-layers can be combined (‘collapsed’) and expanded to reflect the particular focus of a roadmap, within the structure provided by the hierarchical taxonomy, allowing the roadmapping ‘lens’ to focus on and ‘magnify’ the most important issues and parts of the system.
+There are many possible ways of structuring the layers and sub-layers; there is no unique or necessarily best way of doing this, but achieving a good result is critical to the success of the activity. Often a number of possible strategies for the roadmap architecture are identified, with a need to define one primary view that will be incorporated into the structure. Usually, the other views are also important, and should be used as part of the process to define content of the roadmap. For example, for a family of products it would be possible to define layers in terms of the product family members, or the range of functions and performance measures associated with the products. If the product family view is selected for the sublayers, then within each layer (for each product family member) the evolution of function and performance would be charted within the layers.
+Layers and sub-layers should, if possible, be compatible with organizational structures and ‘language’, such as market segments, product architecture and research groups. However, existing groupings are not always the most logical from a roadmapping perspective, so they should not be used blindly, and sometimes it is desirable to use a structure that cuts across existing structures, to encourage new ways of thinking.
+It is important to define the architecture to the right level of granularity. Too much detail can be a mistake (i.e. too many layers and sub-layers), as the architecture can be too complicated and may constrain participants' thinking. On the other hand, too little detail makes organizing information that is captured difficult. Typically, a maximum of 5-8 sub-layers is desirable for any particular layer.
+For product-level roadmaps a good starting point is to consider the various functions that the product performs (or might need to perform) and the qualities (e.g. performance and reliability) that have to be achieved. This encourages the separation of product from technology thinking — often, technology issues ‘contaminate’ the product layer, limiting options by creating an assumption that a particular technology is the only solution. Focusing on functions and qualities emphasizes user requirements, and opens up thinking about possible solutions and technologies that could provide the required functionality and performance, including future developments.
+Generally, a layering strategy should be adopted that results in a high degree of ‘de-coupling’ between layers, and the layers should be clearly differentiated from each other. ‘Evolution’ over time should be able to be charted within each layer, providing a ‘route’ to the future. If a roadmap tells a ‘story’ then each layer or sub-layer represents a ‘chapter’.
+As well as the broad layers (e.g. market, product and technology), it can be desirable to include intermediate layers between them, to highlight key enablers and barriers required for bridging these layers, where action is required. For example, theoretical barriers must be overcome for science to be utilized in technology; technical barriers must be overcome for technology to be implemented in products; non-technical barriers (commercial and organizational) must be overcome for products to succeed in markets.
+The best way to judge if the structure (layers) of the roadmap is appropriate is to test it (does it ‘work’?). ‘Thought experiments’ are helpful, to think through the ‘logic’ implied by the roadmap structure. It should be possible to imagine how each theme will evolve (for each layer and sub-layer), and also how the layers relate to each other, in terms of pull (requirements, from top right to bottom left of the roadmap) and push (capabilities, from bottom left to top right). Pick a specific issue (a market opportunity and/or key technology) and think through this, articulating the associated ‘narrative thread’. For example: “Key trends A, B and C will create a market opportunity D in the medium term and E in the long term, which will require the development of product F and service G, together with manufacturing system H, which means that we will need to invest in technology I and develop a strategic partnership with J”.
+Think about how much space is required for each part of the roadmap (layers and sub-layers, and timeframes). This should reflect both the likely density of information, and the relative importance of each area (depending on context, scope, focus, aims and priorities).
+3.2.	Architecting roadmapping
+It is often claimed that the process of developing a roadmap is more valuable than the roadmap itself, because of the associated communication and consensus generated between functions and organizations. It is important to understand the nature of the roadmapping process, and how this relates to other business processes in the organization (typically strategic planning and innovation or new product introduction in firms). Roadmapping should integrate with and support these processes if it is to have a sustainable impact.
+With reference to Figs. 3 and 4, a roadmap is created in multiple iterations over the views and abstraction levels. The first iteration is done in a short time-span: typically one or a few days. Subsequent iterations require more time: from a few days to a few weeks. The iterations ensure feedback between the why, what and how perspectives. Fig. 8 shows the resulting process funnel, indicating the increasing focus during the roadmap creation.
+Each iteration progresses through the same four phases, as shown in Fig. 9. In the ideation phase the structure and the type of information of the roadmap is ‘designed’, the scope is determined and the roadmap space is populated with existing ideas. The divergence phase is used for further exploration, for instance by creating scenarios, fact-finding and brainstorming to identify opportunities. The convergence phase is used to analyze the resulting ‘playing field’ and to reduce the content to the essential trends, risks, opportunities, design/technology issues and competence questions. A smaller team synthesizes and consolidates this information in a more comprehensive set of visualizations, which can be packaged differently for the specific audiences, such as sales, management or engineering.
+Roadmaps are concerned with the future, mapping vision, goals and possible routes forward. However, the future is uncertain, and a legitimate concern about the format of many roadmaps that are produced is the somewhat convergent appearance of the strategic plans represented by the roadmap, which typically shows the preferred route. A healthy roadmapping process should accommodate the uncertainties associated with future forecasts and aspirations, and where appropriate to communicate these in the roadmap itself. Methods such as sensitivity analysis, scenario planning and options thinking are relevant here (for example, [18,19]).
+4.	Granularity as a key perspective
+During the iterative phases of the roadmapping process, several artifacts (roadmaps) emerge with differing amounts of structuring and differing amounts of information. Fig. 10 shows the amount of information and structure that is typically appropriate per artifact, as well as the iteration and synthesis order.
+The expert view contains lots of information, but not yet a great deal of structure. The amount of structure in the expert view can be limited due to the specialist nature of the expertise, because such a view is more homogenous and shows many relevant facts and trends for the expert. Expert views are typically created by 1 to 5 persons per view, spending a few weeks of effort per month, over a period of a few months.
+The expert views are integrated in a one-page detailed roadmap. The heterogeneous nature of such a roadmap may result in much more structure (periods in columns, subject and aspect areas, relations and threads). At the same time the amount of information has to be reduced. More than 50 information items creates an overcrowded diagram, where the audience drowns in an overload of information. The one-page detailed roadmap is generally the result of a few broad workshops with 8 to 30 participants. These workshops typical require a few days of effort over a period of a few weeks.
+The synthesis of the workshop information is done by a much smaller team, spending a few days of effort over a period of a few weeks. The result of the synthesis is a condensation into essentials: a one-page strategic roadmap, containing 1 to 6 main messages, relating why, what, how and when. Both structure and the selection of information items are focused on supporting and explaining these messages. Too much information and structure would distract from the message, while too much structure makes the roadmap too rigid and structure starts to dominate the purpose. Too little structure or too little information results in a superficial roadmap.
+From the strategic roadmap several directed target views can be derived, for instance for marketing and sales purposes or for higher management. These targeted views typically have a limited amount of information. For example, a target view can be a product roadmap showing the improved performance/price ratio over time for specific systems.
+5.	Roadmapping as a core competence
+The ability to make strategic decisions and implement the associated change programs, are key management competences to develop for any firm that seeks sustainable competitive advantage.
+Organizations deploy a wide variety of management processes, frameworks and techniques to support strategic management. Roadmapping, which uses simple visual frameworks to support the dialogue and communication necessary to develop and deploy strategy, is a key supporting technique. The architectural principles presented in this paper are flexible, and can be adapted to address many strategic issues at different levels of granularity, and can be readily integrated with established business processes and other management frameworks.
+A clear distinction should be made between roadmapping and project (and program) management. Roadmapping is best suited to the ‘front-end’ of the strategy or innovation process funnel (Fig. 8), to support strategic decision making, while project planning is more suited to the middle and later stages of the funnel, to support implementation. The fact that roadmaps and project plans share a similar architecture enables the roadmaps to evolve into project plans, but the processes and aims of the two are different.
+The use of simple and ‘light’ frameworks and processes is desirable, where appropriate, as this encourages a flexible and responsive strategic planning process that can adapt to changes in the external and internal environment, facilitating a rapid process cycle time that enables learning and improvement. Natural tendencies to over-complicate and bureaucratize the process should be resisted, as this will make the process difficult to sustain, and the roadmaps burdensome to update.
+Organizations implement roadmapping in many different ways, including ‘top-down’ and ‘bottom-up’ approaches. The following ‘bottom-up’ pattern has been observed in many large firms, and can enable the learning aspects associated with the development of well-founded and effective roadmapping systems:
+•	Exploratory phase, where ‘pockets’ of roadmapping activity emerge to satisfy local requirements. Often these ‘early adopters’ are not aware of each other, and the approaches vary considerably. This experimentation is healthy, as roadmapping generally needs to be adapted to suit the particular organization and application context.
+•	As roadmapping methods are used more, the activity becomes more visible to senior managers, since a key output from any roadmapping activity is strategic communication. A sensible step at this stage is to form a community of practice, to share experience and to develop common approaches, architectures and graphical elements, where appropriate.
+•	Once a sufficient critical mass is achieved, or the process and its benefits are sufficiently visible and proven, roadmapping may be integrated as part of the core business processes across the organization, such as within the annual strategic planning or budget allocation processes, or as part of stage gates in the innovation or new product introduction processes.
+At this stage the use of enterprise software solutions can be considered, which have the advantage of potentially linking together roadmaps in a large distributed organization. However, the adoption of such systems should be treated with caution, as
+the main benefits of the technique lie in the dialogue and communication associated with their development. The use of simple one-page templates and graphical software, as part of core business processes, supported by a community of practice, is a viable alternative.
+A key success factor for roadmapping is ownership by specific individuals of both the roadmaps and the roadmapping process. A technology roadmap typically represents a simplified, synthesized view of a complex system, and so the overall strategic roadmap is often owned and edited by a system architect. Such individuals need to develop a range of skills and capabilities, including depth and breadth of knowledge. Fig. 11 shows the typical growth path of a system architect, highlighting the prerequisite knowledge and experience. A challenging question is: how to educate such people in preparation for roadmapping?
+The Embedded Systems Institute at the University of Eindhoven provides a curriculum for system architects, as shown in Fig. 12. The foundation is one of the conventional curricula for engineering or science. The next step is to broaden the candidates experience by providing multi-disciplinary methods and techniques focusing on the design of a single system aspect, for example performance, reliability or evolvability. A course on modeling and analysis teaches the more generic capability needed in systems architecting and design. A course on multi-Objective system architecting and design integrates these single aspects into a complete system that needs to fit in the customer context and needs.
+The Systems Architecting course (SARCH) addresses the non-technical aspects of architecting, such as business, application, market, organization and processes (see [20] for architecting capabilities). Roadmapping is one of the modules in SARCH, building on all underlying courses. SARCH course participants have indicated that roadmapping the most used method of those covered in this course [10].
+6.	Conclusions and way forward
+Roadmapping has become one of the most widely used methods for supporting integrated strategic planning, at both company and national levels. However, there has been a proliferation of approaches and frameworks, with little underpinning theory or conceptualization. This paper has sought to make a contribution towards this, focusing on the architectural aspects that are essential if roadmaps and the processes for creating roadmaps are to develop on a sound basis.
+Further work is needed, linking practice, research and teaching. Interesting challenges and opportunities include:
+•	The ‘visual language’ of strategy and innovation needs to be understood better. It is clear from the many examples of published roadmaps that numerous visual forms and approaches are used, and the quality is variable. Yet the visual aspect of roadmaps is one of the main reasons why the method is so attractive. Guidance is needed on how best to use visual devices to support the development of roadmaps as effective aids for knowledge elicitation and communication.
+•	Workshops are an important aspect of most roadmapping processes, providing a forum for bringing together multiple stakeholders. New technologies are being developed that may revolutionize the human interaction needed to develop roadmaps, including collaborative workspaces, interactive displays and web-based collaborative environments. Experimentation is needed to assess how best to use such technology, and to identify requirements for its enhancement.
+•	How roadmapping integrates with other management processes, methods and frameworks is not well understood. Research is needed to determine how organizations have aligned roadmapping with their core business processes (for example, strategic planning and new product introduction), and also how sets of management tools and frameworks word together within these processes (for example, portfolio management and balanced scorecards).
+•	The development of roadmapping has been largely driven by practice (within companies, government agencies and consulting firms), with relatively little academic research to support the theoretical underpinning of the method. Areas such as systems dynamics, mathematics, knowledge management, sociology and psychology are all relevant, and linked research in these areas would be beneficial.
\ No newline at end of file
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+++ b/babor2007 (2).txt	
@@ -0,0 +1,1589 @@
+ INTRODUCTION
+
+Screening, Brief Intervention, and Referral
+to Treatment (SBIRT) is acomprehensive, inte-
+grated, public health approach to the delivery of
+early intervention and treatment services for
+persons with substance use disorders, as well as
+those who are at risk of developing them.
+SBIRT is based on public health principles and
+procedures, and is designed to reduce the bur-
+den of injury, disease and disability associated
+with the misuse of psychoactive substances,
+particularly alcohol, illicit drugs, tobacco prod-
+ucts, and prescription medications with high
+abuse potential. The aims of this review are to
+summarize the research base and state of
+knowledge on SBIRT. For the purposes of this
+review, the following are considered core com-
+ponents of SBIRT:
+
+¢ Screening: SBIRT begins with the intro-
+duction of systematic screening into the
+normal routine at medical facilities and
+other community settings where persons
+with substance use disorders are likely to
+be found. Screening is by definition a pre-
+liminary procedure to evaluate the likeli-
+hood that an individual has a substance
+use disorder oris at risk of negative conse-
+quences from use of alcohol or other
+drugs. Whereas screening tests were ini-
+tially developed to identify active cases of
+alcohol and drug dependence, in recent
+years the aim has been expanded to cover
+the full spectrum ranging from risky sub-
+stance use to alcohol or drug dependence.
+Because the population of persons with
+risk factors is much larger than the popu-
+lation of dependence cases (1), SBIRT
+programs focusing on early intervention
+have generally adopted a broad definition
+of screening.
+
+¢ Brief intervention: The term brief inter-
+vention refers to any time-limited effort
+(e.g., 1-2 conversations or meetings) to
+provide information or advice, increase
+motivation to avoid substance use, or to
+teach behavior change skills that will re-
+duce substance use as well as the chances
+of negative consequences. Brief interven-
+
+tions are typically delivered to those indi-
+viduals at low to moderate risk. Among
+the most cost-effective and time efficient
+interventions are brief motivational con-
+versations between a health care profes-
+sional and a substance user.
+
+¢ Brief treatment: Brief treatment refers to
+the delivery of time-limited, structured
+(or specific) therapy for a substance use
+disorder by a trained clinician and is typi-
+cally delivered to those at higher risk or in
+the early stages of dependence. It generally
+involves 2-6 sessions of cognitive-behav-
+ioral or motivational enhancement ther-
+apy with clients who are seeking help.
+Brief treatment may also include the on-
+going management of substance use dis-
+orders in primary care settings, especially
+with the use of new pharmaceutical agents.
+
+¢ Referral to treatment: Screening often
+identifies those who already have a sub-
+stance-related health condition or a sus-
+pected substance use disorder that warrants
+a formal diagnosis and possible referral to
+treatment. The referral process facilitates
+access to care (including brief treatment)
+for those individuals who have more seri-
+ous signs of substance dependence and re-
+quire a level of care outside the scope of
+brief services.
+
+e Integration and coordination activities:
+In many communities screening and brief
+intervention services are nonexistent, di-
+agnostic and referral services are frag-
+mented and inconsistent, and specialized
+treatment services operate independently
+of the larger health care system. A key as-
+pect of SBIRT is the integration and coor-
+dination of these four components into a
+system of services linking the specialized
+treatment programs in a community with
+a network of early intervention and refer-
+ral activities that are conducted in medical
+and social service settings.
+
+As shown in Figure 1, SBIRT can be de-
+scribed as a set of inter-related services linked
+by decision rules that determine the appropriate
+course of action fora given patient. When riskis
+elevated but in the low range, brief intervention
+is the recommended course. Evidence is lack-
+ing regarding an exact cutoff for moderate risk,
+but several screening tests such as the AUDIT
+(2) have defined a mid-range of risk scores
+where further assessment, monitoring and brief
+treatment are warranted. Conceptually, anyone
+with elevated risk is eligible to begin with brief
+intervention even if the intent of the clinician is
+to deliver areferral to treatment or provide brief
+treatment. And in some cases, screening can
+lead directly to referral without feedback and
+advice. For those at low/moderate risk, the ini-
+tial clinical procedure is brief intervention. For
+
+
+those at moderate or high risk, or with depend-
+ence, the goal would be abrief intervention that
+encourages entry into brief treatment or spe-
+cialty treatment, respectively. Clearly many
+people at higher risk identified by screening
+will not receive specialty treatment. Follow-up
+ovals include arrows pointing back to the risk
+ovals in order to stress the need for continued
+monitoring and referral to further treatment if
+necessary.
+
+The model for SBIRT is based in part on the
+Institute of Medicine (3) report that recom-
+mends the development of integrated service
+systems linking community-based screening
+
+
+10 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+and brief intervention with assessment and re-
+ferral activities. One important function of
+SBIRT is to fill the gap between primary pre-
+vention efforts and more intensive treatment
+for persons with serious substance use disor-
+ders. From a public health perspective, the goal
+of SBIRT is to improve the health of a commu-
+nity by reducing the prevalence of adverse con-
+sequences of substance misuse, including but
+not limited to diagnosable abuse or depend-
+ence, through the coordination of early inter-
+vention and referral to specialized treatment.
+When all components are functioning effec-
+tively in health care and social service agencies
+throughout a community, SBIRT programs
+should be capable of reaching a significant
+proportion of the population using psychoac-
+tive substances.
+
+A BRIEF HISTORY OF SBIRT
+
+Although some SBIRT components date
+back as far as the early 18th century (4), it was
+not until the development of effective screen-
+ing tests for alcohol and drug use in the 1980s
+that SBIRT emerged as a viable public health
+approach to addressing substance misuse.
+Screening instruments such as the MAST, the
+CAGE and the DAST were first developed to
+identify active cases of alcohol and drug de-
+pendence for referral to treatment (5). In the
+1980s a seminal study by Russell et al. (6)
+showed how brief physician advice was capa-
+ble of motivating small but significant numbers
+of patients to stop smoking. Subsequent re-
+search in Malmo, Sweden (7) indicated that
+systematic screening combined with brief in-
+terventions delivered in primary care settings
+were capable of reaching large numbers of
+at-risk drinkers, many of whom reduced their
+alcohol consumption in response to the pro-
+gram. The public health implications of the
+Malmo study for the prevention of alcohol
+problems led the World Health Organization
+(WHO) to initiate a program of clinical and ap-
+plied research on the development of an inter-
+national screening test and the evaluation of
+brief interventions for at-risk drinkers (8). That
+program, begunin 1981, led tothe development
+of the Alcohol Use Disorders Identification
+Test (9) as well as the first cross-national clini-
+
+cal trial of the effectiveness of brief interventions
+in health care settings (10). The WHO program
+was expanded to include a consortium of re-
+searchers investigating ways to implement
+screening and brief intervention technologies
+in primary care settings, as well as the develop-
+ment of national plans to integrate SBIRT ac-
+tivities into the health care systems of both de-
+veloped and developing countries (8). A related
+program was initiated in 1997 to develop a
+screening test and brief interventions for illicit
+drugs as wellas alcohol and tobacco (11). These
+projects have been conducted during a 25-year
+period when there has been a dramatic increase
+in clinical and health services research on
+screening and brief intervention for alcohol and
+other substances. This research has been con-
+ducted primarily in the United States, Australia
+and European countries and has been accompa-
+nied by evaluations of training packages, im-
+plementation models, program costs and sys-
+tems changes necessary to facilitate the adoption
+of SBIRT programs (2).
+
+Perhaps the most significant development in
+this evidence-based movement to test and dis-
+seminate new screening and intervention tech-
+nologies in the US is the Substance Abuse Men-
+tal Health Services Administration’s SBIRT
+initiative, which consists of a variety of demon-
+stration programs operating in 11 states. Other
+large scale programs have beenimplemented in
+Brazil, South Africa and the European Union.
+In the remainder of this review, we will criti-
+cally evaluate the literature supporting the vari-
+ous components of SBIRT, summarizing this
+evidence in terms of its practical applications
+for program planners, administrators, and
+policymakers.
+
+SCREENING
+
+An important prerequisite for the SBIRT ap-
+proach is the accurate identification of people at
+risk as well as active cases of substance abuse
+and dependence. Screening for alcohol, to-
+bacco and other drugs has been gaining popu-
+larity in health care settings because of new
+technologies, expert committee recommenda-
+tions and encouraging research findings about
+the effectiveness of early intervention (5,12, 13).
+Table | provides a compendium of 25 self-report screening tests for alcohol and other sub-
+stance use, abuse and dependence. The tests
+were identified in an extensive review of the lit-
+erature published in peer reviewed journals
+covered in Index Medicus. For each screening
+test, the compendium lists the item content, tar-
+get population, administration mode, number
+of items, scoring time, and the time frame of
+measurement. In this section, we update an
+evaluation of screening tests initially con-
+ducted by Babor and Kadden (5). Screening
+tests for alcohol and drugs are reviewed sepa-
+rately, and in both types of substances we fur-
+ther distinguish between self-report screening
+tests and biological tests that are conducted on
+samples of body fluids.
+
+Alcohol screening tests: One of the first alco-
+hol screening procedures, the Michigan Alco-
+holism Screening Test (MAST, 14), consists of
+24 yes-no questions that list signs and symp-
+toms of chronic alcoholism. The MAST has
+been criticized because of its length, its poten-
+tial for falsification, and its focus on finding
+cases of alcohol dependence rather than early
+identification of risk factors. The shorter
+12-item MAST (15) and the four-item CAGE
+screening test (16) increase the feasibility of
+screening but still maintain a focus on identify-
+ing active alcoholics. Anadded disadvantage of
+these screening tests is their use of questions
+measuring “lifetime” symptoms (“have you
+ever ...”), which can produce false positives
+when the alcohol problems occurred in the past
+but have since remitted. A disguised screening
+test based on the patient’s history of traumatic
+injury (17) was developed to deal with the falsi-
+fication problem, but this was done at the
+expense of sensitivity and specificity.
+
+A number of alcohol screening tests have
+been developed for special populations, includ-
+ing women (18,19) and the elderly (20). The
+World Health Organization developed the
+Alcohol Use Disorders Identification Test
+(AUDIT) (9,2) in order to maximize cultural
+and linguistic generalizability of screening re-
+sults. The AUDIT focuses on both hazardous
+drinking as well as alcohol use disorders. The
+AUDIT has been well validated across differ-
+ent cultural groups ina variety of countries, and
+several shorter adaptations have been devel-
+oped, including the AUDIT-PC, the AUDIT-
+FAST, and the AUDIT-C, all of which focus
+
+mainly on the quantity, frequency and pattern
+of drinking (21,22). Finally, several single item
+screening tests have been developed and vali-
+dated. Williams and Vinson (23) found that a
+single question about the last episode of heavy
+drinking has good sensitivity and specificity in
+detecting hazardous drinking and alcohol use
+disorders. O’ Brien et al. (24) found that by ask-
+ing “How many days do you get drunk?” in a
+typical week, they could identify college stu-
+dents who are at higher risk of alcohol-related
+injury. To the extent that very short screening
+tests may motivate clinicians to screen more
+often, these tests may have value (see, for ex-
+ample, 25). Nevertheless, the value of longer
+tests is that the patient’s responses to questions
+about drinking and alcohol problems can be the
+immediate point of departure for a brief
+intervention, which typically begin with a
+discussion of specific screening results.
+
+Although not recommended for routine
+screening, several biological markers have
+been useful adjuncts to alcohol screening in
+emergency medicine and criminal justice set-
+tings, such as the breath alcohol concentration
+(BAC), gamma-glutamyltransferase (GGT, a
+liver enzyme), and carbohydrate-deficient trans-
+ferrin(CDT). BAC has a short half-life and does
+not provide information about risk behavior
+other than to estimate the extent of recent drink-
+ing. GGT and CDT have not been found to be
+sensitive or specific enough to detect heavy epi-
+sodic drinking (26).
+
+Other psychoactive substances: Given the
+different needs and substance use patterns of
+adults and adolescents, self-report drug screen-
+ing tests have generally been designed and vali-
+dated for one or the other of these populations.
+Two types of self-report tests have been devel-
+oped for adults. The first, exemplified by the
+Drug Abuse Screening Test (DAST; 27), con-
+sists of direct obvious or face valid questions
+about drug use and related problems that yields
+a quantitative score reflecting the severity of
+drug abuse. A later version of the DAST re-
+duced the number of items from 28 to 10 with-
+outcompromising reliability (28). In contrast to
+screening tests that ask directly about substance
+use, several tests have been developed to mea-
+sure risk factors that are associated with actual
+or potential substance use disorders. However,
+tests of this type tend be quite long. For exam-
+ 
+
+14 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+ple, some of these tests are embedded in the
+Minnesota Multiphasic Personality Inventory,
+which contains over 500 items, and thus may
+not be appropriate to use in health care settings
+where there is limited time to administer and
+score the screening test (29).
+
+Recognizing the need for a comprehensive
+screening and referral test for adolescents, the
+National Institute on Drug Abuse (NIDA) de-
+veloped the Problem-Oriented Screening In-
+strument for Teenagers (POSIT) (30). The
+POSIT consists of 139 items that generate
+scores indicating problems in ten functional ar-
+eas that are related directly or indirectly to sub-
+stance use disorders: Substance Use/Abuse,
+Mental Health Status, Physical Health Status,
+Aggressive Behavior/Delinquency, Social Skills,
+Family Relations, Educational Status, Voca-
+tional Status, Peer Relations and Leisure and
+Recreation. The test has demonstrated good re-
+liability and validity in adolescents referred to
+an assessment service for evaluation of sub-
+stance use problems (31), but is too long to
+serve as a brief screening test. One option is to
+use only the Substance Use/Abuse part of the
+test, which would make it more efficient for
+screening in general health care settings.
+
+In addition to the multidimensional screen-
+ing approach used in the POSIT, several shorter
+instruments have been developed to screen spe-
+cifically for substance use among adolescents.
+The Personal Experience Screening Question-
+naire (PESQ) (32) focuses primarily on drug
+use and related problems, but also collects in-
+formation on other areas of concern, such as
+psychopathology. Reliability and validity of
+this 38-item test have been demonstrated in the
+detection of individuals with different histories
+of substance use (33). Another adolescent
+screening test that has been used at adolescent
+treatment programs is the Substance Abuse
+Subtle Screening Inventory (SASSI, 34), a
+78-item self-report instrument that classifies
+adolescents as chemically dependent. Al-
+though the SASSI was designed to prevent de-
+liberate falsification by using indirect ques-
+tions, it has not been found to be very accurate
+(35,36). Other screening tests have been de-
+signed for more specific populations, such as
+the 42-item Drug and Alcohol Problem (DAP)
+Quick Screen, which was developed for use by
+pediatricians (37). Validity data have been re-
+
+ported for the 30-item revision of this test (38).
+Finally, the CRAFFT (Car, Relax, Alone,
+Friends, Forget, Trouble) is brief (6 items) and
+has been validated with adolescents in primary
+care settings (39). Because of its brevity, it is
+more likely to be used than the longer instru-
+ments described above.
+
+Combined screening tests: Despite ad-
+vances in the development of self-report
+screening tests for specific types of psychoac-
+tive substances, there has been considerably
+less attention to instruments that screen for
+multiple substances. To address this deficiency,
+the Alcohol, Smoking and Substance Involve-
+ment Screening Test (ASSIST; 11) was devel-
+oped to screen for at-risk use of psychoactive
+substances as well as related problems. The test
+uses acommon format to screen for 11 psycho-
+active substances as well as injection drug use.
+The scoring procedure estimates the relative
+importance of these different risk behaviors for
+the purpose of prioritizing counseling interven-
+tions. Although the ASSIST is not able to iden-
+tify people who exceed risky drinking limits
+based on quantities of alcohol consumed, these
+questions can easily be added to obtain coun-
+try-specific alcohol risk levels.
+
+A major challenge to combined screening
+for specific substances is provider burden,
+which refers to the skills and time demanded of
+the screening agent. A relatively simple proce-
+dure that addresses this problem is the CAGE
+test adapted to include drugs (CAGE-AID).
+The CAGE-AID was found to be more sensi-
+tive but less specific than the CAGE (40). This
+easy-to-use four-item test nevertheless re-
+quires further questioning if the patient scores
+positive. Thus, efficiency comes at the price of
+specificity, and screening questions using a
+lifetime (ever) approach can result in high
+numbers of false positives.
+
+Biological Screening Methods for Drugs: A
+variety of biological procedures have been de-
+veloped to detect recent drug use through uri-
+nalysis, hair testing, and saliva tests. These
+methods are not capable of detecting sub-
+stance-related problems or even substance use
+beyond a narrow time window (41). Urine
+screening tends to be the preferred method be-
+cause it is less invasive than blood testing and
+the drugs or their metabolites are present in rel-
+atively high concentrations in urine. A recent
+
+
+Overview Id
+
+innovation that facilitates biological screening
+is the commercial availability of self-contained
+urine testing kits. These tests are easy touse and
+provide rapid access to test results, but they can
+only indicate drug use over the previous few
+days. Other problems include a risk of false
+positives by passive drug exposure or ingestion
+of foodstuffs, and false negatives due to the use
+of adulterants.
+
+Summary: There are a number of important
+issues associated with screening instruments
+that should be taken into account in the design
+of an integrated SBIRT program intended to
+serve the needs of a defined population. First,
+the accuracy of most of the self-report tests has
+been evaluated under research conditions,
+which tend to maximize the likelihood of good
+performance. Although most screening tests
+have been found to be valid, performance is
+likely to diminish in routine clinical settings. A
+major concern is the extent to which the results
+of a self-report test can be deliberately faked or
+distorted in an attempt by the patient to preserve
+arespectable self-image in the health care or so-
+cial service setting. Although self-report mea-
+sures of substance use tend to be valid and reli-
+able in the aggregate under most circumstances,
+accuracy in clinical settings depends on the de-
+gree of perceived threat in the data gathering
+situation, the cognitive processes (such as
+memory) that are required to produce answers
+to the questions, and the motivation and other
+personal characteristics of the respondent (42).
+A second consideration is cost and efficiency.
+Self-report tests are free or inexpensive, and
+they can be administered and scored quickly.
+Nevertheless, medical staff sometimes view
+even a small addition to their routine as an un-
+necessary burden. Although some screening
+tests are relatively brief (e.g., CAGE and
+AUDIT-C) and can be administered in one or
+two minutes, others require more time and ad-
+ministration skills. Biological tests can be
+costly to use on aroutine basis and require even
+more time to administer and score. Neverthe-
+less, they are often seen as being more consis-
+tent with routine medical practice, and this may
+affect their acceptability to both patients and
+staff. A third issue is cultural sensitivity. Al-
+though research has not been extensive, there is
+no evidence to suggest that the reliability or va-
+lidity of self-report tests varies across different
+
+ethnic groups (9,11). A final issue is the target
+group of the screening program. Many of the
+adult screening tests described in Table | have
+been designed for finding active cases of alco-
+hol or drug dependence, not to identify risk fac-
+tors for drug or alcohol abuse. These tests (e.g.,
+the DAST) typically avoid direct questions
+about use of specific drugs, focusing instead on
+the problems associated with any substance use
+in the past. Subtle or disguised screening tests
+(e.g., the SASSI) do not appear to be suffi-
+ciently sensitive or specific to identify active
+cases, but may be useful in screening for risk
+factors. Comprehensive screening tests like the
+POSIT and ASSIST are capable of identifying
+both “caseness” and risk factors, but they take
+more time to administer and score. Even single
+item or very brief screening tests like the
+AUDIT-C require further questioning once the
+patient screens positive, so screening tests with
+skip-out instructions like the AUDIT and
+ASSIST may save considerable time because
+most patients need not be screened further after
+negative responses to the first few questions.
+
+BRIEF INTERVENTIONS
+
+A key component of the SBIRT approach is
+the linking of screening results with appropri-
+ate early intervention services or referral to
+treatment. Ifintervention does not exist or is not
+feasible or effective, it is not useful to conduct
+screening. Among the least expensive inter-
+ventions are brief motivational conversations
+between the substance user and a concerned
+physician, a nurse, a physician assistant or a so-
+cial worker. In this section we review efficacy
+studies of brief interventions for excessive al-
+cohol or drug use. Given the volume of research
+literature on this topic for alcohol, we will focus
+primarily on key studies and the results of
+meta-analyses of the cumulative literature.
+Brief interventions for drug use and abuse have
+been studied considerably less, so this literature
+is reviewed in terms of its preliminary findings
+and its deficiencies.
+
+Brief interventions for alcohol abuse and
+at-risk drinking: In the first systematic review
+of research on this subject, Bien et al. (43) eval-
+uated 32 controlled studies involving over 6000
+patients studied prior to 1992. Brief interven-
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+16 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+tions with problem drinkers were often found to
+be as effective as more extensive treatments. It
+was concluded that the course of harmful alco-
+hol use can be effectively altered by relatively
+brief contacts in contexts such as primary
+health care settings and employee assistance
+programs. Kahan et al. (44) reviewed 11 trials
+of physician-based brief intervention in medi-
+cal settings and concluded that brief alcohol in-
+terventions are effective, and their public health
+impact is potentially enormous. Twelve ran-
+domized controlled trials were reviewed by
+Wilk et al. (45), who concluded that brief inter-
+vention is alow-cost, effective preventive mea-
+sure for heavy drinkers in outpatient settings.
+Additional support for these conclusions,
+based on new analyses of many of the same
+studies summarized in previous reviews, has
+been reported by Ballesteros et al. (46). Moyer
+et al. (47) reviewed studies comparing brief in-
+tervention both to untreated control groups and
+to more extended treatments. They found “fur-
+ther positive evidence” for the effectiveness of
+brief interventions, especially among patients
+with less severe problems. In an extensive re-
+view of the literature for the U.S. Preventive
+Services Task Force, Whitlock et al. (13) con-
+cluded that behavioral counseling interven-
+tions for alcohol misuse among nondependent
+primary care patients identified by screening
+are feasible and potentially effective compo-
+nents of an overall public health approach to re-
+ducing alcohol misuse.
+
+Most of the studies cited in these reviews
+were conducted in primary care settings, where
+the prevalence of alcohol abuse and depend-
+ence tends to be lower than what is found in
+emergency and trauma centers. Emergency de-
+partments and trauma centers have been identi-
+fied as high-yield settings for alcohol screening
+(12,48,49). A large randomized trial of brief in-
+terventions in a trauma center (50) found that a
+brief motivational intervention was associated
+with decreased alcohol consumption and a
+reduced risk of trauma recidivism.
+
+In the course of investigating the efficacy of
+brief interventions with at-risk drinkers, re-
+search has also evaluated the extent to which
+behavior change is related to individual differ-
+ences among drinkers, the professional train-
+ing, ethnicity and gender of the intervention
+provider, and the nature of the intervention it-
+
+self (13). In general, behavior change is not de-
+pendent on provider training or characteristics,
+but the dependence severity of the drinker does
+seem to be an important correlate of low re-
+sponse to brief intervention. Regarding the na-
+ture of the intervention, skills training, simple
+advice, and motivational approaches seem to be
+equally effective. In addition, the interventions
+seem to be equally effective with adolescents,
+adults, older adults, and pregnant women.
+
+Despite the general preponderance of posi-
+tive findings, some studies have shown no dif-
+ferences between intervention and control
+groups, and many studies report significant re-
+ductions in control group drinking that are com-
+parable to those of the intervention group (51).
+One explanation for this phenomenonis that the
+screening procedure itself has a motivational
+effect, although one study found no evidence
+that assessment alone was responsible for
+changes in the control group (51). The other ex-
+planation is “regression to the mean,” which de-
+scribes a statistical tendency for extreme values
+such as heavy drinking to return to the group
+average over time.
+
+Briefintervention for drug use and abuse: In
+contrast to the alcohol literature, there have
+been few studies of brief interventions for drug
+abuse. Bernstein et al. (52) found that brief in-
+tervention in a clinical setting can reduce co-
+caine and heroin use. Brief intervention ap-
+peared to facilitate abstinence at 6 months, even
+in the absence of meaningful contact with the
+treatment system. Baker et al. (53) found that
+both the provision of a self-help booklet and a
+single session of motivational interviewing
+were associated with reduced amphetamine
+consumption among regular users. Two studies
+(54,55) have found that general practitioners
+can reduce excessive benzodiazepine use in
+their patients using brief interventions such as
+letters or consultations. Despite these promis-
+ing findings from controlled studies, and simi-
+lar positive results from research described
+below under Brief Treatment, several investi-
+gators have reported negative findings from
+brief interventions with drug users. Marsden et
+al. (56) evaluated the effect of a stimulant-fo-
+cused brief motivational interview (relative to
+the provision of health risk information about
+stimulants) among adolescent and young adult
+stimulant users. No significant differences be-
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+Overview 17
+
+tween groups were found for ecstasy, powder
+cocaine, crack or alcohol. Other research (53)
+with psychiatric inpatients showed similar lack
+of effectiveness.
+
+Summary: Research on brief interventions
+for alcohol and other substance users has accu-
+mulated rapidly during the past two decades.
+Not only are the procedures generally effective
+witha variety of population groups, they can be
+delivered with equal effect by a variety of
+health care providers. Less evidence is avail-
+able regarding the brief interventions for drug
+users, but several studies show positive effects.
+An important question that requires further re-
+search is the extent to which brief interventions
+can be made more effective when combined
+with stepped care strategies that increase the in-
+tensity of the intervention for patients who do
+not respond initially. Among the options are
+brief treatment and referral to specialized
+programs serving persons with alcohol or drug
+dependence.
+
+BRIEF TREATMENT
+
+Brief treatment (BT) refers to the provision
+of as few as two sessions of therapy by a trained
+counselor, social worker, psychologist or psy-
+chiatrist. While brief interventions focus on
+motivating clients to change their substance
+use, brief treatment helps clients develop the
+skills and resources to change. BTs are often
+based on motivational approaches (e.g., Moti-
+vational Enhancement Therapy) or behavioral
+approaches (e.g., Cognitive-Behavioral Ther-
+apy) or a combination of the two. BT typically
+includes a standardized assessment procedure,
+goal-setting, and rapid implementation of
+change strategies. BT should be characterized
+as a self-contained modality, rather than fewer
+sessions of longerterm or traditional therapy, or
+as more sessions of BI. The goals of BT differ
+from those of both longer term, traditional ther-
+apy and of BI. BT tends to focus on the present
+situation and emphasizes the use of effective
+therapeutic tools to make specific behavioral
+changes in a shorter period of time.
+
+Studies have compared BT to more inten-
+sive, traditional treatment approaches and to BI
+approaches. Many have incorporated wait-
+listed control groups in the experimental de-
+
+sign. Stephens, Roffman and Curtin (57) com-
+pared a brief, two-session individual treatment
+with 14 sessions of cognitive behavioral skills
+training. Both treatments produced substantial
+reductions in marijuana use relative to the de-
+layed treatment control condition with treat-
+ment gains maintained at 16-month follow-up.
+The Marijuana Treatment Project (MTP) com-
+pared two treatment therapies with a delayed
+treatment control condition (58). One of the
+therapies consisted of nine individual counsel-
+ing sessions delivered over a 12-week period.
+The other consisted of two motivational en-
+hancement therapy sessions delivered over a
+one-month period. The nine-session interven-
+tion produced significantly greater reductions
+in marijuana use and associated consequences
+than the two-session intervention, and at each
+follow-up point over a 12-month period both
+treatments produced outcomes superior to the
+four-month delayed treatment control condi-
+tion. The results indicate that even a brief
+two-session treatment is associated with sub-
+stantial reductions in marijuana use and related
+problems in chronic marijuana users.
+
+Several studies have demonstrated promis-
+ing evidence that BT is often as effective as lon-
+ger term, traditional therapies for substance use
+disorders (59-61). Moyer et al. (47) found posi-
+tive evidence for the effectiveness of brief ther-
+apies, especially among patients with less se-
+vere problems. For clients with greater problem
+severity, Berglund et al. (62) noted that better
+results were observed with more treatment. Al-
+though studies show that patients who receive
+more outpatient mental health care tend to
+have better short-term substance use outcomes
+(63-65), there is growing evidence that duration
+and continuity of care is more important than
+the amount or intensity of care (66-69). The
+finding that duration of treatment (rather than
+amount of treatment) for alcohol and drug use
+disorders is more closely related to outcome
+suggests that more resources should be de-
+voted to interventions such as brief treatments
+that are linked to other continuity of care op-
+tions (70).
+
+Summary: BT models are consistent with a
+public health approach in which large numbers
+of individuals at risk of developing serious al-
+cohol or other drug problems may be identified
+through primary care screening or through
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+18 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+court-mandated treatment (e.g., stemming from
+DUTarrests). The BT target population has tra-
+ditionally been those individuals with less se-
+vere substance use disorders. However, there is
+agrowing body of evidence to suggest that brief
+treatments are effective with a wide range of
+clients, including persons with mild to moder-
+ate alcohol dependence and regular marijuana
+users. BT may also be appropriate for some pa-
+tients when previous attempts using traditional
+treatment approaches have failed, when there
+are insufficient resources (e.g., client time or in-
+surance coverage) available for longer-term
+therapy, or when there are long waiting lists for
+specialized treatment. There is no question that
+BT is more effective than being ona waiting list
+and could benefit large numbers of clients who
+are seeking and waiting for longer term care
+(71).
+
+MEDICATION-ASSISTED TREATMENT
+IN HEALTHCARE SETTINGS
+
+Recent advances in pharmacological treat-
+ment for alcohol and opioid dependence pro-
+vide significant opportunities to integrate the
+management of substance use disorders into
+primary health care. After screening and brief
+intervention, pharmacotherapy can be initiated
+in health care settings to assist patients under-
+going BT or to facilitate the transition to
+traditional substance abuse treatment.
+
+Medications for alcohol disorders: Four
+FDA-approved medications are available that
+physicians can prescribe to dampen craving, re-
+duce heavy drinking, and/or promote absti-
+nence. These are: naltrexone, a u (mu)-opiate
+receptor antagonist; depot naltrexone, an ex-
+tended-release form of naltrexone that is in-
+jected monthly; acamprosate calcium de-
+layed-release tablets; and disulfiram (under
+supervised administration). These medications
+can be helpful to patients who are struggling to
+maintain sobriety and for preventing relapse
+after referral to treatment.
+
+Of particular interest to SBIRT, one recent
+study looked at whether general internists and
+primary care physicians could treat alcohol-de-
+pendent patients as effectively as addiction spe-
+cialists, using naltrexone (72). Results indi-
+cated that primary care counseling with
+
+naltrexone pharmacotherapy is a promising ap-
+proach that can be effective in selected patients.
+In addition, the long acting, injectable form of
+naltrexone that is now available may enhance
+its use in primary care settings (73).
+
+With the newer medications now available,
+there is increasing interest in whether alco-
+hol-dependent individuals can be treated suc-
+cessfully with FDA-approved medications by
+their primary care physicians in routine medi-
+cal practice. The comprehensive COMBINE
+clinical trial at 11 sites with nearly 1,400 pa-
+tients explored a variety of treatment methods—
+alone and in combination—within the context of
+medical management (74). Alcohol consump-
+tion decreased by 80 percent over a 4-month
+treatment period, which suggests that medical
+management by primary physicians in routine
+practice can be of benefit in treating alcohol de-
+pendence (75). However, the medical manage-
+ment used in the COMBINE trial was relatively
+intensive (nine 20-minute sessions), so the
+minimal level of contact with primary care
+physicians necessary to manage alcohol-de-
+pendent patients has not yet been determined.
+
+The COMBINE Study also found that
+naltrexone in combination with a brief behav-
+ioral therapy delivered by licensed health care
+professionals is more effective than more inten-
+sive behavioral therapy delivered by licensed
+behavioral health specialists (74).
+
+Medications for drug dependence: The Drug
+Addiction Treatment Act of 2000 established a
+new paradigm for the medication-assisted
+treatment of opioid dependence. Qualifying
+physicians in a medical office or other appro-
+priate settings may now apply to the Substance
+Abuse and Mental Health Services Administra-
+tion (SAMHSA) to prescribe and/or dispense
+opioid medications for treating opioid addic-
+tion. Two sublingual formulations of buprenor-
+phine, a long-acting partial agonist of mu-
+opioid receptors, have been approved by the
+FDA for this purpose.
+
+The decision to allow office based treatment
+of buprenorphine was based on a large body of
+clinical experience from other countries and the
+United States (76-78). A Cochrane Review
+meta-analysis of 13 randomized clinical trials
+concluded that buprenorphine is an effective
+intervention for use in the maintenance treat-
+ment of heroin dependence (79).
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+Overview 19
+
+REFERRAL TO ASSESSMENT
+AND SPECIALIZED SUBSTANCE
+ABUSE TREATMENT
+
+Research suggests that brief intervention
+alone may not be sufficient therapy for severely
+dependent drinkers (80). Because many brief
+intervention trials specifically exclude people
+dependent on alcohol or drugs, it is not known
+whether this population may be helped by brief
+interventions alone. Thus, for patients with se-
+vere conditions, SBIRT programs need to make
+referrals to more intensive treatment and to mu-
+tual support groups such as Alcoholics Anony-
+mous (AA), Narcotics Anonymous (NA), and
+Cocaine Anonymous (CA).
+
+Research demonstrates that hospital patients
+identified as substance dependent during medi-
+cal screening (most of whom are not seeking
+treatment) can be effectively referred and en-
+gaged in specialized treatment at rates much
+greater than controls (81-85). In these individ-
+ual program studies, brief interventions have
+increased the percent of patients who show up
+for their first clinic appointment from 5 percent
+among controls to from 50 to 65 percent, with as
+many as 50 percent of patients reporting that
+they continue to be involved in some kind of
+substance abuse treatment or 12-step meetings
+on follow-up (81-83).
+
+Information is limited about the prognosis
+for alcohol- and drug-dependent patients seen
+and referred in other medical settings, where
+patients are highly heterogeneous in terms of
+type, stage, and severity of substance problems,
+with many of these patients not motivated to
+start treatment (86). Prognosis appears to be
+strongly related to the patient’s motivation to
+enter treatment, as well as to change drinking or
+drug-using behavior (85).
+
+The literature provides little information
+about the specific referral processes used by
+various SBIRT programs. However, existing
+evidence suggests that brief motivational inter-
+ventions have positive benefit on patients’ par-
+ticipation in substance abuse treatment and re-
+tention in treatment. For example, when one
+hospital replaced staff referrals with motiva-
+tional interview techniques done by alcohol
+specialists, the percent of referred patients who
+completed treatment increased from 40 to 88
+percent (85). In another study, 65 percent of pa-
+
+tients who received a brief motivational inter-
+vention kept their initial interview at an alcohol
+clinic, compared to 5.4 percent of the control
+group (83).
+
+These findings indicate that SBIRT referral
+methods need to address the patient’s motiva-
+tion to be treated, with the added intention of re-
+ducing the risk of drop-out and assisting the pa-
+tient’s adherence to treatment. Based on the
+available literature, it is not possible to say
+which brief intervention approaches, in which
+settings, and with which patients will be most
+effective for promoting entry and engagement
+into specialized alcohol or drug treatment. The
+research shows that the earlier substance-de-
+pendent patients engage in treatment or mu-
+tual-help groups, the better the outcomes
+
+(87,88).
+
+IMPLEMENTATION, INTEGRATION
+AND COORDINATION ACTIVITIES
+
+A major challenge to the public health im-
+pact of SBIRT is the difficulties involved in in-
+tegrating its components into relevant parts of
+the health care system.
+
+Screening: As noted above, progress has
+been made in the development ofa variety of ef-
+fective screening procedures. Nevertheless, a
+number of practical and logistical issues need to
+be resolved before a screening program can be
+implemented. These issues relate not only to
+who does the screening, but when, where, how
+often and who pays for it. Given the simplicity
+of most self-report screening tests, they are ca-
+pable of being administered in variety of differ-
+ent settings and modalities, such as interview,
+questionnaire, and computer. Dyches et al. (89)
+describe an interactive telephone technology
+for screening with primary care patients. Both
+patients and practitioners had rated the proce-
+dure positively. Under some circumstances,
+impersonal procedures, such as paper and pen-
+cil questionnaires and computer-assisted tele-
+phone interviews, may be more effective than
+face-to-face interviews with physicians. Saitz
+et al. (90) report 50,000 visits a year for screen-
+ing at an internet web site where positive cases
+received advice and information, suggesting
+that an accessible web site can attract high risk
+drinkers for screening and brief intervention.
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+20 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+Inthe absence of routine screening with stan-
+dardized instruments, physicians do not sys-
+tematically apply NIAAA guidelines regarding
+hazardous drinking levels (91) and may be se-
+lective in whom they screen. One study (92)
+found that physicians were least likely to initi-
+ate discussions about drinking with patients
+who are white, female and widows. A Danish
+study of screening by general practitioners (93)
+reported that physicians did not think all pa-
+tients should be screened. The major barriers
+were lack of time and financial incentives, and
+skepticism that patients wanted to be screened.
+
+Some investigators (94) have argued that
+primary health care is not an effective or effi-
+cient place to conduct alcohol screening. Oth-
+ers have suggested alternative sites for screen-
+ing and professionals who can conduct it.
+Anderson et al. (95) identified the clergy as po-
+tential providers of screening and brief inter-
+vention. Hungerford et al. (96) report that
+screening in an emergency department can pro-
+duce high rates of acceptance of counseling
+about alcohol use. In a rural university emer-
+gency department (12), only 3% of the patients
+screened or counseled were uncooperative;
+70% thought the emergency department was a
+good place to help patients with alcohol prob-
+lems. Another potential setting for screening
+programs is traumacenters. Schermer etal. (97)
+found that 70% of trauma patients were suc-
+cessfully screened, with less than 1% refusing.
+Nevertheless, a survey (98) of 50 insurance
+commissioners indicated 38 states where there
+are concerns about the possibility that screen-
+ing will affect insurance payments, which can
+be denied in many states if the patient has been
+drinking.
+
+Brief intervention: According to Roche and
+Freeman (99), physician-based secondary pre-
+vention efforts based on brief interventions for
+hazardous drinking have failed at the imple-
+mentation stage. Barriers to implementation
+include lack of time, poor diagnostic skills,
+negative attitudes, and perceptions of role in-
+compatibility (100). In a survey of 711 trauma
+surgeons (97), 83% agreed that the trauma cen-
+ter was an appropriate setting but only 25%
+used formal screening questionnaires and less
+than one half of problem patients are addressed
+in their hospital stay. Barriers included cost,
+
+time, confidentiality and threats to insurance
+coverage.
+
+To overcome some of these barriers, other
+delivery agents (e.g., nurses) have been consid-
+ered. D’ Onofrio and Degutis (101) describe the
+use of non-physician health promotion advo-
+cates (HPAs) to do SBI and referrals in the
+emergency department.
+
+Another way to expand the use of brief inter-
+ventions is through internet applications. A re-
+view of the small number of web-based inter-
+ventions (102) found that a demand does seem
+to exist for this kind of service and the potential
+impact could be considerable. As noted above,
+Saitz et al. (90) recorded 50,000 screening vis-
+its a year at an internet web site. Positive cases
+received advice and information, suggesting
+that an accessible web site can attract high-risk
+drinkers for brief interventions.
+
+Another barrier to brief intervention is com-
+petition for the provider’s time once a patient
+screens positive. Saitz et al. (103) showed that
+the very act of screening can prompt physicians
+to increase discussions and provide advice.
+Brady et al. (104) found that prompting of pro-
+viders using other means doubled the rate of
+brief intervention. In a study by Kaner et al.
+(105), patient and practitioner characteristics
+predicted who got a brief intervention after
+screening: males, the unemployed, and techni-
+cally trained workers were more likely to re-
+ceive an intervention than females and em-
+ployed persons. In addition, practitioners with
+more training and longer practice experience
+were more likely to deliver interventions.
+Babor et al. (106,107) compared two different
+implementation strategies for Cutting Back, a
+primary care alcohol screening and brief inter-
+vention program for hazardous and harmful
+drinkers. In one model, medical providers were
+responsible for delivering interventions. In an-
+other model mid-level professionals (usually
+nurses) acted as the clinic specialists to provide
+that service. Ina sample of 10 health clinics, the
+mid-level professionals screened a higher per-
+centage of patients than did the medical provid-
+ers during the best month of program operation
+(50% vs. 44%) and over all months of operation
+(24% vs. 19%). Of those patients who screened
+positive, more patients screened by the mid-
+level professionals received an intervention
+than in the provider model (73.1% vs. 57.1%).
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+Overview 2]
+
+The ability of clinics to conduct SBI was signif-
+icantly correlated with both staffing character-
+istics and organizational factors (e.g., prior ex-
+perience, organizational stability, number of
+clinicians trained and the quality of the coordi-
+nator’s work). Lack of time, staff turnover and
+competing priorities correlated negatively with
+SBIRT implementation.
+
+Insummary, the primary obstacles to the use
+of SBIRT in applied settings are: (1) lack of
+time for overburdened health care workers;
+(2) training and motivation of professionals to
+administer screening and intervention to at-risk
+drinkers; and (3) organizational factors includ-
+ing administrative support and competing pri-
+orities. Successful implementation of the tech-
+nology tends to occur at those sites where
+clinicians are reimbursed for their services and
+are well trained for the task. In addition, the ex-
+tent to which a given delivery model is likely to
+work best within a managed care organization
+depends on complex provider and organiza-
+tional characteristics.
+
+Brief treatment: Although there are insuffi-
+cient data to determine which populations
+might benefit most from Brief Treatment, a
+growing literature suggests that BT is effective
+with a wide range of substance abusing clients.
+Further, the majority of clients receiving sub-
+stance abuse treatment stay in therapy for rela-
+tively short periods of time (between 6 and 20
+one-hour sessions). Although this statistic ar-
+gues for a greater use of structured BT ap-
+proaches in current clinical practice, DHHS
+(71) found that many therapists trained in tradi-
+tional approaches were resistant to using struc-
+tured BT models. A related problem is that brief
+treatment is typically developed, evaluated and
+delivered in an individual therapy format,
+whereas traditional treatment tends to be offered
+in group format because of cost considerations.
+
+The demonstration of several efficacious
+brief treatment interventions, especially for
+marijuana dependence, raises questions about
+how best to engage chronic marijuana users in
+treatment and how best to maintain improve-
+ments following treatment. Unfortunately,
+very little research has been conducted in these
+areas. A pilot study was conducted to evaluate a
+program designed to offer a guided self-assess-
+ment (but not treatment) to persons interested in
+changing their marijuana use. It successfully
+
+used a variety of recruitment strategies to at-
+tract participants, including posters, radio and
+newspaper ads, and outreach at various com-
+munity events (108). The check-up program of-
+fered a useful method for reaching non-treat-
+ment-seeking heavy marijuana users, and at
+follow-up program participants reported a sig-
+nificant reduction in the frequency of mari-
+Juana use when compared to those who just got
+information. These results suggest that stand-
+alone programs that provide discrete treatment
+to regular marijuana users may be feasible and
+can reach large numbers of clients if they are
+properly designed and advertised.
+
+Training and technology transfer: Training
+in how to conduct screening and brief inter-
+ventions is clearly a vital component in assur-
+ing effective implementation of SBIRT com-
+ponents. Introducing new screening and
+prevention activities into primary care prac-
+tices and other settings presents significant
+challenges to professional training and contin-
+uing education. Medical schools and residency
+programs devote limited faculty resources and
+curriculum time to substance abuse (109-111) and
+many professionals feel inadequately trained
+when faced with patients who have sub-
+stance-related problems (112,113). Barriers to
+adequate coverage of alcohol and drug-related
+problems in both medical schools and continu-
+ing professional education include traditional
+attitudes about the moral culpability of chronic
+alcoholics, confusion as to whether problem
+drinking is a medical or psychiatric concern,
+lack of faculty role models, lack of training
+materials, and role ambiguity regarding who is
+responsible for screening and intervention
+(114,115). Research on medical education has
+shown that training can be effective in im-
+proving students’ and physicians’ knowledge
+and skills in addressing alcohol issues (106,
+116-118), but changes in knowledge may be
+easier to produce than changes in attitudes and
+behavior (119). A review of the components
+and outcomes of medical education in sub-
+stance-related disorders concluded that the se-
+lection of a combined didactic and interactive
+educational strategy may be the most cost-ef-
+fective learning strategy, but there is little em-
+pirical evidence to support this approach (119).
+
+Although some progress has been made in
+the development of SBIRT for medical practi-
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+22 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+tioners, medical students, and health care orga-
+nizations (1,120,121), a necessary step toward
+dissemination is the development of successful
+training packages that include program imple-
+mentation procedures. Babor et al. (106) found
+that following a relatively short (3-hour) work-
+shop and subsequent supervision, physicians
+experienced an increased sense of confidence
+in performing screening procedures. In addi-
+tion, non-physician clinicians perceived fewer
+obstacles to screening patients after receiving
+the training. When delivered in the context of a
+comprehensive SBIRT implementation pro-
+gram, training was effective in changing pro-
+viders’ knowledge, attitudes, self-efficacy and
+practice of screening and brief interventions for
+at-risk drinking. The results are consistent with
+other studies of provider behavior (116,122,
+123) which show that health care providers
+trained to deliver a brief alcohol intervention
+will counsel their at-risk patients when cued to
+do so and when supported by a primary care of-
+fice system. Adams et al. (123) found that a
+2.5-hour training doubled the rate of alcohol in-
+terventions in high-risk primary care patients.
+Wilk and Jensen (124) used standardized pa-
+tients (i.e., actors who play the role of symp-
+tomatic patients) to train residents to use
+SBI. After training more residents conducted
+screening and brief interventions. Gomel et al.
+(125) compared three strategies to market and
+train primary care physicians. Tele-marketing
+was more cost-effective than academic detail-
+ing and direct mail in promoting uptake of an
+SBI package. Roche et al. (117) compared two
+educational programs to train medical students;
+interactive training was no more effective than
+traditional didactic lectures in developing
+knowledge and skills. These studies suggest
+that SBIRT training can be effective in provid-
+ing skills, increasing self-efficacy, and chang-
+ing provider behavior.
+
+In summary, training programs have been
+developed and adapted to specialty settings
+(e.g., physicians in primary care clinics). Edu-
+cational materials for use with problem drink-
+ers have also been developed. Manuals, pam-
+phlets, and books have been written to help train
+professionals in the process of SBI. Research
+on all of these training packages suggests that
+they increase knowledge about drug misuse,
+but they vary in their ability to change provider
+
+behavior. More research is needed on how in-
+creased knowledge translates into behavior
+changes and what factors help to sustain those
+behavior changes.
+
+ECONOMIC CONSIDERATIONS
+
+There are several important economic issues
+to consider in relation to implementing SBIRT.
+Providers, financial managers, and decision
+makers need accurate information about the
+costs of screening and brief interventions and
+estimates of the revenue potential. Decision
+makers also need to know the cost-effective-
+ness of various SBIRT models in order to
+choose between lower cost/less effective mod-
+els and higher cost/more effective models. Cost
+benefit estimates are needed to assess the net
+costs to health plans or to society of diverting
+resources toSBIRT activities. In this section we
+summarize research on each of these issues.
+
+Cost: SBIRT costs will vary, depending on
+the perspective from which costs are calcu-
+lated, e.g., the provider’s, the payer’s, the pa-
+tient’s, or society’s perspective. For financial
+management purposes, the total costs of SBIRT
+services can be broken down into their compo-
+nents, e.g., screenings, information packets,
+counseling sessions, and case management.
+From the provider’s perspective the cost of
+brief interventions depends primarily on the na-
+ture and severity of the client’s alcohol or drug
+problems, the number of sessions that comprise
+the interventions, the personnel delivering brief
+interventions, the resources to produce and de-
+liver interventions (and treatments) and the set-
+tings in which brief interventions are provided.
+Providers must also consider the one-time costs
+of developing and starting the service plus any
+on-going continuing costs such as continuing
+education of staff. From the client’s perspec-
+tive, the cost of SBIRT includes the amount the
+client pays for the intervention beyond the pre-
+miums for health insurance, as well as time and
+transportation costs to the site where interven-
+tions are furnished. From a payer’s perspec-
+tive, the cost of brief interventions might be de-
+fined as the amount paid for the service minus
+any financial benefit that may accrue from the
+reduction of future costs resulting from the ser-
+vice. From society’s perspective, the cost of
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+Overview 23
+
+brief interventions is expressed in terms of the
+market value of the best alternative use to which
+labor, capital, and other resources may be put
+(i.e., economic or opportunity costs).
+
+Given the various perspectives that could be
+used, it is not surprising that published esti-
+mates of the costs of SBIRT vary considerably.
+For example, Zarkin et al. (126) estimated
+screening costs at $0.42 per patient for a 2-min-
+ute screen versus $16 per patient by Gentilello
+et al. (127) and $497 per patient by Kunz et al.
+(128). Given the fact that fewer than 30 percent
+of patients screened are referred for brief inter-
+ventions, efforts to reduce the initial screening
+costs can significantly reduce the overall cost of
+providing alcohol SBI. There is also broad vari-
+ability in the costing methodology used in the
+literature. For example, brief intervention costs
+have been reported at $2.59 per patient (126),
+$135 per brief intervention session (128), and
+$0.59 median per member per month (insur-
+ance premium cost) (129). Obviously, the un-
+derlying variability of the SBI programs is a pri-
+mary cause for the variation in cost estimates,
+but the lack of a consistent costing methodol-
+ogy contributes to the variability and limits the
+usefulness of cross-program comparisons.
+
+Furthermore, current SBI cost-effectiveness
+and cost-benefit research often presents cost re-
+sults without a detailed description of the cost-
+ing methods used. Many of these studies do not
+adequately address how and what was actually
+costed (e.g., 130). Authors often take national
+wage averages and estimate the amount of time
+for services (e.g., 127). The most thorough cost
+estimate comes from the Cutting Back study
+(126), which used activity-based costing to
+separate start-up costs from ongoing imple-
+mentation costs, a distinction overlooked by
+previous studies. Cutting Back is the only study
+to compare costs across providers and is also
+the first to cost different models of implementa-
+tion. However, the SBI models studied by
+Zarkin et al. (126) were implemented exclu-
+sively for the Cutting Back research project,
+and therefore the authors were forced to make
+judgments as to which costs would likely be
+retained in a non-research setting.
+
+Cost effectiveness analysis: Cost effective-
+ness analysis (CEA) enables decision makers to
+compare the economic merits of alternative
+types of service, suchas brief interventions and
+
+standard care, which represents the care that cli-
+ents would ordinarily receive. Kunz et al. (128)
+found cost-effectiveness ratios for brief inter-
+ventions administered in a hospital emergency
+department of $258 for a one unit reduction in
+the follow-up AUDIT score, $219 for a de-
+crease of one drink per week, and $61 for a one
+percentage point decline in the follow-up prob-
+ability of heavy drinking. In astudy that applied
+estimates from published studies to Australia,
+Wutzke et al. (130) found that brief physician
+advice to at-risk drinkers resulted in additional
+years of life from fewer accidents. Dividing the
+cost of the intervention by the number of
+life-years saved yielded a cost of approxi-
+mately Aus $1,873 per life-year saved. CEA
+does not, however, provide definitive recom-
+mendations on which program should be
+adopted. Rather, it provides decision makers
+with evidence on the relative benefits and costs
+of one program compared to another. For this
+reason, CEA aloneis often not enough to justify
+adoption of a new program.
+
+Cost benefit analysis: Unlike CEA, CBA
+places a dollar value on all outcomes and di-
+rectly compares to the dollar value of a pro-
+gram’s outcomes to the dollar value of its costs.
+As a result, CBA often provides definitive an-
+swers on which programs should be adopted.
+The program with the largest dollar benefit af-
+ter accounting for costs should unambiguously
+be adopted. There are various methods with
+which to compare the benefits of a program to
+its costs, including: net benefit measures in
+which costs are subtracted from benefits; return
+on investment in which the benefits are ex-
+pressed as a percentage return to the investment
+represented by the program costs; and the bene-
+fit cost ratio in which benefits are expressed as a
+ratio of the costs. The choice of CBA measure is
+largely determined by the audience, with return
+oninvestment often appealing more to business
+or corporate audiences and net benefit or bene-
+fit-costratios appealing more to academic audi-
+ences.
+
+The CBA evidence on SBIRT is generally
+very favorable. In arandomized trial of briefin-
+terventions administered in physician offices,
+Fleming and colleagues (131) found that a
+group receiving a brief intervention not only
+had significant reductions in alcohol use, they
+also had fewer hospital days and fewer emer-
+Downloaded by [University of Sydney] at 07:38 02 September 2014
+
+24 ALCOHOL/DRUG SCREENING AND BRIEF INTERVENTION
+
+gency department visits. The intervention cost
+$205 per person ($166 from the clinic perspec-
+tive and $39 from the client’s perspective) and
+saved $712 in health care costs. The benefit cost
+ratio of 4.3 suggests a $43,000 savings in future
+health care costs for every $10,000 spent for
+early intervention. The benefit cost ratio in-
+creased to 39:1 after factoring fewer motor
+vehicle and legal events into the analysis.
+
+In a CBA using published sources, Gentilello
+et al. (127) estimated that the screening and
+brief alcohol interventions provided to injured
+patients treated in an emergency department or
+admitted to a hospital together cost $54 per pa-
+tient, or $16 plus $38, respectively. The net cost
+savings from the screening and intervention
+was estimated at $89 per patient, or $330 for
+each patient receiving an intervention (27 per-
+cent had a positive screen). The benefit, in the
+form of reduced direct health costs, resulted ina
+savings of $3.81 for every $1.00 spent on
+screening and intervention, fora benefit cost ra-
+tio of 3.8:1. If interventions were routinely of-
+fered to injured adult patients nationwide, it
+was estimated that the potential net savings
+might approach $1.82 billion annually.
+
+In a retrospective study of admissions to the
+Naval Medical Center in Portsmouth, Virginia,
+Storer (132) estimated that intervention pa-
+tients had significantly lower hospital readmis-
+sion rates than other patients. The lower read-
+mission rate for intervention patients alone
+generated an estimated savings of $606,400,
+for a total cost of $31,500 (benefit cost ratio of
+19:1), for an average cost of $154 for 205 brief
+interventions.
+
+Summary: Although the findings support the
+use of certain SBIRT components on economic
+grounds, the studies should be used cautiously.
+The cost effectiveness of SBIRT may vary con-
+siderably, depending on how the technology is
+applied. If a program is aimed at a selected,
+high-risk portion of the population (e.g., emer-
+gency room patients with injury or trauma), a
+higher rate of risky drinkers will be identified
+than in a “population approach” (e.g., all mem-
+bers of an HMO), where a cross-section of the
+entire population is screened (133). This issue
+will affect the rate at which people receive an
+SBIRT service and the economic efficiency of
+any such operation. Similarly, the potential for
+cost savings is much greater among a higher
+
+risk portion of the population. The labor cost of
+personnel designated to screen and conduct
+brief intervention, and whether SBIRT is their
+sole function or is incorporated into other func-
+tions will affect cost effective calculations.
+Additionally, the extent of the intervention—
+whether one five-minute session at the time of
+the screening, or multiple sessions of longer
+duration on different days—will significantly
+affect both treatment costs and costs incurred
+by patients.
+
+CONCLUSION:
+TRANSLATING RESEARCH
+INTO PRACTICE
+
+In the parlance of contemporary medical sci-
+ence, “translation” has three inter-related
+meanings: (1) applying what we have learned
+from research to practical settings; (2) making
+scientific knowledge accessible and relevant to
+practitioners; and (3) improving the health of
+the population by broad dissemination of effec-
+tive medical and health promotion technolo-
+gies. Translation from research to practice can
+be considered at two levels: (1) from the labora-
+tory “bench” to the patient’s “bedside”; and
+(2) from bedside to the entire community. In the
+former (called T1 translation), basic science re-
+search leads to new clinical investigation and
+interventions. Examples of T1 bench to bedside
+applications from the 25 years of SBIRT re-
+search considered in this review include the dis-
+covery of biomarkers for alcohol and drug
+screening, the use of psychometric theory to de-
+velop new self-report screening tests, and the
+development of new medications to dampen al-
+cohol craving, reduce heavy drinking, and/or
+promote abstinence. Examples of the second
+form of translation, where clinical investiga-
+tion leads to improved medical practice and en-
+hanced population health (called T2 transla-
+tion), are studies of SBIRT training, program
+implementation and cost effectiveness. Al-
+though much work needs to be done at both T1
+and T2 levels, the findings of this review
+indicate that significant progress has been
+made in translating research into practice. For
+example, since 1980:
+
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+Introduction
+The National Action Alliance for Suicide Prevention established the Research Prioritization Task Force in 2010 to identify interventions capable of reducing the suicide rate by 20% over a 5-year period. Twelve goals emerged. We discuss the 12th: “reduce access to lethal means that people use to attempt suicide” (briefly, means restriction or means reduction).
+A suicidal person’s access to highly lethal means, or methods, of suicide can be reduced through (1) physically impeding access (e.g., using gun locks and bridge barriers); (2) reducing the lethality or toxicity of a given method (e.g., reducing carbon monoxide [CO] content of motor vehicle exhaust); or (3) reducing “cognitive access,”1 that is, reducing a particular method’s appeal or cognitive salience (e.g., discouraging media coverage of an emerging suicide method). We focus here largely on the first two approaches.
+Reducing access to lethal means saves lives when people who cannot readily obtain a highly lethal method either attempt with a method less likely to prove fatal or do not attempt at all (Figure 1). The rationale rests on four well-established observations. First, many suicidal crises are short-lived. A survey of people who had seriously considered suicide in the past year found that for about 30%, the suicidal period lasted under an hour.2 Surveys of attempters have found that the interval between deciding on suicide and actually attempting was 10 minutes or less for 24%-74% of attempters (with the lower end of the range reported by a study of those nearly dying in their attempt).3-5
+Second, the method people use in suicidal acts depends, to a non-trivial extent, on its ready availabil-ity.6,7 Third, the proportion of attempts that result in death (case fatality ratio) varies dramatically across methods, ranging from a high of 85%-90% for firearms to a low of 1%-2% for the methods most commonly used in attempts—medication overdoses and sharp instrument wounds.8 The lethality of the method readily available during a suicidal crisis therefore plays an important role in whether the person survives an attempt; intent matters, but means also matter.
+Reducing a Suicidal Person’s Access to Lethal Means of Suicide
+Fourth, approximately 90% of attempters who survive a nonfatal attempt will not go on to die by suicide thereafter,9 a finding that holds true even in studies focusing only on medically serious attempts, such as jumping in front of a train.10 Therefore, helping people survive periods of acute suicidal risk by reducing their access to highly lethal methods is likely to help many people survive in both the short and long term.
+Reducing access to lethal means saves lives if the methods available for substitution, on balance, are less likely to prove lethal. Firearms account for more than half of suicides in the U.S. and have the highest case fatality ratio. A number of factors are theorized to influence the lethality of a given method. The first is inherent deadliness. For example, car exhaust with a high CO level will be more deadly than car exhaust with a low CO level. The second is ease of use. A method that requires technical knowledge is less accessible than one that does not.
+The third is accessibility. Given the brief duration of some suicidal crises, a lethal dose of pills in the nightstand poses a greater danger than a prescription that must be hoarded over months to accumulate a lethal dose. Similarly, a gun in the closet poses a greater risk than a very high bridge 5 miles away, even if both methods have equal lethality if used. The fourth is ability to abort mid-attempt. More people start an attempt and abort it than carry it through2; therefore, methods that can be interrupted without harm mid-attempt—such as overdose, cutting, CO poisoning, and hanging/suffoca-tion—offer a window of opportunity for rescue or change of heart that guns and jumps do not. The fifth factor is acceptability to the attempter. Although fire, for example, is universally accessible, it is rarely used in the U.S. for suicide.
+At the population level, no measurable impact of means restriction on overall suicide rates is likely to be observed
+(even if, on balance, lives are saved) if the restricted method constitutes a very small proportion of all suicides or if the restricted method is of low lethality. If all sharp instruments magically disappeared, for example, in spite of their frequent use in suicide attempts there would be little measurable impact on suicide deaths, given their low case fatality ratio (sharps constitute only 2% of suicide deaths). Importantly, a possible, though unsubstantiated, unintended impact of reducing access to popular low-lethality methods may be an increase in suicide risk if attempters substitute more lethal methods.
+Research Evidence on Means Restriction
+Population-Level Natural Experiments
+Before 1960, the leading suicide method in the United Kingdom was inhalation of domestic gas. Following discovery of a cheaper, nontoxic source of natural gas in the North Sea, gas suicides fell to nearly zero. Suicides by other methods increased somewhat, but, importantly, the net result was a drop of approximately 30% in the overall suicide rate.11,12 These findings held in other countries where domestic gas was a leading method,13,14 but not in those where gas accounted for a small proportion of total suicides.15-17
+Natural experiments involving decreased toxicity of motor vehicle exhaust and reduced accessibility of barbiturates, firearms, and analgesics (as well as some population-level interventions described below) also illustrate that method-specific suicide rates drop when a method becomes less available or less lethal; however, whether the overall suicide rate drops is equivocal when the method is not commonly used or is of low lethal-ity.1,18-24
+Population-Level Interventions
+Pesticides are the leading suicide method in Sri Lanka. In the 1990s, the Sri Lankan government placed restrictions on sales of the most highly human-toxic agents, following which overall suicide rates dropped by 50%.25 Nearly 20,000 fewer suicides occurred in the 10 years following restrictions compared with the 10 previous years. The decline in suicide was driven by a decline in poisoning suicides; non-poisoning suicides did not decline, nor did nonfatal poisonings. The underlying behavior (swallowing pesticides in a suicide attempt) did not appear to change, but thousands of lives were saved because the lethality of the behavior diminished.
+Pesticide poisoning was a highly lethal, common method of suicide prior to the policy changes. Its lethality dropped following changes; therefore, the overall suicide rate in Sri Lanka dropped driven exclusively by a drop in the pesticide suicide rate. A similarly dramatic drop in suicides was observed in Western Samoa when the pesticide paraquat became less available.26
+Most studies in the United Kingdom on the impact that limiting access to the pain relievers co-proxamol (via market withdrawal)27 and paracetamol (via pack size limits)28 had on poisoning suicides found a significant decline in poisoning deaths by these agents without compensatory increase in other lethal poisonings. Given the small proportion of suicides overall that the two medications comprised, these studies did not look at impact on overall suicide deaths. However, Bateman’s review concluded that pack size restrictions did not reduce paracetamol deaths.29
+Jumping from a very great height is a highly lethal but uncommonly employed method in the U.S. Barriers have been installed at some popular jump sites, such as tall bridges. Most30,31 (but importantly not all32) studies of the impact of these barriers have found that fewer suicides occurred at the protected site without evidence of a compensatory increase in jumping suicides from other sites. Most have not assessed impact on rates overall, given the small proportion that jumps typically constitute of suicides overall.
+An intervention that found a net effect on overall suicide rates, albeit in a small population (i.e., 28 suicides annually on average pre-intervention), involved the Israeli Defense Force.33 Soldier suicides occurred disproportionately on weekends and 90% involved firearms. A 2006 policy aimed at preventing suicide required soldiers to leave their weapons on base during weekend leave. The suicide rate decreased by 40%; weekend firearm suicides dropped significantly, with no significant change in weekday suicides, and no change in nonfirearm suicides.
+Firearms and Suicide in the U.S.
+In the U.S., more suicides are completed with a firearm than by all other methods combined. About one in three homes contain firearms and 51% of all suicides involve firearms.34 Miller et al.34 have provided a review of U.S. firearm suicides. All U.S. case-control studies that have examined the issue35-39 have found that the risk of suicide is two- to five-fold higher in gun-owning homes for all household members, with relative risk being especially high for youth and people without known psychopathology. The higher suicide risk is driven by a higher risk of firearm suicide, with no difference in non-gun suicides. Most studies, but not all, find that among gun households, suicide risk is lower when firearms are stored unloaded, locked, and separate from ammunition.40
+A cohort study found that handgun purchasers in California were more than twice as likely to die by suicide as were their age/sex-matched peers throughout the 6year study period, with the increase in risk attributable to an excess risk of firearm suicide.41 Several ecologic studies in the U.S. bolster findings from the individuallevel studies.42 Time-series43 and cross-sectional studies that have measured firearm prevalence in relation to suicide risk have consistently found a strong association between household firearm ownership rates and rates of overall and firearm suicide (and no significant association between household firearm prevalence and nonfirearm suicide).
+These findings do not appear to be accounted for by differences in underlying suicide risk among persons living in homes with guns. People living in homes with (versus without) guns, for example, are no more likely to screen positive for psychopathology or suicidal ideation, or to report having attempted suicide.44-47 Importantly, the heightened risk of suicide associated with the presence of a household firearm applies not only to the gun owner but to all household members.38,48 In aggregate, the literature on the firearm-suicide connection indicates that access to firearms does not serve as a proxy for an unmeasured third variable that drives suicide risk, but rather increases suicide risk by making it more likely that suicidal acts will involve guns and therefore, on average, prove fatal.
+Applying the Lessons of Means Restriction to the U.S.
+Suicide rates can be substantially reduced—without necessarily changing underlying mental illness or suicidal behavior—by making it more difficult to die in an act of
+deliberate self-harm. Despite evidence across studies (including targeted interventions, natural experiments, case control, cohort, and ecologic studies) of its potential to save lives, means restriction historically has not been prioritized in the U.S.
+One reason may be the misperception that reducing access requires embracing gun control, a politically polarizing issue. It need not. There are a variety of non-legislative approaches that respectfully engage the gun-owning community as partners in suicide prevention. Prime among them is “lethal means counseling”— advising people at risk for suicide, and their friends and family, to keep firearms away from the at-risk person until the person recovers. Below, we highlight suicide methods that may be useful targets for means restriction.
+Firearms have several characteristics that make them particularly suitable targets: They are the leading suicide method in the U.S. (approximately 19,000 deaths a year)49; they are the most lethal50 (substituted methods will be less likely to kill); they are both accessible and cognitively acceptable in U.S. culture; and an attempt with a gun once initiated cannot be reversed (unlike attempts with nearly every other method except jumping). If under an ideal scenario means restriction counseling reached all relevant households (households in which there is a gun and a suicidal person), and if counseling had modest results (one quarter of the households effectively kept the guns from the suicidal person), based on findings from case-control and ecologic studies, an estimated 3,600-3,900 lives would be saved in 1 year.51 This approach is especially promising for youth, whose firearm suicides typically involve a family member’s gun.52
+Medication overdoses are by far the leading method of suicide attempt, with hundreds of thousands occurring each year.53 Although the overall case fatality ratio for medications is below 2%, some medications are markedly more lethal than others, and overdoses account for more than 5,000 deaths annually in the U.S.49 Interventions that reduce the medication load available to at-risk persons to a level that, even when taken all at once, will not pose a severe danger, may prevent deaths and reduce the severity of attempts. Because some of the more-lethal medications also are addictive (e.g., opioids and benzodiazepines), other advantages may accrue from reducing access.
+The drop in deaths associated with motor vehicle exhaust suicides following wider use of catalytic converters suggests that more savings could be realized with further engineering changes.21 Barriers at popular jumping sites, such as the Golden Gate Bridge—particularly when no sites nearby offer comparable acceptability and lethality—will likely save lives. At 700-800 jumping
+suicides annually in the U.S.,49 and about the same number from motor vehicle exhaust, these approaches may save lives but their impact on overall suicide rates may not be apparent given their small numbers.54 Examples of interventions and the mechanisms by which they could save lives are illustrated in Table 1.
+Hanging/suffocation is the second-leading mechanism of suicide death in the U.S. and its use has increased in recent years.49 This method is not amenable to physical means restriction techniques, except in controlled settings like prisons and hospitals. Because it still ranks relatively low among ideators as a planned method,2 means restriction theory suggests that “cognitive access” might be reduced if efforts are made to avoid publicizing this method in traditional and social media. Similarly, care should be taken not to inadvertently increase acceptability of emerging suicide methods (such as highly lethal poisons or drug combinations) by publicizing them in traditional and social media.
+Research Needs
+The body of evidence on means reduction comes from studies examining changes in exposure to suicide methods resulting from natural experiments and interventions at the population level. Individual-level interventions are far more complex. They require identifying at-risk groups, learning the right messages to deliver, finding the right messengers to deliver them, and learning how to change behavior—not insignificant challenges. They also require changing practice among providers, healthcare/ social service systems, families, and community organizations.
+A small body of literature on parents of youth with psychiatric problems suggests that families who were counseled to reduce access to firearms and medications at home were more likely to do so than those not receiving such counseling.55,58,59 This is encouraging, but more intervention research is needed in three broad categories: (1) communications research to identify and test the messages; (2) intervention evaluations to rigorously test the impact of selected interventions; and (3) translation and dissemination work to extend and adapt effective interventions to a variety of populations and settings. In addition, continued foundational research is needed to understand the dynamics governing method choice and planning and to develop a stronger surveillance infrastructure.
+Communications Research
+Communications research with at-risk individuals and their families and friends. Communications research should examine the attitudes and knowledge that at-risk
+individuals and their families hold regarding means restriction and evaluate the acceptability of various strategies, particularly regarding firearms, the method for which reduced access is likely to save the greatest number of lives.60 Examples of useful research questions to pursue via focus groups, surveys, and other methodologies include the following:
+1.	Which specific messages and messengers on safe firearm storage are persuasive to people at risk of suicide and their families, and does the acceptability of the messages vary by reasons for gun ownership (e.g., self-defense, hunting, sport) and by other socioeconomic factors (e.g., political views, education level)?
+2.	Do mistaken assumptions about suicide (e.g., once suicidal, a person remains so; most attempts are well planned long in advance; one method is about as likely to kill as another) pose a barrier to means restriction? Does education on these issues increase families’ safe storage behavior?
+3.	With whom, if anyone, are at-risk persons likely to feel most comfortable temporarily storing their firearms (e.g., a relative, Army buddy, storage facility, or police department)?
+4.	For whom is secure in-home locking (with another person holding the key) a more acceptable solution to off-site storage?
+5.	How should firearm safety messages be tailored when the suicidal person is a minor versus an adult, the gun
+owner versus non-owning member of a gun household, a crisis line caller versus inpatient, or a person at acute versus chronic risk?
+6.	Regarding medication safety, is the protective effect of limiting a patient with an active overdose history to shorter prescription refills (e.g., weekly rather than monthly refills) outweighed by the deleterious consequences of poorer medication compliance? Would a lockable, electronic pill-dispensing machine prove more viable?
+Communications research with providers and gatekeepers. A number of studies have indicated that behavioral health and medical providers do not routinely conduct lethal means counseling with at-risk groups.61,62 Research aimed at remedying this should (1) identify attitudinal and informational barriers that impede and facilitate routine use of lethal means counseling by providers; and (2) evaluate training programs in lethal means counseling to identify the most effective approaches.
+Messaging on firearms safety should be developed in partnership with a broad spectrum of invested parties including, importantly, gun owners, to ensure that messaging is relevant and helpful. Because many suicidal people do not explicitly seek help for their suicidal feelings, non-healthcare-related venues where suicidal people intersect with the system should be identified. The suicide risk of a person who has just been arrested on his third drunk driving charge may be as high as a patient who has been hospitalized for depression. Therefore, defense attorneys and others who see people in trouble (e.g., clergy, batterers’ counselors, social service personnel, probation/parole officers, marriage counselors, divorce attorneys) may be useful “gatekeepers” to refer people at risk and convey firearm safety messages.
+Communications research with gun owner groups.
+Gun owner groups such as gun shops, shooting and hunting clubs, firearm rights groups, gun magazines, and firearm training classes offer an environment in which to deliver a basic rule of firearm safety: Be alert to signs of suicide in household members and keep guns from them until they recover.63 Gun owner groups typically have a strong safety culture focused on preventing the 600 unintentional firearm deaths that occur annually in the U.S.; expanding that focus to prevent the 19,000 firearm suicides is a natural next step.49
+Communications research with these groups could (1) identify facilitators and barriers to gun owner organizations embracing the role of reducing the misuse of firearms in suicide; (2) collaborate with gun owner
+groups to develop communications tools such as brochures, posters, training modules, and sample newsletter blurbs; and (3) test “uptake” of these communications tools (the extent to which groups use the tools when provided).
+The ultimate goal of communications research is to develop an interdisciplinary approach that will make reducing a suicidal person’s ready access to firearms as “normative” in 10 years as the “friends don’t let friends drive drunk” message64 is today. In addition to messaging research outlined above, research is needed to clarify whether broad-scale media campaigns that raise awareness about suicide and warn families to keep guns from those at risk exert a protective, neutral, or harmful effect (the latter by normalizing suicide).65
+Intervention Outcomes Research
+Controlled clinical trials. As effective messages are developed, rigorous studies are needed to test the impact of lethal means counseling. Although these necessarily will be on a small scale as protocols are tested,55 ideally, they will be tested in populations large enough (e.g., Veterans Affairs, military, large healthcare network) to detect changes in suicide outcomes. In smaller populations, impact on individuals’ self-reported storage of guns and medications should be tested as interim outcome measures. Because these studies are conducted with suicidal individuals, researchers must attend carefully to human subjects considerations to protect study subjects.
+Other outcomes research. At the same time, evaluations aimed at other approaches should be undertaken, including (1) technical interventions (e.g., locked electronic pill dispensers, algorithms to flag potentially dangerous prescribing in electronic medical records, personalized firearms that can only be fired by the gun owner); (2) policy interventions (e.g., amend insurance companies’ mandatory 90-day prescription policies to exempt patients at risk of overdose); and (3) outreach interventions (e.g., incorporate suicide awareness/means restriction messages in firearm safety materials).
+Translation/Dissemination Research
+The next step after effective methods of lethal means counseling (and other interventions) are identified, is institutionalizing these practices in standard clinical care among medical and behavioral health providers, and among non-traditional groups like firearm safety instructors and defense attorneys. Translation research will help identify the most effective strategies to promote implementation of effective interventions. As lethal means
+counseling becomes more widespread, it will be necessary to find safe storage and disposal options for firearms and toxic medications.
+Foundational Research
+In concert with developing, testing, and disseminating interventions, we must deepen our understanding of the factors that govern method choice and deliberation in suicidal behavior, and incorporate what is learned, iteratively, into ongoing interventions. Unanswered questions include the following:
+1.	When a suicidal person’s access to a lethal method is blocked, what determines whether he or she substitutes a more lethal versus less lethal method, or abandons an attempt entirely?
+2.	Under what conditions might blocking access to a low-lethality method (e.g., locking the medicine cabinet) have an unintended harmful effect of leading attempters to substitute more lethal methods?
+3.	What role do online and personal social networks play in method choice and technical knowledge?
+4.	Have method-specific case fatality ratios changed over time as the capacity for greater technical knowledge of methods increases and medical interventions change?
+5.	Have prescribing practices affected the severity of attempts?
+6.	How do gun storage practices affect suicide risk to the gun owner and household members by age and sex?
+7.	Among youth who die by firearm suicide, does the source of the firearm (e.g., parent’s gun, youth’s gun acquired illegally) vary across racial/ethnic/socioeco-nomic groups?
+Foundational research relies upon the existence of accessible, current, and valid data. The National Violent Death Reporting System provides detailed information on suicide deaths and should be expanded from its current 18 states to all 50.66 The Behavioral Risk Factor Surveillance System has supplied valuable information on state- and national-level gun ownership rates and storage practices; however, its gun items have not been asked since 2004 and should be repeated every 2-3 years.67 Linked hospital, pharmacy, and death certificate data will enable researchers to examine the impact of prescribing and method switching.
+Changing the Paradigm
+Currently, the suicide prevention field focuses on identifying people at risk and getting them into treatment. A challenge facing the field is to shift the paradigm such that researchers, practitioners, patients, and the broader
+population understand that reducing a suicidal person’s access to lethal means also has important life-saving potential. A first step is educating researchers and practitioners during training and continuing education about the evidence base.
+Reducing the availability of highly lethal and commonly used suicide methods has been associated with declines in suicide rates of as much as 30%-50% in other countries. Research on how to apply these lessons to the U.S.—including communications research to identify effective messages and messengers, clinical trials and other intervention research to identify effective interventions, and translational research to ensure broad uptake of these interventions—has the potential to substantially reduce the number of suicide deaths.
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+Introduction
+Suicide is a global public health concern, claiming more than 1 million lives each year (World Health Organization, 2010). While suicide results from a variety of factors, there is convincing evidence that the media have a powerful influence over those at risk. Media reports of suicide may be associated with increased suicidal behavior when coverage is unnecessarily prominent, detailed, repeated, or when reports sensationalize, romanticize, or glorify suicide (Pirkis & Blood, 2001).
+The phenomenon of imitative suicides has driven worldwide efforts to promote more sensitive coverage of suicide, primarily via dissemination of reporting guidelines for media professionals (Pirkis, 2009). Guidelines have been developed by numerous government and nongovernment organizations in many countries, although the reporting style they promote is generally similar and in line with World Health Organization recommendations. These recommendations primarily encourage journalists to reduce the prominence, detail, and sensationalism of suicide reports, and to educate the public about suicide and treatment options (World Health Organization, 2008).
+Despite similarities in content, there is significant variation in the degree to which media professionals have been involved in the development, dissemination, and promotion of guidelines. It has been suggested that this aspect may significantly influence the use and effectiveness of guidelines (Pirkis, Blood, Beautrais, Burgess, & Skehans, 2006). Suicide reporting guidelines were recently developed in Asia, in Hong Kong, Malaysia, Sri Lanka, the Republic of Korea, and China (Beautrais et al., 2008) as well as in Europe, North America, and Australasia. Some have been developed by governments, while others have been developed by the media themselves or nongovernment (e.g., suicide prevention) organizations (Pirkis et al., 2006). A notable example of guidelines developed with significant media collaboration are those disseminated by the MediaWise Trust in the UK, while others, such as those developed in New Zealand by the Ministry of Health, have been strongly criticized for their lack of media consultation (Pir-kis et al., 2006).
+While most guidelines focus on traditional forms of reporting such as newspapers, some recently made efforts to incorporate new and more dynamic formats such as social media and the internet (e.g., Recommendations for Report
+ing on Suicide, 2011, www.reportingonsuicide.org). Additionally, media organizations in several countries such as Denmark, Estonia, Germany, Norway, Lithuania, Sweden, and Australia developed their own code of ethics or guidelines for suicide reporting (Tully & Elsaka, 2004). In some places, notably New Zealand, coronial powers may also be invoked to restrict reporting on suicide (Tully & Elsaka, 2004).
+Critically, compared to the widespread development and distribution of guidelines, very little evaluation has been undertaken, with a review by Pirkis et al. (2006) finding that there was too little evaluative research to draw conclusions about guideline effectiveness. Effectiveness can be measured in terms of improvements in the quality of news reports of suicide, or more directly in terms of a reduction in suicide rate or suicidal behaviors.
+This study reviews the published literature examining the effectiveness of media guidelines for suicide reporting. We build upon previous reviews of this topic, such as the 2006 article by Pirkis et al. In particular, to develop a clear picture of guideline effectiveness, we bring together quantitative and qualitative research findings.
+Methods
+Search Strategy
+We used a systematic search strategy over multiple databases to identify all relevant literature in the area of mediabased interventions. An electronic search was conducted via Medline, Scopus, CINAHL Plus, and PsycInfo using a keyword search (suicide AND (media OR journalist) AND (guideline OR intervention OR program)). In addition, the we searched the world wide web (WWW) and article reference lists. Government or nongovernment organization reports were also included if relevant evaluative data were presented. Abstracts (and where necessary, the full document) were reviewed, and articles meeting the inclusion criteria were reviewed in full.
+Inclusion Criteria
+Studies evaluating the effectiveness of media guidelines for the reporting of suicide were included. Preliminary searches suggest there is a paucity of research in this area, so that we did not place restrictions on study location or outcome measures. Studies detailing qualitative interviews with media professionals concerning guidelines were also included. Since the association between media reporting of suicide and imitative behavior has been examined thoroughly (Pir-kis & Blood, 2001; Pirkis et al., 2006; Stack, 2003, 2005), we did not include studies exploring this relationship.
+Results
+Location
+Publications meeting the inclusion criteria are shown in Table 1 and were drawn from the United States (Jamieson, Jamieson, & Romer, 2003), Australia (Pirkis et al., 2009; Skehan, Greenhalgh, Hazell, & Pirkis, 2006), New Zealand (Collings & Kemp, 2010; Tully & Elsaka, 2004), Austria (Etzersdorfer & Sonneck, 1998;Niederkrotenthaler& Son-neck, 2007; Sonneck, Etzersdorfer, & Nagel-Kuess, 1994), Switzerland (Michel, Frey, Wyss, & Valach, 2000), the United Kingdom and China (Fu & Yip, 2008; Fu, Chan, & Yip, 2010).
+Outcome Measures
+Suicide rate, awareness and use of guidelines, and quality of media reports were the outcome measures used to assess guideline effectiveness. Only two studies examined suicide rates as the primary outcome measure (Etzersdorfer & Son-neck, 1998; Niederkrotenthaler & Sonneck, 2007). Most evaluated the effect of guidelines on the quantity and quality of media reports (Fu & Yip, 2008; Fu et al., 2010; Jamieson et al., 2003; Michel et al., 2000; Pirkis et al., 2009). Assessments of quality were based on the presence or absence of a variety of features either recommended or discouraged by the guidelines. Three studies examined report quality both before and after implementation (Fu & Yip, 2008; Michel et al., 2000; Pirkis et al., 2009), but one examined only compliance with guidelines after implementation (Fu et al., 2010). Rather than measuring report quality, both Niederkrotenthaler and Sonneck (2007) and Jamieson et al. (2003) used the number of articles mentioning suicide in the headline as a measure of the newsworthiness of suicide. Most studies examined reports in newspapers only (Fu& Yip, 2008; Fuet al., 2010; Jamieson et al., 2003; Michel et al., 2000; Niederkrotenthaler & Sonneck, 2007), although one examined newspaper, television, and radio items (Pirkis, 2009).
+Several studies report qualitative data based on interviews with media professionals regarding the use, awareness, and attitudes toward guidelines (Skehan et al., 2006; Tully & Elsaka, 2004). In addition, Skehan et al. (2006) compared the effects of two different distribution methods on media professional’s use and awareness of guidelines.
+Major Findings
+Effect on Suicidal Behavior
+Several studies suggest an association between the introduction of media guidelines and reduced suicide rate. Guidelines introduced in response to a dramatic increase in
+subway suicides in Vienna were followed by a reduction in suicide rates (Sonneck et al., 1994). A decrease in subway suicides of 75% was observed following the introduction of the guidelines in 1987 (Sonneck et al., 1994), and, importantly, the number of subway suicides remained low for the following 5 years. Niederkrotenthaler and Sonneck (2007) examined both Viennese and national Austrian sui
+cide data from as recently as 2004/2005, demonstrating a national decrease of 81 suicides annually since guideline introduction, in addition to the sharp decrease in Viennese subway suicides observed by Sonneck et al. (1998). Importantly, the reduction in suicides was limited to areas in which compliant newspapers reached more than 67% of the population, with no significant impact in areas in which
+48.2% and 38.6% of the population were covered by compliant media (Niederkrotenthaler & Sonneck, 2007).
+Effect on Reporting
+Studies examining the effect of media guidelines on news reports demonstrate mixed results. As well as a reduction in suicide rate, Niederkrotenthaler and Sonneck (2007) demonstrated that the mention of “suicide” or “self-murder” (Selbstmord) in article headlines also decreased significantly following intervention and remained low. Interestingly, several authors report increased publication of suicide articles following the introduction of guidelines (Fu & Yip, 2008; Michel et al., 2000; Pirkis et al., 2009).
+Michel et al. (2000) examined the effect of guidelines on suicide reports in Swiss newspapers. Significant reductions were found in the number of headlines on the front page (20% to 4%), use of sensational headlines (62% to 25%) and text (49% to 14%), glorifying headlines (17% to 3%) and text (33% to 6%), and inclusion of pictures (43% to 8%). No change was observed in the mentioning of name, time, or place in the headline, repetition of articles about the same case, or the mentioning of suicide method in the headline or text.
+Guidelines developed for the Australian Mindframe and Mental Health program (Commonwealth of Australia, 2002) appear to have had a significant effect on reporting, with several qualities of newspaper, television and radio reports on suicide improving significantly between 2000/2001 and 2006/2007 (Pirkis et al., 2009). Notable decreases were found in the use of inappropriate language (41.7% to 6.1%), photographs, footage or diagrams (13.5% to 4.1%), details of method (49.6% to 14%), reference to celebrity status (91.2% to 13.7%), and use of suicide in the headline (29.5% to 21.2%). Additionally, the inclusion of help service information increased (6.5% to 17.7%), and a decrease in the number of articles portraying suicide as a “social phenomenon” rather than being related to mental illness (47.4% to 23.6%) was also noted.
+Jamieson et al. (2003) examined short- and long-term changes in New York Times articles on suicide using three timepoints: one prior to guideline introduction (1990) and two following (1995 and 1999). Use of suicide in the headline decreased after guideline introduction (59.1% in 1990 to 46.2% in 1995) but subsequently normalized to 57.9% in 1999. Suggestion of the cause of suicide in reports decreased initially from 69.7% to 53.8% but subsequently increased to 60.4%. Depression was mentioned more after the intervention (3.9% in 1990, 11.3% in 1995), but by 1999 this had decreased (7.5%). The frequency of suicide story placement in the first nine pages increased significantly over time from 38.5% in 1990 to 50.9% and 76.6% in 1995 and 1999, respectively. The authors also examined the frequency of stories on murder-suicide and assisted suicide. Reports of murder suicide were similar between the first two timepoints (33.3% and 31.1%) but increased by
+the third (44.9%). By contrast, the number of assisted suicide stories fell from 19.7% to 9.4%, dropping further to 1.9% in 1999.
+Two publications examined article content in Asia and indicate the limited effectiveness of the guidelines. Fu and Yip (2008) examined report quality in five Hong Kong newspapers between 2004 and 2006, thus before and after the November 2004 launch of media guidelines (Center for Suicide Research and Prevention, 2004). The percentage of articles publishing photographs showed a small but significant decrease (92.0% to 90.7%), and mentioning the reason for suicide in the headline also decreased (65.9% to 59.6%) (Fu & Yip, 2008). However, no significant changes were observed in the mention of suicide in the headline (89.5% to 90.4%), front-page placement (1.9% to 1.8%), graphical representation 3.3% at both timepoints), or inclusion of positive messages (4.9% at both timepoints).
+A more recent study by Fu et al. (2010) found low compliance with the WHO suicide reporting recommendations in newspaper reports in Guangzhou, Hong Kong, and Taiwan. While suicide reports were generally not considered front-page news, photographs were included in 33.6%, 89.3%, and 26.7% of articles in Guangzhou, Hong Kong and Taiwan, respectively. In addition, suicide method (75.7%, 91% and 69.8%) and gender (71.7%, 83.3% and 54.9%) were also commonly mentioned in article headlines, and preventative advice was rarely provided. The authors compared suicide data and newspaper reports, demonstrating an underreporting of female suicide in Guangzhou and Taiwan, and overreporting of youth suicides in all regions. Of note when interpreting these findings is that the authors did not describe an active guideline promotion strategy, so that the likely exposure of journalists to the guidelines cannot be determined.
+Awareness and Use of Guidelines by the Media
+Several studies reporting qualitative data concerning the awareness and use of guidelines by journalists suggest that journalists are either unfamiliar with the guidelines or do not use them. Tully and Elsaka (2004) found that most New Zealand journalists interviewed did not use the guidelines, and moreover there was significant opposition to the guidelines on the grounds that they promote censorship and paternalism. Another New Zealand study by Collings and Kemp (2010) found similar results: Few journalists were aware of the guidelines, and none reported actively using them. A UK report by MediaWise Trust also found that most journalists interviewed had no detailed knowledge of any of the existing guidelines on suicide reporting.
+Journalists appear to be generally sceptical about the association between reporting style and imitative suicidal behavior (Collings & Kemp, 2010; Tully & Elsaka, 2004); nevertheless, they are concerned that their own suicide sto
+ries might influence vulnerable readers (Collings & Kemp, 2010). In the Collings and Kemp (2010) study, journalists considered it in the public interest to report on newsworthy suicides and saw restrictions as perpetuating “taboos” around suicide. While most journalists reported resistance to guidelines, those interviewed were generally already using responsible reporting practices when covering suicide (Collings & Kemp, 2010; Tully & Elsaka, 2004). Journalists also placed importance on the right of the media to speak freely, and emphasized their role in educating the public and stimulating discussion (Collings & Kemp, 2010).
+The study by Skehan et al. (2006) examined both quantitative and qualitative measures of awareness, uptake, and perceived value of the Mindframe and Mental Health guidelines (Commonwealth of Australia, 2002). Most journalists whose organization had been fully briefed on the guidelines in 2005 were aware of the resources in 2006 (67% and 28% for printed and online versions, respectively). Similar awareness was achieved for both drop-in (50% and 28% for printed and online versions, respectively) and mail-out distribution (50% and 19%, respectively), while in organizations where no guidelines were distributed, fewer journalists were aware of the resources (29% and 9%, respectively). Use of resources was also high, with 63% and 83% of respondents using the resources in organizations receiving either the drop in or briefing session, respectively. For mail-out distribution and control (no active dissemination), 38% and 45% used the resources, respectively. Much of the qualitative research addressed the respondent’s perceptions of the briefing sessions; journalists reported increased awareness of how best to report suicide and valued the face-to-face delivery method as well as the information provided on the link between suicide reporting and imitative behavior (Skehan et al., 2006).
+Discussion
+An analysis of the literature concerning guideline effectiveness revealed several important themes. First, the research suggests that guidelines can be effective in both reducing suicide and changing reporting behavior. The Austrian and Australian data provide proof of principle that guidelines can work when there is an active dissemination strategy and media industry cooperation (Etzersdorfer & Sonneck, 1998; Niederkrotenthaler & Sonne, 2007; Pirkis et al., 2009; Skehan et al., 2006).
+On the basis of this review, one of the most important factors that appear to influence the effectiveness of guidelines is media participation in development. For example, in Austria and Australia, where media was intimately involved throughout the development, dissemination, and training processes, guidelines appear to be effective in changing reporting and reducing imitative suicide. At the opposite end of the spectrum, when there is little media
+“buy-in,” guidelines unsurprisingly have minimal effect. In China, for example, where media guidelines were released but were not developed in collaboration with media, reporting quality remains low. Similarly, in New Zealand guidelines were criticized for lack of consultation with media during their development, and journalists do not appear to be using them (Collings & Kemp 2010; Tully & Elsaka, 2004).
+Importantly, and a serious limitation to the potential effectiveness of guidelines, qualitative data suggest that journalists’ use and opinion of guidelines is generally low. The qualitative data suggest that journalists are likely to resist guidelines when the development process is not sufficiently consultative or when a threat to their journalistic freedom is perceived (Collings & Kemp, 2010; Tully & Elsaka, 2004). Any program in this area should seek to maintain a balance between sensitive reporting (to protect people at risk of suicide) and the preservation of free speech. Thus, programs targeted at journalists should not be directive, instead using an information-sharing approach, presenting the research on suicide and reporting in an unbiased way.
+Importantly, guidelines are not an interactive form of education. Programs may also be most effective when they encourage journalists to explore the issues in a practical and self-directed way, enabling journalists to come to their own conclusions about how to report suicide. The Australian data (Skehan et al., 2006) reported high levels of awareness within organizations that had been directly contacted during guideline dissemination, but low levels in organizations in which no active dissemination was conducted. One important aspect influencing media guideline effectiveness is the degree to which their dissemination is associated with journalist training, education, and maintenance activities. There are clear examples of the importance of training in the reviewed literature, for instance, even using a strategy “based on direct approaches and repeated, educational advertising, and discussions.” Niederkrotenthaler and Sonneck (2007) cite the need to “refresh” journalists’ knowledge in order to maintain adherence to guidelines, and to bring to the attention of new journalists the importance of sensitive reporting of suicides. Monitoring may also play a critical role in ensuring sensitive reporting is maintained in the long term. Niederkrotenthaler and Sonneck (2007) detailed a strategy of monitoring news reports and responding by delivering guidelines to journalists and inviting discussion. The Austrian case also highlights the importance of targeted training that addresses locally specific trends in imitative suicides. As well as developing guidelines for suicide reporting, the strategy was particularly targeted at changing reporting of subway suicides in order to prevent suicides by this method. Concerted efforts toward this end were effective and culminated in a moratorium on reporting of subway suicides and a subsequent large decline in subway suicide rates.
+Any program involving the media industry must take into account the commercial, editorial, practical, and professional contexts within which journalists operate (Collings & Kemp, 2010). For example, for commercial inter
+ests and competitive reasons it is generally unreasonable to expect journalists not to cover suicides of celebrities or well-known figures (Jamieson et al., 2003). Moreover, journalists often deem reporting on suicide to be in the public interest and consider suicide a legitimate topic for discussion in the media (Jamieson et al., 2003). Certainly, this proposition is supported by the evidence showing that the introduction of guidelines often does not precipitate a decrease in reports about suicide (Maillart, Reisch, & Michel, 2007; Michel et al., 2000; Pirkis et al., 2009), though it may improve the quality of the reports (Maillart et al., 2007; Michel et al., 2000; Pirkis et al., 2009).
+There is not enough evidence to know whether changes in reporting of suicide are long-lasting. In fact, what little research there is suggests the opposite: Media professionals revert to sensational reporting over the long term (Jamieson et al., 2003; Maillart et al., 2007). Likely to be crucial to long-term effectiveness are strategies for ongoing monitoring of reports, as in Austria, in addition to recurrent training for journalists in order to reinforce responsible reporting behaviors and the evidence behind them.
+Adherence to guidelines appears to be particularly low in Asia, where suicides are highly newsworthy events. Considering the high suicide rate in some areas of Asia, and the prominent influence of media on imitative suicidal behavior (Chen et al., 2010; Cheng, Hawton, Lee, & Chen, 2007), it is crucial to develop strategies that effectively engage the media industry in the prevention of suicide.
+The involvement of the media in suicide prevention must now move beyond the “toning down” of media reports and focus on the positive contributions that can be made. There is a significant scope for the media industry to help foster support from government and the general public, with informed and sensitive discussions of suicide by the media likely to help to drive suicide prevention higher on the public health agenda (Bale, 2001). Engagement with media also presents an opportunity to increase mental health literacy. Stigma is one the primary barriers to helpseeking behavior in vulnerable individuals, and a public discussion of suicide and mental illness by the media could help generally to destigmatize mental illness and suicide, and to normalize help-seeking behavior (Gould, 2001; Pir-kis et al., 2009).
+There are several factors that limit comparisons between studies and influence the likely effectiveness of media guidelines. One lies in appreciating the specific local context and the influence of cultural factors. The imitative effect of sensational suicide reporting is strong in Asia, although there is very little research evaluating the effectiveness of guidelines or other programs seeking to change journalist reporting behavior. The two studies cited in this review suggest that sensationalist reporting of suicide still occurs. It would be very useful to conduct qualitative research with journalists in Asian countries, to examine journalist’s awareness, use, and perception of the guidelines. The importance of existing levels of mental health literacy cannot be underestimated and will likely determine wheth
+er sensitive reporting is likely to occur, with or without the presence of guidelines. For example, in countries in which mental health literacy is high among the general public, news reports are more likely to reflect this and focus more on mental illness and report strategies for treatment and help-seeking. On the other hand, in less developed regions, where mental illness remains stigmatized and mental health literacy may be low, journalists may be less likely to report sensitively, be less equipped to incorporate discussions of mental health in their reports, and be less able to recognize the possible negative effects of sensational reporting.
+Cultural differences are important to consider for several reasons. First, differences in strength of the imitative effect of sensational reporting are likely to influence both the importance of sensitive reporting as well as the likelihood of journalists following guidelines. For example, in Asia where the imitative effect of sensational suicide reporting is strong, there is a greater need for journalists to report sensitively than in a country where suicide is less strongly linked with media reports. Second, if journalists perceive that the link between sensational reporting and imitative suicide is weak in their own country, they may be less likely to place importance on following guidelines for responsible reporting. For example, Tully and Elsaka (2004) report that in New Zealand, which has a high suicide rate despite comparatively severe restrictions on reporting of suicide, journalists doubt the relevance of research on imitative suicide and concurrently report little use of guidelines. These examples highlight the importance of considering the unique local sociocultural context when discussing guidelines and examining their effectiveness.
+Finally, in a rapidly changing and global media environment, it is important that efforts to improve suicide reporting and raise awareness of mental health literacy move beyond traditional media such as newspapers, television, and radio and begin to make use of domains such as the internet and social media to promote awareness and ensure quality reporting. Some countries such as the United States have begun to grapple with this issue, updating their guidelines to specifically target social media (e.g., Recommendations for reporting on suicide, 2011, http://www.reportingonsuicide.org).
+Conclusions
+Media guidelines are a widely implemented suicide-prevention strategy, although the overall body of evidence suggests that media guidelines can be effective in improving reporting and reducing suicide only when accompanied by media endorsement, active dissemination strategies, and ongoing training and monitoring. Other countries should follow the lead of Austria and Australia, which have developed guidelines in collaboration with media and which have devoted significant resources to monitoring and training. In addition, more rigorous research is needed to evaluate the usefulness of guidelines; the need for more follow-
+up studies evaluating guideline effectiveness is also clear. As it stands, the meager evidence suggests varying effectiveness in modifying reporting behavior. Interventions in Europe and Australia appear to have been successful, at least in the short term, while the two evaluations in Asia suggest guidelines have been only modestly effective. Where guidelines have been effective, more research is needed to determine the optimal frequency of guideline dissemination campaigns necessary to sustain changes in reporting behavior over the long term.
+Approaches that engage media professionals in their development and convey high-quality, unbiased information in person are most likely to result in changes in journalists’ reporting behavior. An understanding and appreciation of the local media context is critical for engaging media professionals in responsible suicide reporting, as are constructive, respectful, and positive partnerships.
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+| J Routledge
+Taylor & Francis Croup
+Introduction
+Suicide presents a significant public health concern, affecting all regions of the world, including both high- and low-income countries. Globally, the World Health Organization estimates that close to 800,000 people die by suicide every year (World Health Organization, 2018a). Rates of suicide
+attempts are estimated at around 20 times this number, and suicide bereavement is said to affect between 48 million and 500 million people each year (World Health Organization, 2018a). Prevention, therefore, is a central concern for policymakers and health care professionals all over the world.
+Suicide is a complex phenomenon resulting from multiple biological, psychological, social and cultural factors (Zaheer, Eynan, Lam, Grundland, & Links, 2018). As a result, risk factors vary across different populations and sociodemographic groups, with several high-risk groups identified in the literature. In some studies, immigrants, refugees, and ethnic minorities have been found to be at increased risk of suicidal behavior compared to their host populations, whereas others have found rates to be lower than the host population (Forte et al., 2018). Research has found a relationship between immigration and higher rates of psychological distress and mental illness which is thought to be a key risk factor for suicide (Fazel, Wheeler, & Danesh, 2005). Other common risk factors for suicide in this population include difficulties with cultural assimilation and acculturation, social isolation, language barriers, and separation from family (Lai, Li, & Daoust, 2017).
+Australia has one of the largest multicultural populations in the world, with cultural and linguistic diversity a defining feature (Minas et al., 2013). In 2018, there were approximately 7.3 million migrants living in Australia (out of a total population of 25 million) (Australian Bureau of Statistics, 2019). As a result, people from culturally and linguistically diverse backgrounds are a priority population for suicide prevention in Australia. The term Cultural and Linguistic Diversity (CALD) was introduced by the Australian Bureau of Statistics (ABS) in 1999 as a broad descriptive measure to identify the various multicultural populations living in Australia (Australian Bureau of Statistics, 1999). CALD replaces the previous designation “non-English language speaking” and was intended to capture a wider subset of the population under the one descriptor. In most population and census data in Australia, the country of birth is the primary indicator of CALD status.
+CALD populations have unique identities and experiences of mental health and suicide, with multicultural differences, trauma, and experiences of discrimination and stigma pertinent to effective suicide prevention approaches (Life in Mind, 2019). Very little is known about suicide and suicidality among this population in Australia, with current data sources said to be not sufficiently complete, comprehensive or accessible to answer key suicide prevention questions (Sutherland et al. 2018). Currently in Australia, only Queensland and Victoria collect surveillance data on suicide, with the Queensland Suicide Register (QSR) and Victorian Suicide Register
+(VSR) collecting data dating back to 1990 and 2009 respectively. In their most recent report, for the period 2011-2013, the QSR recorded 389 (20.3%) suicide deaths for people born outside Australia. This was a rate of 8.41 deaths per 100,000 people, which was lower than the overall rate for Queensland and Australia (13.42 and 15.12 deaths per 100,000 people respectively) (Potts, Kolves, O’Gorman, and De Leo, 2015). Similarly, for the period 2009-2019 in Victoria, 239 people of CALD backgrounds died by suicide (representing 21.2% of the total number of deaths by suicide) (Sutherland et al., 2018). A study by Ide et al. (2012) found that rates of suicide among first-generation migrants between 1974 and 2006 varied considerably depending on country of birth, and that suicidal behavior is “influenced by the cultures, traditions, ethnicities and, possibly, genetic predispositions of their homelands” (p. 1922). A follow-up study in 2014 examined suicide rates of second-generation immigrants in Australia and found no difference between first and second-generation migrants (Law, Kolves, & De Leo, 2014). Beyond this, no further research was found examining factors associated with suicidality among CALD populations in Australia. Research on mental illness - a key risk factor for suicide (World Health Organization, 2018b) - is also scarce in this population, with a 2012 report from Mental Health in Multicultural Australia concluding that research findings on the prevalence of mental health disorders in immigrant and refugee populations were “incomplete and contradictory” (Minas, Klimidis & Kokanovic, 2007, p. 29).
+Prevention efforts for suicide among CALD communities in Australia are also limited. An assessment of activities funded as part of the Australian Federal Government’s National Suicide Prevention Program showed that the uptake of mainstream suicide prevention initiatives by CALD communities was poor, and that there were only a handful of small scale suicide prevention programs targeted specifically at people from CALD backgrounds (Australian Government Department of Health, 2014; 2018). Furthermore, there have been no studies or evaluations examining the effectiveness of these programs, nor any research into suicide prevention more broadly within this population in Australia. To inform suicide prevention efforts for any population, it is important to understand the factors associated with suicidality, as well as considering the effectiveness of suicide prevention efforts elsewhere. Therefore, the aim of this paper was to review the international literature on suicidality and suicide prevention in CALD communities as a means of informing suicide prevention research, policy, and practice in the Australian context.
+Methods
+This systematic review was conducted in accordance with the PRISMA recommendations for systematic reviews (Liberati et al., 2009).
+4 @ M. BOWDEN ET AL.
+Eligibility criteria
+Studies were included if they met the following criteria:
+•	Examined factors associated with suicidality (i.e. suicidal ideation, suicide attempts or suicide plans), suicide deaths or suicide prevention in CALD populations. Studies examining prevalence rates were not included).
+•	Described suicide prevention interventions in CALD populations.
+•	Were conducted in western countries, to ensure comparability with Australia. Western countries in this context were defined as members of the Organization for Economic Cooperation and Development (OECD) and include Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Hungary, Republic of Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovakia, South Korea, Spain, Sweden, Switzerland, Turkey, United Kingdom, and the United States.
+•	Used qualitative or quantitative methods.
+•	Were published in English (given the Australian context).
+•	Were published in 2000 or later (to ensure the most up to date research was captured).
+As a term introduced by the Australian Bureau of Statistics (ABS), CALD is a term not commonly used outside of the Australian literature. The ABS outlines the following variables which constitute CALD status: country of birth; country of birth of father; country of birth of mother; ancestry; religious affiliation; year of arrival in Australia; proficiency in spoken English; first language spoken; main language spoken at home; main language other than English spoken at home; languages spoken at home (with a minimum of four variables required to meet the definition) (ABS, 1999). As a result, inclusion criteria were broadened to include refugees, immigrants (including both first and second-generation populations), ethnic minorities and multicultural populations. MeSH (Medical Subject Headings) terms were used to identify other related search terms. Aboriginal and Torres Strait Islander and Indigenous groups were excluded because they have unique experiences of suicide specific to the Australian context, which is outside the scope of this review.
+Identification and selection of studies
+Literature was identified through a search of five online databases on 19 March 2019, including Medline, PsycINFO, Embase, Emcare, and CINAHL. These databases were selected because of their focus on mental
+health, population health, psychology, and human behavior. Reference lists of several key papers were also searched for additional citations. A full list of search terms is shown in Figure 1.
+Data extraction and analysis
+Data were extracted from qualitative and quantitative studies separately due to the substantial differences in methodology and presentation of findings. For qualitative studies, country, data collection, sampling methodology, study population, sample size, area of focus and key findings were extracted. For quantitative studies country, data collected, sampling methodology, sample size, study population, area of focus and key findings were extracted.
+Thematic analysis was undertaken for all included studies and guided by three steps. Step one involved reading and reviewing in detail the included full-text articles. In step two, key factors related to suicidality in the population of interest, prevention, intervention, and future recommendations were summarized and reviewed. In step three, common factors and themes were grouped together under several broader themes.
+In addition to examining study methodology and data collection strategies, sample size, the general area of focus and the findings were also extracted. Further, findings were examined with specific attention to the
+factors associated with and influencing suicidality, prevention and intervention strategies, and recommendations for future prevention activities.
+Quality Assessment
+Methodological quality of the qualitative studies was assessed using the Critical Appraisal Skills Program (CASP) qualitative checklist (CASP, 2019). This tool contains a list of nine checklist criteria for assessing the following areas: aims, methodology, design, data collection, data analysis, ethical considerations, interpretation, findings and value of the research. Studies which meets 8-9 of the criteria are considered to be high quality; 6-7 of the criteria are moderate quality; and those which meet less than six criteria are considered to be poor quality. Quantitative studies were assessed using the Quality Assessment Tool for Quantitative Studies (Effective Public Health Practice Project, 1998). This tool comprises eight components (selection bias, study design, confounders, blinding, data collection methods, withdrawals and dropouts, intervention integrity and analysis) which are scored as strong, moderate or weak. The total rating of a study is “strong” if none of its components are rated “weak”. Studies are rated as “moderate” if only one of its components was rated “weak”; and rated as “weak” if two or more of its components are rated as “weak”. Although the instrument assesses the integrity of the intervention and analyses (e.g. analysis by intention to treat status), this was not applicable for the studies included in this review, and therefore excluded for the purposes of this review.
+Results
+The search generated a total of 715 citations. Abstracts were screened to identify studies that met the eligibility criteria. After application of these criteria at the title and abstract stage, 31 articles were included for full-text review. A further five citations were identified through a manual search of the reference lists of selected papers. Full-text articles were read to confirm they met the inclusion and exclusion criteria with 18 studies excluded at this point to produce 18 papers for inclusion in the final analysis. (See Figure 2). Citations were managed using EndNote X9 reference management software (EndNote, 2019).
+Study characteristics
+Study characteristics are outlined in Table 1 (qualitative studies) and Table 2 (quantitative studies). Of the 18 included studies, five were from the US, three from Canada, two from Germany, two from The Netherlands, and
+one each from Sweden, France, Switzerland, Norway and Greece. One study utilized multicentre data from 11 European countries.
+Qualitative studies
+Ten of the 18 studies utilized qualitative research methods. Sample sizes ranged from 10 to 83. Two studies used focus groups; five used semi-structured individual interviews; two used psychological autopsies and one used both semi-structured interviews and focus groups. Two studies were conducted with females only, and all 10 studies included only adult participants. Three studies were concerned with Bhutanese refugees living in the US. The remaining seven were concerned with immigrants living in other countries, including Chinese immigrants in the US, Asian immigrants living in the US, Chinese immigrants living in Canada, Korean immigrants living in Canada (two studies), Turkish immigrants living in Germany, and immigrants generally living in Greece. Two studies examined data from people who had died, with a focus on risk factors associated with death by suicide (one comparing immigrants to national population). The remaining eight studies collected data from those who had either attempted suicide (two studies), those who had identified as having suicidal behaviors or suicidality (five studies), or from community members affected by suicide bereavement (one study).
+14 @ M. BOWDEN ET AL.
+Quality assessment
+Using the CASP tool, four of the ten studies were rated as poor quality, three were rated as moderate quality, and three rated as high quality. The main methodological limitations related to the lack of justification for sample size, selection bias, and observer bias. (See Table 3).
+Quantitative studies
+Eight of the 18 studies utilized quantitative research methods. Sample sizes ranged from 185 to 4.4 million. All eight studies collected demographic data, with three studies using validated psychometric instruments. Five studies used existing datasets and conducted retrospective data analysis (including two national registry datasets). One study examined women only, and two studies examined adolescents only. Two studies examined the experience of specific immigrant groups living in other countries, including Turkish women living in Germany and Turkish women in The Netherlands. Four studies examined the experience of those with an immigrant background living in other countries, including one from Norway, one from France, one from The Netherlands and one from the WHO/ EURO multicentre study (which included data from 11 countries). The remaining two studies were based on national registry data and utilized immigrant status and country of birth as defining variables. Two studies examined suicide deaths, three studies examined suicide attempts, and three studies examined suicide ideation or suicidal behavior.
+Quality assessment
+Using the Quality Assessment Tool for Quantitative Studies, the overall quality of the quantitative studies was mixed, with six rated as weak, and two rated as moderate. The main methodological limitations related to the overall study design (i.e. not randomized and reliance on cross-sectional, once-off data collection methods), lack blinding of participants, and selection bias. (See Table 4).
+Key themes
+Several key themes were observed across the 18 included studies. These included acculturation difficulties, stigma, the influence of social networks and family, heterogeneity of CALD populations, and suggested prevention strategies.
+Theme 1: acculturation difficulties
+Acculturation refers to the process of integration or assimilation into another culture. More broadly it refers to “changes in beliefs, attitudes, values, and behaviours - generally as a result of migration from one country to another” (Zaheer et al., 2018, p.505). Acculturation stress describes the psychological difficulties which arise during the acculturation process as a result of the conflict between trying to maintain one’s own culture while trying to adapt to a new culture (Berry, 1997). Seven studies pointed to acculturation difficulties as a risk factor for suicidality (Brown et al., 2019; Bursztein Lipsicas et al., 2012; Chung, 2012; Eylem et al., 2019; van Leeuwen, Rodgers, Regner & Chabrol, 2010; Zaheer et al., 2018). Resettlement challenges, including language barriers, culture shock, employment difficulties, and financial stress were most commonly reported. These findings were observed across studies from a range of host countries including USA, France, Canada, and The Netherlands, as well as in the WHO/EURO multicentre study, and in both the qualitative and quantitative literature. Several studies found that greater adherence to one’s ethnic culture was associated with a higher risk of suicide in immigrants and refugees, whereas active participation in the host culture acted as a protective factor against suicide (Eylem et al., 2019; van Leeuwen, Rodgers, Regner, & Chabrol, 2010). Consideration and awareness of acculturation issues were highlighted by several studies as being necessary for effective suicide prevention initiatives (Brown et al., 2019; Eylem et al., 2019; Zaheer et al., 2018).
+Theme 2: stigma
+Stigma, which is defined as a mark of disgrace or infamy, or a reproach on one’s reputation (Barney, Griffiths, Jorm, & Christensen, 2006), was highlighted by five studies as a key barrier to help-seeking for suicidal ideation and behavior (Chung, 2012; Han & Oliffe, 2015; Heredia Montesinos et al., 2019; Paraschakis et al., 2014; Puzo, Mehlum, & Qin, 2018). Stigma most commonly stemmed from cultural norms and shame associated with suicide and mental illness. Self-stigma about having a mental illness was also highlighted. This stigma often also related to family and social pressure, and the need to maintain a good reputation within the community. This theme was highlighted in Turkish women (Heredia Montesinos et al., 2019), Asian immigrants (Chung, 2012) and Korean-Canadian immigrants (Han & Oliffe, 2015). Anti-stigma awareness campaigns were cited in two studies as a possible way to help improve help-seeking behavior (Heredia Montesinos et al., 2019; Puzo et al., 2018).
+Theme 3: social networks and family
+Family relationships and social support were found to influence suicidal behavior - as both risk and protective factors. Nine studies pointed to family relationships and/or social support as a contributing factor to suicidality (Brown et al., 2019; Chase & Sapkota, 2017; Chung, 2012; Chung, Caine, Barron, & Badaracco, 2015; Hagaman et al., 2016; Han, L. Oliffe, & S. Ogrodniczuk, 2013; Han & Oliffe, 2015; Heredia Montesinos et al., 2019; Zaheer et al., 2018). Changes in social networks and family structures as a result of the migration and resettlement process were shown to increase suicidality, especially for Bhutanese, Korean and Turkish individuals for whom family relationships are of important cultural significance (Brown et al., 2019; Chung et al., 2015; Han et al., 2013; Heredia Montesinos et al., 2019). Additionally, conflict with family (including between partners, parents and children or wider family networks) was found to be a stressor associated with suicidality in several studies (Brown et al., 2019; Chung et al., 2015; Hagaman et al., 2016; Han et al., 2013; Heredia Montesinos et al., 2019). At the same time, several studies found that family support and connectedness with family was a protective factor for many immigrants (Chase & Sapkota, 2017; Chung et al., 2015; Han & Oliffe, 2015). Degradation of social networks, isolation, lack of social support and loneliness were considered to be contributing factors for suicidality in nearly all included studies.
+Theme 4: heterogeneity of CALD populations
+Of the 18 studies, 10 examined the experiences of a particular ethnic group within another country (Aichberger et al., 2015; Brown et al., 2019; Chase & Sapkota, 2017; Chung, 2012; Eylem et al., 2019; Hagaman et al., 2016; Han et al., 2013; Han & Oliffe, 2015; Heredia Montesinos et al., 2019; Zaheer et al., 2018), and the remainder looked at immigrants, refugees or asylum seekers as a group more broadly (Bursztein Lipsicas et al., 2012; Chung et al., 2015; Goosen et al., 2011; Paraschakis et al., 2014; Puzo et al., 2018; van Leeuwen, Rodgers, Regner, & Chabrol, 2010;; Vazsonyi, Mikuska, & Gassova, 2017; Westman, Sundquist, Johansson, Johansson, & Sundquist, 2006). Findings from both groups of studies highlighted the need to consider CALD populations as a heterogeneous group when considering prevention strategies. Populations examined within the 18 studies were very diverse, with different risk and protective factors unique to each cultural or ethnic group. This was highlighted as a key limitation in both the studies that looked at a single ethnic group as well as those that examined immigrants as a broader group. For those studies which looked at the experiences of a specific cultural group, the authors warned that findings were contextual to that particular group and could not be generalized to the
+broader immigrant/refugee experience. Conversely, for the studies which examined the experience of immigrants as a broader group, the authors warned that results may have been skewed, or that findings lacked cultural context.
+Theme 5: suggested intervention/prevention directions
+Twelve studies offered recommendations for future prevention efforts, most of which centered on the development of culturally sensitive and multidimensional approaches. In highlighting the relationship between social connectedness and suicidality, two studies called for interventions aimed at strengthening social connectedness and integration, both within the individual’s own cultural community as well within the broader local community (Chase & Sapkota, 2017; Puzo et al., 2018). Building on this theme, one study highlighted the need to develop “holistic mental health treatment and suicide prevention approaches”, with a greater focus on the impact of social environments and relational processes (Brown et al., 2019). Similarly, two studies also pointed to initiatives which harnesses family networks as key to suicide prevention (Chase & Sapkota, 2017; Vazsonyi et al., 2017).
+At the policy level, three studies called for improved access to culturally appropriate health services (Chung, 2012; Hagaman et al., 2016; Han & Oliffe, 2015), as well as cultural competence of mainstream health services (Goosen et al., 2011; Heredia Montesinos et al., 2019) as a means of breaking down barriers to access for CALD populations. The timing of prevention efforts was also highlighted, with one study suggesting interventions should target newly arrived immigrants or refugees (Hagaman et al., 2016). Several studies acknowledged the link between mental health and suicide and recommended that intervention efforts focus on improving the quality of, and access to, mental health services more generally (Han & Oliffe, 2015; Puzo et al., 2018; Westman et al., 2006).
+Discussion
+Summary of key findings
+This review found no relevant Australian studies, highlighting an important gap in the Australian research on suicide prevention and suicidality among CALD populations. Furthermore, the search did not identify any intervention studies in any context. The review does provide a snapshot of some of the factors associated with suicidality in international CALD populations, as well as suggesting directions for suicide prevention initiatives. A mix of qualitative and quantitative literature was included in the review, with five key themes identified, including acculturation difficulties, stigma, social
+networks and family, heterogeneity of CALD populations and suggested prevention directions.
+Acculturation
+Acculturation was identified as a key factor associated with suicidality within many of the included studies. Acculturation stress is identified in the broader literature as a common outcome in migrants, refugees and asylum seekers (Berry, 2005). It occurs often as a result of the breakdown of social and cultural norms, which can then lead to individual psychological distress. Participants across the studies identified issues and difficulties assimilating into the host culture. This most commonly included language barriers, which then led to not being able or willing to access professional support services or connect with the local community. Acculturation issues were also shown to be associated with difficulties gaining employment, financial stress, social isolation, and poor living conditions, all of which increased the risk of suicide among study participants. This finding is consistent with previous reviews which has found a relationship between acculturation stress and both depression and suicidal ideation in refugee and immigrant populations (Lai et al., 2017; Wyatt, Ung, Park, Kwon, & Trinh-Shevrin, 2015).
+Acculturation issues were present across many of the studies including groups from a range of cultural and ethnic backgrounds, so it is likely that these findings are generalizable to the Australian context. A small amount of research has examined acculturation stress in refugees in Australia and its link to mental health and wellbeing more generally (Kartal, Alkemade, Eisenbruch, & Kissane, 2018; Kovacev & Shute, 2004; Lumley, Katsikitis, & Statham, 2018; Milner & Khawaja, 2010). While none of this research addresses suicide specifically, it does illustrate that such issues are also relevant to the Australian context, especially given the link between psychological distress, mental illness, and suicide. Future prevention efforts should aim to improve the assistance given to newly arrived migrants or refugees with their transition into the Australian community as a means of mediating the stress associated with transition into a foreign culture. Suicide need not be the primary focus of such initiatives, as the benefits of addressing acculturation stress could help to ameliorate associated risk factors such as a lack of social support, language difficulties and barriers to employment and education.
+Stigma
+Stigma was highlighted as a key barrier to help-seeking by participants in several of the included studies. A number of reviews have shown stigma to have a detrimental impact on help-seeking (Farrelly et al., 2015; Thornicroft, 2008; Thornicroft et al., 2016). Several studies in the review recommended the
+implementation of anti-stigma campaigns as a possible prevention approach that would be suitable and acceptable for this population (Heredia Montesinos et al., 2019; Puzo et al., 2018). Several interventions aimed at reducing stigma around mental illness and suicide have been developed for CALD communities in Australia, including face-to-face psychoeducation and web-based interventions. However, a 2013 review concluded that there is only limited evidence for their effectiveness (Reavley & Jorm, 2013), highlighting that while such an approach may be acceptable and feasible for CALD populations, further evaluation is required.
+Social networks and family support
+Within the included studies, family relationships and social support were found to be both risk and protective factors, a finding consistent with broader suicide research. The interpersonal theory of suicide emphasizes the role of interpersonal stressors - including family conflict and social isolation - in suicidal behavior (Van Orden et al., 2010), suggesting that these are not risk factors unique to the CALD population. From a sociological perspective, this offers a good starting point for suicide prevention. Social support has been found to be a key protective factor against suicide (Kleiman & Liu, 2013), with multiple studies establishing a clear link between social support and help-seeking (Suka, Yamauchi, & Sugimori, 2015; Whitlock, Wyman, & Moore, 2014). Strong relationships with adults have been shown to enhance help-seeking in adolescents for emotional distress and suicide (Pisani et al., 2012). Additionally, adolescents who are engaged at school, and who perceive clear social supports available to them, are more likely to seek help for suicidal behavior and mental illness (Pisani et al., 2012; 2013). School-based interventions which utilizes existing structural supports typically offered by school communities may be an option for targeting suicide prevention for CALD communities. Further research would be required to evaluate the acceptability and effectiveness of such an approach in this population.
+Limitations
+Several limitations and gaps were identified in the existing literature, in particular regarding the types of studies included in the review. Firstly, no Australian studies were identified through the search. While inferences can be made about the applicability of the findings to the Australian context, further research involving CALD populations within Australia is required to gain an accurate understanding of the associated factors and develop appropriate suicide prevention approaches.
+The review included a mix of both qualitative and quantitative studies, as well as studies with vastly different sample sizes (ranging from 15 to 4.4 million), and methodology. No longitudinal studies or intervention studies were identified, and therefore it was not possible to look at the impact of suicidality over time in a given population or examine the effectiveness of existing prevention interventions. The quality assessment indicated that studies were not of strong methodological quality, and hence caution should be exercised when generalizing the findings.
+One of the key themes identified in the review was the heterogeneity of CALD populations. Most studies examined specific cultural groups making it difficult to generalize the findings. Furthermore, some studies involved immigrant populations, whereas others involved refugees; two groups with different risk profiles. It is therefore difficult to identify particular risk or protective factors specific to the Australian CALD population as a whole due to the diversity of cultures, ethnicities, identities, and experiences (migrant or refugee) which make up this group. A key challenge for suicide prevention initiatives, therefore, will be to develop appropriately targeted and culturally-appropriate interventions which reaches as wide an audience as possible. From a policy perspective, universal prevention approaches may be unsuitable for this population as they fail to take into account individual risk or protective factors. It may be the case that numerous smaller-scale, targeted prevention approaches are introduced instead as a means of addressing the needs of the numerous and diverse CALD communities in Australia.
+Policy, research, and practice implications
+This review clearly highlights the need for a greater focus in Australia on suicide prevention research, policy and practice for CALD communities. To gain a greater understanding of effective prevention approaches, research into prevalence rates is required as the first step to assess the magnitude of the problem and appropriately plan future prevention interventions. Given the heterogeneity of the CALD population, it would be most useful to identify particular ethnic or cultural groups who may be at increased risk in order to plan more targeted prevention efforts. In Australia, data on rates of suicide are limited, with many calling for improvement and expansion of surveillance data (Sutherland et al., 2018). While Victoria and Queensland both have suicide registers (including data on CALD status), there is a need for national registry data to capture the rates of suicide among CALD populations across all states and territories.
+In addition to investigating rates of suicide, further research looking at risk and protective factors associated with suicide in this population is also required to inform both policy and practice around culturally appropriate
+and targeted prevention initiatives. A key challenge for researchers and policymakers is the rapidly changing characteristics of the Australian population. Over the past 20 years, the number of refugees and migrants has increased, with shifting trends among different ethnic and cultural groups. It is therefore difficult to accurately capture factors associated with suicide in this population as a whole. Research needs to be more targeted in its sample selection and examine the experience of single cultural or ethnic groups only (something which could be guided by prevalence data).
+In Australia, while government policy is shifting in focus toward greater spending on mental health research and services, there is still only limited funding allocated to suicide prevention each year (Australian Institute of Health & Welfare, 2019). Examples of current suicide prevention efforts in Australia include an array of digital health information resources on mental health and suicide (for example, Beyond Blue, headspace and Black Dog Institute), crisis support helplines (for example Lifeline), and most recently a national media campaign (“YouCanTalk”) aimed at family and friends of those who may be experiencing suicidal behavior as a way of empowering them to have a conversation about. Unfortunately, these initiatives are all limited in their accessibility for CALD populations, as they take a universal prevention approach, and do not offer culturally-targeted variations. Australia’s Fifth National Mental Health and Suicide Prevention Plan call for action to increase access to services from people from CALD communities, particularly digital delivery service platforms (Council of Australian Governments Health Council, 2017).
+The development of new, culturally-appropriate prevention initiatives comes at huge expense, and adapting existing mainstream prevention programs could be more cost-effective. Here the focus would be on improving engagement of CALD communities in existing programs, as a means of improving inclusion and accessibility for all members of the Australian population. Airhihenbuwa’s PEN-3 cultural model of health behavior may be useful for guiding this approach (Airhihenbuwa, Ford, & Iwelunmor, 2013). Developed as a means of placing culture at the center of public health and health education, the model can be used to inform intervention and promotion approaches by identifying the positive aspects of culture. The model emphasizes that “(a) all cultures have positive attributes, which is particularly important when health promotion efforts occur outside of an interventionist’s own culture; and (b) family and community contexts are important determinants of health behaviors”. (p.79). Shown to be effective in a range of health settings (Iwelunmor, Newsome & Airhihenbuwa, 2014), such a model could also be applied in the suicide prevention context.
+Co-design (or participatory design) principles of involving those people for whom a product or service is designed for in the decision-making process may also be useful in this context (Hagen et al. 2012). Co-design seeks to understand the lived-experience of users, and would directly involve
+members of CALD communities having input on decision making and recommendations. Working directly with community members in the design of suicide prevention responses may lead to new ways of understanding the factors associated with suicide in that population, and better tailoring of prevention initiatives. It may also help to build credibility and rapport with community members, which could then also improve engagement and uptake.
+Finally, there is a need for evaluation of existing programs. The small number of suicide prevention programs for CALD populations in Australia are yet to be evaluated - highlighting another key gap. Evaluation could look at how to improve access to existing mainstream suicide prevention programs or adapt programs to be more culturally appropriate.
+Conclusion
+This review highlights the need for a greater focus on suicide prevention in Australian CALD communities across policy, research, and evaluation. The absence of any published Australian studies means that the findings have drawn on international literature to examine initiatives in reducing suicide in overseas CALD populations. The review also highlights the complexity of conducting research with this population, due to the broad range of cultural and ethnic groups within it. While broad themes related to acculturation difficulties, social connectedness and stigma were identified, many of the studies presented results that were context-specific, and not generalizable outside of the particular cultural group of interest. In the current geopolitical climate, trends in migration can shift in a relatively short period of time. The makeup of different cultural groups within CALD communities can shift rapidly, and so exploring the experience of CALD communities as a whole may not be useful in designing and implementing suicide prevention initiatives. Prevention efforts need to be targeted to specific cultural groups, while also exploring how to better engage CALD populations in existing mainstream programs and services.
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+1.	Introduction
+In recent years, research on loneliness has attracted growing attention, not least by the demonstration that lonely individuals are at higher risk for mental and physical health conditions carrying high societal costs. These include depression (Cacioppo et al., 2010; Domènech-Abella et al., 2019; Santini et al., 2015), anxiety (Anderson and Harvey, 1988), psychotic disorders (Lim et al., 2018; Michael and Park, 2016), cardiovascular conditions (Winterton and Quintana, 2019), chronic health conditions (Barlow et al., 2015), and im-munological/inflammatory changes (Moieni et al., 2015a). Additionally, neurodivergent individuals with conditions including autism are now recognized as having greater vulnerability to the distress evoked by social isolation and to negative physical consequences associated with loneliness (Ee et al., 2019; Stickley et al., 2017).
+In sum, increased self-report of loneliness is associated with higher morbidity and mortality. Such links have been investigated in recent meta-analyses. It was reported that the negative impact of loneliness on mortality was consistent across 35 articles included in a systematic
+review, and this relationship was found across gender and age groups (Rico-Uribe et al., 2018). The relationship between loneliness and depression has also been the object of investigation in a recent metaanalysis, in which loneliness was shown to consistently have moderate effects on depressive symptoms across studies regardless of sampling strategies, publication type and publication year (Erzen and Çikrikci, 2018). Thus, with the growing amount of research and interest in identifying possible mechanisms by which loneliness impacts on health, the present work sets out to provide a broader view of the literature on the effects of loneliness on both physical and mental health.
+In this narrative review, we provide an overview of the current literature on the multi-directional interactions between loneliness, loneliness distress, affective symptomatology, emotional processing and interoceptive control. We highlight interoception as the sensory interface between the dynamic regulation of the internal physiological state of the body and the neurocognitive (mental) processes that support motivational behaviors, affective and emotional feelings, and even an integrated sense of selfhood: Interoception is an umbrella term encompassing body-to-brain communication through distinct neural and
+humoral channels, the neural representation and integration of this afferent information, the impact of these representations on perceptive, cognitive and behavioral processes, and the psychological expression of these bodily representations as conscious sensations, emotional feelings and affective states (Craig, 2003; Critchley, 2004; Quadt et al., 2018). Importantly, interoception is intrinsically linked with affective states, to the extent that some theories posit a direct relationship between bodily and emotional changes (James, 1884; Schachter and Singer, 1962). Correspondingly, bodily and emotional processes share neural architecture (Zaki et al., 2012), with the insular cortex as a hub for interoceptive mechanisms and conscious affect (Critchley et al., 2004).
+After reviewing evidence from psychophysiology, self-report and neuroimaging research on the impact of loneliness on mental and physical health, we formulate a model of loneliness, loneliness distress, and affective symptomatology based upon aberrant predictive interoceptive processing. Matthews and Tye (2019) put forward the hypothesis that there are coordinated adaptations across discrete neural circuits that function to maintain “social homeostasis”. We aim to pick up this hypothesis and extend the concept to a proposed model of social allostasis. This model addresses how the predictive engagement of neural circuits, interoceptive pathways, and autonomic mechanisms interact to navigate the complex social environment.
+1.1.	Terminology and methodology
+The term ‘loneliness’ can be quite elusive, with different explicit and implicit definitions used in everyday language, and also in academic research on the effects of loneliness. For this reason, it makes sense to delineate the most important terms and outline how we will use them in this review.
+One commonly used definition describes loneliness as the actual or perceived absence of meaningful social relationships in an individual’s life (Cacioppo et al., 2010; Petitte et al., 2015; Weiss, 1973). Therein, however, lies the crux, as the quantity of social contacts does not necessarily define the quality of social relationships. Efforts are therefore often made to keep these apart conceptually, for example by distinguishing emotional loneliness (the perceived lack of meaningful attachment) from social loneliness (the lack of membership to a group, Weiss, 1973), or social isolation (a low number of social contacts). In this manner, the philosopher Paul Tillich wrote: “Loneliness expresses the pain of being alone. Solitude expresses the glory of being alone” (Tillich, 1959). Although there is a common understanding that loneliness reflects psychological distress, scientific investigations into loneliness tend not to incorporate measures of both emotional loneliness and social loneliness/isolation. This conceptual distinction between quality and quantity of social relationships is not explicitly represented in most scientific measures of loneliness.
+The most commonly used instrument to measure loneliness is the UCLA Loneliness Scale (Russell, 1996). The 20 items of the scale indeed focus on the subjective perception of loneliness (e.g., “How often do you feel that there are people you can turn to?”, “How often do you feel that your relationships with others are not meaningful?”). Many studies use a shorter version of the UCLA Loneliness Scale, an 8-item
+questionnaire (ULS-8, Hays and DiMatteo, 1987), or the Social Function Questionnaire (SFQ, Tyrer et al., 2005). In the latter, out of the eight questions, only one directly asks about loneliness (“I feel lonely and isolated from other people”), some questions only indirectly touch upon social connections (e.g., “I get on well with my family and other relatives”), and others not at all (e.g., “I enjoy my spare time”). However, in all questionnaires, the vast majority of questions do not make clear the distinction between chosen solitude and involuntary distress at being lonely. Therefore, while the UCLA Loneliness scales and SFQ remain the most widely used self-report measures (for which there is general agreement that they measure loneliness in terms of being upset about being alone), neither explicitly asks whether perceived instances of social isolation actually cause distress.
+To reflect these important distinctions, we will use the term ‘social isolation’ to reflect a low number of social relationships (Weiss, 1973) ‘solitude’ to reflect the chosen state of being alone (Tillich, 1959), ‘loneliness’ as the perceived state of having few meaningful social relationships (Cacioppo et al., 2010), and ‘loneliness distress’ as the upset caused by the perceived absence of intimate social relations (Quadt et al., 2019b)(see Table 1).
+1.2.	The neuroscience of loneliness
+Neuroscientific approaches to the investigation of loneliness and its representation or impact within the human brain have grown over the past decade. Despite increasing knowledge of the neural correlates that underpin social perception and cognition (Adolphs, 2009; Lieberman, 2013), the neurobiological basis and consequences of loneliness are much less well understood. Approaches to investigate the neural correlates of loneliness in humans include task-based and resting-state functional neuroimaging as well as structural neuroimaging, although the evidence for connectivity, functional, and structural changes in lonely individuals is sparse and often discrepant, possibly due to different sampling strategies, both in terms of the number of participants and different age groups tested (Duzel et al., 2019). In addition, animal models offer valid platforms to explore the cellular and molecular mechanisms through which loneliness can impact brain structure and function.
+Task-related brain imaging, now mostly using functional magnetic resonance imaging (fMRI), is a widely adopted strategy to define proximate mechanisms underlying individual differences in affective reactivity. Here, with appropriate experimental design, inferences concerning loneliness can be made from patterns of neural response to social and non-social stimuli across lonely and non-lonely individuals. At the cortical level, evidence suggests involvement of visual, atten-tional, and emotion-related neural systems (Cacioppo and Ortigue, 2011). However, there are conflicting findings regarding regional neural responses to social stimuli in lonely and non-lonely individuals that make it challenging to draw definitive conclusions about the differential contributions of specific brain areas. For instance, one study reported an attenuation of activation within the ventral striatum in lonely versus non-lonely individuals during the processing of pleasant social stimuli, suggesting reduced representation (and feelings) of
+reward in response to positive social images (Cacioppo et al., 2009). However, another study later reported an amplification of ventral striatal activation to the same stimuli in lonely individuals, compatible with a form of ‘social craving’ (Inagaki et al., 2016). More recently, a functional imaging study of younger and older adults reported no association between loneliness and ventral striatal activation in response to pleasant social images of strangers or depictions of loneliness (D’Agostino et al., 2019). Such contradictions extend to related areas of social neuroscience, for example, increased amygdala activation was previously observed to track larger social network size in young adults (Von Der Heide et al., 2014), yet this finding was not replicated in a more recent fMRI investigation (D’Agostino et al., 2019).
+Task-related fMRI investigations of loneliness are potentially problematic because, rather than directly addressing trait loneliness, they emphasize the measurement of neural responses to different stimuli in the moment, and therefore concern state, rather than trait, loneliness (Yi et al., 2018). Other approaches are arguably more appropriate: For example, resting state fMRI measures the strength and coordinated pattern of communication between centers across the brain from dynamic fluctuations in indices of ‘spontaneous’ neural activity (Biswal et al., 1995). Such measures are well suited to tracking individual differences in cortical processing and may thus provide a better measure of functional brain architecture associated with trait loneliness (Yi et al., 2018). Using Granger Causality, higher loneliness scores have been associated with relatively weaker influence of the ‘dorsal attention network’ (which includes the superior and inferior frontal gyrus, superior and inferior parietal gyrus, middle occipital gyrus, postcentral gyrus, and supramarginal gyrus) on activation within the ‘ventral attention network’ (which includes the supramarginal, superior, middle and inferior temporal gyrus, and inferior frontal gyrus, Vossel et al., 2012). Similar claims are made for a decrease in causal influence from a predominantly subcortical ‘affective’ network (including putamen, amygdala, caudate, and pallidum; Price, 2003) to the visual cortical network (that includes extrastriatal regions in middle occipital gyrus, calcarine fissure, fusiform gyrus; Greicius et al., 2007; Tian et al., 2017). These findings can be interpreted in light of previous studies that indicate, in lonely individuals, weaker reactivity to positive social pictures within the affective network and increased reactivity to negative pictures within the visual network when compared to non-lonely individuals (Cacioppo et al., 2009), indicating that impaired processing of social cues in lonely individuals may render them more attentive to negative as opposed to positive social stimuli (Tian et al., 2017).
+In addition to functional neuroimaging studies, loneliness has been associated with more specific correlates in measures of brain structure. For example, one voxel-based morphometry study in young adults reported lower gray matter (GM) volume in the region of the posterior superior temporal sulcus (pSTS) of lonely as opposed to non-lonely participants (Kanai et al., 2012). The pSTS is implicated in the early stages of social perception (Allison et al., 2000), hence the observed lower pSTS GM volume was also observed to predict poorer performance on a social perception task. Nevertheless, loneliness did not predict social perception ability. One interpretation is that, rather than mediating the feeling of loneliness per se, abnormalities within the pSTS impact basic social perception skills, which may then contribute to the feeling of loneliness. Moreover, the directionality of these relationships remains to be determined, since it is unclear whether loneliness determines GM volume reductions in the pSTS, or whether lower pSTS volume leads to impaired social skills and, consequentially, to loneliness (Kanai et al., 2012). Moreover, as the authors point out in their discussion, it is likely that structural changes in additional regions are also associated with higher levels of loneliness, and that some associations may have failed to be identified due to the small sample size employed. Accordingly, a later study with a much larger sample size (766 young adults) has in fact reported white matter density reductions in regions besides the pSTS, including the bilateral inferior parietal lobule (IPL), right anterior insula (AI), posterior temporoparietal
+junction (pTPJ), dorsomedial prefrontal cortex (dmPFC) and ros-trolateral prefrontal cortex (RLPFC) (Nakagawa et al., 2015). On the other hand, a more recent investigation of the structural correlates of loneliness in older adults (N = 319) did not report any changes in volume within the pSTS (Duzel et al., 2019). Voxel-based morphology revealed decreased GM volume associated with increasing ratings of loneliness within amygdalo-hippocampal complex (left anterior amygdala, hippocampus, left parahippocampal cortex, left posterior parahippocampal gyrus) and left cerebellum. When including age as a covariate, the analysis revealed that, while older participants reporting higher loneliness displayed reduced GM volumes in the dlPFC, amygdala, hippocampus, and ACC, younger participants displayed higher volumes in these regions, suggesting that loneliness exerts stronger negative effects on brain structure with advanced age (Duzel et al., 2019). Thus, it is possible that, in association with high levels of loneliness, age-related changes in brain structure may account for the different findings observed in studies employing younger and older adults, and further longitudinal investigations will be necessary to provide a clear picture of the long-term effects of loneliness on brain structure in otherwise healthy individuals. While these changes were associated with loneliness, they did not predict objective social network size. Region of interest (ROI)-based analysis, based on data from previous functional neuroimaging studies of loneliness, on the other hand, revealed negative associations between loneliness and GM volume within the dlPFC, hippocampus, insula, and anterior cingulate cortex (Duzel et al., 2019), key regions implicated in both emotion regulation and self-reflective processes (Johnson et al., 2006).
+Human behavioral and neuroimaging approaches, which can provide a direct indication of structural and functional correlates of loneliness, can be complemented by insights from animal studies investigating the neurobiological implications of social deprivation at the level of cellular/molecular mechanisms. However, loneliness is different from social isolation. While animal studies can model the effects of social isolation, e.g. by changing the housing conditions of animals and separating them from cage mates, the subjective experience of selfreported loneliness and loneliness distress cannot be studied in animals. Nevertheless, both loneliness and loneliness distress are likely not unique to humans, rather they may represent a trans-species discrepancy between the desired and actual social relations that can also affect other animals (Cacioppo et al., 2015). While preclinical studies of the effects of social isolation are abundant and often report altered dopamine signaling (Fabricius et al., 2010; Hall et al., 1998; Yorgason et al., 2016), the neurobiological consequences of loneliness in animal models remains underexplored, likely due to the challenges associated with recapitulating such subjective feelings in animals. Preliminary findings, however, point to a role of dopaminergic neurons within the dorsal raphe nucleus (DRN) in the expression of loneliness (Matthews et al., 2016). Using in vivo calcium imaging to measure responses of DRN dopamine neurons in mice, neuronal activity was observed to increase during social contact following previous social isolation. Furthermore, photoactivation of DRN dopaminergic neurons favoured social preference in group-caged rodents, while activating these neurons in the absence of a social target was aversive, and photoinhibition prevented the social-seeking behaviours that were typically observed following isolation. These findings suggest that these neurons may detect and/or resolve disparity between the desired and actual social environment of the animal, and that their activation may represent a loneliness-like state, where a negative affective state drives increased seeking of social contact (Matthews et al., 2016). More recently, however, increased firing of DRN dopaminergic neurons was observed not only in singlehoused male mice upon first encounters with female intruders, but also in response to non-social salient stimuli, suggesting that the activity of at least some sub-populations of DRN dopaminergic neurons may not be specific to social engagement (Cho et al., 2017), thus calling for further investigations into their specific contribution to the feeling of loneliness in animal models.
+1.3.	Demographic overview
+The experience of feeling lonely can affect individuals of all age groups, although much research on loneliness focuses on its expression within elderly populations (Matthews et al., 2019). However, some age groups seem to be more strongly affected than others. Loneliness appears to be particularly prevalent among younger and older, relative to middle-aged adults (Lauder et al., 2004; Richard et al., 2017). Recently, survey data from the Environmental Risk Longitudinal Twin Study in the UK, in which loneliness was measured at age 18 in 2232 individuals, showed that about 30 % of young people report feeling lonely at least sometimes, with a small percentage (5-7 %) reporting feelings of loneliness with much higher frequency (Matthews et al., 2019).
+The same study found that individuals with higher loneliness scores were also more likely to have a diagnosis of conditions such as ADHD, anxiety, and depression, and carry a higher risk for substance (alcohol and cannabis) misuse and self-harm (Matthews et al., 2019). A large-scale survey with over twenty thousand US-American individuals aged 18 or older also reported a large prevalence of loneliness among younger individuals. Overall, younger individuals reported higher loneliness scores when compared with individuals over the age of 65 (Bruce et al., 2019). Moreover, even though young adults report larger social network size compared to late middle-aged adults, they report feeling lonely and isolated for twice as many days (Child and Lawton, 2019). Despite the evidence on the relative prevalence of loneliness across different age groups, large-scale longitudinal studies to address the question of how chronic loneliness affects quality of life throughout the lifespan are lacking. Furthermore, additional factors may contribute to a higher incidence of loneliness in different populations, such as lower socioeconomic status or living without a partner (Bruce et al., 2019).
+As discussed in the next sections, loneliness also appears to be more prevalent in individuals suffering from chronic physical conditions, neurological disorders, hearing and vision impairments, as well as mental health conditions, including depression, anxiety, and psychosis. This speaks for a need to determine how loneliness relates to such conditions, and the extent to which it is implicated in the neural and physiological mechanisms that underlie them.
+2.	Loneliness and mental health conditions
+To limit the scope of this narrative review, we have chosen to focus on four mental health conditions for which substantial evidence is available: depression, anxiety, psychotic disorders, and suicidality. This is not to say, however, that there are not relevant associations between loneliness and other mental health conditions. A novel approach in psychiatry defines psychological and psychiatric conditions as inherently social, which suggests that loneliness and loneliness distress may play a vital role in all mental health conditions (Schilbach, 2016). Others have provided evidence for an association between loneliness and addiction (Âkerlind and Hornquist, 1992), eating disorders (Levine, 2012), disordered sleep (Simon and Walker, 2018), and Borderline/ Emotionally Unstable Personality Disorder (Liebke et al., 2017).
+2.1.	Depression
+Depressive episodes are marked by persistent affective and cognitive symptoms, including low mood, irritability, restlessness, pervasive negative rumination, and intense feelings of hopelessness (ICD-10, 2016). Somatic symptoms like psychomotor slowing, a leaden feeling in the extremities, and aches and pains also occur frequently in depressed patients and are independent of cultural context (Kirmayer, 2001; Tylee and Gandhi, 2005). Behavioral changes often accompany depressive episodes. Most notably, patients show so-called sickness behaviors that include fatigue, reduced food and fluid intake, anhedonia, and social withdrawal (Dantzer et al., 2008). Sickness behaviors are a stereotyped
+behavioral pattern that may have evolved as a defensive response to infectious threats to the organism, reducing bodily strain (e.g., fatigue), and risk of infection through social isolation (Miller, 1964). Combined with research indicating heightened levels of inflammation in depressed individuals (Dantzer et al., 2008; Harrison et al., 2009b), sickness behaviors in depression may be part of an initially adaptive response that becomes maladaptive when regulatory mechanisms fail to revert the organism back to equilibrium (Quadt et al., 2018). Social isolation might therefore be part of an autonomic reaction at the onset of depression, fostering feelings of loneliness that frequently co-occur with other depressive symptoms. However, it is widely acknowledged that loneliness and depression often go hand in hand, yet the exact temporal and causal relationship between the two states remains elusive.
+The co-occurrence of depression and loneliness is common, to the extent that some diagnostic tools include feelings of loneliness as a defining characteristic of a depressive episode (e.g., Centre for Epidemiologic Studies Depression Scale [CES-D], Radloff, 1977). Despite this regular comorbidity, studies show that depression and loneliness are statistically separable (Cacioppo et al., 2006a, b) and functionally distinct (Adam et al., 2006; Hawkley et al., 2006). Depression can be conceptually separated from loneliness, in that depression concerns general feelings, while loneliness expresses how people feel specifically about their social connections (Weiss, 1973).
+Several longitudinal studies now demonstrate that increased scores on self-report measures of loneliness predict both the onset of, and symptomatic changes during, depressive episodes (Cacioppo et al., 2006a, 2010; Cacioppo et al., 2006b; Jaremka et al., 2014). Importantly, loneliness is found to put people at much higher risk of depression than social isolation (Cornwell and Waite, 2009; VanderWeele et al., 2011), indicating that the subjective perception and evaluation of social relationships plays an important role in offsetting depressive episodes. In a longitudinal study (van Winkel et al., 2017) applying the Experience Sampling Method (ESM, Myin-Germeys et al., 2009) to track depression, it was found that social company was judged more negatively after feeling lonely, and this predicted the frequency with which company was subsequently avoided. Additionally, trait loneliness predicted the transition into a depressive episode even after controlling for sub-clinical depression at baseline. These results suggest that the negative appraisal of social relationships and the subsequent withdrawal from social contact may play a role in the onset of depression (van Winkel et al., 2017).
+Structural and functional neuroimaging studies have enhanced our understanding of neural mechanisms underpinning depressive symptoms. Prefrontal regions, most notably the medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (dlPFC), and anterior cingulate cortex (aCC), interact with subcortical centers associated with the processing and representation of reward, notably the nucleus ac-cumbens in the ventral striatum, and with regions implicated in motivational learning, notably the amygdalo-hippocampal complex (Rive et al., 2013). Genome-wide association studies have identified a set of genes associated with a decrease in calbindin-positive interneurons in depressed patients (Kim and Webster, 2010). Similar gene expression patterns within the dlPFC are reported in association with levels of loneliness 5 years ante-mortem, suggesting molecular mechanisms by which loneliness contributes to neurobiological changes in brain areas associated with depression (Canli et al., 2018).
+However, little is known regarding how the brain changes specifically in relation to interactions between loneliness and depression. To date, few studies have directly investigated such changes, and most primarily focus on elderly populations with late-life depression. Older individuals with recurrent late-life depressive episodes are observed to report higher levels of loneliness (even compared to those with single depressive episodes). This was also reflected in structural differences within left striatal areas (putamen, caudate, and pallidum, Sin et al., 2018). In fact, GM volume positively correlated with loneliness in the single episode subgroup, and negatively in the multiple episode
+subgroup, suggesting that loneliness interacts with depressive symptoms to compromise further the structural integrity of striatal reward-related circuitry (Sin et al., 2018). A recent study investigating both functional and structural changes in relation to loneliness in individuals with late-life depression, on the other hand, revealed no significant structural connectivity differences in depressed versus control participants. However, functional connectivity within areas of the default mode network (DMN, middle frontal gyrus, posterior cingulate, middle and inferior temporal gyrus) and the cortico-striatal network (including lateral orbitofrontal and thalamus) positively correlated with loneliness scores in depressed participants (but negatively in healthy controls) when processing negative affective stimuli (Wong et al., 2016). These findings remain consistent with reports of impaired down-regulation of the DMN in depressed patients when viewing negative stimuli (Sheline et al., 2009), and with impairment of cortico-striatal connectivity and reward processing in depression (Heller et al., 2009). Additional research is needed to establish whether and how loneliness determines and/or exacerbates structural and functional changes associated with depression.
+From a clinical perspective, it is important to consider research that shows the effectiveness of treating loneliness in the context of depression. Social isolation reduces responsiveness to standard treatment of depression (Trivedi et al., 2005), making the alleviation of isolation a promising target of interventions. Indeed, two recent longitudinal intervention studies confirm that facilitating meaningful social interaction effectively prevents and reduces depressive symptoms and relapse rates (Cruwys et al., 2013, 2014). Although joining more social groups led to larger improvements in depressive symptomatology, identification with a social group and a feeling of belonging crucially best predicted positive outcomes. However, for those participants with an existing history of depressive disorders, being a member of a larger number of social groups proved to be a more powerful predictor of remission than for those without a history of depression (Cruwys et al., 2013). In summary, although subjective feelings of loneliness are more predictive of the occurrence of depression than the objective number of social connections, increased exposure to meaningful social contacts may help to prevent and treat depressive symptomatology through regulating and hindering negative biases that maintain negative affect and cognitions.
+2.2.	Anxiety disorders
+Social withdrawal and peer rejection increase feelings of loneliness in childhood (Boivin et al., 1995), and loneliness occurring as a result of peer rejection in pre-kindergarten children is associated with higher anxious/depressed symptoms during adolescence, suggesting a mediating effect of loneliness in the relationship between social rejection early in life and subsequent development of mental disorders (Fontaine et al., 2009). Loneliness is a recognized risk factor in the development and maintenance of social anxiety. In particular, the relationship between peer acceptance and social inclusion, loneliness and social anxiety has been investigated in children and adolescents, with a recent meta-analysis revealing positive longitudinal associations between loneliness and social anxiety (Maes et al., 2019). One fMRI investigation (Jarcho et al., 2019) employed a ‘virtual school paradigm’, where participants learn associations between social evaluations and virtual peers providing the evaluation. Virtual peers classified as ‘mean’ always provide negative social evaluations; peers classified as ‘nice’ always provide positive social evaluations; and ‘unpredictable’ virtual peers provide positive and negative evaluations 50 % of the time, respectively (Jarcho et al., 2019). Children experiencing high levels of peer victimization report more loneliness and social dissatisfaction. High victimization also correlates with increased right amygdala reactivity to positive evaluations, suggesting a possible link between social distress, loneliness, and social anxiety symptoms prompted by heightened amygdala responses to social stimuli (Jarcho et al., 2019).
+Links between loneliness and social anxiety have also been reported in other age groups. A recent longitudinal study with over 1000 participants aged 18-87 measured loneliness over a period of six months and revealed that early state loneliness predicted later state social anxiety, as well as paranoia and depression (Lim et al., 2016). The relationship between social anxiety and loneliness appears to be bi-directional, such that earlier state social anxiety also predicts later state loneliness (Lim et al., 2016). A high-performance electroencephalographic (EEG) study, which included participants aged 18-44, reported differences in event-related potentials between lonely and non-lonely participants. Although the study did not report significant differences in self-reported social anxiety between lonely and non-lonely individuals, possibly because this only included healthy university students, the neuroimaging results provide interesting insights into brain mechanisms underlying processing of social stimuli in lonely vs. non-lonely individuals (Cacioppo et al., 2016). According to an evolutionary theory of loneliness, lonely individuals display heightened attention for social threat during the early stages of processing of social stimuli (Cacioppo et al., 2015). Results from this EEG investigation provide supporting evidence to the following claims: in contrast to non-lonely participants, who displayed common brain microstates early (60 — 248 ms) during processing of social and non-social threat, lonely individuals displayed differences in processing of social and non-social threat stimuli in the first 116 ms of information processing. In addition, social threat elicited differential activation of regions associated with attention, self-representation, and threat perception, including the inferior and superior temporal gyrus, dlPFC, parahippocampus, supramarginal gyrus, and possibly amygdala and insula (Cacioppo et al., 2016).
+Despite the compelling evidence linking loneliness to social anxiety across various age groups, research on the relationship between loneliness and other forms of anxiety remains scarce. While theoretical approaches suggest that loneliness leads to a generalized hypervigilant state, with consequences that affect physical as well as mental health (Hawkley and Cacioppo, 2010), more studies are needed to establish clear links to anxiety. Longitudinal investigations including both behavioral and structural/functional neuroimaging approaches will be necessary to determine how loneliness affects the brain, and the extent to which these changes are associated with an increased risk of anxiety disorders.
+2.3.	Psychotic disorders
+Psychosis represents a symptom of several psychiatric, neurodeve-lopmental and neurologic conditions, being the defining feature of schizophrenia spectrum disorders (Ffytche et al., 2017; Heckers et al., 2013; Holtzman et al., 2013). Psychotic symptoms negatively impact an individual’s ability to seek social interactions and to maintain close relationships, with both positive (e.g. hallucinations) and negative (e.g. anhedonia) symptoms reported to negatively influence social activities (Rabinowitz et al., 2013). While the effect of loneliness on depressive and anxiety symptomatology is now widely recognized (Cacioppo et al., 2010), the relationship between loneliness and psychotic disorders remains largely underexplored (Badcock et al., 2015).
+The ‘social defeat hypothesis’ attempts to identify social isolation and loneliness as risk factors for developing psychosis (Selten and Cantor-Graae, 2005), drawing from evidence revealing that migrants (both first and second generation) are more at risk of developing schizophrenia. This trend is reported to be largely independent from the country of origin, thus suggesting causes that go beyond genetic or biological factors (Cantor-Graae and Selten, 2005). However, while the theory addresses possible links between social adversity, social isolation and psychosis, it does not account for the experience of subjective loneliness, as an individual who faces social adversity or isolation does not necessarily experience loneliness (Lim et al., 2018).
+Given the high percentage of individuals suffering from psychotic disorders reporting feeling lonely, with loneliness affecting over 80 %
+of individuals with psychosis (Stain et al., 2012), identifying potential mechanisms by which loneliness affects the onset and worsening of psychotic symptoms is of crucial importance. Insights come from an investigation in which loneliness predicted symptoms of subjective thought disorder and loss of pleasure (anhedonia) in individuals with a diagnosis of a psychotic disorder (including schizophrenia, schizoaffective disorder, depressive psychosis, and bipolar disorder with psychotic features), and poorer cognitive functions, indexed by lower digit symbol coding scores in individuals who reported feeling more lonely (Badcock et al., 2015). There are associations between anhe-donia and impairments of processes such as reward evaluation, decision-making, anticipation and motivation, with severity of anhedonia negatively correlating with activity in frontal-executive areas including the orbitofrontal cortex, ventromedial and dorsolateral PFC in schizophrenic patients (Harvey et al., 2010; Park et al., 2009). It is thus possible that loneliness may contribute to the further worsening of cognitive functions and contribute to maintaining subjective thought disorder and anhedonia in individuals with different forms of psychosis (Badcock et al., 2015). However, since this study only assessed loneliness by means of a single question asking participants whether, in the past 12 months, they had felt lonely (and to rate their answer on a 4-point scale, rather than employing a more psychometrically valid scale), it is difficult to generalize its findings to other studies in the literature.
+Another theoretical approach to the relationship between social isolation and symptoms of psychosis is the ‘social deafferentation hypothesis’ of schizophrenia (Hoffman, 2007). According to this hypothesis, plastic re-organization of cortical areas involved in social cognition takes place following experiences of social withdrawal, thus yielding hallucinations and delusions with social content that carry emotional value, resembling phenomena like visual hallucinations following loss of vision, or phantom limbs following amputations (Hoffman, 2007). Understanding one’s body and its boundaries is crucial for the ability to distinguish self from others, and is therefore essential for adaptive social functioning (Park and Nasrallah, 2014; Petkova et al., 2011). Interestingly, loneliness has been associated with a greater incidence of hallucinations in individuals with Alzheimer’s disease (AD) and healthy elderly controls (El Haj et al., 2016), possibly indicating that loneliness may determine the onset of psychotic symptoms even in the absence of an underlying neurological or psychiatric condition. Self-disturbances and anomalous bodily experiences often accompany schizophrenia, and susceptibility to induced proprioceptive illusions, including the Pinocchio Illusion (Lackner, 1988), in which the experience that one’s nose is growing is generated, is reported to be higher in individuals with schizophrenia. These findings extend the social deafferentation hypothesis to tactile sensations and suggest that social isolation also contributes to aberrant sensations across exteroceptive and proprioceptive dimensions (Michael and Park, 2016).
+A general limitation arising from the current literature on the relationship between loneliness and psychotic disorders appears to be the lack of longitudinal investigations assessing how loneliness may impact the development of a psychotic disorder in individuals with or without additional risk factors, such as genetic vulnerability. While a picture is emerging regarding how loneliness poses an additional burden on the quality of life of individuals with psychosis (Switaj et al., 2018), the mechanisms by which it contributes to symptoms of psychosis remain poorly understood.
+2.4.	Suicidality
+Suicidal behavior is a complex biopsychosocial process that includes thoughts, plans and attempts to end one’s own life. It is the leading cause of death for middle-aged males in the UK (Samaritans, 2019), with close to 800 000 people world-wide dying by suicide every year, thus now being a priority of the World Health Organization (WHO, 2019). Similarly to loneliness, suicidal behavior is called a world-wide
+epidemic that occurs in low- and high-income countries across all age groups (Naghavi, 2019).
+Loneliness is significantly associated with an increased risk of mortality and it is possible that suicidal behavior plays an important role in this association. However, there is little research investigating how loneliness and suicidality are linked. Some existing studies focus on specific age groups, finding that adolescents (Garnefski et al., 1992), middle-aged (Miret et al., 2014), and elderly individuals (Li et al., 2016) are vulnerable to both loneliness and suicidal behavior. However, there are only few population-wide studies that show a positive correlation between loneliness and suicide. Drawing on data from the Quebec Health Survey in 1987 (Boyer et al., 1992), an early study shows that prevalence of suicidal behavior increases with the degree of both subjective loneliness and having fewer social relationships, i.e., objective loneliness (Stravynski and Boyer, 2001). Gender differences were found across lifespan, with a stronger link between loneliness and suicidal ideation in men than women. It should be noted, though, that the measures of loneliness in this survey are very sparse, with two questions enquiring about marital status and whether the individual has friends (“objective measure of loneliness”), and only one question used to establish subjective feelings of loneliness (“How often do you feel alone?”). Nevertheless, the contribution of loneliness to suicidal behavior has been recognized in several studies and across different countries (Conroy and Smith, 1983; McKinnon et al., 2016; Osgood, 1991; Peck, 1983) using more thorough self-report measures.
+Analysis of the Adult Psychiatric Morbidity Survey (McManus et al., 2009), in which over 7000 households in the UK were interviewed, checked for a link between loneliness and suicidal behavior across multiple factors (Stickley and Koyanagi, 2016). More specifically, the study set out to disentangle the complex relationship between loneliness, common mental disorders (CMD) such as anxiety and depression, and suicidal behavior, since the role of each of these components is unclear. Research on the association between the three has hitherto been conflicting, with one study stating that loneliness is a crucial factor in suicide attempts independent of depression (Wiktorsson et al., 2010), while others report that the link between loneliness and suicidal ideation is fully mediated by depression (Lasgaard et al., 2011). The analysis of the population-wide survey shows that higher rates of loneliness (as measured with the Social Functioning Questionnaire) are indeed related with a higher prevalence of suicidal ideation and attempts. Regarding the role of CMD and loneliness for risk of suicidal behavior, it was found that being lonely without CMD, and CMD without being lonely was associated with elevated odds of engaging in suicidal behavior. However, being lonely with a CMD increased the odds significantly, showing that the combination of both creates the largest risk.
+Although poor mental health exacerbates suicidal behavior in a lonely person, it is important to understand how loneliness affects suicidal behavior independently of the presence of mental illness (Stickley and Koyanagi, 2016). One potential mediator might be stress; not only are lonely individuals prone to perceiving life as more stressful (Hawkley and Cacioppo, 2003, 2007), chronic stress is also related with increased suicidal behavior (Feskanich et al., 2002; Grover et al., 2009).
+A recent narrative meta-analysis found that the main social constructs that are associated with suicidal outcomes (i.e., ideation and attempt) are having no partner or partners, living alone, social isolation, feeling lonely, feeling alienated from others, and feeling not to belong (Calati et al., 2019). Interestingly, the subjective feeling of loneliness appeared to have the strongest impact on both suicidal ideation and suicide attempts. This finding is corroborated by a systematic, integrative meta-analysis showing that although structural/ quantitative social relationships do have an effect on suicidal behavior in elderly individuals, the function and quality of social relationships is much more predictive of suicidal ideation and attempt (Chang et al., 2017).
+Given these findings, it appears that meaningful social relationships
+indeed are a protective factor against suicidal behavior and that interventions against loneliness could be a promising strategy to reduce suicidal ideation, plans, and attempts.
+3.	Loneliness and neurodiversity
+Autism Spectrum Conditions (ASC) and Attention Deficit and Hyperactivity Disorder (ADHD) are terms for a cluster of neurodeve-lopmental phenotypes. Autistic individuals often present with stereotypical and restricted behavioral patterns, altered sensory reactivity, and non-typical social and emotional processing (Frith, 2014). ADHD individuals typically display inattentiveness, hyperactivity and impulsiveness according to the Diagnostic Statistical Manual (DSM-V, 2016). Both neurodiversities are linked with an increased risk for mental and physical health conditions, such as depression (Hollocks et al., 2019), anxiety (Fuller-Thomson et al., 2016; Joshi et al., 2013), chronic pain conditions (Baeza-Velasco et al., 2018; Stickley et al., 2016), and immunological challenges (Casavant et al., 2012; Marchezan et al., 2018). There is an increasing number of research showing that autistic and ADHD adults are significantly more likely to feel lonely than neurotypical adults (Mazurek, 2014; Stickley et al., 2017).
+3.1.	ADHD and loneliness
+There are very few studies with partially conflicting results that investigate a potential connection between ADHD and loneliness. In children and adolescents, no link between diagnosis of ADHD (Houghton et al., 2015) or presence of ADHD symptoms (Diamantopoulou et al., 2005) was found. However, a longitudinal study showed that young adults who were diagnosed with ADHD as children reported high rates of loneliness (Weiss and Hechtman, 1993). In older adults with ADHD, the diagnosis was linked to increased loneliness, but not social isolation (Michielsen et al., 2015).
+Despite the sparse evidence, more research into ADHD and loneliness is vital, given that individuals diagnosed with ADHD are more prone to conditions previously linked with increased loneliness (Stickley et al., 2017). Studies with ADHD adults have shown that they often experience decreased social satisfaction in the form of poorer quality of relationships (Das et al., 2012), difficulties in marital relationships (Eakin et al., 2004), and poor social support (Brod et al., 2012). Individuals with ADHD are also more vulnerable to mental health conditions such as depression and anxiety (Kessler et al., 2006). Although the exact pathways remain unclear, a recent study shows that depressive symptomatology and loneliness are highly correlated in a sample of elderly ADHD individuals, indicating the potential importance of depression for a link between ADHD symptoms and loneliness (Michielsen et al., 2015). Drawing on a nationally representative sample of the UK through the Adult Psychiatric Morbidity Survey (McManus et al., 2009), ADHD symptomatology and symptom severity is strongly associated with loneliness scores, even after full adjustment for potentially confounding factors (Stickley et al., 2017). This association is, in accordance with previous studies, partially mediated by the presence of common mental health conditions such as depression, anxiety and obsessive-compulsive disorder. Importantly, even those with fewer symptoms and milder symptom severity were still significantly more likely to experience high scores on loneliness measures.
+3.2.	Autism and loneliness
+There is substantial evidence that autistic children and adolescents show higher rates of loneliness than their neurotypical peers (Bauminger et al., 2008; Orsmond et al., 2004; van Asselt-Goverts et al., 2015; Whitehouse et al., 2009). In young autistic individuals, self-reported anxiety correlated with levels of loneliness (White and Roberson-Nay, 2009). Specifically, autistic children and adolescents report feeling more lonely and display higher levels of social anxiety
+compared to both clinical and non-clinical controls (Deckers et al., 2017). Loneliness is reported to correlate negatively with social skills and social competence, and to correlate positively with social anxiety in both autistic children and adolescents and neurotypical controls (Deckers et al., 2017). However, loneliness in autistic adults is much less researched. Recent large-scale studies suggest that rates of loneliness are up to four times higher in autistic than non-autistic adults (Mazurek, 2014; National Autistic Society, 2018). Data from 220 autistic and 146 non-autistic adults, gathered in the Australian Longitudinal Study of Adults with Autism (ALSAA), furthermore shows elevated rates of loneliness in autistic adults, where the presence of an autism diagnosis contributed the greatest variance in loneliness scores (Ee et al., 2019). The number of support persons in their social network significantly correlated with loneliness scores in non-autistic participants, while dissatisfaction with social support was more significant in autistic participants. Anxiety was highly correlated with loneliness in autistics, but not non-autistics, pointing to an important role of mental health conditions in this association. A qualitative analysis of comments from autistic participants revealed high inter-individual variation in the conceptualization of loneliness; where some participants scored high of items of the ULS-8 such as “I feel like I lack companionship”, they did still not perceive themselves as lonely as not socializing was their choice.
+The hitherto neglected topic of how autistic adults understand loneliness has recently been explored in a small-sample qualitative study using focus groups and individual interviews (Elmose, 2019). Previously, it was reported that autistic children and adolescents typically conceptualize loneliness in non-affective terms, referring to more socio-structural dimensions like being alone or having no one to play with (Bauminger and Kasari, 2000). When asked about social challenges and support in daily life, autistic adults view themselves as profoundly isolated and having difficulty initiating interaction, but still longing for intimacy and connection (Causton-Theoharis et al., 2009; Müller et al., 2008). In accordance with these findings, four major recurring themes are proposed supported by a study using phenomenological-based thematic analysis (Elmose, 2019). The four themes include experience of loneliness, being autistic, discrepancies in social relationships, and ease of interaction. Similar to neurotypicals, loneliness was mostly seen as a distressing emotion of an absence that is not within one’s control, and associated with feelings of depression. All themes suggest that there is a profound experience of disconnection from the neurotypical world, where feelings of not being accepted and not belonging pose a great risk factor for loneliness. The inherent difference between autistic and non-autistic individuals may contribute to feelings of loneliness, often starting in early childhood for autistic children. Research shows that differences in communication-style, nonverbal social interactive cue processing, and emotional expression goes both ways between autistic and non-autistic individuals, although a much larger research effort has been made to explain how autistics misunderstand non-autistics, not adequately reflecting the issue from an autistic viewpoint (Milton and Bracher, 2013). The discrepancy in mutual understanding and reciprocity in communication between autistic and non-autistic individuals has been described as the ‘double empathy problem’ (Milton, 2012), and is likely one reason for autistic people reporting a large number of negative social experiences that further social withdrawal and negative mental health outcomes (Ee et al., 2019). A recent review (Milton et al., 2018) summarizes research on the double empathy problem and concludes that, rather than being a deficit in autistic communication skills, breakdown of communication and subsequent isolation of autistic individuals is a two-way street.
+Taken together, neurodivergent individuals are not only much more vulnerable to mental health conditions associated with loneliness, but also experience loneliness and negative social contact more often than neurotypicals. These findings suggest that affective symptomatology and loneliness are tightly correlated and that future interventions need to consider not only the higher likelihood of mental illness, but also
+increase awareness about neurodiversity in neurotypicals to warrant mutual respect and acceptance.
+4.	Loneliness and physical health
+4.1.	Cardiovascular health
+Perceived social isolation determines an increased vigilance for social threat associated with reductions in health-related behaviours, poorer quality of sleep, and increases in stress (Hawkley and Cacioppo, 2010). These can, in turn, affect peripheral physiology through neuroendocrine (e.g. increased cortisol levels) and autonomic dysregula-tion, with enhanced sympathetic activity and parasympathetic withdrawal (Steptoe and Kivimaki, 2012). The detrimental effects of loneliness on physical health are well-documented, with recent metaanalyses reporting a 30 % increased risk for stroke, myocardial infarction, and mortality in individuals reporting feeling lonelier (Holt-Lunstad et al., 2015; Steptoe et al., 2013; Valtorta et al., 2016).
+One of the mechanisms by which loneliness impacts physical health is a reduction in one’s ability to self-regulate emotions, thoughts, and behaviors. Lonely individuals tend to associate the social world with threatening situations (Hawkley and Cacioppo, 2010) and are less inclined to perform physical activity (Hawkley et al., 2009). Reduced selfcontrol that accompanies loneliness has also been suggested as a contributing risk factor for alcohol abuse (Akerlind and Hornquist, 1992). Other studies, however, have failed to identify differences in health behaviors between high and low loneliness individuals (Cacioppo et al., 2002).
+Social isolation during childhood and adolescence predicts both adult social isolation and increased cardiovascular risk factors in adulthood, including BMI, systolic blood pressure, and high-density lipoprotein (HDL) cholesterol levels (Caspi et al., 2006). Studies have also found positive relationships between loneliness and increased systolic blood pressure (SBP) in middle-aged adults, where the rate of blood pressure increase over a four-year period was significantly increased by trait loneliness (Hawkley et al., 2006; Hawkley and Cacioppo, 2010). Evidence indicates that elevated total peripheral resistance (TPR) in individuals aged 30-49 predicts rises in SBP in late adulthood (Franklin et al., 1997), and individuals who report feeling lonelier also display elevated TPR (Hawkley and Cacioppo, 2003). Therefore, it is suggested that loneliness-mediated rises in TPR represent a candidate mechanism for the acceleration in the rate of increase in SBP in lonely individuals at older ages (Hawkley and Cacioppo, 2010).
+Neuroendocrine changes can occur as a result of chronic loneliness, and are regarded as key contributors to cardiovascular morbidity associated with long-term chronic loneliness (Hawkley and Cacioppo, 2010). These mechanisms include increases in hypothalamic-pituitary-adrenocortical (HPA) axis activation, which, as discussed below, contributes to glucocorticoid resistance and increased inflammatory responses. Altered HPA activity and resulting increases in pro-inflammatory processes are in turn associated with cardiovascular problems including hypertension, atherosclerosis, and coronary artery disease (Girod and Brotman, 2004; Nijm and Jonasson, 2009; Whitworth et al., 2001). A second mechanism is thought to involve over-activation of the sympathetic nervous system (SNS). High levels of epinephrine and norepinephrine are measured in lonely middle-aged and older adults (Cole et al., 2015b; Hawkley et al., 2006), and similar results indicating increased sympathetic activity are reported in monkeys (Capitanio et al., 2019). Given the well-documented links between hypertension and cardiovascular alterations and increased SNS activity (Grassi, 2010), such findings provide early insights on the mechanisms by which loneliness exerts its detrimental effects on cardiovascular physiology. Oxytocin is one potential ‘upstream’ regulator of social interaction and autonomic cardiac control. Released during social interaction (Grewen et al., 2005), oxytocin is linked to loneliness by the
+identification of oxytocin receptor polymorphisms in humans associated with higher degrees of both social and emotional loneliness (Lucht et al., 2009).
+When investigating the effects of intranasal oxytocin on autonomic cardiac control in healthy individuals, Norman et al. (2011), found that loneliness, here measured using the UCLA loneliness scale, predicted high-frequency heart rate variability (HF-HRV) responses. HF-HRV is a product of respiratory sinus arrhythmia (RSA), which signals changes in the tonic inhibitory control of vagal parasympathetic activity, such that, with inspiration, inhibitory vagal control is reduced, and heart rate increased (Grossman and Taylor, 2007). This is a good indicator of cardiac autonomic function (Berntson et al., 1997, 1993). Social interactions have been reported to have positive effects on HRV. For example, married individuals display higher levels of HF-HRV (Randall et al., 2009), and HRV is increased in shy individuals (who generally display lower HRV levels) during social interactions with close others (Schwerdtfeger et al., 2020). In response to intranasal administration of oxytocin, individuals with lower scores on the UCLA loneliness scale displayed significant increases in HF-HRV, as opposed to individuals with high loneliness scores, who did not display significant responses compared to the placebo group (Norman et al., 2011). On the other hand, upon oxytocin administration, decreases in pre-ejection period (PEP), which indexes increased sympathetic cardiac control (Berntson et al., 1997), occurred independently of loneliness scores. Together, these results suggest that, for lonely individuals, oxytocin-mediated autonomic cardiac control is shifted from a co-activation of autonomic branches towards a selective sympathetic activation, highlighting a potential mechanism by which a perceived lack of social interactions may impact cardiovascular health (Norman et al., 2011). Conversely, the degree of social integration in international students over time after arrival in their host country has been associated with changes in heart rate and HF-HRV (Gouin et al., 2015). While loneliness, here measured using the UCLA scale, did not predict changes in HF-HRV or HR, bivariate correlations revealed that high levels of social integration predicted increases in HF-HRV over time, whereas low levels of social integration were associated with higher HR and lower HF-HRV 2- and 5months post-arrival. Reduced HF-HRV has been associated with cardiovascular disease, cardiac events in individuals with no previous history of cardiac problems (Hillebrand et al., 2013), as well as predicting future development of cardiovascular disease (Stein et al., 2008). Thus, the observed changes in HF-HRV in lonely individuals support the notion that reduced autonomic regulation plays a role in the deleterious effects of loneliness on cardiovascular health.
+However, the exact mechanisms by which social isolation and loneliness impact cardiovascular health remain elusive. As recently suggested, providing clear distinctions between different aspects of social isolation, in relation to different types of social bonds (e.g. spousal or friendship relationships), will be necessary to elucidate potential mechanisms by which loneliness affects cardiovascular health, and whether differential effects occur depending on the type and number of relationships compromised (Winterton and Quintana, 2019).
+4.2.	Chronic health conditions
+One theoretical framework formulates possible pathways by which loneliness leads to physical illness (Hawkley and Cacioppo, 2010). Chronic conditions such as diabetes, cardiovascular disease, and migraine are linked to loneliness, despite sparse empirical evidence about how loneliness mechanistically affects physical health (Christiansen et al., 2016). Besides some evidence for a directional effect from loneliness to chronic health problems, several studies report the effects that chronic health conditions have on both social isolation and loneliness. In children and adolescents, having a chronic physical condition may render inclusion in a peer group more challenging. For instance, children and adolescents with a chronic condition are at risk of school absenteeism (Emerson et al., 2016), and present lower quality
+friendships, less peer acceptance, and less support from peers (Martinez et al., 2011; Pinquart and Teubert, 2012). This, in turn, may lead to increased feelings of loneliness (Heinrich and Gullone, 2006). A recent meta-analysis investigated loneliness in children and adolescents with chronic conditions including neurological disorders, visual and hearing impairments, haematological conditions, cancer, endocrine diseases and heart conditions. This investigation revealed significant associations between loneliness and chronic illness, particularly neurological conditions and visual/hearing impairments, possibly due to the higher communication difficulties in children and adolescents with such diagnoses (Maes et al., 2017).
+Given the growing evidence on the long-term effects of loneliness on both physical and mental health, it would be interesting for future longitudinal analyses to investigate whether loneliness in young individuals with chronic illness is linked to further physical health problems later in life, as well as to a higher incidence of mental disorders. This need is further supported by studies reporting a linear increase in loneliness in individuals with chronic health conditions over an eightyear longitudinal investigation (Barlow et al., 2015). This study also found that health-related self-protection, which includes psychological processes that aim at ameliorating emotional well-being in the context of health threats, such as positive re-appraisal and reducing self-blame (Heckhausen et al., 2013), appeared to mitigate the effects of chronic illness on the experience of loneliness (Barlow et al., 2015).
+How loneliness relates to three chronic conditions (migraine, cardiovascular disease, and diabetes) has been investigated in some detail (Christiansen et al., 2016). Here, loneliness was assessed using the Danish version of the three-item loneliness scale (‘How often do you feel isolated from others?’; ‘How often do you feel you lack companionship?’; ‘How often do you feel left out?’; Hughes et al., 2004), based on the revised UCLA loneliness scale. In order to characterize possible relationships between loneliness and chronic health conditions, the study assessed the effect of moderating factors, including smoking, poor diet, physical inactivity, high stress, poor sleep quality and duration, and alcohol consumption (Christiansen et al., 2016). Such moderators mediated significant relationships between loneliness and diabetes, migraine, and cardiovascular disease, suggesting that loneliness exerts its detrimental effects on health via indirect, rather than direct, pathways. Because stress appeared to be the strongest predictor of poor health in relation to loneliness, it is possible that it plays a unique role in poor health outcomes in lonely individuals (Christiansen et al., 2016).
+Further supporting the role of health-related behaviors in the detrimental effects that loneliness has on physical health, the English Longitudinal Study of Ageing reported that physical activity was lower in older adults with high levels of social isolation, independent of other factors including mobility limitations and depressive symptoms, although a relationship was not observed between sedentary behaviors and loneliness (Schrempft et al., 2019). Another study investigated the structural (living with or without a partner; degree of contact with family and friends; which could be translated to social isolation according to the terminology of the present review) and functional (degree to which one feels to be able to count on help from others in case of illness, which could reflect loneliness as described in the present review) aspects of social relations in individuals with type 2 diabetes (Hempler et al., 2013). Although a causal relationship between social isolation/ loneliness and diabetes was not identified, the study reported that individuals with type 2 diabetes were more likely to live without a partner, less likely to have contacts with family and friends, and less certain that they could receive support from others in case of illness (Barlow et al., 2015).
+A recent correlational study found significant relationships between loneliness and depression in cancer patients and reported that depression and loneliness were predictors of mortality among patients. However, when accounting for depressive symptoms, high loneliness surprisingly predicted low mortality in cancer patients (D’ippolito et al.,
+2017). Although such a relationship will need further validation, these findings indicate that loneliness distress, and subsequent development of depressive symptoms, rather than loneliness itself, contributes to poorer prognosis in cancer patients. Similar investigations should be carried out in other clinical populations to obtain clearer insights on the effects of social isolation, loneliness, and loneliness distress on the prognosis of diverse health conditions.
+4.3.	Immunology
+Poor physical health has been suggested to be associated with disruptions in inflammatory regulation and exacerbation of inflammatory responses that occur as a result of loneliness (Shankar et al., 2011). The interaction between social behaviors and inflammation is thought to be bi-directional. Inflammatory responses play a role in shaping behavior by signals sent from inflammatory cytokines onto, for example, afferent vagal nerves (Goehler et al., 1997). These inflammatory signals elicit ‘sickness behaviors’ such as sleepiness, fatigue, and social withdrawal (Dantzer and Kelley, 2007). The effects of inflammation on social behavior are well-documented in animal models, and include, for example, social withdrawal from unfamiliar animals but increased contact with familiar cagemates in both female and male rats (Yee and Prendergast, 2010). In humans, inflammation has been reported to increase feelings of social disconnection (here described as loneliness) as well as sensitivity to both positive and negative social stimuli in humans (Moieni et al., 2015c; Muscatell et al., 2016). The effect of social isolation and loneliness on inflammation, on the other hand, is suggested to be a preventive response mechanism of the body to an increased vulnerability to wounding and infection that arises from lack of protection from others. Inflammation is not only regulated peripherally, but also neurally, resulting in increased immune system activation when the environment signals an increased risk for wounding and infection (Eisenberger et al., 2017). This is achieved by a dual mechanisms: chronic stress leads to repeated activation of SNS fibers that innervate lymph nodes and coordinate immune responses (Sloan et al., 2007), together with continuous activation of the HPA axis and glucocorticoid outputs, which in turn lose efficiency at down-regulating inflammatory responses via glucocorticoid resistance (Avitsur et al., 2001).
+In an effort to determine possible biological mechanisms by which loneliness may contribute to poor physical health and mortality, Cole et al., 2007 investigated the differential expression of genes associated with chronically high levels of subjective social isolation. Their investigation revealed a highly activated, proliferative phenotype in circulating leukocytes (indexed by over-expression of genes involved in cell growth and differentiation and cell cycle progression, such as ERG1 and CDC25), doubled levels of C-reactive protein (CRP, a marker for systemic inflammation), decrease in glucocorticoid receptor (GR) target genes (indexing glucocorticoid resistance) and increase in nuclear factor (NF)-kB target genes (indexing increased transcription of pro-inflammatory cytokines). These results were indicative of a shift towards pro-inflammatory states and impairments in the GR-mediated anti-inflammatory effects. Other studies have investigated the relationships between inflammation and sensitivity to social isolation. This measure represents a composite score obtained from the UCLA loneliness scale, the Experiences in Close Relationships Questionnaire (anxious attachment subscale), the Brief Fear of Negative Evaluation Questionnaire, and the Merhabian Sensitivity to Rejection Scale (Moieni et al., 2015a), which, according to the terminology laid out in the present review, can be viewed as a composite measurement of loneliness and loneliness distress. Such studies document that, rather than baseline levels of circulating pro-inflammatory mediators, what is exacerbated in individuals with high as opposed to low sensitivity to social isolation is the inflammatory response to a stressor, and this is especially documented in young adults (Moieni et al., 2015a). Recently, administration of a Salmonella typhi capsular polysaccharide vaccine
+was shown to elicit an increase in IL-6 which was positively correlated with scores on the UCLA Loneliness Scale (Balter et al., 2019).
+The interaction between inflammation, sensitivity to social isolation, and depressive symptoms is consistent with the ‘social signal transduction hypothesis’ of depression, whereby increased social-environmental stress drives increased inflammation, which results in depression (Slavich and Irwin, 2014). In a sample of 115 healthy adults, depressed mood was increased by infusion of an endotoxin (E. coli), with the increase being moderated by baseline levels of sensitivity to social isolation. In addition, high levels of sensitivity to social isolation determined an increase in endotoxin-induced activation of pro-inflammatory transcription control pathways (AP-1 and NF-kB) (Irwin et al., 2019).
+While subjective loneliness is generally associated with changes in immune system activation, more so than the objective size of the social network (Cole et al., 2007), an opposite trend is also seen, where objective social isolation, rather than self-reported loneliness, predicts increased CRP levels indicating inflammation (Shankar et al., 2011). Despite the evidence, because studies reporting relationships between loneliness and inflammation often only assess objective or subjective social isolation (Balter et al., 2019; Heffner et al., 2011), it is essential for future research to investigate concurrently the impact that both dimensions have on inflammation.
+5.	Loneliness in a social allostasis model
+5.1.	The social homeostasis model of social isolation
+From an evolutionary perspective, social isolation and loneliness can be seen as both adaptive and maladaptive mechanisms, depending on the context of the individual. A classic evolutionary model of loneliness posits that the feeling evolved to ensure survival and reproduction of the human species, where offspring go through a long period of dependency to caregivers (Cacioppo et al., 2006a), and where even short amounts of time without group protection could mean substantial risk to harm from predators. According to this model, the negative affect associated with loneliness serves as a mechanism to draw the individual back to its group where it is safer from environmental threats. Neuroscientific findings that identify overlapping brain regions for physical and social pain (Eisenberger et al., 2003) are taken as evidence for the claim that social pain is interpreted similarly to (and can be just as threatening to) the organism as physical pain. Loneliness, from this perspective, is an initially adaptive affective response to the potential threats of social isolation. However, social isolation can be beneficial to the individual in the case of illness, where isolating oneself from external sources of additional infection is advantageous. In this case, social isolation is an initially adaptive behavioral response as part of sickness behaviors (Dantzer et al., 2008). Loneliness distress can be seen as a signal to return to the group after recovery and seek reconnection.
+A novel model of loneliness adapts and extends these claims, positing that the negative affect related with loneliness is triggered by an adaptive response to perceived social deficits (Matthews and Tye, 2019). In this ‘social homeostasis model’, the organism detects that a specific set point associated with social needs is not met, which elicits control systems to trigger appropriate behavioral responses that will lead the system back to the set point. The evidence reviewed for this model is mainly based on findings from small animals, but suggestions are made that similar mechanisms can be found in humans. In the remainder of this chapter, we will lay out the social homeostasis model as it is suggested for humans and then introduce an alternative model of loneliness, loneliness distress and affective symptomatology as an aberrant process of predictive social allostasis.
+The social homeostasis model considers three different neural and behavioral response patterns to social isolation. These initially adaptive reactions are suggested to underlie disease states associated with
+loneliness when their intended short-term engagement is prolonged (Matthews and Tye, 2019). These homeostatic responses to social deficits are hypervigilance and/or heightened arousal, social motivational mechanisms, and passive coping for self-protection.
+The classic evolutionary model of loneliness (Cacioppo et al., 2006a) assumes that loneliness originated to promote hypervigilance to guard against potential threats to the socially isolated individual. This claim is supported by empirical findings showing that humans with increased self-reported loneliness also show increased anxiety (Ginter et al., 1994; Stednitz and Epkins, 2006), and heightened responses to aversive social stimuli (Cacioppo et al., 2016). In both rodents and humans, acute social distress like isolation or exclusion recruits fastacting mechanisms that regulate arousal, vigilance and attention. As reviewed above, a major system involved in reactions to social isolation is the HPA axis. While its activation is deemed adaptive in the shortterm to prepare the individual for potential threats, chronic HPA axis activation leads to poor regulation of daily cortisol output associated with self-reported loneliness in humans (Adam et al., 2006; Doane and Adam, 2010). Corticotropin-releasing factor (CRF) pathways initiate neuroendocrine responses to social isolation, and in rodents activate social connection-seeking behaviors (Füzesi et al., 2016). In humans, it is suggested that HPA axis, CRF pathways and LC noradrenergic systems play a role in the increased arousal, attention and vigilance associated with loneliness.
+Social deficits activate a “social monitoring system” with the purpose of increasing attention towards socially relevant information (Gardner et al., 2005). Additionally, reward processing dopaminergic systems that facilitate social behavior and the hypothalamic oxytocin system are suggested to be engaged in motivating the individual to seek social reconnection in the case of isolation and loneliness (Matthews and Tye, 2019). Lonely humans may exhibit increased negative affect (Cacioppo et al., 2006b), with reduced sensitivity to physical noxious stimuli and attenuated emotional sensitivity (DeWall and Baumeister, 2006). These patterns are suggested to be strategies for protection against emotional distress associated with loneliness.
+The social homeostasis model furthermore includes findings on immunological reactions to social isolation, where perceived isolation leads to increased pro-inflammatory activity as a preparation for potential physical injury and as a consequence of the decreased risk of contagious viral infections (Eisenberger and Cole, 2012; Eisenberger et al., 2010b). Importantly, there appears to be a bi-directional relationship between immunological response and loneliness, as changes to the immune system predict loneliness (Cole et al., 2015b).
+Discussing the subjective nature of social experience, the authors point out that there is an important difference between actual and perceived social isolation, and that the latter is what is repeatedly found to be associated with increased morbidity and mortality (Matthews and Tye, 2019). They conclude that a model of social homeostasis for humans needs to account for this subjective assessment of social experience; an aspect that is strongly dependent upon internal bodily signals and interoceptive processes. It is this aspect we will pick up in order to develop our model of loneliness, loneliness distress and their relation with affective symptomatology.
+5.2.	The basics: interoceptive predictive processing and allostasis
+We will base our model on previous proposals on the flexible and predictive nature of interoceptive processing and emotional regulation (Quadt et al., 2019a, 2018), which we will briefly review in this section. This model will be enriched with the notions of allostasis, allostatic load/overload (McEwen, 1998; McEwen and Stellar, 1993), and the recent characterization of psychosocial stress as uncertainty within the free energy principle (Peters et al., 2017).2
+Interoceptive predictive processing (Barrett and Simmons, 2015; Clark, 2016; Seth and Critchley, 2013) is based upon the general hypothetical framework of predictive processing (Clark, 2013) or predictive coding (Friston, 2012; Hohwy, 2013), a popular algorithmic theory about cortical organization and neural function. Given the basic assumption that the brain only has indirect access to environmental and bodily states, an inference process is required to arrive at the most probable hidden cause of the multitude of sensory signals. Filtering out regularities on different temporal and spatial timescales, noise and irregularities are cancelled out (Hohwy, 2013). Underlying this process is the generation of predictive models of the most likely incoming sensory signals and the improvement of model probability through feedback loops that are steered by error signals (i.e., the mismatch between predicted and received signal). Prediction errors function in two distinct ways that result either in generating perception (i.e., perceptual inference, where error signals update predictive models) or in generating action (i.e., active inference, where error signals elicit changes in behavior, Friston, 2012). Within cortical hierarchies, timescales from slow to fast signal processing differ with respect to their degree of abstraction; where concrete sensory signal properties are computed at lower, fast levels, highly abstract regularities are represented on slower, higher levels of this hierarchy (Hohwy, 2010). Within this dynamic cascade of top-down and bottom-up information, both prediction errors and predictive models undergo precision estimation and optimization by the reduction or increase the synaptic gain of error units (Friston, 2010). The overarching goal of these complex processes is to steer the organism towards adaptive responses to external and internal stimuli and efficient navigation of behavior and experience (Friston, 2009).
+PP is formulated as an instance of a larger principle, namely the Free Energy Principle (Friston and Stephan, 2007). Different organisms face different homeostatic challenges. While cats may survive on their own rather early in life, humans depend on social relationships in several ways. These necessary circumstances can be expressed in a probabilistic way - being alone in an ally is a more probable state for young cats than children to find themselves in. This implies that if a system moves out of its expected or probable set of states, this state will be - in information theoretical terms - surprising, as it is unexpected. For an organism to survive, it must strive to minimize surprise and stay within the range of expected states as much as possible. More formally, the probability of internal and external states must have low entropy, where entropy is a measure of disorder and uncertainty (Friston, 2010). By minimizing prediction error and thereby uncertainty, PP instantiates the Free Energy Principle (Clark, 2013).
+In the more specific case of interoceptive predictive processing (IPP), it is hypothesized that predictions about internal states are generated in visceromotor regions within the prefrontal (caudal visceromotor-prefrontal cortex [VMPFC]/orbitofrontal cortex [OFC]), ante-rior/mid cingulate cortices and anterior insula cortex (AIC, Barrett and Simmons, 2015). These predictive signals are compared to internal viscerosensory input that ascends from the nucleus of the solitary tract (NTS), parabrachial nucleus and thalamus, arriving at the primary interoceptive cortex, i.e., mid/posterior insular cortex (Barrett et al., 2016). Reduction of the error signal is either reached through perceptual inference which implies a change in feeling state, or active inference, involving autonomic or behavioral responses.
+Ultimately, this predictive interoceptive system adapts to anticipated demands and deviations in an effort to efficiently regulate needs and resources. Prior experience, contextual information and the current state of the organism inform the generation of predictions, and the comparison between predicted and actual incoming internal signal in accordance with precision estimations serve the goal of keeping the organism within the expected range of integrity (Quadt et al., 2018).
+The flexible adaptation to internal and external changes are crucial in this process that can be conceptualized as “allostasis” or “predictive regulation” (Sterling, 2012).
+5.3.	Loneliness as social allostatic load
+We propose that social allostasis is a predictive mechanism encompassing neural, physiological, autonomic and interoceptive processes with the goal of steering perception, emotions and social behavior in an adaptive manner. In healthy individuals, the complex and dynamic interaction between external and internal needs and resources is balanced by the engagement of appropriate neural and autonomic systems that shape the control of bodily states and behavior. Feeling states arise and fluctuate in concert with the dynamic regulation of internal states and as adaptive responses to social stimuli. Navigating the social environment is of importance to humans for a variety of reasons. From a phylogenetic perspective, groups provided much needed protection from environmental threats, but at the same time social isolation as a sickness behavior ensured limited exposure to infectious threats from others. Ontogentically, humans rely on caregivers for survival for a long timespan, thus separation from others can be seen as an inherent threat. Keeping an organism alive and well therefore requires a flexible and context-sensitive balance of withdrawal and seeking connection.
+The research reviewed here shows that loneliness as social stress are associated with decreased health in a vast variety of conditions and is linked with increased morbidity and mortality. This suggests that the internal mechanisms reacting to and steering social interaction are of an equally large and wide variety. Similar to the social homeostasis model, we assume that the over-activation of initially adaptive mechanisms ultimately underlies increased morbidity and mortality associated with loneliness. Social allostatic load expresses the metabolic effort of the system to steer itself back into a range of expected states, for example, finding companionship, re-building social relationships, asking others for help. If these efforts do not resolve stress, and engagement of originally short-term mechanisms is prolonged, interoceptive impairments and ultimately negative affective symptomatology are the consequence.
+Importantly, the social allostatic process is not without cost to the organism. The successful and adaptive coordination of neural, neuroendocrine, immunological and autonomic systems to ensure homeostatic balance demands a considerable amount of metabolic energy (Juster et al., 2010). The strain on the system of this concerted effort is expressed in the term “allostatic load” (McEwen, 1998), referring to the ‘wear and tear’ of the body during repeated allostatic activation as a response to stress. When exposed to chronic stress, allostatic load can turn into “allostatic overload”, which is the point at which morbidity and mortality start to threaten the organism as a consequence of prolonged and now exhausted activation of adaptive mechanisms (McEwen and Wingfield, 2003).
+Many reviews have summarized the role of poor social relationships and psychosocial stress on allostatic load (e.g., Beckie, 2012; Juster et al., 2010; McEwen and McEwen, 2015), and stress was recently reframed as uncertainty within the larger concept of the Free Energy Principle (Peters et al., 2017). When circumstances change for an organism, new adaptations need to be made, which elicits an acute stress response involving ACC, amygdala, midbrain and brainstem nuclei (Barrett and Simmons, 2015). Initial reactive HPA-axis secretion of glucocorticoids activates energetic resources for fight-or-flight responses, recruiting neuroendocrine, immune and inflammatory systems (Juster et al., 2010). These so-called primary mediators, which include norepinephrine, epinephrine, cortisol, dehydroepiandrosterone sulfate, insulin-like growth factor-1, and interleukin-6 affect organs and tissues, and allow for quick adjustments to preserve systemic integrity (Beckie, 2012; Juster et al., 2010; McEwen and Wingfield, 2003). In PP terms, neural models of the world must be adjusted to the internal and/or external changes if they no longer adequately predict sensory input.
+This first systemic reaction to psychosocial stressors represents a hypervigilant state activated via descending projections from the ACC-amygdala complex to the locus coeruleus (LC) in the brain stem. Here, norepinephrine boosts information transmission at cortical synapses (Aston-Jones and Cohen, 2005), again increasing the brain’s energy demand (Harris et al., 2012b). The selective enhancement of sensory information transmission can improve and adjust internal models to a now changed environment and thereby habituate and consolidate systemic responses (Mather et al., 2016). Additionally, the ACC descends projections to the HPA-axis where cortisol is released, passing the blood-brain barrier and binding to glucocorticoid receptors and mi-neralcorticoid receptors in neuronal populations located in the amygdala, cerebral cortex and hippocampus (Harris et al., 2012a). However, if no appropriate update is found and stress continues, ACC-transmitted error signals prevail, signaling continued entropy (uncertainty). The ACC-amygdala-complex remains in a hyperaroused and -vigilant state, which requires heightened energy output through the allostatic network, i.e., sympathetic nervous system and HPA-axis (Hitze et al., 2010). If these acute responses do not resolve uncertainty and stress becomes chronic, allostatic load grows and burdens the organism.
+These primary allostatic effects now lead to secondary outcomes of systemic dysregulation of cardiovascular, inflammatory and metabolic markers. As a response to the over- or underproduction of primary mediators, these systems alter their function, which can lead to sub-clinical levels of cardiovascular (e.g., blood pressure, heart rate, heart rate variability), metabolic (e.g., high- and high density lipoprotein cholesterol, glucose, insulin), and immune (c-reactive protein, fibrinogen) parameters (McEwen, 1998). Finally, the wear and tear leads to allostatic overload and can cause systemic damage manifesting itself as cardiovascular disease, chronic pain, cognitive dysfunction, or depressed mood (Peters et al., 2017). Tertiary outcomes then refer to the stage of allostatic overload, where the cluster of physiological efforts to return the system to homeostasis (allostatic load) manifests as mental and physical health decrease, poor quality of life and increased risk of mortality (Juster et al., 2010).
+5.4.	The burden of loneliness distress: social allostatic overload
+Perceptual content within predictive processing is proposed to be steered by the estimated precision of prior experience and incoming sensory signals (Clark, 2018; Hohwy, 2012). It is proposed that interoceptive inference plays a role in the occurrence of affective symptoms; when interoceptive signals are unexpected, they reach conscious awareness (Van den Bergh et al., 2017). Bodily sensations, according to this proposal, are interpreted as symptoms when the predictive model with the highest probability contains information representing disease-related causes. Negative or anxious affect could therefore result from aberrant social allostatic mechanisms that fail to return the system to the desired state of social connection; as expectations on higher levels, based on systemic needs, fail to accurately predict social stimuli, large prediction errors ensue and populate consciousness as negative affect signaling that needs are not met. Importantly, failure to return the system to more desirable social states may also and even primarily rely on external circumstances, where no social support system is available or has been removed due to loss, re-location, ongoing bullying, or other causes that are not within the control of the individual.
+However, how does perceived isolation from meaningful social connections elicit negative affect? The classic model of loneliness suggests that perceived social isolation is the starting point of a vicious cycle, in which the initial hypervigilance for social threats leads to confirmatory attentional, memory and behavioral biases (Cacioppo et al., 2014). These biases in turn lead to increased negative behavioral displays, social interactions and affect, causing the social environment to withdraw from the individual and thereby increasing social isolation. This cascade of negativity is underpinned by the activation of neuro-biological mechanisms that increase morbidity and mortality. The focus
+of this model is on the overall effects of perceived social isolation, where negative affect is both consequence and cause for loneliness. The social homeostasis model suggests that the prolonged engagement of mechanisms normally recruited for short-term adaptations plays a vital role in the negative consequences of perceived isolation (Matthews and Tye, 2019). In our model, we will concentrate on the effects of loneliness and loneliness distress on affective symptomatology.
+When adopting the notion of stress as uncertainty, the subjective experience of controllability takes on major relevance. Stress research shows that the perception of controllability of a stressful event is tightly related to the negative impact on the individual (e.g., Dickerson and Kemeny, 2004; Henderson et al., 2012; Maier and Seligman, 2016). Beliefs about uncertainty have been shown to mediate the magnitude of individual stress responses, and the adaptability of beliefs about uncertainty improves prediction of future outcomes (De Berker et al., 2016). This indicates that habituation to stressful experiences through the perception and judgment of uncertainty and controllability leads to more adaptive responses. Arguably, social stressors are not always in the control of an individual and can easily be perceived as uncontrollable. The perception of social stress as outside of one’s control may therefore not only causes the onset of the allostatic stress response, but may play a major role in the maintenance of social allostatic overload. Where habituation to stressors does not occur, allostatic load keeps increasing until it leads to systemic damage (Peters et al., 2017).
+Particularly when associated with sickness behaviors as a response to increased inflammatory signals, aberrant social allostatic processes that are associated with imbalanced emotional processing may drive affective symptoms of depression via social allostatic overload. The experimental manipulation of inflammatory levels (Eisenberger et al., 2010a; Harrison et al., 2009a; Rosenkranz et al., 2005) reveals interoceptive pathways that are neurally mediated via a discrete set of neural circuits, including basal and posterior ventromedial thalamus, dorsal mid and posterior insula (Harrison et al., 2009c). The ensuing increase in inflammation leads to the display of sickness behaviors, and specific functional changes within interoceptive brain regions can be identified for different components of these behaviors. While the sub-genual cingulate appears to underlie change in mood (Harrison et al., 2009a), the insula is implicated in the subjective experience of social disconnect (Harrison, 2017). The loss of interest in social interactions is associated with an increase in right anterior insula metabolism (Hannestad et al., 2012). Importantly, these are the same regions that underlie emotional and affective regulation, supporting the notion that inflammation, through interoceptive processing, enhances mechanisms that are associated with feelings of loneliness, social disconnect and the impairment of social cue processing (Moieni et al., 2015b).
+Depressed mood is associated with the negative appraisal of social company and the withdrawal from others (van Winkel et al., 2017). These findings fit with the hypothesis that depression is associated with a “locked-in” state of the brain, where negative assumptions about the environment are not corrected due to reduced exposure to potentially corrective stimuli and insensitivity to prediction errors containing corrective information (Barrett et al., 2016). In combination with the enlisting of sickness behaviors to conserve energy, this may lead to inefficient energy regulation that could underlie negative affect and biases the organism towards social withdrawal and general avoidance behaviors (Schwartenbeck et al., 2015). More specifically, dysfunction in visceromotor cortical regions, where interoceptive prediction errors are claimed to originate, may cause imbalances in responses to bodily needs through overpredicting metabolic energy demands (Nieuwenhuizen and Rutters, 2008). Ensuing HPA axis overactivity will then increase pro-inflammatory cytokines and cause changes in the endocrine and immune system (Barrett and Simmons, 2015). Ultimately, this process leads to aberrant coupling of interoceptive predictions and inputs at the thalamocortical level and may result in an increase in interoceptive prediction errors. In an attempt to down-regulate these noisy error signals through precision units leaves them
+less likely to influence and change predictions. The system is now “locked-in”, as sensitivity to prediction errors decreases, enabling faulty predictions to maintain the high demand of metabolic energy (Stephan et al., 2016). When the endocrine and immune system have reached their limit, depression ensues as a result of enlisting sickness behaviors to conserve energy (Barrett et al., 2016). In the case of loneliness, this process might set in as a result of HPA axis overactivity to shield against potential threats. Distress and negative affective symptomatology set in when the system does not revert to equilibrium, but stays “locked-in”.
+5.5.	Breaking the cycle: mechanisms, causes and interventions
+It remains an open empirical question how increased loneliness and loneliness distress are causally linked to higher mortality and morbidity. It is unclear whether external, social-environmental factors leading to feelings of loneliness distress set in motion the allostatic process that ultimately leads to systemic damage, or whether chronic allostatic load precedes feelings of loneliness and - through sickness behaviors such as social withdrawal - causes loneliness and distress. It is also possible that both social stressors (e.g., loss of a loved one, exposure to bullying) and internal stressors (e.g., onset of a depressive episode) can set in motion the allostatic process that is not resolved, which again might have either external or internal reasons.
+A recent meta-analysis discusses the mechanistic and causal role of loneliness in psychotic disorders (Michalska da Rocha et al., 2018). While it is confirmed that loneliness and psychotic symptoms are significantly interrelated, the causal structure of these relationships remains unclear. However, there is some evidence that loneliness -through elicitation or exacerbation of negative affect and low self-esteem - might play a role at the onset or at subclinical stages of psychosis (Garety et al., 2001). Others suggest a self-perpetuating cycle where psychotic symptoms foster isolation and disconnect from potentially helpful contacts and support, and loneliness enables the maintenance of symptoms through low self-esteem and negative affect, which in turn increase isolation (Gayer-Anderson and Morgan, 2013; van der Werf
+et al., 2010).
+Data from randomized controlled trials shed some light on how to break the cycle of loneliness, negative affect and further isolation. When compared to the effectiveness of Active Cognitive Therapy, Befriending showed equal improvements in both positive and negative symptoms (Jackson et al., 2008). Befriending consists of the simple practice of the therapist talking with the patient about general things in their life, or jointly participating in activities when verbal interaction proves difficult for the patient (Sensky et al., 2000). Furthermore, subjective increase in quality of life and self-reported recovery in patients diagnosed with schizophrenia or schizoaffective disorder was positively correlated with social support, and negatively correlated with feelings of loneliness. In a population of depressed patients, interventions counteracting social withdrawal and enabling meaningful social contact alleviated depressive symptomatology and may even prevent relapse (Cruwys et al., 2013, 2014). These findings suggest that the exposure to social stimuli might indeed counteract the “locked-in” state of the brain and facilitate the correction of negative assumptions, thus breaking the vicious cycle of perpetuating negative feelings of loneliness.
+However, a meta-analysis comparing different types of interventions in different designs found that those addressing maladaptive social cognition via Cognitive Behavioral Therapy-based interventions showed the greatest reduction in loneliness scores (Masi et al., 2011). It is concluded that increasing opportunities for social contact does not necessarily lead to an increase in meaningful relationships that would alleviate feelings of loneliness. Furthermore, these results speak for an important role of aberrant social cognitive mechanisms in contributing to feelings of loneliness, such as negative evaluations of the social environment, being stuck in a hypervigilant state that is caused by heightened perceptions of social threat, and memory biases towards negative social events (Masi et al., 2011).
+We suggest that, in line with the evidence reviewed here, feelings of loneliness and loneliness distress can play a causal role for the onset and maintenance of mental and physical health problems. The underlying
+mechanisms involve the recruitment and, most importantly, exhaustion of processes adapted to shield the individual from stressors. Although interventional studies show that improved social cognitive processing alleviates feelings of loneliness, the exact temporal and causal relationships between loneliness, physical and mental health are yet to be determined. The detrimental effects of loneliness on health are likely complex and multifaceted, where chronic illness - mental and physical - can lead to social withdrawal, setting the vicious cycle in motion, or where extended social stress leads to chronic illness via social allostatic overload (Fig. 1).
+6.	Conclusion and future research
+Loneliness and loneliness distress are phenomena that appear across all age groups and cultures. Given the evolutionary importance of belonging to a group, being distressed by the perceived or actual lack of social contact appears like a reasonable response. However, as shown in this narrative review, the effects of prolonged loneliness can be detrimental to the entire organism. The impact of loneliness on an individual includes not just maladaptive behavioral patterns, but appear to initiate a cascade of complex body-brain interactions that make the whole organism more vulnerable to mental and physical health conditions. It is thus of utter importance to clarify the causal directions between social isolation, negative feelings of loneliness, and brain and body responses. This review aimed at giving an impression of the current state of research on that topic.
+While research on loneliness and its impact on health is growing, there are many important areas still to explore. Our model of social allostasis proposes how affective symptomatology arises as a consequence of loneliness. This model, however, is based on research on neurotypical individuals with mental and physical health conditions and as such does not necessarily apply to neurodivergent individuals. It is an open question whether the same interoceptive and allostatic processes are involved in the impact of loneliness in neurodivergent individuals, or whether their heightened loneliness rates are due to more socio-structural reasons. One possibility, for example, is that they are exposed to social isolation more often due to the double empathy problem, which neurotypicals are usually less likely to be concerned with. However, once social isolation sets in, are the same processes involved in generating vulnerability to disease in neurotypical and neurodivergent individuals? More empirical research is needed to reveal the reasons for heightened loneliness in neurodiversity, their specific notion of loneliness and loneliness distress, and how these relate to the increased vulnerability to health conditions.
+In the case of neurotypical individuals with depression, only a subset of depressed individuals shows signs of heightened inflammation as a potential factor for their symptoms (Harrison, 2017), and there is a potential role of inflammation-induced mechanisms fostering feelings of loneliness and associated affective symptomatology. This leads to an empirical question that, to our knowledge, has not been investigated yet: Are depressed individuals with inflammatory issues lonelier and more distressed by social isolation than those without inflammatory markers of depression? The answer to this question could provide further hints about the role of inflammation and interoceptive pathways that are involved in affective symptomatology linked with loneliness distress and depression.
+From a clinical perspective, only few studies have investigated potential interventions to minimize loneliness and its negative effects. One way to break the vicious cycle of social isolation, social withdrawal and negative affect appears to be the exposure to meaningful social interaction. However, additional research is necessary to examine whether interventions have long-term positive effects and whether these effects can reverse potential damage that is already done.
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+INTRODUCTION
+Suicide causes concern among doctors, other health professionals, policy makers and the general public. The fundamental question of what drives a person to take their own life remains unanswered despite numerous studies. The major obstacle to an understanding of suicide is that the victim cannot be interviewed and the reason directly ascertained. One solution has been the development of the psychological autopsy. This
+1 Address for correspondence: Dr J. T. O. Cavanagh, University of Glasgow, Department of Psychological Medicine, The Academic Centre, Gartnavel Royal Hospital, Great Western Road, Glasgow G12OXH.
+technique is based upon a combination of interviews of those closest to the deceased and an examination of corroborating evidence from sources such as hospital and general practice case-notes, social work reports and criminal records. From this information an assessment is made of the suicide victim’s mental and physical health, personality, experience of social adversity and social integration. The aim is to produce as full and accurate a picture of the deceased as possible with a view to understanding why they killed themselves. Psychological autopsy is probably the most direct technique currently available for determining the relationship between particular risk factors and suicide. This
+395
+type of intensive approach not only provides direct information but also aids the interpretation of studies employing other techniques (Hawton et al. 1998).
+There are now in excess of 150 reports on psychological autopsy studies of suicide in the world literature. In this paper we report a systematic review of these studies. We aimed to answer two questions. First, which variables, of those studied, have the strongest association with suicide. Secondly, what is the potential impact of effectively treating such problems on suicide rates.
+METHOD
+All reports of psychological autopsies conducted to investigate death by suicide that included direct interviews with informants were eligible for inclusion (Beskow et al. 1991). We first searched three computerized databases up to June 2000; MEDLINE (from 1/1966), BIDS ISI (from 1/ 1981) andPSYCHLIT (from 1/1990). Our search used the key words [PSYCHOLOGICAL AUTOPSY] or [SUICIDE] or [PSYCHOLOGICAL AUTOPSY and SUICIDE]. All on-line abstracts were reviewed and reprints of all potentially eligible reports were obtained. We supplemented this search strategy by obtaining all reports cited in the reference lists of these papers.
+Data extraction
+All the reports were reviewed independently by two investigators (J.C. and A.C.). A structured proforma was used to assess eligibility and to extract relevant data. The proforma recorded the sampling technique, geographical area sampled and representativeness of the sample, the method of psychological autopsy, psychiatric diagnostic criteria used and methods of analysis. The findings of the studies were recorded in the following categories, which were chosen as they have been linked to suicide epidemiologically and are commonly used for clinical risk assessment.
+Medical
+Medical details were recorded for the following five categories.
+Presence of mental disorder
+Both individual diagnoses according to the Diagnostic and Statistical Manual (DSM) III, III-R
+or IV (American Psychiatric Association, 1994) categories and the presence of two or more DSM diagnoses, including mental disorder and substance abuse disorder was noted.
+History of deliberate self-harm
+History of deliberate self-harm was recorded as actual attempts rather than threats.
+Service contact
+This was defined as lifetime contact with mental health professionals.
+Communication of intent
+Communication of intent by deceased to health professional over lifetime was recorded.
+Presence of physical ill-health
+Social
+Adverse life events
+This was defined as interpersonal difficulties and/or loss (Rich et al. 1988; Heikkinen et al. 1992), and was divided into three time periods for case series: within the final 3 months; from 6 to 12 months; and in the last year before death.
+Social isolation
+Social isolation was defined as living alone, having little or no contact with support networks of family and friends and being unmarried.
+The results of data extraction by each rater were compared and any discrepancies resolved by a third investigator (S. L.). When more than one report of data from the same patient sample was identified, the report with the largest number of participants recording the variable of interest was used.
+We chose not to calculate a numerical quality score for each report, as has been recommended for randomized trials, but to explore the effects of various possible biases as recommended for systematic reviews of observational studies (Egger et al. 1998).
+Statistical analysis
+The median rates and 95 % confidence intervals were calculated (Gardner & Altman, 1998) for each of the studied variables and the range of scores described. To quantify the impact of any one factor or group of factors, population attributable fractions (PAFs) were calculated (Last, 1988). PAFs estimate the proportion of
+cases (e.g. suicides) in a population that can be attributed to the exposure (e.g. mental disorder) and can be interpreted as the fraction of cases in a population that could be avoided by reducing or eliminating the exposure. This assumes a direction of causality, which can be justified in suicide; for obvious reasons suicide cannot cause any of the studied variables. PAFs can only be calculated from controlled studies. When cases and controls are matched for one or more factors, the PAF can be calculated according to the following formula: P (E/D) x (RR — 1)/RR, where P (E/D) was the proportion of cases exposed to the factor and RR was the risk ratio (Coughlin et al. 1994).
+To examine for possible publication bias and heterogeneity, funnel plots were constructed of the number of subjects in case series studies by the percentage of those with the variable of interest.
+Heilig et al. 1982; Litman, 1984; Andersson- substance abuse are of note. Although the me-Segesten, 1989; Beskow et al. 1990; Garza-Leal dian proportions of social adversity and isolation & Landron, 1991; Little, 1992; Murphy et al. and previous communication of intent are simi-1992; Runseon & Rich, 1992; Brent et al. 1993 b, lar, the ranges and confidence limits of these are 1999 ; Clark, 1993 ; Mendelson & Rich, 1993 ; far wider.
+Hendin, 1994; Shneidman, 1994;Akuskai, 1995; In controlled studies, clear differences were Conwell et al. 1995; Duberstein, 1995; Seguin evident in the median proportions of cases (90%) et al. 1995; Shaffer et al. 1995; He & Lester, 1997; and controls (27%) with mental disorder and Malmberg et al. 1997; Gould et al. 1998; Heila the 95% CIs do not overlap (88 to 95% v. 14 et al. 1998, 1999a, b; Isometsa & Lonnqvist, to 48%). The medians for affective disorder 1998 ; Marttunen et al. 1998 ; Lindeman et al. and co-morbidity were notably different in 1999;Pearsonetal. 1999;Pirkolaetal. 1999b,c; cases and controls, but the only non-overlap of Saarinen et al. 1999).	95 % CIs was for co-morbid mental disorder
+Table 2 describes the median proportion, 95% with substance abuse (19 to 57% and 0 to 13% confidence intervals and ranges of all the vari- respectively). However, the large confidence inables studied by both case series and case-con- tervals show a lack of precision in the estimates trol techniques. In the case series, the clearest and interpretation should be accordingly cau-association between suicide and any of the study tious. The differences in median proportions variables was with mental disorder - 91 % (95 % were slightly less marked for the social variables, CI 81 to 98%) of suicides had a mental disorder. and the confidence intervals generally wider, Although the range is wide (23 to 100 %), this reflecting both the greater range of study results was solely attributable to a single study of suicide and the smaller number of pertinent studies. by self-burning among Indian women (Rao et al.	Table 3 shows the results of the population
+1989). The second lowest rate for mental disorder attributable fraction calculation from the seven was 74%. The majority of the cases of mental case-control studies (Shafii et al. 1988; Brent disorder was ofdepression. However, the rates of et al. 1993 a; Lesage et al. 1994; Shaffer et al. co-morbidity with other mental disorders and 1996; Appleby et al. 1999; Foster et al. 1999;
+Vijayakumar & Rajkumar, 1999) that reported data on both cases and controls and were not focused solely on one subgroup of diagnoses. All seven studies examined young adult or adolescent populations and all seven used living community control groups. They found that between 47 and 74% of the suicides examined were attributable to mental disorder. Affective disorders (21-57%) made the most substantial contributions. However, co-morbid mental disorders (41-52%) and co-morbidity between mental disorder and substance abuse (23-46 %) also appear to be of importance. Examination of the funnel plots revealed no evidence of publication bias in studies that examined the association between mental disorders and suicide
+(Fig. 1). This applied to both mental disorder as a general category and individual diagnostic categories. There was, however, evidence of a ceiling effect for mental disorder. Funnel plots of studies reporting on social variables did not conform to any pattern whatsoever, often simply due to a lack of data. We found no clear evidence of publication bias, or any clear reasons for apparent heterogeneity.
+DISCUSSION
+This systematic review found that, of the variables most often studied and regarded as clinically useful, mental disorders had the strongest associations with suicide. This held for subjects
+suffering from one diagnosis and those with both a mental disorder and an addiction problem. These findings are fairly homogeneous across studies. We found less evidence of association between suicide and the other variables examined. This appears to be attributable to both wider ranging results and a smaller number of studies. Overall, the results of case-controlled studies should be viewed on an individual basis in light of what cases and controls consist. The Population Attributable Fractions suggest that between a half and three-quarters of suicides could be avoided were it possible to have completely effective treatment, or prevention, of mental disorders.
+Is our finding of a strong and consistent association between mental disorder and suicide reliable? First, the psychological autopsy technique is regarded as valid in determining accurate diagnosis and in systematically measuring other variables post-mortem (Kelly & Mann, 1996). Secondly, our results are consistent with the strong positive evidence for the role of mental disorder throughout the suicide literature. Thirdly, we believe our study covers all the relevant papers in the area and we detected no suggestion of publication bias. However, there are several potential problems with our study. Of most importance is attribution bias. Coroners, and their equivalents in other countries, may be more likely to return suicide verdicts in those with known mental disorders; although the legal process is informed by a variety of sources and verdicts are not determined by the opinion of one legal officer. A further source of bias is the potential for the ‘ search after meaning ’. Informants may, in retrospect, identify a number of causal factors that could explain the suicide, and while some may focus on external factors such as adverse life events, others may well identify internal factors such as mental disorder. The use of corroborating evidence from informants other than close relatives, as well as medical and other records, in psychological autopsy studies should however offset such biases. Finally, the studies from which the PAFs were calculated were on younger age groups and the available data suggests that the importance of mental illness in suicide increases with age. Our results may therefore represent an underestimate of the true PAF.
+Why was the range of results for the other variables wider? The studies were from different
+geographical locations, but there was no evidence pointing towards systematic variations between countries. Furthermore, the vast majority of studies were conducted in Western Europe and the USA, societies that are relatively similar in cultural background. Neither was there evidence of systematic international difference in the ascertainment of suicide. An alternative explanation is that mental disorder, as currently defined, is a relatively homogeneous concept in most cultural groups but psychosocial factors are less so. Thus, while the interaction between a subject and a mental disorder may be more predictable and lead to a specific outcome -hopelessness and suicidal ideas are defining characteristics of depression - the interaction between a subject and social adversity may be less predictable. This is, however, speculative and we found no clear evidence for heterogeneity between different studies. It appears that there are simply insufficient data on specific diagnoses and psychosocial factors available from previous psychological autopsy studies; although the same evidence is sufficient to demonstrate the importance of mental disorders generally.
+What are the clinical implications of these findings? Strategies aimed at reducing suicide currently fall into two main groups (Rose, 1992). First, population-based methods focus on reducing the availability of means, for example by fitting catalytic converters in cars to reduce the lethality of exhaust fumes. These strategies are supported by recent reports of an overall drop in suicide rates (McClure, 2000). However, suicide rates among young males remain higher than 20 years ago and one method of suicide is often replaced by another. The second approach is to focus on those at increased risk. However, the lack of sensitivity and specificity of risk factors in the context of a relatively rare event like suicide have led some to question the usefulness of risk factors such as mental disorder (Powell et al. 2000). Furthermore, as a result of substantial overlap in risk factors, it can be difficult to interpret some of the risk data and decide which group or risk factor to target. Thus, reports of high relative and attributable risks associated with unemployment may be overestimates due to lack of controlling for the association with mental disorder (Mortensen et al. 2000).
+On the basis of this review we would suggest that it is at least legitimate and may be most
+effective to aim to optimize the management of those with mental disorder in general. Some might argue that it would be better to concentrate specifically on hopelessness and im-pulsivity, but specific treatments for these are currently experimental and not readily accessible, particularly in primary care. By contrast, effective pharmacological and psychological interventions are much more readily available for most mental disorders. We suggest that improving the detection and treatment of all disorders, particularly in primary care, may be the most effective way of reducing suicide rates. It may be necessary to use particular strategies in specific settings (Katon et al. 1996) rather than revert simply to the idea of more education (Thompson et al. 2000).
+What are the research implications ? We are not saying that particular disorders or psychosocial factors are unimportant. Rather, the available studies do not offer clear evidence that they are the most important variables. Further studies should focus on the interactions between particular mental disorders, social adversity, social isolation and previous communications of intent. In the meantime, it is our opinion that the core responsibility of doctors in trying to reduce suicide rates remains the identification and treatment of mental disorders.
+We are grateful to Ms Catherine Allen of the University of Edinburgh, Scotland for her assistance with this study.
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+INTRODUCTION
+Mental disorders are among the leading contributors to the global burden of disease.1 The global lifetime and past-year prevalence of common mental and substance use disorders are estimated to be 29% and 18%, respectively.2 Yet, the treatment gap, defined as “the absolute difference between the true prevalence of disorder and the treated proportion of individuals affected by the disorder”3—exceeds 50% in most countries.3 In lowand middleincome countries, the treatment gap often exceeds 90%.4 This excess burden has stimulated increased investment in global mental health implementation research intended to reduce the treatment gap, improve the quality of mental health services, and increase demand for care, thereby reducing the community prevalence of mental disorders.5,6 Despite growing evidence supporting the effectiveness of mental health services7 and the ability of innovative implementation strategies to increase access to care, these discoveries have not translated into a reduction in the prevalence of mental disorders at the population level.8 This may be explained, in part, by challenges translating into practice the evidence generated through rigorously controlled research studies.9
+Research on barriers and facilitators of mental health services implementation in low-resource settings has advanced our understanding of the common challenges that exist at the patient, provider, intervention, organization, community, and sociopolitical levels.10,11 Research on the relationships among these determinants remains limited, however, particularly in the context of advancing health equity in settings that face structural and social barriers to implementing mental health services. The majority of strategies developed and tested globally in response to common barriers to implementation of mental health services have focused on increasing access to care through task sharing, capacity building, and service integration.12-14 Although these strategies are necessary for reducing the treatment gap, particularly in low-resource settings (where the treatment gap is the largest),4 they may not be sufficient solutions to sustainably reduce the prevalence of mental disorders in complex and dynamic settings over time.
+Sustainability of mental health services is defined as continued delivery and behavior change that is maintained beyond the initial period of implementation and continues to produce benefits for patients, communities, and health systems.15 Although sustainable mental health services may evolve over time to meet changing local needs and resources, they ultimately continue to be delivered and benefit the community and system within which these services are implemented. To increase the likelihood of the sustainability of evidence-based mental health interventions and their population-level impact on health outcomes, strategies must address the multilevel, time-varying, and interrelated determinants of implementation and mental health outcomes. Failing to account for the ways in which communities and systemlevel factors interact over time in complex systems may explain, in part, why we have not observed an enduring decline in the burden of mental disorders at the population level despite efforts to increase the availability of evidence-based services.16
+Systems science is a discipline that has developed translational methods aimed to understand the behavior of complex systems, including how specific components of a system behave in relation to other components over time.17 Integrating principles of systems science into global mental health implementation research provides tools to anticipate emergent barriers and unintended consequences of interventions that hamper their long-term public health impact and to identify novel opportunities to intervene that may more efficiently reduce the burden of mental disorders.18 Applying these methods to understand the relationships among determinants of implementation may enable identification of efficient leverage points to improve the delivery and sustainability of mental health services in low-resource settings facing a complex network of implementation challenges amid a scarcity of services.10 The objective of this study is to characterize the relationships among common determinants (i.e., barriers and facilitators) and implementation outcomes of mental health services in low-and middle-income countries in order to explore how these relationships may affect the sustainability of services and the prevalence of mental disorders over time. By characterizing these relationships, we aim to identify key implementation processes that are critical to reducing the burden of mental disorders.
+As described in Supplemental Table 1, http://links.lww.com/HRP/A162, we first conducted a review of reviews to compile a comprehensive list of barriers and facilitators to implementing mental health services in low- and middle-income countries. We developed a search strategy for PubMed/MEDLINE that included terms related to mental disorder, intervention implementation, barriers and facilitators, and low- and middle-income countries (the full search strategy and flow diagram are provided in Supplemental Figures 1, http://links.lww.com/HRP/A164, and 2, http://links.lww.com/HRP/A165). Eligible articles included any type of review that presented challenges, barriers, or facilitators to implementing interventions for common and severe mental disorders, substance use disorders, or mental disorders more generally in low- and middle-income countries. Low- and middle-income countries were defined using the World Bank’s country and lending groups.19 We extracted information on determinants of mental health services implementation and the relationship between determinants from included studies, which are detailed in Supplemental Table 2, http://links.lww.com/HRP/A163.10’11’20-73 Determinants were defined as the barriers and facilitators of mental health services implementation. Barriers and facilitators identified in the review were categorized by at least two authors independently according to the domains described in the integrated sustainability framework (ISF).74 This framework was chosen because it includes domains common across implementation frameworks (e.g., inner and outer context) while also framing these domains as determinants of sustainability. Discrepancies were resolved through discussion. We also extracted information on the antecedents and consequences of barriers and facilitators described in this review to elucidate potential causal pathways among barriers, facilitators, and implementation outcomes.
+We transcribed the determinants of mental health services implementation that were extracted from the review articles onto notecards and arranged them on a large whiteboard. We drew arrows from antecedents to barriers/facilitators as well as from barriers/facilitators to consequences in order to display causal sequences of determinants and outcomes as they were described in the review articles. A simplified electronic depiction of the resulting interrelationship digraph (IRD) is provided in Supplemental Figure 3, http:// links.lww.com/HRP/A176, for illustrative purposes. We examined the IRD to identify key implementation, services, and patient outcomes that were influenced by the determinants identified in this review. Causal sequences that were not linked (directly or indirectly) to key mental health services outcomes were omitted from the final IRD. We examined the IRD to identify emergent categories of determinants that were then used to organize the barriers and facilitators within the ISF domains.
+We then constructed a theoretical framework informed by the ISF and Proctor’s implementation outcomes that included the key outcomes and determinant categories identified in the IRD. 74,75 To elaborate the theoretical framework and IRD into a causal loop diagram, we examined the IRD to identify determinants that displayed linkages to the outcomes included in the theoretical framework directly or indirectly via other determinants. We positioned these determinants in relation to the implementation, services, and patient outcomes as they were displayed on the IRD, and began to elaborate this model with causal
+feedback loops. Causal loops were pathways identified from the IRD (i.e., as described in the included review articles) that linked a series of determinants to the implementation, services, and patient outcomes and that fed back into the original determinant to produce a closed feedback loop. The direction of the arrows in the causal loop diagram indicates the directionality of the association between determinants or outcomes as described in the included studies. The polarity of each relationship was noted using a positive sign (+), indicating a positive association, or a negative sign (-), indicating an inverse association between the two constructs linked in the causal loop diagram. For example, a positive arrow linking risk factors to mental disorder may indicate that higher levels of the risk factor in the population will yield a higher prevalence of mental disorder. Conversely, a negative arrow linking implementation barriers to delivery of services would describe that greater implementation barriers result in a reduction in delivery of mental health services. Then, each feedback loop was defined as either a reinforcing (R) or balancing (B) loop. Reinforcing loops are amplifying processes whereby changes in the determinants and outcomes described in the loop result in an exponential growth or decline unless regulated by external forces. Balancing loops are self-regulating processes that limit growth or maintain equilibrium of a process, leading to a plateau or steady state over time.76,77 The direction and polarity of the arrows together determine the type of feedback loop (i.e., reinforcing or balancing), which can be used to understand how these processes collectively influence implementation outcomes. The elaborated causal loop diagram was then evaluated by study authors to determine whether the relationships were consistent with scientific knowledge and applied experience. Study authors reviewed the logic underlying each causal loop and iteratively refined the causal loops until they best reflected both the data extracted from the review articles and the study authors’ knowledge and experience.
+We retrieved 6180 articles from our search conducted in PubMed/MEDLINE (see Supplemental Figure 2, http://links.lww.com/HRP/A165). Four hundred ninety-four articles were excluded at the title/abstract screening stage because they were not review articles. The remaining 5594 articles excluded at the title/abstract phase either did not reference a mental health intervention/service in the title/abstract or were not conducted in a low-or middle-income country. Ninety-two articles were eligible for full text review. Twenty articles were excluded because they were not review articles, and seven review articles were excluded because they did not include studies in low- and middleincome countries. Fifty-six review articles reporting on barriers and facilitators to mental health programs in low- and middleincome countries were included in this review. The types of review article varied across studies and were described as critical reviews, desk reviews, literature and document reviews, narrative reviews, nonsystematic reviews, rapid appraisal reviews, review of government documents and guidelines, scoping reviews, and systematic reviews that sometimes included a meta-analysis.
+The majority of these studies reported on mental health programs in low- and middleincome countries generally.10,11,20,23,27,29-31,37-39,41,42,44,47,49,51,52,54,56,58,60,61,63,73 other reviews focused on specific continents (Africa,32,36,64 Asia28,68), countries or
+33	50,53	72	55	46,59	22,45
+sovereign states (Brazil, China, Colombia, Georgia, Ghana, India,
+Harv Rev Psychiatry. Author manuscript; available in PMC 2022 June 02.
+Tnr/lon 48 IZanvo 66 T ahntiAti 21,71 Dnlnctino 69	43 Dlulinmnoc 70 QTclon/lc 35
+joiddii, Kenya, Lebanon, ’ Palestine, Peru, Philippines, Solomon islands, South Africa,25,40 Uganda34), regions (Latin America,57 the Middle East,26,62 countries neighboring Syria65), or specific contexts (humanitarian or conflict settings24,67). Most reviews focused on mental health programs intended for the general population,10,11,20-28,30-39,42-47,50-53,56-61,63,64,66,68-71 whereas others focused on programs for children and adolescents,67,73 conflict-affected populations,49 refugees,48,62,65,72 internally displaced persons,55,72 or women during the perinatal period.29,41,54 Most reviews presented challenges for implementing general mental health services, with some focusing on the use of specific strategies such as telemental health,28,44,60,62,71 task sharing,27,29,30,36,47,50,54 integrated services,10,42,61 or communitybased approaches to improving mental health.32,37,41,46,52,59 Most reviews focused on services for mental health problems generally, though some focused on services for specific disorders, including depression,29,41,54 posttraumatic stress disorder,63 schizophrenia and psychotic disorders,22,31,69 or substance use disorders.49
+Key Barriers and Facilitators of Mental Health Services in Low- and Middle-Income Countries
+We identified three implementation and mental health services outcomes in relation to the prevalence of mental disorders that were commonly influenced by the barriers and facilitators described in the included articles: (1) demand for services (e.g., help seeking, utilization, continued engagement, retention in care),10,11,21-26,28,32,37-39,41,44,51,56,57,62,65,68-71 (2) supply of services (e.g., reach, access, implementation),10,20-24,26,29,30,32,34,36,39,43,44,46-52,54,55,57,58,61,63-67,69,71,72 and (3) quality of services (e.g., effectiveness, quality of care).10,24,26,34,38,52,58,59 We mapped the barriers and facilitators onto the domains of the iSF (Table 1).74 The iSF is composed of outer contextual factors, inner contextual factors, interventionist/population characteristics, intervention characteristics, and processes—all of which interact dynamically to determine sustainable service delivery and health impact.
+We stratified the barriers and facilitators within each of these domains by the three implementation and services outcomes (i.e., demand, supply, and quality of services; Table 1). Broadly, demand for mental health services was driven by characteristics of the population and outer context,11,21-26,28,29,31,32,38,39,47,56,57,62,68-70,72 the relationship between communities and the health system/providers,25,26,39,50,57,65,68,70 the sociocultural relevance of mental health interventions,26,29,41,42,44 and processes for engaging diverse stakeholders in the implementation of mental health services.23,26,59 The supply of mental health services, which included both access and implementation, was largely determined by outer and inner contextual factors. Supply of mental health services was influenced by the government’s commitment to supporting mental health services through policies, plans, and resources.10,21,23,32,42,43,49-51,55,58,61 Within the inner context, barriers such as poor health system infrastructure,10,27,33,46,51,64,66,67 limited resources,10,22,23,26,31,51,55,63,69 competing priorities,10,49,55 and a toxic institutional climate10 prevented delivery of mental health services. Having well-organized44,50 and decentralized services23,46 staffed by multidisciplinary and collaborative provider teams10,50,59,64 facilitated the implementation of mental health services. Other processes, including coordination,10,61 training and career
+development opportunities for staff,10 and engagement and collaboration among traditional healers, medical providers, governments, and service users,10,42,50 facilitated access and implementation of mental health services. The quality of mental health services was the outcome with the fewest determinants described in the literature. Community trust in the mental health system, including providers and interventions,34,52 and mechanisms to ensure accountability (e.g., monitoring, evaluation, and health information systems)34,55 improved the quality of mental health services.
+Relationships Between Barriers and Facilitators of Mental Health Services in Low- and Middle-Income Countries
+From the IRD we identified seven key categories of determinants that influenced the implementation of mental health services and the prevalence of mental disorders. These included the following: community stakeholder engagement; cultural relevance; stigma; human resource capacity; organization of services; governance, policy, and financing; and sociopolitical and community context. Together, these concepts represented determinants of mental health implementation and services outcomes across levels of the ISF (Table 1 and Figure 1).
+At the center of the IRD were the core relationships describing the association between key implementation and services outcomes, which informed the theoretical framework. As specified in our study objective a priori, we added the primary patient outcome—the prevalence of mental disorders—to our theoretical framework. These relationships suggested that in contexts with a void of mental health services, help seeking (including continued help seeking [i.e., retention]) initially increased the supply of services. However, once supply meets this demand and reaches an equilibrium (i.e., the treatment gap is reduced), greater supply will ultimately be associated with less demand. Demand for mental health services also led to increased quality of care; as a result, receipt of effective mental health services reduced the prevalence of mental disorders and subsequent treatment demand (bolded arrows, Figure 2/Supplemental Figure 4, http://links.lww.com/HRP/A178). We identified 13 feedback loops that explained and expanded upon the associations described in our theoretical framework (Figure 2/Supplemental Figure 4, http://links.lww.com/HRP/A178; see Text Box 1 for description of causal loops).
+Relationship between supply and demand for mental health services—The relationship between supply and demand for services was largely explained by two distinct processes. First, the availability of human resources for mental health was identified as a critical determinant of access to, and implementation of, mental health services. A shortage of providers was exacerbated by high treatment demand, which led to provider strain and turnover. Training and capacity building strengthened provider competency, motivation, and self-efficacy, thereby mitigating staff turnover and improving access to mental health care. Another mechanism describing the relationship between supply and demand for mental health services was community stakeholder engagement. The demand for mental health services stimulated community stakeholder engagement, increasing the relevance, acceptability, and efficiency of mental health services and mobilizing multisectoral stakeholders and resources to improve access to, and quality of, mental
+health services. There was also a reciprocal relationship whereby increased stakeholder engagement amplified demand for mental health services.
+Prevalence of mental disorders and the supply of, and demand for, mental health services—Social and structural stigma were key determinants of demand for mental health services. Social problems, discrimination, inequality, and setting insecurity increased the prevalence of mental disorders while also weakening health systems and infrastructure, which compromised supply of mental health services. These sociopolitical and contextual adversities were exacerbated by social stigma, which reduced demand for services. Since this review focused on collecting determinants of implementation, not all determinants of the prevalence of mental disorders are included in the causal loop diagram; risk factors and protective factors are included in the causal loop diagram as a single exogenous determinant representing the numerous biopsychosocial factors that contribute to the prevalence of mental disorders beyond the implementation determinants.
+Improving the quality of mental health services—Finally, the causal loop diagram describes the capacity of governments, coupled with a political commitment to mental health (which is often stimulated by community-level advocacy), to enact mental health policies and plans, dedicate funding and resources, strengthen health systems, and develop mental health information systems to promote accountability of the health system. The quality of mental health services was also influenced by community ownership of mental health services, which involved community trust, buy-in, and the inclusion of multiple health providers and healers (i.e., medical pluralism). These processes ultimately improved mental health services and outcomes.
+DISCUSSION
+In this study we identified 56 reviews describing barriers and facilitators to mental health services in low- and middle-income countries. These results revealed notable consistencies in barriers and facilitators across reviews, which categorized as follows: stigma; cultural relevance; community stakeholder engagement; human resource capacity; organization of services; governance, policy, and financing; and sociopolitical and community context. These categories were developed based on themes that emerged across the determinants we identified and were consistent with findings from recent reviews examining implementation determinants of mental health programs in low- and middle-income countries using similar frameworks, such as the Consolidated Framework for Implementation Research.78 For example, Esponda and colleagues (2020)10 reported that resources (including human resource capacity), social needs, provider stigma, and the fit of mental health services were critical determinants of implementation in low- and middle-income countries. As recommended by Esponda and colleagues,10 this study extends findings from previous reviews by applying systems thinking to understand the relationship among these determinants and implementation outcomes. Findings from this study reveal substantial complexity and multilevel interactions among these determinants that warranted a systems science approach to identify sustainable opportunities for improving the population-level impact of mental health and psychosocial services in complex, low-resource settings.
+The causal loop diagram identified multiple leverage points for improving the populationlevel impact of mental health services. Leverage points in complex systems are considered “places within a complex system where a small shift in one thing can produce big changes in everything.”79 These leverage points include strengthening community engagement and demand for mental health services, sustainable financing, capacity building, and alleviating social adversity and stigma. Capacity building was involved in multiple processes that influenced mental health implementation outcomes by increasing the number of trained providers, enabling decentralized and integrated service delivery models, and reducing provider stigma. The majority of global mental health implementation research has focused on increasing access to care through training and capacity building, which are essential strategies for improving mental health in settings with limited services and have been shown to be feasible in numerous low-resource settings.80 Our causal loop diagram indicates that increased investment in improving demand for mental health care may yield stronger and more sustainable impacts because of its centrality and multi-finality within the system. This result is consistent with prior literature suggesting that, in order to reduce the community prevalence of mental disorders, efforts to increase supply and reduce the treatment gap must be complemented by strategies to increase help seeking and quality of care.6 The difference between the biomedical model of mental health endorsed by most mental health specialists and the way in which psychological distress is experienced and understood in communities, referred to as the “credibility gap,” must be bridged in order to reduce the burden of mental health problems in communities.81 The necessary strategies include many of the processes identified in the causal loop diagram, including: (1) ensuring cultural relevance and understanding local needs, (2) integrating services, not only within the health system, but across sectors, and (3) providing more holistic care to reduce distress that may not require specialized interventions.81 Increasing demand for care may have a downstream impact on all aspects of the system, including, directly, by improving the quality and supply of services and, indirectly, via community engagement. Determinants of demand may be modified at the communityand health-system levels through increases in the supply of mental health services, community engagement, and reductions in stigma, as well as further upstream factors.
+Additionally, community stakeholder engagement displayed a reciprocal, reinforcing relationship with increased demand for mental health services. Community stakeholder engagement appears to be central to processes that increase demand for mental health care and that support the continued implementation and quality of mental health services to reduce the prevalence of mental disorders. The mechanisms through which community stakeholder engagement influences these processes include ensuring community needs are understood and acceptably addressed, increasing community trust and buy-in, including nontraditional stakeholders in mental health service delivery (e.g., nonmedical healers and religious healers) to promote culturally relevant and holistic care, and advocating for political prioritization of mental health services. This finding reveals the potential of a community-engaged, ground-up approach to changing the mental health system in an effort to promote impact and sustainability. Identifying this potential is consistent with calls to more meaningfully engage and empower communities to strengthen the relevance, impact, and sustainability of mental health services in low-resource settings.82
+We identified other pathways to improving population-level mental health that have been underutilized and underdeveloped in global mental health implementation research. The most proximal determinants of the prevalence of mental disorders include stigma, social problems, and the quality of mental health services. The severity of social problems, which is driven by contextual instability and inequality, directly affected the prevalence of mental disorders. Inequality and discrimination are consequences of stigma and also determinants of mental health. Qualitative research in India found that psychological symptoms attributed to social adversity may also reduce demand for mental health services because of low perceived need, which suggests an additional pathway by which social adversity may reduce population mental health.83 Social determinants of health and stigma must be considered when developing mental health initiatives that intend to have a population-level impact. Among the implementation and services outcomes that comprised the conceptual framework (Figure 1) and causal loop diagram, the quality of mental health services was the outcome with the fewest determinants that we identified in this review. The quality of mental health services is influenced by community buy-in and trust, medical pluralism (i.e., holistic care integrating multiple medical systems, such as Western and traditional medicine), the strength of the health system, and mental health information, monitoring, and evaluation systems. In an effort to uncover determinants of quality that were not identified through this literature review, future intervention and implementation research should explore strategies targeting these proximal determinants of mental health services quality.
+Relative to the implementation and services outcomes included in our theoretical framework (supply of mental health services, demand for mental health services, and quality of mental health services), the sustainability of mental health services (defined here as continued delivery of mental health services beyond the initial period of implementation and continued patient and community benefit) was rarely reported as an outcome in the eligible review articles, providing further evidence that sustainability is an understudied outcome in global mental health implementation research. Sustainable funding and resources for mental health services emerged as necessary determinants for improving the population-level impact of mental health services. However, national health budgets often have limited, if any, resources earmarked for mental health.84 Mental disorders receive less than one U.S. dollar of development assistance per disability-adjusted life year in low- and middle-income countries, which is significantly lower than most other disorder categories.85 As observed in the causal loop diagram, financial resources increased access to care by building mental health services capacity, strengthening health systems to increase demand for services, and increasing the quality of services by supporting mental health information, monitoring, and evaluation systems. Without reliable financing, significant tradeoffs and compromises to supply and quality will arise and reduce the capacity to sustain the implementation of mental health services.86 National health system strengthening initiatives in middle-income countries have identified lack of sustainable financing and low demand as the biggest threats to sustainability.87,88 Identifying determinants of sustainable financing is needed to overcome this substantial bottleneck in mental health service delivery in low- and middleincome countries.89 Community stakeholder engagement, mental health awareness, and political prioritization of mental health services were determinants of sustainable resources and funding identified in this review, and may be critical points of leverage to ensure that
+financial resources are allocated appropriately to improve the sustainability and populationlevel impact of mental health services in low-resource settings. Taken together, the causal loop diagram reinforces that the whole of the system must be taken into consideration when making investment decisions.
+The causal loop diagram enabled exploration of the potential impacts of changing system elements on sustainability of service delivery. One limitation, however, was that we were unable to capture qualitative changes in the nature (i.e., polarity) of the included relationships over time. For example, several included articles described the demand for mental health services increasing with supply in low-resource settings with a large treatment gap; this association may be true, however, only for settings with large treatment gaps due to a near absence of mental health services. As supply increases and a greater proportion of the demand for mental health services is met, the polarity of the relationship between supply and demand is expected to shift to one where increased supply reduces demand (i.e., reaches an equilibrium). Furthermore, as demand stabilizes and meets population needs, resources may be shifted elsewhere, leading to reduced supply and a resurgence of mental disorders. These scenarios have implications for sustainability. For instance, relying on training and capacity building as a singular strategy to improving population-level mental health may be compromised if mechanisms are not in place to regulate the initial increase in the demand for mental health care. In low-resource settings where the treatment gap is large enough that the relationship between the supply of, and demand for, services has not reached equilibrium, the overwhelming demand for mental health care may lead to provider burnout, attrition, and ultimately a shortage of mental health providers. Strategies that target implementation outcomes or demand for services under these conditions must supplement such efforts with strategies that provide support to providers and that increase the quality of services to ensure that the burden of mental disorders is ameliorated and that demand is regulated until the supply of services is able to accommodate the demand. To further explain the relationships depicted in this causal loop diagram, future research needs to use alternative system dynamics methods that allow these complex and nonlinear relationships to be represented as processes that drive the accumulation and depletion of constructs, such the unmet demand for mental health services and the community prevalence of mental disorders,
+Some important limitations of this review include that the data used for this study were restricted to peer-reviewed review articles indexed in PubMed. Therefore, this review did not include individual studies that were not captured in the eligible reviews and also did not include reviews from other databases or the gray literature. Despite this limitation, we identified substantial convergence and consistency of reported barriers and facilitators across studies, providing some evidence of saturation. The causal loop diagram reflects the information available in the current academic literature, which is likely to overrepresent treatment and specialized interventions. Group model-building approaches were designed to leverage community tacit knowledge.90 Relying on published literature is unlikely to capture some barriers and facilitators that may be revealed through a participatory group model-building process, including non-biomedical approaches to promoting mental health, alleviating distress, or addressing disorder.91 Further research to reproduce or refine the causal loop diagram produced in this study with community stakeholders in a low-resource
+setting is likely to reveal important pathways, feedback loops, determinants, and outcomes that were not identified in this study. One previous study that utilized a participatory group model-building approach to examine low demand for mental health services in Afghanistan revealed several similarities to the causal loop diagram produced in this study including the following: (1) stigma as a barrier to help seeking, (2) social problems and discrimination exacerbating mental health problems, (3) increased mental health system capacity improving the receipt of mental health services, and (4) awareness of mental health problems reducing stigma toward people living with mental disorders.92 In order to evaluate the generalizability of these results, future research should also examine whether relationships among determinants are modified by population, mental health condition, and context.
+Despite these limitations, this study extends our characterization of the common determinants of mental health services in low- and middle-income countries from a descriptive to a relational perspective. These findings reinforce the importance of community stakeholder engagement, strengthening health systems and human resources for mental health, addressing social determinants of mental health, and building political commitment—all interacting to promote sustainable mental health services that will produce a population-level impact. Fully elaborating these relationships will require further integration of systems science into ongoing implementation research to refine and confirm the relationships described in this study, which were derived from published literature. Applying systems-science methodologies to implementation science enables us to discover promising leverage points to improve the sustainability and population-level impact of mental health services, implementation strategies, and policies.
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+Suicide is the third leading cause of death in American adolescents, accounting for 1,371 deaths of youth between the ages of 12 and 18 years in 2005.1 In addition, every year nearly 1 million adolescents attempt suicide, leading to high emotional costs for families and financial costs for the health system.2 Unfortunately, no medication and few (< 10) psychotherapy studies have focused on suicidal youth.3 Existing studies demonstrate only a slight reduction in suicide
+attempts or ideation, and experimental treatments have rarely been superior to treatment as usual.4-7 Cognitive Behavioral Therapy (CBT) and/or medication successfully reduced mild suicidal ideation in a depressed sample (Treatment for Adolescents with Depression Study [TADS]8) but not in a more severely depressed and suicidal population.9 Overall, the efficacy of CBT and other forms of psychotherapy for suicidal youth has been mixed,3,10 and debate continues over whether medication exacerbates or reduces suicidal thinking.11 Clearly, research on alternative intervention approaches is warranted.
+Family-based treatments for youth suicide are promising for a number of reasons. First, nega
+tive family functioning (e.g., high conflict, low cohesion, poor attachment, ineffective parenting, etc.) is a strong risk factor for youth suicide and depression.12 Family conflict may precede 20% of suicide deaths and 50% of nonfatal suicidal acts.13 Second, family conflict is frequently a negative moderator of treatment outcome.8,9,14 Third, family cohesion, emotional support, and appropriate supervision are protective factors in preventing suicide and depression.12
+Not surprisingly, several family intervention studies have shown promise for treating suicidal youth. Harrington et al. found routine care plus family intervention more effective at reducing suicidal ideation than routine care alone, but only for youth without major depression.15 Huey et al. found Multisystemic Family Therapy more effective than hospitalization for decreasing suicide attempts but not suicidal ideation, depressive affect, or hopelessness.16 Brent et al. found CBT and family therapy for depressed adolescents had significant reductions in suicidal ideation compared with supportive therapy post-treatment, but no difference at follow-up.14
+One promising intervention is Attachmentbased Family Therapy (ABFT), the first manual-ized family therapy specifically designed to target family processes associated with depression and suicide. ABFT aims to improve the family's capacity for problem solving, affect regulation, and organization. This strengthens family cohesion, which can buffer against suicidal thinking, depression, and risk behaviors.12 In a study of ABFT for depressed adolescents, the treatment was found to significantly reduce both depression and suicidal ideation.17
+This study was designed and powered specifically to examine the efficacy of ABFT for reducing suicidal ideation in adolescents. Although many adolescents may think about suicide sometime in their life, severe and persistent ideation is a serious risk factor for suicidal behavior.2,3,18 In addition, these youth present a worrisome challenge for parents, teachers, and mental health and medical professionals, all of whom fear that ideation may lead to attempts. Unfortunately, adolescents with severe suicide ideation are often excluded from clinical trials because of the safety risks,19 and few prevention or treatment studies target youth with severe ideation. Moreover, few studies have included a large sample of inner-city, minority youth, a group whose risk for suicide has risen dramatically in the last de
+cade.20 With funding from the Centers for Disease Control and Prevention, we aimed to test whether ABFT reduced suicidal ideation and depressive symptoms more than Enhanced Usual Care (EUC). Because treatment retention has been a challenge in treating suicidal youth,21,22 we also aimed to see whether ABFT was associated with better retention than usual care in the community.
+METHOD
+Procedures
+The research occurred in the Department of Psychiatry at the Children's Hospital of Philadelphia (CHOP). Patients were recruited mainly from CHOP's primary care offices and emergency room. During routine clinical interviews, patients identified as having suicidal thoughts were referred to the clinic social worker for an assessment. Adolescents who endorsed scores above 31 on the Suicidal Ideation Questionnaire (SIQ-JR)23 and above 20 (i.e., moderate depression) on the Beck Depression Inventory (BDI-II)24 were referred to our study. On a second screening, generally 2 days later (mean = 2.21 days, SD = 1.34), if both suicidal ideation and depression scores remained above the cut-offs, patients were eligible for the study. For this high-risk group, a longer waiting period was deemed unethical. Patients had to be between 12 and 17 years of age with a parent or guardian willing to participate.
+Adolescents were not eligible if they 1) needed psychiatric hospitalization, 2) were recently discharged from a psychiatric hospital, 3) had current psychosis, or 4) had mental retardation or history of borderline intellectual functioning. Patients could remain on antidepressant medication if they had started it at least 12 weeks before randomization.
+Eligible adolescents and parents provided written, informed consent and were then randomized to either ABFT or EUC. Assessments were conducted at baseline, 6 weeks, 12 weeks (post-treatment), and 24 weeks (follow-up, 12 weeks after treatment). All assessment staff had a B.A. in psychology and were trained, certified, and supervised by the project coordinator. The study was approved by the IRB at CHOP.
+We used an adaptive or “urn” randomization procedure, maintained by the statistician, with four stratification variables: age, gender, past suicide attempt, and family conflict. The project coordinator was blinded to the randomization sequence until the baseline assessment was complete. Given limited grant resources and clinical severity of the patients, blinding follow-up assessors to treatment assignment was not feasible. Assessors needed knowledge of risk circumstances and available services to assess safety.
+Seven Ph.D.- or M.S.W.-level therapists provided ABFT (four female and three male). Six were Cauca
+sian and one was Asian-American. Each was trained, certified, and supervised by the first and fourth authors. Fidelity to treatment was monitored through weekly case discussion, videotape review, and live supervision.
+Treatments
+Attention-Based Family Therapy. ABFT17,25 emerges from interpersonal theories that suggest that depression and suicide can be precipitated, exacerbated, or buffered against by the quality of family relation-ships.26 Therefore, ABFT focuses on strengthening parent-adolescent attachment bonds to create a protective and secure base for adolescent development. Therapy begins with a discussion of what prohibits the adolescent from turning to his or her parent(s) for help when contemplating suicide. Family barriers included the following: 1) stress resulting from the adolescent's depression; 2) a history of negative interactions and communication; 3) abuse, neglect, abandonment; and 4) parental psychopathology. However, even in the absence of chronic family level stress, families needed help managing these suicidal crises.
+Although ABFT therapists use behavior, cognitive, and psychoeducational interventions, the model is primarily a process-oriented, emotion-focused treatment, guided by a semistructured treatment protocol. Treatment is conceptualized as five specific tasks, each with a distinct process and goal. Treatment begins with the Relational Reframe Task, conducted with relevant family members, and aims to make strengthening of relationships (as opposed to behavior or symptom management) the initial treatment goal. The Adolescent Alliance Task, with the adolescent alone, then identifies core family conflicts linked to suicide and prepares the adolescent to discuss these with his or her parents (one to two sessions).
+Next, the Parent Alliance Task with the parents alone aims to amplify parental love and empathy, and to teach emotionally focused parenting skills that will increase affective attunement (one to two sessions). These sessions culminate in the Reattachment Task, in which families come back together to discuss identified problems and practice new communication, problem solving, and affect regulation skills (three to four sessions). As attachment relationships strengthen, therapists focus on the Competency Task. This task promotes adolescent autonomy (e.g., improving school functioning, increasing pleasant activities) while maintaining family connection. This ideal progression of tasks is modified as needed.
+Enhanced Usual Care. EUC was a facilitated referral process with ongoing clinical monitoring. We found providers, set up initial appointments, and encouraged attendance. It was the treating therapist's responsibility to engage and retain the adolescent in treatment.
+Safety Monitoring. We monitored patients' clinical status weekly using the SIQ-JR and BDI-II, administered either face-to-face (ABFT) or over the phone (EUC). Information regarding service use was gathered weekly. In addition, a project staff member was always on call for emergencies.
+Outcome Measures
+All outcome assessments were conducted face-to-face for all patients. The study had two declared primary outcomes: suicidal ideation and depressive symptoms. The self-report SIQ-JR23 and assessor-administered Scale for Suicidal Ideation (SSI)27 were used to assess the trajectory of change in suicidal ideation over time (total scores). For clinical recovery, we used the SIQ-JR <13 (based on a nonclinical inner city, minority sample28) and SSI = 0. The SSI was dichotomized because the data were not normally distributed, to evaluate the treatment based on the absence of ideation.29 The self-report BDI-II was used to assess trajectory of change (total score) in depressive symptoms and clinical response (BDI-II < 9).14,24
+Treatment retention was a secondary outcome, measured as number of sessions attended. For descriptive purposes and subgroup analyses, psychiatric diagnosis was determined using the clinician-administered, computerized Diagnostic Interview Schedule for Children (DISC).30
+Data Analysis
+Statistical Methods. Tests of baseline differences in demographic and clinical characteristics were conducted using independent-samples t tests for continuous variables and Chi-square or Fisher exact tests of independence for categorical variables. Implementation of various Box-Cox transformations indicated that a square-root transformation would correct the deviations in normality for the SIQ-JR and the BDI-II (both were negatively skewed). No substantial improvement in near normality was achieved for the SSI, so it was dichotomized.29
+Using all four assessments and intent to treat, hierarchical linear modeling (HLM) was used to investigate treatment differences.31 For the SIQ-JR and the SSI, the main efficacy analysis was determined to be piecewise, modeling the rate of change from baseline to 12 weeks (end of active treatment), and then again at 24 weeks (follow-up). For the BDI-II, the piecewise model ran first from baseline to 6 weeks (mid-treatment) and then to 24 weeks. For the binary outcomes, including clinical response, we implemented a hierarchical generalized linear model that accommodates the binary nature of the data and the clustering within each subject. Effect size was measured using Cohen's d for the continuous measures and odds ratios (OR) for the binary measures.
+The OR corresponded to the number of times an event was more likely to occur for ABFT compared with EUC at each assessment.
+Nonparametric Wilcoxon rank sum tests were used to compare number of sessions attended. The patternmixture model approach was used to assess whether missing data had a substantive influence on results. All analyses were conducted using SPSS Version 15 and SAS Version 9.1.3.
+Statistical Power. We originally estimated a required sample size of 120 patients for the longitudinal analysis of SIQ-JR. Given the time needed for creating a referral infrastructure with primary care and the short recruitment window (26 months), we did not achieve the targeted goals. The obtained sample sizes of 35 (ABFT) and 31 (EUC), with a 14.4% missing data rate at the 24-week follow-up assessment, and an approximate intrasubject correlation of 0.40 (estimated value of 0.34 for the SIQ-JR scale), indicated the study power of 74.5%. Such power would yield a statistically significant result for an effect size of 0.60, power of 81.0% for an effect size of 0.65, and power of 86.4% for an effect size of 0.70.
+RESULTS
+Enrollment Statistics
+Between March 2005 and May 2007, 341 adolescents were referred for the first screening (Figure 1). Of the 248 who were excluded, 188 did not meet study criteria, 36 became unreachable before the second screening, 14 refused study participation, six were hospitalized, and four were excluded for other reasons. Of the 93 who attended the second screening, 11 no longer met study criteria, eight were unreachable before consent, and five refused study participation. Of the 69 who qualified based on the second screening, three reported psychotic features during intake and were excluded. Of the final sample, 50 participants (75%) were referred from primary care, and 16 (25%) were referred from emergency departments.
+Demographic, Clinical, and EUC Characteristics There were no differences in any demographic or clinical variables between the two treatment conditions (Table 1). The mean age was 15.1 years (SD = 1.5), 49 (74%) were African American, and 55 (83%) were female. Of the parents, 27 (41%) had an income of less than $30,000, 17 (26%) were married, and 46 (70%) had no more than a high school diploma. A total of 31 (47%) adolescents met criteria for a depressive disorder, 44 (67%)
+met criteria for any anxiety disorder, and 38 (57%) met criteria for an externalizing disorder (i.e., attention deficit/hyperactivity disorder [ADHD], oppositional disorder, or conduct disorder). Of the participants, 41 (62%) reported having made a past suicide attempt, and nearly three-quarters of those patients (30 of the 41 attempters) reported making multiple attempts. Twenty (30%) participants reported a history of suicide attempts in their families. Upon study entry, six patients were stable (> 12 weeks) being treated with antidepressant medication, three in each treatment condition.
+Of the patients in EUC, seven (23%) were referred to private practice, and 24 (77%) were referred to community mental health centers. Sixteen (52%) received individual therapy, six (19%) received group therapy, two (6%) received family therapy, one (3%) received case management, and six (19%) did not attend treatment. There were no differences in the completion rates of assessments between the two treatment groups at any of the time points. Implementation of the pattern-mixture approach indicated group comparisons were not biased because of completion status.
+Trajectory of Change: Suicidal Ideation
+On self-reported suicidal ideation (i.e., SIQ-JR), ABFT demonstrated a significantly greater rate of improvement during the treatment period (12 weeks) than EUC (F(1, 64) = 12.60, p = .001) (Figure 2 and Table 2). No significant difference in rates of change was observed between treatments during the follow-up phase (F(1, 64) = 0.01, p < .91). During the treatment phase, the amount of change on the square-root of the SIQ-JR scale was -5.32 (standard error [SE] = 0.38) for ABFT and -3.35 (SE = 0.40) for EUC. This corresponded to a significant difference of -1.97 (SE = 0.55) with a large effect size (d = 0.95) in favor of ABFT (t(64) = 3.55, p = .0007). The total change from baseline to follow-up on the square-root of the SIQ-JR for ABFT was —4.37 (SE = 0.40) and for EUC -2.34 (SE = 0.43). This corresponded to a significant difference of 2.03 (SE = 0.59) with a large effect size (d = 0.97), in favor of ABFT (t(64) = —3.45, p = .001). Therefore, despite the slight increase of suicidal ideation for both groups during the follow-up phase, the large effect in favor of ABFT was maintained (Table 3).
+Regarding the trajectory of change of clinicianrated suicidality (SSI), ABFT demonstrated a significantly greater rate of change on the log-odds scale than EUC during the treatment phase (F(1,64) = 6.32, p = .014). During the follow-up phase, there was no significant difference in rate of change between the two groups (F(1,64 ) = 0.20, p = .65). During the treatment phase, the amount of change on the log-odds SSI for ABFT was 2.51 (SE = 0.51) and for EUC was 0.77 (SE = 0.47). This corresponded to a significant difference of 1.74 (SE =
+J
+126 www.jaacap.org
+0.69) with a medium to larger effect size (d = 0.62) in favor of ABFT (t(64) = 2.51, p = .014). The total change from baseline to follow-up on the log-odds SSI scale for ABFT was 3.21 (SE = 0.61) and for EUC 1.14 (SE = 0.53). This corresponded to a significant difference of 2.07 (SE = 0.80) with a medium to large effect size (d = 0.64) in favor of ABFT (t(64) = 2.58, p = .012). Therefore, although both groups improved after 12 weeks, ABFT continued to have a slightly higher rate of improvement, with a medium to large effect.
+-jal of the American Academy of Child & Adolescent Psychiatry
+VOLUME 49 NUMBER 2 FEBRUARY 2010
+Clinical Recovery: Suicidal Ideation
+For clinically meaningful change on the selfreported SIQ-JR (Table 4), ABFT had a significant effect at all time points and large effect sizes represented by the odds ratios. At 6 weeks, 69.7% of ABFT participants and 40.7% of EUC partici-
+pants reported suicidal ideation in the normative range (odds ratio [OR] = 3.35, 95% confidence interval [CI] = 1.15-9.73; y2(1) = 5.07, p = .02). At 12 weeks, 87.1% of ABFT participants and 51.7% of EUC participants reported ideation in the normative range (OR = 6.30, 95% CI = 1.7622.61, y2(1) = 8.93, p = .003). At 24 weeks, 70.0% of ABFT participants and 34.6% of EUC participants reported ideation in the normative range (OR = 4.41, 95% CI = 1.43-13.56; y2(1) = 7.01, p = .008).
+Clinical response on clinician-rated SSI demonstrated similar patterns as the SIQ-JR. At 6 weeks, 40.7% of ABFT participants and 33.3% of EUC participants reported no suicidal ideation in the past week (OR = 1.38, 95% CI = 0.44-4.32; y2(1) = 0.30, p = .59). At 12 weeks, 69.2% of the ABFT participants and 34.6% of the EUC participants reported no suicidal ideation in the past week (OR = 4.25, 95% CI = 1.33-13.56; y2(1) = 6.24, p = .01). At 24 weeks, 82.1% of the ABFT participants and 46.2% of the EUC participants reported no suicidal ideation in the past week (OR = 5.37, 95% CI = 1.56-18.49; y2(1) = 7.66, p
+= .006). Over the 6 months of the study, 11 patients made low lethality suicide attempts (ABFT= 4 [11%]; EUC = 7 [22%]). These numbers are too small for statistical analysis.
+Depressive Symptoms
+The trajectory of change on self-reported depressive symptoms (BDI-II) showed a marginally significant differential rate of change during the first 6 weeks of treatment between the two groups (F(1, 64) = 3.00, p = .09). From midtreatment to follow-up, there was no significant difference in rate of change between the two groups (F(1, 64) = 0.33, p = .57). During the first 6 weeks, ABFT demonstrated an average reduction of 2.16 (SE = 0.24), whereas EUC had an average reduction of 1.55 (SE = 0.26) on the square root BDI-II. This corresponded to a near
+significant difference of 0.62 (SE = 0.353) with a small to medium effect size (d = 0.37; t(64) = 1.75, p = .08). For the total change from baseline to follow-up there was a nonsignificant difference of 0.36 (SE = 0.51) with an effect size (d = 0.22) in favor of ABFT (t(64) = -0.72, p = .48).
+For clinically meaningful change in depressive symptoms (i.e., BDI-II > 9), we found marginally significant effects and large effect sizes represented at all time points. At mid-treatment, 34.4% of ABFT participants and 11.1% of EUC participants reported depression scores in the nonclini-cal range (OR = 4.19; 95% CI = 1.03-17.07; y2(1) = 4.38, p = .04). At post-treatment, 54.8% of ABFT participants and 31.0% of EUC participants reported nonclinical depression scores (OR = 2.70,95% CI = 0.94-7.71; y2(1) = 3.46, p = .06). At follow-up, 58.1% of ABFT participants and 38.5%
+of EUC participants reported nonclinical depression scores (OR = 2.21, 95% CI = 0.76-6.42; ^2(1) = 2.17, p = .14).
+Treatment Retention
+ABFT patients attended a significantly greater number of sessions (mean = 9.71, SD = 5.26) than EUC patients (mean = 2.87, SD = 3.3; Z = —4.74, p < .001). To determine whether treatment rather than the number of sessions had an impact on the SIQ-JR scores, we explored the trajectory of change for the subsample of patients who received no more than six sessions in either treatment (ABFT = 9; EUC = 27). Similar to the findings in the full sample, analysis of the subsample demonstrated that ABFT cases showed a marginally greater rate of change during the active treatment period compared with EUC (F(1, 34) = 2.84, p = .10).
+Subsample of Adolescents Diagnosed with Depression
+To facilitate comparison with other treatment studies, we looked at rates of change on SIQ-JR for those patients who met criteria for clinical depression during the past year (n = 35, 53.0%; EUC = 19,61.3%; ABFT = 16; 45.7%; ^2(1) = 1.60, p = .21). Similar to findings from the full data set, ABFT demonstrated a significantly greater rate of change than EUC for these depressed participants during the treatment phase (F(1,33) = 6.16, p = .02). During the follow-up phase, there was no significant difference in rate of change be
+tween the two groups (F(1, 33) = 0.10, p = .75). The total change, from baseline to follow-up was -4.35 (SE = 0.66) for ABFT and -2.19 (SE = 0.62) for EUC on the square-root SIQ-JR. This corresponded to a significant difference of 2.16 (SE = 0.91) with an effect size (d = 1.00) in favor of ABFT (t(33) = -2.39, p = .02). Therefore, the large effect on suicidal ideation in favor of ABFT for clinically depressed subjects is similar to the effect found for the full sample.
+DISCUSSION
+The findings from this study are promising. Compared with usual care in the community, youth treated with ABFT demonstrated significantly greater and more rapid reductions in suicidal ideation during the treatment. These differences persisted at follow-up, with an overall large effect size (.97). These findings are strengthened by the consistency across self-report, clinician ratings, and for the subsample of patients who met criteria for a depressive diagnosis. ABFT was also associated with greater rates of clinical recovery. For this, we used the stringent criteria of mean SIQ-JR scores (< 13) in a nonclini-cal sample, rather than the SIQ-JR clinical cut-off of 31. On both the SIQ-JR and the SSI, patients in ABFT at post-treatment and follow-up, were at least four times more likely to show scores in recovery than patients in EUC. This is one of the few studies to demonstrate a research treatment to be more effective than treatment as usual for reducing suicidal ideation in adolescents.3 Al
+though the rates of suicide attempts during the 6 months of the study were too small for statistical comparisons, the percentages for ABFT (11%) were as low or lower than reported in other studies.5-7
+ABFT also demonstrated nearly significant reductions in depressive symptoms by mid-treatment compared with EUC, with no difference at post-treatment and follow-up. Effect sizes, however, at all time points were very strong for ABFT suggesting that a larger sample size may have produced statistical significance. In addition, long-term differences between treatments for depression are uncommon,8,14 possibly because of the episodic nature of the disorder. Still, ABFT provided more rapid relief from depressive symptoms than community care, an important advantage when youth are at risk for suicide. These findings were supported by the measures of clinical recovery for depression. Compared with EUC, patients in ABFT were 3 times more likely to report BDI-II total scores in the nonclini-cal range (<9) at mid-treatment and 2.3 times more likely at follow-up.
+The data on retention were particularly important. ABFT retained adolescents in treatment longer than usual care, even with the additional support and monitoring offered in EUC. These findings are meaningful, given the difficulties of engaging suicidal youth in outpatient treatment, but are not surprising, given the multiple barriers to services in community settings.22 Still our retention rates were as good as, or better than, most clinical trials with suicidal youth.3,7,21,22 Given that parents typically bring adolescents to therapy, our focus on parent engagement may explain this success. More research is needed to test this hypothesis.
+Unfortunately, the low EUC retention rate also raises questions about the internal validity of the study. In other words, what is really causing the change? Differential outcomes may be a function of the treatment dose and not treatment type. Perhaps an equal dose of community care would have achieved outcomes similar to ABFT. Alternatively, maybe outcome and retention effects were due to therapist or context differences. This study compared trained, research therapists receiving close supervision (ABFT) to a range of providers, with varied training backgrounds, working in a range of treatment settings. In addition, the ABFT treatment was free, with no waiting list and flexible scheduling, characteris
+tics not common in the community. These confounds plague most studies using TAU as a comparison group.32
+Despite these limitations, several reasons support enthusiasm for the validity of the results. First, when comparing all patients who received a low dose of either treatment, patients in ABFT continued to show better outcomes. Second, if we compare post-treatment scores on the SIQ-JR across different studies, for which most samples start above a score of 31, ABFT (mean = 8) consistently does as well or better than other experimental treatments: mean = 26 for Peer Nominated Support,5 mean = 24.5 for skill-based treatment,7 mean = 11.8 for CBT plus fluoxetine, 14.4 for fluoxetine alone, mean = 11.4 for CBT alone and 15 for placebo.8 Although these points are encouraging, conclusive claims about the efficacy of ABFT for the treatment of suicidal youth will depend on a future study that can address the design confounds outlined above.
+Other limitations should be considered. First, assessors were not blinded to treatment assignment. Although not uncommon in studies of high-risk participants,29 the consistency across self-report and interviewer measures, as well as across multiple clinical domains, lends some confidence that assessor bias did not compromise the findings. Second, three-fourths of the sample was African American, and nearly one-half lived below the poverty level. Thus, our findings may not generalize to more affluent or culturally heterogeneous samples. Research on ABFT with more diverse populations is needed. Third, we lack data to suggest when family treatment might be contraindicted (e.g., family conflict is too high or is absent). Certainly no treatment is perfect for all patients, although helping any family through a youth's suicidal crisis seems clinically appropriate. Fourth, the relatively short follow-up limits our understanding of long-term treatment benefits. Longer follow-up would better reflect treatment benefits. Finally, just to be clear, based on this study, we cannot claim that ABFT prevents suicidal behavior, only that it reduces suicidal ideation.
+An important goal of the National Strategy for Suicide Prevention is the development, testing, and dissemination of effective treatments for youth suicide.2 Family-based interventions have been shown to be effective for treating these youth, and ABFT in particular seems a promising treatment for reducing suicidal ideation and de
+pressive symptoms. However, more empirical evidence on the efficacy and mechanisms of change for ABFT is warranted. &
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+BACKGROUND
+Description of the condition
+Worldwide, more than 25% of people develop one or more mental or behavioural disorders, during their entire lifetime (WHO 2001). Schizophrenia is one illness heavily contributing to the numbers of people considered severely mentally ill. The lifetime prevalence of schizophrenia alone is 0.58% in the adult population (Warner 1995). Currently this one illness is 26th on the list of diseases ranked according to contribution to overall burden in term of disability-adjusted life years (DALYs). Its ranking is projected to rise to 20th by the year 2020, with more than 17 million DALYs lost (accounting for 1.25% of overall burden, (Murray 1996). Other psychiatric/psychological conditions, however, can also cause very considerable problems in a person’s functioning. Many people with other types of non-organic psychotic illness, or even non-psychotic disorders such as personality disorder, can be considered severely mentally ill.
+There has been lack of consensus about the definition of ’severe mental illness’ but the most common dimensions used to identify this group are i. diagnosis, ii. disability, iii. duration and iv. abnormal behaviour. There is, however, little consistency between dimensions and thresholds used in different settings (Slade 1997). The definition of severe mental illness with the widest consensus is that of the National Institute of Mental Health (NIMH) (Schinnar 1990). Their definition is based on three criteria: i. diagnosis of non-organic psychosis or personality disorder; ii. duration characterised as involving ’prolonged illness’ and ’long term treatment’ and operationalised as a two-year or longer history of mental illness or treatment; and iii. disability, which includes dangerous or disturbing social behaviour, moderate impairment in work and non-work activities and mild impairment in basic needs (National Institute of Mental Health 1987).
+A survey was conducted in Europe aiming to calculate prevalence rates of severe mental illness according to the NIMH definition. This put the total population-based annual prevalence at approximately two per thousand (Ruggeri 2000).
+Description of the intervention
+Since the 1960s there has been an almost worldwide trend towards the closure of institutions for the mentally ill. Coupled with these closures, many government policies have focused on reducing the number of hospital beds for people with severe mental illness in favour of providing care in a variety of non-hospital settings - outpatient clinics, day centres or community mental health centres. These changes were consistent with the increasing shift from hospital-based care in favour of a more community-focused approach (Malone 2007).
+A major thrust towards community care has been the development of community mental health teams (Bouras 1986; Bennett 1991). Over the last 20 years various types of approaches to working within community mental health teams have evolved. These approaches fall into two main categories: i. services with well-delimited aims, such as crisis resolution and home treatments teams, vocational rehabilitation and early intervention service; and ii. services aimed at meeting a wide range of patient needs, such as Assertive Community Treatment (ACT) and Case Management (CM) (Ruggeri 2008).
+Assertive Community Treatment and Case Management are community-based packages of care, developed in the in the early 1970s. These packages of care were initially conceived to co-ordinate the care of severely mentally ill people discharged from closing mental hospitals. They were, however, soon more widely applied as a means of caring for severely mentally ill patients who did not require immediate admission (Thompson 1990).
+Assertive Community Treatment and Case Management do share common goals such as maintaining contact, reducing hospitalisation (and hence cost) and improving outcome (Table 1). There are, however, at least in theory and respect to the original models, important structural distinctions between them. Nonetheless, through clinical practice, across time, the two interventions have evolved and tended to converge into a package of care that can be called ’Intensive Case Management’, containing elements from both the two original models (Scott 1995; Burns 2008). Brokerage case management was rapidly abandoned in favour of Clinical Case Management (Holloway 1995) and more sophisticated, but poorly defined models were developed. In these models case managers have clinical training, provide at least some clinical services, and operate with low caseloads (Rubin 1992; Solomon 1992). Assertive Community Treatment is sometimes described as a teambased case management where Case Management involves individual caseloads, but this distinction is not maintained consistently (Burns 2001). Thus, in both clinical trials and clinical practice, what is presently called ’Case Management’ is likely to contain some elements of ACT practice. These models can be called ’Clinical Case Management’, ’Intensive Case Management’ and ’Strengths Case Management’ (Solomon 1992). However, Intensive Case Management is a broader term often used interchangeably with Assertive Community Treatment but distinguished from it on the grounds that it often lacks one or more ACT programme elements (Burns 2001). Intensive Case Management emphasises the importance of small caseload (usually considerably less than 20) and high intensity input, but is otherwise not clearly defined (Marshall 2008). Intensive case managers are usually clinicians who act as therapist in addition to their case management duties (Marshall 2008).
+How the intervention might work
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+7
+The theory behind care in the community is that it enables people to live as independently as possible within their own homes or ’homely settings’ out of hospital, because unnecessary hospital care is wasteful, un-therapeutic and stigmatising. It was hoped that living in the community would increase opportunities for people with severe mental illness to achieve their full potential as autonomous members of society (Department of Health 1990). Community care policies are also aimed at promoting choice and independence for people experiencing mental health difficulties. Intensive Case Management is an intervention at the level of local service organisation. It is a way of organising teams rather than a specific treatment model (Johnson 2008). Intensive Case Management should provide a mental health service which is a reliable, systematic, flexible and a co-ordinated care method, addressed to answer the unique combination of health and social care needs of people with severe mental illness. It represents a long term intensive approach to the patient in the community (Killaspy 2008), providing a comprehensive range of treatment, rehabilitation and support services (Scott 1995). Intensive Case Management aims to helping people to acquire material resources (such as food, shelter, clothing and medical care) and to improve psychosocial functioning; to provide sufficient support to keep the patient involved in community life and to encourage growth toward greater autonomy; to develop coping skills to meet the demands of community life; to insure continuity of care among treatment agencies (Stein 1980). Key purposes of Intensive Case Management are to improve outcome, reduce hospitalisation and prevent loss of contact with services.
+Recently it has been suggested that the success of Intensive Case Management depends on its fidelity to the Assertive Community Treatment model (i.e. if a team approach is properly implemented) and on the setting (i.e. it would work better where there is a high baseline level of bed use) (Burns 2007).
+Why it is important to do this review
+With the evolution of the original intervention models there was a need to update and merge two previous relevant Cochrane reviews (Marshall 2000a; Marshall 2000b) and to take into account the findings of work by the same authoring team (Burns 2007). During the last ten years intervention models have not only been modified, merged and become more difficult to distinguish in practice, but also research has been more widespread with new studies evaluating these approaches outside of the USA.
+Inpatient treatment remains the major cost of healthcare (Burns 2001). The effects of the currently implemented packages of care in different settings should be fully understood across a range of outcomes.
+OBJECTIVES
+1. Primary objective
+To assess the effects of Intensive Case Management as a means of caring for severely mentally ill people in the community in comparison with non-Intensive Case Management (caseload >20) and with standard community care. The review does not distinguish between models of Intensive Case Management.
+2. Secondary objective
+To assess whether the effect of Intensive Case Management on hospitalisation (mean number of days per month in hospital) is influenced by intervention’s fidelity to Assertive Community Treatment model and by the rate of hospital use in the setting where the trial was conducted (baseline level of hospital use).
+METHODS
+Criteria for considering studies for this review
+Types of studies
+All relevant randomised controlled trials were considered, and economic evaluations conducted alongside included randomised controlled trials. Quasi-randomised studies, such as those allocating by using alternate days of the week, were excluded. Where trials were described in some way as to suggest or imply that the study was randomised and where the demographic details of each group’s participants were similar, trials were included and sensitivity analysis was undertaken to the presence or absence of these data.
+Types of participants
+A majority of participants were required to be i. within the age range 18 to 65, ii. suffering from severe mental illness preferably as defined by National Institute of Mental Health 1987 but, in the absence of that, from illness such as schizophrenia, schizophrenialike disorders, bipolar disorder, depression with psychotic features or/and personality disorder; and iii. not acutely ill. Substance abuse was not considered to be a severe mental disorder in its own right, however studies were eligible if they dealt with people with both diagnoses, that is those with severe mental illness plus substance abuse. Dementia and mental retardation are not considered to be a severe mental disorder, hence studies focusing on these populations were excluded. We considered only participants treated in the community-care setting.
+Types of interventions
+We considered only interventions and management packages not focused primarily on alternatives to acute hospital admission.
+1.	Intensive Case Management
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+8
+We defined Intensive Case Management as where the majority of people received:
+a.	A package of care shaped either on the:
+•	Assertive Community Treatment model, being based on the Treatment in Community Living, Assertive Community Treatment (Stein 1980); or
+•	Assertive Outreach model (Witheridge 1982, Witheridge 1991) (i.e. multidisciplinary team-based approach, practicing ’assertive outreach’, providing 24 hrs emergency cover, providing care themselves)(McGrew 1995); or the
+•	Case Management model (Intagliata 1982) however described as such in the trial report.
+b.	With a caseload up to and including 20 people.
+2.	Non-Intensive Case Management
+We defined non-Intensive Case Management as where the majority of people received:
+a.	A package of care shaped either on the:
+•	Assertive Community Treatment model, being based on the Treatment in Community Living, Assertive Community Treatment (Stein 1980), or
+•	Assertive Outreach model (Witheridge 1982, Witheridge 1991) (i.e. multidisciplinary team-based approach, practicing ’assertive outreach’, providing 24 hrs emergency cover, providing care themselves)(McGrew 1995); or
+•	on the Case Management model (Intagliata 1982) however described as such in the trial report.
+b.	With a caseload over 20 people.
+3.	Standard care
+We defined standard care as where the majority of people received: a. A community or outpatient model of care not specifically shaped on either the model of Assertive Community Treatment and Case Management, and not working within a specific designated named package or approach to care.
+b. If data were available on the standard care caseload, a final sensitivity analysis was undertaken testing how prone the primary outcomes were to change when trials comparing Intensive Case Management with standard community care only (caseload >20) were included.
+Types of outcome measures
+All outcomes were grouped by time into short term (up to 6 months), medium term (7 months to 12 months) and long term (over 12 months). Where available, 24 months was the preferred follow-up point for calculating mean days per months in hospital. If more than one follow-up point within the same period were available, we reported the latest one.
+Primary outcomes
+1. Service use
+1.1 Hospitalisation: mean number of days per month in hospital
+1.2 Not remaining in contact with psychiatric services
+Secondary outcomes
+1.	Adverse effects
+1.1 Death - all causes and suicide
+2.	Service use
+2.1	Admitted to hospital
+2.2	Hospital admission rate
+2.3	Use of services outside of mental health provision (i.e. emergency services)
+3.	Global state
+3.1	Leaving the study early (lost to follow-up)
+3.2	Relapse (as defined in trial)
+3.3	Not improved to a clinically meaningful extent (as defined in trial)
+3.4	Not improved
+3.5	Average endpoint score
+3.6	Average change score
+3.7	Compliance with medication
+3.8	Average endpoint score
+3.9	Average change score
+4.	Social functioning
+4.1	Contact with legal system (i.e. police contacts, arrests, imprisonments)
+4.2	Employment status (number unemployed at end of study)
+4.3	Accommodation status (number homeless or not living independently during or at the end of the study, mean days homeless and mean days in stable accommodation per month in study)
+4.4	Alcohol use
+4.5	Illicit drug use
+4.6	Average endpoint score
+4.7	Average change score
+5.	Mental state
+5.1	General symptoms
+5.1.1 Not improved to a clinically meaningful extent (as defined in trial)
+5.1.2 Not improved
+5.1.3 Average endpoint score
+5.1.4 Average change score
+5.2	Specific symptoms
+5.2.1 Positive symptoms (delusions, hallucinations, disordered thinking)
+5.2.1.1 Not improved to a clinically meaningful extent (as defined in trial)
+5.2.1.2 Not improved
+5.2.1.3 Average endpoint score
+5.2.1.4 Average change score
+5.2.2 Negative symptoms (poor volition, poor self-care, blunted affect)
+5.2.2.1 Not improved to a clinically meaningful extent (as defined in trial)
+5.2.2.2 Not improved
+9
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+5.2.2.3 Average endpoint score
+5.2.2.4 Average change score
+5.2.3 Mood depression
+5.2.3.1 Not improved to a clinically meaningful extent (as defined in trial)
+5.2.3.2 Not improved
+5.2.3.3 Average endpoint score
+5.2.3.4 Average change score
+6.	Behaviour
+6.1	General behaviour
+6.2	Not improved to a clinically meaningful extent (as defined in trial)
+6.3	Not improved
+6.4	Average endpoint score
+6.5	Average change score
+6.6	Specific behaviours (i.e. self-harm; injury to others or property)
+7.	Quality of life
+7.1	Not improved to a clinically meaningful extent (as defined in trial)
+7.2	Not improved
+7.3	Average endpoint score
+7.4	Average change score
+8.	Satisfaction
+8.1	Participant satisfaction
+8.1.1 Not improved to a clinically meaningful extent (as defined in trial)
+8.1.2 Not improved
+8.1.3 Average endpoint score
+8.1.4 Average change score
+8.2	Carer satisfaction
+8.2.1 Not improved to a clinically meaningful extent (as defined in trial)
+8.2.2 Not improved
+8.2.3 Average endpoint score
+8.2.4 Average change score
+9.	Costs
+Measures of cost were expressed as mean monthly costs per patient in the trial. Three main types of cost were reported:
+9.1	Direct costs of psychiatric hospital care
+9.2	Direct health care costs (including all medical and psychiatric care and the costs of case management, but excluding accommodation other than hospital care)
+9.3	Direct costs of all care (including costs of accommodation and subtracting benefits, such as earnings, where these are known)
+Search methods for identification of studies
+For search methods of first versions of this review please see Appendix 1 and Appendix 2.
+Electronic searches
+1.	Cochrane Schizophrenia Group Trials Register (February 2009) The register was searched using the phrase:
+(*ca?e management* OR *cpa* OR *community treatment* OR *community team* OR *community cent* OR *community care approach* OR *madison model* OR *outreach* OR *hostel* OR *aftercare* OR *residential* OR *housing* OR *transitional* OR *posthospital* OR *partial hospitali?ation* OR *Foster* OR *Guardianship* OR *daily living programme* OR *crisis intervention* OR *early intervention* OR *Ambulatory treatment* OR *Ambulatory care* OR *community living* OR *social support* OR *patient care team* OR *community mental health* OR *patient participation* OR *assertive outreach* OR *drop-in hospital* OR *drop-in care* OR *drop-in treatment* OR *drop-in cent* OR *drop-in unit* OR *drop in hospital* OR *drop in care* OR *drop in treatment* OR *drop in cent* OR *drop in unit* OR *day hospital* OR *day care* OR *day treatment* OR *day cent* OR *day unit* OR *Intensive care* OR *Inten-sive interven* OR *Intensive treat* OR *Intensive therap* OR *Intensive management* OR *Intensive model* OR *Intensive programmem* OR *Intensive team* OR *Intensive service* OR *mobile care* OR*mobile interven* OR*mobile treat* OR*mo-bile therap* OR *mobile management* OR *mobile model* OR *mobile programmem* OR *mobile team* OR *mobile service* OR *outreach care* OR *outreach interven* OR *outreach treat* OR *outreach therap* OR *outreach management* OR *outreach model* OR *outreach programmem* OR *outreach team* OR *outreach service* OR *community care* OR *community interven* OR *community treat* OR *community therap* OR *com-munity management* OR *community model* OR *community programmem* OR *community team* OR *community service* OR *community base* OR *home care* OR *home interven* OR *home treat* OR *home therap* OR *home management* OR *home model* OR *home programmem* OR *home team* OR *home service* OR *home base* OR *aggressive outreach* OR *broker* OR *programme* OR *care programme approach* OR *care programme* in title, abstract, index terms of REFERENCE and in interventions of STUDY) OR (*Pact* OR *tcl* In title) OR (Pact* OR tcl* in abstract and index terms of REFERENCE and in interventions of STUDY)
+This register is compiled by systematic searches of major databases, hand searches and conference proceedings (see Group Module -Specialised Register).
+Searching other resources
+1. References
+Should an included or excluded study suggest that another study was of relevance, the reference was identified and the full text acquired.
+2. Personal contact
+Where required for additional data we contacted authors of trials for this information. We did not systematically contact all authors for additional papers.
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+10
+Data collection and analysis
+Selection of studies
+The principal reviewer (MD) inspected all abstracts of studies identified as above and identified potentially relevant reports. In addition, to ensure reliability, CBI inspected a random sample of these abstracts, comprising 10% of the total. Where disagreement occurred this was resolved by discussion, or where there was still doubt, the full article was acquired for further inspection. The full articles of relevant reports were acquired for reassessment and carefully inspected for a final decision on inclusion (see Criteria for considering studies for this review). Once the full articles were obtained, in turn MD and CBI inspected all full reports and independently decided whether they met inclusion criteria. MD and CBI were not blinded to the names of the authors, institutions or journal of publication. Where difficulties or disputes arose, we asked author MM for help and if it was impossible to decide, these studies were added to those awaiting assessment and the authors of the papers contacted for clarification.
+Data extraction and management
+1.	Extraction
+1.1	Data regarding criteria and outcomes
+The principal reviewer (MD) extracted data from all included studies. In addition, to ensure reliability, CBI independently extracted data from a random sample of these studies, comprising 10% of the total. Again, any disagreement was discussed, decisions documented and, if necessary, authors of studies were contacted for clarification. With remaining problems MM helped clarify issues and those final decisions were documented. Data presented only in graphs and figures were extracted whenever possible, but were included only if two reviewers independently had the same result. Attempts were made to contact authors through an open-ended request in order to obtain missing information or for clarification whenever necessary. Where possible, we extracted data relevant to each component centre of multi-centre studies separately.
+1.2	Additional data
+1.2.1 Fidelity
+This rating related to the Intensive Case Management intervention. This rated fidelity of the intervention to assertive community treatment on the ’team membership’ and ’team structure and organisation’ sub-scales of the Index of Fidelity to Assertive Community Treatment (IFACT) (McGrew 1994).
+This index was derived from a survey of 20 clinical experts in assertive community treatment and validated in a survey of 18 programmes.
+a	. The ’team membership’ sub-scale comprises four items:
+-	ratio of patients to staff
+-	total size of team
+-	extent of psychiatric input
+-	extent of nursing input to the team
+b	. The ’structure and organisation’ sub-scale comprises seven items, whether the team is:
+-	the primary source of care for its patients
+-	is situated away from the hospital
+-	meets daily
+-	shares responsibility for caseloads
+-	is available 24 hours a day
+-	has a team leader who is also a case manager
+-	offers unlimited time for its services
+We chose IFACT because the sub-scales are brief and are possible to be completed from published or unpublished text. For each item on the index, a score of one indicates high fidelity to the model. Score ranges from 0 to 11, where the maximum score available on ’team membership’ sub-scale is 4, and on ’structure and organisation’ sub-scale is 7, with higher scores indicating higher fidelity to the model.
+We obtained fidelity data from published and unpublished trial reports, direct contact with trialists, and data previously obtained directly from trialists by previous reviews (Burns 2001, Catty 2002, Burns 2007). Two raters (MD and CBI) independently combined these data into a single fidelity score. Multicentre trials of Intensive Case Management often struggle to implement a uniform approach, with centres operating at different degrees of fidelity. Where possible, we rated each component centre separately.
+1.2.2 Baseline hospital use
+The average number of days per month in hospital for all participants in the two years before the study began.
+We obtained this data from published and unpublished trial reports and from direct contact with trialists.
+1.2.3 Service use: hospitalisation
+We obtained the primary outcome mean number of days per month in hospital for the included studies from published and unpublished trial reports, direct contact with trialists and data previously obtained directly from trialists reported by a previous review (Burns 2007).
+2.	Management
+2.1	Forms
+Data were extracted onto standard, simple forms.
+2.2	Data from multi-centre trials
+Where possible MD and CBI verified independently calculated centre data against original trial reports.
+3.	Scale-derived data
+We included continuous data from rating scales only if:
+a. the psychometric properties of the measuring instrument had been described in a peer-reviewed journal (Marshall 2000); and b. the measuring instrument was not written or modified by one of the trialists for that particular trial; and
+c. the measuring instrument is either i. a self-report or ii. completed by an independent rater or relative (not the therapist).
+4.	Endpoint versus change data
+We preferred to use scale endpoint data, which typically cannot have negative values and is easier to interpret from a clinical point
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+of view. Change data are often not ordinal and are very problematic to interpret. If endpoint data were unavailable, we used change data.
+5.	Skewed data
+5.1	General
+Continuous data on clinical and social outcomes are often not normally distributed. To avoid the pitfall of applying parametric tests to non-parametric data, we aim to apply the following standards to all data before inclusion: a) standard deviations and means are reported in the paper or obtainable from the authors; b) when a scale starts from the finite number zero, the standard deviation, when multiplied by two, is less than the mean (as otherwise the mean is unlikely to be an appropriate measure of the centre of the distribution, (Altman 1996); c) if a scale started from a positive value (such as PANSS which can have values from 30 to 210) the calculation described above was modified to take the scale starting point into account. In these cases skew is present if 2SD>(S-S min), where S is the mean score and S min is the minimum score. Endpoint scores on scales often have a finite start and end point and these rules can be applied. When continuous data are presented on a scale which includes a possibility of negative values (such as change data), it is difficult to tell whether data are skewed or not. Skewed data from studies of less than 200 participants were entered in additional tables rather than into an analysis. Skewed data pose less of a problem when looking at means if the sample size is large and were entered into syntheses.
+5.2	Specific - mean number of days in hospital
+We implemented one exception to the above rule (5.1) in order to present more data, recognising that this is a ’post hoc’ decision, but also that the rules as regards management of skewed data and how robust skewed data are within meta-analysis is unclear (Higgins 2008). Where mean number of days in hospital data were skewed, and they were provided by studies of less than 200 participants, we nevertheless entered those data into a sub-group of the overall analysis. We also presented the overall effect from all data pooled. 6. Common measure
+To facilitate comparison between trials, we intended to convert variables that can be reported in different metrics, such as days in hospital (mean days per year, per week or per month) to a common metric (e.g. mean days per month).
+7.	Conversion of continuous to binary
+Where possible, efforts were made to convert outcome measures to dichotomous data. This could be done by identifying cut-off points on rating scales and dividing participants accordingly into ’clinically improved’ or ’not clinically improved’. It was generally assumed that if there had been a 50% reduction in a scale-derived score such as the Brief Psychiatric Rating Scale (BPRS, Overall 1962) or the Positive and Negative Syndrome Scale (PANSS, Kay 1986), this could be considered as a clinically significant response ( Leucht 2005a, Leucht 2005b). If data based on these thresholds were not available, we used the primary cut-off point presented by the original authors.
+8.	Direction of graphs
+Where possible, we entered data in such a way that the area to the left of the line of no effect indicates a favourable outcome for Intensive Case Management.
+9.	Summary of findings table
+We anticipated including the following long term main outcomes in a summary of findings table.
+1.	Service use
+1.1 Hospitalisation: mean number of days per month in hospital 1.2 Hospital admission across time
+2.	Global state
+2.1	Relapse (as defined in trial)
+2.2	Leaving the study early (lost to follow-up)
+3.	Mental state: general symptoms
+3.1.	Not improved to a clinically meaningful extent (as defined in trial)
+4.	Adverse effect
+4.1 Death - suicide
+5.	Social functioning
+5.1 Employment - unemployed at end of study
+Assessment of risk of bias in included studies
+Again working independently, the principal reviewer (MD) assessed risk of bias of all included studies and the second reviewer (CBI) assessed risk of bias from a random sample of these studies, comprising 10% of the total. MD and CBI assessed risk of bias using the tool described in the Cochrane Collaboration Handbook (Higgins 2008). This tool encourages consideration of how the sequence was generated, how allocation was concealed, the integrity of blinding at outcome, the completeness of outcome data, selective reporting and other biases. We would have excluded studies where allocation was clearly not concealed.
+Trials with high risk of bias (defined as at least three out of five domains categorised as ’No’) were not included in the meta-analysis. If the raters disagreed, the final rating was made by consensus with the involvement of another member of the review group. Where inadequate details of randomisation and other characteristics of trials are provided, authors of the studies were contacted in order to obtain further information. Non-concurrence in quality assessment was reported.
+Measures of treatment effect
+1.	Binary data
+For binary outcomes we calculated a standard estimation of the random-effects risk ratio (RR) and its 95% confidence interval (CI). It has been shown that RR is more intuitive (Boissel 1999) than odds ratios and that odds ratios tend to be interpreted as RR by clinicians (Deeks 2000). For statistically significant results we had planned to calculate the number needed to treat to provide benefit /to induce harm statistic (NNTB/H), and its 95% confidence interval (CI) using Visual Rx (http://www.nntonline.net/)
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+taking account of the event rate in the control group. This, however, was superseded by Summary of findings for the main comparison and the calculations therein.
+2.	Continuous data
+2.1 Summary statistic
+For continuous outcomes we estimated a random-effects mean difference (MD) between groups. We preferred not to calculate effect size measures (standardised mean difference SMD). However, in the case of where scales were of such similarity to allow presuming there was a small difference in measurement, we calculated it and, whenever possible, we transformed the effect back to the units of one or more of the specific instruments.
+Unit of analysis issues
+1.	Cluster trials
+Studies increasingly employ ’cluster randomisation’ (such as randomisation by clinician or practice) but analysis and pooling of clustered data poses problems. Firstly, authors often fail to account for intra-class correlation in clustered studies, leading to a ’unit of analysis’ error (Divine 1992) whereby p values are spuriously low, confidence intervals unduly narrow and statistical significance overestimated. This causes type I errors (Bland 1997, Gulliford 1999).
+Where clustering is not accounted for in primary studies, we presented data in a table, with a (*) symbol to indicate the presence of a probable unit of analysis error. In subsequent versions of this review we will seek to contact first authors of studies to obtain intraclass correlation coefficients for their clustered data and to adjust for this by using accepted methods (Gulliford 1999). Where clustering had been incorporated into the analysis of primary studies, we present these data as if from a non-cluster randomised study, but adjusted for the clustering effect.
+We have sought statistical advice and have been advised that the binary data as presented in a report should be divided by a ’design effect’. This is calculated using the mean number of participants per cluster (m) and the intra-class correlation coefficient (ICC) [Design effect = 1+(m-1)*ICC] (Donner 2002). If the ICC was not reported it was assumed to be 0.1 (Ukoumunne 1999).
+If cluster studies has been appropriately analysed taking into account intra-class correlation coefficients and relevant data documented in the report, synthesis with other studies would have been possible using the generic inverse variance technique.
+2.	Cross-over trials
+A major concern of cross-over trials is the carry-over effect. It occurs if an effect (e.g. pharmacological, physiological or psychological) of the treatment in the first phase is carried over to the second phase. As a consequence on entry to the second phase the participants can differ systematically from their initial state despite a wash-out phase. For the same reason cross-over trials are not appropriate if the condition of interest is unstable (Elbourne 2002). As both effects are very likely in severe mental illness, we only used data of the first phase of cross-over studies.
+3.	Studies with multiple treatment groups
+Where a study involved more than two treatment arms, if relevant, the additional treatment arms were presented in comparisons. Where the additional treatment arms were not relevant, these data were not reproduced.
+Dealing with missing data
+1.	Overall loss of credibility
+At some degree of loss of follow-up, data must lose credibility (Xia 2007). For any particular outcome should more than 50% of data be unaccounted for, we did not reproduce these data or use them within analyses. If, however, more than 50% of those in one arm of a study were lost, but the total loss was less than 50%, we marked such data with (*) to indicate that such a result may well be prone to bias.
+2.	Binary
+In the case where attrition for a binary outcome is between 0 and 50% and where these data were not clearly described, data were presented on a ’once-randomised-always-analyse’ basis (an intention to treat analysis). Those leaving the study early were all assumed to have the same rates of negative outcome as those who completed, with the exception of the outcome of death. A sensitivity analysis was undertaken testing how prone the primary outcomes were to change when data from only those who completed the study were compared with intention to treat data using the assumption outlined above.
+3.	Continuous
+3.1	Attrition
+In the case where attrition for a continuous outcome is between 0 and 50% and data from only those who completed the study are reported, we have reproduced these.
+3.2	Standard deviations
+3.2.1 General
+Where there are missing measures of variance for continuous data, but exact standard errors or confidence intervals for group means, or either ‘p’ or’t’ values for differences in means, we calculated standard deviation value according to method described in Section 7.7.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2008). If standard deviations were not reported and could not be calculated from available data, we asked authors to supply the data. In the absence of data from authors, we used the mean standard deviation from other studies.
+3.2.2 Standard deviation mean number of days per month in hospital
+For the primary outcome, mean number of days per month in hospital, if standard deviations were not reported and could not be calculated from available data, we asked authors for additional information. In the absence of data from authors, we imputed the missing standard deviations using a regression analysis of SD against mean from those trials that provided both. We documented in Table 2 in what studies we imputed SDs according to the above technique.
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+3.3	Last observation carried forward
+We anticipated that in some studies the method oflast observation carried forward (LOCF) would be employed within the study report. As with all methods of imputation to deal with missing data, LOCF introduces uncertainty about the reliability of the results. Therefore, where LOCF data has been used in the trial, if less than 50% of the data had been assumed, we reproduced these data and indicated that they are the product of LOCF assumptions. 3.4 Incomplete data for meta-regression
+In some cases we anticipated that IFACT score variables would not all be available. If IFACT score could not be calculated from available data, we imputed it by multiple imputation using the mi library in R (R 2008). As explained above, we only made these assumptions if we were able to directly rate over 50% of the data. We documented in Table 3 in what studies we calculated IFACT score according to the above technique.
+In some cases we anticipated that baseline hospital use data would not all be available. Missing data was imputed as for the IFACT scores. As explained above, we only make these assumptions if we were able to directly rate over 50% of the data. We documented for which studies we calculated baseline hospital use data according to the above technique (Table 3).
+A sensitivity analysis was undertaken testing how prone the results from meta-regression were to change when data from only those who completed the studies were compared with the imputed data using the assumption outlined above.
+Assessment of heterogeneity
+1.	Clinical heterogeneity
+We considered all included studies initially, without seeing comparison data, to judge clinical heterogeneity. We simply inspected all studies for clearly outlying situations or people, which we had not predicted would arise. When such situations or participant groups arose, these were fully discussed.
+In addition two potential sources of heterogeneity were specified a priori (fidelity and baseline level of hospital use (Data extraction and management). These data were extracted as described above. 2. Methodological heterogeneity
+We considered all included studies initially, without seeing comparison data, to judge methodological heterogeneity. We simply inspected all studies for clearly outlying methods which we had not predicted would arise. Should such methodological outliers arise these will be fully discussed.
+3.	Statistical heterogeneity
+3.1	Visual inspection
+We visually inspected graphs to investigate the possibility of statistical heterogeneity.
+3.2	Employing the I2statistic
+Heterogeneity between studies was investigated by considering the I2 method alongside the Chi2 ’p’ value. The I2 provides an estimate of the percentage of inconsistency thought to be due to chance (Higgins 2003). The importance of the observed value of I2 de
+pends on i. magnitude and direction of effects and ii. strength of evidence for heterogeneity (e.g. ’p’ value from Chi2 test, or a confidence interval for I2). I2 estimate greater than or equal to 50% accompanied by a statistically significant Chi2 statistic, was interpreted as evidence of substantial levels of heterogeneity (Section 9.5.2 - Higgins 2008). When substantial levels of heterogeneity were found in the primary outcome, we explored reasons for heterogeneity (Subgroup analysis and investigation of heterogeneity).
+Assessment of reporting biases
+Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results (Egger 1997). These are described in Section 10 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2008). We are aware that funnel plots may be useful in investigating reporting biases but are of limited power to detect small-study effects. We did not use funnel plots for outcomes where there were ten or fewer studies, or where all studies were of similar sizes. In other cases, where funnel plots were possible, we sought statistical advice in their interpretation.
+Data synthesis
+Where possible we employed a random-effects model for analyses. We understand that there is no closed argument for preference for use of fixed or random-effects models. The random-effects method incorporates an assumption that different studies are estimating different, yet related, intervention effects. According to our hypothesis of an existing variation across studies, to be explored further in the meta-regression analysis despite being cautious that random-effects methods does put added weight onto the smaller of the studies - we favoured using random-effects model.
+Subgroup analysis and investigation of heterogeneity
+1.	Subgroup analyses
+We anticipate two sub-group analyses. For the first version of the protocol for this review we did not anticipate any sub-group analyses. On further consideration we now realise that analysis at separate time periods could be thought of as sub-groups. The second sub-group is within the primary outcome and relates to skewed and non-skewed data. This has been introduced late into this protocol and could be considered post hoc. However, we are also conscious that our original rule for management of these data could be considered overly cautious and result in some important data not being presented (Higgins 2008).
+2.	Investigation of heterogeneity
+2.1 Anticipated heterogeneity - outcome of mean days per month in hospital
+Investigation of heterogeneity formed part of the secondary objectives of the review. We hypothesised that the effect of Intensive Case Management on one of our primary outcomes (mean number of days per month in hospital) differs according to fidelity of
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+intervention to the assertive community treatment model and the baseline level of hospital use.
+The association of the IFACT score and the baseline number of days in hospital with the treatment effect was examined by performing random-effects meta-regression analysis in R (R 2008). The script we used to perform meta-regression analyses is reported in Appendix 5. Meta-regression was also carried out using both variables within the same model. The relationship between the treatment effect and the two variables was also examined using a thin plate spline. If possible data from multi-centre studies were to be entered in the meta-regression disaggregated into the component centre with outcome and fidelity data for each.
+Meta-regression was performed:
+a)	only if at least ten studies per comparison are available (Higgins 2008)
+b)	all included studies were entered into the meta-regression. Comparison type was also tested as an additional regressor in the model. 2.2 Unanticipated heterogeneity - other outcomes
+2.2.1 For outcomes other than the second primary outcome (not remaining in contact with psychiatric services)
+If inconsistency was high this was reported and no exploration undertaken.
+2.2.2 For outcome ’not remaining in contact with psychiatric services’
+If inconsistency was high this was reported. First we investigated whether data had been entered correctly. Second, if data had been correct, the graph was visually inspected and studies outside of the company of the rest were successively removed to see if heterogeneity was restored. Should this occur with no more than 10% of the data being excluded, data were presented. If not, data were not pooled.
+Should unanticipated clinical or methodological heterogeneity be obvious we simply stated hypotheses regarding these for future reviews or versions of this review. We did not anticipate undertaking analyses relating to these.
+Sensitivity analysis
+1.	Implication of randomisation
+We aimed to include trials in a sensitivity analysis if they are described in some way as to imply randomisation. For the primary outcomes we included these studies and if there was no substantive difference when the implied randomised studies were added to those with better description of randomisation, then all data were employed from these studies.
+2.	Standard care caseload
+If data were available, a sensitivity analysis was undertaken testing how prone the primary outcomes were to change when trials comparing Intensive Case Management to standard community care caseload = 20 were compared with trials comparing Intensive Case Management to standard community care caseload >20. If there was a substantial difference, we reported results and discussed them but continued to pool the data.
+3.	Assumptions for lost binary data
+Where assumptions had to be made regarding people lost to followup (see Dealing with missing data) we compared the findings of the primary outcomes when we used our assumption compared with completer data only. If there was a substantial difference, we reported results and discussed them but continued to employ our assumption.
+4.	Assumptions for incomplete data for meta-regression
+Where assumptions had to be made regarding missing SDs data in studies entering meta-regression (see Dealing with missing data), we compared the findings of the meta-regression on our primary outcome when we used our assumption compared with data taken from only those who completed the studies. A sensitivity analysis was undertaken testing how prone results from meta-regression were to change when data from those who completed were compared with imputed data using the assumption outlined above. If there was a substantial difference, we reported results and discussed them but continued to employ our assumption.
+RESULTS
+Description of studies
+See: Characteristics of included studies; Characteristics of excluded studies; Characteristics of studies awaiting classification; Characteristics of ongoing studies.
+Results of the search
+1.	Selection of studies (see Selection of studies)
+When CBI inspected a random sample of study abstracts identified as above (see Search methods for identification of studies) comprising 10% of total abstracts the principal reviewer (MD) had inspected, disagreement did occur. Full articles were therefore acquired for further inspection. At this next stage MD and CBI had full agreement on the total sample of reports selected for further inspection, with the exception of Huang-China which now awaits assessment.
+When MD and CBI in turn inspected all full articles of relevant reports and independently decided whether they met inclusion criteria (see Criteria for considering studies for this review), they fully agreed and no difficulties or disputes arose on any report. It was not necessary contact third reviewer (MM) for clarifying issues.
+2.	Data extraction (see Data extraction and management) When CBI independently extracted data from a random sample of included studies (10% of total) disagreements were discussed. MD and CBI reached full agreement on final decisions.
+3.	Original Cochrane reviews (Marshall 2000a, Marshall 2000b) 3.1 ACT (Marshall 2000b)
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+Fourteen trials had met inclusion for the ACT versus standard care comparison. The trials included were Aberg-Wistedt-Sweden,Audini-UK, Bond-Chicago1, Bond-Indiana1, Chandler-California1, Hampton-Illinois, Herinckx-Oregon, Jerrell-SCarolina1, Lehman-Maryland1, Morse-Missouri1, Quinlivan-California, Rosenheck-USA, Solomon-Pennsylvania and Test-Wisconsin. All trials were included in the current review in the comparison ICM versus standard care.
+For the ACT versus hospital-based rehabilitation comparison, three trials had been eligible for inclusion (De Cangas-Canada, Lafave-Canada, Marx-Wisconsin). All three have been excluded from the current update as they did not met inclusion criteria.
+Six trials were included in the ACT versus case management comparison (Bush-Georgia, Essock-Connecticut1, Jerrell-SCarolina1, Morse-Missouri2, Quinlivan-California, Solomon-Pennsylvania). Of the original six studies described above, two were included in the ICM versus standard care comparison as both are three arm studies comparing ICM (in two arms) versus standard care (Jerrell-SCarolina1, Solomon-Pennsylvania). Three trials were included in the ICM versus non-ICM comparison (Bush-Georgia, Essock-Connecticut1, Quinlivan-California). Quinlivan-California was included in both comparisons ICM versus standard care and ICM versus non-ICM, as it had three arms comparing three differentiated interventions (ICM, non-ICM and standard care). The sixth trial, Morse-Missouri2, although previously included, was excluded from the current update because it contained no usable data, as number for treatment groups were not presented.
+3.2 Case Management (Marshall 2000a)
+Ten randomised controlled trials were included in the comparison of case management versus standard care (Curtis-New York, Ford-UK, Franklin-Texas, Jerrell-SCarolina1, Macias-Utah, Marshall-UK, Muijen-UK2, Quinlivan-California, Solomon-Pennsylvania, Tyrer-UK). Of these eight are now included (Curtis-New York, Ford-UK, Jerrell-SCarolina1, Macias-Utah, Marshall-UK, Muijen-UK2, Quinlivan-California, Solomon-Pennsylvania). Franklin-Texas and Tyrer-UK had to be excluded as they did not meet inclusion criteria on type of intervention.
+4.	2009 search
+The February 2009 update search of Cochrane Schizophrenia Group’s Register of trials yielded 2565 references. We selected 55 for further inspection. Of these 14 trials met the inclusion criteria and were included (Bjorkman-Sweden, Drake-NHamp, Essock-Connecticut2, Harrison-Read-UK, Johnston-Australia, Morse-Missouri3 , Muller-Clemm-Canada, Okpaku-Tennessee, OPUS-Denmark, Pique-California, REACT-UK, Sytema-Netherlands, Salkever-SCarolina, UK700-UK). Thirtyone trialswere excluded. We added ten English language trials and five Chinese trials to those awaiting assessment and sought further information. Three trials which had been previously been awaiting assessment were able to be included as more reports had become available (Fekete 1998 now included as McDonel-Indiana, Holloway-UK, Shern-USA1).
+4.1 Problematic trials
+There are two problematic trials that are worth special mention. Jerrell-SCarolina1 is a three arm trial, with two of the arms qualifying as Intensive Case Management (Programme Assertive Community Treatment and Intensive Broker Case Management) and one a control (standard care). As results are reported separately for each arm, it was not possible to present continuous data from two ICM arms pooled together. One option was to treat each arm as a separate ’site’, effectively treating the study as two trials, but with the same control group. A second option was to include only one of the experimental arms. Although conscious of excluding potentially useful data on an arbitrary basis, we decided to include only one of the arms compared to standard care, according to the second option. The main reason for this was avoiding a unit of analysis error occurring in the first option. A sensitivity analysis was undertaken testing how prone results were to change when this trial was not included in meta-analysis.
+Curtis-New York is a trial comparing ICM with standard care and presents two main difficulties to the reviewer. The first is regarding the ICM caseload size. Study reported caseload ratio as 1:35 (therefore above the 1:20 ratio defining an ICM intervention). As we derived estimation of caseload size by dividing the number of intervention participants by the number of whole time equivalent clinical staff in the team (not just those who are formally classified as ’case managers’), we found that the actual staff:participant ratio is about 1:17. We therefore stated this trial as eligible. The second issue is regarding the peculiar way this trial provides the ICM intervention. Both experimental and control interventions are community-oriented and they fully fit into the review’s inclusion criteria, but the ICM team is located in hospital. The fact that the team office is based in hospital is undesirable but not unusual. In any case, the case management was given to people in the community. We, therefore, confirmed its inclusion, aiming not to penalise it because it had reported details that are not available for all trials. We found, however, a discrepancy in data Curtis-New York provided on service use outcomes (average number of days in hospital per month, admitted to hospital). This study was an outlier, being the only one clearly favouring standard care over ICM. A sensitivity analysis was undertaken testing how prone results were to change when this trial was not included in the metaanalysis. Neither for the primary outcome ’average number of days in hospital’ nor ’admitted to hospital’ results changed significantly when Curtis-New York was dropped, but it did significantly affect the level of heterogeneity. We could just advance the hypothesis that reason for heterogeneity could be the unusual way the intervention was provided in this trial (Table 4).
+Included studies
+The current update of previous two reviews includes 176 reports describing 38 studies. This review now includes data on 7328 randomised people from within these 38 separate trials. Twenty four of these studies had already been included in two original
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+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+reviews (as included or awaiting assessment), with 14 more derived from the 2008 search. Twenty six trials provide data for the ICM versus standard care comparison, 11 for the ICM versus non-ICM comparison and only one for both comparisons. Please note that it was possible to report data for several separate centres of seven multi-centre trials (Bond-Indiana1 (3), Chandler-California1 (2) Drake-NHamp (7) Hampton-Illinois (2) McDonel-Indiana (2) Rosenheck-USA (9) and UK700-UK (4)). Several of these centres are reported separately in the table of Characteristics of included studies.
+1.	Study length
+Only one study fell into the short term category, with a maximum length of six months (Bond-Indiana1 ); nine studies reported medium term data, between them only one reported data by seven months (Okpaku-Tennessee) as all the remaining eight reported data by 12 months (Bond-Chicago1; Bush-Georgia, Hampton-Illinois, Johnston-Australia, Lehman-Maryland1, Morse-Missouri1, Solomon-Pennsylvania, Sytema-Netherlands). The remaining 28 all fell into the long term category with a maximum length of four years (Test-Wisconsin) and an average length of 23.5 months.
+2.	Design
+All included studies presented a parallel longitudinal design. Twelve were multi-centre trials, but only seven provided data for single centres (see above).
+3.	Participants
+A total of 7328 participants are included from 38 trials. Most trials included from previous reviews were conducted in USA (16 trials, including 3474 participants) and only five were European (including 345 participants). In the current revision we added 1964 participants from seven trials conducted in Europe, and 1545 from ten studies conducted in Australia, Canada and the USA. Nineteen studies included participants with severe mental illness. None provided operationalised definitions addressing dimensions of diagnosis, duration and disability. Thirteen, however, provided criteria for either the diagnosis, impairment or level of service use. Six studies did not provide any criteria for defining serious mental illnesses at all. Diagnoses of severe mental illness varied across studies from schizophrenic disorder alone, to wider diagnostic groups including schizophrenic disorder, affective and personality disorder. Of the remaining 19 studies, 17 involved participants with various diagnoses, but the great majority had some psychotic disorder and most trials reported criteria for service use and/or impairment. Two studies included participants with a high level of impairment or service use due to psychiatric illness, but not any diagnostic criteria for inclusion (Harrison- Read-UK, Okpaku-Tennessee). Most trials (22) involved participants that had been diagnosed using operationalised criteria (DSM, ICD, OPCRIT, RDC, SADS, see Characteristics of included studies footnotes) whilst 16 (ten in the group including participants with serious mental illnesses and five in the group of studies including people with various diagnoses) did not report using any diagnostic tool, but only stated type of
+illness or level of impairment. Only OPUS-Denmark included participants with a first episode of psychotic illness.
+Four studies included a total of 742 dually diagnosed participants ( Drake-NHamp, Essock-Connecticut2, Morse-Missouri3, Muller-Clemm-Canada) and eight 1337 homeless participants.
+Data on mean age were available from 31 trials (6339 participants). The average age was about 38 years old. Only Macias-Utah did not report information on participant age.
+4.	Settings
+As stated in the inclusion criteria, all the included studies took place in a community setting, provided both by private and public mental health services. No study was carried out in a low-income country, as all the included studies were from Europe or USA.
+5.	Interventions
+Twenty seven trials were included in the comparison ICM versus standard care and 12 in ICM versus non-ICM comparison. Quinlivan-California was a three arm trial (ICM, non-ICM and standard care) and provided data for both comparisons. REACT-UK was considered in the ICM versus non-ICM comparison due to our assumption that standard care could be considered as Care Programme Approach, even if not clearly reported by trial authors. The Care Programme Approach was introduced in England in the mid-1990s and become standard care thereafter: it is a combination of non-Intensive Case Management and care from a Community Mental Health Team (CMHT), hence to be considered as non-ICM, according definitions in this review.
+5.1	ICM
+On average the ICM included in this review was well defined. The majority of experimental interventions were explicitly modelled on the assertive community treatment model, being based on the Treatment in Community Living model of Stein 1980. Only a few studies based ICM on the case management model. The experimental intervention was provided either by an already existing team or Intensive Case Management services newly established for the trial.
+5.2	Non-ICM
+There were no discernable differences in the practice of non-ICM and ICM except for the intensity of contact. Staff’s degree of training and skill were similar in the ICM teams and non-ICM teams. In some studies non-ICM was itself an experimental intervention, but mostly it represented the average standard care, as what in this review we call ’standard care’ increasingly shifted towards nonICM across decades. Mental health systems increasingly included elements from ICM, melting them with community mental health service. English mental health policy is one example, where the Care Programme Approach was introduced in United Kingdom in the mid-1990s and become standard care thereafter. It is a combination of non-Intensive Case Management and care from a community mental health team (CMHT), hence to be considered as non-ICM, according definitions in this review. Therefore the REACT-UK study was considered in the ICM versus nonICM comparison due to our assumption that standard care could
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+be considered as Care Programme Approach, even if not clearly reported by trial authors.
+5.3	Standard care
+On average the standard care definition was blurred as this intervention was modelled on a generalist model. Its core was being provided by a community mental health service, but its features were variable across trials run in different countries at different time periods. Presence of further specialised services, such as rehabilitation or psychotherapist services, were variable within standard care services. In a few studies both ICM and standard care were incorporating services for substance abuse treatment and homelessness care.
+6.	Outcomes
+Many trials used different scales in assessing treatment effects in various outcomes (global state, mental state - general and specific symptoms, social functioning, behaviour, quality of life and satisfaction). As most of the scales were used by only one study in each comparison group, it was not possible to enter these data in a unique analysis. Even where studies used the same scale, they often applied different rating scores. Therefore, again, data could not be entered together in the analysis. Some studies failed to clearly report the rating score they used for a pre-stated scale. This is noted in the Risk of Bias tables (see ’Outcomes’ in Characteristics of included studies table). No studies assessed improvement measuring it on scales. We did not calculate effect size measures (standardised mean difference SMD, see Measures of treatment effect). 6.1 Outcome scales: only details of the scale that provided usable data are shown below. Reasons for exclusions of data are given under ’Outcomes’ in Characteristics of included studies table.
+6.1.1 Global state
+6.1.1.1 Global Assessment Scale (GAS) (Endicott 1976): in Audini-UK, Muijen-UK2, Rosenheck-USA
+This is an observer-rated scale for evaluating the overall functioning of a person during a specified time period on a continuum from psychological or psychiatric sickness to health. Score ranges from 0 to 100, where higher score indicates a better outcome.
+A modified version of the GAS was included in DSM-III as the Global Assessment of Functioning Scale (GAF) (APA 1987): in Bjorkman-Sweden. Outcomes from the two scales are reported together as GAF, as these two scales are very similar and they report results on the same score range.
+6.1.1.2 Health of the Nation Outcome Scale (HoNOS) (Wing 1998; Stein 1999): in Harrison-Read-UK, REACT-UK Provides a systematic summary of behaviours and functioning, measuring mental health and social/behavioural functioning. It consists of four areas (behaviour, impairment, symptoms and social), each assessed through 12 items on a five-point scale (0 to 4). Ratings are from 0 to 48; high score means severe dysfunction.
+6.1.2 Global state: compliance with medication
+6.1.2.1 Rating of Medication Influence (ROMI) (Weiden 1994): in Harrison-Read-UK, REACT-UK
+This is a 20 item scale, measuring the influence of factor on med
+ication adherence. Each item is rated according to the degree of influence on medication-taking behaviour: none (1), mild (2) and strong (3). It has 2 sub-scales - patient reported compliance (items 1-7) and patient-reported non compliance (items 8-20). A high score on the compliance sub-scale indicates high compliance, high score on the non-compliance sub-scale indicates high non-compliance. Results from the two studies are presented on different range score (in Harrison-Read-UK range score of 1 to 3; in REACT-UK range score is not clearly reported).
+6.1.3 Social functioning
+6.1.3.1 Disability Assessment Schedule (DAS) (WHO 2001a): in Holloway-UK
+The WHO’s Psychiatric Disability Assessment Schedule (DAS) is a measure of functioning and disability. It contains 36 items with six domains of functioning including: understanding and communicating, getting around, self care, getting along with others, household and work activities and participation in society. Higher scores indicate a worse outcome.
+6.1.3.2 Interview Schedule for Social Interaction - abbreviated version (ISSI) (Henderson 1980; Unden 1989): in Bjorkman-Sweden The ISSI scale is a self-report scale, it consists of 30 items and measures social integration and attachment. The maximum score is 30 points and higher scores indicate a better social integration and attachment.
+6.1.3.3 Life Skills Profile (LSP) (Rosen 1989; Parker 1992): in REACT-UK
+The Life Skills Profile is a clinician-rated questionnaire that was developed in Australia primarily for use with people with psychotic illnesses. There are 39-item and five sub-scales that assess the general domain of disability over the last 3 months. The five sub-scales measure self-care, non-turbulence, social contact, communication and responsibility. For each of the 39 items on a scale ranging from ’not at all disabled’ to ’extremely disabled’.
+6.1.3.4 REHAB Scale (REHAB) (Baker 1983): in Marshall-UK. An observer-rated measure of social functioning, covering social activity, self-care, speech disturbance and community skills. It rates the frequency of items of embarrassing or disruptive behaviour, such as violence, self harm, shouting and swearing, and sexual offensiveness (deviant behaviour - REHAB DB); and lack of general skills (general behaviour - REHAB GB) .The scale ranges from 0144 with higher scores indicating poorer functioning.
+6.1.3.5 Role Functioning Scale (RFS) ( Green 1987): in Jerrell-SCarolina2
+Self-report scale whereby the total of four sub-scales measures global role functioning. Higher scores indicate better functioning. 6.1.3.6 Social Adjustment Scale (SAS) (Weissman 1971, Weissman 1974): inAudini-UK, Muijen-UK2
+Measures social functioning in a number of life domains (work, social, extended family, marital, parental, family unit, and economic adequacy). Score ranges from 1 to 7, with high score indicating poor outcome.
+6.1.3.7 Social Adjustment Scale-II (SAS-II) (Schooler 1979): in
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+Jerrell-SCarolina1
+Revised version of the Social Adjustment Scale (see above), used to assess social adjustment. Self-reported scale, similar to SAS, but adapted for schizophrenia; it is composed of 24 items covering seven areas including social, family and work functioning. The scoring system appears to differ between the two versions, perhaps because this was an adapted version. High score indicates better outcome.
+6.1.3.8 Social Functioning Questionnaire (SFQ) (Tyrer 1990, Tyrer 2005): in Harrison-Read-UK
+An eight-item self-report scale (score range is 0 to 24). It provides a quick assessment of perceived social function. Higher score indicates poorer social functioning.
+6.1.3.9 Strauss-Carpenter scale (Strauss 1972; Strauss 1974): in Bjorkman-Sweden
+Strauss-Carpenter Outcome Scale assesses a total of 21 items exploring frequency of social contacts, employment duration, symptomatology, and duration of rehospitalisation. The scaling of each item extends from 0 (maximal negative) to 4 (maximal positive). The scoring range of the scale extends from 0 (maximal negative) to 84 (maximal positive).
+6.1.4 Social functioning: substance abuse
+6.1.4.1 Alcohol Use Scale (AUS) (Mueser 1995; Drake 1996): in Drake-NHamp
+A five point scale based on clinicians’ ratings of severity of disorder, ranging from one (abstinence) to five (severe dependence).
+6.1.4.2 Dartmouth Assessment of Lifestyle Interview (DALI) ( Rosenberg 1998): in Sytema-Netherlands
+An 18-item, interviewer-administered scale, addressing the detection of substance use disorder in people with severe mental illness. DALI focuses on alcohol, cannabis, and cocaine use disorders. DALI-alcohol: scores range from -4 to +6, higher scores indicate higher risk of alcohol abuse. DALI-drugs: scores range from -4 to +4, higher scores indicate higher risk of drugs abuse. As scale ranges from negative to positive value, skew is difficult to detect. Therefore data from this scale were entered in additional tables rather than into an analysis.
+6.1.4.3 Substance Abuse Treatment Scale (SATS) (McHugo 1995; Drake 1996): in Drake-NHamp
+An eight-point scale indicating progression toward recovery ranging from one (early stages of engagement) to eight (relapse prevention). Higher scores indicate greater progression.
+6.1.4.4 Time-Line-Follow-Back (TLFB) (Sobell 1980): in Drake-NHamp
+Scale administered by an interviewer to assess days of alcohol and drug use over the previous six months. Outcome reported as binary data.
+6.1.5 Mental state: general symptoms
+6.1.5.1 Brief Psychiatric Rating Scale (Overall 1962): in Audini-UK, Drake-NHamp, Muijen-UK2, REACT-UK, Sytema-Netherlands (BPRS 24-items - Velligan 2005); in Ford-UK, Rosenheck-USA (BPRS 18-item)
+The BPRS is a scale measuring positive symptoms, general psychopathology and affective symptoms. The original scale has 16 items, but a revised 18-item scale is commonly used. There are several ways symptoms are reported (i.e. on a scale of 0 to 6 or a scale of 1 to 7), but the most common is that each item is rated on a seven-point scale (1 = not present to 7 = extremely severe). The 18-item scale might range from 18 to 126, or from 0 to 108 (as in Ford-UK and Rosenheck-USA). There is a further version, a 24-item scale, it ranges from 24-168. For all the scales high scores indicate more severe symptoms.
+6.1.5.2 Brief Symptom Inventory (BSI) (Derogatis 1983): in Rosenheck-USA
+A brief rating scale used by an independent rater to assess the severity of psychiatric symptoms. Scores range from 0-4 with higher scores indicating more symptoms.
+6.1.5.3 Comprehensive Psychopathological Rating Scale (CPRS) (Asberg 1978): in Holloway-UK, UK700-UK
+This is an interview rating scale covering a wide range of psychiatric symptoms, and can be used in total or as sub-scales. It consists of 65 items that covers the range of psychopathology over the preceding week (40 symptom items are rated by the participant and 25 observed items are rated by the rater during the interview). Each item is rated on a 0-3 scale varying from ’not present’ to ’extremely severe’, with high scores indicating more severe symptoms. High scores indicate a worse outcome.
+6.1.5.4 Colorado Symptom Index (CSI) (Shern 1994): in Lehman-Maryland1, Shern-USA1
+A brief rating scale used by an independent rater to assess the severity of a range of psychiatric symptoms. A lower score indicates more symptoms.
+6.1.5.5 Krawiecka Scale (KS) alias Manchester Scale (Krawiecka 1977): in Harrison-Read-UK
+This scale rates severity of psychiatric symptoms. It consists of eight categories of symptoms assessed on a five-point scale. These are depression, anxiety, hallucinations, delusions, flattened and incongruous effect, psychomotor retardation, incoherence and irrelevance of speech, and poverty of speech. A score of zero or one denotes an absence of pathology while ratings of two, three or four denote the presence of the target symptoms in increasing severity. Rating is from 0 to 36. Higher scores indicate a worse outcome. 6.1.5.6 Present State Examination (PSE) (Wing 1974): in Audini-UK, Muijen-UK2
+This is a clinician-rated scale measuring mental status. 140 symptom items are rated and combined to give various syndrome and sub-syndrome scores. A short version, covering the first 40 “neurotic” symptoms, has been used in several population surveys. Score range from 1 to 120. Higher scores indicate greater clinical impairment.
+6.1.5.7 SCL-90 (Hopkins symptoms check list) (Derogatis 1974): in Bjorkman-Sweden
+This is a self-report clinical rating scale of psychiatric symptomatology containing 90 symptom related questions. It consists of 90
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+items, with 83 items representing nine sub-scales: somatisation (12 items), obsessive-compulsive (10 items), interpersonal sensitivity (9 items), depression (3 items), anxiety (10 items), angerhostility (6 items), phobic anxiety (7 items), paranoid ideation (6 items) and psychoticism (10 items). Seven additional items include disturbances in appetite and sleep. The SCL-90 also utilises three global distress indices: Global Severity Index (GSI), Positive Symptom Distress Index (PSDI), Positive Symptom Total (PST). The participant assesses the degree of severity of each symptom. Items are rated on a 5-point Likert scale, ranging from “not at all distressing” (0) to “extremely distressing” (4), with higher scores indicating greater symptomatology.
+6.1.6 Mental state: specific symptoms
+6.1.6.1 Beck Depression Inventory (BDI) (Beck 1979): in Holloway-UK
+A 21-item self-rating scale for depression. Each item comprises 4 statements (rated from 0 to 4) describing increasing severity of the abnormality concerned. Person completing the scale is required to read each group of statements and identify the one that best describes the way they have felt over the preceding week. Score ranges from 0 to 84, with higher score indicating more severe symptoms.
+6.1.6.2 Hospital Anxiety and Depression Scale (HAD-S) ( Zigmond 1983): Harrison-Read-UK
+This scale is a questionnaire composed of statements relevant to either generalised anxiety or depression referring to the past week. The number of items in the questionnaire are seven reflecting anxiety, and seven reflecting depression. Each item is answered by the participant on a four point (0-3) response category, so the possible scores range from 0 to 21 for anxiety and 0 to 21 for depression. High score indicates a worse outcome.
+6.1.6.3 Scale for the Assessment of Negative Symptoms (SANS) (Andreasen 1982, Andreasen 1989): in Holloway-UK, UK700-UK
+This assesses five symptom complexes to obtain clinical ratings of negative symptoms in people with schizophrenia over the preceding week. They are: affective blunting, alogia (impoverished thinking), avolition/apathy, anhedonia/asociality and disturbance of attention. The final symptom complexes seem to have less obvious relevance to negative symptoms than the other four complexes. Assessments are conducted on a six-point scale (from 0 indicating ’not at all’ to 5 indicating ’severe’). High scores indicate a worse outcome.
+6.1.7 Behaviour
+6.1.7.1 Social Behaviour Schedule (SBS) (Wykes 1986): in Holloway-UK
+The SBS is a 21-item scale designed to assess a range of areas of functioning in people with long term mental illness. The scale covers areas such as social behaviour and communication, self-care and inappropriate behaviour. The respondent’s behaviour on each item during the previous month is scored by someone familiar with him or her. Each item is rated on a five-point Likert scale
+(from 0 to 4), with higher scores indicating greater deficits.
+6.1.8 Quality of life
+6.1.8.1 Lancashire Quality of Life Profile (LQoLP) (Oliver 1996; Oliver 1997): in Bjorkman-Sweden, Holloway-UK, UK700-UK A structured self-report interview with 105 items, combining objective and subjective measures in the following nine life domains (range of values 1-7): living situation, social relationships, work and education, legal status and safety, religion, family relations, leisure activities, finances and health. The LQoLP also measures the following additional areas: positive and negative affect (with the Bradburn Affect-Balance Scale), self-esteem, global well-being (Cantril’s Ladder and Happiness Scale), perceived quality of life and the quality of life of the patient independently of the patient’s own opinion (with the Quality of Life Uniscale). The measures from LQoP used in Bjorkman-Sweden were: overall quality of life (which is the mean of subjective quality of life in nine life domains), and global well-being. Higher score indicates better subjective quality of life/satisfaction.
+6.1.8.2 Manchester Short Assessment of Quality of Life (MANSA) (Priebe 1999): REACT-UK, Sytema-Netherlands
+A 16-item scale composed of 4 objective and 12 subjective questions. 12 subjective items are rated on a 7 point scale (from ’couldn’t be worse’ to ’couldn’t be better’, scored from 1 to 7, range 12-84) assessing satisfaction with life ’in general’, and in a range of domains such as vocational, financial, friendships, leisure, personal safety, physical health and mental health. Four objective items, answered yes or no, assess the existence of a close friend, contacts with friends per week, accusation of a crime and victimisation of physical violence. Higher score indicates better quality of life. In REACT-UK and Sytema-Netherlands score is reported as a mean ranging from 1 to 7.
+6.1.8.3 Lehman’s Quality of Life Interview (QOLI) (Lehman 1988): in Drake-NHamp, Lehman-Maryland1, Shern-USA1; ( Lehman 1993) in Ford-UK; (Lehman 1983) in Marshall-UK The QOLI contains 153 items that measure global life satisfaction as well as objective and subjective quality of life, in eight life domains (living situations, daily activities and functioning, family relations, social relations and finances, work and school, legal and safety issues and health). Rated on a 7-point scale with higher scores indicating better quality of life. Subjective assessment of general life satisfaction ranges from 1 to 7 (terrible to delighted). Ford-UK reported objective quality of life and Marshall-UK reported subjective quality of life.
+6.1.9 Client satisfaction
+6.1.9.1 Camberwell Assessment of Need interview (CAN) (Phelan 1993): in Bjorkman-Sweden, Harrison-Read-UK, UK700-UK The Camberwell Assessment of Need assesses the health and social needs of people with mental health problems. This scale assesses 22 areas to yield numbers of met and unmet needs as rated by the participant. Possible scores range from 0 to 22, with a higher score indicating poorer level of met needs.
+6.1.9.2 Abbreviated Camberwell Assessment of Need Interview
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+(CANSAS)(Slade 1999, Phelan 1995): in REACT-UK, Sytema-Netherlands
+This is an abbreviated form of the above CAN.
+6.1.9.3 Client Satisfaction Questionnaire (CSQ) (Larsen 1979): in Audini-UK; in Muijen-UK2, OPUS-Denmark, Sytema-Netherlands
+The CQS is a self-report instrument designed to measure global patient’s satisfaction with services. Items are concerned with quality of services received, how well services met the client’s needs and general satisfaction. The CSQ is substantially correlated with treatment attrition, number of therapy sessions attended, and with change in client-reported symptoms. It consists of 8 items which are scored on 4-point Likert scales (1 to 4). Total score ranges from 8 to 32. Higher scores indicate greater satisfaction.
+6.1.9.4 Client Satisfaction Questionnaire (CSQ) - modified version (Larsen 1979, Gerber 1999): in REACT-UK
+The survey has 35 questions covering the location of services, services clients expect, delays in obtaining services, clients’ input into treatment, information received about drug treatment, satisfaction with treatment, access to clinical files, satisfaction with the therapist, family involvement in treatment, the treatment process, and overall satisfaction. Possible responses to most items range from 1 (most negative) to 7 (most positive). A rating of zero on certain items enables the respondent to indicate that the question was not relevant to his or her situation. Six items within the questionnaire, also on a 7-point scale, form a general satisfaction questionnaire. Higher score indicates greater satisfaction.
+6.1.9.5 Patients’ satisfaction with health services (Tyrer 1979): in UK700-UK
+A self-reporting questionnaire that rates nine components of satisfaction with services each on a four-point scale (1-4). Scores may range from 9 to 36, higher values indicating less satisfaction.
+6.2 Missing outcomes
+No trial reported or rated relapse, positive symptoms and carer satisfaction.
+Excluded studies
+We excluded 81 studies from the review. Half had been excluded by authors of the original Assertive Community Treatment and Case Management reviews and another 31 had to be excluded after the 2009 search.
+Four trials included in the original reviews have been excluded in this update as they now do not match the new inclusion criteria (De Cangas-Canada, Franklin-Texas, Lafave-Canada, Marx-Wisconsin). Morse-Missouri2 had to be excluded in this update as it did not report the number of people randomised to each treatment group.
+One further trial (Tyrer-UK), originally included in the CM review, was now excluded because of a methodological issue.
+Tyrer-UK is a trial comparing ICM to standard care. The first issue was to clarify the ICM caseload. Thanks to further information provided from author, it was clarified there were 25 key workers
+in the service looking after 400 patients on the register. Therefore each key worker had a caseload of 1 to 16 or ’high intensity’ case management by this review’s definition. The second issue arose for the case managers in the treatment group being also workers in the control group. The problem was therefore one of contamination, as requiring someone to carry out close monitoring of one participant in the treatment group, that could affect their care of a similar participant in the control group in an unpredictable way. We decided to treat this as an excluded study on the grounds that we cannot be sure that high intensity case management was really being compared with standard care.
+Three trials, which had been awaiting assessment in the old Cochrane reviews, were also excluded for different reasons (Jerrell-California, Godley-Illinois, Mulder-Missouri, more details are given in Characteristics of excluded studies).
+Jerrell-California had been a partially published trial and had been previously classified as ’awaiting further assessment’ because information was required on number of people excluded after randomisation (participants were excluded if they refused to participate after randomisation or if they had not been discharged from hospital within 6 months of entering the study). As these data did not become available, the trial has now been excluded.
+Godley-Illinois was an unpublished two-centre trial and it was initially classified as ’awaiting further assessment’ because it was not possible to determine if the intervention was ACT or CM. During the current update the intervention was classified as ICM versus standard care. It had to be excluded because it contained no usable data due to incomplete data reporting, and no further information became available (i.e. there was an apparent error in the reporting of numbers admitted to hospital; in one table admission rates are reported as: 31/52 experimental group and 33/ 45 control group; in another table admission rates are reported as: 31/52 experimental and 25/45 control).
+Mulder-Missouri was a report of data from randomised and nonrandomised participants. These data were not reported separately and, in addition, the intervention also did not fit inclusion criteria (ICM is compared to acute hospital admission).
+Overall, 27 studies were excluded because they were not randomised or because randomisation was compromised (Jerrell-California). Five studies had to be excluded as participants were requiring immediate hospital admission (Fenton-Canada, Hoult-Australia, Muijen-UK1, Mulder-Missouri, Stein-Wisconsin), one (Martin-UK) because participants were dually diagnosed with intellectual disability and mental illness, and one more because the majority of participants were simply homeless and not clearly ill (Toro-New York). Most trials had to be excluded because of the intervention: 26 because the experimental intervention was not ICM. Modcrin-Kansas had to be excluded as caseload was not reported in either experimental or control group. Ten trials were excluded as the comparison intervention was not standard care nor non-ICM. Ten trials were excluded as the intervention administered to the experimental group was not only
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+ICM (Chandler-California2, COAST-UK, Cosden-California, Gold-SCarolina, Grawe-Norway, Lehman-Maryland2, LEO-UK, McHugo-Washington DC, Shern-USA2, Shern-USA3). Finally two trials, Godley-Illinois and Morse-Missouri2, were excluded because no usable data could be extracted from the study report (as previously explained).
+1. Awaiting classification
+Fifteen trials, of which five are in the Chinese language are awaiting classification until further information is obtained (Agius-Croatia, Dick-UK, Johnson-UK, Kane-Virginia, Klotz-California, Linszen-Netherlands, O’Donnell-Australia, Rivera-New York, Sells-Connecticut, Verhaegh-Netherlands, Guo-China, Huang-China, Li-China, NCT00781079, TanChina).
+2. Ongoing studies
+One of the two trials described as ’ongoing’ in previous reviews has now been included (UK700-UK ), while the second trial (Hu-California) has not been included as a trial itself, but it has been moved to reference both Chandler-California1 and Jerrell-SCarolina1. We are aware of only one study currently ongoing (Walsh-Connecticut).
+Risk of bias in included studies
+For multi-centre trials provided data for individual single centres, we did not assess the risk of bias for each centre. Our judgments regarding the overall risk of bias in individual studies is illustrated in Figure 1.
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+Allocation
+All 38 studies were stated to be randomised, but only ten provided descriptions of the methods used to generate the sequence. These studies were therefore classified as of high quality with a low risk of selection bias. The most common method was random allocation according to a sequence of random numbers generated by a computer programme (Bjorkman-Sweden, Essock-Connecticut2, Ford-UK, Harrison-Read-UK, OPUS-Denmark in one of two sites). Three trials used permuted block (Marshall-UK, REACT-UK, Sytema-Netherlands), one used table of random permutation (Pique-California) and one coin tossing (Rosenheck-USA). In one of the two sites of OPUS-Denmark trial (Aahrus site) allocation was performed by drawing lots - from among five red and five white lots from a black box. Overall, however, most studies are classified as of unclear quality with a moderate risk of selection bias and an overestimate of positive effect, as no description of the methods used to generate the sequence had been provided.
+Regarding the allocation concealment, only four studies were rated as high quality as they provided descriptions of the methods used to conceal random allocation (OPUS-Denmark, REACT-UK, Sytema-Netherlands, UK700-UK). In all four cases it was a centralised allocation carried on by telephone, fax or mail. All remaining thirty-three studies are classified as of unclear quality with a moderate risk of selection bias and overestimate of positive effect.
+Blinding
+We classified blinding in respect only to primary outcomes. Due to intervention characteristics, i.e. being a model of service organisation, we assumed participants and clinicians being implicitly not blind to treatment assignment. We also assumed that primary outcomes were likely to be influenced by participant and clinician lack of blinding, as the knowledge of treatment allocation could determine both performance and attrition bias at a level which is difficult to predict/quantify. Whereas we did not consider the primary outcomes as interviewer mediated, hence we assume that lack of interviewer blinding would produce less detection bias. Therefore all studies providing primary outcome data are classified as of unclear quality with a moderate risk of performance and attrition bias. This gathers further potential for overestimate of positive effects and underestimate of negative ones.
+We report blinding to secondary outcomes in the risk of bias table but we do not account for it in the global rating of the study blinding risk of bias. Again, if the secondary outcome was clini-cian/participant mediated, we rated it as unclear. If it was interviewer rated, we assessed it according to information provided in the study. Only Shern-USA1 was rated at high risk of bias, as it provided only secondary outcome data and was only interviewer mediated. It was therefore possible to assess its’ risk of bias with
+higher confidence.
+Incomplete outcome data
+Where information were available, we assessed incomplete outcome data separately for primary and secondary outcome and presented both assessments in Figure 1 . We, however, only rated the risk of bias in respect to primary outcome. Only three trials provided information for incomplete primary and secondary outcome data apart, and the risk of bias could be assessed separately (Holloway-UK, Johnston-Australia, REACT-UK). Nine trials were judged as adequately addressing incomplete outcome data and rated as low risk of attrition bias. Four of these were so rated because there were no missing outcome data (Bush-Georgia, Holloway-UK, REACT-UK, Sytema-Netherlands). Three more did make the number and reason for missing data explicit as well as balanced across groups (Audini-UK, Essock-Connecticut1, Johnston-Australia). The last two because they undertook intention to treat analysis (OPUS-Denmark, UK700-UK).
+Bond-Chicago1 and Ford-UK were judged as high risk of attrition bias because, although clearly reporting number and reason for missing data, reasons for missing outcome data were likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention group. However, our protocol somewhat compensated for this (see Dealing with missing data) and despite the high rating, data from these studies remains included.
+The remaining twenty-seven trials were rated as ’unclear’ with a moderate risk of attrition bias. They either did not address this issue or presented insufficient information of attrition/exclusions to permit judgment (i.e. no reasons for missing data provided or number of randomised not stated. Jerrell-SCarolina1 reported only number of randomised participants completing the study period).
+Some specific examples may serve to illustrate the difficulty in rating this issue.
+Essock-Connecticut2 was not an intention to treat analysis - seven participants were excluded from the study straight after randomisation because they were lost to follow up, but authors failed to provide information on what intervention those participants had been allocated and reason for leaving the study early. As this study was providing only continuous outcome data, we reproduced completer-only data which were reported. No action was undertaken to deal with other missing data.
+Macias-Utah had three problems. First, the study was not an intention to treat analysis - seven participants were excluded from the study because they were lost to follow up. The authors of this study broke with usual practice by failing to provide any data on participants lost to follow up, in particular, data on admissions
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+to hospital. Second, one participant, (presumably randomised to the treatment group) was excluded after randomisation, having refused to participate (again, it was not clear whether this person had been admitted to hospital). Third, five further participants were added (randomly) to the treatment and control groups part way through the study, some as late as ’late 1990’ (final assessments took place in February 1991). No further information has become available since first review publication, which potentially could substantially affect findings.
+In Morse-Missouri1 28 further participants were added (randomly) to the initial randomised sample, to replace participants leaving the study within the first month after entering. As replacement was carried out through randomised assignment, reviewers did not raise any question on the replacement issue and the study was included. We presented data from the final sample, obtained after randomised replacement occurred.
+In Muller-Clemm-Canada the number randomised was not clearly reported, as authors declared that “Clients who withdrew from the study within the first 6 months were replaced by other clients”. Finally Sytema-Netherlands randomised 119 participants, but one was excluded because moved to another area directly after randomisation (We performed intention to treat analysis on remaining 118 participants).
+Selective reporting
+Most of the trials (23) were rated as of low quality with a high risk of reporting bias as they presented data in reports in a way we could not consider as free of suggestion of selective outcome reporting (i.e. pre-specified outcomes were not reported or reported incompletely so that they could not be entered in the analysis, or reported outcomes not pre-specified). Thirteen studies were rated as of high quality in reporting outcome with a low risk of reporting bias. For two studies the risk of bias was assessed as unclear with a moderate risk of reporting bias.
+Other potential sources of bias
+The presence of other potential threats to validity was unclear in only Hampton-Illinois. In this trial it was unclear whether the study was interrupted early in one of the two centres. Therefore the risk of other potential sources of bias was rated as moderate in this trial.
+All remaining trials were rated as low risk of other potential sources of bias, as no evidence of other bias occurred. Most were publicly funded. No declaration of interest was made by authors, and we assume there was none to be made. However, many study authors were active pioneers of developing and implementation of the experimental intervention model across the scientific community and clinical world. This raises the issue on how researcher beliefs could affect the entire process of evaluating an intervention in a randomised clinical trial. Although conscious of this issue, we
+decided not to make any attempt in rating it as it is very difficult to judge, and erroneous quantification could drive bias into our conclusions.
+Effects of interventions
+See: Summary of findings for the main comparison INTENSIVE CASE MANAGEMENT compared with STANDARD CARE for severe mental illness; Summary of findings 2 INTENSIVE CASE MANAGEMENT compared to NON-INTENSIVE CASE MANAGEMENT for severe mental illness
+The nine main indices of outcome were: i. service use; ii. adverse effects; iii. global state; iv. social functioning; v. mental state; vi. behaviour vii. quality of life; viii. satisfaction; and ix. direct costs. Each index will be considered in turn for each of the two comparisons.
+1. COMPARISON 1: INTENSIVE CASE MANAGEMENT versus STANDARD CARE (Summary of findings for the main comparison)
+Primary outcomes 1.1 Service use
+1.1.1 Service use: Average number of days in hospital per month - at about 24 months
+Data were available from five studies presenting skewed data from a sample size greater than or equal to 200 participants and from nineteen trials reporting skewed data from sample sizes less than 200. We entered these data in separate subgroups.
+In the first sub-group analysis (i.e. skewed data from studies with sample size greater than or equal to 200 participants) we found no significant difference in length of hospitalisation per month (n=1812, 5 RCTs, MD -0.46 CI -0.95 to 0.03), although data suggested a trend favouring ICM (p=0.06). In the second subgroup analysis (i.e. skewed data from study sample size less than 200 participants) there was a significant difference between groups, favouring the ICM group in reducing length of hospitalisation (n= 1783, 19RCTs, MD-1.01 CI-1.74to-0.28), but thesedatawere heterogeneous (I2=77%, p<0.00001).
+When synthesising data from the two sub-groups, we found that the length of hospitalisation was significantly reduced in the ICM group (n=3595, 24 RCTs, MD -0.86 CI -1.37 to -0.34), but the level of heterogeneity was high (P=74%, p< 0.00001, Figure 2). We investigated the heterogeneity by checking again for correctness of data and removing one outlier study from the analysis (Curtis-New York), as it was the only one favouring standard care. By excluding Curtis-New York the level of heterogeneity was still high (I2=59%, p< 0.0002). Therefore we removed the second most outlier study from the analysis (one of three centres from a multicentre study, Bond-Indiana1 (A)) as this was the most extreme result (favouring ICM). By excluding Bond- Indiana1 (A) data remained significant, favouring ICM (n=3245, 22 RCTs, MD -0.79 CI -1.22 to -0.36). The heterogeneity was reduced to just within our cut off point (I2=49%, p=0.005). Removing two further out-
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+Meta-regression was run on 52 trials providing data for primary outcome ’Average number of days in hospital per month - at about 24 months’ (combining data from all ICM studies within Comparison 1 and 2). Within the meta-regression we found that i. the more ICM is adherent to the organisation model, the better it is at decreasing time in hospital (’organisation fidelity’ variable coefficient -0.36 CI -0.66 to -0.07, Figure 5); and ii. the higher the baseline hospital use in the population, the better ICM is at decreasing time in hospital (’baseline hospital use’ variable coefficient -0.20 CI -0.32 to -0.10, Figure 6). Combining both these vari
+ables within the model, ’organisation fidelity’ is no longer significant (regression coefficient -0.24 CI -0.52 to 0.04, p=0.089), but ’baseline hospital use’ result is still significantly influencing time in hospital, although it seems to lose some of its potency (regression coefficient -0.18 CI -0.29 to -0.07, p=0.0027) (Figure 7). Figure 7 shows the interaction of the two variables on study outcome graphically through the use of thin plate spline modelling. The plot provides a locally weighted two dimensional representation of the collinearity between the variables used in the regression.
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+1.1.2 Service use: Not remaining in contact with psychiatric services
+We included only one short term study and found no significant difference between treatment group (n=95, 1 RCT, RR 0.54 CI 0.28 to 1.05).
+Medium-term data were available from three studies showing a significant difference between treatment groups, favouring the ICM group, where participants had a lower risk of not remaining in contact with psychiatric services compared with participants in the SD group (n=1063, 3 RCTs, RR0.51 CI 0.36 to 0.71).
+Six long term studies data confirmed this trend favouring ICM,
+showing a significant advantage for the ICM group (n=653, 6 RCTs, RR 0.35 CI 0.18 to 0.68), but data were heterogeneous (I2=63%, p=0.02). Herinckx-Oregon seemed to be the sole cause of this (n=475, 5 RCTs, RR 0.27 CI 0.11 to 0.66, P=44%, p= 0.13) and on further consideration, post hoc, we think we were in error to include the outcome from this study because it was defined so differently from the other trials. Herinckx-Oregon did not include refusing to re-interview, moving out, and death - as all the other studies had done - and it was impossible to amend this at this stage. We therefore feel justified in removing this study
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+altogether from this part of the review outcomes.
+Overall, when pooling together studies from different time subgroups, we found a significant advantage in the ICM group, where people were less likely to be lost to psychiatric services than people in the standard care group (n=1633, 9 RCTs, RR 0.43 CI 0.30 to 0.61, I2=49%, p=0.05, Figure 8).
+We did not use a funnel plot for this outcome as there were less than ten studies (see Assessment of reporting biases).
+Secondary outcomes
+1.1.3 Service use: Admitted to hospital across time
+Data were available from two short term studies showing no significant differences between treatment groups (n=244, 2 RCTS, RR0.61, CI 0.22 to 1.69), but these datawereheterogeneous (P= 81%, p=0.02).
+We found medium term data in five studies and that these favoured the ICM group which had less admission to hospital across time compared with standard care (n=1303, 5 RCTs, RR 0.85 CI 0.77 to 0.93).
+Long-term data were provided by eleven studies. As in the short term data, they showed no significant differences between treatment groups (n=1516, 11 RCTs, RR 0.96 CI 0.74 to 1.23), but
+these data were heterogeneous (I2=70%, p=0.0003). Only one study reported data by long term, but referring to the number of admissions across the previous year. This, therefore, could not be entered in the long term data sub-group analysis. It showed a significant effect favouring the ICM group (n=547, 1 RCT, RR 0.67 CI 0.52 to 0.86), therefore not consistent with long term data showed above. As these findings were based on data from one study only, they are considered less robust than those available from the eleven long term studies.
+1.1.4 Service use: Hospital admission across time
+Data were available from one medium term and three long term studies, describing the average number of admissions per month. All these data were skewed and did not enter the analysis. Data by the medium term study suggested a trend favouring the ICM
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+group. Data from long term studies did not show any trend favouring one specific group over the other. Audini-UK and Muller-Clemm-Canada did not report variance measurements. We assumed consistency between studies and used the fully reported variance for Sytema-Netherlands and employed these data for Audini-UK and Muller-Clemm-Canada as well.
+1.1.5 Service use: Use of Emergency Room
+Data were available from three studies: one providing binary data as ’number admitted to ER’ (Emergency Room) and two providing continuous data as ’average number of admission to ER’. The first study describing ’number admitted to ER’, showed a non significant difference between two group (n=178, 1 RCT, RR 1.13 CI 0.72 to 1.76). The two studies describing the average number of admission to ER reported skewed data. Skewed data were not consistent as one did not show any trend in the direction of effect and the other showed a trend favouring the ICM group. As in one study (Jerrell-SCarolina1) the variance measurement was not reported in the study, the SD was carried over from the other available study (Lehman-Maryland1).
+1.2	Adverse events
+1.2.1 Adverse event: Death all causes
+By the short term, two deaths occurred in the 81 people treated with ICM compared with two in the 80 people treated with standard care (n=161, 2 RCTs, RR 1.04 CI 0.16 to 6.91).
+We found the same result by medium and long term. By medium term five deaths occurred in 453 people treated with ICM compared with six deaths in 448 people treated with standard care (n= 901, 6 RCTs, RR 0.78 CI 0.23 to 2.62). By long term twenty-four deaths occurred in 741 people treated with ICM compared with twenty-seven deaths in 715 people treated with standard care (n=1456, 9 RCTs, RR0.84 CI 0.48 to 1.47).
+1.2.2 Adverse event: Suicide
+We found similar results in mortality due to suicide as we found in mortality due to all causes. Data by short term were available from two studies, where no suicides occurred in 62 people treated with ICM compared with two suicides in 65 people treated with standard care (n=127, 2 RCTs, RR 0.35 CI 0.04 to 3.27). Data by medium term were available from four studies where two suicides occurred in 412 people treated with ICM, compared with two suicides in 407 people treated with standard care (n=819, 4 RCTs, RR 0.98 CI 0.17 to 5.60). Finally data by long term were available from nine studies, where ten suicides occurred in 741 people treated with ICM, compared with fourteen suicides in 715 people treated with standard care (n=1456, 9 RCTs, RR 0.68 CI 0.31 to 1.51).
+1.3	Global state
+1.3.1 Global state: Leaving the study early
+We included five short term studies and found no significant differences between treatment groups for number of participants leaving the study early (n=598, 5 RCTs, RR 0.74 CI 0.41 to 1.33), but data were heterogeneous (I2=86%, p=0.0001).
+We included eight medium term studies and found the risk of
+leaving the study early was lower for participants in the ICM group (n=1699, 8 RCTs, RR 0.60 CI 0.51 to 0.70).
+Data from thirteen long term studies confirmed data by medium term studies, showing a significant advantage for ICM (n=1798, 13 RCTs, RR 0.68 CI 0.58 to 0.79).
+1.3.2 Global state: GAF average endpoint score
+We found GAF score favoured ICM during short term assessment in four studies (n=797, 4 RCTs, MD 2.07 CI 0.28 to 3.86) and during long term assessment in 5 studies (n=818, 5 RCTs, MD 3.41 CI 1.66 to 5.16). Medium-term GAF data from 3 studies (n=722, 3 RCTs, MD 0.09 CI -3.11 to 3.28) were equivocal, not showing any significant difference between groups.
+1.3.3 Global state: Not compliant with medication
+We found data only from one long term study, favouring the ICM group (n=71, 1 RCT, RR 0.35 CI 0.15 to 0.81).
+1.4	Social functioning
+1.4.1 Social functioning: Contact with legal system
+For short term outcomes we found data only from one study, describing the outcome ’contact with the police’. This study did not reveal any significant difference in the rate of contact with the police between treatment groups (n=61, 1 RCT, RR 2.57 CI 0.73 to 9.04).
+For medium term outcomes we found three studies, describing the outcome ’number of arrested’. They failed to show a significant difference between the two intervention groups (n=604, 3 RCTs, RR 1.08 CI 0.61 to 1.90). Only one medium term study was available providing data on ’contact with the police’. These data favoured the ICM group in reducing the number of contacts with the police (n=88,1 RCT, RR0.23 CI 0.09 to 0.55).We foundfour medium term studies describing ’number of imprisoned’ and we found no significant advantage for ICM group (n=361, 4 RCTs, RR 0.80 CI 0.39 to 1.64).
+For long term outcomes we found data from one study, describing the outcome ’number of arrested’ and it showed no significant advantage for ICM (n=178, 1 RCT, RR 0.66 CI 0.32 to 1.37). We found also four long term studies reporting data on ’number of imprisoned’, again not showing any significant advantage for ICM in reducing the number of participants imprisoned by long term (n=361, 4 RCTs, RR 0.72 CI 0.31 to 1.67).
+1.4.2 Social functioning: Employment status
+We found medium term data for various outcomes. One study reported data on ’not competitively employed at the end of trial’ and these data did not show a significant advantage for ICM in improving the number of competitively employed people (n=88, 1 RCT, RR 1.00 CI 0.91 to 1.10). We found four medium term trials reporting the same outcome also failing to show a significant difference, although data did suggest a trend favouring ICM (n= 1136, 4 RCTs, RR 0.89 CI 0.79 to 1.00), but heterogeneity was present (I2=75%, p=0.008).
+The same outcome ’number of not employed at the end of the trial’ was described in four long term studies. As in the medium term comparison, data again failed to show a significant difference,
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+although they suggested a trend favouring the ICM group (n= 1129, 4 RCTs, RR 0.70 CI 0.49 to 1.00), but, again, there is considerable heterogeneity (I2=94%, p<0.00001).
+1.4.3 Social functioning: Accommodation status
+We found data on outcome ’homelessness’ from one short term study. This small study revealed a significant reduction in the rate of homelessness in the ICM group (n=95, 1 RCT, RR 0.04 CI 0.00 to 0.70).
+Medium-term data on ’homelessness’ outcome were available from one small study. Data did not reveal any significant difference between groups in the rate of homelessness by the medium term (n= 88, 1 RCT,RR0.32CI0.03 to2.95).Medium-termdatafromfive trials were also available on ’not living independently’ outcome. These data showed a significant advantage for ICM in reducing the number of not living independently (n=1303, 5 RCTs, RR 0.80 CI0.66 to 0.97).
+Data on the accommodation status were available by long term, describing different outcomes. We found ’homelessness’ data in three long term studies, not revealing any significant difference between intervention groups (n=418, 3 RCTs, RR 0.78 CI 0.34 to 1.82).
+We found four studies providing data on ’not living independently’. These data favoured the ICM group where the incidence of not living independently was lower compared with the standard care group (n=1185, 4 RCTs, RR 0.65 CI 0.49 to 0.88).
+The outcome ’not living in stable accommodation’ was available only from one study. We found that data favoured the ICM group in reducing the number of not living in stable accommodation (n=168, 1 RCT, RR0.80 CI 0.65 to 0.98).
+Skewed data were available on ’average days in stable accommodation’ and ’average days per month in sheltered homes’. Mediumterm data on ’average days in stable accommodation’ showed a trend favouring the ICM group, therefore consistent to results previously described on ’not living in stable accommodation’ by long term.
+Long-term data on ’average days per month in sheltered homes’ were provided by two studies. These data were equivocal as data from one study favoured ICM whilst data from the second study favoured the standard care group.
+1.4.4 Social functioning: Substance abuse
+Long-term data were available from one study, reporting alcohol and illicit drug abuse. Data failed to show a significant difference both in alcohol (n=547, 1 RCT, RR 0.55 CI 0.26 to 1.17) and illicit drug abuse (n=547, 1 RCT, RR 0.96 CI 0.63 to 1.47) between groups.
+We were unable to enter two types of data into the analysis: medium term data assessing alcohol and drug abuse on DALI scale, and other skewed data. As the DAS scale averages values from positive to negative, skew is very difficult to detect, we did not enter these data in the analysis. These data tended to favour the standard care group both on alcohol and drug abuse outcome. Medium and long term data from the outcome ’days of substance
+use per month’ were equivocal, but skewed.
+1.4.5 Social functioning: Average endpoint score (various scales) Three studies provided data from five different scales (DAS, ISSI, RFS, SAS-adapted version, Strauss-Carpenter scale) assessing social functioning by short, medium and long term. As no more then one study per time period used the same scale, we did not enter more than one study per sub-group. Data from each available time-period failed to show any significant difference between treatment groups.
+Data by short term were available on the RFS (n=80, 1 RCT, MD 0.62 CI -0.99 to 2.23) and SAS-adapted scale scores (n=80, 1 RCT, MD 3.34 CI -0.87 to 7.55).
+Data by medium term were available on the DAS (DAS scale: n= 55, 1 RCT, MD 0.10 CI -0.40 to 0.60), RFS (n=80, 1 RCT, MD 0.86 CI -1.00 to 2.72), and SAS-adapted scale scores (n=80, 1 RCT, MD 3.30 CI -1.23 to 7.83).
+Data by long term were available on the DAS (n=58, 1 RCT, MD -0.20 CI -0.67 to 0.27), ISSI (n=62, 1 RCT, MD -3.20 CI -6.29 to -0.11), RFS (n=80, 1 RCT, MD 2.35 CI 0.65 to 4.05), and SAS-adapted (n=80, 1 RCT, MD 2.75 CI -1.63 to 7.13), and Strauss-Carpenter scale scores (n=60, 1 RCT, MD -0.10 CI -1.37 to 1.17). Skewed data on SAS scale score were available by short, medium and long term from two studies. These data were equivocal and not consistent between two studies. Long-term data on REHAB scale score were provided by one study, these data tended to favour the ICM group but were also skewed.
+1.5.1 Mental state: General symptoms
+Two sets of data were available: i. non skewed data or skewed data from a sample size greater than or equal to 200 participants per study: entering analysis together; ii. skewed data: not entering analysis.
+i. Short-term data were available from different scales. Short-term data mental state scores assessed with BPRS scale were not significantly different between two groups (n=668, 2 RCTs, MD -1.56 CI -6.85 to 3.73), but data were heterogeneous (I2=92%, p= 0.0004). Short-term mental state scores assessed with BSI scale were available from the same two studies providing BPRS short term data. Again data were not significantly different (n=668, 2 RCTs, MD -0.06 CI -0.19 to 0.06), but, different from the BPRS results, these data were homogeneous. One further trial was available providing short term data on mental state assessed with CSI. These data show a significant difference favouring the ICM group (n=125, 1 RCT, MD 0.56 CI 0.28 to 0.84).
+Medium-term data were available from different scales. Mental state scores assessed with BPRS and BSI scales were both not significantly different between the two groups (BPRS scale: n=662, 2 RCTs, MD -0.96 CI -2.42 to 0.51; BSI scale: n=662, 2 RCTs, MD -0.02 CI -0.15 to 0.10), but medium term data assessed with CSI favour the ICM group (n=125, 1 RCT, MD 0.35 CI 0.05 to 0.65).
+We found significant results in mental state by long term: data were available from two studies assessing mental state scores with
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+BPRS and BSI scale. We found in both sub-groups data favouring the ICM group, where people reached a better mental state score by long term (BPRS scale: n=647, 2 RCTs, MD -2.65 CI -4.11 to -1.20; BSI scale: n=647, 2 RCTs, MD -0.18 CI -0.31 to -0.06). Finally one study assessed the mean change from baseline on CSI scale, data showed no difference between groups (n=168, 1 RCT, MC -0.32 CI -0.53 to -0.11).
+ii. Skewed data were available from six studies assessing mental state by different time periods and with different scales (BPRS-18 items, BPRS-24 items, CPRS, PSE, SCL-90). Data failed to show any significant trend favouring one group over the others, this report being consistent across different studies and different rating scales. These data were considered not robust as they were skewed, but were in accord with short and medium term data, but not long term data.
+1.5.2 Mental state: Specific symptoms
+We found data on specific symptoms from only one study. This study provided data on depression incidence and there was no significant difference between groups by medium and long term (by medium term: n=547, 1 RCT, RR 0.77 CI 0.56 to 1.04; by long term: n=547, 1 RCT, RR 0.83 CI 0.57 to 1.21).
+We found a second small study (n=70) providing skewed data on depression symptoms assessed with BDI scale, and negative symptoms assessed with SANS scale. Neither set of data is entered into the analysis. Skewed depression scores favoured the ICM group at medium and long term, whilst skewed negative symptoms scores by medium and long term were equivocal.
+1.6	Behaviour: Specific - self harm
+Medium and long term data showed there were no significant differences in number of participants who committed self-harm between groups: by medium term 30 events occurred in 312 people treated with ICM compared with 30 events in 308 people treated with standard care (n=620, 2 RCTs, RR 0.99 CI 0.61 to 1.59); by long term 11 events occurred in 190 people treated with ICM compared with 13 events in 184 people treated with standard care (n=374, 2 RCTs, RR 0.82 CI 0.38 to 1.78). These data were confirmed from long term data on self-harm during the previous twelve months available from a single study (n=547, 1 RCT, RR 0.81 CI 0.47 to 1.38).
+Skewed data were available from one small study (n=70) assessing behaviour with SBS scale by medium and long term. These data tended to favour the ICM group.
+1.7	Quality of life: Average endpoint score (various scales) We found six studies assessing quality of life with various scales by different time periods.
+The only significant result we found was by short term: data were available from a single study and showed a significant higher quality of life in the ICM group as assessed on the QOLI general-wellbeing sub-scale (n=125, 1 RCT, MD 0.53 CI 0.09 to 0.97).
+Medium-term data assessing quality of life with LQoLP (one study) and MANSA (one study) did not show a significant difference between groups (LQoLP scale n=52, 1 RCT, MD 0.09 CI -
+0.60 to 0.78; MANSA scale n=81, 1 RCT, MD 0.20 CI -0.29 to 0.69).
+Long-term data on quality of life were assessed with LQoLP (two studies) and QOLI (two studies). As with the medium term data, results did not suggest significant differences between groups (LQoLP scale: n=113,2 RCTs, MD -0.23 CI -0.55 to 0.08; QOLI scale: n=132, 2 RCTs, MD 0.09 CI -0.24 to 0.42).
+We found one further study providing data by long term for this comparison, but it was not entered in the analysis as data were skewed, measuring mean change from baseline on QOLI scale. These data tended to favour the ICM group.
+1.8	Participant satisfaction/need
+We found that participant satisfaction assessed with CSQ scale was significantly greater in the ICM group compared with standard care in all three time period assessments. Short-term data were available from only one small study and showed a significant difference between groups favouring the ICM intervention (n=61, 1 RCT, MD 6.20 CI 2.60 to 9.80); medium term data from two studies confirmed these results (n=500, 2 RCTs, MD 1.93 CI 0.86 to 3.01) and long term data was also favourable for the ICM group (n=423, 2 RCTs, MD 3.23 CI 2.31 to 4.14). One further small trial provided short term data but it was skewed, the standard care arm’s attrition was higher than 50%. Participant satisfaction was assessed with CSQ scale and it tended to favour the ICM group. We found more skewed data from three studies assessing participant need on two other scales (CAN, CANSAS). Medium-term data from one study failed to show any difference between groups. Long-term data were assessed with two different scales in two different studies: one study, using the CANSAS, did not show any trend in the direction of effect, data from the second study, using the CAN, tended to favour the ICM group.
+1.9	Costs
+Studies reported cost data assessing ’direct costs of psychiatric hospital care, ’health care costs’ and ’costs of all care’ at different periods of time.
+Direct medium term costs of psychiatric hospital care were available from two studies (Chandler-California1 (A), Chandler-California1 (B),) reporting skewed data but with a sample size greater than 200. Data favoured ICM (n=426, 2 RCTs, MD -US$143.74 CI -272.40 to -15.08). Four more studies did describe ’direct costs of psychiatric hospital care’, but data were markedly skewed. Some of these data showed a trend favouring ICM, some standard care. Therefore we could not highlight any trend confirming the findings from meta-analysis.
+Long-term data for direct health care cost were also available from two studies: again studies reporting skewed data but with a sample size greater than 200 so could be entered into a meta analysis. These data were inconclusive (n=873, 2 RCTs, MD US$-529.24 CI -2143.59 to 1085.10), as they were highly heterogeneous (I2= 94%, p<0.0001) with inconsistency in direction of effect.
+Other skewed, medium term data from a study with a sample size of less than 200 could not be entered into the meta analysis, this
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+data did not show any significant difference between interventions. 2. COMPARISON 2: INTENSIVE CASE MANAGEMENT versus NON-INTENSIVE CASE MANAGEMENT (Summary of findings 2) Primary outcomes 2.1 Service use
+2.1.1 Service use: Average number of days in hospital per month - at about 24 months
+We found three studies reporting skewed data but with a sample size greater than or equal to 200. There was no significant dif
+ference between groups for reducing length of hospitalisation (n= 694, 3 RCTs, MD -0.58 CI -1.93 to 0.76). These findings were in accord with the second sub-group analysis - skewed data from 18 studies with a sample size less than 200 participants. These data again did not show a significant difference between groups (n= 1526, 18 RCTs, MD -0.03 CI -0.33 to 0.28). Overall, combining the two pools of studies, found no clear difference between ICM and non-ICM (n=2220, RCTs 21, MD -0.08 CI -0.37 to 0.21, Figure 9).
+A funnel plot did not show any significant reporting bias (Figure 10).
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+2.1.2 Service use: Not remaining in contact with psychiatric service
+Short-term data were not available.
+Medium-term data were available from one small study showing a significant difference favouring ICM group (n=73, 1 RCT, RR 0.27 CI0.08 to 0.87).
+Long-term data were available from three studies. Pooled data were not statistically significant (n=1182, 3 RCTs, RR 0.82 CI 0.34 to 1.98) but data were heterogeneous (I2=82%, p=0.004) with inconsistency in direction of effect.
+When we pooled studies from medium and long term, data did not show significant differences between interventions but data hetero-
+geneitywassubstantial (n=1255, 4 RCTs, RR0.63 CI0.27to1.49, I2=81%, p=0.001). We addressed this finding checking again for correctness of data and explored heterogeneity by dropping each study out of the analysis. Only by removing both Drake-NHamp and UK700-UK is homogeneity restored. We could not ascertain any clear reason for the heterogeneity. We have therefore chosen not to pool these data as it could be misleading to quote an average value for the intervention effect - particularly as this is the case, when there is inconsistency in direction of effect (Figure 11). We did not use a funnel plot for this outcome as there were less than ten studies (see Assessment of reporting biases).
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+Secondary outcomes
+2.1.3 Service use: Use of hospital
+Binary data describing this outcome were available from three long term studies, reporting ’number of admitted to hospital’. These data showed a not significant difference in number of admitted to hospital between groups (n=1132, 3 RCTs, RR0.91 CI 0.75 to 1.12).
+Only one long term study provided continuous data on this outcome, reporting ’average number of admission’. Data were skewed, but as the trial sample size was greater than or equal to 200, we entered these data in the analysis. They failed to show any significant differences between groups (n=678, 1 RCT, MD -0.18 CI -0.41 to 0.05).
+A trial that included less than 200 participants presented skewed data that we could not meta analyse. Data was reported for the outcome ’average number of admissions’ by medium term. As with previous findings, they failed to show any trend in effect between groups.
+2.2	Adverse events: Death
+2.2.1 Adverse events: Death all causes
+Short-term data on mortality were available from one study (n= 193), not reporting any deaths and therefore a measure of effect was not estimable.
+Medium-term data were available from three studies, where one death occurred in 148 people treated with ICM, compared with no deaths in 146 people treated with non-ICM. There were no significant differences in mortality between groups (n=294, 3 RCTs, RR 2.92 CI 0.12, 69.43).
+We found long term data in five studies, reporting 16 deaths occurring in 816 people treated with ICM, compared with 18 deaths in 821 people treated with non-ICM. These data confirmed medium term findings not showing differences in mortality between groups (n=1634, 5 RCTs, RR 0.90 CI 0.46, 1.75).
+2.2.2 Adverse events: Suicide
+Short-term data on suicide mortality were available from one study (n=193), again not reporting any deaths and therefore a measure of effect was not estimable.
+Medium-term data were available from six studies, where five sui
+cides occurred in 464 people treated with ICM, compared with three suicide in 465 people treated with non-ICM. There were no significant differences in the suicide rate between groups (n=929, 6 RCTs, RR 1.61 CI 0.26 to 9.85).
+Long-term data were available from three studies, reporting six suicides occurring in 577 people treated with ICM, compared with seven suicides in 575 people treated with non-ICM. These data confirmed medium term data overall mortality and on suicide, with no significant differences in the suicide rate between groups (n=1152, 3 RCTs, RR 0.88 CI 0.27 to 2.84).
+2.3	Global state
+2.3.1 Global state: Leaving the study early
+We found studies providing medium and long term data, but not short term.
+Medium-term data showed no treatment effect in reducing number of lost to follow up (n=225, 2 RCTs, RR 0.64 CI 0.13 to 3.07), but these data presented a substantial level of heterogeneity (I2= 84%, p=0.01). In addition, there was inconsistency in the direction of effect between the two studies.
+Long-term data were available from seven studies and we found a significant advance for ICM in reducing the number of lost to follow up (n=1970, 7 RCTs, RR 0.70 CI 0.52 to 0.95). Different from the medium term data, long term sub-group analyses did not show substantial heterogeneity. However, pooling data from two time sub-groups, we found data still significant, showing an advantage for ICM in reducing the number of lost to follow up (n= 2195, 9 RCTs, RR0.72 CI 0.52 to 0.99). Although heterogeneity was reduced in comparison to the medium term sub-group data, these pooled data still showed a substantial level of heterogeneity (I2=59%, p=0.01).
+2.3.2 Global state: HoNOS scale
+One study reported long term data on global state assessed with HoNOS scale. These data were skewed, but entered the analysis as the study sample size was greater than or equal to 200 participants. Data failed to show a significant difference between interventions (n=239, 1 RCT, MD -0.40 CI -1.77 to 0.97).
+We found skewed data describing global state with HoNOS scale
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+by medium and long term. These data trended to favour the standard care group.
+2.3.3 Global state: Compliance with medication
+One study reported medium term data for the binary outcome ’number of participants not compliant with medication’. There was no significant difference between groups (n=73, 1 RCT, RR 1.14 CI 0.42 to 3.05).
+Long-term compliance scores assessed with the ROMI compliance and non-compliance sub-scale were not significantly different (compliance sub-scale: n=239, 1 RCT, MD 0.60 CI -0.05 to 1.25), (non-compliance sub-scale, n=239, 1 RCT, MD -0.60 CI -1.63 to 0.43), although both sub-scale scores tended to favour ICM.
+Medium and long term skewed data were available from one study assessing compliance scores with the ROMI compliance and noncompliance sub-scale. These data tended to favour standard care, where participants had a higher level of compliance.
+2.4	Social functioning
+2.4.1 Social functioning: Contact with legal system
+Medium-term data were available from one study reporting ’contact with the police’. We found no significant difference between groups (n=73, 1 RCT, RR 0.32 CI 0.04 to 2.97).
+Long-term data were available both on binary outcome ’imprisoned’ and ’arrested’. We found two studies reporting the outcome ’imprisoned’ and data failed to show any difference in the number of people imprisoned (n=959, 2 RCTs, RR 1.15 CI 0.64 to 2.08). Only one study provided data on the outcome ’arrested’. Again there was no clear difference between groups (n=251, 1 RCT, RR 0.87 CI 0.53 to 1.42).
+2.4.2 Social functioning: Employment status
+Data were available from one medium term study, reporting ’participant who spent >1 day employed’ and ’participants on paid employment’. Both these outcomes did not show significant difference between groups (’spent >1 day employed’: n=73, 1 RCT, RR 1.46 CI 0.45 to 4.74; ’on paid employment’: n=73, 1 RCT, RR0.97 CI0.14to6.54).
+2.4.3 Social functioning: Accommodation status
+The outcome ’living in supported accommodation’ was only available for the medium term, as reported from one study. Data failed to show a significant difference between groups (n=73, 1 RCT, RR 2.59 CI 0.75 to 9.01).
+The outcome ’homelessness’ was only available for the long term (one study). There were no significant differences between treatment groups in the number of people being homeless (n=251, 1 RCT, RR 0.69 CI 0.34 to 1.38).
+The continuous outcome ’average days per month in stable accommodation’ was available for short, medium and long term (2 RCTs). Data did not find significant differences between groups at any time period (by short term: n=203, 1 RCT, MD -0.20 CI -2.48 to 2.08; by medium term: n=203, 1 RCT, MD 0.10 CI -2.15 to 2.35; by long term: n=901, 2 RCTs, MD -0.19 CI -1.37 to 1.00).
+2.4.4 Social functioning: Substance abuse
+Data describing this outcome were available both as a binary and continuous measure. Long-term binary data on ’substance abuse’ differentiated between ’alcohol abuse’ and ’illicit drug abuse’. There was no difference in the number of people abusing alcohol (n=251, 1 RCT, RR 1.10 CI 0.67 to 1.83) nor in those abusing illicit drug (n=251, 1 RCT, RR 1.08 CI 0.69 to 1.71). Binary data on ’remission from alcohol use disorder’ (defined AUS score < 3) also did not show any significant difference between groups by long term (n=223, 1 RCT, RR 0.86 CI 0.65 to 1.14).
+Short, medium and long term continuous data were available, assessing the substance abuse with SATS scale. These data failed to show significant difference between groups at any time period assessment (short term: n=203, 1 RCT, MD 0.07 CI -0.28 to 0.42; medium term: n=203, 1 RCT, MD -0.11 CI -0.55, 0.33), (long term: n=203, 1 RCT, MD 0.11 CI-0.41 to 0.63).
+Skewed data were available from one study assessing ’days using alcohol’ and ’AUS scale score’ by short, medium and long term. Data on ’days using alcohol’ showed a trend towards a higher alcohol consumption in the ICM group in each assessed time period. This trend was confirmed by the short and medium term findings on the AUS scale, where the ICM group rating had a worse outcome than the non-ICM group, however, long term data showed a worse outcome in the non-ICM group.
+2.4.5 Social functioning: Scale data
+We found LSP social functioning score did not favour any group over the other by long term (n=239, 1 RCT, MD 4.00 CI -0.61 to 8.61).
+Skewed data on SFQ social functioning scores showed equivocal results by medium term, and a worse outcome in the ICM group by long term.
+2.5	Mental state
+2.5.1 Mental state: General symptoms
+We found BPRS mental state scores did not favour any group during the short, medium and long term analysis (by short term, n=203, 1 RCT, MD -0.65 CI -3.99 to 2.69; by medium term, n= 203, 1 RCT, MD -1.62 CI -4.76 to 1.52; by long term, n=203, 1 RCT, MD -0.22 CI -3.32 to 2.88). These data were confirmed by long term findings on the CPRS, where there was no significant difference between groups (n=595, 1 RCT, MD 0.40 CI -1.83 to 2.63).
+Medium and long term skewed data from the Krawiecka Scale could not be entered into meta analysis, along with long term skewed data from the BPRS scale. These data consistently suggested a better mental state outcome in the non-ICM group.
+2.5.2 Mental state: Specific symptoms
+Long-term continuous data on negative symptoms from one long term trial did not favour either group (n=593, 1 RCT, MD 0.20 CI -2.32 to 2.72).
+Skewed data were available on anxiety and depression symptoms assessed with HADS anxiety and depression sub-scale by medium and long term. These data, provided by the same study, are not
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+consistent between medium and long term, as they showed a better outcome in the ICM group by medium term and a worse outcome in the ICM by long term, compared with the non-ICM group.
+2.6	Behaviour (various measures)
+Medium-term data reporting ’harm to self or others’ did not find significant difference between groups (n=73, 1 RCT, RR0.88 CI 0.40 to 1.90).
+Long-term data were available both on ’patient committing selfharm’ and ’patient committing injury to others’. There were no significant differences in these two data sets (’patient committing self-harm’: 708, 1 RCT, RR 1.06 CI 0.70, 1.61; ’patient committing injury to others’: n=959, 2 RCTs, RR 1.09 CI 0.85 to 1.40). 2.7 Quality of life
+We found three studies assessing quality of life with three different scales (LQoL, MANSA, QOLI) at different time period. There were no significant differences in any of these measures - at any time period. Data on QOLI scores were available by short and medium term from one study (short term QOLI: n=203, 1 RCT, MD -0.02 CI -0.43 to 0.39; medium term QOLI: n=203, 1 RCT, MD -0.04 CI -0.43 to 0.35). Long-term data were available from three different studies measuring quality of life with three difference scales, therefore not entering the analysis together. None of them showed any significant difference between groups (LQoL: n=526, 1 RCT, MD 0.03 CI -0.10 to 0.16; MANSA: n=166, 1 RCT, MD 0.10 CI -0.19 to 0.39; QOLI: n=203, 1 RCT, MD 0.10 CI -0.25 to 0.45).
+2.8	Participant satisfaction/need
+Long-term data were available from one study, assessing participant need with CAN, and participant satisfaction with health service with the Homonym scale. There were no significant differences between groups as assessed with the two scales (CAN: n= 585, 1 RCT, MD -0.29 CI -0.69 to 0.11; Satisfaction with Health Service: n=490, 1 RCT, MD -0.40 CI -1.25 to 0.45).
+Skewed data from a different study suggested a long term better outcome on satisfaction with treatment for ICM group (data assessed with CSQ scale).
+One study provided data suggesting a difference between intervention in participant need as assessed with CAN scores, but these data were skewed. They showed a worse outcome in the ICM group in the medium and long term.
+2.9	Costs
+Costs were assessed measuring ’direct costs of psychiatric hospital care’ and ’direct cost of all care’. No data were available on ’direct health care costs’.
+Skewed data on direct costs of psychiatric hospital care were available from two studies - by medium and long term. Medium and long term data from one study did not find a significant difference between groups, whilst the second long term study data found costs significantly lower for the ICM group. The latter study was a small one (Quinlivan-California, n=60), where the first one was larger (Harrison-Read-UK, n=193).
+Direct cost of all care were available from one study (UK700-UK)
+reporting skewed data but with a sample size greater than 200. There was no significant difference between groups for reducing direct cost of all care (n=667, 1 RCT, MD 77.00 CI -66.63 to 220.63), but findings showed a trend suggesting greater cost in the ICM group. This trend suggested a cost increasing of £77 per person per month by long term, referred to fiscal year 1997/98. One study (Johnston-Australia, n=58) reported skewed data on the same outcome (direct cost of all care). This set of data did not show any significant differences between groups by the medium term, substantially confirming data by long term.
+3. SENSITIVITY ANALYSES
+As anticipated in the Methods section (see Methods, Sensitivity analysis), we performed the following sensitivity analysis.
+3.1	Implication of randomisation
+3.1.1 COMPARISON 1: INTENSIVE CASE MANAGEMENT versus STANDARD CARE
+All the included studies were described as randomised and none were described in a way as to imply randomisation, therefore we did not include any trials in the anticipated sensitivity analysis.
+3.1.2 COMPARISON 2: INTENSIVE CASE MANAGEMENT versus NON-INTENSIVE CASE MANAGEMENT
+All the included studies were described as randomised and none were described in a way as to imply randomisation, therefore we did not include any trials in a sensitivity analysis
+3.2	Standard care caseload is over 20
+3.2.1 COMPARISON 1: INTENSIVE CASE MANAGEMENT versus STANDARD CARE
+Between studies providing primary outcomes, only six studies reported the ratio staff to patients, and for each study it was >1: 20 (Bond-Chicago1, Bond-Indiana1, Herinckx-Oregon, Jerrell-SCarolina1, OPUS-Denmark, Sytema-Netherlands).
+When we entered these studies (where standard care group caseload was greater than 20) in the analysis, the first primary outcome ’average days per month in hospital’ showed a significant favourable effect in ICM group in reducing the length of hospitalisation (n=951, 7 RCTs/study centres, MD -2.01 CI -3.36, -0.67), but heterogeneity was substantial (I2=70%, p=0.003). These findings confirm those obtained when all studies were entered in the analysis, regardless of caseload in the standard care comparison group. The second primary outcome ’not remaining in contact with psychiatric services’ showed that participants were significantly less likely to lose contact with psychiatric services than participants in the ICM group compared to the standard care group by medium, long term and overall (n=931, 4 RCTs, MD 0.38 CI 0.23, 0.63), but heterogeneity was substantial (I2=67%, p=0.03). These findings confirm those obtained when all studies were entered in the analysis, regardless of caseload in the standard care comparison group. But data in the sensitivity analysis showed a substantial level of heterogeneity not present when all studies were entered in the analysis.
+3.3	Assumptions for lost binary data
+3.3.1 COMPARISON 1: INTENSIVECASEMANAGEMENT
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+versus STANDARD CARE
+In this comparison, no assumptions had to be made regarding people lost to follow-up at the primary binary outcome ’not remaining in contact with psychiatric services’, therefore we did not include any trials in the anticipated sensitivity analysis.
+3.3.2 COMPARISON 2: INTENSIVE CASE MANAGEMENT versus NON-INTENSIVE CASE MANAGEMENT
+In the second comparison, assumptions had to be made regarding people lost to follow-up for the primary binary outcome ’not remaining in contact with psychiatric services’ for one trial (REACT-UK). When we compared the findings of the primary outcomes when we used our assumption compared when we used completer data only, results did not show any substantial difference.
+3.4	Assumptions for lost continuous data (for meta-regression)
+Sensitivity analysis was undertaken, removing 13 out of 52 trials originally entering meta-regression. These were trials where primary outcome SD was imputed (see Table 2). Therefore meta-regression was run on the remaining pool of 39 non-imputed studies. Results did no longer reach significance for both variables ’organisation fidelity sub-score’ and ’baseline hospital use’ as p is at trend ~ 0.07 (’organisation fidelity sub-score’: regression coefficient -0.2 CI -0.48 to 0.02, p=0.067; ’baseline use of hospital’: regression coefficient -0.18 CI -0.39 to 0.02, p=0.077). Removing a high influence outlier (Rosenheck-USA-NP (C)), significance returns for the effect of’baseline hospital use’ (-0.26 CI -0.51 to -0.01, p= 0.046), but not for ’organisation fidelity’. When combining both two variables within the model (’organisation fidelity sub-score’ and ’baseline hospital use’), the pattern of results did not change for the non-imputed studies.
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+DISCUSSION
+Summary of main results
+1.	COMPARISON 1: INTENSIVE CASE MANAGEMENT versus STANDARD CARE (Summary of findings for the main comparison)
+Primary outcomes 1.1 Service use 1.1.1 Service use: Average number of days in hospital per month - at about 24 months
+ICM does seem to reduce length of hospitalisation when compared with standard care (by 0.86 day/month over 24 months). These data are from a heterogeneous analysis - when certain studies are removed from the analysis, the now homogeneous result suggests that the saving is in the region of 0.6 days/month. We are not quite sure which of the two figures is really the most reliable, but in any event trial findings suggest a saving of time in hospital per person of between about 7 and 10 days in hospital per year.
+What is important, however, is that there was a high duration of hospital stay for these people in the two previous years (averaging 6 days/month). This is higher than for those in the ICM vs nonICM comparison (averaging 3.4 days/month) - which found no difference between groups (see Figure 9). The impressive saving of time in hospital for ICM was greater than that of standard care, but the saving in standard care was also considerable (absolute decline for ICM is about 3 days, standard care is 1.8 days). Well organised standard care, for people with what would seem to be a history of considerable periods spent in hospital over the last 24 months, does seem to reduce the time in hospital for the ensuing two years. ICM, however, adds more than an extra day to that gain.
+The meta-regression is a tool of limited power, employed on weak data. The results are, however, in keeping with the results of one other review (Burns 2007) which suggests that the gain of ICM was not so much linked with the total fidelity score, the staff subscale, or the comparison group. It did suggest a link with the organisational sub-score of the IFACT scale (0.36 day/month out of hospital gained by each point increase of this sub-score). It also suggested that ICM effect is linked to length of hospitalisation during the previous two years (0.2 day/month out of hospital gained by each day increase in the day in hospital/per month during previous two years).
+In other words this means that:
+•	if the ICM team ratio staffclient is less than 1:20 (calculated dividing the number of active clients on the caseload by the number of full time equivalents of direct service staff on the team), then it makes no difference how much the caseload is lower than 1:20;
+•	the ICM team size does not matter (calculated as the number of full-time clinical staff equivalents, as defined earlier);
+•	the availability of a psychiatrist on the team is not pivotal; and/or
+•	the availability of a nurse on the team is not pivotal
+It also suggested that gains in IFACT scores can reduce average hospital stay. McGrew 1994 and McGrew 1995 suggest that gain can be mediated by:
+•	ensuring that the ICM team performed the role of primary therapist for the client (the primary therapist role designates the person, within the local mental health system, with primary clinical and record keeping (e.g., treatment plans) responsibility for the client);
+•	the ICM team’s offices being located in a separate building from the parent agency’s main offices (i.e. usually away from the hospital site);
+•	the ICM team sharing caseloads (rated as the degree to which all staff members on the team had contact with all clients on a regular basis, e.g., through rotation), in contrast to individual caseloads in which specific staff workers are responsible for specific clients;
+•	the ICM team meeting as a group each weekday to discuss their entire caseload;
+•	the ICM team’s supervisor devoting at least halftime to client contacts, either co-jointly during supervision of team members, or individually as part of his or her duties as a member of the team;
+•	the ICM team providing 24-hr direct access to the team (If access to the ICM team was triaged through the community mental health centre (CMHC) emergency 24-hr on-call service, intermediate score of 0.5 is obtained, therefore the advantage on decreasing days in hospital per month would be halved (-0,2 days in hospital per month)); and/or
+•	the ICM team serving clients without any expectation of transferring them to another programme
+1.1.2 Service use: Not remaining in contact with psychiatric services
+Overall, we found ICM to be better than standard care for retaining people in psychiatric services (n=1811, 10 RCTs, RR 0.45 CI 0.34 to 0.61). This effect, however, was not seen for the short term analysis (n=95, 1 RCT, RR 0.54 CI 0.28 to 1.05), although confidence intervals are compatible with an effect favouring ICM (p=0.07). Medium-term findings did suggest an overall effect of ICM being more effective in reducing loss to follow up to psychiatric service contact (n=1063, 3 RCTs, RR 0.51 CI 0.36 to 0.71). In the longer term this same effect was heterogeneous, due to an outlying finding of a single study (Herinckx-Oregon). This trial had a peculiar definition for this outcome which was different to the other studies. For Herinckx-Oregon ’not remaining in contact with psychiatric services’ did not include refusing re-interview, moving out and death, whereas for the other trials it did. If Herinckx-Oregon is excluded and only five long term trials are retained in the analysis, then ICM appears to be effective in preventing loss to follow (n=475, 5 RCTs, RR 0.27 CI 0.11 to 0.66). The result of a better retention in care for ICM group strengthens
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+the relevance of the first primary outcome (of ICM decreasing time in hospital). ICM decreases days of hospitalisation in a severe mentally ill population where patients are kept in close contact with services. Therefore, the ICM effect on reducing days in hospital might not be dissipated by a higher rate of loss to follow up. Secondary outcomes
+1.1.3 Service use: Use of hospital
+We found ICM to reduce the number of people admitted to hospital more than standard care - at least in the medium term (n= 1303, 5 RCTs, RR 0.85 CI 0.77 to 0.93). Skewed data for ’average number of admissions in the medium term’ are in accord with these first findings. Synthesis of short and-long term studies in this outcome produce heterogeneous findings. The short term data are from only two studies and when the most positive one (Bond-Indiana1 ) is removed the finding becomes clearly null. The long term data is from eleven trials, removing the three clearly outlying studies (Curtis-New York, Macias-Utah, Test-Wisconsin) also restores homogeneity and moves the finding squarely towards the null.
+Overall the effect of ICM on admission to hospital is not strong. Whereas the time spent in hospital does seem to be less if allocated ICM at least for those whose baseline use of hospital was high, the number of admissions is not greatly changed. Admissions are shorter but not by much, if any less frequent.
+1.1.4 Service use: Use of services outside of mental health provision
+The only outcome available was ’use of Emergency Room’ as well as ’rate of use of Emergency Room’. Studies did not report a convincing difference in use. We do not, however, consider these data very robust as they were based on data from one study only or on skewed data that are very difficult to interpret.
+1.2 Adverse event: Death
+This review did not find any difference in mortality either due to ’all causes’ or to ’suicide’ in the short, medium and long term. Although death is a rare adverse event, the duration of the longer studies (~24 months) means that some deaths would have been expected to occur and did (3.2% ICM vs 3.8% standard care). This difference is not statistically significant but is homogeneous and it suggests a trend in term of risk reduction (RR 0.84 CI 0.48 to 1.47). The same trend is confirmed in the ICM effect on mortality due to ’suicide’ by long term (suicide rate1.3% ICM vs 2% standard care). Again this difference is not statistically significant but is homogeneous and stronger compared to ’death all causes’. If the suicide risk is really so much reduced (RR 0.68 CI 0.31 to 1.51) this would be a really important finding, also considering that the quality of evidence is moderate (Summary of findings for the main comparison).
+Few things have changed the outcome of death for people with schizophrenia. If, for this group of people, set in the context of a standard care with a high baseline risk of admission, ICM does really decrease death, and by so much, this really is a strong argument in favour of the ICM. These data are the findings of eight
+studies with only a total number of participants of 1164. One larger trial might be able to confirm this important suggestion. 1.3 Global state
+1.3.1 Global state: Leaving the study early
+For the short term ICM data regarding preventing number of lost to follow up were heterogeneous. However we found ICM to be more advantageous than standard care in reducing rate of lost to follow up both in the medium term (n=1699, 8 RCTs, RR 0.60 CI 0.51 to 0.70) and long term period (n=1798, 13 RCTs, RR 0.68 CI 0.58 to 0.79). There remains the impression that ICM, and again with the proviso that these findings may apply most specifically to a group with high baseline admission, holds on to people more tightly across time. This may not lower admission rate much but loss to follow up and length of admission may decrease. Overall these data are not of high quality (Summary of findings for the main comparison) but there is an impression that ICM has more advantage over standard care for the higher risk groups. For example, in groups with only 10% loss to follow up across the long term only three more people are not lost for every 100 given ICM. However, with more realistic figures of 50% loss to follow up, this figure rises to 15 more people out of every 100 who are kept in care compared with those allocated standard care.
+1.3.2 Global state: GAF
+Short-term studies indicated a better improvement in GAF endpoint score for participants on ICM compared with those allocated standard care (short term: n=797, 4 RCTs, MD 2.07 CI 0.28 to 3.86). This effect was confirmed by the long term data (n=818, 5 RCTs, MD 3.41 CI 1.66 to 5.16). Whilst this is favourable for those allocated ICM we are unsure of the clinical meaning of these data as changes of two or three points on a scale that runs to 100 does not seem much. We have found no reference to the clinical meaning of such small changes.
+1.3.3 Global state: Not compliant with medication
+Only one long term study provided data on compliance with medication and these indicated a higher compliance level in those allocated to ICM intervention compared with people in the standard care group (n=71, 1 RCT, RR 0.35 CI 0.15 to 0.81). Again this is an important finding and should be replicated. We are surprised that such easily recorded data are not reported in more studies.
+1.4 Social functioning
+1.4.1 Social functioning: Contact with legal system
+Studies measuring contact with legal system used different definitions over a variety of periods of time. This makes interpretation difficult. There is no real suggestion that ICM increases or decreases the measures used to record these issues. The ‘arrested’ and ‘imprisoned’ outcomes were the only ones with some consistency. They did not show any significant difference in the intervention effect between groups. The ‘contact with the police’ outcome findings were from only one study and were not significant in the short term, but became significantly different in the medium term (favouring the ICM group). Overall the legal outcomes are not convincing but there is more need for consistency in approach to
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+this area of research.
+1.4.2 Social functioning: Employment status
+This review did not reveal any significant difference between ICM and standard care in employment status, as measured by different outcomes. Medium term findings on ‘number of people not competitively employed’ were based on only one trial. Both medium and long term data reporting ‘not employed’ tended to favour ICM, although data were heterogeneous and overall quality of evidence was very low (Summary of findings for the main comparison). Again, this is an important outcome for which more data are needed before anyone can draw firm conclusions.
+1.4.3 Social functioning: Accommodation status
+Data on outcome ’homelessness’ were not convincing either by short, medium and long term, but had usually been derived from few trials.
+The outcome ’not living in stable accommodation’ was scarcely reported, as it was available just from one long term study.
+Regarding the ’not living independently’ outcome, we found people allocated to ICM were more likely to live independently compared with those in the standard care group - in the medium term and, even more so in the long term. This has to be another important finding of this review. If the risk of not living independently really is substantially reduced by this ICM package (18% ICM vs 26% standard care, long term) at least for people with high baseline risk of admission and, if this is a desired outcome for this particular client group then, combined with the other moderate but cumulative advantages, this further highlights ICM’s advantage over standard care.
+1.4.4 Social functioning: Substance abuse
+Only one study (n=547) reported usable binary data for alcohol and drug abuse. We found a long term single study failed to show any advantage for participants treated with ICM compared to standard care. Skewed data was supplied by Sytema-Netherlands (n= 81) with DALI scores tending to favour the ICM group for both drug use and alcohol consumption. We, however, are not clear of the clinical meaning of these scores. Morse-Missouri3 (n=103) reported skewed data on days substances were used per month. There is no indication of any difference between groups. Currently, there is not compelling evidence that ICM effects abuse of substances or alcohol.
+1.4.5 Social functioning: Various scales
+Few studies reported usable data for social functioning scale and outcome data were complicated by the use of different scales by single studies making meta-analysis impossible. In this confusion of evidence, we see no advantage for participants treated with ICM compared to those in standard care in terms of social functioning measures. This does not seem to concur with other findings re independent living. It could be that the fine grain measures of functioning are picking up subtle parameters of social function not effected by the package. It could also be that they are not sensitive enough measures to broad and important issues of social function. 1.5 Mental state
+Mental state rating in these trials illustrate the confusion of how such symptoms are recorded in randomised studies. Timings of use of the scales differ and the findings are so problematic to interpret from the clinical perspective that were are left making safe but bland conclusions.
+There does not seem any compelling evidence that, in this group of people, set where the baseline risk of admission is high, ICM really substantially effects a person’s mental state.
+1.6	Behaviour: self-harm
+Self harm was not convincingly reduced by use of the ICM model and these are based on findings from the larger of the studies. The mortality finding discussed above, however, does seem to suggest that ICM reduced the risk of death. These findings are a little at odds with each other, although not entirely, but give all the more reason to continue to research into this area for these, the simplest of outcomes.
+1.7	Quality of life
+Few studies report relevant outcomes. Short and medium term outcome data were complicated by the use of different scales by single studies making meta-analysis impossible. We found one short term study (n=125) showed a better quality of life in the ICM group on the QOLI scale but more medium term and long term data failed to show any advantage for participants treated in ICM group compared with standard care. The few skewed data seems to concur with the impression that for the quality of life measures used in trials, ICM confers no advantage over standard care.
+1.8	Participant satisfaction/need
+ICM participants were more satisfied with their treatment compared with the standard care in these trials. These findings are based on quite strong data (short term n=61, medium term n= 500, long term n=423). More satisfaction with care could enhance medication compliance, the will to keep in services, housing status and a host of other variables. We are left with the doubt about the size and meaning of the overall finding. We are not sure of how encouraged we should be that these packages of care deliver an average of a two to three point improvement in the Client Satisfaction Questionnaire.
+Several of the smaller trials did measure need and unmet need. These skewed data are difficult to interpret but do not convincingly seem to favour either of the groups.
+1.9	Costs
+With respect to cost of in-patient psychiatric care ICM was consistently superior to standard care ’direct costs of psychiatric hospital care’, findings suggesting a saving of money per person of about US$144 per month (fiscal year 1990). Those data were provided from two studies, taking part in the same multi-centre trial. Skewed data did not show any trend confirming or disputing this data, as skewed data were contradictory.
+No difference was found respect to direct health care costs or direct costs of all care.
+2. COMPARISON 2: INTENSIVECASEMANAGMENTver-
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+47
+sus NON-INTENSIVE CASE MANAGMENT (Summary of findings 2)
+Primary outcome
+2.1	Service use
+2.1.1 Service use: Average number of days in hospital per month - at about 24 months
+This review failed to show a significant advantage in reducing the average length of hospitalisation when ICM is compared with non-ICM. This could be an important finding and we see no clear reason not to trust this result. The implications from this could be that if services are already providing non-ICM there is no point in investing in further intensiveness. Currently we know of no review comparing non-ICM with standard care and reporting relevant outcomes. This should be undertaken. It is possible that there are other features of ICM that may improve outcome but we have not stipulated that we should specifically investigate for these. What was different between the two sets of trials is the baseline risk of admission in the previous two years (about 6 days/month Comparison 1 vs about 3.4 days/month Comparison 2). This was highlighted to us by the meta-regression process. This generates further hypotheses. Baseline hospital risk is linked to service provision, service culture, severity of illness, and other issues. We do not have the sophistication of data to investigate these. What we are left with is the possibility that in a situation where people with severe mental illness have a duration in hospital of <4 days/month in the two years preceding the ICM package of care, the increased intensity of approach may not be justified.
+2.1.2 Service use: Not remaining in contact with psychiatric service
+We found ICM to be more effective in increasing the number of people retained in contact with psychiatric service in the medium term, but these findings were not considered robust as they were based only on one small trial (n=73). No difference was found between interventions in the long term data, but data were heterogeneous. Overall, when pooling medium and long term data, we did not find any advantage for participants treated in the ICM compared to non-ICM regarding better retention in psychiatric service but, again, these data were heterogeneous and we found no obvious explanation for the heterogeneity (n=1255, 4 RCTs). Secondary outcomes
+2.1.3 Service use: Use of hospital
+This review did not reveal any difference in the risk of being admitted to hospital in the long term (n=1132, 3 RCTs, RR 0.91 CI 0.75 to 1.12). These findings were confirmed by data from one long term study (n=678) on the average number of admission, where no advantage was shown between treatments in reducing number of admissions in the long term.
+Therefore data on frequency of admission and those data on length of hospitalisation are consistently showing no effect of ICM on both outcomes.
+2.2	Adverse events: Death all causes and suicide
+This review did not find provide strong evidence on mortality rate
+either due to all causes or to suicide in the short, medium and longterm. These data are quite informative, especially those from long term studies where the study length might balance the rarity of the event in detecting any difference between intervention effects. In the long term studies some deaths occurred (2.0% ICM vs 2.2% non-ICM), (n=1634, 5 RCTs, RR0.90 CI 0.46,1.75). This impression was confirmed for studies reporting suicide, as shown in Summary of findings 2, where low quality evidence showed no difference between groups.
+2.3	Global state
+2.3.1 Global state: leaving the study early
+For the short term outcome, no studies were available.
+By the medium term data showed no difference between interventions, but those data were not strong as they came from a small sample of two studies (n=225) and were heterogeneous with inconsistency of effect.
+By the long term we found ICM to be more advantageous than non-ICM in reducing rate of lost to follow-up.
+Overall pooling studies from two time sub-groups, we found heterogeneous data, but substantially confirming those homogeneous ones obtained by long term. They confirmed ICM to be more advantageous than non-ICM in reducing rate of lost to follow-up (n=2195, 9 RCTs, RR 0.72 CI 0.52 to 0.99). If we consider this outcome as proxy of a better retention in care, it might overcome the inconsistency of data on “number of people remaining in contact with psychiatric service” (see Discussion - Primary outcomes - 2.1.2 Service use: Not remaining in contact with psychiatric service). Therefore ICM seems to positively reduce number of lost to follow-up, but not affecting length and frequency of admission. These data are not of high quality (Summary of findings 2) but there is an impression that ICM has more advantage over nonICM for the higher risk groups. For example, in groups with only 10% loss to follow up across the long term only three more people are not lost for every 100 given ICM. However, with more realistic figures of 50% loss to follow up this figure rises to 14 more people out of every 100 are kept in care compared with those allocated standard care.
+2.3.2 Global state: HoNOS scale
+Not enough studies were available to run a meta-analysis on data derived from the HoNOS scale or from any other scales assessing global state. Considering how comprehensive the scale assessment of other outcomes is, it appears that global state as an outcome is under-recorded by trialists despite being informative and relevant from a clinical point of view.
+2.3.3 Global state: Compliance with medication
+As for the previous outcome, not enough studies were available to run a meta-analysis. Again, a very meaningful outcome from a clinical point of view is neglected.
+2.4	Social functioning
+2.4.1 Social functioning: Contact with legal system
+As it was for studies included in the first comparison (see Results 1. COMPARISON 1 INTENSIVE CASE MANAGEMENT ver
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+48
+sus STANDARD CARE), studies measuring contact with legal system used different definitions over a variety of time periods. This makes interpretation difficult. Besides only few studies addressed this outcome (3 trials overall). There is no real suggestion that ICM increases or decreases the measures used to record these issues. The ’imprisoned’ outcome was the only one with some consistency. It did not show any significant difference in the intervention effect between groups. The ’contact with the police’ and ’arrested’ outcome findings were from only one study each and were not significant in the short and long term respectively. Overall the legal outcomes are not convincing but there is more need for consistency in approach to this area of research.
+2.4.2 Social functioning: Employment status
+Data available on this outcome are substantially inconclusive, being provided by only one small trail (n=73). It measured employment status according to two different definitions ’spent > 1 day employed’ and ’on paid employment’. Both findings were not significant in the medium term and overall quality of evidence was low (Summary of findings 2). Again, this is an important outcome underestimated in the current studies and at this stage more data are needed before anyone can draw firm conclusions.
+2.4.3 Social functioning: Accommodation status
+Surprisingly scarce data were available on this outcome, as it was available just from two studies measuring ’living in supported accommodation’ and ’homelessness’ respectively. Both medium term data on ’living in supported accommodation’ and long term data on ’homelessness’ were based on only one trial and they were not significant. Again, this is an inconclusive finding due to the scarceness of available data, despite being easily recorded and very relevant from a perspective of a community based service.
+2.4.4 Social functioning: Substance abuse
+Studies measuring substance abuse were only two and they described this outcome as a binary and continuous measures, and not consistently across studies. This makes interpretation difficult and findings not convincing, as a single study entered each measurement, therefore we could not carry out any meta-analysis. As far as we can assess there is no indication of any long term difference between groups in ’number of people abusing alcohol’, in ’number of people abusing illicit drug’ nor in ’remission from alcohol use disorder’ (defined as AUS score <3). These findings were confirmed by those continuous ones, assessing the substance abuse with SATS scale. These data failed to show a significant difference between groups at any time period assessment. As for the first comparison, currently there is no compelling evidence that ICM effects abuse of substances or alcohol.
+2.4.5 Social functioning: Scale data
+Findings were equivocal on scale data measuring social functioning as provided by only one study. We do not think that future studies should address this outcome by use of scale measurement. Scales are not sensitive measures for social functioning. More effort should be placed on consistent and wide measurements of the main issues of social functioning (such as accommodation status,
+employment status, contact with legal system, rate of permanent social benefits).
+2.5	Mental state: General symptoms and Specific symptoms Again, outcomes measured on scales are substantially inconclusive as the data are spread across single studies on different scales and at different time periods, making meta-analysis impossible.
+According to the low quality of data available, there does not seem any compelling evidence that ICM really substantially effects mental state.
+2.6	Behaviour: self-harm and injury to others
+This review did not reveal any long term significant difference between ICM and non-ICM in the risk of committing self-harm or injury to others. These data are based on findings from the larger of the studies (UK700-UK). They are consistent to the mortality findings discussed above, where no significant difference was shown in death rate between groups, either for suicide and for all causes. Although these data are suggestive of no difference of effects between interventions, they are still quite weak due to limited sample size. More trials should address this outcome, one of the simplest ones to collect.
+2.7	Quality of life
+Quality of life rating in these trials illustrate the confusion of how such symptoms are recorded in randomised studies. The scales differ and timings of use of the scales also differ. There was such an inconsistency in approach to this area of research to make metaanalysis impossible. None of the available findings showed any significant difference between interventions. There does not seem any compelling evidence that ICM really substantially effects a person’s quality of life.
+2.8	Participant satisfaction/need
+Findings tended to favour ICM participants in gaining a better satisfaction with health services and in reducing their need. But this difference was not significant and both findings were based on data from the same trial, the largest one (UK700-UK, n=585). Therefore we can not draw any conclusions, but to highlight a possible favourable effect in the ICM group, that needs to be confirmed.
+2.9	Costs
+Studies assessed ’direct costs of psychiatric hospital care’ and ’direct cost of all care’. Regarding first outcome, findings are based on skewed data, provided from one small trial (Quinlivan-California, n=60) and a larger one (Harrison-Read-UK, n=193). There does not seem any compelling evidence that ICM really substantially effects ’direct costs of psychiatric hospital care’ either by medium and long- term. Again, findings on long term ’direct cost of all care’ did not show any difference between interventions.
+Overall completeness and applicability of evidence
+1.	Completeness
+1.1	Duration of follow up
+49
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+The majority of studies presented long term data, i.e. over one year of follow up. This is a reasonable time length to assess sensibly any difference in the intervention effects.
+1.2	Coverage of outcomes
+As the experimental intervention is a service organisation model, its realisation involves health policy and research should account for efficacy and cost evaluation. The outcomes reported were mainly service use and social functioning oriented. No studies reported data on relapse (see Summary of findings for the main comparison, Summary of findings 2), positive symptoms, carer satisfaction and family burden. Participant satisfaction was scarcely reported and in a fragmented way, therefore available data are only partially informative on the effects of these approaches. Few studies provided cost data.
+2.	Applicability
+2.1	Origin
+The origin of the data has changed in the last decade since the two original reviews (Marshall 2000a, Marshall 2000b). There are now more included trials from Europe whereas in the past the data source was largely North America with a few from Australia. Now 30% of the total sample included in the review is composed of randomised people from Europe. For the primary outcome these studies add power to the result, narrowing the confidence intervals, but not otherwise substantially changing the findings. Furthermore, as all the included studies were from Europe or North America and Australia, this review findings are still lacking applicability to low-income countries and, more generally, to countries where mental health systems are not community-based.
+2.2	People
+Studies included people presenting a variability that we feel is likely to reflect the heterogeneous population a clinician faces in daily practice when treating people affected by severe mental illness. This variability was in terms of diagnosis (where participants were affected by a wide diagnostic group including schizophrenic disorder, affective and personality disorder); comorbidity (where four studies (Drake-NHamp, Essock-Connecticut2, Morse-Missouri3, Muller-Clemm-Canada) included dually diagnosed participants); and social characteristics (where eight trials included homeless participants). On average, studies included people with a long history of illness, as just OPUS-Denmark included participants with a first episode of psychotic illness. This fits with the concept of severe mental illness, where this label includes certain criteria relating to length of illness.
+2.3	Interventions
+Some studies showed a greater applicability, because the experimental intervention was provided by pre-existing team, therefore closer to the real world and less contaminated by the experimental setting.
+The majority of new included trials compare ICM with non-ICM (8 trials out of 14). This is confirming the trend of psychiatric services to increasingly include some elements of the original model, but also to dilute and contaminate them with the current organi
+sation. Therefore what we call ’standard care’ is converging toward non-ICM. For those of us who practice in Europe it is the second of the two comparisons in this review that may well be more applicable to everyday care. This comparison, it is important to recall, substantially did not illustrate much difference between ICM and non-ICM.
+Quality of the evidence
+As illustrated in Figure 1 there is the impression of a moderate overall risk in these trials of bias. This would mean, therefore, a moderate risk of overestimate of positive effect. Also, making the difficult judgements about quality has been helped greatly by a discernable improvement in reporting of methodology.
+Potential biases in the review process
+There are several potential biases. We have worked mainly with published reports, and only in few cases with unpublished material. By doing this we may be perpetuating a reporting and publishing bias. It would be better to have much more original individual patient data. This review follows on from two past Cochrane reviews (Marshall 2000a, Marshall 2000b) as well as much work already published in paper format (Burns 2007). The conduct of these reports has influenced this document and it is possible that we have failed to identify systematic biases in the way we have done the reviews across time.
+The authors of this review do include an active pioneer of developing and implementation the experimental intervention model across the scientific community and clinical world (MM) and one included study is his (Marshall-UK). As a team we have tried to ensure that decisions are made by rational consensus and not to have an expert in the team would have been an inadvisable omission.
+In some cases protocol rules were not clear enough and need for subsequent clarification arose and post hoc decisions had to be taken (see Differences between protocol and review). In various cases this could affect the review process. We think, probably, this has lowered the quality of data included in the review but not to include so much, for example, skewed data would have omitted much information. Also by breaking down studies into their centres, many fall below the 200 participant cut off point. We have included these data in the <200 category whereas they would have been in the >200 in previous versions. The impression of the overall effect of the changes in protocol are that we have a more inclusive review, with data that are more heterogeneous and also more favourable for the experimental interventions than otherwise would have been the case should we have used are more limited data set. Nevertheless, we do feel it reasonable to present all these data for the reader to consider.
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+50
+We pre-specified what characteristics of studies could be associated to heterogeneity and therefore we stated in the protocol what variables were to be explored in the meta-regression before inspecting studies’ results. Despite this pre-specification, we were not blind to what variables were more probably related to heterogeneity, as we were familiar to some study results previously published. Therefore the undertaken exploration of heterogeneity might at best lead to generation of hypothesis, but it can not provide reliable conclusions.
+Agreements and disagreements with other studies or reviews
+This review merges two older Cochrane reviews fully bringing up to date how these data should be considered. It is not that this version disagrees with the older reviews - it just replaces them with a more current viewpoint of the data. A major improvement of this version are data on duration of admission - previously lacking from past reviews. Paper versions of this area do not provide a full summary of available evidence on ICM effects across various outcomes (Burns 2007). This Cochrane review does not disagree with the paper version - it is simply much more comprehensive. Where it comes to the meta-regression, this reviews substantially confirms the hypothesis stated elsewhere (Burns 2007), that the baseline hospital use and fidelity to the model effects outcome.
+AUTHORS’ CONCLUSIONS
+Implications for practice
+1.	For people with severe mental illnesses
+ICM was found effective in ameliorating many outcomes relevant to people with severe mental illnesses. In fact ICM was shown to reduce hospitalisation, in term of less frequent and shorter admission to hospital; increasing retention in care; probably reducing the risk of death and suicide; globally improving social functioning in terms of a better accomodation status, employment status, and showing a trend reducing contact with legal system. Although it remains not clear the effect on mental state and quality of life, ICM effects seem to significantly help global state compared with standard care. It is not clear, however, what gain ICM provides on top of a less formal non-ICM approach. The latter may suit some people with severe mental illnesses better than the more intensive full ICM model. Data on satisfaction with care for the ICM versus non-ICM models are very few and difficult to interpret.
+2.	For clinicians
+ICM formalises a holistic approach to care of people with severe mental illnesses in the era of limiting hospital admissions and subsequent hospital closure. What this review suggests is that this
+formalising is helpful across several outcomes over and above a standard care - the latter largely based in outpatients departments - and that this seems acceptable to people with severe mental illnesses. However, when the fully formal holistic approach (ICM) is compared with the less formal but also holistic non-ICM the differences are not so clear. This could be seen as encouraging, as many clinicians are unlikely to rigidly apply full ICM for various reasons. This does not abrogate the need to know and apply key components of the model of care within ICM.
+3.	For policy makers
+What we know at this juncture is that ICM is of value at least to people with severe mental illnesses who are in the sub-group of those with a high level of hospitalisation (about 4 days/month in past 2 years). The intervention should be performed close to the original model, therefore training should be planned for relevant mental health workers. Data on costs are still scarce and conclusions cannot be drawn on cost-effectiveness. Where ICM features are already available in the community psychiatric service (the non-ICM intervention), it is not clear if additional full development to the rigid model of ICM is of value. Results of this review could guide policies on introduction of such an ICM service in those countries where a community psychiatric service is already set up but ICM is not in use, and in countries where a shift from hospital-based care in favour of a more community-focused approach has still to be developed. Particular consideration should be placed on the setting where ICM is going to be set up, as its value was shown where the level of hospitalisation is high. It is not clear whether the introduction of just some of the ICM features (the non-ICM intervention) is of value, compared with the community-based standard care, as more research is needed to clarify the non-ICM effect compared with standard care.
+Implications for research
+1.	General
+First, as has already been said, reporting of research does seem to have improved across time and as a result it is much easier to understand details of the practice of modern studies.
+However, this review illustrates how scale measurements are much more widespread than simple clinical questions for assessing clinical outcomes. Binary data, we suggest, are less ambiguous than continuous. Further complicating matters is that there are many scales for the same outcome: we often found many studies assessing the same outcome on different scales and did not feel justified to run a meta-analysis. For example, regarding social functioning outcomes, there was need for consistency in approach to this area of research. Heterogeneous measurements were used to describe the same outcome. This is not very informative - but this review illustrates how opportunities have been lost by researchers. Now, as it is possible that the time for more studies has past, we, as a result of not having consistency, will always be left in doubt about
+51
+Intensive case management for severe mental illness (Review)
+Copyright ©2011 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+important effects of care. Finally, we presume that use of scales may discourage any worker committed to patient care to take part in an experimental study.
+More attention should be placed on patient and family prospec-tives, in terms of detecting patient and carer satisfaction, quality of life, and family burden.
+2.	Specific
+2.1 More reviews
+Currently we know of no review comparing non-ICM with standard care and reporting relevant outcomes. This should be undertaken. In addition several good studies are excluded from this review as they are evaluating mixed models of care, or models plus other interventions such as Cognitive Behavioural Therapy. These studies do merit further attention from reviewers and could help clarify further ways by which either the ICM model develops or new and yet more holistic approaches evolve.
+2.2 Developing this review
+A full data set with all individual patient data would help avoid some biases and allow reanalysis using unified definitions of outcome. Any relevant studies in this area should make provision for prospectively providing data compatible with this review (Walsh-Connecticut).
+2.3 More trials
+We do not think that more trials comparing current ICM with standard care or non-ICM are justified. What should be researched
+are the features of ICM that may improve outcome, as it might be that the model of intervention is effective just because of some of its features. This work may involve more observational studies in order to evolve the ICM model to new and better packages of care.
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+Introduction
+Suicide attempt and suicide are leading causes of global morbidity and mortality. Approximately 800,000 people die by suicide annually [1], of which about one-third are under the age of 30 [2]. The prevalence of suicide attempt is significantly greater than that of suicide death and is associated with heightened risk of later death by suicide [3,4] as well as psychosocial adversities that persist later in life [5]. For every suicide death, it is estimated that approximately 135 people are affected [6]. Over the course of a lifetime, the proportion of people exposed to the suicide of a relative, friend, or acquaintance is approximately 21% [7]. Exposure to suicide has been linked to increased risk of physical disease and adverse mental health including depression, posttraumatic stress disorder, and complicated grief [8,9]. The deleterious effects associated with exposure to suicide may also render some people, particularly adolescents and young adults, at increased risk of suicide and suicide attempt [10].
+Combined, the large number of people exposed to suicide and the potential increased risk of suicide-related outcomes (suicide, suicide attempt, and suicidal ideation) in others mean that exposure to suicide is a significant public health concern [1]. This is reflected in several national suicide prevention strategies that recommend postvention interventions for those bereaved by suicide [11], as well as several international frameworks for the prevention of suicide-related contagion, and the management of suicide and self-harm clusters [12-15]. These public health strategies have largely focused on exposure to suicide, despite a growing body of evidence that suggests that exposure to suicide attempt, the behavior most proximal to suicide, may also be associated with increased risk of suicide-related outcomes [16-19].
+Distinguishing between the potential independent effects of exposure to suicide and suicide attempt is important since measures of morbidity and mortality have markedly different public health implications. Yet evidence regarding the independent effects of exposure to suicide and suicide attempt on subsequent suicide-related outcomes is unclear. A systematic review
+and meta-analyses by Geulayov and colleagues [20] showed that exposure to suicide and exposure to suicide attempt of a parent were associated with increased risk of suicide and suicide attempt in offspring. However, the authors pooled mean effect sizes across subgroups within studies and did not take into account the dependencies between effect sizes, an approach that may distort the results of the meta-analyses [21]. Another systematic review by Crepeau-Hob-son and Leech [19] reported that both exposure to suicide and exposure to suicide attempt were associated with subsequent suicide-related behavior among friends or acquaintances. But the authors did not adequately control for studies that reported lifetime prevalence, leaving the causal direction between exposure to suicide attempt and subsequent suicide-related outcomes unclear.
+Lack of guiding evidence has impeded translation of the evidence into practice. For example, it is not currently clear which populations may be at risk, nor whether the risk differs across outcomes involving suicide, suicide attempt, and suicidal ideation. Sveen and Walby [22] found inconclusive evidence supporting a relationship between exposure to suicide and increased risk of suicide-related behavior in others. However, the authors combined studies reporting exposure in relatives and friends or acquaintances, which may incur different suicide risk. More recently, systematic reviews that investigated exposure to suicide in friends and acquaintances have reported a positive association between exposure to suicide and subsequent suicide-related outcomes [19,23]. Yet, as noted previously, the causal direction between exposure and outcome measures were confounded by the inclusion of studies that reported lifetime prevalence of exposure and outcome measures. Lastly, some studies included outcome measures that combined suicidal ideation with suicide attempt [24,25] or combined exposure to suicide and exposure to suicide attempt as a composite measure of exposure to suicidal behavior [26-28]. Composite measures of exposure to suicidal behavior prevent us from identifying whether the observed effect is influenced by a true association or the result of a cumulative effect.
+Consequently, the effects of prior exposure to suicide and suicide attempt on suicide-related outcomes have not been reliably quantified, and the factors that moderate this risk are not currently known. We therefore aimed to conduct a systematic review and multilevel meta-analysis investigating the independent association between prior exposure to suicide, suicide attempt, and suicidal behavior (composite measure—suicide or suicide attempt) and subsequent suicide, suicide attempt, and suicidal ideation in relatives, friends, and acquaintances. In doing so, we aimed to quantify the association between exposure to suicide and suicide attempt and the full range of suicide-related outcomes, and to identify whether factors such as relationship to the person who engaged in the initial suicidal act, age of the study population, and study design characteristics moderate this risk. By using multilevel meta-analyses, we were able to account for dependencies among multiple effect sizes taken from the same cohort within a study, an extremely common and challenging aspect of conducting meta-analyses of epidemiological studies [29].
+Methods
+This work adheres to PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses) [30] and MOOSE (Meta-analysis of Observational Studies in Epidemiology) [31] guidelines (S1 Text) and was prospectively registered with PROSPERO (CRD42018104629). Deviations from the protocol include the use of exposure to suicidal behavior (composite) and statistical analyses using multilevel meta-analyses. The association between exposure to suicide and suicide attempt and grief and mental health outcomes will be reported in a separate systematic review and meta-analysis.
+Electronic search strategy
+We searched MEDLINE, Embase, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Applied Social Sciences Index and Abstracts (ASSIA), Sociological Abstracts, International Bibliography of the Social Sciences (IBSS), and Social Services Abstracts from inception through 19 November 2019 for observational studies examining the effects of exposure to suicide, suicide attempt, or suicidal behavior on 1 or more outcomes relating to suicide, suicide attempt, or suicidal ideation. Search terms relating to exposure to suicide and suicide attempt as well suicide bereavement, suicide contagion, and suicide clusters were combined using Boolean logic (S2 Text). The search was not limited by time, location, year of publication, or language (articles written in a language other than English were translated using Google Translate). Additional articles were identified by scanning the reference lists of included articles and previous reviews. One author (NTMH) conducted the initial search and screening of titles and abstracts. Three authors independently screened the full text of each potentially eligible article (NTMH, AB, KA, and KW). Discrepancies were resolved by the first author (NTMH), who also contacted the corresponding authors of primary studies for additional information.
+Study selection and eligibility criteria
+Eligible studies reported dichotomous events (both the exposure and outcome were reported as having occurred or not occurred, yielding a 2 x 2 matrix) or odds ratios (ORs) for exposure to suicide, suicide attempt, or suicidal behavior and subsequent suicide, suicide attempt, or suicidal ideation. Exposure to suicide, suicide attempt, or suicidal behavior was determined from self-reported measures, informant interviews, official records (such as hospital admission records), or data linkage to death certificates. Outcomes involving suicide, suicide attempt, or suicidal ideation were determined from self-reported measures, informant interviews, or official records, such as death certificates, coroner reports, or hospital admission records. Cohort, case-control, and cross-sectional study designs were eligible if the study was reported in a peer-reviewed journal and the temporal sequence between the exposure and outcome was specified. For cross-sectional studies, the temporal sequence between exposure and outcome was established if the outcome measurement occurred after the exposure (e.g., the study asked participants if they had made a suicide attempt after exposure to the suicide of another). Participants of any age who were exposed to prior suicide or suicide attempt were eligible if the sample was mainly, or solely, drawn from the general population, as opposed to a clinical or other high-risk population (e.g., inpatients or prison detainees). Eligible control groups included individuals who did not report prior exposure to suicide, suicide attempt, or suicidal behavior in others.
+Studies were excluded if findings from a non-exposed (control) group were not reported, or the control group was composed of participants exposed to other modes of death (e.g., accident or natural causes). Studies that reported estimates of lifetime prevalence as well as studies that did not establish the temporal sequence between exposure and suicide-related outcomes (e.g., the study reported 12-month prevalence of the outcome, but prior exposure to suicide was not indicated) were excluded. Finally, studies that reported outcomes following exposure to media reports of suicide (including fictional and non-fictional portrayals) or non-suicidal self-injury were excluded.
+Data collection and coding
+Two independent reviewers (NTMH and KK) extracted data using a standardized data collection form. A description of the a priori moderators of risk included in the study are presented
+in S1 Table. Dichotomous data were favored over ORs. When dichotomous events were not available, unadjusted ORs were recorded. For studies with multiple follow-up time points, only data from the longest time point were extracted [32]. Studies that included participants from the same population during overlapping time periods (e.g., nationwide data registry studies that reported suicide deaths from overlapping time periods) were included only if the studies reported different relationships (e.g., relatives and friends and acquaintances) or different suicide-related outcomes. When studies combined measures of exposure to the suicide of a relative or friend, we contacted primary authors for disaggregated data. If these data were not available, the relationship between the exposed individual and the individual(s) who engaged in suicidal behavior was determined by a majority rule (the relationship that occurred most frequently as indicated in > 50% of the total sample). Similarly, if the age of participants included a combination of youths and adults, the age of the population was categorized in favor of the age group that exceeded 50% of the overall population. Study-level data are provided as S1 Data.
+Multilevel meta-analysis rationale and data analysis
+Since 16/34 (47%) studies reported multiple exposure and/or outcome measures in the same sample of participants, the assumption of independent estimates for a traditional meta-analysis was not met. We therefore used a 3-level meta-analysis, which parallels traditional random effects meta-analyses. The main difference is that dependent effect sizes (due to multiple subgroups or outcome measures within studies) are nested within studies (level 2) before these are pooled across studies (level 3). Thus, t2^ is the variance within studies while t^ is the variance between studies. This approach allows for the investigation of heterogeneity not only between but also within studies [33]. For clarity, we use the general term “multilevel” throughout to describe our analyses.
+We conducted a multilevel meta-analysis with the maximum likelihood estimation method using the metaSEM package [34] for R version 3.6.0. For the main analysis, we used dichotomous event data to calculate the pooled OR with the accompanying 95% confidence interval (CI) for risk of suicide, suicide attempt, and suicidal ideation within exposed and non-exposed individuals. When event data were not available, we used unadjusted ORs. Meta-analyses were conducted separately for exposure to suicide, suicide attempt, and suicidal behavior. Heterogeneity was quantified as variance in true effects within (t^2^) and between (t^) studies. We also report the 12 statistic, which represents the proportion of variance in true effects out of total variance for each level (i.e., I22j and 123)), along with its 95% confidence interval. Maximum likelihood mixed-effects analyses were used to examine effect moderators via subgroup analysis and to explain heterogeneity (quantified as R2) for each level. Since the multilevel model does not provide study-level effect estimates, forest plots present the mean OR of each study but report the pooled 3-level estimate. Small study effect (“publication bias”) was assessed by visually inspecting funnel plots of mean log ORs against standard error for asymmetry [35]. When at least 10 studies were available for analysis, we formally assessed funnel plot asymmetry using a multilevel analogue of Egger’s test of the intercepts [36].
+Risk of bias and quality assessment
+Study quality was assessed using the National Heart, Lung, and Blood Institute quality assessment tool for observational studies [37]. The original tool contains 14 criteria that determine potential sources of bias in the study population and selection of participants, outcome and exposure measurement, blinding, confounding, and attrition. An overall rating of “good,”
+“fair,” or “poor” was provided for each independent study. Three independent reviewers conducted assessments (NTMH, AP, and AC), and any discrepancies were settled through discussion and finalized by the primary author (NTMH).
+Results
+Study selection
+The initial search identified 21,868 records, of which 8,320 were duplicates. A total of 13,548 records were screened based on title and abstract (Fig 1). The full-text versions of 760 records were assessed, 10 of which were obtained from searching the reference lists of existing reviews. The authors of 6 studies were contacted [38-43], and information or additional data provided for 2 studies [39,41]. A total of 167 records reported outcomes relating to suicide, suicide attempt, or suicidal ideation. Of these, 73 articles reported lifetime prevalence estimates, 35 studies involved overlapping populations or superseded time points, and 2 studies did not report ORs or accompanying effect sizes: These articles were therefore excluded from the metaanalysis. One study [44] was excluded because it reported an OR of 36.4, and 1 study [45] was excluded because it reported an OR of 18; both studies were prone to artifacts introduced by quasi-separation (S2 Table; S3 Text). The final dataset included 34 independent studies, which comprised 71 effect sizes (exposure to suicide: k = 42 across n = 22 studies; exposure to suicide attempt: k = 19 across n = 13 studies; exposure to suicidal behavior: k = 10 across n = 5 studies).
+Characteristics of studies
+Thirty-four studies were included in the meta-analysis (N = 13,923,029; Table 1). In terms of exposure to suicide, 22 studies (N = 13,607,708) provided a total of 42 effect sizes for suicide (k = 24), suicide attempt (k = 15), and suicidal ideation (k = 3). For exposure to suicide attempt, 13 studies (N = 342,516) provided a total of 19 effect sizes for suicide (k = 3) and suicide attempt (k = 16). For exposure to suicidal behavior (composite measure—suicide or suicide attempt), 5 studies (N = 2,145) provided a total of 10 effect sizes for suicide (k = 7) and suicide attempt (k = 3). Studies were from a range of geographic settings including Australia/New Zealand [46-48], North America [16,18,28,49-57], Europe [17,41,58-63], East Asia [26,27,64-69], the Middle East [39,42], and South America [70]. Overall, 20/34 studies involved youths aged 25 years or less. Overall exposure was determined by informant interviews in 14/34 (41%) studies, self-report measures in 12/34 (35%) studies, and official death records in 8/34 (24%) studies. A total of 6/34 (18%) studies reported separate effect sizes for exposure to suicide and exposure to suicide attempt, and 5/34 (15%) studies reported effect sizes for both exposure in relatives and exposure in friends. In terms of outcome measurements, most studies (23/34, 68%) used official hospital admission or death records, followed by self-report measures (10/ 34,29%) and informant interviews (1/34, 3%). One study (1/34, 3%) reported outcomes for both suicide attempt and suicidal ideation following exposure to suicide. No studies reported suicidal ideation outcomes following exposure to suicide attempt or suicidal behavior. Lastly, 3 studies included exposure and outcome measurements of deliberate self-harm, irrespective of intent [48,59,62]. The remaining studies did not define suicide attempt [16,18,42,52,54,57, 59,70], or defined suicide attempt as an act involving explicit intent to die [16,17,28,39,47,4951,53,56,64].
+Study quality
+Studies were most commonly rated fair (13/34) and good (13/34), followed by poor (8/34; S3 Table). The 13 good-quality studies tended to comprise cohort or case-control study designs
+exposure to suicide and suicide attempt into a composite measure of exposure to suicidal behavior, did not provide adequate definitions of exposure to suicide or suicide attempt, and did not provide information on case ascertainment for suicide-related outcomes.
+Results of the multilevel meta-analysis
+Exposure to suicide. Across 42 effect sizes from 22 studies, exposure to suicide was associated with 2.94-fold (95% CI = 2.30 to 3.75, P < 0.001; Fig 2) increased odds of suicidal behavior (suicide or suicide attempt). Heterogeneity within and between studies was comparable (t22) = 0.13, 722) = 47%, 95% CI 15% to 94%; t23) = 0.132,123) = 48%, 95% CI 1% to 81%). The funnel plot revealed evidence of asymmetry, which may indicate evidence of small study effect (Egger’s intercept = 0.675, 1-tailed P = 0.06; S1 Fig). Results from the subgroup analysis showed that exposure to suicide was associated with increased odds of suicide (k = 24, OR = 3.23, 95% CI = 2.32 to 4.51, P < 0.001) and suicide attempt (k = 15, OR = 2.91, 95% CI = 2.01 to 4.23, P < 0.001). However, there was no evidence of an association with suicidal ideation (k = 3, OR = 1.85, 95% CI = 0.97 to 3.51, P = 0.06; Q between subgroups = 2.22, df = 2, P = 0.33, R22j = 11.8%, R23) = 0%). The odds of later suicidal behavior were comparable when the exposure to suicide occurred in relatives (k = 34, OR = 3.07, 95% CI = 2.35 to 4.01) and friends and acquaintances (k = 8, OR = 2.42, 95% CI = 1.50 to 3.91; Q = 0.77, df = 1, P = 0.38, R22j = 0%, R23) = 2.7%). No further significant moderators relating to study design characteristics were identified (Table 2).
+Exposure to suicide attempt. Across 19 effect sizes from 13 studies, exposure to suicide attempt was associated with 2.99-fold (95% CI = 2.19 to 4.09, P < 0.001; Fig 3) increased odds of suicidal behavior. Heterogeneity within studies was 9% (t22) = 0.022, I22) = 9%, 95% CI 1% to 54%), while heterogeneity between studies was substantially larger (-r^ = 0.22, I23) = 88%, 95% CI 42% to 97%). Inspection of the funnel plot did not reveal evidence of small study effect (Egger’s intercept = -0.453, P = 0.33; S2 Fig). Results from subgroup analysis revealed that exposure to suicide attempt was associated with greater odds of subsequent suicide attempt (k = 16, OR = 3.53, 95% CI = 2.63 to 4.73, P < 0.001) but not suicide death (k = 3, OR = 1.64, 95% CI = 0.90 to 2.98, P = 0.10; Q between subgroups = 4.22, df = 1, P = 0.04, R22) = 0%, R23) = 3.8%). Significant between-group differences were observed for study design, with cross-sectional studies reporting greater odds of subsequent suicidal behavior (k = 2, OR = 8.23, 95% CI = 4.70 to 14.30, P < 0.001) compared to case-control studies (k = 10, OR = 2.74, 95% CI = 2.04 to 3.69, P < 0.001) and cohort studies (k = 7, OR = 2.69, 95% CI = 1.82 to 3.99, P < 0.001; Q between subgroups = 7.35, df = 2, P = 0.02, R22) = 0%, R23) = 72.8%). Finally, moderator analyses revealed that psychological autopsy studies (k = 3, OR = 1.64, 95% CI = 0.90 to 2.99, P = 0.127) were associated with reduced odds of suicidal behavior compared to non-psy-chological autopsy studies (k = 16, OR = 3.53, 95% CI = 2.63 to 4.73, P < 0.001, Q-between subgroups = 4.22, df = 1, P = 0.03, R,2) = 0%, R,3) = 38.4%). No further significant differences were observed for the remaining moderators (Table 3).
+Exposure to suicidal behavior. Across 10 effect sizes from 5 independent studies, exposure to suicidal behavior (composite measure—suicide or suicide attempt) was associated with 2.58-fold (95% CI = 1.25 to 5.35, P = 0.01) increased odds of suicidal behavior (Fig 4). Heterogeneity within and between studies was comparable (rL = 0.2831,y = 38%; tL = 0.40, I,L = (2)	i2)	(3)	(3)
+53%). Visual inspection of the funnel plot did not reveal evidence of small study effect (S3 Fig). However, a formal test of asymmetry was not conducted due to insufficient studies. Results from the subgroup analysis revealed that exposure to suicidal behavior was associated with greater odds of suicide (k = 7, OR = 3.83, 95% CI = 2.38 to 6.17, P < 0.001) but not suicide attempt (k = 3, OR = 1.10, 95% CI = 0.69 to 1.76, P = 0.90; Q between subgroups = 5.02, df = 1, P = 0.02, R22) = 31.6%, R23) = 100%). The odds of suicidal behavior were also greater when the exposure occurred in relatives (k = 8, OR = 3.09, 95% CI = 1.53 to 6.26, P = 0.001) compared to friends and acquaintances (k = 2, OR = 1.33, 95% CI = 0.69 to 2.92, P = 0.48; Q between subgroups = 5.20, df = 1, P = 0.02, R22) = 86.2%, R23) = 0%). No significant differences were observed for the remaining moderators (Table 4).
+Discussion
+Based on findings from 34 studies of mostly good and fair quality, encompassing 13,923,029 participants and 71 effect sizes, we found that prior exposure to suicide was associated with significantly greater odds of suicidal behavior (suicide or suicide attempt; OR = 2.94). Results of the moderator analysis revealed that prior exposure to suicide was associated with 3.23-fold increased odds of suicide and 2.91-fold increased odds of suicide attempt, while there was no evidence of an association between exposure to suicide and subsequent suicidal ideation. These findings remained robust across cohort, case-control, and cross-sectional studies, as well as exposure and outcome measurements encompassing informant interview, self-report, and official records (e.g., coroner reports, hospital admission records, or data linkage with birth and death registries).
+Exposure to suicide attempt was associated with increased odds of suicidal behavior (OR = 2.99). However, moderator analyses revealed that the association of exposure to suicide
+attempt with suicide-related outcomes was significant only for suicide attempt (OR = 3.53), not for suicide death (OR = 1.64). These findings were demonstrated across 19 effect sizes from 13 studies of mostly fair quality, and corroborated by 3 large population-based studies using data linkage or hospital admission records for suicide attempt [48,59,62]. Exposure to suicidal behavior (suicide or suicide attempt) was associated with a 2.58-fold increased odds of suicidal behavior, but moderator analysis revealed that this was significant only for outcomes relating to suicide death (OR = 3.83), not suicide attempt (OR = 1.10). These findings were demonstrated across 10 effect sizes from 5 studies, the majority of which involved psychological autopsy methodologies.
+Our analyses update and further specify the findings from previous systematic reviews, which included estimates from studies reporting lifetime prevalence or did not differentiate between the independent effects associated with exposure to suicide and exposure to suicide attempt [19,22,23]. The finding that exposure to suicide was associated with an increased odds of suicide and suicide attempt—in contrast to exposure to suicide attempt, which was associated with an increased odds of suicide attempt only—indicates that exposure to suicide and suicide attempt do not incur uniform risk across the range of suicide-related outcomes. This was corroborated by our analysis of exposure to suicidal behavior, which found that this composite measure was associated with increased odds of suicide but not suicide attempt, a finding that was inconsistent with our separate analyses of exposure to suicide and exposure to suicide attempt. Taken together, the present findings raise questions about the conceptual value of combining suicide and suicide attempt as a composite measure of suicidal behavior, and suggest that future research and public health policies should refrain from combining these exposures and outcomes into 1 composite measure of suicidal behavior.
+Evidence from 2 studies [46,57] suggests that exposure to suicide may be associated with increased risk of suicidal ideation, especially in older adults [46]. Conversely, results from a single cohort study in youths [57] indicate higher risk for suicide attempt than for suicidal ideation, pointing once more to lack of uniformity across populations and outcomes. Moreover, theoretical and empirical accounts suggest that while exposure to suicide may contribute to subsequent suicidal ideation to some extent, its effect on people with a history of suicidal ideation maybe more pronounced [71], as this experience might reduce cognitive and practical barriers to acting on one’s suicidal thoughts [46,72,73]. A more comprehensive look at this interaction may have important practical implications for developing specific interventions for this high-risk population, in particular interventions guided by the “ideation-to-action framework” [71] that aim to reduce acquired capability for suicidal behavior among individuals exposed to suicide.
+The increased risks associated with exposure to suicide for outcomes relating to suicide and suicide attempt in the current meta-analyses suggest that further consideration should be given towards developing interventions that target suicide-related outcomes in those bereaved by suicide. To date, interventions targeting those exposed to suicide have largely focused on bereavement-related factors such as grief, reduced social support, and stigma [74,75]. Although previous studies have shown that these factors are elevated among those bereaved by suicide as opposed to other modes of death, there remains a dearth of studies that investigate the effectiveness of interventions on suicide and suicide attempt behavior. A recent review by Andriessen and colleagues [74], for example, found 3 controlled studies [76-78] that investigated the effectiveness of an intervention on suicidal ideation and found no studies that included outcomes related to suicide or suicide attempt.
+Although we did not observe a significant association between exposure to suicide attempt and subsequent suicide, the specific relationship between exposure to suicide attempt and subsequent suicide attempt is noteworthy, since suicide attempt is associated with significant
+Odds Ratio
+disruptions to an individual’s milieu, and has been linked to adverse psychosocial and mental health stressors that persist later in life [5]. The findings from our analysis of exposure to
+suicide attempt also provide some insight into the mechanisms underlying the observed association between exposure to suicide and exposure to suicide attempt and the suicide-related outcomes. Arguably, the absence of bereavement-related factors and the specific association between exposure to suicide attempt and subsequent suicide attempt support the hypothesis that suicidal individuals may model, or imitate, suicide-related behavior that they see in others [10]. An imitation model is consistent with previous studies that have shown that increased risk of suicide-related behavior following exposure to both suicide and suicide attempt is not significantly moderated by preexisting risk factors such as depression, anxiety, and hospital admission for mental health [79,80]. The finding that exposure to suicide is associated with significant increased odds of suicide attempt is important since public health approaches for the prevention of behavioral contagion of both suicide and suicide attempt, such as frameworks for the prevention of suicide and self-harm clusters [12-15], have focused largely on mitigation efforts following exposure to suicide and therefore may benefit from the inclusion of exposure to suicide attempt in future mitigation efforts.
+Limitations
+The current systematic review and meta-analysis is the first to our knowledge to quantify the association between exposure to suicide and suicide attempt and the full spectrum of suicide-
+related outcomes and has many strengths, including the use of multilevel meta-analysis, the large sample size, and the exclusion of estimates of lifetime prevalence that do not take into account the temporal sequence between exposure and suicide-related outcomes. Despite this, several limitations exist. Whilst we conducted an extensive search of 21,868 records, there is the possibility that some relevant studies were not detected. Such studies are likely to create a bias towards the null (i.e., the exposure not having a significant effect). This is a limitation that is common to many systematic reviews and was mitigated to the best of our ability through adherence to a screening protocol developed a priori.
+Furthermore, since most studies adjusted for different covariates, we restricted our analysis to unadjusted events and ORs. Whilst this is consistent with previous meta-analyses in the field [81,82], it meant that we could not investigate other risk factors, such as frequency of exposure, duration since exposure, and baseline mental health diagnoses, and how these might moderate the association between exposure to suicide and suicide attempt and suicide-related outcomes. For example, a previous systematic review on pre- and post-loss features of suicide bereavement in young people found evidence of a cumulative effect of exposure to suicide on subsequent suicide risk [83]. In the present meta-analysis, 2 out of 34 studies included in our analyses provided separate estimates for multiple exposures to suicide [62] and suicide attempt [48]. In 1 study [62], exposure to 2 or more suicide deaths affected less than 1% of the population, but was associated with 9.8-fold greater odds of suicide attempt, compared to an OR of 3.8 among those who had been exposed to the suicide of 1 relative. Similarly, those exposed to the suicide attempt of 2 parents were 5.67 times more likely to make a suicide attempt, compared to ORs of 2.89 and 3.89 (for paternal and maternal exposures, respectively) among youths who had been exposed to the suicide attempt of 1 parent [48].
+Indeed, in the present multilevel meta-analysis, within-study heterogeneity remained largely unchanged by study-level moderators for both exposure to suicide and exposure to suicide attempt. For example, we did not find evidence of a significant difference in suicide-related outcomes when the exposure to suicide or suicide attempt occurred in relatives compared to friends and acquaintances. Although previous registry-based studies have shown a
+6-fold increase of suicide among biological relatives of adoptees who have died by suicide [84], in the present meta-analysis it was not possible to delineate between relatives who resided in the same household, and therefore shared many of the same environmental risk factors, and relatives who did not [9]. Understanding these factors is important for identifying specifically who within in the general population is most at risk. However, the pooling of observational studies meant that analyses of these factors were outside the scope of the present study. An important next step forward would therefore be examinations of exposure to suicide and suicide attempt while taking these risk factors into account using individual participant data meta-analyses.
+In the present multilevel meta-analysis, between-study heterogeneity remained moderate (I23) = 52.2%) across studies measuring exposure to suicide, which was not sufficiently explained by any of the included study design moderators. By contrast, study design characteristics accounted for 72.8% of between-study heterogeneity (123) = 87.8%) across studies measuring exposure to suicide attempt. In this instance, cross-sectional studies reported significantly larger ORs (OR = 8.23) compared to case-control (OR = 2.74) and cohort (OR = 2.69) studies. In general, cross-sectional studies are prone to an inherently greater number of biases, compared to case-control and cohort studies. This may be particularly pronounced in studies that measure suicide attempt because recall of suicide attempt may be less salient than recall of suicide death, and is prone to multiple interpretations and definitions [85].
+It is noteworthy that we did not find evidence to support the role of age as a risk moderator, as suggested in previous reviews [9,10]. Yet these results should be interpreted with caution, as
+ANOVA, analysis of variance.
+https://doi.org/10.1371/journal.pmed.1003074.t004
+the dichotomization of study populations into the categories of youths and adults was based on a majority rule in 13 out of 34 studies [26-28,39,58,60,61,63-65,68-70]. The finding that age was not a risk moderator may therefore be an artifact introduced by the imprecise age classification of the included population in individual studies. Furthermore, whilst similar patterns were observed across studies examining exposure to suicide attempt in youths versus adults, only 1 out of 13 studies [70] reported outcomes among adults, which may have impacted our ability to detect a statistically significant difference.
+Finally, the results of the present study do not allow causality to be inferred, and although we show evidence of a temporal association between prior exposure to suicide and suicide attempt and subsequent suicide-related outcomes, cross-sectional studies, by virtue of study design, do not provide incidence estimates. To account for this limitation, we only included cross-sectional studies where participants were explicitly asked about suicidal acts that occurred after exposure to suicide or suicide attempt. But this approach does not mitigate errors in recall and other biases that are inherently more common in cross-sectional studies.
+Conclusions
+Our findings suggest that prior exposure to suicide is associated with increased risk of suicide and suicide attempt. By contrast, exposure to suicide attempt is associated with increased risk of suicide attempt, but not suicide death. Future studies should refrain from combining suicidal behaviors into composite measures of suicide exposures and outcomes as the relationships between exposure to suicide and suicide attempt and suicide-related outcomes are markedly different. Lastly, future studies should consider interventions that target suicide-related outcomes in those exposed to suicide and include efforts to mitigate the adverse effects associated with exposure to suicide attempt.
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+For many policy makers, paying healthcare providers for performance is an intuitively appealing way of improving quality and value in services. However, the results from incentive programmes in healthcare have been inconsistent,1-4 and progress in designing them has been slow.5 6 Careful calibration of incentives is essential when determining performance indicators and setting targets. As well as being aligned with professional values, targets must be challenging but attainable, and payments must be large enough to promote high quality care without distorting clinical practice. The financial implications for both payers and providers can lead to contentious contractual disputes. We discuss how targets are set in pay for performance programmes and look at the experiences of a national incentive scheme for general practitioners in the UK.
+How do payers set targets?
+Most pay for performance schemes use either relative targets or absolute targets. The relative target approach compares performance between providers and is used in tournament style programmes, where higher performers are rewarded and low performers may even be penalised—for example, Medicare’s physician value based payment modifier.7 Payers must decide what proportion of providers to reward but don’t make judgments on acceptable levels of performance. This approach has several drawbacks for providers: as they do not know the targets in advance they lack a firm reference point to guide quality improvement efforts, and high levels of achievement may not be rewarded, which is particularly unfair when the distribution of performance scores is narrow and differences between providers are clinically insignificant.
+With absolute targets, also known as criterion referenced targets, payers determine performance targets in advance, which requires more work on evaluation. This approach is less directly competitive because all providers can “win” or “lose” depending on their level of achievement, and payers must therefore accept greater uncertainty about programme costs. In contrast to relative targets, where targets are adjusted automatically, absolute targets need to be reset in response to provider performance so that they remain appropriate.
+Quality and Outcomes Framework: points mean prizes
+The UK’s primary care pay for performance programme—the Quality and Outcomes Framework (QOF)—was introduced in 2004 to provide quality related payments to family practices in addition to their existing capitation payments8 and encourage better practice.
+The scheme uses a criterion referenced approach, with absolute targets set across several domains (box 1). Most of the targets relate to management of chronic disease and public health measures (such as smoking cessation and immunisation), but organisational and patient experience factors are also included. As an example, table 1^ shows the current QOF targets (“indicators”) for the clinical domain of stroke and transient ischaemic attack.
+QOF sets a lower and upper threshold for each indicator. Practices can earn an increasing number of points depending on their level of achievement between the lower and upper threshold. The lower threshold sets the minimum level of achievement required to receive any remuneration and the upper
+threshold the achievement to receive maximum remuneration with a linear scale between (figured). In the absence of robust evidence on performance, thresholds were initially set through discussion between the Department of Health and the British Medical Association (BMA), the representative body for doctors. All lower thresholds were set at 25%, and upper thresholds were set at 50% to 90%, depending on the estimated difficulty of achieving individual indicators.
+Because the targets are absolute and size of payments varies over a range of performance, all practices performing below the upper threshold have an incentive to make incremental improvements. However, the financial incentive to improve diminishes as the upper threshold is approached and disappears once it is exceeded. This may partly explain why the practices with the lowest baseline achievement rates improved at the fastest rate,10 although similar patterns have been observed in other incentive schemes.11 12 It could also explain the lack of improvement in achievement rates after the first three years of the scheme (figured).
+Difficulties of setting QOF thresholds
+Meeting QOF targets has become an established part of clinical practice in the UK. Performance is routinely monitored through practices’ clinical computing systems and over 90 000 practice years of data are now publicly available. Until recently this wealth of information was not used to set thresholds. In 2006-07 (the third year of the scheme) most minimum thresholds were
+arbitrarily raised to 40% and upper thresholds were raised by 5-20 percentage points for 12 of 50 clinical indicators, and lowered by 10 percentage points for one. No other changes were made until 2011, despite average practice achievement rates exceeding the upper thresholds for 66 out of 71 clinical indicators.13
+There were three main reasons for this inertia. Firstly, the purpose of payment thresholds was not well defined at the outset. For example, upper thresholds were intended to represent the “maximum practically achievable level to deliver clinical effectiveness,”14 but no formal definition was provided and it was unclear whether thresholds were intended to maintain performance levels or to encourage improvement. Secondly, changes to QOF must be agreed in annual contract negotiations between the Department of Health and the BMA, and BMA representatives sought to protect practice incomes by resisting threshold increases. Contract changes are also applied uniformly across all practices, precluding piloting. Thirdly, the annual QOF review process prioritised clinical evidence on management of chronic disease over evidence on the effect of incentives on clinician behaviour, so while detailed recommendations were made for developing quality indicators,15 performance thresholds were not substantially revised.
+The National Institute for Health and Care Excellence (NICE) took over the annual QOF review process in 200916 and explored alternative methods for setting targets. On reviewing national performance, researchers from the University of Manchester proposed basing upper thresholds on the 75th percentile of
+ANALYSIS
+achievement in the previous year and lower thresholds on the fifth percentile of achievement (box 2).17 The aim was to calibrate targets against historical performance so that they remained appropriate, while retaining a criterion based approach that potentially allowed all practices to obtain maximum rewards. This approach was rejected in the 2010 contract negotiations. Instead upper thresholds for 2011-12 were increased by one percentage point for two indicators and lower thresholds were left unchanged. The following year lower thresholds were increased by 5-10 percentage points for most indicators and upper thresholds were increased by 4-10 percentage points for 13 indicators. In most cases upper thresholds remained below the average practice achievement rates.
+In 2012 the Department of Health imposed a series of changes to the general practitioners’ contract, including setting upper thresholds for QOF indicators at the 75th percentile of achievement. To allow practices time to adapt, the new thresholds were applied to 20 indicators in 2013-14, with the remaining indicators due to follow in 2014-15.9 Under this new system upper thresholds would increase by an average of 14.7 percentage points (range 2-46 percentage points).18
+However, two design decisions diluted the effect of these changes to the upper threshold. Firstly, lower thresholds were fixed at 40 percentage points below the upper thresholds.
+Because the distribution of achievement for most QOF indicators is compressed over a narrow range most practices could therefore earn close to the maximum payment without increasing achievement. For example, for indicator DM17—the proportion of patients with diabetes and a cholesterol concentration <5 mmol/L—median achievement in 2012-13 was 81.3%, which was within four percentage points of the 75th percentile of achievement (85.1%); hence most practices would earn at least 90% of maximum remuneration without further improvement (table 2^). Secondly, the base payment per point was increased by 17%, from £133.76 to £156.92. The combined effect of these changes is that under the new system practices would, on average, earn 87.5% of their existing income from the clinical indicators if they did not make any improvements. In monetary terms, this represents a mean loss of £8324 a year, with practices in the most deprived areas losing less.18 Although this is not a trivial threat to income, it is much less than that posed by the original NICE working group proposal (table 2^).
+BMA representatives were nevertheless resistant to the changes19 and succeeded in postponing the threshold changes planned for 2014-15 by a year and in removing several unpopular indicators from the scheme.20 Most of the money earmarked for these indicators (representing 23.8% of total QOF funding) will be redirected into increased capitation payments.
+Does increasing targets improve performance?
+If providers are primarily motivated by extrinsic (financial or reputational) rewards, increasing upper thresholds should improve performance. Whether this improvement is meaningful or is secured through gaming (for example, by inappropriately excluding patients) will depend on the provider. If intrinsic (professional or altruistic) motivations drive performance—which is consistent with practices overachieving relative to the existing thresholds—raising thresholds may have a limited effect, although practices may still respond to new benchmarks for high quality care.
+As the adjustments to QOF thresholds have so far been limited it is difficult to draw conclusions on the effect of the changes. Although thresholds for several QOF indicators were raised in 2006-07 and 2012-13, they remained below most practices’ existing levels of achievement. Raising the thresholds seemed to produce little response from the minority of low achieving practices affected by them, particularly in 2012-13 (figured). To date the change that has had the greatest effect on performance was the increase in the threshold for CHD10 (prescription of p blockers for patients with coronary heart disease) from 50% to 60% in 2006-07. In 2005-06, 1748 practices (21%) reported achievement rates of between 50% and 60%. Mean reported achievement for these practices, which had increased by just 0.3 percentage points in the year before the threshold increase (2004-05 to 2005-06), increased by 11.1 percentage points in the year after (from 55.7% to 66.8%). However, actual prescribing rates increased by just 1.9 percentage points; the apparent improvement in performance was largely the result of practices excluding more patients (exception reporting rates increased from 15.8% to 25.8%).
+Assessment of the effects of threshold changes has been further hindered by the fact that they are applied to all UK practices, preventing comparative analysis. However, in 2006-07, a natural experiment arose when the upper threshold for immunising patients with coronary heart disease against influenza was raised from 85% to 90%, while thresholds for immunising patients with three other conditions (chronic obstructive pulmonary disease, diabetes, and stroke) remained unchanged. The small increase in the upper threshold was associated with a 0.41 percentage point increase in immunisation rates and a 0.26 percentage point increase in exception reporting rates for patients with coronary heart disease, relative to increases for patients with other conditions. Increases in immunisation rates were greater for practices with lower baseline performance.21
+Conclusions
+QOF was introduced to deal with an existing emphasis on volume rather than quality of care that ran “counter to general practitioners’ professionalism, the interests of the NHS, and the interests of patients.”14 However, performance thresholds were set relatively low and not adequately adjusted, allowing most practices to secure close to maximum remuneration despite wide variations in performance. Because payments are adjusted for list size and disease prevalence, QOF effectively became a weighted capitation payment with minimum quality requirements, rather than a discriminatory quality payment. The NHS now has two choices: retreat from financial incentives and secure value by alternative means or reinvigorate pay for performance. Recent contract changes move in both directions at once; reallocating a quarter of QOF funding to core capitation payments reduces the share of remuneration linked to quality measures, while setting upper thresholds at the 75th percentile of achievement creates a more discriminating quality scheme. Future decisions on reforming pay for performance should be based on assessments of the benefits and costs of incentive programmes. This will require not only clinical evidence on individual quality indicators but evidence on how incentive structures affect provider behaviour and patient outcomes. Opportunities to generate evidence on the effects of changing performance thresholds have repeatedly been missed in QOF, with arbitrary changes simultaneously applied to all practices. Given that the framework directly affects the care of over 12 million patients and represents an investment of over £1bn a year, the need to trial changes to incentive structures is as great
+as the need to trial clinical interventions. In the absence of trials, causal effects are best estimated by exploiting natural experiments, and the phasing of the latest threshold changes over two years will, at least, provide such an opportunity.
+Contributors and sources: TD, EK, DR, and MS conduct research on the intended and unintended consequences of incentive schemes in healthcare, and between 2009 and 2012 worked as external academic contractors supporting the National Institute of Health and Care Excellence in the development of indicators for the Quality and Outcomes Framework. As part of this work they provided recommendations to the QOF indicator advisory committee on alternative methods for setting performance thresholds. AR conducts research on pay for performance and public quality reporting in healthcare, and the impact of incentive schemes on inequalities and discrimination in healthcare settings. TD and AR designed the paper; EK extracted the data; TD wrote the manuscript; and EK, DR, MS and AR edited the manuscript. TD is the guarantor of the article. The data we used for original analyses are freely available and we have provided references and links in the manuscript.
+Competing interests: All authors have read and understood the BMJ Group policy on declaration of interests and declare the following: TD was supported by a National Institute for Health Research career development fellowship, EK was partly supported by a NIHR school of primary care research fellowship, and AR was supported by a career development award from the Agency for Healthcare Research and Quality. From 2009 to 2012 TD, EK, DR, and MS held external contracts with NICE for work on setting thresholds and removing indicators from the Quality and Outcomes Framework.
+Provenance and peer review: Not commissioned; externally peer reviewed.
+1	Gillam S, SiriwardenaA, Steel N. Pay-for-performance in the United Kingdom: Impact of the Quality andOutcomes Framework—asystematic review. AnnFamMed2012;10:461-8.
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+Introduction
+People with a mental disorder have three to four times higher mortality rates than those without a mental disorder.1 This is a serious public health problem and the WHO has called for national targets to reduce the excess mortality among people with mental disorders.2 The quality of future efforts depends on a comprehensive and detailed overview of current mortality trends. Data from previous studies suggest that people with severe mental disorders have on average a 10-20 year shorter life expectancy than the general population.3,4 However, it is not entirely clear whether the mortality gap has grown over recent years; findings for schizophrenia suggest it has,5 while studies looking at other mental disorders show the gap is closing.3,4 So far, only a few studies have investigated mortality trends related to mental disorders in national samples and the evidence base is limited by selected samples and short follow ups.6,7
+Studying mortality differences between people with and without mental disorders over time is complex because these vary by age and cause of death. The excess
+mortality relates not only to external causes of death, such as suicides, accidents, and homicides,1,6 but also to medical diseases and disorders, such as cardiovascular disease, malignant neoplasms, respiratory diseases, and endocrine and metabolic disorders.1,4,6,8,9 Additionally, serious concerns regarding the methodological approaches of previous studies have been raised; calculating life expectancy of people with mental disorders from age 15 years onwards by using mortality rates for this population implies assuming that all individuals were diagnosed at this age.10 However, in many cases, mental disorders first manifest during early adulthood with a consequential later age of onset. Hence, an appropriate approach is needed to account for different ages of onset when calculating life expectancy in people with mental disorders. Several measures for studying mortality differences exist;10,11 however, life-years lost allows for a comprehensive comparison of mortality between those with and without mental disorders by taking account of the age at diagnosis and by allowing for assessment of cause-specific changes over time (panel).11,12
+Thanks to national longitudinal hospital registers, Danish data offer a unique possibility for investigating mortality patterns with respect to diagnosed mental disorders.13 The objective of this study was to assess differences in mortality between people with and without mental disorders as well as changes over time between 1995 and 2014 using Danish register data. This was done by quantifying the contribution by specific causes of death to the excess life-years lost between people with and without mental disorders.14
+Methods
+Study design and population
+Using a cohort design, we linked nationwide population data from the Danish Civil Registration System to information about hospital contacts from the Psychiatric Central Research Register and deaths from the Danish Cause of Death register. Data were linked on an individual-level using the unique personal registration number.8 All linked data sources contained longitudinal See Online for appendix information covering all people living in Denmark over the study period of 1995-2014.
+All individuals aged between 15 and 94 years who were registered as living in Denmark at some point between Jan 1, 1995, and Dec, 31, 2014 were included in the study population. Individuals who during follow-up fulfilled the age criterion or migrated into the country were included on the date of their 15th birthday or date of entry, respectively. The study period lasted until date of: 95th birthday, emigration, death, or Dec 31, 2014, whichever occurred first. The project was approved by the Danish Data Protection Agency.
+Measures
+People were regarded as exposed from the date of a psychiatric diagnosis. All full-time or part-time patients
+have been recorded in the Psychiatric Central Research Register since 1969. From 1995 onwards, outpatients and emergency department patients were also included in the registry. Based on the ICD, we included all people who had been diagnosed with a mental disorder (ICD-8 290-315; ICD-10 F00-F99) during psychiatric hospitalisation or outpatient treatment. Contacts for an organic or symptomatic mental disorder (ICD-8: 290-09, 290-10, 290-11, 290-18, 290-19, 292-x9, 293-x9, 294-x9, 309-x9; ICD-10: F00-F09), intellectual disability (ICD-8: 311-xx, 312-xx, 313-xx, 314-xx, 315-xx; ICD-10: F70-F79), and contacts exclusively for examinations and observations (ICD-8: Y01-Y19; ICD-10: Z00-Z99) were not considered. People with organic disorders or intellectual disabilities were considered on equal terms with other individuals, eg, only if a person with an organic mental disorder also was registered with a mental disorder, such as depression, would the person be deemed as having a severe mental disorder as per study definitions. Historical data dating back to 1969 were assessed (appendix p 1 provides an overview of the data coverage). Free access to mental health care is available for all Danish citizens and less than 10% of all admissions are involuntarily.
+Outcomes
+Causes of death were identified using the primary cause listed in the Registry on Causes of Death in which date of death also is recorded. Deaths were categorised according to the ICD-10 as: infectious diseases (ICD-10: A00-A09, A20-A99, B00-B89, B91-B99), neoplasms (ICD-10: C00-D09), diabetes mellitus (ICD-10: E10-E14), heart diseases (ICD-10: F03-9, I00-I25, I27, I30-52, I60-I99, R54), respiratory diseases (ICD-10: J00-J99), digestive diseases (ICD-10: K00-K93), alcohol misuse (ICD-10: F10, I85, K70), suicide (ICD-10: X60-X84, Y87-0),
+Articles
+accidents (ICD-10: V01-X59, Y10-Y86, Y87-2, Y88-Y89), and other causes of death (all remaining ICD-10 codes).
+Statistical analysis
+Cause-specific death rates were calculated for people with and without mental disorders by single age increments and over 5-year periods. Age-standardised and period-standardised mortality rate ratios (MRR) were obtained using direct standardisation in which mortality rates were calculated for each single age. Using this approach, survival curves can be fitted for each single age and those for people at age 40 years were calculated as an example. Average life expectancy at diagnosis was calculated as follows: let e (a j,t) denote life expectancy between age aj and the age t>aj, for those diagnosed with mental disorders at age a j.15,16 Life expectancies were calculated for every onset age a 1(, from age 15 until an upper limit set at age t=94 (ie, well above the life expectancy for the Danish population). These were combined into an average life expectancy at diagnosis until age 94 weighted by the n individuals with mental disorders, as
+n
+1E e (aiT)
+i = 1
+This implies that the life expectancy at age 15 years is weighted by the number of people at that age and added to the life expectancy at age 16 years, which is also weighted by the number of people at this age, and so on. For this reason, this measure is therefore not directly comparable to the life expectancy at age 15 years but gives a more precise figure for people with mental disorder’s actual years lived. Thus, the average life expectancy takes into account the differential numbers at age of diagnosis, hence avoiding the problem of being based on only one age. This approach has been described elsewhere and it is especially useful for comparisons between groups.10 Similarly, an average life expectancy was obtained for an age-matched comparison group consisting of people without mental disorders.
+Life-years lost describes the number of years lost due to death and it is used for analysing differences in mortality between subpopulations.10,11 It is calculated as the number of years between death and a set upper limit (ie, upper value minus life expectancy). We used life-years lost to work out the excess life-years lost (ie, life-years lost for people with mental disorders minus life-years lost for people without mental disorders). The specific calculations of life-years lost are described here below.
+Let 3 (a j,t) denote the number of life-years lost between the age of onset a j and the upper limit t> a j, for people with mental disorders. Life-years lost complements the life expectancy by adding to the total possible number of years between the two ages a j and t, as
+(t-a 1)=e (a 1, t)+>( a 1, t)
+where m are the causes of death and aj(aj,t) the number of life-years lost ‘due to cause j’. Corresponding calculations were conducted for people without mental disorders, denoted as a*( a j,t).
+The approach of calculating average life-years lost based on average life expectancy has not previously been described, neither has the difference between life-years lost of those with and without mental disorders that is denoted as excess life-years lost:
+In this additive model, the sum of years lost to mutually exclusive and exhaustive causes of death equals the total average life-years lost. Excess life-years lost was calculated separately for men and women. The appendix (p 2) provides details of the applied approach.
+The analysis was done with SAS (version 9.4) and R software.
+Role of the funding source
+The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to
+all the data in the study and had final responsibility for the decision to submit for publication.
+Results
+Between 1995 and 2014,	6 107 234 individuals
+(3 026 132 men and 3 081 102 women) aged 15-94 years were living in Denmark. The study population was observed over 89 216 177 person-years (men: 43 914948; women 45 301 229). By 2014, 589 327 people (262 005 men and 327 322 women) had been diagnosed with a mental disorder. People with mental disorders were observed for 5 999 290 person-years or 6-7% of all person-years (men: 2 582 871 [6%]; women: 3 416 420 [8%]).
+During the 20 years of study, 1 072 158 people (men: 539 262; women: 532 896) died. Of these deaths, 134 150 (13%) people (61 855 [12%] men; 72 295 [14%] women) had been diagnosed with a mental disorder. The total mortality rates for men with and without mental disorders were 27-1 and 11-4 per 1000 person-years, respectively. For women, the corresponding values were 21-2 and 11-0. As shown in table 1, the MRR between people with and without mental disorders was highest for suicide and alcohol misuse in both men and women. Nevertheless, an increased rate ratio was noted across all causes of death for people with mental disorders (table 1).
+We recorded a lower probability of surviving beyond the age of 40 years for people with mental disorders compared with those without (figure 1). By combining similar calculations for each single age, the average life expectancy at diagnosis between ages 15-94 was 29-3 years for men with mental disorders compared with 39-5 years in age-matched men without mental disorders. Estimated life-years lost for men with a mental disorder was 26-3 years compared with 16-1 years for men without a mental disorder (ie, a difference of10-2 years). For women, the average life expectancy was 34-6 years for those with a mental disorder and 41-9 years for those without one, corresponding to 19-6 and 12-3 life-years lost, respectively, with a difference of 7-3 excess life-years lost.
+The excess life-years lost were further disentangled into components by cause of death. As shown in table 2, the excess life-years lost for men with mental disorders were due mainly to alcohol misuse, which caused 2-8 (27-8%) years of the 10-2 excess life-years lost. Other causes of deaths, accidents, and suicides, as well as respiratory diseases and heart diseases also contributed substantially to the excess life-years lost. Men without mental disorders lost more life-years to neoplasms than those with mental disorders and additional analysis showed that men with mental disorders to a larger extent had died ahead of those ages where neoplasm rates peaked (data not shown). In women, other causes, respiratory diseases, and alcohol misuse were responsible for the major shares of excess life-years lost followed by heart diseases and suicide (table 2).
+With regards to changes over time, the number of average life-years lost decreased from 27-8 years in 1995-99 to 24-5 years in 2010-14 for men with mental
+disorders (appendix p 4). However, men without mental disorders experienced a decrease with a similar magnitude from 17-7 to 14-4 life-years lost in the same time period. Hence, the difference between those with and without mental disorders, or excess life-years lost, was similar (3-2 and 3-3 years for 1995-99 and 2010-14, respectively). Similarly for women, a decrease from 20-4 to 18-3 years was noted for those with a mental disorder while life-years lost decreased from 13-1 to 11-1 years for those without a mental disorder—ie, the overall excess life-years lost did not change over time.
+Figure 2 shows the distribution of life-years lost by causes of death. Neoplasms accounted for an increasing share of life-years lost; life-years lost to neoplasms rose for men with mental disorders from 4-8 (17-2%) to 5-2 (21-2%) and in women from 4-5 (22-2%) to 4-8 (26-4%) between 1995 and 1999 and 2010 and 2014. For heart diseases, improvements over time were noted for both groups, albeit more pronounced in people without mental disorders. The number of life-years lost due to respiratory diseases increased from 1-0 (11-1%) to 1-3 (13-7%) for women with mental disorders over time. A decrease in life-years lost due to suicide from 2-3
+(6-8%) for men and 1-4 (7-0%) to 0-7 (4-0%) for women over the study period.
+In figure 3, where zero indicates no excess life-years lost, the negative numbers listed for neoplasms (-0-5 years) showed that men with mental disorders experienced fewer life-years lost than those without between 1995 and 1999. This number rose to 0-1 in 2010-14. Women also experienced an increase in excess life-years lost due to neoplasms over time; from 0-2 to 0-6 years when comparing changes from 1995-99 with 2010-14. Similarly, we recorded increases for heart diseases (men: 1-2; women 0-3) and respiratory diseases (men: 0-3; women: 0-2; figure 3). No improvements were achieved for digestive disorders (men: -0-1; women: 0-0) and alcohol misuse (men: 0-1; women: 0-0); the latter accounting for 2-5 excess life-years lost among men with mental disorders over the study period (figure 3). A decrease in excess life-years lost was noted for suicide (men: -0-7; women: -0-5) and accidents (men: -0-9; women: -0-5). Still, the absolute difference remained quite high, especially for men in whom suicide and accidents accounted for 1-4 and 1-3 life-years lost, respectively, between 2010 and 2014 (appendix p 4).
+(8-4%) to 1-6 (6-5%) was recorded for men and from 1-2 (6-1%) to 0-7 (3-9%) for women with mental disorders between 1995 and 1999 and 2010 and 2014 whereas only a marginal difference was noted for people without mental disorders. In people with with mental disorders, life-years lost due to accidents decreased from 2-8 (10-0%) to 1-7
+Discussion
+This is, to our knowledge, the first nationwide study to quantify which causes of death contributed to the excess life-years lost between people with and without mental disorders. A shift in the excess life-years lost by cause of death was noted over time; death related to medical
+diseases and conditions became more significant over time while excess mortality due to suicides and accidents decreased.
+Neoplasms, heart diseases, and respiratory diseases accounted for the major shares of life-years lost in people with mental disorders. Heart disease has been linked to mental disorders (eg, myocardial infarction and depression).17 Almost 20% of the excess life-years lost in women with mental disorders was due to respiratory diseases, including chronic obstructive pulmonary disease. Smoking is common in people with mental disorders and might possibly affect women more adversely than men; consequently, chronic obstructive pulmonary disease and pneumonia are likely to be common in women with mental disorders.18 A lack of both good nutrition and physical activity is prevalent and might explain the large contribution of neoplasms and digestive disorders to the mortality gap.19,20 Additionally, physical disorders might be detected later and given less than optimal treatment in patients with mental disorders compared with those without.21,22
+The excess number of life-years lost related to suicides, accidents, and alcohol misuse is supported by findings from previous studies.1,23 Particularly, alcohol misuse accounted for a large share of excess life-years lost. Substance misuse is a common comorbid disorder in people with mental disorders24 and could possibly explain some of the excess life-years lost related to accidents.20
+Although improvements in life-years lost were achieved for both people with and without mental disorders over time, the difference between the two groups remained; however, we noted a shift with respect to causes of death. A significant decrease in excess life-years lost was recorded for suicides and accidents, which tend to peak in younger age groups. Hence, the increase reported for neoplasms, heart diseases, and respiratory diseases could suggest that people with mental disorders now to a larger extent are living longer and, consequently, are exposed to risks of dying from diseases with a later onset (ie, competing risks).
+People diagnosed with mental disorders experienced fewer life-years lost to suicide over the examined period compared with before while the numbers did not change for those without mental disorders. Of note, the overall suicide rate has fallen in Denmark25 and our findings suggest that this is mainly attributable to a decrease in people with mental disorders—ie, those who have been diagnosed. Means restrictions (ie, measures to limit access to suicide methods) have previously been noted as being effective suicide prevention strategies.26 Given that people with mental disorders might be more impulsive or have preference for specific methods,27,28 it is possible that increased attention to reducing available means for this group might have been effective. Furthermore, medical treatment and psychosocial therapy might be contributing factors to the reductions recorded in suicide mortality.29,30
+Expanding and applying a new approach for calculating average life expectancy resulted in smaller differences (ie, 10-2 excess life-years lost for men and 7-4 years for
+women) than noted in previous studies.9 Instead of assuming that all people with mental disorders were diagnosed at age 15 years, the current study presented how an average life expectancy based on onset ages between ages 15-94 years can be expressed as life-years lost and excess life-years lost.
+Nationwide register data were collected on a uniform basis for the entire study population without loss to follow up. By including historical and complete data for psychiatric hospital contacts dating back to 1969, we were able to identify a comprehensive share of the population with severe mental disorders, limiting any selection bias. The long follow up improved the consistency of the findings. By applying average life expectancy and life-years lost, this study took a more exact approach to assessing changes in mortality than previous efforts focusing on measures referring to one fixed age. Furthermore, life-years lost is an easily conveyable measure, which complements life expectancy and allows presentation of complex scenarios—eg, with the purpose of informing health-care staff and policy makers. By using an innovative approach, we were able to quantify excess mortality of a transient population and address changes over time.
+However, limitations of this study should be acknowledged. By using contact to psychiatric hospital as a proxy for severe mental disorders, less severe cases seen in general practice might have been missed. Although it is reasonable to assume that more severe cases would have been seen at hospital, for some the actual onset age might not have been captured correctly. Still, diagnoses determined by medical staff in psychiatric hospitals might have been of higher validity. The change from ICD-8 to ICD-10 in 1994 is not estimated to have affected the probability of being included in this study. However, the inclusion of outpatients from 1995 probably widened the sample size and possibly lowered the level of severity in terms of mental disorder. Because this change was implemented ahead of the follow up, it should not have affected the observed changes. Also, we assumed that the registration of causes of death was valid; although different diseases might contribute to dying, only one cause was considered for each death. However, it is possible that we did not capture first onset of mental disorders for all people with such a disorder, particularly among older birth cohorts, leading to conservative estimates. Furthermore, we applied a restrictive approach for identifying deaths due to alcohol misuse, thus, possibly underestimating its effect. People with mental disorders constitute a heterogeneous group, hence limiting the interpretation.
+With regards to clinical implications, our findings underline the importance of preventing suicide and accidents in patients with mental disorders. Improving healthy habits, in terms of reducing smoking and alcohol misuse, improving nutrition and exercise, as well as addressing social challenges might help reduce mortality in people with mental disorders. Interventions to improve
+healthy habits in people with mental disorders have only to some extent been explored and promising findings are scarce.31,32 It would be especially desirable to have effective interventions for reducing alcohol misuse in this group.
+It has been suggested that people with severe mental disorders might be underserved with respect to certain medical procedures, such as cardiovascular disease treatments,2,7 emphasising the need for careful physical examinations during hospital contacts.33 In view of the fact that most people with a mental disorder spend only fragments of their life in hospitals, detection and treatment of physical disorders should not be left to secondary and tertiary care providers; follow-up visits in primary care could play an important role, as could routine screening at any health-care contact. Future studies might apply the same approach for assessing life-years lost for specific diagnoses, use a wider definition of mental disorders by including prescription data, or assess the effect of interventions aimed at reducing mortality among people with mental disorders.
+In conclusion, the excess mortality in people with mental disorders was quantified using average life expectancy and life-years lost, allowing for a comprehensive overview of cause-specific changes over time. Alcohol misuse, suicide, and accidents accounted for the largest share of excess life-years lost in people with mental disorders, followed by heart diseases and respiratory diseases. Overall, life-years lost decreased over the studied period but the excess life-years lost in people with mental disorders did not improve. However, we noted a shift in causes of death; the number of excess life-years lost due to suicide and accidents decreased while an increase was noted for neoplasms, heart diseases, and respiratory diseases. These findings will assist an evaluation of future focus areas for interventions.
+Articles
+26	Mann JJ, Apter A, Bertolote JM, et al. Suicide prevention strategies. JAMA 2005; 294: 2064-74.
+27	Huisman A, van Houwelingen CAJ, Kerkhof AJFM. Psychopathology and suicide method in mental health care. J Affect Disord 2010; 121: 9^99.
+28	Mann JJ, Waternaux C, Haas GL, Malone KM. Toward a clinical model of suicidal bahavior in psychiatric patients. Am J Psychiatry 1999; 156: 181-86.
+29	Erlangsen A, Lind B, Stuart EA, et al. Short and long term effects of psychosocial therapy provided to people after suicide attempt: a register-based, nationwide multicentre study using propensity score matching. Lancet Psychiatry 2014; 2: 47-58.
+30	Hawton K, Witt KG, Taylor Salisbury TL, et al. Psychosocial interventions for self-harm in adults. Cochrane Database Syst Rev 2016: Cd012189.
+31	Caemmerer J, Correll CU, Maayan L. Acute and maintenance effects of non-pharmacologic interventions for antipsychotic associated weight gain and metabolic abnormalities: a meta-analytic comparison of randomized controlled trials. Schizophr Res 2012; 140: 159-68.
+32	Speyer H, Christian Brix Norgaard H, Birk M, et al. The CHANGE trial: no superiority of lifestyle coaching plus care coordination plus treatment as usual compared to treatment as usual alone in reducing risk of cardiovascular disease in adults with schizophrenia spectrum disorders and abdominal obesity. World Psychiatry 2016; 15: 155-65.
+33	Briskman I, Bar G, Boaz M, Shargorodsky M. Impact of co-morbid mental illness on the diagnosis and management of patients hospitalized for medical conditions in a general hospital. Int J Psychiatry Med 2012; 43: 339 48.
+www.thelancet.com/psychiatry Vol 4 December 2017
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+Young adulthood (ages 18-34) is a time of higher risk for suicide deaths among racial and ethnic minorities in the United States, relative to older adulthood,1 and overall, regardless of race or ethnicity, young adults (YAs) have a higher rate of suicide attempts (SAs) than do older adults.2 In fact, suicide is the second leading cause of death among YAs between the ages of 18 and 34.3 While available national data do not compare young adult SA rates by race/ ethnicity, data from the 2017 Youth Risk Behavior Survey of high school students suggest that Black adolescent girls have a higher 12-month prevalence of non-fatal SAs (13%) compared to White adolescent girls (7%), while Black and Hispanic adolescent boys have a higher 12-month prevalence of SAs (7% and 6%, respectively), compared to White adolescent boys (5%).4 One way of understanding risk and hence preventative strategies for SAs is to examine events that precipitate SAs.5-7 Because SAs increase risk for suicide deaths,8 it is imperative to understand factors that precipitate SAs and how they vary across racial/ethnic groups. Thus, the present study adds to the existing literature by examining racial/ethnic differences in SA precipitants to inform prevention and intervention.
+Research examining SA precipitants has focused primarily on interpersonal and intrapersonal factors, with fewer studies examining achievement-related factors. Interpersonal factors are events that occur outside the self and are primarily influenced by a third party or entity, such as bullying and
+familial conflict, while intrapersonal factors are events originating from within the self, including self-reported depressive symptoms and loneliness. In contrast, achievement-related factors relate to academic or occupational goals, and may be either interpersonally (e.g., pressure from others to perform) or intrapersonally (e.g., ambition) motivated. A focus on interpersonal precipitants is consistent with the Interpersonal Theory of Suicide, which suggests that interpersonal factors, such as thwarted belonging and perceived burdensomeness, in conjunction with intrapersonal factors, such as hopelessness, interact to increase suicidal desire.9,10 Interpersonal conflict with peers, spouse, or family members increases risk for, and often precipitates, SAs.11-13 Interpersonal factors are thus important to consider to better identify the varied contexts that precipitate SAs among different racial/ethnic groups.
+Along with interpersonal factors, intrapersonal factors, such as non-adaptive cognitions and negative mood, are also associated with SAs.14,15 One study found that diagnosis was the second most common SA risk factor endorsed (18%) after interpersonal conflict (46%) among 15-24-year-olds (N = 264) admitted to a psychiatric hospital in Singapore following a SA.12 Further, evidence from a systematic review indicates that intrapersonal factors, such impulsivity, hostility, aggression, and anxiety, predict SAs,16 and findings among emerging adults show that other intrapersonal factors, including mood and disruptive behavior disorder
+diagnoses, anger, and affect dysregulation distinguished between single and multiple suicide attempters, with multiple attempters exhibiting greater depressive symptom severity.17 Thus, intrapersonal factors, including cognitions, mood, and affective states, are associated with SAs.
+Achievement-related SA precipitants, such as perceived failure in academic or occupational settings, have received less attention than interpersonal and intrapersonal factors. Some studies indicate that suicide attempters endorse educational problems as SA precipitants, although the proportion of educational/academic problems compared to interpersonal or intrapersonal precipitants is relatively small,6’18 and evidence on the relationship between achievement and SA is mixed.13’19 Given that emerging adulthood marks a period during which college students are concerned with accomplishing educational and occupational aspirations,20 the present study also examined achievement-related factors that precipitated SAs among this population.
+Suicide attempt precipitants among underrepresented racial/ethnic groups
+While racial/ethnic minorities are underrepresented in suicide research,21 some studies have examined common risk factors for SAs among different racial/ethnic groups. For instance, evidence suggests that intimate partner violence, hopelessness, suicide ideation, sadness, and feelings of worthlessness predict SAs among Black women.22-24 National data show that among Black adults (18 years and older), mood and anxiety disorders significantly predict higher odds of planning and attempting suicide.25 Moreover, findings in community samples indicate that hopelessness is associated with more lethal SAs among both Black and White adults.26 In contrast, hope serves as a protective factor against suicide ideation among Black college students and against SAs among Black women with a history of intimate partner violence.27,28 Although both interpersonal and intrapersonal factors are associated with SAs among Black adults, intrapersonal factors appear to have a stronger association with SAs.
+Research with Hispanic youth suggests that interpersonal factors may precipitate their SAs. A national study examining common predictors of SAs among Hispanic adult subgroups found that familial conflict was associated with over six times higher odds of reporting a lifetime SA.29 Qualitative research suggests that interpersonal conflict with family and peers precipitates SAs among Hispanic adoles-cents.15 Still, one recent study examining cultural meanings of suicide among Hispanic, White, and Asian American adults found no differences in interpersonal motivations for suicide.30 However, the study investigated “reasons why” a person may consider suicide versus factors that actually precipitated SAs; further, participants were adults with a history of either suicide ideation, plans, or attempts. We are interested in factors that precipitate SAs and how these may vary across different racial/ethnic groups. Reasons for considering suicide may be different from factors that actually precipitate SAs.
+Research examining SAs among Asian Americans is also relatively limited, and findings are mixed. One national study among Asian American undergraduate students found that interpersonal and achievement-related factors (i.e., family conflict and academic problems) were the most common precipitants of suicide ideation.31 Data from another national study suggest that family conflict is positively associated with SAs among Asian Americans.32,33 Still, other research has found that family conflict—conceptualized as demands family members place on the individual—is inversely associated with SAs, suggesting that familial conflict, although unpleasant, may still be protective, because the presence of the family provides a sense of belonging.34 Notably, this latter study was conducted with Filipino Americans only, and the findings may not generalize to all Asian American subgroups. Interpersonal factors such as family conflict may increase suicide ideation and attempts among some Asian Americans.
+The present study
+Overall, racial/ethnic minorities are underrepresented in suicide research.21,35 Since racial/ethnic minorities are most at risk of suicide in their youth,1 identifying distinct precipitants that differentially increase risk for SAs may inform prevention and intervention among racially and ethnically diverse populations. The present study examined racial/eth-nic group differences in three types of SA precipitants relevant to YAs—interpersonal, intrapersonal, and achievement-related precipitants. We hypothesized that among suicide attempters: (1) Hispanic and Asian American YAs would more often report interpersonal precipitants than Black and White YAs; and (2) Black YAs would more often report intrapersonal precipitants than White, Hispanic, and Asian American YAs. As the research to date has been limited and inconclusive, we did not have specific hypotheses about racial/ethnic differences in achievement-related precipitants, and thus this research question was exploratory. Far less is known about the factors that increase suicide risk among biracial/multicultural YAs. Although we did not have specific hypotheses about SA precipitants among biracial YAs, we included them in our analyses, because they comprised a substantial proportion of our sample of suicide attempters (17%).
+Method
+Participants
+Undergraduates from an urban public commuter college in the Northeastern United States, who spoke and understood English, took part in the present online study. Only data from adults aged 18-34 who reported at least one SA were analyzed, as this is the age group for which suicide is the second leading cause of death.1’3 Further, this age group is representative of the population from which we sampled. Students from this population tend to be nontraditional
+students, from low socioeconomic status (SES), and from ethnically diverse backgrounds.
+Measures
+Participants completed an 8-item suicidal behavior survey that assessed lifetime SA history (“Have you ever made a suicide attempt?” “How many?”), method of attempt, whether they received medical care for the SA, and reason for attempting suicide (“What caused you to want to try to kill yourself?”). This latter item was coded by three independent judges. Specifically, the judges coded the factors that led people to attempt suicide as either interpersonal, intrapersonal, or achievement-related precipitants. Interpersonal precipitants included events or conflicts involving other people (e.g., family conflict, bullying, death of a loved one; k = .80-.92). These interpersonal precipitants were then coded into type of interpersonal precipitants (family, peer/friend, and romantic; k = .77-.87). In contrast, intrapersonal precipitants included factors related to the person’s internal experience (k = 81-.92). These were further coded into cognitive-affective precipitants and included cognitive factors, such as hopelessness, worthlessness, loneliness, or emotions, such as frustration and anger, and as well as self-reported diagnoses. The interrater reliability for type of intrapersonal precipitant was good, k = .79-.86. Achievement-related precipitants included performance-related stress in both academic and workforce environments (k = .61-.90). Judges also coded the total number of interpersonal (a =.86-.92), intrapersonal (a = .95-.97), and achievement-related precipitants (a = .62-.81), as well as the total of number precipitant categories (a = .90-.92), and the total number of all precipitants, including those
+outside of the three categories described above (a = .89-.93). Table 1 contains examples of each precipitant category along with the frequency of precipitants reported per category.
+Data analysis
+Chi-square tests of independence were conducted to test whether YAs from different racial/ethnic groups were more or less likely than chance to report interpersonal, intrapersonal, or achievement-related SA precipitants, or type of interpersonal and intrapersonal SA precipitants, and to identify gender differences among the variables of interest and determine whether to include gender as a covariate in hierarchical logistic regression analyses. One-way analyses of variance were conducted to examine racial/ethnic differences in number of interpersonal, intrapersonal, achievement-related, and total number of individual SA precipitants reported.
+Hierarchical logistic regression analyses were conducted to examine whether race/ethnicity was statistically associated with reporting interpersonal (e.g., family, peer/friend, romantic, other) or intrapersonal (e.g., cognitive-affective, mental health diagnosis) SA precipitants. Because only eight participants reported achievement-related SA precipitants, this was not examined as an outcome in a logistic regression analysis. Gender differences emerged (see below), so we used it as a covariate in the logistic regression analyses. Gender was entered into the first block of the analysis, race/ ethnicity was added in the second block, and the third block included the interaction between gender and race/ethnicity. Because the two-way interaction was not statistically associated with any of the outcomes, we removed it from the final
+analyses. The outcome variables included any interpersonal and intrapersonal precipitants, coded as 0 (not reported) and 1 (reported). One set of analyses was conducted with White YAs as the reference group, and another set of analyses was conducted with Black YAs as the reference group. These reference groups were chosen to allow comparisons between racial/ethnic minorities and White YAs and comparisons between racial/ethnic minority groups.
+Results
+Two hundred and sixty-eight YAs endorsed a SA history in a survey of 3,218 students (suggesting a base rate of lifetime SAs of 8%). Of the 268 undergraduates who reported a SA, 8 (3%) were excluded because they were older than 34, fifteen (6%) were excluded because they failed to provide their age, and 16 (6%) were excluded because they self-identified as “Other” as their race/ethnicity (and given the limited research and heterogeneity in this group, they were excluded from analyses). The final sample consisted of 229 YAs (87% women), ages 18-33 (Note: No 34-year-olds participated in the study) who self-identified as either Hispanic (n = 95; 42%), White (n = 42; 18%), Biracial (n = 38; 17%), Black (n = 33; 14%), or Asian (n = 21; 9%).
+Of the 229 participants in the present sample, 150 YAs (66%) reported one SA, and 79 (34%) reported multiple attempts (range = 2-15). Forty-five (20%) participants were hospitalized following the attempt, while 39% (N = 89) reported that no one knew about the SA. Ingestion (n = 153; 67%), cutting (n = 100; 44%), and choking/asphyxiation (n = 27; 12%) were the most frequently reported methods used for making SAs (Note: These categories were not mutually exclusive, as some participants reported more than one method.). Of the 200 participants who reported a precipitant, 70% reported intrapersonal precipitants, 42% reported interpersonal precipitants, and 4% reported achievement-related precipitants. Overall, 49% (out of 200) reported one precipitant, while 51% reported between 2 and 9.
+Chi-square tests of independence revealed that Asian YAs were less likely than chance to provide a response to events precipitating their SAs (Z = —2.5, p <.01). Further, Black YAs were less likely than chance to report interpersonal SA precipitants (Z = 2.7, p < .01). There were no racial/ethnic differences in either intrapersonal or achievement-related SA precipitants, neither were there racial/ethnic differences in
+type of interpersonal (family, peer/friend, and romantic) or intrapersonal (cognitive-affective and mental health diagnosis) SA precipitants reported (see Table 2).
+Further, chi-square tests of independence revealed that male were less likely than chance to provide a response to events precipitating their SAs (24 [75%]; Z = —2.0, p <.01), whereas women were more likely chance to provide a response (178 [90%]; Z = 2.0, p <.01), / = 4.41, p < .05. There were no gender differences in interpersonal or intrapersonal SA precipitants, or type of either interpersonal or intrapersonal SA precipitants.
+Univariate analyses of variance showed no racial/ethnic differences in number of interpersonal, F(4,195) = 1.25, p = .29, or intrapersonal precipitants, F(4,195) = 0.16, p = .96, nor were there racial/ethnic differences in total number of precipitant categories, F(4,195) = 0.76, p = .56, or total number of individual SA precipitants reported, F(4,195) = 0.54, p = .71. In addition, there were no gender differences in any of these variables (p = ns).
+Hierarchical logistic regression analyses
+We conducted hierarchical logistic regression analyses to investigate racial/ethnic differences in interpersonal and intrapersonal SA precipitants. When White YAs were entered as the reference group, race/ethnicity was not statistically associated with either interpersonal or intrapersonal SA precipitants. However, when Black YAs were entered as the reference group, Hispanic (O.R. = 3.97), Asian (O.R. = 4.80), and Biracial YAs (O.R. = 3.60) had higher odds of reporting an interpersonal precipitant, after adjusting for gender. There were no differences between Black and White YAs in odds of reporting interpersonal SA precipitants. Moreover, after adjusting for gender, there were no differences between Black YAs and YAs from other racial/ethnic groups in odds of reporting intrapersonal SA precipitants. Table 3 reports on the logistic regression analyses.
+Discussion
+Overall, YAs in our sample who reported SA precipitants reported that their suicide attempts were most often precipitated by intrapersonal factors (70%), followed by interpersonal factors (42%), consistent with previous research examining motivation for suicide across three different racial/ethnic groups.30 Achievement-related SA precipitants
+were least often reported, and this is consistent with previous work,6 as well. Our findings support previous research and theoretical models proposing that both interpersonal and intrapersonal factors are most relevant to SAs9,10,35 and suggest that multiple factors may contribute to a person’s decision to attempt suicide—51% (out of 200) YAs reported two or more SA precipitants. As proposed by cognitive models of suicide, when a person’s perceived levels of distress surpass a threshold, they may transition from considering to attempting suicide.16 Thus, the interaction of various external and internal factors may significantly contribute to suicide risk.
+Findings in the present study also revealed some racial/eth-nic group differences in SA precipitants. The data partially supported the first hypothesis: Hispanic and Asian YAs had almost 4- and 5-times higher odds, respectively, of reporting interpersonal SA precipitants, compared to Black YAs, adjusting for gender (but not different from White YAs). Biracial YAs also had almost 4 times higher odds of reporting interpersonal precipitants than Black YAs. More generally, Black YAs were less likely to report interpersonal SA precipitants. These findings are consistent with previous work suggesting that Black YAs may be less vulnerable to interpersonal stressors and more susceptible to the effects of intrapersonal factors.24-28 Further, the findings are consistent with research suggesting that both Hispanic and Asian YAs may be more susceptible to interpersonal stressors that may precipitate SAs.15,29,33
+Notably, when White YAs were used as the reference group in regression analyses, no racial/ethnic group differences emerged; however, when Black YAs were used as the reference group, group differences emerged between racial/ ethnic minorities. These findings have important theoretical, clinical, and research implications. First, the findings suggest that racial/ethnic minorities are similar to White YAs in the proportion of intrapersonal SA precipitants reported, which would be consistent with most theories of suicide not specifying cultural differences in factors that may increase suicide risk among different racial/ethnic groups. However, only using White individuals as the reference group for
+comparison masks significant group differences within racial/ethnic minority groups and does not provide suicide researchers or clinicians with information relevant to the development and implementation of culturally sensitive suicide prevention and intervention strategies.
+Second, by analyzing different outcomes, such as interpersonal and intrapersonal precipitants, and using two different ethnic groups as reference groups, we were able to identify which SA precipitants vary across race. Although previous research suggests that Hispanic and Asian adults are susceptible to interpersonal vulnerabilities that may increase suicide risk,15,29,31-33 it is unclear why SAs by Black YAs are less often precipitated by interpersonal factors than their racial/ethnic minority peers. Perhaps Black YAs in highly critical interpersonal environments may have habituated to these stressors, and such stressors may not constitute enough distress to surpass the tolerance threshold proposed in cognitive models of suicide.36 These hypotheses are speculative and warrant further research.
+Contrary to predictions, no racial/ethnic groups differed from chance in their tendency to report intrapersonal precipitants; nor were there differences between them in regression analyses. These findings indicate that YAs across different racial/ethnic backgrounds experience intrapersonal stressors that precipitate their SAs about equally. These findings support a focus on intrapersonal risk factors in theories of suicide and in clinical intervention. Intrapersonal factors were the most frequently reported among participants across all racial/ethnic groups. Thus, our findings show that although interpersonal factors vary across racial/ethnic groups, intrapersonal precipitants are experienced in similar proportions across racial/ethnic groups.
+Regarding type of interpersonal and intrapersonal precipitants, YAs across all racial/ethnic groups reported similar frequencies of SA precipitants. One explanation might be that the specific type of interpersonal or intrapersonal precipitant may not be as critical in predicting suicide risk as the overarching category and their underlying mechanisms. For instance, YAs susceptible to interpersonal precipitants
+may share rejection sensitivity as an underlying mechanism that increases risk.37 Understanding differences within types of interpersonal precipitants may not be as important as understanding that interpersonal factors, in general, may increase risk for individuals who are sensitive to rejection. This explanation is also speculative, as we did not measure factors such as rejection sensitivity. However, future research should examine such potential underlying factors.
+One important finding was that Asian YAs were less likely than chance to provide a response for events precipitating their SAs—which may have clinical implications: Asian YAs may feel less comfortable discussing SAs and events preceding them. These findings are in line with previous work suggesting that Asian American youth are reluctant to seek mental health services, because they may not perceive mental health as important and because many prefer to confide in their peers and religious groups.38 More generally, these findings support seminal research showing that ethnic minorities are less to likely disclose suicide ideation in self-report assessments compared to their White peers39; ethnic minorities were deemed “hidden ideators” because their suicide ideation became apparent only after a clinician conducted a suicide risk assessment. Given mixed research evidence (e.g., whether familial conflict serves as a risk or protective factor among Asian Americans), it is imperative to conduct more research with this population to better understand the characteristics that increase suicide risk among them and how these may be distinct from those of other racial/ethnic minorities. The findings in this research may benefit clinicians who work in college and university counseling centers. Understanding factors that increase risk of suicide attempts among students from different racial/ethnic backgrounds may facilitate better assessment and intervention. Specifically, clinicians are encouraged to be more proactive in inquiring about suicidal thoughts and attempts among YAs.
+Strengths and limitations
+Some study limitations should be noted. First, the study was retrospective, and there was variability in when YAs had attempted suicide. Seventy-nine out of 229 YAs (34%) reported multiple attempts, and it was unclear whether they reported precipitants for all attempts, the most recent, or the most salient. Second, very few people endorsed achievement-related precipitants. Arguably, achievement-related precipitants can be either interpersonally or intrapersonally motivated, which may explain the low endorsement of “pure” achievement-related precipitants. Third, participants were predominantly women, limiting generalizability of the findings to men or gender expansive YAs. Still, the present study has several strengths. First, participants were from a public college in which most students come from economically disadvantaged backgrounds, transfer from community colleges, and commute to classes, characteristics which may more closely resemble the general population, compared to those of more traditional colleges/universities. Second, the undergraduate students were from underrepresented racial/
+ethnic groups, which provides greater generalizability to racial/ethnic minority groups than typical studies of suicidal behavior in the US. Third, our study used both qualitative and quantitative methods grounded in theory-driven research. Future work should replicate these findings in a larger sample of participants who report a recent SA (vs. a lifetime history of SAs) with a larger representation of men.
+Conclusion
+Overall, the findings in the present study suggest that YAs from different racial/ethnic backgrounds are sensitive to distinct contextual factors that precipitate SAs. Compared to Black YAs, Hispanic, Asian, and Biracial YAs had higher odds of reporting interpersonal SA precipitants, suggesting that racial/ethnic minorities may respond differently to interpersonal stressors. Importantly, these differences between racial/ethnic minorities may go unnoticed, depending on the group serving as a point of comparison. These findings have important clinical and theoretical implications. Future work should consider the intersectionality of contextual factors that may vary by race/ethnicity, gender, SES, and sexual orientation to identify distinct risk factors in these subgroups of young adults.
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+INTRODUCTION
+Suicide and attempted suicide are major public health concerns. At least 804,000 people take their own lives annually and 25 times that number attempt suicide (WHO, 2014). In recent decades, there have been many welcome developments in understanding and preventing suicide, as well as good progress in intervening with those who have attempted suicide. Despite these developments, though, many challenges remain. In this article, we explore both the recent developments and the challenges ahead for the field of suicide research and prevention. Instead of relying solely on our individual perspectives, we consulted experts in suicide research and prevention from across the globe. To this end, we contacted all of the contributors to the 2nd edition of the International Handbook of Suicide Prevention (O’Connor and Pirkis, 2016) and asked them to nominate, in their
+view (i) the top 3 most exciting new developments in suicide research and prevention in recent years, and (ii) the top 3 challenges in the field of suicide research and prevention. We were fortunate to receive responses from about one third of the authors representing 12 countries and spanning four continents (see section “Acknowledgments”). We reviewed their nominations, combined them where they described an overarching theme and then classified them into whether they referred to research or practice1 . We also expanded upon their brief comments and added supporting references (largely in the new developments sections) to elucidate the specific development or challenge. Needless to say, these are fuzzy boundaries and some of the entries could be classified into more than one category. It is important to highlight that this Perspective article is not a review of the literature, although we did draw from the suicide research literature to obtain evidence to elucidate the responses from the contributors. Given the nature of the task, some of the new developments/challenges are very specific and others are more general. The interpretations of the contributors’ submissions are ours and do not necessarily reflect those of the individual contributors. It is also important to emphasize that this appraisal of the developments and challenges within the field is not exhaustive and it reflects our biases and those of the contributors; it is our combined view (together with our international experts’ views) of the recent past within the field and our thoughts about the future. It could also be argued that, as the contributors all wrote chapters for a single handbook, they are all like-minded individuals with a particular view on suicide prevention. Nonetheless, we believe that this synthesis will be helpful to guide those involved in suicide research and prevention as it highlights hot topics in the field. We also highlight at the outset that despite the developments in understanding suicide risk, our ability to predict suicide remains no better than chance and in many countries across the globe suicide rates continue to increase (O’Connor and Pirkis, 2016; Franklin et al., 2017).
+New Developments in Research
+The use of new technologies (including social media and naturalistic real-time monitoring via smartphones) to increase understanding of suicidal behavior and to better identify suicide risk were the most frequently cited new research developments nominated by our contributors (see Panel 1). With the proliferation of smartphone ownership globally, in low- and middle-income countries (James, 2014) as well as in high-income countries, the growth in interest is not surprising (de Beurs et al., 2015). Given the field’s continued inability to predict suicidal behavior with sufficient sensitivity/specificity (O’Connor and Nock, 2014; de Beurs et al., 2015; Franklin et al., 2017), the use of smartphone technologies affords the opportunity to assess risk factors repeatedly, in real-time and in naturalistic settings (de Beurs et al., 2015; Michaels et al., 2015). It is hoped that the use of such technologies will better capture the ‘waxing and waning’ nature of suicidal ideation (Joiner and Rudd, 2000; Zisook et al., 2009) and account
+for the complex interaction between the risk factors which predict the transition to suicide attempts (de Beurs et al., 2015; O’Connor and Kirtley, 2018). If the promise of new technologies is realized, individuals or clinicians may be able to better identify windows of acute risk in real-time (based, in part, on social media and moment-to-moment monitoring), alert others and hopefully receive interventions to alleviate that risk. Needless to say, there are many practical and ethical barriers that have yet to be overcome, but they are not insurmountable.
+The use of ecological momentary assessment (via mobile phones) has already been shown to be feasible (Palmier-Claus et al., 2011; Husky et al., 2014) and it offers considerable promise in enhancing our prediction of the suicidal ideation-suicide attempts gap (Myin-Germeys et al., 2009). In terms of social media, Twitter and Facebook are now being harnessed to help understand the transmission of risk of suicide and self-harm. For example, a recent study from Japan has shown that social media coverage of celebrity suicides varies as a function of characteristics of the celebrity, with large volumes of traffic happening when the celebrity is a relatively young entertainer (Ueda et al., 2017). Also in Asia, text mining and machine learning approaches have been applied to Chinese social media to identify language markers of suicide risk and emotional distress (Cheng et al., 2017). Social media is also being used a lot by young people as a means of communicating distress (Marchant et al., 2017). More generally, although Facebook is rolling out safety protocols that aim to identify social media users at-risk of suicide via their online posts, it is not clear whether such interventions are effective. As such initiatives develop it is vitally important that they are rigorously evaluated and potential unintended consequences (e.g., do they lead to more social isolation as these initiatives lead to a reduction in sharing on social media?) considered. These developments have important implications for theories of suicide risk and contagion as well as suicide prevention efforts more generally. As noted above, although these developments are exciting, best practice guidelines need to be developed to ensure these technologies are implemented safely and ethically (Michaels et al., 2015).
+The second most frequently cited development was the growth in theories of suicidal ideation and behavior. This is, perhaps, unsurprising given that at least 12 theories have been put forward since the mid-1980s (O’Connor et al., 2016) beginning with Shneidman’s cubic model of suicide (Shneidman, 1985) [obviously Durkheim pre-dates all of these contemporary models (Durkheim, 1897)]. Three of the recent predominant theories (the interpersonal theory, the integrated motivational-volitional model and the three step theory) have received considerable research attention; each fitting within the ideation-to-action framework (Joiner, 2007; Van Orden et al., 2010; O’Connor, 2011; Klonsky and May, 2015; O’Connor et al., 2016; O’Connor and Kirtley, 2018; O’Connor and Portzky, 2018) which describes those theories which posit that the factors associated with suicidal ideation are distinct from those that govern behavioral enaction, i.e., a suicide attempt/suicide (O’Connor and Nock, 2014; Klonsky et al., 2016, 2017).
+Although each of these theories emphasizes different factors that lead to the emergence of suicidal ideation and behavior, they have shaped our understanding of the suicidal process: historically, theories of suicide did not explicitly specify the conditions that led to suicidal ideation as being distinct from those associated with a suicide attempt/death by suicide. In brief, these new theoretical developments have been important not only to enhance understanding of the complexities of the suicidal process but they are also forming the basis for the development of psychological interventions to reduce risk of suicide and self-harm. For example, a recent brief psychosocial intervention (a volitional helpsheet) which draws from the integrated motivational-volitional model of suicidal behavior offers promise in reducing risk of repeat self-harm in some individuals following a suicide attempt (Armitage et al., 2016; O’Connor et al., 2017). The recent focus on safety planning and crisis response planning interventions is also consistent with the ideation-to-action framework (Stanley and Brown, 2012; Bryan et al., 2018) and are welcome additions to the field.
+Given that suicide and suicide attempts are transdiagnostic phenomena, the move away from a focus on individual mental disorders coupled with the introduction of the National Institute of Mental Health’s Research Domain Criteria (RDoC) was identified as a positive development for the field (Glenn C.R. et al., 2017). Indeed, in a novel approach Glenn and colleagues conducted a meta-analysis of transdiagnostic dimensions (Glenn et al., 2018). Rather than focusing on risk factor domains, they viewed the predictors of suicidal behavior through the lens of the RDoC domains. Perhaps unsurprisingly, they found that limited prospective research, to date, fits within the RDoC transdiagnostic framework and even less addresses protective factors. Where there was evidence, it tended to be for the Negative Valence Systems domain (e.g., hopelessness) but there was also growing evidence for suicide theory-related factors (e.g., burdensomeness, defeat/entrapment) (Van Orden et al., 2010; O’Connor, 2011; Glenn et al., 2018). One of the key messages for future research from Glenn and colleagues’ recent meta-analysis is that we need to move beyond “the ‘usual suspects’ of suicide risk factors (e.g., mental disorders, sociodemographics) to understand the processes that combine to lead to this deadly outcome.”(Glenn et al., 2018).
+The use of innovative study designs and new techniques were also identified as important developments. Four such designs or
+techniques were highlighted by our contributors. The first is the use of big data and machine learning. Consistent with other areas of psychopathology, the statistical and computing power of big data and machine learning is now being applied to suicide risk assessment. Such approaches have the advantage of being able to combine a large number of risk factors in the prediction of suicide risk and they have already been shown to be moderately successful (Franklin et al., 2017; Hettige et al., 2017; Kessler et al., 2017). As the machine learning field develops, it will be interesting to determine the extent to which the algorithms can be applied to real-world clinical contexts to inform treatment planning (see also Research challenges below).
+The second technique is the retrospective timeline followback (TLFB) methodology (Sobell and Sobell, 1992) which systematically assesses behaviors/events in the days/weeks preceding an index event. Although TLFB is not new (Sobell and Sobell, 1992), its application within a case-crossover design to understand suicide risk in the days and hours preceding a suicide attempt is novel. Building on the work of Conner et al. (2012) showing that interpersonal stressful life events may lead to a suicide attempt within the same day, Bagge et al. (2013) conducted a TLFB study but focused on the 48 h preceding a suicide attempt. In the first study of its kind, they demonstrated that negative life events (NLEs) were triggers for a suicide attempt and that NLEs occurred more often on the day of, rather than the day before, a suicide attempt (Bagge et al., 2013). Given that we know relatively little about the factors that trigger suicide attempts in the preceding hours, we would urge others to consider employing the TLFB method.
+Third, the innovative work on predicting suicidal behavior using implicit cognitions toward death has been a welcome addition to the literature (Nock et al., 2010). Implicit cognitions assess one’s automatic associations with life or death. For example, it may be possible that an individual’s unconscious association with wanting to live or die changes as their mood decreases - and this could be incorporated into a real-time warning system. Not only do implicit measures overcome the issue of one’s reluctance to disclose suicidal intent but they also tap directly into the automatic processes that govern behavior (alongside reflective processes) (Strack and Deutsch, 2004). Although this is an exciting development, there are many unanswered questions, including, how stable are implicit cognitions, how are they formed, over what time frame and
+with whom are they predictive as well as how are they related to existing risk factors? (Dickstein et al., 2015; Hussey et al., 2016; Glenn J.J. et al., 2017). Indeed, a recent study, conducted across two research labs in United States and Scotland, found that implicit attitudes can be activated by low mood in those with a suicidal history (Cha et al., 2018).
+Finally, network analysis is a new statistical technique that has been applied to psychopathology in general and suicidal behavior specifically in recent years (de Beurs, 2017; de Beurs et al., 2017; Fried et al., 2017). The advantage of network analysis is that it allows researchers to investigate the complex associations between risk factors or symptoms. It also determines which symptoms are central within a network thereby highlighting specific treatment targets with the potential to be most powerful in reducing risk of suicidal behavior. To our knowledge only one prospective study of suicidal behavior has been published to date (de Beurs et al., 2017) so it is unclear which symptoms will have optimal predictive power. Nonetheless, we urge researchers to embrace this new statistical technique.
+The past 20 years of research has also been witness to new developments in brain imaging techniques and epigenetics (van Heeringen, 2014; van Heeringen and Mann, 2014; Sudol and Oquendo, 2016). With respect to the former, in a recent review of 12 neuroimaging techniques, 5 yielded important findings specific to suicide attempts [namely, magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission tomography (SPECT)] (Sudol and Oquendo, 2016). Taken together the brain imaging studies have identified both structural and functional abnormalities in the prefronal and limbic areas of the brain in individuals with a suicidal history. Obviously, brain imaging is only part of the answer in piecing together the suicide risk puzzle, but such approaches continue to make an important contribution to our understanding of suicide-specific markers of risk. For example, such research helps to explain, in part, the deficits in emotional regulation and decision-making that often characterize suicide risk (Sudol and Oquendo, 2016).
+The neurobiology of suicidal behaviour and epigenetics were also highlighted. There is an established body of research, employing different study designs (including in vivo, experimental and post-mortem techniques) finding that impairments in the serotonergic and hypothalamic-pituitaryadrenal axis stress response systems, in particular, are associated with increased vulnerability to suicide (van Heeringen and Mann, 2014; Lutz et al., 2017). The growing recognition of the influence of external factors, including early life adversity, on gene expression, has also led to a step change in our interpretation of the relationship between stress, mental disorders and suicide vulnerability (van Heeringen and Mann, 2014; Lutz et al., 2017). Indeed, there may be unique epigenetic processes (including altered cortisol responses and altered glutamate signaling) at play that increase suicide risk, with Lutz et al. (2017) arguing that understanding the former “has contributed to one of the most meaningful changes to the neuroscience landscape in the past 15 years (Lutz et al., 2017)”. There is also evidence that microRNAs may play a critical role in suicide
+risk (Serafini et al., 2014). Finally, it is also noteworthy that neurobiological scientists are incorporating key psychological and social factors into their modeling of suicide risk (Turecki and Brent, 2016; Lutz et al., 2017).
+No single discipline can address the complex challenge of understanding risk, as suicide is the end product of a complex interplay of neurobiological, psychological, and social processes. Indeed, social factors, including social isolation, disconnection (Stack, 2000; Macrynikola et al., 2018), loneliness (Bennardi et al., 2017) and social disadvantage (Batty et al., 2018) were flagged by a number of respondents as key determinants of suicide risk which have received welcome attention in recent years. What is more, social disconnection and social isolation (O’Connor and Nock, 2014) (including the absence of social support) feature in the interpersonal theory of suicide (Van Orden et al., 2010) the integrated motivational-volitional model of suicidal behavior in particular (O’Connor and Kirtley, 2018) and the 3 step theory (Klonsky and May, 2015).
+The social context is crucial to understanding suicide risk especially given the evidence that suicide is socially patterned being significantly more prevalent in areas of social disadvantage compared to more affluent areas (Platt, 2016; Batty et al., 2018). Although there have been some developments in understanding how changes in the social role may contribute to suicide risk, more needs to be done to better understand how conceptualizations of masculinities may elevate suicide vulnerability (Scourfield et al., 2012). Finally, an incredibly positive development in the field in recent decades has been the recognition of the importance of postvention and those with lived experience (including suicide attempt survivors and suicide bereavement survivors) as key to suicide research and prevention activities. Lezine recently described the vital work of suicide prevention through personal experience (Lezine, 2016) which we would urge everyone involved in suicide research to read.
+Challenges in Research
+The most frequently cited challenge was the issue of the reliability of suicide data due to low base rates and small samples in intervention studies, in particular (see Panel 2). This is a significant issue as we endeavor to build the evidence base for what works to prevent suicide. The lack of evidence for the efficacy of psychological and pharmacological treatments to prevent suicide specifically may be, in part, attributable to this issue of scale. As suicide is a low base-rate outcome, almost all clinical RCTs have been underpowered to detect changes in suicide rates and, at best, they have employed suicide attempts as the primary outcome. Even then, the sample sizes have tended to be modest, thereby precluding the a priori investigation of whether interventions may be effective in some groups of participants but not in others. For example, even where there is evidence for clinical efficacy of psychosocial interventions to reduce self-harm (Hawton et al., 2016), we cannot say whether they are effective for men as well as for women. One potential solution might be to include suicide-related measures as secondary outcomes in all psychological treatment trials and then potentially aggregating findings across studies, where appropriate (Holmes et al., 2018). Another solution relates to the
+second challenge. Given that the size of suicide research field is relatively modest and the challenge around statistical power, the establishment of national and international networks may facilitate large-scale research opportunities.
+Relatedly, the scarcity of translational research was also noted as a challenge. This is an important consideration, as new developments in suicide research are frequently not translated into saving people’s lives. The issue of translation relates to ensuring that research evidence, for example the potential effect of government austerity policies on health, is translated into a change in government policy. Another example would be the limited interest in some countries in implementing strategies in restricting the access to the means of suicide (see also practice challenges). We also need to re-double efforts to increase the likelihood of evidence-based treatments for suicidal behavior being accessible to those who need them. Consistent with the call within the Lancet Psychiatry Commission on psychological treatments research (Holmes et al., 2018) to focus on implementation, the same onus is on suicide researchers specifically. The second challenge is also related to the issue of scale, arguably. Excluding large-scale national linkage database type research, the majority of risk factors/predictors research in the field has tended to focus on suicide ideation and attempts as outcome variables - as well as being limited to a small number of predictors. This is an important limitation and perhaps the establishment of the networks or suicide research hubs (as suggested above) can address this dearth in the literature.
+As noted earlier in this paper, the field of suicide research has to continue to move beyond traditional risk factors (e.g., psychiatric illness) and to embrace complexity. We need a renewed focus on novel risk factors and multivariable risk factors; this is especially urgent as our ability to predict suicide has not improved since the 1960s and it remains no better than chance (Franklin et al., 2017). With a few exceptions, limited research has focused on short-term acute risk, with researchers often directing their attention to the long-term follow-up of
+patients who are high risk (O’Connor et al., 2009; Glenn et al., 2016; Glenn J.J. et al., 2017). Although the latter is important, arguably such studies have limited clinical utility and given that there is increased risk of suicidal behavior in the days and weeks following discharge from hospital (Owens et al., 2002; Chan et al., 2016; Ribeiro et al., 2016), we need to know more about this acute window. In addition, next on the list of challenges relates to the systematic exclusion of high-risk participants from our studies. We need to develop new protocols to include the very people who are most likely to benefit from our research findings. Obviously there are ethical and safety challenges, but, arguably it is unethical to exclude this vital group of participants in research studies.
+There have been considerable improvements in terms of interdisciplinary working in recent years. For example, in 2008, one of us (RCOC) organized the European symposium on suicide and suicidal behavior (ESSSB12; the leading suicide research conference in Europe) in Glasgow, Scotland. This was a huge success; it had the theme of ‘working together to prevent suicide: research, policy and practice’ and there were countless examples of interdisciplinary research showcased therein. What is more, 10 years later, the other one of us (GP) organized the same conference in Ghent, Belgium (ESSSB17), again with the explicit plea for high-quality multidisciplinary research. Despite these developments, we should not be complacent; there remains a considerable need to continue building the linkages across disciplines and to involve everyone who has a stake in suicide prevention in our research. Indeed one of our experts highlighted that such working is essential especially to maximize the opportunities afforded by new technological developments. The lack of an international consensus in the terminology used in the field remains a challenge (Silverman, 2016); mutual cross-disciplinary respect is also required to ensure that interdisciplinarity flourishes.
+Although Durkheim, one of the pioneers in suicide research, highlighted the central role of social context and social factors in the etiology of suicide in 1897 (Durkheim, 1897), without
+question, their roles have not received adequate attention in the 121 years since. This is especially alarming given the scale of the socio-economic gradient in suicide and the established relationships with unemployment and disadvantage (Platt, 2016; Batty et al., 2018). However, the renewed focus on adverse childhood experiences in suicide risk is welcome (Turecki and Brent, 2016; O’Connor et al., 2018). At the other end of the social context spectrum are social media influences and other volitional factors (O’Connor and Kirtley, 2018) which are implicated in suicidal behavior and self-harm. We do not know enough about how such factors act to increase suicide risk in terms facilitating social modeling and increasing cognitive accessibility (O’Connor and Nock, 2014; O’Connor et al., 2014; Mars et al., 2015; Biddle et al., 2016). Another major challenge is the relative dearth of research into suicide in low and middle-income countries (LMIC). Despite the fact that the vast majority of the world’s suicides occur in LMIC (WHO, 2014), (although this is changing) there is still insufficient research focus in LMIC.
+The advent of machine learning techniques and the use of “big data” algorithms were identified as exciting new developments in the preceding section. However, caution is also urged by our contributors as such algorithms may not adequately appreciate the person-specific nature of suicide warning signs. This highlights the importance of adopting a multi-method approach to understanding suicide risk. As noted above, no single approach, method or discipline has all of the answers; if we are to make further progress in predicting suicide, this is most likely to succeed if we integrate multiple approaches and crucially we should not throw the baby out with the bathwater.
+The next three challenges are inter-related as they each refer to a more fine-grained appreciation of sub-types of individuals who are at increased/decreased risk. The first in this triad is also featured in the new developments section, as it calls for a better understanding of the transition from suicidal thoughts to suicide attempts/suicide. As noted above, the ideation to action theories (Van Orden et al., 2010; Klonsky and May, 2015; O’Connor and Kirtley, 2018) are beginning to address this important challenge more systematically, though the findings from the World Mental Health surveys have also contributed to our understanding of these pathways (Nock et al., 2008). Nonetheless, we believe that this is one of the biggest challenges in the field as there are so many gaps in our understanding of this transition (Nock et al., 2008; O’Connor et al., 2012; Dhingra et al., 2015). We also need more research regarding the factors that differentiate between those who make low-lethality attempts versus those who make high-lethality attempts. Although a few high quality studies exist (Rivlin et al., 2013; Marzano et al., 2016; Anestis et al., 2018), focused on specific populations, more in-depth research is required. In addition, although there has been growing focus on the relationship between sleep and suicide risk (Littlewood et al., 2017), among those who have attempted suicide, insomnia may be associated with a more violent method (Pompili et al., 2013).
+The final challenge in this group calls for increased understanding of the causes of suicidal behavior in people who do not have psychiatric illnesses. Despite the evidence that most people in Western countries who die by suicide have a diagnosed mental illness (Cavanagh et al., 2003), there
+are cultural variations (Phillips, 2010) and some argue that the association between mental illness and suicide is not as marked as is commonly reported (Hjelmeland and Knizek, 2017). As the relationship between mental illness and suicide is often ascertained via a psychological autopsy, researchers should implement the recommendations on the next generation of psychological autopsy studies that aim to increase the accuracy of data collected (Conner et al., 2011). These latter three challenges highlight the more general point that we still often treat people at risk of suicide as an homogeneous group but we need to move beyond such characterization and identify distinct profiles of people at risk: the precision medicine approach.
+As the determinants of suicide are many, spanning neurobiology, psychology and social factors, it is not surprising that there is a tension between where our research effort should be focused. A number of contributors argued for an increase in attention to the neurobiological determinants of suicide as these would inform treatment targets and a reduced focus on epidemiology. As noted above, no one discipline has all of the answers so it is important that all areas receive attention.
+New Developments in Practice
+The most frequently reported new development in research (i.e., the use of new technologies) is also the most frequently mentioned new development in practice (see Panel 3). So called ‘new’ technologies such as the internet and mobile phones not only increase our understanding of suicidal behavior but they can also be harnessed for treating and connecting with individuals who are suicidal (Hom et al., 2015; de Beurs et al., 2015; Hetrick et al., 2017; Nuij et al., 2018).
+Given that less than one third of suicidal individuals seek help or make use of mental health services it is clear that there are numerous barriers to traditional forms of treatment such as stigma and shame, low perceived need, a preference for selfmanagement, availability and high cost of care (Bruffaerts et al., 2011; Andrade et al., 2014). Online interventions are well placed, therefore, to overcome many of these barriers as they are easily accessible anywhere at anytime, at low cost, and are mostly anonymous or highly confidential (Hom et al., 2015).
+Although there have been several studies sharing positive effects of online interventions in the reduction of suicidal ideation (Christensen et al., 2013; Saulsberry et al., 2013; Williams and Andrews, 2013; Mewton and Andrews, 2015) many of these online interventions were developed to manage depression and were not designed to target suicidal ideation specifically. In a rare exception, van Spijker et al. (2014) examined the effectiveness of an online intervention specifically targeting suicidal ideation in a Dutch community sample. The results were promising, showing a significant reduction in suicidal ideation thereby highlighting the potential for managing suicidal ideation via an online intervention. However, in this study individuals with severe suicidal ideation were excluded and a controlled followup period was missing. This online intervention has also been examined in an Australian community sample by use of a randomized controlled trial (van Spijker et al., 2018). Although the intention-to-treat analyses showed significant reductions in the severity of suicidal thinking at post-intervention, 6 and
+12 months, no overall group differences were found. It is, thus, clear that more research is needed and it is encouraging that this online intervention is currently being examined in other countries such as Denmark and Belgium (De Jaegere, submitted).
+Although it is not surprising that suicide experts have focused on suicide-specific tailored interventions, it is important to direct attention at suicidal thoughts and behavior as treatment targets rather than viewing them exclusively as symptoms or epiphenomena of mental disorder. Indeed, many previous studies have focused on the treatment of depression and viewed suicidal ideation as a secondary outcome that may improve if the intervention for depression is effective. Therefore, the growth in clinical trials that have investigated the efficacy of suicide-specific prevention and intervention strategies aimed at reducing suicidal behavior is a welcome development.
+To this end, there has been growing evidence for suicide-specific psychosocial interventions which show promise in reducing suicidal behavior (Hawton et al., 2016). For example, Gysin-Maillart et al.’s (2016) 24-month follow up randomized clinical trial (the Attempted Suicide Short Intervention Program; ASSIP) of a novel brief therapy for patients who had attempted suicide was effective in reducing suicidal behavior in a real-world clinical setting. ASSIP was associated with a circa 80% reduced risk of repetition of at least one suicide attempt and ASSIP participants spent 72% fewer days in the hospital during follow-up compared to controls. ASSIP consists of three therapy sessions followed by regular contact through personalized letters over 24 months (Tarrier et al., 2008). The development of the Collaborative Assessment and Management of Suicidality (CAMS) approach has been an exciting new development in the field which also offers considerable promise (Comtois et al., 2011; Jobes, 2016).
+Another brief psychological intervention with a volitional helpsheet (VHS) has also yielded promising results (O’Connor et al., 2017). Although the VHS had no overall effect, post hoc analyses suggested that this brief adjunct intervention might be effective in reducing the number of self-harm repetitions following a suicide attempt among those with a self-harm history. In another modest sized RCT which compared a 6-week telephone-based positive psychology (PP) intervention with a cognition-focused (CF) control intervention, those who received the CF intervention reported greater improvements in hopelessness at 6 weeks but not at 12 weeks (Celano et al., 2017). This study recruited patients who had recently been hospitalized
+for depression and suicidal ideation or behavior. There was also greater improvement in suicidal ideation, depression and optimism at 6 and 12 weeks after receipt of the CF intervention. Contrary to the authors’ hypothesis, however, it was the CF intervention that was superior in improving hopelessness, other suicide risk factors and positive psychological constructs rather than the PP intervention (Celano et al., 2017).
+These latter studies add to the established evidence that CBT has a significant effect in reducing suicidal behavior (O’Connor and Nock, 2014; Hawton et al., 2016). However, a systematic review and meta-analysis from 10 years ago still nicely summarizes the gaps in our knowledge (Tarrier et al., 2008). Tarrier et al.’s (2008) subgroup analyses confirmed significant treatment effects for CBT in adult samples (but not in adolescent samples), for individual treatment delivery (but not for group administration) and for CBT when compared to minimal treatment or treatment as usual (but not when compared to another active treatment; Tarrier et al., 2008).
+As noted in the new research developments section, the insights offered by those with lived experience into the design and improvement of suicide prevention programs were also highlighted by our experts. Although their contributions have taken different forms, there is now a considerable body of qualitative evidence on the aftermath of a suicidal crisis or attempt based on interviews with those with lived experience. Such studies have deepened our understanding of what it is like to be suicidal and how to help those who are vulnerable (Lin et al., 2009; Oliffe et al., 2012; Vatne and Naden, 2016).
+More recently, attention has been paid to ensuring that such insights and testimonials inform the development and implementation of suicide prevention programs. For example, Jones et al. (2018) explored the views of health and human service workers with regard to the development of a suicide prevention training program. This included meaningful involvement of someone with lived experience in the development and delivery of the training. The authors concluded that the inclusion of a person with lived experience of suicidality resonated strongly with the participants and provided a powerful learning experience for those involved. A strong positive element was that the person with lived experience gave participants crucial insights into how to have conversations around suicide and how best to ask the questions about thoughts of suicide directly. Although this study indicates the positive effect of including people with lived experience the authors also caution that the
+inclusion of a person with lived experience must be appropriate and safe. In addition, despite the progress in involving people with lived experience to improve research and practice, we should not be complacent; we have a long way to go in terms of maximizing their involvement to the mutual benefit of all.
+The growth in specific interventions for high-risk groups was also highlighted as an important new development. By way of an example, Vijayakumar et al. (2017) reported the effectiveness of CASP, an intervention by community volunteers among refugees, to reduce suicidal behavior. This intervention involves contact between community volunteers and refugees and the use of safety planning cards. The findings from the RCT were positive as the intervention was associated with a significant reduction in suicidal behavior among the refugees. Interventions for another high-risk group, older adults, are also encouraging. Specifically, a recent systematic review of interventions to prevent and reduce suicidal behavior in older people showed that several interventions are effective, with at least some evidence for multi-faceted primary care-based depression screening and management programs, pharmacotherapy and psychotherapy, and telephone counseling (Okolie et al., 2017). In short, there is the increasing recognition that the one size fits all approach to suicide prevention initiatives is not effective and we must tailor interventions to fit specific at-risk populations.
+The growing evidence for effective school-based programs was also highlighted by our experts. There is now some evidence for peer-support prevention programs and skills-based training programs, which show positive outcomes regarding coping skills and referral to help (Katz et al., 2013; Hetrick et al., 2014, 2017). There is also some evidence that screening programs have some utility in identifying young people at risk (Robinson et al., 2013). Although it is important to note that there are many challenges with screening programs including the issue of false positives and the need for available resources to support those who are identified as high risk. School-based awareness programs have also been shown to significantly improve knowledge, attitudes and help-seeking behavior (Cusimano and Sameem, 2011). However, the most promising findings are those from the Saving and Empowering Young Lives in Europe (SEYLE) study (Wasserman et al., 2015). In this multicentre cluster-randomized controlled trial, adolescents who received the youth aware of mental health (YAM) program reported a significant reduction in incident suicide attempts and severe suicidal ideation compared with the control group at 12-month followup. As with all areas of practice, it is important to replicate these findings.
+New anti-psychotic drug treatments were identified as a final new development. Although there is “at least modest evidence suggesting that antipsychotic medications protect against suicidal risk” (Kasckow et al., 2011), a combination of psychosocial and pharmacological management is recommended. Of all the anti-psychotic medications, though, the best evidence is for clozapine (Kasckow et al., 2011). Consistent with the pharmacological treatment literature more generally, there remains a dearth of large-scale treatments of anti-psychotic medications.
+Challenges in Practice
+The experts’ opinions regarding the challenges in practice were diverse, with the majority of the suggestions only being mentioned by one expert (see Panel 4).
+The only challenge which was mentioned multiple times was the need for more research and evidence for universal and selective prevention strategies. This is, perhaps, unsurprising as the USI (Universal, Selective, Indicated) prevention model forms the basis for much suicide prevention activity worldwide. Despite its use as an overarching framework, it is obvious from the research literature that there are extensive gaps in our knowledge about what works to prevent suicide and how the different levels of intervention (USI) interact. However, there have been recent efforts to investigate the synergies between the different components of suicide prevention strategies (Harris et al., 2016). Given that suicide rates continue to rise in some countries (e.g., in the United States), perhaps it is time to reconsider whether a paradigm shift, rather than a ‘more of the same’ approach to suicide prevention is required.
+As highlighted above, there has been welcome attention on indicated prevention strategies, focusing on those who are already suicidal. However, new thinking about universal and selective prevention is urgently required so that we can promote populations to be more resilient, to increase their coping skills and protect them from suicide risk. It is a major problem for those involved in suicide prevention including policy makers that there is so little evidence for universal and selective prevention strategies. Indeed, one of the few evidence-based strategies within universal prevention is the implementation of universal school awareness programs (Zalsman et al., 2016); much more research is urgently required.
+More broadly, we also need to challenge attitudes, beliefs and knowledge regarding the preventability of suicide. Although suicide prevention is very difficult in practice, it is not impossible; but the existence of negative attitudes and beliefs that suicide is inevitable, that it cannot be prevented, are unhelpful. The establishment of the Zero Suicide (https://zerosuicide.sprc.org) movement has been very important in this regard, promoting the message that every suicide is one death too many. Related to the latter are the attitudes toward means restriction, especially in the context of restricting access to guns in the United States. Although restricting access to the means of suicide is one of the most effective suicide prevention strategies (Zalsman et al., 2016) in practice it is challenging to convince policy makers, gatekeepers, or mental health professionals of the need to implement a means restriction strategy. It is hoped that the recent focus on means safety (Anestis et al., 2018) rather than means restriction per se may help in this regard. The major challenge of integrating conversations about lethal means safety into standard primary care, mental health and substance use clinics was also identified by one of our experts.
+There is also a lack of evidence regarding the most effective ways of implementing suicide prevention methods and strategies in different institutions/organizations including hospitals, mental health institutions, local governments and schools. However, there is evidence from the UK that implementing mental health
+service recommendations in particular the provision of 24 hour crisis care, having local policies for dual diagnosis and conducting a multidisciplinary review after a suicide are associated with falling suicide rates (While et al., 2012). Indeed, a major challenge is the reality that at least one quarter of people who die by suicide have had previous contact with mental health services (NCISH, 2017). We await with considerable interest the findings from on-going evaluations of the implementation of Zero Suicide (Mokkenstorm et al., 2017) initiatives as they will provide valuable insights into the challenges and facilitators of rolling out multi-level suicide prevention interventions in health care settings.
+The chain of care and the need for continuity and high quality care are key to successful suicide prevention efforts especially among those who present to clinical services following selfharm. Indeed, in many countries throughout the world there are recommended standards of care and aftercare, but the reality is that these standards are frequently not implemented. Mehlum and Mork describe these challenges but crucially they have also identified key solutions which should improve adherence and sustainability over time (Mehlum and Mork, 2016). There is also considerable need for better linkages between crisis intervention, statutory services, therapists and the myriad of community and voluntary sector organizations that work in suicide prevention. Although it is difficult to set up studies to evaluate optimal chains of care, we urgently need more evidence about how to organize and implement linkages between different services and mental health settings.
+There have been several studies regarding the clinical management of patients who present to hospital following suicidal behavior, nonetheless there is still a need for more evidence to inform best practice for the evaluation, treatment and follow-up of these patients. Although it is recommended that all patients who attempt suicide or self-harm should receive a comprehensive psychosocial assessment, at the hospital level there are wide variations in the clinical management such that the proportion of clinical presentations receiving psychosocial assessment (range 22-88%), medical (22-85%) or psychiatric (0-21%) admission and referral to non-statutory services (4-62%) (Cooper et al., 2013) varies markedly. However, there seems to be little association between these differences in hospital management and the repetition rate of self-harm (Cooper et al., 2015). As this is surprising, future studies should focus more on understanding the processes underlying the
+different management and treatment styles of suicidal people at hospital level and their relationship to patient outcomes.
+As mentioned in the new developments in practice section the increased involvement of people with lived experience can only be positive for the field. Moreover, there is the possibility that including people with lived experience of suicidal ideation and/or behavior in suicide prevention programs might have other positive effects by providing a more powerful learning experience. Their involvement may yield similar effects to the Papageno effect that has been shown in relation to the media portrayal of suicide. The Papageno effect describes the positive and preventive effects of using positive testimonials of people with lived experience who have survived a suicide attempt and who have learned to overcome a suicidal crisis in a media portrayal of suicide. See Pirkis et al. (2016) for an overview of media influences and suicidal behavior literature.
+Whereas the positive effect of including positive testimonials of people with lived experience in media portrayal is well established (Niederkrotenthaler et al., 2009, 2010) it ought to be extended to other areas of suicide prevention. Although there are some studies reporting the involvement of people with lived experience in the field of mental health these studies have tended not to evaluate this approach (Repper and Breeze, 2007). The Jones et al. (2018) study noted in the new developments in practice section is one of the few studies to explore the benefits of involving people with lived experience of suicidality in such studies. There is a strong need, therefore, for more evaluation studies investigating the effects, opportunities, and risks of involving people with lived experience in suicide prevention programs.
+Given that male deaths by suicide vastly outnumber female suicides (Turecki and Brent, 2016), there is a huge challenge to gain more knowledge regarding how to approach and more effectively reach men with suicide prevention strategies. Men seek less help and communicate less about mental health problems so the challenge is how to motivate them for prevention and treatment. We also need to be careful that we do not blame men for not seeking help especially given that the support and services may not have been designed with them in mind. The stigma regarding mental health problems is also higher for men than for women, therefore, it is important that public health campaigns should direct their focus on men, specifically. As noted earlier, the extent to which existing interventions to reduce risk of suicide are effective for men is largely unknown.
+Another challenge identified by our experts was the need to focus more on suicide risk in adolescents. In particular we need to identify and treat first onset suicidal ideation in adolescents. Indeed, if we are to better identify adolescents early on in the suicidal process we need to learn more about how to help them manage their suicidal ideation, how to enhance their coping skills, their social skills and capacity to solve social problems. We need to develop evidence-based interventions to increase their resilience that could help to buffer against suicide risk early in life. Targeting suicidal ideation in early adolescence should thus be a specific focus for suicide prevention efforts. As noted above, school-based suicide prevention programs should be considered in this regard (Katz et al., 2013; Robinson et al., 2013).
+As a discipline, we should remain open to learning from other areas of public health and applying any such lessons to suicide prevention. For example, a very novel but intriguing suggestion by one of our experts is to study the effects of a dental health approach to determine whether it could be effective in improving mental health and prevent suicide. Translating the dental health approach into a mental health approach could involve us engaging in mental health self-care for a few minutes, say twice a day and this could be supplemented by an annual or 6 months check-up by a health care professional. Needless to say, a key challenge for such an approach would be around how we change existing attitudes, beliefs and knowledge regarding mental health to ensure that mental health self-care becomes an obvious and vital part of our daily health care, just like dental health care is. We should also look at what has been done with respect to stroke, heart disease and cancer; all areas of public health where the death rates have decreased markedly in recent decades while suicide rates have remained stable or increased.
+Another fundamental methodological challenge for both practice and research is how to increase the integration of clinical knowledge into the clinical evidence-base. Indeed, well designed empirical studies and theoretical models to understand suicidal behavior often lack clinical knowledge; their translatability and implementation would benefit markedly from such knowledge. The complexity of the suicidal process and the individual differences associated with the development of suicidal ideation and behavior that are often observed in clinical practice are frequently overlooked in research, thereby limiting their clinical utility. A related issue is that there are few suicide-specific treatments that are actually available in the real world. It is incumbent on health care managers and policy makers to prioritize the accessibility of evidence-based treatments to those who need them when they need them. In short, the clinical management of patients who are suicidal remains a huge challenge, both in terms of the evidence base for tailored interventions and the accessibility of such interventions for those who are most vulnerable.
+CONCLUSION
+It is an exciting time to be working in suicide research and prevention. In many countries throughout the world there have been important developments in understanding and preventing
+suicide. The four panels highlight key opportunities, challenges and pointers to move the field forward. We hope that their contents will guide the future research agenda, acting as a catalyst for new thinking in suicide prevention research and practice. However, it is important to reiterate the limitations of this Perspective article. Although the new developments and challenges outlined herein are extensive, they are not exhaustive but represent the views of 32 researchers or practitioners in the field. Nonetheless, the new developments identified by our experts are exciting, harnessing new technologies and approaches to better understand who is most at risk of suicide and why. However, many challenges remain; first, our ability to predict suicide is still not much better than chance and although there has been a welcome focus on suicide prevention interventions (both at the public health and clinical level), many gaps in our knowledge remain. None of us has all of the answers, and we hope that the suggestions reported herein will encourage new synergies and opportunities for interdisciplinary research. Finally, we are optimistic that the new developments and the field’s determination to overcome the identified challenges will combine to save more lives across the globe.
+O’Connor and Portzky
+Suicide Research and Prevention
+Nadine Dougall (Scotland), Cassie Glenn (United States), Bergljot Gjelsvik (England), Onja Grad (Slovenia), David Gunnell (England), Peter Gutierrez (United States), Yari Gvion (Israel), Gergo Hadlaczky (Sweden), Keith Hawton (England), David Jobes (United States), Ad Kerkhof (Netherlands), Olivia Kirtley (Belgium), Karolina Krysinska (Australia), Mark Ilgen
+(United States), Matt Miller (United States), Allison Milner (Australia), Jane Pirkis (Australia), Michael Phillips (China), Zoltan Rihmer (Hungary), Steve Stack (United States), Kees van Heeringen (Belgium), Danuta Wasserman (Sweden), Karen Wetherall (Scotland), Alan Woodward (Australia), and Clare Wyllie (England).
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+Zalsman, G., Hawton, K., Wasserman, D., van Heeringen, K., Arensman, E., Sarchiapone, M., et al. (2016). Suicide prevention strategies revisited: 10-year systematic review. Lancet Psychiatry 3, 646-659. doi: 10.1016/S2215-0366(16) 30030-X
+Zisook, S., Trivedi, M. H., Warden, D., Lebowitz, B., Thase, M. E., Stewart, J. W., et al. (2009). Clinical correlates of the worsening or emergence of suicidal ideation during SSRI treatment of depression: an examination of citalopram in the STAR*D study. J. Affect. Disord. 117, 63-73. doi: 10.1016/j.jad.2009.01.002
+Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
+Copyright ©2018 O’Connor and Portzky. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
+Frontiers in Psychology | www.frontiersin.org
+14
+November 2018 | Volume 9 | Article 2139
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+INTRODUCTION
+Researchers have consistently shown that mortality rates in patients with a severe mental illness (SMI) are excessively high. SMI patients die about 10-20 years earlier than the general population (1-5). Most systematic reviews showed that all-cause mortality in SMI patients is 2 to 3.5 times higher than in the general population (3-18). There is also evidence that this mortality gap has been increasing over time (4, 19, 6). This seems to indicate that SMI patients benefit less than the general population from progressions in healthcare (6). Reduced life expectancy in SMI patients is not only explained by unnatural causes of death like suicide and accidents but also by physical illnesses (8, 20-23). Studies have shown that cardiovascular diseases (CVD), respiratory and metabolic diseases
+are the main physical illnesses in patients with SMI, followed by cancer and infectious diseases (15, 20, 21, 24, 25).
+The World Health Organisation has clustered different risk factors for excess mortality in SMI patients into three groups (26). Premature death in SMI is considered to be the result of 1) individual factors (disorder specific: e.g. genetic factors, severity of disorder, family history, early age of onset, reduced responsiveness to pain; behaviour-specific: e.g. poor life-style), 2) health system factors (e.g. deficiencies in leadership, financing, information and medications) and (3) social determinants of health (e.g. insufficient public policies, socio-economic position, environmental and social support vulnerabilities) (5, 27).
+Although many studies have shown evidence of excess mortality in SMI patients, this has not led to a reduction of the mortality gap. In fact, underlying mechanisms are still not fully known and the data are fairly inconsistent (2). Several reviews showed for example that there is still considerable debate about the long-term effects of antipsychotic medication (27-30). There can be a delay of several years between drug-induced weight gain, the development of metabolic syndrome and premature death (27). Finally, there is a relative paucity of studies evaluating the link between symptom severity (in the mental disorder) and other baseline measures like psychotropic side effects and premature death. To our knowledge, only one study has found an association between disease characteristics like symptom severity and mortality (31), and only one study took place in the Netherlands. In this study we aim to contribute to the knowledge of risk factors and correlates of excess mortality in SMI patients. We hope that this allows us to develop better prevention programmes and interventions targeting predictors of early mortality.
+This study is part of a larger study program (32-37). The objective of the present study was to examine premature mortality among SMI patients treated by mental healthcare institutions of Amsterdam (The Netherlands), and to calculate mortality ratios standardised to the general population. Baseline predictors of mortality in SMI patients were identified over a 6-year period and for purpose of exploration a prediction model was constructed. We hypothesised that cardiovascular diseases, older age, cigarette smoking, more severe psychiatric symptoms and more severe psychotropic side effects, and alcohol or drug use were independent risk factors for mortality. With previous literature lacking theoretical support for interaction between the above-mentioned predictors, no specific prediction model was a priori expected.
+METHODS
+Design
+This prospective longitudinal study was conducted between 2005 and 2011 in a survey sample of SMI patients treated by the mental healthcare institutions Arkin, Geestelijke Gezondheidszorg [GGZ (mental healthcare)] InGeest, Regionale Instelling voor Bescherm Wonen [(RIBW [Regional Institute for Sheltered Housing)] and Huis voor Onbehuisden [HVO (Sheltered housing for Homeless people)] Querido in
+Amsterdam, The Netherlands) (37), which are responsible for the treatment of SMI patients residing in Amsterdam. The main objectives were to evaluate changes in quality of life, disease characteristics, general functioning, care needs, social network, inclusion in society and victimisation (32-37). The current study describes the baseline predictors of mortality in SMI patients over a 6-year period. Baseline measurements started on August 2005. Data about sociodemographics, severity of psychopathology, substance use, use of antipsychotics, polypharmacy and side effects were obtained at baseline. The study was approved by the Dutch Association of Medical-Ethical Appraisal Committees (NVMETC) for mental health organisations.
+Population, Inclusion and Exclusion Criteria
+For this research program we were specifically interested in a population of patients with functional impairment and chronic conditions, as treated by long-term transmural treatment teams [like (function) assertive community treatment teams] or patients treated in psychiatric hospitals, with a history of intensive mental healthcare during the previous 2 years. Patients were included when they fulfilled: 1) the criteria for a psychiatric diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV). 2) Where treated by long-term transmural treatment teams or treated in a psychiatric hospital. 3) Had a history of intensive mental healthcare during the previous 2 years. To define persistency of the psychiatric illness we used the criterion “a history of intensive mental healthcare during the previous 2 years”, which is based on SMI definitions by Delespaul et al. (38), Parabiaghi (39), Scheurs and Wiersma (40), Kroon et al. (41) and Ruggeri et al. (42). Further inclusion criteria for the target population were: adequate mastery of Dutch or English and residence in the Amsterdam district for at least 1 year. Exclusion criteria included being unable to understand questions or communicate, or an inability or unwillingness to give informed consent. Patients with comorbid substance use disorders were also included when they fulfilled the criteria listed here. Diagnoses were made by the attending psychiatrist (32, 33).
+Enrollment
+Stratified random sampling was used, to select 876 patients from 2,846 patients treated in outpatient teams, sheltered housing facilities and inpatient care facilities. The aim was to include an equal number of patients from three care settings. After written informed consent was obtained, the face-to-face assessment was conducted by a trained psychologist, research assistants, or a senior researcher. The interviews took place in the patients’ own homes or, if preferred by the patients, at the healthcare centre. The interviews took approximately one and a half hours and patients received €15. Included patients were assessed again 6 years after inclusion (32, 33, 37).
+Representativeness of the Sample
+Representativeness analyses of our sample were done and published by Theunissen et al. (37). These analyses showed
+that included outpatients had more often a psychotic diagnosis, were slightly older, had a lower Global Assessment of Functioning (GAF), than patients who did not participate in the study. This may indicate that included outpatients were more severely ill than outpatients who did not participate in this study. There were no significant differences between involved inpatients and inpatients who did not participate in this study. In addition, inpatients were over-represented in our sample group (38%) by comparison with the general SMI population (13%) (37).
+Measures
+Data about causes of death were obtained from electronic case files. Case notes were examined by a psychologist and a senior researcher to identify all causes of death between 01/08/2005 and 02/03/2012, and death certificates were obtained from the city +++records.
+The Brief Psychiatric Rating Scale-Expanded or BPRS-E, a semi-structured interview, was used to measure psychiatric symptoms (43, 44). It consists of 24 items that can be scored on a seven-point Likert scale on the basis of observations during the interview and patient self-reports. The items are grouped in four subscales: positive symptoms, negative symptoms, depression and disorganisation (44). The psychometric properties of the BPRS-E are mostly good (45-47).
+Measurements in the Addictions for Triage and Evaluation (MATE) was used to measure total number of years of smoking, current alcohol and drugs use, substance dependence and abuse. The MATE provides 20 different assessment scores and inter-rater/interviewer reliability is good (48).
+Antipsychotic polypharmacy was defined as the use of more than one antipsychotic agent, which is a broadly used definition (13, 27, 28, 49-52).
+Side effects of neuroleptics were assessed with The Liverpool University Neuroleptic Side Effect Rating Scale (LUNSERS). Psychological, neurological, autonomic, hormonal and other miscellaneous side effects were assessed using 41 items. Ten (‘red herring’) items were used to detect response tendencies. Patients described side effects on a five-point scale ranging from ‘not at all’ to ‘very much’ side effects, with a higher score indicating more severe side effects. The test-retest reliability of the LUNSERS is good (r = .811, P < .001), as is concurrent validity by comparison with the ‘Udvalg for Kliniske Undersogelser’ (UKU, task force for clinical investigations) Side Effect Rating Scale for Neuroleptics (r = .828, P < .001) (53).
+Data Analysis
+Frequency distributions were used to describe causes of death and the corresponding ICD-10 codes (54).
+Standardised mortality ratios (SMRs) were used to describe the relative risk for SMI patients and the general population. SMRs were calculated for the study period using the number of deaths observed divided by the expected number of deaths, controlling for age and gender. The expected number of deaths came from gender-specific mortality statistics for the Dutch population
+between 2005 and 20111. The change in gender-specific expected number of deaths were estimated for each year that an individual became older during follow-up. Because not all ages were equally prevalent, we weighted by age. An SMR greater than 1.0 indicates that the relative risk of death for SMI patients is higher than that of the general population (10).
+The defined daily dose (DDD) system of the World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology was used as an equivalent to compare prescriptions of antipsychotics at baseline. The DDD is the assumed average maintenance dose per day for a drug used for its main indication in adults (WHO). It is the most frequently used system in academic articles and reports, and it is a reliable tool for the standardisation of drug doses (55). The following WHO formula was used to calculate the DDD for each antipsychotic:
+Drug usage (DDDs) = (items issued x amount of drug per item)/DDD. DDDs greater than 1 indicate a dosage that is higher than average. DDDs greater than 1.5 were classified as excessive (56-58).
+Univariate Cox proportional hazard regression analysis was used to assess associations between baseline variables and mortality (REF). The following variables were used as baseline measures: age, gender, ethnicity, diagnosis, symptom severity, substance use, antipsychotic treatment and dose, polypharmacy and side effects.
+Cox proportional hazard multivariate regression analysis was used to assess the effects of covariates (backward method) on mortality rates (59). Covariates were: symptoms of disorganisation and duration of cigarette smoking. Time to death was used as the time variable. The presence of anticholinergic side effects was omitted as a covariate because of the violation of the proportional hazards assumption. We considered ethnicity as potential confounders in the multivariate Cox proportional hazard regression (enter method). To avoid multi collinearity between the predictors age and duration of cigarette smoking, we omitted age as a predictor. To examine if there was relevant confounding a change of more than 10% between regression coefficients was used (59).
+Sensitivity analyses were performed to examine if predictors were influenced by heterogeneity in diagnoses. All analyses belonging to Table 2 were re-done, and only applied to patients with a psychotic disorder with high levels of psychiatric burden (The Global Assessment of Functioning (GAF) scale < 50) (41). However, number of observations were too limited to apply a sensitivity analysis to the multivariate Cox regression model.
+All analyses were performed with SPSS 22 (SPSS Inc., 2009), and statistical significance was defined as P < 0.05.
+RESULTS
+In 2006, 876 patients were selected. Some patients refused to participate (25.9%) and others did not participate for unknown
+reasons (25.5%). Another 2.9% of the patients were not included because their clinicians deemed that inclusion would have a negative impact on clinical status. Patients who were no longer receiving treatment (8.3%) were also excluded. Another patient (0.1%) was excluded because no diagnosis was present. Data for five patients (0.6%) were missing: one removed his patient file and four left the country. These five patients were excluded because their survival status could not be verified. The remaining 322 (36.8%) patients were included in the study.
+The mean age of the included patients was 54.9 at baseline. Approximately 80% of the patients had a psychotic disorder; 21% had non-psychotic disorders: often a severe mood (46.3%), substance use (50.7%) or personality disorder (3%). Almost 80% of the patients were using antipsychotic medication (SD = 0.34; range, 1-5) at baseline.
+During the follow-up period of 6 years, 35 patients died (10.9%: men 11.6%; women 9.8%). The mean age of death for all these patients was 59.6 (SD = 12.19; range, 32-88), 59 for men (SD = 10.85; range, 32-78) and 60 for women (SD = 14.89; range, 41-88). The overall SMR for the cohort was 4.51 (95% CI 3.07-5.95 for all SMI patients; 4.89, 95% CI 2.97-6.80 for men, and 3.94, 95% CI 1.78-6.10 for women). Table 1 lists the causes of death.
+Death from natural causes accounted for 86% of deaths. More details about natural causes found on five occasions or more can be found below. Death from diseases of the circulatory system accounted for 31% of mortality, with most of these patients dying from ischaemic heart disease or acute myocardial infarction in a psychiatric hospital. Fifty percent of the patients who died at the age of 50 or lower died from diseases of the circulatory system. Five patients (14.3%) died from diseases of the digestive system. Five patients (14.3%) died from unknown causes.
+Death resulted from unnatural causes in five patients (14.3%). Almost 6% committed suicide and approximately 9% dies as a result of euthanasia.
+Table 2 lists the results of the univariate analysis of associations between mortality and sociodemographics, clinical characteristics, substance use and medication at baseline. Mortality was elevated in older subjects, those with western ethnicity, more symptoms of disorganisation, a longer duration of smoking and with more severe anticholinergic side effects at baseline. A trend was found for an association between non-psychotic disorders and mortality. Mortality seemed higher in males but this association was not statistically significant. The prevalence of cigarette smoking was almost 70%. Fourteen percent smoked 10 years or less, approximately 35% smoked 11-30 years, and 20% smoked for more than 31 years. Sensitivity analyses revealed that in patients with a psychotic disorder, same predictors were found, with the exception of anticholinergic side effects.
+To further our understanding, we performed a Cox regression (backward method), entering the significant predictors from Table 2 as covariates. In the analysis we adjusted for ethnicity. Table 3 lists the results of the Cox regression model. We found the mortality risk of patients with SMI increases by a factor of 1.036 (0.36%) for each year that a patient has smoked. Finally, the mortality hazard increases with a factor of 2.310 (131%) for each one-point increase in the disorganisation score.
+DISCUSSION
+This study confirms that SMI patients have an increased risk of premature mortality. We found a standardised
+mortality rate after 6 years of follow-up that was four to five times higher than in the general population in the Netherlands. In our random sample, almost 11% of the SMI patients died in a period of 6 years, in particular from cardiovascular disease. Death from natural causes accounted for 86% of the excess mortality and unnatural causes for 14%. The highest mortality rates were found in patients receiving
+residential treatment in psychiatric hospitals. Reduced survival was predicted by: higher age, Western background, more years smoking cigarettes, symptoms of disorganisation and anticholinergic side effects of antipsychotics. In multivariate analyses, number of years of cigarette smoking, and symptoms of disorganisation remained as predictors of a higher risk of premature death.
+Total SMR (4.51) in our cohort was higher than in other studies (4.51, as compared to 1.2-4.9) (3, 6, 8-10, 14, 15, 18, 60), with most studies reporting an SMR of 2.5 (3). Most patients died of natural causes, including diseases of the circulatory system (cardiovascular-related), a finding that was in accordance with our expectations and the results of other studies, (2, 7, 10, 15, 20, 61). Moreover, this finding is also in line with the common causes of death in the general population2. We found lower rates of cancer than Brown et al (7).
+Most patients died in psychiatric hospitals. This is not surprising given the poorer medical and psychiatric condition of these patients by comparison with outpatients (11). In line with prior research (15, 62-65) with a follow-up range from 4 to 10 years, we found a suicide rate of 0.6%. The distribution of natural (86% vs 60-67.3%) and unnatural (14% vs 17.5-40%) deaths in our study is only slightly different from that in other studies (4, 20-22). In our study, three patients underwent euthanasia. It is worth noting that the Netherlands was the first country in the world where euthanasia was legal for patients suffering unbearable pain with no prospects of recovery. Euthanasia has now also been legalised in Belgium, Luxembourg, Colombia and Canada (66), several states of the United States and Mexico. As a result, data about euthanasia are very scarce and comparisons are therefore impossible. It is known that fatal accidents are more prevalent in SMI patients than in the general population (2, 10, 67). By contrast with previous studies, none of our patients died due to an accident (2, 8, 68).
+In line with our expectations relating to severe psychiatric symptoms, we identified disorganisation as a predictor of premature death. To our knowledge, no other study has found this specific association. Previous studies have found that more positive symptoms (31) and fewer negative symptoms (69) were associated with higher mortality rates in SMI patients. One potential explanation for the higher risk of premature mortality in disorganised patients is that these patients may have the most severe psychiatric problems, possibly impairing their ability to live healthily and to seek somatic care when needed.
+The finding that there was no difference in the association between antipsychotic monotherapy or polypharmacy and mortality is consistent with studies from Tiihonen et al. (27), Tenback et al. (24) and Baandrup et al. (70), but it contradicts
+research from Auquier et al. (20), Joukamaa et al. (13) and Waddington et al. (52), who found that polypharmacy was associated with a higher mortality risk. More specifically, we also found no association between DDDs and mortality. Other studies have found that higher prescribed doses of antipsychotics are linked to a higher mortality rate (31) and to an increased risk of mortality from coronary heart disease and stroke (61).
+Unfortunately, the heterogeneity of the designs of these other studies preclude direct comparison. Furthermore, most of the studies mentioned here looked at drug prescriptions throughout the study period although, like us, Joukemaa et al. (13) and Loas et al. (31) measured drug prescriptions at baseline.
+A high mortality risk was also seen in patients with more severe anticholinergic side effects. To our knowledge, no other study has found this specific association. However, the interpretation of this finding, in which only this type of psychotropic side effect is associated with early death, is challenging and, in addition, future research should test this finding further before proceeding to interpret it. Contrary to our hypothesis, we found no association between substance use and mortality. This could be explained by the fact that substance use was very low in our SMI group. Despite low hazard ratios, our findings support the evidence that cigarette smoking is a risk factor for premature mortality (3, 6, 8, 18, 71). The finding that Western ethnicity was associated with reduced life expectancy is in accordance with a study by Olfson et al. (15). Further research is needed to clarify the mechanism underlying this finding.
+Limitations
+This study has several limitations. Firstly, we have to address the issue of survivor treatment selection bias, also known as immortal time bias (72, 73). Which was for example correctly addressed by Tiihoonen et al. (27); Cullen et al. (74); en Tornianen et al. (75), while others did not applied sensitivity analyses to address immortal time bias. In our study, baseline measurements were at one point in time. Patients who did not use any medication but started for example a few days after baseline were classified as the non-users group. This may have resulted in misinterpretations of the classifications. Also, patients who moved to foreign countries and therefore dropped out of treatment, may have died. From these patients we could not check the death registers. Although this happened not very often, this may have led to slightly underestimated death rates. It is also unclear if polypharmacy is related to an increased mortality risk, due to a direct toxic effect. Or that here may be a consequence of a cohort effect: patients with polypharmacy may be psychiatrically and physically sicker than patients with monotherapy. Thus when patients with and without polypharmacy were compared, underlying individual characteristics may differ. Baseline measurements only were used to predict mortality. We were therefore unable to take changes in medication or dosage, or the discontinuation of pharmaceutical treatment, into consideration, even though these are not uncommon (76). The total duration and cumulative antipsychotic dosage may therefore be a better measure to use in studies of reduced survival in SMI patients (61, 77). Moreover, we did not corrected for changes from one
+antipsychotic to another. Some patients might need to decrease one antipsychotic gradually, whilst raising another antipsychotic agent. Despite the fact that we do not think that this was highly prevalent, it is possible that the polypharmacy rate is slightly overestimated. Therefore the results of antipsychotics exposures among people with SMI has to be interpreted carefully. Secondly, our sample consisted of people with SMI with a mean age of 54.9 years at baseline and many of these people will have been ill for many years. Some patients died before it was possible to include them in the present study. This means that there may have been oversampling of healthy people. However, in clinical practice, we still see people of this age with a long history of SMI. Furthermore, we are interested in the risks to which this age group is exposed. A better understanding of these risk factors will help clinicians to address these the risk factors and identify potential solutions.
+Thirdly, this is an observational study and so it was not possible to control for all potential confounding variables. Important predictors of mortality rates such as body weight and lifestyle factors such as physical exercise and diet may exist (30), but, unfortunately, they were not assessed in this study.
+Fourthly, we did not control for confounding from indication in relation to polypharmacy, DDD or somatic medication, because the specific reason for the prescribed antipsychotic (combination) treatment was not recorded. Antipsychotics are also sometimes (off-label) prescribed for patients with without a psychotic disorder (78). We assumed that the psychiatrist made an informed decision and that medication prescriptions were tailored to each patient’s individual need. Therefore, we did not checked for contraindication. Future research should address this point.
+Fifthly, another issue is that we included only patients who were receiving psychiatric care and this may have led to an underestimate of mortality in patients with SMI. Patients with SMI without psychiatric care probably live in poorer circumstances and may have an even higher SMR. Our results may therefore not be representative for the general SMI population and SMR ratios may have slightly been underrated. Another issue is the representativeness of our sample. It seems that our subgroup of outpatients are a more severe group and that inpatients were overrepresented. The representativity of the subgroups of inpatients and patients living in a sheltered care facility was good. In contradiction to our limitation what we mentioned before, this may have resulted in overrated SMR ratios for patients who receive psychiatric care.
+Moreover, conclusions were based on small numbers, with limited statistical power. Therefore, results has to interpreted carefully. Finally, it was impossible to perform reliable statistical analyses to identify the determinants of specific diseases since the numbers in the different ICD-10 categories were too low.
+CONCLUSIONS AND IMPLICATIONS
+The high SMR and the incidence of CVD-related deaths in SMI patients in our study justify concern, particularly given
+the apparent potential for improvement given the fact that SMI patients with cardiovascular disease and diabetes use healthcare less than other diagnostic groups and others in the community with the same level of physical health problems (79). It is also alarming that most excess mortality is associated with preventable conditions (21, 80, 77). Our findings therefore imply that improvements in care are necessary to reduce excess mortality in SMI patients. The evidence suggests that the following modifiable risk factors should be covered by an intervention to reduce excess mortality in SMI: metabolic risk factors (abdominal obesity, elevated blood pressure, dyslipidaemia and insulin resistance, endothelial dysfunction, inflammatory activity and stress), lifestyle risk factors (cigarette smoking, substance use, physical inactivity and poor diet) and improved access to medical health services (3, 25, 71, 80, 81).
+Our findings support the idea that we should improve early medical screenings to identify acute medical disease and lifestyle interventions focused on reducing the risk of premature death (77). In the light of the high mortality rates in our study, we started to provide counselling through an online intervention:www.Leefstijlinbeeld.nl. This interactive tool allows patients to assess their lifestyle, either under the supervision of their counsellor or on their own. It provides patients with direct feedback, for example about smoking, nutritional intake and other lifestyle factors, and suggestions for improvement. However, the long-term effects of lifestyle interventions are rather poor and future research should explore the effectiveness of modified interventions. Finally, the excess mortality we found highlights the importance of an increased focus from professionals on the reduction of modifiable risk factors and the establishment of a policy for integrating mental and physical healthcare in order to prevent avoidable deaths.
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+1.	Introduction
+Suicide prevention is an important global health issue (World Health Organization, 2018). Albeit suicidal ideation and behavior have a complex biopsychological etiology, from a public health standpoint, knowledge about modifiable risk factors is especially beneficial because they can be used as targets for prevention. In recent years, the negative ramifications of loneliness have come to the fore of mental health research (Hawkley and Cacioppo, 2010). Loneliness has drawn particular attention in the context of the COVID-19 pandemic with researchers cautioning that public health interventions such as stay-at-home orders and other social distancing measures could aggravate loneliness in the population, especially in already vulnerable groups (Dahlberg, 2021),
+thereby leading to higher suicide rates (Reger et al., 2020).
+Loneliness is defined as a subjective discrepancy between the social connections one desires and those that are available. This mismatch entails dysphoric emotional states (Mushtaq et al., 2014; Perlman and Peplau, 1984). The role of loneliness and related constructs has been a focus of numerous influential theories of suicidal ideation and behavior for a long time. Durkheim (1897) proposed that individuals were more susceptible to suicide if they lacked a connection to something which transcended themselves, such as a religious community. By contrast, Shneidman’s (1998) theory of suicide focused on individual factors, with unbearable psychological and emotional pain (“Psychache”) as the main cause for suicide. He asserted that Psychache is so intense and intolerable because it results from basic needs which have been
+thwarted, seven of which are most common among suicidal individuals (including e.g., affiliation). These map onto the more global “need to belong” proposed by Baumeister and Leary (1995). Correspondingly, the authors noted that an unfulfilled need to belong results in great psychological pain, from which suicide offers an escape (Baumeister, 1990). More recently, the Interpersonal Theory of Suicide (Van Orden et al., 2010) posited that Thwarted Belongingness is an important factor that causes the desire to die by suicide. Thwarted Belongingness, much like loneliness, has been characterized as a mentally painful, unfulfilled wish for social connection (Van Orden et al., 2012).
+Plenty of empirical investigations have attested to the associations of loneliness with suicidal ideation and behavior, including large, population-representative cohort studies (Calati et al., 2019; Klein et al., 2021; Leigh-Hunt et al., 2017; Mushtaq et al., 2014; Skogman and Ojehagen, 2003; Stickley and Koyanagi, 2016; Stravynski and Boyer, 2001). However, it has been noted that risk factors interact, hence their importance might vary dependent on a person’s characteristics (such as gender) and over the life span (Kraemer et al., 2001). Unfortunately, gender-sensitive or gender-specific investigations are scarce. This is a research gap for a number of reasons. First, there are marked epidemiological gender differences with respect to suicidal ideation and behavior: Women have higher prevalence rates of internalizing disorders such as depression which includes suicidal ideation (Kuehner, 2017). Further, the Gender Paradox of Suicide (Canetto and Sakinofsky, 1998; Freeman et al., 2017) describes that women are more likely to attempt suicide while men are more likely to die by suicide (Turecki and Brent, 2016). Secondly, loneliness might affect women and men in different ways. Gender roles become especially salient in social relationships (West and Zimmerman, 1987), and women and men have differed regarding the needs for and the use of relationships (Day and Livingstone, 2003; Ptacek et al., 2016). Regarding gender-dependent ramifications of loneliness, however, research has yielded inconsistent results. Loneliness had stronger effects on depression and anxiety symptoms in women (Chang, 2018). On the other hand, Zebhauser et al. (2014) reported closer relations of loneliness and mental health (i. e. low life satisfaction, less resilience) in men. A recent meta-analysis even showed stronger associations of loneliness and mortality in men (Rico-Uribe et al., 2018). Lastly, previous research has proposed that the most important stressors preceding suicidal ideation and motives for suicidal behavior might differ between men and women (Alessi et al., 2019; Canetto and Sakinofsky, 1998; Cleary, 2019; Listabarth et al., 2020; Skogman and Ojehagen, 2003). However, it has been noted that the attributions of motives for a man’s or a woman’s suicide are also shaped by culture-dependent gendered conceptions and stereotypes (Andriolo, 1998; Canetto and Sakinofsky, 1998). Historically, there has been a stronger emphasis on social factors in women’s suicidal crises while in men, aspects of professional attainment and gratification have been underscored (“She died for love and he for glory”) (Canetto, 1995; Gerisch, 2008).
+There is also a need to investigate potential age-dependent associations of loneliness and suicide outcomes as the prevalence rates of suicidal ideation, suicide attempts, and suicide deaths vary between younger and older individuals (World Health Organization, 2019). For instance, middle-aged adults accounted for the largest proportion of suicides whereas suicidal ideation was most common among the young (Turecki and Brent, 2016). Furthermore, loneliness was particularly prevalent in younger people (Beutel et al., 2017; Hawkley and Cacioppo, 2010). As loneliness has often been studied from a gerontological perspective, there is a lack of investigations of the complete adult age range. This is a research gap as loneliness might have different implications across the life span (Qualter et al., 2015).
+1.1.	Aims of the study
+We hypothesized that loneliness was positively related to higher levels of suicidal ideation. In order to find out whether loneliness
+constitutes an equally important risk factor for suicidal ideation in women and men of different ages, we tested gender- and age-dependent effects (while controlling for potential confounders).
+2.	Materials and methods
+2.1.	Recruitment and data collection
+Data was collected in 2014 by the institute USUMA (German acronym for Independent service for surveys, methods and analyses) by order of the University of Leipzig. The survey was approved by the institutional ethics review board of the University of Leipzig (Az063-14-10032014). Before providing informed consent, all participants were informed of the study procedures, data collection and anonymization of personal data. Participating households and individuals were chosen using random route procedures and Kish selection (Kish, 1949). Inclusion criteria were an age of 14 years or older, sufficient German language skills, and the provision of informed consent. The response rate was 55%. Participants were individually interviewed at their home by a trained interviewer and completed several questionnaires (more information about the procedure: Kliem et al., 2017). In total, 2,527 individuals were questioned by trained interviewers. As this study focused on the Beck Scale for Suicide Ideation (BSS) which was (in accordance with its original instructions) only filled out by individuals of legal age, we excluded individuals younger than 18 years from all analyses which reduced the sample to N=2,450.
+2.2.	Participants’ characteristics and measures
+Sociodemographic information included in the present study comprised participants’ gender, age, and income. The equivalised income was calculated according to the OECD (OECD 2011) (Household income/-^(people in household). Monthly income was categorized as follows: 1=< 1,250€, 2=1,250-2,500€, 3=>2,500€.
+We used the German Beck Scale for Suicide Ideation (BSS) to measure suicidal ideation. Participants are asked to rate 21 aspects of suicidal ideation on a three-point scale (0 to 2). The response options represent different intensities of suicidal ideation, with higher scores indicating greater severity. The first five items constitute the BSS screen which assesses suicidal ideation over the course of the last week. Its sum score ranges from 0 to 10. The BSS screen has previously shown excellent psychometric properties and measurement invariance with regard to gender, depression status, and age (Kliem et al., 2017). In the present sample, internal consistency was excellent (rn=.97). The main analyses concern the BSS screen. (The full BSS is only completed by risk groups who answer at least one of two filter questions with “yes”. Only for calculations focusing on previous suicide attempts, we used the respective item of the full BSS.)
+To assess loneliness, we used the established three item short form developed by Hughes et al. (2004). Its construction was based on the twenty item R-UCLA Loneliness Scale (Russell et al., 1980). The measure assesses basic aspects of the subjective experience of loneliness: perceived isolation, disconnectedness from others, and not belonging to others. These three items are rated on a five-point Likert scale ranging from 0=never to 4=very often. The sum score ranges from 0 to 12. Previous studies have confirmed its unidimensionality, good internal consistency and have attested to its construct validity (Richter and Weinhardt, 2014). Measurement invariance across gender and age has been established for the German version used in the present study (Klein et al., 2021). In our sample, the scale showed good internal consistency (rn=.85).
+The survey also contained the four-item-version of the Patient Health Questionnaire (PHQ-4) which assesses mental distress (Lowe et al., 2010). This widely used screening tool combines the GAD-2 (anxiety symptoms nervousness and worrying) and PHQ-2 (depression symptoms depressed mood and anhedonia). It starts with the question: “Over the
+last two weeks, how often have you been bothered by the following problems?”. Response options range from 0=not at all to 3=nearly every day, yielding a sum score ranging from 0 to 12. A cut-off score of >6 has been empirically validated (Lowe et al., 2010). In the present study, the PHQ-4 showed good internal consistency (rn=.86).
+2.3.	Statistical analyses
+Sample characteristics are reported as numbers and percentages or as means and standard deviations, respectively. P-values correspond to two-tailed tests.
+The multiple linear regression model of suicidal ideation (using the sum score of the BSS screen) comprised the predictors loneliness (entered as a continuous score), gender (coded 0=men; 1=women), age (in decades) and four interaction terms (loneliness x gender, loneliness x age, gender x age, loneliness x gender x age). Variables were meancentered prior to computing product terms. We also included three potential confounders: Household income (equivalized income), mental distress (PHQ-4 sum score) and living situation (coded 0=living alone; 1=living with others). Given the cross-sectional design, the large sample size and the few missing observations (< 1% per variable), we used listwise deletion. A post-hoc sample size calculation showed that the sample was large enough to detect even small effects (f2=.02) in the regression model containing ten predictors. For a statistical power level of 0.8, the minimum required sample size was N=818.
+We used the variance inflation factor (VIF) to check all regression models for multicollinearity. All scores were < 5 (10 was defined as the critical threshold (Myers, 1990)), indicating no concerning level of multicollinearity. Effect sizes and regression coefficients are interpreted following Cohen (1992).
+3.	Results
+3.1.	Sample characteristics and uni-/bivariate analyses
+We analyzed data of 1,130 men and 1,320 women. Table 1 shows the sample characteristics as well as correlations of the continuous variables. There were moderate positive correlations of the reported intensity of loneliness with mental distress and with suicidal ideation. Table 2 presents characteristics and correlations stratified by gender. Among men, loneliness, distress symptoms, and suicidal ideation showed slightly higher correlations than among women. Among women, loneliness was related to age and income (older women and those with a lower household income reported more loneliness, although these correlations were very weak). Among women, mean scores of loneliness (t (2434)=-2.12, p=0.034, d=0.09) and mental distress (t(2425)=-4.37, p< .001, d=0.18) were higher than among men. Men and women did not differ regarding the mean level of suicidal ideation (BSS screen). They were also just as likely to report previous suicide attempts (Men: N=24; Women: N=33). Loneliness was more pronounced among those who reported to live alone (M=3.11, SD=2.58) compared to participants who lived together with others (M =1.95, SD=2.16) (t(2434) = 11.57, p< .001, d=0.50). A similar difference, although of smaller size, was observed for mental distress (living alone: M=2.10, SD=2.48; living with others: M =1.52, SD=1.98) (t(2425)=6.12, p< .001, d=0.27).
+3.2.	Main analyses: linear regression analyses of suicidal ideation
+Table 3 reports the linear regression analysis of suicidal ideation within the whole sample. The model explained 13.1% of the outcome’s variance. There was a positive association of loneliness and suicidal ideation (p =.410, p< .001) independent of participants’ income, living situation, and mental distress. However, the association was moderated by gender (p=-.281, p=.024) and by age (p=-.255, p=.033) in the sense that it was stronger among men and among younger participants. The
+interaction of loneliness and gender is visualized in Fig. 1.
+We also carried out separate analyses for women and men (Table 4). The same set of predictors explained a greater portion of the variance of suicidal ideation within men compared to women (adjusted R2=.178 vs. R2=.094). Loneliness was a highly significant statistical predictor of suicidal ideation in both samples. In men, its relation to suicidal ideation was comparatively stronger (f=.192, p=.001 in men vs. f=.113, p=.035 in women). Also, living together with others was associated with lower levels of suicidal ideation in men (f=-.072, p=.019). Living together with others was not associated with women’s suicidal ideation. In the male sample, we observed a significant interaction of loneliness and age, indicating that the association of loneliness and suicidal ideation was stronger in younger men (f=-.149, p=.014) (Fig. 2).
+4.	Discussion
+Based on a representative population survey, we investigated gender- and age-dependent associations of loneliness and suicidal ideation. The results showed that loneliness and suicidal ideation were related on a population level irrespective of a person’s income, living situation, and other mental distress symptoms. The findings are in line with the sizable amount of empirical research that attests to the
+interpersonal nature of suicidal crises (Chu et al., 2017; Van Orden et al., 2010). They also corroborate previous population-based studies that reported higher levels of suicidal ideation in lonely individuals (Ben-nardi et al., 2019; Beutel et al., 2017; Stravynski and Boyer, 2001).
+By demonstrating close links of loneliness and suicidal ideation in women and men in gender-specific analyses, the study contributes to the disproval of gender-stereotypical “suicide myths” positing that suicidality in men has little to do with social connectedness (Canetto, 1995; Cleary, 2019). The comparatively stronger relation of loneliness and suicidal ideation in men could even indicate that reporting loneliness is a particularly serious warning signal for them. Men might struggle to admit feelings of loneliness because of cultural reasons such as a traditionally masculine socialization and gender self-concept (Dahlberg et al., 2015) which also constitute risk factors for suicide in men (Cleary, 2019; Granato et al., 2015).
+Along these lines, it is important to note that young men living in individualistic cultures had the highest risk to experience loneliness (Barreto et al., 2020). Our study adds that the relation of loneliness and suicidal ideation was especially strong in younger men. Young people might have been overlooked by the bulk of loneliness research that has construed it as a topic affecting primarily the elderly (Franssen et al., 2020; Qualter et al., 2015). This study underscores the need to mitigate
+loneliness in all age groups. Its results also have implications for mental health promotion in the context of the current COVID-19 pandemic, during which younger individuals reported particularly large increases in loneliness (Niedzwiedz et al., 2021). As they experienced comparatively greater changes to their daily lives than older generations and might have missed important developmental milestones, young people might be particularly at risk for negative outcomes.
+Moreover, the observation that loneliness was closely related to suicidal ideation - irrespective of structural indicators of social integration such as living in a shared household - attests to the importance of studying aspects of subjective social connectedness. Loneliness needs to be taken seriously as a risk factor for suicidal ideation and screening individuals depending on characteristics such as living conditions might not be sufficient to detect those who are vulnerable. A recent longitudinal study reported that both loneliness and social isolation (as well as their combination) were related to mortality (Beller and Wagner, 2018).
+International, population-based investigations showed that living in a shared household was protective against loneliness during the COVID-19 pandemic as well (Bartres-Faz et al., 2021; Bu et al., 2020). Furthermore, longitudinal survey data from Brazil highlighted the paramount importance of the subjective feeling of loneliness beyond other risk factors for suicidal ideation, including social distancing (Antonelli-Salgado et al., 2021). However, so far, gender-specific approaches were scarce. In previous research, having a smaller social network, rather than living alone, was associated with loneliness regardless of gender (Zebhauser et al., 2014). By contrast, Beutel et al. (2017) reported that living alone aggravated feelings of loneliness in men and women.
+In our study, separate analyses in men and women revealed differential effects of living together with others: living alone was related to lower levels of suicidal ideation in men, but there was no such effect in women. This finding is in line with previous research that indicated gender-dependent mental health benefits of living together as a couple or a family (Rosenfield and Mouzon, 2013). It is a common observation that men depend more on their spouses than women do and that wid-owhood/being single are greater risk factors for depression in men as they lose comparatively more in social ties than women (Tibubos et al., 2019; Van de Velde et al., 2010).
+We observed no differences between men and women regarding their levels of suicidal ideation. A possible explanation for this finding could
+be the contents of the BSS (screen) as the instrument does not only assess manifestations of distress, but also other aspects such as a wish to live. Such dimensions could be less affected by gender role expectations and societal norms than e.g., internalizing symptoms.
+By contrast, women were more likely to report loneliness than men, mirroring previous results from German community samples (albeit the differences were small) (Beutel et al., 2017; Luhmann and Hawkley, 2016). Other investigations found no gender differences (Pinquart and Sorensen, 2001; Zebhauser et al., 2014), or even higher levels of loneliness among men (Borys and Perlman, 1985; Nicolaisen and Thorsen, 2014). We do not know whether these diverging results reflect actual differences (between samples, countries, or over time) or whether they are an artefact associated with different instruments. For one thing, the present results add to a large body of research that indicates structural determinants of mental health, such as income. Thus, there might well be related differences between countries and samples. Purely methodical issues can likely be precluded as both the BSS screen and the UCLA Three-Item Loneliness scale showed measurement invariance across gender (Klein et al., 2021; Kliem et al., 2017).
+Along these lines, more reports of loneliness and mental distress among women may be shaped by women’s greater willingness to admit distressing emotional states to themselves and to others (Kuehner, 2017; Yousaf et al., 2015). Researchers have noted that women find it easier to label themselves as lonely (Borys and Perlman, 1985). Hence, univariate differences in loneliness and distress measures do not allow for the conclusion that men generally feel better than women do, or that they are less likely to experience situations in which they feel lonely. Rather, there is a need to deepen the knowledge of potentially gender-related risk factors and expressions of suffering in order to inform targeted prevention and intervention efforts. In this respect, the present study’s approach does not give insight into the underlying mechanisms shaping men’s and women’s suicidality. More comprehensive, future studies could specifically test gender-dependent risk factors. For instance, substance abuse issues including alcoholism (Grazioli et al., 2018) or experiences of military service (Willmund et al., 2019) are more common in men. On the other hand, given the smaller proportion of explained variance of the regression model within female participants, our survey might have omitted risk factors specifically important for women’s suicidal ideation. In previous studies, these included experiences of sexual violence and harassment (Gradus et al., 2013; Mallon et al., 2016).
+4.1.	Limitations
+The study’s results need to be interpreted in light of its limitations. First, the cross-sectional design cautions against causal interpretations and against speculation about the direction of effects (e.g., of the association of loneliness and suicidal ideation). Further, we did not assess established risk factors for different suicide outcomes whose prevalence differs between men and women (such as alcoholism). As a result, the study can only indicate few targets for prevention and intervention in clinical practice. By contrast, experts have noted that suicide research will benefit from studies that use large amounts of data to investigate many risk factors in combination (O’Connor and Portzky, 2018). Along these lines, the regression model that included the whole sample explained only 13.1% of the outcome’s variance. Although this is a relatively large proportion for a population-based survey study, it suggests that important risk or protective factors might not have been accounted for, for instance early or recent stressful life events and individual differences such as coping styles (for an overview, see Franklin et al. (2017).
+Research has shown that risk factors for suicidal behavior differ from those of suicidal ideation (Meta-analysis: May and Klonsky, 2016). The present study only concerns the latter. However, as more pronounced gender differences have been observed with regard to suicide attempts and suicide deaths, the investigation of gender-dependent risk factors
+for these outcomes might be particularly important. Nonetheless, research in suicidal ideation is also valuable as death wishes are an indicator of grave distress. Research has shown that suicidal ideation was related to suicide mortality (Khang et al., 2010; Louzon et al., 2016).
+This work aimed to study gender-dependent risk factors. It is thus a clear limitation that we only addressed men and women although nonbinary individuals are an especially vulnerable group (Horwitz et al., 2020). The survey from which we drew our data was conducted in 2014 and it forced a choice of the options woman or man. It did not differentiate between trans- and cisgender women and men either. Future research endeavors should explicitly include and study those members of the population who are most affected by gender-based discrimination and violence. This also applies to people who do not identify as heterosexual (Ploderl and Fartacek, 2005).
+5.	Conclusions
+Loneliness was related to suicidal ideation on a population level despite the statistical control of other distress symptoms, participants’ living situation, and household income. This association was observed in men and women. Structural indicators of social integration (such as living with others) had more positive effects for men than for women, suggesting gender-dependent effects of risk and protective factors for
+suicidal ideation in the community. However, the results also indicate that in order to screen for individuals at risk, an assessment of objective indicators of social integration is not enough. Rather, the subjective, distressing feeling of being disconnected from others needs to be captured. The study also indicated that younger men might be an especially vulnerable group of the community who should receive particular attention in suicide prevention research and practice.
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+Suicide is a leading cause of death worldwide.1-3 In order to ultimately prevent suicide, we need to be able to predict who is at greatest suicide risk so targeted interventions can be employed. Over the past few decades, impressive gains have been made in identifying lifetime, or long-term, risk and protective factors for suicide deaths and suicide attempts (i.e., nonfatal suicidal behavior).4-9
+However, one of the most important jobs of clinicians is to determine who is at short-term, or imminent, risk for suicide. This decision is extremely difficult because alarmingly little is known about the short-term risk factors for suicide. Not surprisingly, Aspirational Goal 3 of the National Action Alliance for Suicide Prevention’s Research Prioritization Task Force is to improve prediction of short-term, or imminent, suicide risk.
+The purpose of this paper is to highlight gaps in our knowledge about the short-term prediction of suicide and to suggest the types of breakthroughs needed to fill
+these gaps. Suicide death is challenging to study because it is a low base rate event. Nonfatal suicidal behavior (e.g., suicide attempt) is much more common, often leads to serious harm in itself, and is currently the most robust risk factor for suicide death.5,8,10 Therefore, as a key first step we outline the research needed to improve the prediction of suicidal behavior, which, given its frequency, increases the statistical power of prediction studies.
+What We Know
+Most of what is known about the prediction of suicidal behavior comes from epidemiologic studies of lifetime and 12-month suicide ideation and attempts.
+Approximately 9.2% of adults have seriously considered suicide, 3.1% have formulated a suicide plan, and 2.7% have attempted suicide in their lifetime.6 In regard to 12-month prevalence, approximately 2% of adults report past-year suicide ideation, 0.6% report suicide plans, and 0.3% report a suicide attempt.8
+Suicide ideation and attempts are relatively rare in childhood, but rates increase dramatically during adolescence and remain high throughout adulthood.3,11 Of particular importance for short-term prediction, approximately one third of individuals with suicidal thoughts will transition to make a suicide plan, and one third of those with suicidal thoughts will make a suicide
+attempt in their lifetime.6 More than 60% of individuals who progress from thinking about suicide to attempting suicide will do so within the first year after the onset of suicide ideation,6,9,11 suggesting that the year after the first onset of suicide ideation is a particularly high-risk time. However, few variables have been identified that predict which individuals with suicide ideation go on to make a suicide attempt. Therefore, the field knows much less about who is likely to act on their suicidal thoughts, and, for those at risk, when they are likely to take suicidal action.
+Moreover, very little research has focused on the persistence of suicide ideation and attempts. In one of the only studies to do so, Kessler and colleagues,3 using the WHO World Mental Health (WMH) Surveys data, found that for most individuals with suicidal thoughts and behaviors (51% of suicide ideators, 59% of planners, and 70% of attempters), the behavior will not persist for more than 1 year. For the remaining percentage of individuals, however, suicidal thoughts and behaviors can be chronic and even lifelong.
+A range of lifetime and 12-month risk factors for suicide ideation and attempts have been identified, including sociodemographic factors, stressful life events, family history of psychopathology, presence and accumulation of mental disorders, and past suicidal thoughts and behaviors.2,5 However, there is still much to learn about how these factors confer risk for future suicidal behavior. For instance, although past suicidal behavior is one of the most robust risk factors for future suicidal behavior,5,8,10 60% of previous attempters will not make another suicide attempt in their lifetime,3 and many people who die by suicide have no previous history of suicidal behavior.12 Moreover, it is unclear whether these long-term variables hold any value for the short-term prediction of suicidal behavior. Time-invariant factors, like gender, clearly do not. In contrast, time-varying factors, such as the presence of multi-morbidity, may be predictive of short-term risk. However, we are unaware of any existing studies with this temporal resolution.
+Breakthroughs Needed
+Below, we present four key questions that highlight the gaps in our current knowledge of short-term predictors of suicidal behavior, and suggest breakthroughs needed to advance existing research.
+1.	What Factors Predict the Transition from Suicidal Thoughts to Attempts?
+Although suicide ideation is a well-documented risk factor for suicidal behavior, the majority of those with suicidal thoughts do not go on to make a suicide plan or
+attempt. Therefore, it is vital to improve prediction of which individuals are likely to act on their suicidal thoughts. Unfortunately, most identified risk factors, such as major depression, predict suicide ideation but not attempts among those thinking about suicide.3,13 Data from the WHO WMH Surveys indicate that whereas known risk factors account for 62.4% and 80.3% of the variance in predicting suicide ideation and attempts, respectively, these same risk factors account for only 7.1% of the variance predicting suicide attempts among ideators.3
+Recent research has started to identify some risk factors that do differentiate suicide attempters from suicide ideators, including younger age; low income or unemployment; history of childhood adversities8,10; disorders characterized by agitation, impulsiveness, and aggression8,13; parental history of panic and antisocial behavior8,14; and history of sexual violence.15 However, much more progress is needed in this direction.
+2.	What Factors Predict This Transition Over the Next Hours, Days, or Weeks?
+Previously identified long-term risk factors have unknown abilities for predicting the transition from suicidal thoughts to actions over the short term, and research suggests there may be important differences between the risk conferred by short- and long-term variables. For instance, Fawcett et al.16 found that hopelessness and suicide ideation predicted suicide over the longer term (2-10 years), whereas anxiety, insomnia, and anhedonia predicted suicide death over the next 12 months. Although long-term risk factors may indicate who is more likely to engage in suicidal behavior in their lifetime, distinct short-term risk factors are necessary for indicating when individuals are likely to act on their suicidal thoughts.16,17 Research on short-term risk has been hampered by three key methodological limitations.
+First, though the field generally agrees on the overall distinction between chronic and acute risk factors, there is no consensus definition for what constitutes shortterm, or imminent, risk (i.e., subsequent hours, days, or weeks). Second, most studies measuring suicide risk factors use a long assessment window (i.e., lifetime or past year). Studies that have examined more short-term risk factors indicate that suicidal behavior is often closely preceded by acute substance use,18 interpersonal negative life events,17 and extreme anxiety, agitation, or other negative affective states.12 However, because most previous studies used small, selective samples without a comparison group, it is unclear how these results will generalize to other populations, or how unique these risk factors are to suicide, as compared to psychiatric crises
+more broadly. A third limitation is the reliance on retrospective self-reports, which, though a valuable source of information, can be limited by bias and unreliability (e.g., forgetting). What are needed now, in addition to such studies, are prospective examinations of short-term (i.e., over the next hours, days, and weeks) predictors of suicidal behavior.
+3.	What Are the Most Important Objective Markers of Short-Term Risk?
+The current state of the art in acute suicide risk assessment is to ask individuals questions such as: Do you have any plan or intent to kill yourself? Research has therefore been limited by an almost exclusive reliance on selfreported likelihood of future engagement in suicidal behavior, which may be biased for a variety of reasons (e.g., motivation to conceal suicide plans and intentions).12,19 Given the limitations of self-report methods, the field needs new and objective ways of measuring suicide risk (i.e., assessment not biased by opinion or interpretation).
+Importantly, measures and tests that objectively assess suicide risk are currently being developed, including (1) behavioral measures of implicit suicidal cognition20; (2) neurocognitive measures of difficulties in attention, working memory, and executive functioning21; and (3) biological tests of dysfunction in the serotonergic system and hypothalamic-pituitary-adrenal axis.4 Though promising, these tests are not currently used in practice to assess risk, and it is not yet clear if and how they might be combined with existing risk factors to improve the accuracy of resulting predictions.
+4.	What Method of Combining Information About Risk and Protective Factors Yields the Best Prediction?
+There is currently no empirically supported method for incorporating these variables in a way that informs our determination of an individual’s risk for future suicidal behavior (i.e., low, moderate, high, or imminent risk). In the absence of a tool for synthesizing this information, clinical judgment or intuition is currently used, rather than science, to combine details about risk factors—a problematic method given the superiority of actuarial over clinical methods for predicting human behavior.22
+To address this gap, more research is needed to identify a set of risk factors that maximize prediction sensitivity (i.e., to accurately identify suicides) and specificity (i.e., to accurately identify non-suicides). The development of such prediction is complicated by the low base rate of suicide death.23 It is unlikely that a single risk factor will effectively predict suicide with both
+high sensitivity and high specificity. Therefore, research needs to move from bivariate to multivariate prediction models examining combinations of multiple risk factors in the same large sample.
+Notably, several recent studies have tested different methods of combining suicide risk factors with some initial success.8,10,24 These risk indices included known sociodemographic and psychiatric risk factors and were able to accurately classify a substantial portion of individuals (areas under the curve [AUCs] ranged from .74 to .88). This means that a randomly selected suicide attemp-ter could be distinguished from a randomly selected suicide ideator with 74%-88% accuracy.8,10 Of note, risk indices were moderated by factors such as attempt planning, suggesting that risk factors may vary among suicidal subgroups. Although promising, these risk indices included lifetime and 12-month risk factors, which provide less information about short-term risk, and are not currently used in naturalistic settings. Future research is needed that incorporates short-term factors into these risk indices, examines how risk indices vary across subgroups, and translates these tools into a form that is easily accessible and interpretable in clinical practice.
+Short-Term Research Objectives
+Following directly from the limitations and needed breakthroughs described above, the suggested shortterm research objectives are to identify (1) factors that predict the transition from ideation to attempts; (2) risk factors more closely temporally linked to engagement in suicidal behavior; (3) objective risk markers; and (4) scientifically informed methods for combining risk factors. There are many avenues to pursue in order to make these advances. We propose two possibilities for illustrative purposes below.
+Large Representative Samples
+For this line of research, it would be ideal to follow large, representative, and demographically diverse samples (i.e., > 10,000 individuals, consistent with sample sizes used in national9 and cross-national epidemiologic research)3 over a number of years while assessing a large number of risk factors yearly, or more frequently (e.g., via email/ smartphone). Data from these large-scale samples could be used to examine prospective predictors of the transition from suicide ideation to suicide attempts or completions (e.g., among those without a history of prior attempts), as well as to inform the development of risk indices and algorithms for predicting suicidal behavior, including moderators of multivariate risk index models.
+Small High-Risk Samples
+A second set of studies could more intensively monitor small samples (e.g., > 100 individuals, consistent with studies using real-time monitoring techniques)25,26 at high risk for suicide (e.g., previous suicide attempts) to identify acute risk factors more closely linked to suicidal behavior. High-risk studies could focus on frequent monitoring of state-related risk factors (e.g., agitation, suicide planning, recent negative life events) on a weekly, or ideally daily, basis using real-time monitoring.26 This type of research could improve understanding of specific triggers for suicidal behavior and, moreover, could help identify acute risk factors that predict suicide attempts among ideators. In addition, small samples are ideal for testing the efficacy of existing objective tools, as well as developing novel objective measures.
+Long-Term Research Objectives
+The ultimate goal of this research is to use information about short-term risk to help prevent suicide. To this end, after short-term risk factors are identified, the first long-term objective is to develop tools for clinical practice that can more accurately identify who is at risk, as well as when and where they are at risk for engaging in suicidal behavior. It will be important for these tools to integrate information about known long-term and shortterm risk factors, as well as both subjective and objective measures in a way that is useful in clinical settings. Ideally, these tools could be used to inform decisions about appropriate treatment (e.g., indicating when hospitalization is warranted).
+A second long-term objective is to design intervention and prevention strategies that target and manipulate known risk factors to examine whether they are causally related to suicidal behavior and can ultimately help decrease the likelihood of future suicidal behavior. For example, studies using objective tools may suggest that interventions aimed at improving specific aspects of memory, attention, or executive functioning could help decrease suicide risk.
+Conclusions
+Although advances have been made in the long-term prediction of suicidal behavior, there are significant gaps in our knowledge about the short-term prediction of suicide risk. We suggest four key targets for future research that could help improve short-term prediction. Preliminary steps have been made in some of these areas, but a great deal of work is needed to more accurately predict these dangerous behaviors and ultimately help prevent suicide.
+Publication of this article was supported by the Centers for Disease Control and Prevention, the National Institutes of Health Office of Behavioral and Social Sciences, and the National Institutes of Health Office of Disease Prevention. This support was provided as part of the National Institute of Mental Health-staffed Research Prioritization Task Force of the National Action Alliance for Suicide Prevention.
+The research was supported, in part, by a grant from the National Institute of Mental Health (F32 MH097354) awarded to Catherine R. Glenn.
+No financial disclosures were reported by the authors of this paper.
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+Introduction
+In the early 1990s Sri Lanka (population 19 million) had one of the highest suicide rates in the world. Between 1950 and 1995 rates increased 8-fold to a peak of 47 per 100 000 in 1995. In the 10 years after 1995, Sri Lanka’s suicide rates declined by 50% (Figure 1). Understanding the reasons for this transformation in the incidence of suicide in Sri Lanka may have important implications for suicide prevention in other low-and middle-income countries.
+Research in high-income countries indicates that the main potential contributors to temporal trends in suicide rates are: economic recession, increases in divorce, changing levels of alcohol misuse, periods of war, improvements in the treatment of mental disorder and changes in the availability or lethality of commonly used suicide methods.1-7
+Pesticide self-poisoning is one of the most commonly used methods of suicide in Asia with an estimated 300 000 deaths each year.8,9 Previous research indicates that pesticide poisoning accounts for two-thirds of Sri Lanka’s suicide deaths.6,10,11 Case-fatality following pesticide self-poisoning can be over 50 times higher than following paracetamol poisoning,12 the most commonly used method of self-poisoning in Britain and other developed countries. The use of pesticides in acts of self-harm may therefore have profound effects on the epidemiology of suicide in countries where pesticides are commonly ingested in acts of self-poisoning.8
+Pesticides vary greatly in their toxicity to humans. For example, paraquat self-poisoning has a case-fatality in excess of 60%,47 whereas case-fatality following chlorpyrifos (an organophosphate) self-poisoning is 8% and with
+Methods
+Suicide data
+We obtained data on the number of suicides in Sri Lanka from the following sources: (i) total suicides and suicide rates between 1940 and 1975 from two previous analyses of suicide in Sri Lanka both based on data from Sri Lanka’s Registrar General16,17; (ii) age-, sex- and method-specific suicide data from 1975 to 2005 from the Department of Police, Division of Statistics, Sri Lanka. Due to civil war, suicide data from two districts (Killinochi and Mullaitivu) do not appear in the recent suicide statistics. These districts comprise <2% of the population of Sri Lanka and so are unlikely to influence observed patterns.
+For most of the time period covered by the data on methodspecific suicides (1975-96) the method of death was categorized into one of eight groups: poisoning/acetic acid, hanging, jumping in front of a train, drowning, burning, shooting, use of sharp cutting instruments and 'other means’. Since the terms 'acetic acid’ and 'poisoning’ were used interchangeably up to 1996, we have categorized both as poisoning. In 1997, the number of specific methods of suicide was expanded to include pesticide poisoning and in 2002 six additional categories were added (explosives, ingestion of acids, ingestion of fuel, plant poisoning, jumping from a height and addictive drug ingestion/ injection). The most commonly recorded method of suicide in the study period was 'other means’—accounting for 38% of all suicides. We have four reasons to believe those coded as 'other’ were mainly deaths from self-poisoning. First, in 2 years when the numbers of poisonings doubled (1983 and 1984) the number of suicide coded as 'other’ halved, but there was no effect on the numbers of suicides using other specified methods (hanging, etc.) (Figure 2). Second, previous studies of suicide in
+Sri Lanka in the 1980s and 1990s indicate that the principle method used for suicide is self-poisoning. In three populationbased studies in different regions of Sri Lanka, self-poisoning with pesticides accounted for over two-thirds of suicides.6,10,11 Third, as noted earlier, pesticide suicides were categorized as a separate specific method of suicide from 1997 onwards and in that year 2589 pesticide suicide deaths were recorded. In the same year the total number of 'poisoning’ suicides dropped from 1954 in 1996 to 231; likewise the number of deaths coded as due to 'other means’ declined from 3818 in 1996 to 2027 in 1997 and 1397 in 1998 and continued to decline to 31 by 2005. At this time, no other specific methods of suicide showed a marked increase in numbers. This suggests that most, if not all, the 'transfer’ from 'other means’ to specific means was to the pesticide poisoning category. Lastly, for the period covered by our analysis, the specific methods of suicide listed in the mortality data were self-poisoning (acetic acid), hanging, drowning, selfburning, firearms, self-piercing or jumping in front of a train— and that these seven methods account for over 90% of suicide deaths in most countries.18,19 The 'other’ category, therefore, is unlikely to consist of a different (unspecified) method. Based on these observations, in our analysis of trends in method-specific suicide rates, we combined 'other’ and 'all self-poisoning’ categories into a single class—self-poisoning.
+To assess whether any changes in suicides were due to declines in pesticide self-poisoning, we obtained data on the number of hospital admissions for pesticide poisoning and the number of in-hospital deaths from pesticide poisoning throughout the whole of Sri Lanka from published sources20 and from Sri Lanka’s Health Statistics Unit 1980-2003 (Colombo).
+Population data
+To estimate age- and sex-specific suicide rates we used estimated mid-year population data (in 5 year age/sex bands) obtained from the Registrar General for Sri Lanka. Censuses were carried out in 1971, 1981 and 2001 in Sri Lanka. To calculate sex-specific age-standardized suicide rates, we used the World Standard Population. Based on the 2001 census it is estimated that 14.6% of the population live in
+Municipal or Urban areas and the predominant religion is Buddhism (77% of the population) (http://www.statistics. gov.lk/census2001/index.html).21 A total of 74% of the population of Sri Lanka are Sinhalese and around 18% Tamil.
+Data on risk factors for suicide
+Trends in national suicide rates are adversely influenced by economic recession/unemployment as well as rises in levels of divorce and alcohol misuse. 1-5 We obtained data on levels of unemployment between 1971 and 2005 from figures published by the Central Bank of Sri Lanka.22 To investigate trends in the use and misuse of alcohol we obtained data on trends in alcohol consumption and cirrhosis mortality from Sri Lanka’s Department of Census and Statistics (http://www.statistics. gov.lk/index.asp,accessed 10 July 2006).21 We used information in published papers to assess trends in divorce in Sri Lanka. 23
+Periods of war tend to be associated with reductions in suicide.6,7 There has been a longstanding civil war in Sri Lanka. The conflict is between the Sri Lankan Government and the Liberation Tigers of Tamil Eelam (LTTE) who are fighting to gain independence for a separate state for Tamils in the North and East of the country.24 The main period of civil war in the period studied was 1983-2002.
+To assess whether there were changes in the total amount of pesticides available in Sri Lanka we obtained published data on pesticide imports and consumption in Sri Lanka (1995-2000) (http://www.fao.org/docrep/008/af340e/af340e0k.htm).25
+Four further initiatives that may have influenced suicide rates in Sri Lanka were: (i) the Control of Pesticides Act that created the position of the 'Registrar of Pesticides’ in 1983—this post carries the authority to set regulations and standards for pesticides; (ii) the establishment of National Poisons Information Centre in 1988;26 (iii) the creation of the Presidential Task Force on Suicide, which developed a National Policy and Action Plan on the Prevention of Suicide published in December 199727 and (iv) the de-criminalization of suicide in Sri Lanka in 1998.
+Analysis of time trends in suicide
+We used graphical approaches to investigate secular trends in age-, sex- and method- specific suicide rates. We related these trends to the timing of specific interventions by Sri Lanka’s Registrar of Pesticides.
+From the late 1970s until the early 1990s, WHO Class I ('extremely or highly toxic’)28 organophosphorus (OP) insecticides such as parathion, methyl parathion, monocrotophos and methamidophos were the commonest poisons taken in fatal self-harm.29,30 The Registrar of Pesticides banned methyl parathion and parathion in 1984 and over the following years gradually phased out all the remaining Class I organophosphate pesticides, culminating in July 1995 with bans on the remaining Class I pesticides monocrotphos and methamido-phos. During this transitional period, farmers switched their agricultural practice to the Class II ('moderately hazardous’)28 (http://www.who.int/ipcs/publications/pesticides_hazard_rev_3. pdf, accessed 22 May 2007) organochlorine pesticide, endosulfan, which proved clinically to be highly toxic.15 Imports of endosulfan were banned in December 1998.15
+Results
+Overall suicide rates
+Sri Lanka’s suicide rates rose gradually from 6.3 per 100 000 in 1940 to 9.9 per 100000 in 1960. Thereafter, the rate of suicide increased more sharply, doubling between 1961 and 1971 and doubling again between 1971 and 1983, before reaching a peak of 47 per 100 000 in 1995. The period of rapid rise in suicides in the late 1950s and early 1960s coincided with the first reported cases of pesticide-associated deaths in Sri Lanka.31 There were brief interruptions to this year on year rise in 1975-78 and in 1990-94. Rates halved between 1995 and 2005 (Figure 1). There were 19 769 fewer suicides in 1996-2005 as compared with 1986-1995.
+The rises in suicide rates between the late 1970s and the early 1990s were similar in males and females. In 1975, age standardized suicide rates were 25.9 per 100 000 in males and 10.9 per 100 000 in females. Rates rose by 3-fold in both sexes to a peak in 1995 of 77.9 per 100 000 in males and 27.8 per 100 000 in females (Figure 3). Subsequently rates halved in both sexes. By 2005, suicide rates were 37.3 per 100 000 and 9.7 per 100 000 in males and females, respectively. The highest rates occurred between 1987 and 1995 in both men and women and the timing of the reduction in rates was identical.
+There appeared to be little impact on suicides of the bans placed on methyl parathion and parathion in 1984, other than perhaps a slowing of the rapid rise in suicide rate noticed between 1961 and 1983. However, the ban on the final permitted class I pesticides methamidophos and monocrotophos (1995) and the class II pesticide endosulfan (1998) were both followed by marked reductions in overall suicides. The National Suicide Prevention Policy (December 1997) post-dated the decline in suicides and the National Poisons Information Service was introduced 7 years before the suicide reductions commenced (1988).
+The method-specific suicide data in Figure 4 indicate that the reduction in suicide occurred largely as a consequence of a reduction in self-poisoning and 'other’ methods of suicide.
+This interpretation is supported by hospital mortality data which show that in-hospital death rates from pesticide selfpoisoning halved from 12.0 per 100000 in 1998 to 6.5 per 100 000 in 2005. By 2005, in-hospital pesticide poisoning mortality rates (6.5 per 100 000) were lower than they had been in 1975 (6.9 per 100 000). Of note, however, there was a rise in in-hospital pesticide poisoning deaths from 8.7 per 100 000 in 1995 to 12.0 in 1998 following the 1995 ban on Class I pesticides (see Supplementary data at IJE on online).
+The reduction in suicides was not due to a reduction in levels of self-poisoning. Between 1993 and 1995 47411 people were admitted to hospital following pesticide poisoning (accidental and deliberate), this number increased by 16% to 55 160 in 2003-05. Furthermore, the reduction in suicides was not due to the reduced overall availability of pesticides. In 1995, 1736 metric tones of insecticides, herbicides and fungicides were used in Sri Lanka, this amount decreased only slightly (by 2%) to 1696 metric tons in 2000 (http://www.fao.org/docrep/008/af340e/ af340e0k.htm).25
+Age-specific suicide rates
+Age-specific suicide rates were investigated to determine whether the patterns of rise and decline differed by age (see Supplementary data at IJE on online). There was some evidence that in 17-35-year-old males and females, rates peaked earlier (the late 1980s) than in the older age groups. However, in all three age groups, in both males and females, the main period of decline in suicide was after 1995.
+Trends in risk factors associated with suicide
+Data on unemployment in Sri Lanka prior to 1990 were sparse.
+Available data (see Figure on web) show that the highest
+recorded levels of unemployment were in 1971 for males (14.3% unemployed) and 1981 for females (32.9%). Levels of unemployment declined steadily throughout the 1990s. Male and female unemployment declined in the 5 years before, and continued to decline in the 5 years after, the marked down turn in suicides in 1995. Male unemployment declined from 11.1% in 1990, to 9% in 1995 and 5.5% in 2005; female unemployment declined from 23.4% in 1990 to 18.7% in 1995 and 11.5% by 2005.
+Data on alcohol sales indicate that per capita alcohol consumption increased year on year from 1.81 litres per capita in 1981 to 7.37 litres per capita in 2003. Similarly, deaths from alcoholic liver diseases rose between 1991 and 1999. Such trends are likely to have an adverse rather than a beneficial impact on suicide rates.
+Published data indicate that divorce is unusual in Sri Lanka and there is little evidence of a sizeable change in divorce in recent years. Around 0.4% of 45-49-year old Sri Lankan women were divorced in the 1990 compared with 0.5% around 2000.23 The temporal changes in suicide do not appear to correspond with the period of civil war (1983-2002).24 By the time of the outbreak of the civil war in 1983, Sri Lankan suicide rates were already extremely high—37.6 per 100 000 in 1982. In subsequent years, suicide rates stabilized. The civil war was still in progress at the time suicide rates began declining in 1995. By the time of the ceasefire in 2002, rates had already halved and suicides rates were relatively stable in the 3 years after the ceasefire.
+Discussion
+The period of rapid rise in Sri Lanka’s suicide rate coincided with the emergence of the first reported cases of pesticide
+poisoning and a rapid rise in pesticide-related deaths between 1954 and 1963.31 Around this period the proportion of suicides due to poisoning increased from 37% (1959) to 72% (1969).32 The marked decline in Sri Lanka’s suicide rates in the mid-1990s coincided with the culmination of a series of legislative activities that systematically banned the most highly toxic pesticides that had been responsible for the majority of pesticide deaths in the preceding two decades.15 Other, earlier measures, such as the 1980 Control of Pesticides Act and the establishment of the National Poisons Information Centre in 1988 may have contributed to the levelling off of the epidemic rise in suicide in the 1970s but preceded the marked reductions by over 10 years. The decriminalization of suicide in 1998 is unlikely to be responsible for the decline in suicides after that year. If anything, decriminalization would be expected to lead to an increased willingness to report suicides, thereby resulting in an underestimation of suicide reductions occurring after that year.
+Of note, these reductions in suicide occurred against a background of rising levels of hospital admissions for pesticide self-poisoning.15 Nevertheless, increases in admissions do not necessarily signal increases in incidence; increased admissions could be due to changes in help-seeking, referral or admission policies. Whilst the earlier restrictions on pesticides in the 1990s were not associated with a marked impact on suicide rates, the halting of the year on year rise in suicides in the early 1990s occurred against a background of increases in pesticide self-poisoning incidents providing some evidence of their beneficial impact. In keeping with these findings, research in two different regions of Sri Lanka has documented a decline in the case-fatality of pesticide self-poisoning during 1997-2002 14 and 1991-2000.15
+In-hospital pesticide deaths increased for a short period after the ban on class I pesticides. This was most likely due to the increased incidence of endosulfan poisoning after 1995.14,15 Endosulfan is a highly toxic pesticide, with a delayed time to death, resulting in more patients surviving to hospital admission. The Class I OP pesticides previously available had a far quicker onset of action with many patients dying before hospital admission.
+The impact of the import bans in 1995 and 1998 on the pesticides used for self-poisoning are seen in two studies documenting changes in the pesticides ingested in acts of self-poisoning in Sri Lanka.14,15 Following the ban on Class I pesticides, there was a rise in the use of endosulfan for selfpoisoning and a marked reduction in the use of Class I pesticides. The last cases of class I pesticide poisoning deaths were seen in 1997 in Anuradhapura and 1999 in southern Sri Lanka. Endosulfan fatalities had declined markedly by 2000 in Anuradhapura and by 2002 in southern Sri Lanka from peaks in 1997/1998.14,15
+Before accepting that the reduction in suicide is the result of Sri Lanka’s restrictions on the sales of toxic pesticides it is important to review alternative explanations. We found no evidence that the trends were specifically associated with beneficial changes in levels of employment, alcohol sales, divorce or with periods of civil war. Data on alcohol sales must be treated with caution as these figures do not include illegally brewed alcohol that is consumed in large quantities in
+rural areas, but we are aware of no evidence of declining use of such alcohol between 1995 and 2005.
+The effects of any improvements in the recognition and treatment of depression following the Presidential Task Force’s report on suicide in 199727 are not possible to assess, but its publication post-dated the large fall in suicides between 1995 and 1997 and we are aware of no national mental health initiatives in Sri Lanka that are likely to have resulted in the observed 50% reduction in suicide. We were unable to investigate whether the favourable trends in suicide were due to changes in coding practices, i.e. self-inflicted deaths being more often coded as accidental. We feel this is unlikely. First, because following the decriminalization of suicide in 1998 it is likely that, if anything, more deaths previously coded as accidental would be coded as suicidal. Second, a detailed analysis of deaths in one region between 1990 and 2002 indicated that only 12% of fatal cases of pesticide poisoning were accidental.14 Third, rates of suicide by hanging increased and we would expect any changes in the classification of suicides to accidental deaths to affect all methods.
+As well as restricting or withdrawing a number of pesticides, Sri Lanka has also actively pursued a number of initiatives to reduce use and increase the safety of pesticide use by farmers— ‘Integrated Pest Management’. Furthermore, there has been considerable research interest in Sri Lanka in the medical management of self-poisoning and so it is possible that the improved management of pesticide self-poisoning has contributed to the favourable trends we observed.26,33-35 However, there has been no consistent marked improvement in the practice of, or facilities for, management of pesticide poisoning during this time that might explain the 50% reduction in suicides reported here.
+It has previously been hypothesised that the rise in suicide in Sri Lanka in the 1950-80s might have been due to a combination of increased levels of internal migration, with consequent social disruption, and high levels of youth unemployment in the context of improved education leading to changes in career aspirations.17,36 Over the period covered by our analysis we documented falling levels of unemployment, but these falls preceded the reductions in suicide by over 5 years.
+Limitations
+An important weakness in our analysis is the poor quality of the method-specific suicide data. Over the period of time covered by the data, the categories used to identify the specific methods used for suicide changed. Furthermore, extensive use was made of the 'other means’ of suicide category, despite the most commonly used methods of suicide in Sri Lanka (i.e. hanging, poisoning, drowning, burning and jumping in front of trains6,10,11) being available as specific coding categories throughout the study period. Nevertheless, it is of note that deaths by hanging, the second commonest method of suicide in Sri Lanka, rose throughout the study period. This provides some evidence that favourable trends in overall suicides were driven by a reduction in poisoning deaths rather than some other factor influencing all suicides regardless of method. Whilst deaths by self-poisoning and ‘other’ methods of suicide declined after 1995, the number of hospital
+admissions for self-poisoning continued to rise14,15 indicating either a reduction in the toxicity of the agents consumed or an improvement in their medical management. This suggestion is supported by the reduction in case-fatality from pesticide poisoning in the 1990s.14,15
+The second limitation of our analysis is that the data are ecological, thus limiting causal inference. Whilst the timing of reductions in suicide rates corresponds with pesticide related interventions and such downturns did not appear to be associated with changes in other risk factors for suicide, we cannot rule out the impact of other (unrecognised) factors in the absence of a control population. Our data are, however, supported by recent declines in the case-fatality of pesticide poisoning in Sri Lanka.14,15 Furthermore, data from two other Asian countries where pesticide regulation has not occurred— India and Korea—reveal differing trends in suicide. In India, Sri Lanka’s closest neighbour, self poisoning (mainly insecticides) accounts for 37% of suicides and there has been no equivalent decline in suicide rates.37 Suicide rates in India were 9.7 per 100000 in 1995, 10.8 per 100000 in 1998 and 10.3 per 100000 in 2005.37,38 In Korea, rates of pesticide suicide doubled between 1991 and 2001.39
+The effect of suicide on changes in the availability of commonly used suicide methods
+There is a growing body of evidence showing that changes in the availability of a commonly used method of suicide may influence not only method-specific, but also overall suicide rates. The strongest evidence for this comes from the natural experiment afforded by the detoxification of the domestic gas supply in Britain in the 1960s.2 Similar effects on method specific and overall suicides have been documented following the enactment of gun-control legislation40-43 and restricting the availability of barbiturates in Australia.44
+The most relevant example of the influence of the easy availability of lethal methods of suicides in relation to pesticide suicide comes from Western Samoa.45 In the 1970s and 80s there were marked fluctuations in Western Samoa’s imports of paraquat, a pesticide associated with a particularly high case-fatality (>60%46,47). These variations closely followed rise and falls in fatal episodes of paraquat self-poisoning and overall suicide. Indeed in the late 1970s/early 1980s, the period associated with the highest levels of paraquat imports, paraquat suicides accounted for around three quarters of suicides in West Samoa.45
+A concern with using method restriction as an approach to suicide prevention is that substitution of methods may occur. This will have an adverse effect on population suicide rates if the new methods are associated with a higher case-fatality than those they are replacing. The small rise in hanging (Figure 4) may signal such an effect is occurring in Sri Lanka. However, hospital admission data show there has been no decline in admissions for pesticide self-poisoning (see Results section) over the years suicides declined. This suggests people continue to self-poison with pesticides that have not been banned, thus case-fatality has declined because the available products are less toxic to humans.
+Implications
+Pesticides are readily available in most rural households in low income countries, they are commonly used by young people who impulsively poison themselves in moments of crisis.48-50. Pesticides are one of the most frequently used methods of suicide worldwide.8,9 For this reason, strategies to reduce pesticide self-poisoning deaths may have a major impact on global patterns of suicide.
+A number of strategies have been suggested to reduce these deaths.8,51 Our data suggest that restricting the availability of toxic pesticides should be prioritised. Such a response would be in keeping with previous calls for the WHO to introduce a minimum pesticides list to restrict the use of pesticides to a small number of pesticides least dangerous to humans.52 As well as import restrictions such as those imposed in Sri Lanka, other means of restricting the availability of pesticides include ensuring they are stored safely in locked boxes in rural households and restricting their sale to licensed premises and to licensed farmers.
+The high number of deaths from pesticide poisoning has been recognized for over 25 years20 but the international response has been slow and disjointed.53 In part, this reflects obvious tensions between the benefits and harms of pesticide use. The pesticide industry has developed its own response—most notably, the ‘Safe Use’ campaign in the early 1990s54 and several nations, such as Sri Lanka, have developed their own legislative frameworks to limit the use of the more toxic pesticides.
+We propose other countries such as China and India, where pesticide self-poisoning is a major health problem,18,55,56 follow Sri Lanka’s example in comprehensively regulating pesticide imports and sales. At the same time there is an urgent need for public health research to identify clearly the full range of costs and benefits associated with the widespread use of pesticides. This will allow nations to make policy decisions based on agricultural, environmental, nutritional and health grounds.
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+Suicide is a leading cause of premature mortality in low and high income countries. Despite this, information to guide prevention strategies is limited. One of the few approaches with a strong evidence base is preventing access to methods that are highly lethal and commonly used in suicidal acts.1 The rationale for this approach is based in four observations: suicide attempts are often impulsive, crises are often fleeting, prognosis is good after non-fatal attempts (<10% of people go on to die by suicide), and acts are more likely to be fatal when highly lethal methods are used.
+Placing barriers on bridges and other sites from which suicide by jumping is common therefore seems sensible. Jumping is highly lethal and deaths are often very public,2 leading to media reporting and possible contagion.3 Reviews and national guidelines have called for safety measures at “suicide hotspots.”4-6
+In the linked study, Sinyor and colleagues describe the apparent failure of the Bloor Street Viaduct barrier in Toronto to reduce suicide by jumping.7 They assessed the yearly rates of suicide by jumping at the Bloor Street Viaduct from 1993 to 2001 (nine years before the barrier) and from July 2003 to June 2007 (four years after the barrier). Although the barrier prevented suicides from the bridge—deaths fell from 9.3 a year to zero—this had no effect on the rates of suicide by jumping in the region as a whole (although suicide rates overall fell); indeed, there was a possible compensatory rise in suicides from other bridges.
+What do these findings mean for the efficacy of preventive barriers? A few notes of caution are important before abandoning their use at selected sites. Firstly, the relatively small number of suicides from the Bloor Street Viaduct (about 9.3 a year before the barrier; <4% of suicides in Toronto) make it impossible to draw any conclusions about the net effect of the barrier on suicide deaths overall. Secondly, suicides from other bridges may have been increasing in the years before the barrier was erected.7 Thus, the rise in suicides from other bridges may have resulted from an increase in the popularity of bridge jumping rather than substitution. Thirdly, other studies of bridge barriers (with similar sample size limitations) have been more favourable5: barriers on the Clifton Suspension Bridge (United Kingdom) led to a halving of deaths from the bridge and a reduction in overall suicides by jumping in the area by males (9 0% of those who jumped from the Clifton Suspension Bridge before the barriers were put in place were male).8 Fourthly, as the authors point out, people who jump from bridges that, like the Bloor Street Bridge, span hard surfaces (rather than water) and do not have the iconic status or aesthetic distinction of the Golden Gate or Clifton Suspension Bridge, may differ from those who jump from iconic sites. Moreover, jumpers may be less impulsive than
+people who use common suicide methods. Data from the US National Violent Injury Statistics System, for example, show that jumpers are less likely to have same-day crises, such as interpersonal conflicts on the day of the suicide, than people who use methods other than jumping (most of whom die in their homes); this suggests that restricting access to highly lethal household methods may, on average, be more effective.9 Lastly, suicide barriers reduce the number of people who witness gruesome, public suicides and the kind of high profile coverage of these violent deaths that may, in turn, trigger additional suicides.3
+From a population perspective, with the exception of city states, such as Hong Kong and Singapore, jumping is a relatively rare (<10% total) method of suicide4; deaths from specific hotspots are fewer still. If the ultimate goal of means restriction is to reduce the incidence of suicide, the most promising targets are therefore not hot spots, important as these may be, but methods that are commonly used, highly lethal, and readily accessible in or near the home (such as toxic pesticides in developing counties and firearms in the United States).
+To be effective in reducing the overall incidence of suicide, however, substitute methods must also be less lethal. Thus the detoxification of the domestic gas supply in the UK, bans on highly toxic pesticides in Sri Lanka, and decreases in household firearm ownership in the United States were followed by marked reductions in method specific suicides and overall suicide rates in these countries.10'12 These reductions occurred because there was limited substitution by other methods, or if substitution did occur, less lethal methods were used. Moreover, these reductions were measureable and significant at the population level because the restricted method accounted for a large proportion of deaths from suicide.
+Sinyor and colleagues’ study reminds us that means restriction may not work everywhere, that characteristics of the means targeted are important to consider, and that we have much to learn about the determinants of the choice of method in suicidal acts. Yet, where and when means restriction works, it may save more lives than other suicide prevention strategies, especially in children and young adults, who tend to act impulsively in fleeting suicidal crises.
+1	Mann JJ, Apter A, Bertolote J, Beautrais A, Currier D, Haas A, et al. Suicide prevention strategies: a systematic review. JAMA 2005;294:2064-74.
+2	Vyrostek SB, Annest JL, Ryan GW. Surveillance for fatal and nonfatal injuries—United States, 2001. MMWR Surveill Summ 2004;53:1-57.
+3	Pirkis J, Blood RW. Suicide and the media. Part I: reportage in nonfictional media. Crisis 2001;22:146-54.
+4	Gunnell D, Nowers M. Suicide by jumping. Acta Psychiatr Scand 1997;96:1-6.
+5	Beautrais A. Suicide by jumping. Crisis 2007;28:58-63.
+An increasing number of nations are developing national strategies to try to prevent some of the estimated million suicides that occur annually throughout the world. Detection of people at risk is a key component of such programmes. Although attempted suicide (or self harm) is an important risk factor,1 the risk of suicide after attempted suicide seems to differ between countries. In the United Kingdom, the risk of suicide in the first year after self harm, although 60-100 times greater than in the general population,2-4 is much lower than that reported from other countries.4
+In the linked cohort study, Runeson and colleagues assess the effect of the method of the attempted suicide on the risk of subsequent completed suicide.5 Using data from Swedish national case registers, the authors found that one in 10 people (12%) admitted to hospital after attempted suicide between 1973 and 1982 died by suicide during long term follow-up (to the end of 2003), 4.2% within the first year. These figures are three to four times greater than those found in UK samples.2-4 Possible explanations include differing sources of patient samples (all those in the Swedish study had been admitted for inpatient medical care), variations in characteristics of patient populations, and differences in general population levels of suicide.
+The most important finding from the Swedish study was that the risk of completed suicide varied considerably according to the methods used in earlier attempts. Compared with patients who had tried to poison themselves, those who used hanging (strangulation or suffocation) had a particularly high risk of subsequent suicide, with more than half eventually dying by suicide (and over two thirds of these people dying in the year after the index attempt). Risk was also higher in people who had tried to gas themselves, jumped from a height, used firearms, and attempted to drown. However, relative lethality should not be assumed from the general method used for self harm—in another Scandinavian study of patients who had attempted suicide by poisoning, those who had used more lethal poisons had a greater subsequent risk of suicide.6
+Although the absolute and relative levels of suicide risk after attempted hanging reported in Runeson and colleagues’ study are far greater than found in a recent UK study,7 the important point is that people who use more lethal methods in non-fatal attempts have a greater risk of subsequent suicide. One question that Runeson and colleagues could not answer is whether this is because people who use more lethal methods have greater suicidal intent (wish to die). Assessment of suicidal intent is a key element
+of the psychosocial assessment of patients who self harm.8 Higher suicidal intent indicates increased risk of eventual suicide, especially in the short term,9 although this measure can be a poor predictor in individual patients.10 Assessment of lethality and suicidal intent, which may be poorly correlated in some patients,11 is likely to be the most informative.
+As shown elsewhere,12 Runeson and colleagues found that patients who had psychiatric disorders diagnosed after suicide attempts had a greater risk of suicide. But within diagnostic categories the method used in attempts was also indicative of relative risk. Patients with psychotic disorders who used hanging in their attempts had a very high risk of eventual suicide (84.1% of men and 84.4% of women died during follow-up). Risk was also increased in patients with affective disorders (a more common diagnosis in patients who self harm) who used hanging. This highlights the importance of carefully looking for signs of psychiatric disorder during psychosocial assessment of patients with self harm.8
+Most people who died by suicide during follow-up in the Swedish study used the same method as in their non-lethal attempt, except where the initial method had been self cutting. This phenomenon was particularly pronounced for hanging, with more than nine of 10 people who used this method in their initial attempt doing so in their fatal act. Continuity of method was also high for drowning, jumping, and, in men, use of a firearm. This has relevance to restricting availability of methods that might be used for suicide in those at risk.
+The results of Runeson and colleagues’ study have important implications for assessment and aftercare of patients who self harm; however, caution is needed in their interpretation. Self poisoning is the most common method of self harm in people who present to general hospitals, and most people who subsequently died by suicide in the Swedish study had taken overdoses in the index attempt. Self cutting is the second most common method used for self harm in patients seen in general hospitals. In the Swedish study, such patients had a similar risk of subsequent suicide to those who self poisoned. Although use of more lethal methods of self harm is an important index of suicide risk, it should not obscure the fact that self harm in general is a key indicator of an increased risk of suicide.
+1	Foster T, Gillespie K, McClelland R, Patterson C. Risk factors for suicide
+independent of DSM-III-R axis I disorder. Br J Psychiatry 1999;175:175-9.
+2	Hawton K, Zahl D, Weatherall R. Suicide following deliberate self-harm: long-term follow-up of patients who presented to a general hospital. Br J Psychiatry 2003;182:537-42.
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+BACKGROUND
+Description of the condition
+The term ’self-harm’ is used to describe all non-fatal intentional acts of self-poisoning or self-injury, irrespective of degree of suicidal intent or other types of motivation (Hawton 2003a). Thus it includes acts intended to result in death (’attempted suicide’), those without suicidal intent (e.g., to communicate distress, to temporarily reduce unpleasant feelings), and those with mixed motivation (Hjelmeland 2002; Scoliers 2009). The term ’parasuicide’ was introduced by Kreitman 1969 to include the same range of behaviour. However, clinicians in the USA have used ’parasuicide’ to refer specifically to acts of self-harm without suicidal intent (Linehan 1991), and the term has largely fallen into disuse in the UK and other countries. The Diagnostic and Statistical Manual of Mental Disorders , fifth edition (DSM-5) includes two types of self-harming behaviour as conditions for further study, namely non-suicidal self-injury (NSSI) and suicidal behaviour disorder (SBD). Many researchers and clinicians, however, believe this to be an artificial and somewhat misleading categorisation (Kapur 2013). We have therefore used the approach favoured in the UK and some other countries where all intentional self-harm is conceptualised in a single category, namely self-harm (SH). Within this category, suicidal intent is regarded as a dimensional rather than a categorical concept. Readers more familiar with the NSSI and SBD distinction may regard SH as an umbrella term for these two behaviours (although it should be noted that neither NSSI nor SBD include non-fatal self-poisoning).
+SH has been a growing problem in most countries over the past 40 years. In the UK, researchers estimate that there are now more than 200,000 presentations to general hospitals per year (Hawton 2007). In addition, self-harm often occurs in adults in the community and does not come to the attention of clinical services or other helping agencies (Borges 2011). SH consumes considerable hospital resources in both developed and developing countries (Carter 2005; Claassen 2006; Fleischmann 2005; Gibbs 2004; Kinyanda 2005; Parkar 2006; Schmidtke 1996; Schmidtke 2004).
+Unlike suicide, in most countries SH usually occurs more commonly in females than males, although this gap decreases over the life cycle (Hawton 2008). It has also decreased in recent years (Perry 2012). SH predominantly occurs in young people, with 60% to 70% of individuals in many studies aged under 35 years. In females, rates tend to be particularly high among those aged 15 to 24, whereas in males the highest rates are usually among those in their late 20s and early 30s. SH is also less common in older people but then tends to be associated with high suicidal intent (Hawton 2008), with consequent greater risk of future suicide (Murphy 2012).
+Many people who engage in SH are facing acute life problems, often in the context of longer-term difficulties (Hawton 2003b). Common problems include disrupted relationships, employment
+difficulties, financial and housing problems, and social isolation. Alcohol abuse and, to a lesser extent, drug misuse are often present. There may be a history of adverse experiences, such as physical abuse, sexual abuse, or both. In older people, physical health problems, bereavement, and threatened loss of independence become increasingly important.
+Many patients who present to hospital following SH have psychiatric disorders, especially depression, anxiety, and substance misuse (Hawton 2013). These disorders frequently occur in combination with personality disorder (Haw 2001).
+Both psychological and biological factors appear to increase vulnerability to SH. Psychological factors include difficulties in problem-solving and a tendency to show black and white (or all or none) thinking patterns, low self-esteem, impulsivity, vulnerability to having pessimistic thoughts about the future (i.e., hopelessness) and a sense of entrapment ( O’Connor 2012: Williams 2000; Williams 2005). Biological factors include disturbances in the serotonergic and stress-response systems (Van Heeringen 2014).
+SH is often repeated, with 15% to 25% of individuals who present to hospital with SH returning to the same hospital following a repeat episode within a year (Carroll 2014; Owens 2002). Studies from Asia suggest a lower risk of repetition (Carroll 2014). There may be other repeat episodes that do not result in hospital presentation.
+The risk of death by suicide within one year amongst people who attend hospital with SH varies across different studies, from nearly 1% to over 3% (Carroll 2014; Owens 2002). This variation reflects differences in the characteristics of the SH population and background national suicide rates. During the first year after a SH episode, the risk in the UK is 50 to 100 times that of the general population (Cooper 2005; Hawton 1988; Hawton 2003b). Of people who die by suicide, over half will have a history of SH (Foster 1997), and at least 15% will have presented to hospital with SH in the preceding year (Gairin 2003). A history of SH is the strongest risk factor for suicide across a range of psychiatric disorders (Sakinofsky 2000). Repetition of SH further increases the risk of suicide (Zahl 2004).
+Description of the intervention
+Psychosocial interventions include a wide variety of treatments, for example cognitive behavioural therapy (CBT), problem-solving therapy, behaviour therapy, and dialectical behaviour therapy (DBT). Treatments may vary in relation to the initial management; the location of treatment; the continuity, intensity or frequency of contact with a therapist; and the mode of delivery (individual or group-based). We included treatments that focused on specific subgroups of SH patients in this review. These subgroups may be defined in terms of age, psychological characteristics or psychiatric diagnoses, substance misuse, and history of repetition of self-harm. We also included studies of strategies to maintain contact with patients, such as visits to patients with poor thera
+How the intervention might work
+The mechanisms of action of psychosocial interventions might include helping people improve their coping skills and self-esteem, tackle specific problems, overcome psychiatric disorders, increase their sense of social connectedness, and reduce impulsivity, aggression and unhelpful reactions to distressing situations.
+CBT-based psychotherapy
+CBT aims to help patients identify and critically evaluate the way in which they interpret and evaluate disturbing emotional experiences and events and then change the way they deal with problems (Westbrook 2011). The therapy has three steps. First, therapists help patients change the way in which they interpret and evaluate distressing emotions. Secondly, patients learn strategies to help them change the way they think about the meaning and consequences of these emotions. Lastly, with the benefit of modified interpretation of emotions and events, the therapy helps patients to change their behaviour and especially to develop positive functional behaviour (Jones 2012).
+Problem-solving therapy, which is an integral part of CBT, assumes that psychopathological processes such as SH are ineffective and maladaptive coping behaviours. Patients might overcome them by learning skills to actively, constructively and effectively solve the problems they face in their daily life (Nezu 2010). Therapists might achieve this by encouraging patients to consciously and rationally appraise problems, reduce or modify the negative emotions generated by problems, and develop a range of possible solutions to address their problems (D’Zurilla 2010). Treatment goals include helping patients to develop a positive problem-solving orientation, use rational problem-solving strategies, reduce the tendency to avoid problem-solving, and reduce the use of impulsive problem-solving strategies (Washburn 2012).
+Our rationale for including CBT and problem-solving therapy approaches in a single category of CBT-based psychotherapy in this review is that they share common elements. For example, problem-solving therapy incorporates other elements of behaviour therapy and constitutes a key part of cognitive behavioural therapy; also, cognitive-behavioural strategies are important for effective problem-solving therapy (Hawton 1989; Westbrook 2011)
+Interventions for multiple repetition of SH/probable personality disorder
+The goal of emotion-regulation training is to help patients find adaptive ways to respond to distress instead of trying to control, suppress or otherwise avoid experiencing these emotions through behaviours such as SH (Gratz 2007). Emotion-regulation training
+therefore helps patients in four stages: first, to become more aware and accepting of their emotional experiences; second, to engage in goal-directed behaviours whilst inhibiting the expression of impulsive ones; third, to use appropriate strategies to moderate the intensity and duration of their emotional responses; and fourth, to become more accepting of negative emotional experiences within their daily life (Gratz 2004).
+Dialectical behaviour therapy (DBT) combines problem-solving training, skills training, cognitive modification training and mindfulness techniques to encourage patients to accept their thoughts, feelings, and behaviours without necessarily attempting to change, suppress, or avoid these experiences (Lynch 2006; Washburn 2012). Within this framework, the aim of DBT is to help patients better regulate their emotions, achieve a sense of interpersonal effectiveness, become more tolerant of distressing thoughts and feelings, and become better at managing their own thoughts and behaviours (Linehan 1993b; Linehan 2007). The primary treatment goals of DBT are therefore threefold: to reduce SH, to reduce behaviours that interfere with treatment success (e.g., treatment nonadherence), and to reduce any other factors that may adversely affect the patient’s quality of life (e.g., frequency and duration of psychiatric hospitalisations) (Linehan 1993b). As the aim of DBT is to help patients change or adjust to significant personality characteristics, treatment is intensive and relatively prolonged.
+Mentalisation refers to the ability to understand the actions of both the self and of others as meaningful given knowledge of the desires, beliefs, feelings, emotions, and motivations that underscore their behaviour (Bateman 2004; Choi-Kain 2008). During times of interpersonal stress, however, individuals may fail to represent experiences in terms of mental states and instead become overwhelmed with negative thoughts and feelings about the self (Rossouw 2013). Behaviours such as SH may then represent an escape from these negative self-evaluations. Mentalisation therapy aims to improve patients’ ability to empathise with others in order to develop the ability to see how their own behaviours may have an impact on the feelings of others, and to regulate their own emotions more effectively (Rossouw 2013).
+Case management
+Case management in mental health services has mainly been developed for more severely ill patients. “In its simplest form . . . case management is a means of co-ordinating services. Each . . . person is assigned a ’case manager’ who is expected to assess that person’s needs; develop a care plan; arrange for suitable care to be provided; monitor the quality of the care provided; and maintain contact with the person (Holloway 1991)” (Marshall 2000a, p. 2). Case management might have a significant role in the aftercare of selfharm patients because of the recognised problem of poor treatment adherence in many patients and the heterogeneous nature of the problems patients are often facing (Hawton 2003b; Lizardi 2010). It has included, for example, provision of a care manager, crisis intervention, problem solving, assistance with getting to clinical
+appointments, and assertive outreach, each provided according to individual patient need (Morthorst 2012).
+Treatment adherence enhancement approaches
+These approaches include specific efforts to maintain contact with patients, such as following up patients in the community who fail to attend outpatient appointments (Van Heeringen 1995). It also includes strategies to encourage adherence with treatment (Hvid 2011).
+Having the same clinician who assessed a patient initially also providing any aftercare intervention may increase treatment adherence and may also have an advantage in that the clinician is already acquainted with the patient’s problems and needs.
+Remote contact interventions
+Remote contact interventions typically involve sending regular letters or postcards to patients. Patients may view this kind of intervention as a ’gesture of caring’ that may help to counteract the sense of social isolation many SH patients experience (Cooper 2011). This sense of “social connectedness” may, in turn, have a stabilising emotional effect (Motto 2001).
+Another type of remote contact intervention involves the use of emergency cards, which may encourage patients to seek help when they feel distressed as well as offering provision for on-demand emergency contact with psychiatric services (Kapur 2010).
+The fact that in many countries most individuals have their own general practitioner (GP) can also facilitate provision of care directly following SH. Interventions may include guidance for GPs on treating and managing problems commonly experienced by SH patients (e.g., depression, substance misuse, life problems). Such advice may also include advising GPs on referral of SH patients to local community services (Bennewith 2002).
+Telephone contact with patients following discharge from hospital can also help to ensure a continuing sense of contact with the service and be used to provide advice and possibly psychotherapy. The immediacy that psychotherapy by mobile telephone can achieve, when compared with standard clinic-based psychotherapy, may help with crisis management in times of distress (Marasinghe 2012).
+Why it is important to do this review
+SH is a major social and healthcare problem. It is responsible for significant morbidity, is often repeated, and has strong links to suicide. It also leads to substantial healthcare costs (Sinclair 2011). Many countries now have suicide prevention strategies (WHO 2014), which include a focus on improved management of patients presenting with SH due to their greatly elevated suicide risk and their high levels of psychopathology and distress. The National Suicide Prevention Strategy for England (Her Majesty’s
+Government Department of Health 2012) and the US suicide prevention strategy (Office of the Surgeon General 2012), for example, highlight SH patients as a high risk group for special attention.
+In recent years there has been considerable focus on improving the standards of general hospital care for SH patients. The Royal College of Psychiatrists published consensus guidelines for such services in 1994 and a further guideline in 2004 (Royal College of Psychiatrists 1994; Royal College of Psychiatrists 2004). While these guidelines focus particularly on organisation of services and assessment of patients, there clearly also need to be effective treatments for SH patients. These may include both psychosocial and pharmacological interventions. In 2004 the National Institute for Clinical Excellence (NICE) produced a guideline on self-harm, which focused on its short-term physical and psychological management (NCCMH 2004). More recently, NICE produced a second guide focused particularly on long-term management, using some interim data from the present review as the evidence base for therapeutic interventions (NICE 2011). A similar guideline was produced in Australia and New Zealand (Boyce 2003). We had previously conducted a systematic review of treatment interventions for SH patients in terms of reducing repetition ofSH (including suicide); this review highlighted the paucity of evidence for effective treatments, at least in terms ofthis outcome (Hawton 1998; Hawton 1999). The first NICE guideline essentially reinforced this conclusion (NCCMH 2004). However, there was emerging evidence for beneficial effects of short-term psychological therapy on other outcomes (depression, hopelessness, and problem resolution) (Townsend 2001). Using interim data from the present review, the second NICE guideline concluded that there was evidence showing clinical benefit of CBT-based psychotherapeutic interventions in reducing repetition of self-harm, compared with routine care (NICE 2011).
+We have now fully updated our original review in order to provide current evidence to guide clinical policy and practice. Previous versions of this review included SH patients of any age and both psychosocial and pharmacological interventions. We have now divided this research into three separate reviews, one of interventions in children and adolescents (Hawton 2015a), another of pharmacological interventions in adults (Hawton 2015b), and this, the third, focused on psychosocial interventions in adults. We have also now included data on treatment adherence, depression, hopelessness, problem-solving, and suicidal ideation.
+OBJECTIVES
+To assess the effects of specific psychosocial treatments versus either treatment as usual, enhanced usual care or other forms of alternative psychotherapy, in adults following SH.
+10
+Criteria for considering studies for this review
+Types of studies
+We included randomised controlled trials, including cluster-randomised, multi-arm, and cross-over trials of specific psychosocial interventions versus any comparator (e.g., treatment as usual [TAU]/enhanced usual care [EUC]/otheralternative forms ofpsy-chotherapy) in the treatment of adult SH patients.
+Types of participants
+Participant characteristics
+Participants were adult men and women (aged 18 and over) of all ethnicities. We also included trials where there were a small minority (< 15%) of adolescent participants. However, we undertook sensitivity analyses to assess the effect of including such studies.
+Diagnosis
+We included participants who had engaged in any type of non-fatal intentional self-poisoning or self-injury in the six months prior to trial entry resulting in presentation to clinical services. There were no restrictions on the frequency with which patients engaged in SH; thus, for example, we included trials where participants had frequently repeated SH (e.g., those with self-harming behaviour associated with borderline personality disorder).
+We defined SH as any non-fatal intentional act of self-poisoning or self-injury, irrespective of degree of suicidal intent or other types of motivation. Thus we included acts intended to result in death (attempted suicide), those without suicidal intent (e.g., to communicate distress, to temporarily reduce unpleasant feelings), and those with mixed motivation. Self poisoning includes both overdoses of medicinal drugs and ingestion of substances not intended for consumption (e.g., pesticides). Self-injury includes acts such as self-cutting, self-mutilation, attempted hanging, and jumping in front of moving vehicles. We only included trials where participants presented to clinical services as a result of SH.
+Co-morbidities
+There were no restrictions on included patients in terms of whether or not they had psychiatric disorders nor with regard to the nature of those disorders, with the exception of intellectual disability, as any SH behaviour is likely to be repetitive (e.g., head banging), and the purpose of this behaviour is usually different from that involved in SH (NICE 2011).
+Setting
+Interventions delivered in inpatient or outpatient settings were eligible for inclusion, as were trials from any country.
+Subset data
+We did not include trials in which only some participants had engaged in SH or trials in people with psychiatric disorders where SH was an outcome variable but not an inclusion criterion for entry into the trial.
+Types of interventions
+Comparisons included in this review were between any psychosocial intervention and any comparator (e.g., TAU/EUC/other alternative forms of psychotherapy, or placebo). As the trials included in this review assessed a wide variety of interventions, we developed categories or groups of interventions. This categorisation was based on consensus discussions within the review team and included decisions about combining trials in which there were superficial differences between treatments but the key methodologies between trials were similar. In some cases we sought more details of the therapy from authors to assist this process. Categorisation reflected both prior views on types of psychotherapy and the types of interventions that were identified as a result of the systematic review of the literature.
+Experimental interventions
+These included:
+1.	CBT-based psychotherapy;
+2.	interventions for multiple repetition of SH/probable personality disorder;
+3.	case management;
+4.	treatment adherence enhancement approaches;
+5.	mixed multimodal interventions;
+6.	remote contact interventions;
+7.	other mixed interventions.
+Comparator interventions
+Treatment as usual
+As treatment as usual (TAU) is likely to vary widely between settings, following previous work we defined TAU as any care that patient would receive had they not been included in the trial (i.e., routine care) (Hunt 2013).
+Enhanced usual care
+Enhanced usual care (EUC) refers to TAU that has in some way been supplemented, for example through the provision of psycho-
+1 1
+education, assertive outreach, or more regular contacts with case managers.
+Treatment by expert
+This typically consists of a treatment by a widely recognised authority with significant experience in treating individuals following SH.
+Other alternative forms of psychotherapy
+This included other forms of psychotherapy designed to be of lower duration or intensity than the experimental intervention and could include:
+1.	brief or short-term psychotherapy;
+2.	standard case management;
+3.	standard dialectical behaviour therapy (DBT).
+Discharge from hospital without further aftercare
+Types of outcome measures
+Primary outcomes
+The primary outcome measure in this review was the occurrence of repeated SH (defined above) over a maximum follow-up period of two years. Repetition was identified through at least one of the following: self-report, collateral report, clinical records, or research monitoring systems. As we wished to incorporate the maximum amount of data from each trial, we included both self-reported and hospital records of SH where available. We also assessed frequency of repetition of SH.
+Secondary outcomes
+1.	Treatment adherence
+This was assessed using a range of measures of adherence, including the proportion of participants that both started and completed treatment, pill counts, and changes in blood measures.
+2.	Depression
+This was assessed either continuously, as scores on psychometric measures of depression symptoms (for example total scores on Beck Depression Inventory (BDI; Beck 1961) or scores on the depression sub-scale of the Hospital Anxiety and Depression Scale (HADS; Zigmond 1983)), or dichotomously, as the proportion of patients reaching defined diagnostic criteria for depression. We included both patient- and clinician-reported instruments.
+3.	Hopelessness
+This was assessed as scores on psychometric measures of hopelessness, for example total scores on the Beck Hopelessness Scale (BHS; Beck 1974). We included both patient- and clinician-reported instruments.
+4.	Suicidal ideation
+This was assessed suicidal ideation either continuously, as scores on psychometric measures (for example total scores on the Beck Scale for Suicidal Ideation (BSSI; Beck 1988)), or dichotomously, as the proportion of patients reaching a defined cut-off for ideation. We included both patient- and clinician-reported instruments.
+5.	Problem solving
+Problem solving ability was assessed either continuously, as scores on psychometric measures (for example total scores on the Problem-Solving Inventory (PSI; Heppner 1988)), or dichotomously, as the proportion of patients with improved problems. We included both patient- and clinician-reported instruments.
+6.	Suicide
+This included both register-recorded deaths and reports from collateral informants such as family or neighbours.
+Timing of the outcome assessment
+We reported outcomes for the following time points.
+1.	At the conclusion of the treatment period.
+2.	Between 0 and 6 months after the conclusion of the treatment period.
+3.	Between 6 and 12 months after the conclusion of the treatment period.
+4.	Between 12 and 24 months after the conclusion of the treatment period.
+Hierarchy of outcome assessment
+Where a trial measured the same outcome (e.g., depression) in two or more ways, we used the most common measure across trials in any meta-analysis, but we also reported scores from the other measure in the text of the review.
+Search methods for identification of studies
+Electronic searches
+1. The Cochrane Depression, Anxiety and Neurosis Review Group Specialised Register (CCDANCTR)
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+12
+The Cochrane Depression, Anxiety and Neurosis Group (CC-DAN) maintains two clinical trials registers at their editorial base in Bristol, UK: a references register and a studies-based register. The CCDANCTR-References Register contains over 39,500 reports of randomised controlled trials on depression, anxiety and neurosis. Approximately 60% of these references have been tagged to individual, coded trials. The coded trials are held in the CC-DANCTR-Studies register and records are linked between the two registers through the use of unique study ID tags. Coding of trials is based on the EU-PSI Coding Manual. Please contact the CC-DAN Trials Search Coordinator for further details.
+Reports of trials for inclusion in the group’s registers are collated from weekly generic searches of MEDLINE (1950 to date), EM-BASE (1974 to date) and PsycINFO (1967 to date), as well as quarterly searches of the Cochrane Central Register of Controlled Trials (CENTRAL).
+We searched the CCDANCTR (Studies and References) database on 29 April 2015 using terms for self-harm (condition only), as outlined in Appendix 1.
+We applied no restrictions on date, language, or publication status to the search.
+2. Additional electronic database searches
+Sarah Stockton, librarian at the University of Oxford, conducted earliersearches (1998 to October2013) ofMEDLINE, EMBASE, PsycINFO and CENTRAL (The Cochrane Library), following the search strategy outlined in Appendix 2. As the CCDANCTR already contains relevant reports of RCTs from these databases, it was unnecessary to re-search these. Additionally, KW searched the Australian Suicide Prevention RCT Database (Christensen 2014). KW also conducted electronic searches of ClinicalTrials.gov and the ISRCTN registry using the keywords random* AND suicide attempt* ORself$harm* to identify relevant ongoing trials.
+Both the original version of this review as well as an unpublished version also incorporated searches of the following databases: SIGLE (1980 to March 2005) and SocioFile (1963 to July 2006). We updated the search of ClinicalTrials.gov and the ISRCTN registry to 29 April 2015.
+Searching other resources
+Handsearching
+For the original version of this review the authors hand-searched 10 specialised journals within the fields of psychology and psychiatry, including all English language suicidology journals, as outlined in Appendix 3. As these journals are now indexed in major electronic databases, we did not repeat hand-searching for this update.
+Reference lists
+We checked the reference lists of all relevant papers known to our review team as well as the reference lists of major reviews that include a focus on interventions for SH patients (Baldessarini 2003; Baldessarini 2006; Beasley 1991; Brausch 2012; Burns 2000; Cipriani 2005; Cipriani 2013; Comtois 2006; Crawford 2007a; Crawford 2007b; Daigle 2011; Daniel 2009; Dew 1987; Gould 2003; Gray 2001; Gunnell 1994; Hawton 1998; Hawton 1999; Hawton 2012; Hennen 2005; Hepp 2004; Hirsch 1982; Kapur 2010; Kliem 2010; Lester 1994; Links 2003b; Lorillard 2011a; Lorillard 2011b; Luxton 2013; Mann 2005; McMain 2007b; Milner 2015; Moller 1989; Moller, 1992; Montgomery 1995; Muehlenkamp 2006; Muller-Oerlinghausen 2005; Nock 2007; Ougrin 2011; Ougrin 2015; Tarrier 2008b; Tondo 1997; Tondo 2000; Tondo 2001; Townsend 2001; Van der Sande 1997b).
+Correspondence
+We consulted trial authors and other experts inthe field of suicidal behaviour to find out if they were aware of any ongoing or unpublished RCTs concerning the use of psychosocial interventions for adult SH patients.
+Data collection and analysis
+For details of the data collection and analysis methods used in the original version of this review, see Appendix 4.
+Selection of studies
+For this review update all review authors independently assessed the titles of trials identified by the systematic search for eligibility. A distinction was made between:
+1. eligible trials that compared any psychosocial intervention with a control (e.g., treatment as usual (TAU), enhanced usual care (EUC), or other alternative forms of psychotherapy);
+2. general treatment trials (without any control treatment).
+All trials identified as potentially eligible for inclusion underwent a second screening. Pairs of review authors, working independently from one another, screened the full text of relevant trials to identify whether the trials met our inclusion criteria.
+We resolved disagreements following consultation with KH. Where we could not resolve disagreements based on the information reported within the trial, or where it was unclear whether the trial satisfied our inclusion criteria, we contacted authors to provide additional clarification.
+Data extraction and management
+In the current update, KW and one other author (TTS, EA, DG, PH, ET or KvH) independently extracted data from included trials using a standardised extraction form. In case of disagreement, authors resolved them through consensus discussions with KH.
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+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+Data extracted from each eligible trial included participant demographics, details of the treatment and control interventions, and information on the outcome measures used to evaluate the efficacy of the intervention. We contacted study authors to obtain raw data for outcomes that were not reported in the full text of included trials.
+We extracted both dichotomous and continuous outcome data from eligible trials. As the use of non-validated psychometric scales is associated with bias, we extracted continuous data only if the psychometric scale used to measure the outcome of interest had been previously published in a peer-reviewed journal and was not subjected to item, scoring, or other modification by the trial authors (Marshall 2000b).
+We planned the following main comparisons.
+1.	CBT-based psychotherapy versus TAU or other alternative forms of psychotherapy.
+2.	Interventions for multiple repetition of SH/probable personality disorder versus TAU or other alternative forms of psychotherapy.
+3.	Case management versus TAU or other alternative forms of psychotherapy.
+4.	Treatment adherence enhancement approaches versus TAU or other alternative forms of psychotherapy.
+5.	Mixed multimodal interventions versus TAU or other alternative forms of psychotherapy.
+6.	Remote contact interventions versus TAU or other alternative forms of psychotherapy.
+7.	Other mixed interventions versus TAU or other alternative forms of psychotherapy.
+Assessment of risk of bias in included studies
+Given that highly biased trials are more likely to overestimate treatment effectiveness (Moher 1998), KW and one of TTS, EA, DG, PH, ET or KvH independently evaluated the quality of included trials by using the criteria described in Higgins 2008a. This tool encourages consideration of the following domains:
+1.	Random sequence generation.
+2.	Allocation concealment.
+3.	Blinding of participants and personnel.
+4.	Blinding of outcome assessment.
+5.	Incomplete outcome data.
+6.	Selective outcome reporting.
+7.	Other bias.
+We judged each source of potential bias as conferring low, high or unclear risk of bias, and we incorporated a supporting quotation from the report to justify this judgment. Where the original report provided inadequate details of the randomisation, blinding, or outcome assessment procedures, we contacted authors for clarification. We resolved disagreements through discussion with KH and reported risk of bias for each included trial in the text of the review. For cluster-randomised and cross-over trials, we used
+appropriate methods of assessing bias as outlined in Higgins 2011, sections 16.3.2 and 16.4.3.
+Measures of treatment effect
+Dichotomous outcomes
+We summarised dichotomous outcomes, such as the number of participants engaging in a repeat SH episode and the number of deaths by suicide, using summary odds ratios (OR) and the accompanying 95% confidence interval (CI), as the OR is the most appropriate effect size statistic for summarising associations between two dichotomous groups (Fleiss 1994).
+Continuous outcomes
+For outcomes reported on a continuous scale, we used mean differences (MD) and accompanying 95% CI where trials employed the same outcome measure. Where studies used different scales to assess a given outcome, we used the standardised mean difference (SMD) and its accompanying 95% CI.
+We only aggregated trials for the purposes of meta-analysis if treatments were sufficiently similar. For trials that could not be included in a meta-analysis, we have instead provided narrative descriptions of the results.
+Unit of analysis issues
+Zelen design trials
+Trials in this area increasingly employ Zelen’s method, in which investigators obtain consent after randomisation and treatment allocation. This design may lead to bias if, for example, participants allocated to one particular arm of the trial disproportionally refuse to provide consent for participation or, alternatively, if participants only provide consent provided they are allowed to cross over to the active treatment arm (Torgerson 2004). We included four trials that employed Zelen’s method in this review (Carter 2005; Hatcher 2011; Hatcher 2016a; Hatcher 2015). Given the uncertainty of whether to use data for the primary outcome based on all those randomised to the trial, or only those who consented to participation, we extracted data for the primary outcome measure using both sources where possible. We also conducted sensitivity analyses by excluding these trials to investigate what impact, if any, their inclusion had on the pooled estimate of treatment effectiveness.
+Cluster-randomised trials
+Cluster randomisation, for example by clinician or general practice, can lead to overestimation of the significance of a treatment effect, resulting in an inflation of the nominal type I error rate,
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+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+unless there is appropriate adjustment for the effects of clustering (Donner 2002; Kerry 1998). We planned to statistically adjust for the effects of clustering following the guidance outlined in Higgins 2008b, section 16.3.4. Clustering was an issue in one included study (Bennewith 2002); however, we were unable to adjust for the effects of clustering in subsequent analyses as the study authors could not provide us with either the intercluster coefficient or the design effect.
+Cross-over trials
+A primary concern with cross-over trials is the ’carry-over’ effect, in which the effect of the intervention treatment (e.g., pharmacological, physiological, or psychological) influences the participant’s response to the subsequent control condition (Elbourne 2002). As a consequence, on entry to the second phase of the trial participants may differ systematically from their initial state despite a wash-out phase. This, in turn, may result in a concomitant underestimation of the effectiveness of the treatment intervention (Curtin 2002a; Curtin 2002b). One trial included in the current update used cross-over methodology (i.e., Marasinghe 2012). To protect against the carry-over effect, we only extracted data from the first phase of this trial, prior to cross-over.
+Studies with multiple treatment groups
+Two trials in the current review included multiple treatment groups (Stewart 2009; Wei 2013).As both intervention arms in the Stewart 2009 trial investigated the effectiveness of CBT-based psychotherapy, we combined dichotomous data from these two arms and compared them with data from the TAU arm. For outcomes reported on a continuous scale, we combined data using the formulagiven in Higgins 2011, section 7.7.3.8. Wei 2013 compared two different psychotherapies with TAU, namely CBT-based psychotherapy and telephone contact. Therefore we included this trial in both categories of intervention using the data from the relevant experimental arm. As we did not combine these interventions in any meta-analysis, we used the same TAU data for both analyses.
+Studies with adjusted effect size estimates
+None of the trials included in the current update calculated adjusted effect sizes. In future updates of this review, however, where trials report both unadjusted and adjusted effect sizes, we will include only unadjusted effect sizes.
+Dealing with missing data
+We as review authors did not impute missing data, as we considered that the bias that would be introduced by doing this would have outweighed any benefit (in terms of increased statistical power) that may have been gained by the inclusion of imputed data. However, where authors omitted standard deviations (SD) for contin
+uous measures, we estimated these using the method described in Townsend 2001.
+Dichotomous data
+Although many authors conducted their own intention-to-treat analyses, few presented intention-to-treat analyses as defined by Higgins 2008b. Therefore, we based outcome analyses for both dichotomous and continuous data on all information available on trial participants. For dichotomous outcomes, we included data on only those participants whose results were known, using as the denominator the total number of participants with data for the particular outcome of interest, as recommended (Higgins 2008b).
+Continuous data
+For continuous outcomes, we included data only on observed cases.
+Missing data
+Where data on outcomes of interest were incomplete or excluded from the text of the trial, we contacted authors to request further information.
+Assessment of heterogeneity
+It is possible to assess between-study heterogeneity using either the Chi2 or I2 statistic. In this review, however, we used only the I2 statistic to determine heterogeneity, as Higgins 2003 considers this to be more reliable. The I2 statistic indicates the percentage of between-study variation due to chance and can take any value from 0% to 100% (Higgins 2003). We used the following values to denote unimportant, moderate, substantial, and considerable heterogeneity, respectively: 0% to 40%, 30% to 60%, 50% to 90%, and 75% to 100% as per the guidance in the Cochrane HandbookforSystematic ReviewsofInterventions (Deeks 2008, section 9.5.2). Where we found substantial levels of heterogeneity (i.e., > 75%), we explored possible reasons. We also planned to investigate heterogeneity even when the I2 statistic was lower than 75% but either the direction or magnitude of a trial effect size was clearly discrepant from that of other trials included in the metaanalysis (see Subgroup analysis and investigation of heterogeneity section for further information on these analyses).
+We also report heterogeneity in the results section but only when we observed substantial levels, as indicated by an I2 statistic of 75% or greater.
+Assessment of reporting biases
+Reporting bias occurs when the direction and significance of a particular trial’s results influence the decision to publish a report on it (Egger 1997). Research suggests, for example, that trials with statistically significant findings are more likely to be submitted and
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+subsequently accepted for publication (Hopewell 2009), leading to possible overestimation of the true treatment effect. To assess whether trials included in any meta-analysis were affected by reporting bias, we entered data into a funnel plot but only, as recommended, when a meta-analysis included results of at least 10 trials. Where evidence of any small-study effects were identified, we explored reasons for funnel plot asymmetry, including the presence of publication bias (Egger 1997).
+Data synthesis
+For the purposes of meta-analysis, we calculated the pooled OR and accompanying 95% CI using the random-effects model, as this is the most appropriate model for incorporating heterogeneity betweentrials (Deeks 2008, section 9.5.4). Specifically, we usedthe Mantel-Haenszel method for dichotomous data and the inverted variance method for continuous data. However, we also undertook a fixed-effect analysis to investigate the potential effect of method choice on the estimates of treatment effect. We descriptively report any material differences in ORs between these two methods in the text of the review. All analyses were conducted in Review Manager, version 5 (RevMan 2014).
+Sensitivity analysis
+We undertook sensitivity analyses, where appropriate, as outlined below.
+1.	Trial/s that used Zelen’s method of randomisation (see Unit of analysis issues section).
+2.	Trial/s that contributed substantial levels of between-study heterogeneity (see Subgroup analysis and investigation of heterogeneity section).
+3.	Trial/s that included some adolescent participants.
+4.	Trial/s that specifically recruited individuals diagnosed with borderline personality disorder.
+Summary of findings table
+We prepared a ’Summary of findings’ table for the primary outcome measure, repetition of SH, following recommendations outlined in Schunemann 2008a, section 11.5. This table provides information concerning the overall quality of evidence from each included trial. We prepared the ’Summary of findings’ table using GRADEpro software (GRADE profiler). We assessed quality of the evidence following recommendations in Schunemann 2008b, section 12.2.
+Subgroup analysis and investigation of heterogeneity
+RESULTS
+Subgroup analyses
+In the original version of this review, we planned to undertake subgroup analyses by repeater status (i.e., history of multiple episodes of SH vs first known episode of SH) and gender but found there were insufficient data. Consequently, in this update we only undertook a priori subgroup analyses by sex or repeater status where there were sufficient data to do so.
+Investigation of heterogeneity
+Where meta-analyses were associated with substantial levels of be-tween-study heterogeneity (i.e., as indicated by an I2 statistic > 75%), KH and KW first independently triple-checked the data to ensure that the review team had correctly entered the data. Assuming this was the case, we investigated the source of heterogeneity by visually inspecting the forest plot and removing each trial that had a very different result from the general pattern of the others until homogeneity was restored as indicted by an I2 statistic < 75%. We have reported the results of this sensitivity analysis in the text of the review alongside hypotheses regarding the likely causes of heterogeneity.
+Description of studies
+Results of the search
+In this update, the search strategy outlined in Appendix 1 and Appendix 2 yielded a total of 23,725 citations. We identified a further 10 trials that were ongoing at the time of the systematic search through correspondence and discussion with researchers in the field. All have subsequently been published.
+In consultation with CCDAN, we divided the original review (Hawton 1998; Hawton 1999) into three separate reviews: the present review focuses on psychosocial interventions for adults, a second review deals with pharmacological interventions for adults (Hawton 2015b), and the third assesses interventions for children and adolescents (Hawton 2015a). Nine of the additional 10 trials evaluated psychosocial interventions for adults, and were therefore included in the present update. The remaining trial evaluated an intervention for children and adolescents; this is included in the related relevant review (i.e., Hawton 2015a).
+After deduplication, the initial number of citations decreased to 16,700. Ofthese, we excluded 16,459 after screening the titles and abstracts and a further 217 after reviewing the full texts (Figure 1).
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+no information on the proportion of the sample with a history of multiple episodes of SH.
+In around half of the included trials (k = 25; 45.4%), the authors stated either within the trial report or through correspondence that they included participants irrespective of whether or not the episode of SH involved suicidal intent (Bateman 2009; Beautrais 2010; Bennewith 2002; Clarke 2002; Davidson 2014; Fleischmann 2008; Harned 2014; Hatcher 2016a; Hatcher 2015; Hatcher 2011; Hawton 1981; Hawton 1987a; Hvid 2011; Kapur 2013a; Liberman 1981; Linehan 2006; McAuliffe 2014; McMain 2009; Patsiokas 1985; Slee 2008; Tapolaa 2010; Torhorst 1987; Turner 2000; Tyrer 2003; Van Heeringen 1995; Waterhouse 1990). Seven trials included participants following a ’suicide attempt’ (i.e., suggestive of evidence of suicidal intent) (Allard 1992; Cedereke 2002; Morthorst 2012; Salkovskis 1990; Torhorst 1988; Van der Sande 1997a; Wei 2013).A further five trials included participants only if there had been evidence of suicidal intent (Brown 2005; Kawanishi 2014; Marasinghe 2012; Stewart 2009; Welu 1977), whilst in an additional two, the majority of participants (76.5% and 74.0% respectively) expressed a wish to die (Guthrie 2001; Husain 2014). Gratz 2014 included participants only if there was no evidence of suicidal intent, whilst only 2.0% in one trial expressed a wish to die (Vaiva 2006). Thirteen trials did not report information on suicidal intent (Carter 2005; Crawford 2010; Dubois 1999; Evans 1999a; Evans 1999b; Gibbons 1978; Gratz 2006; Hassanian-Moghaddam 2011; Linehan 1991; McLeavey 1994; Morgan 1993; Priebe 2012; Weinberg 2006).
+Twenty-five trials did not report information on the methods of SH for the index episode (Allard 1992; Bateman 2009; Cedereke 2002; Davidson 2014; Dubois 1999; Evans 1999b; Fleischmann 2008; Gratz 2006; Gratz 2014; Hvid 2011; Kapur 2013a; Linehan 1991; Linehan 2006; Marasinghe 2012; McMain 2009; Morthorst 2012; Patsiokas 1985; Priebe 2012; Salkovskis 1990; Stewart 2009; Tapolaa 2010; Turner 2000; Tyrer 2003; Wei 2013; Weinberg 2006). One trial provided information on the methods used in all episodes of SH (including the index episode) in the two years preceding trial entry (Liberman 1981). A total of 55.7% of these episodes involved self-poisoning and 44.3% involved self-injury. One additional trial provided information on methods used in the three months prior to trial entry with a total of 61 (67.8%) participants in this trial engaging in self-injury at least once over this period; however, investigators did not specify the methods that the remaining 29 participants used for SH (Slee 2008). We present methods of SH for the remaining 28 trials in Table 2. In these trials, the majority of participants had engaged in self-poisoning (k = 28; 79.9%). Two trials included only those who engaged in self-injury (Harned 2014; Welu 1977), whilst in a further trial the majority of participants (75.6%) had engaged in self-poisoning using pesticides (Husain 2014).
+Co mo rbidities
+We present information on current psychiatric diagnoses for all 55 trials in Table 3. Eight trials focused specifically on participants diagnosed with borderline personality disorder (Bateman 2009; Gratz 2006; Gratz 2014; Linehan 1991; Linehan 2006; McMain 2009; Turner 2000; Weinberg 2006), three focused on participants diagnosed with any personality disorder (Davidson 2014; Evans 1999b; Priebe 2012), one focused specifically on participants diagnosed with alcohol use (Crawford 2010), and one focused on participants with comorbid post-traumatic stress disorder and borderline personality disorder (Harned 2014).
+Details on comorbid diagnoses were not reported in the majority of trials (k = 49; 89.1%) (Allard 1992; Bateman 2009; Beautrais 2010; Bennewith 2002; Cedereke 2002; Clarke 2002; Crawford 2010; Dubois 1999; Evans 1999a; Evans 1999b; Fleischmann 2008; Gibbons 1978; Gratz 2006; Guthrie 2001; Harned 2014; Hassanian-Moghaddam 2011; Hatcher 2016a; Hatcher 2015; Hatcher 2011; Hawton 1981; Hawton 1987a; Husain 2014; Hvid 2011; Kapur 2013a; Kawanishi 2014; Liberman 1981; Linehan 1991; Linehan 2006; Marasinghe 2012; McAuliffe 2014; McLeavey 1994; Morgan 1993; Morthorst 2012; Patsiokas 1985; Priebe 2012; Salkovskis 1990; Stewart 2009; Tapolaa 2010; Torhorst 1987; Torhorst 1988; Turner 2000; Tyrer 2003; Vaiva 2006; Van der Sande 1997a; Van Heeringen 1995; Waterhouse 1990; Wei 2013; Weinberg 2006; Welu 1977). In Brown 2005, most participants (85.0%) were diagnosed with more than one psychiatric disorder; however, the authors did not provide information on specific diagnoses. In an additional three trials (Carter 2005; McMain 2009; Slee 2008), the median number of psychiatric diagnoses was greater than two, suggesting that participants in these trials were also diagnosed with more than one psychiatric disorder; however, none of the three reported further information on specific diagnoses. In one trial, 45.0% of the participants were diagnosed with comorbid personality disorder and substance misuse (Davidson 2014), whilst in another, just under half of the sample (45.9%) had a comorbid personality diagnosis (Gratz 2014).
+Setting
+Of the 55 independent RCTs included in this review, most took place in the United Kingdom (k = 17) or the United States (k = 12), followed by New Zealand (k = 4), Australia (k = 2), Canada (k = 2), Denmark (k = 2), France (k = 2), Germany (k = 2), the Netherlands (k = 2), and one each from Belgium, China, Finland, Iran, Japan, Pakistan, the Republic of Ireland, Sri Lanka, and Sweden. One trial was a multicentre study conducted in a number of countries.
+Interventions
+The trials included in this review investigated the effectiveness of various forms of psychosocial intervention.
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+19
+1.	CBT-based psychotherapy versus TAU (k = 18: Brown 2005; Davidson 2014; Dubois 1999; Evans 1999b; Gibbons 1978; Guthrie 2001; Hatcher 2011; Hawton 1987a; Husain 2014; McAuliffe 2014; Patsiokas 1985; Salkovskis 1990; Slee 2008; Stewart 2009; Tapolaa 2010; Tyrer 2003; Wei 2013; Weinberg 2006).
+2.	Interventions for multiple repetition of SH versus TAU (k = 6: Bateman 2009; Gratz 2006; Gratz 2014; Linehan 1991; McMain 2009; Priebe 2012) or other alternative forms of psychotherapy (k = 3: Harned 2014; Linehan 2006; Turner 2000).
+3.	Case management versus TAU (k = 4: Clarke 2002; Hvid 2011; Kawanishi 2014; Morthorst 2012).
+4.	Treatment adherence enhancement approaches versus TAU (k = 1: Van Heeringen 1995) or other alternative forms of psychotherapy (k = 1: Torhorst 1987).
+5.	Mixed multimodal interventions versus TAU (k = 2: Hatcher 2016a; Hatcher 2015).
+6.	Remote contact interventions versus TAU (k = 11: Beautrais 2010; Bennewith 2002; Carter 2005; Cedereke 2002; Evans 1999a; Hassanian-Moghaddam 2011; Kapur 2013a; Marasinghe 2012; Morgan 1993; Vaiva 2006; Wei 2013).
+7.	Other mixed interventions versus TAU (k = 5: Allard 1992; Crawford 2010; Fleischmann 2008; Van der Sande 1997a; Welu 1977) or other alternative forms of psychotherapy (k = 5: Hawton 1981; Liberman 1981; McLeavey 1994; Torhorst 1988; Waterhouse 1990).
+Outcomes
+Information on the primary outcome, repetition of SH, was available for all but one of the included trials (Patsiokas 1985). In the case of four trials, we obtained information on this outcome following correspondence with authors (Marasinghe 2012; McAuliffe 2014; McMain 2009; Tapolaa 2010). For around half of the trials (k = 24; 43.6%), information on repetition of SH was based on self-report (Brown 2005; Cedereke 2002; Davidson 2014; Evans 1999b; Gratz 2006; Gratz 2014; Harned 2014; Hassanian-Moghaddam 2011; Hawton 1981; Hawton 1987a; Husain 2014; Liberman 1981; Linehan 1991; Linehan 2006; McMain 2009; Priebe 2012; Slee 2008; Tapolaa 2010; Torhorst 1988; Turner 2000; Vaiva 2006; Van Heeringen 1995; Wei 2013; Weinberg 2006), whilst in a further eight trials, collateral reports, hospital/clinical records, or both supplemented the selfreported information (Allard 1992; Bateman 2009; Bennewith 2002; Fleischmann 2008; Guthrie 2001; Hvid 2011; Morthorst 2012; Tyrer 2003). In 11 trials, information on repetition of SH was based on hospital re-presentations (Beautrais 2010; Carter 2005; Clarke 2002; Crawford 2010; Evans 1999a; Hatcher 2016a; Hatcher 2015; Hatcher 2011; Kapur 2013a; Waterhouse 1990; Welu 1977), whilst in three others, investigators obtained the information from hospital or medical records (Gibbons 1978;
+Morgan 1993; Van der Sande 1997a). McLeavey 1994 used collateral records supplemented by hospital records to determine repetition of SH. McAuliffe 2014 used mixed methods to determine repetition of SH, using self-reported information at the post-intervention and six-month follow-up assessments and data on hospital re-presentations at the 12 month follow-up assessment. Patsiokas 1985 did not report information on repetition of SH, and our review team was unable to obtain this information through correspondence. The remaining seven trials provided no details about the source of the data on repetition of SH (Dubois 1999; Kawanishi 2014; Marasinghe 2012; Salkovskis 1990; Stewart 2009; Torhorst 1987).
+The 19 trials that recorded information on treatment adherence assessed this using a variety of methods, including: the proportion of participants who completed the full course of treatment (Bateman 2009; Harned 2014; Husain 2014; Linehan 1991; McMain 2009; Priebe 2012; Slee 2008; Torhorst 1987; Turner 2000), the proportion of participants who attended at least one treatment session (Bennewith 2002; Cedereke 2002; Hawton 1981), and the number of treatment sessions attended (Brown 2005; Evans 1999b; Van Heeringen 1995). Three trials assessed treatment adherence using both the proportion of participants that completed the full course of treatment and the total number of treatment sessions attended (McAuliffe 2014; McLeavey 1994; Torhorst 1988), whilst the remaining trial assessed treatment adherence using both the proportion of participants who attended at least one treatment session and the total number of treatment sessions attended (Van der Sande 1997a).
+Investigators assessed depression using the BDI in 13 trials (Bateman 2009; Fleischmann 2008; Gibbons 1978; Guthrie 2001; Hawton 1987a; Husain 2014; Linehan 1991; Marasinghe 2012; McAuliffe 2014; McMain 2009; Salkovskis 1990; Slee 2008; Tapolaa 2010), the Hamilton Rating Scale for Depression (HRSD; Hamilton 1960) in 3 trials (Harned 2014; Linehan 2006; Wei 2013), both the BDI and HRSD in 1 trial (Turner 2000), the Depression Anxiety Stress Scales (DASS; Lovibond 1995) in 2 trials (Gratz 2006; Gratz 2014), the Montgomery-Asberg Depression Rating Scale (MADRS; Montgomery 1979) in 1 trial (Van der Sande 1997a), the depression sub-scale of the HADS in 6 trials (Davidson 2014; Evans 1999b; Hatcher 2016a; Hatcher 2015; Hatcher 2011; Tyrer 2003), both the BDI and the HRSD in 1 trial (Brown 2005), and both the BDI and the Zung Self-Rating Depression Scale (ZSRDS; Zung 1965) in a further trial (Liberman 1981). In one trial it was not clear what scale the researchers used to assess depression (Torhorst 1988).
+All 14 trials that recorded information on hopelessness assessed this using the BHS (Brown 2005; Hatcher 2016a; Hatcher 2015; Hatcher 2011; Husain 2014; Kawanishi 2014; Linehan 1991; McAuliffe 2014; McLeavey 1994; Patsiokas 1985; Salkovskis 1990; Stewart 2009; Van der Sande 1997a; Waterhouse 1990). Ten trials assessed suicidal ideation using the BSSI (Cedereke 2002; Davidson 2014; Guthrie 2001; Hatcher 2011; Husain
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+2014; Marasinghe 2012; McAuliffe 2014; Patsiokas 1985; Stewart 2009; Turner 2000), two trials used the Suicide Behaviors Questionnaire (SBQ; Linehan 1981) (Linehan 2006; Weinberg 2006), one trial used the Scale for Suicidal Ideators (SSI; Schotte 1982) (Linehan 1991), and one the suicidal ideation sub-scale of the Psychiatric Status Schedule (PSS; Spitzer 1970) (Waterhouse 1990). Three trials measured suicidal ideation dichotomously as the proportion with self-reported suicidal ideation (Hassanian-Moghaddam 2011; Liberman 1981; Wei 2013).
+Hatcher 2011 assessed problem-solving using the Social Problem-SolvingInventory-Revised (SPSI-R; D’Zurilla 1996); Husain 2014 used the Coping Resource Inventory (CRI; Marting 1988); Patsiokas 1985, the Means-Ends Problem Solving test (MEPS; Maydeu-Olivares 1996); Salkovskis 1990, the Personal Questionnaire Rapid Scaling Technique (PQRST; Mulhall 1977); McAuliffe 2014 and McLeavey 1994, both the MEPS and SelfRated Problem Solving Scale (SRPSS; McLeavey 1987); Slee 2008, the oriented coping subscale of the Coping Inventory for Stressful Situations (CISS; Endler 1994); and Fleischmann 2008 (at one site of the World Health Organization’s (WHO) multisite trial SUPRE-MISS, as reported in Xu 2012), the problem-solving sub-scale of an idiosyncratic problem-solving questionnaire. Gibbons 1978 and Hawton 1987a measured problem-solving dichotomously as the proportion of participants self-reporting improved problems at follow-up.
+In about half of the 44 trials (k = 24; 54.5%) that recorded information on completed suicide, the method used to assess this outcome was unclear (Bateman 2009; Beautrais 2010; Brown 2005; Clarke 2002; Davidson 2014; Dubois 1999; Gratz 2006; Gratz 2014; Guthrie 2001; Harned 2014; Husain 2014; Kapur 2013a; Linehan 1991; Linehan 2006; Marasinghe 2012; McMain 2009; McLeavey 1994; Priebe 2012; Salkovskis 1990; Slee 2008; Stewart 2009; Tapolaa 2010; Torhorst 1987; Weinberg 2006). In the 20 remaining trials, investigators used a variety of methods to assess completed suicide, including: collateral reports (Crawford 2010; Fleischmann 2008; Hawton 1987a; Wei 2013), Coroner’s records (Hatcher 2016a; Hatcher 2015; Hatcher 2011; Hvid 2011; Tyrer 2003), hospital records, medical records or both (McAuliffe 2014; Morthorst 2012), mortality statistics (Carter 2005; Cedereke 2002; Kawanishi 2014; Van Heeringen 1995), collateral reports supplemented by medical records or Coroner’s records (Allard 1992; Hassanian-Moghaddam 2011), collateral report supplemented by mortality statistics (Van der Sande 1997a), hospital records, medical records or both, supplemented by mortality statistics (Vaiva 2006), or mortality statistics supplemented by Coroner’s records (Evans 1999a).
+Excluded studies
+We excluded a total of 217 articles from this update: 94 in which not all participants engaged in SH; 60 that used a non-randomised clinical trial design; 27 that were reviews, editorials, letters to the editor, or conference proceedings; 23 that were trial protocols; 11 where SH could have occurred at any point rather than within six months of randomisation; one that only presented data from one trial arm (although a related publication that presented data for both the intervention and control arms was eligible for inclusion), and one that reported data reported for a period beyond two years (although articles reporting data for earlier follow-up periods for this trial were eligible for inclusion).
+We excluded one trial that had been included in the original version of this review following advice from CCDAN due to bias in the method used to randomise participants to the intervention and control groups (Chowdhury 1973). We also had to exclude one further trial that otherwise met inclusion criteria for this review as correspondence with authors revealed that information on nonfatal SH could not be disaggregated from information on completed suicide (Chen 2013).
+We provide details on the reasons for excluding 52 trials clearly related to psychosocial interventions for suicidality in the Characteristics of excluded studies section.
+Ongoing studies
+We identified a total of 20 ongoing trials of psychosocial interventions for SH in adults. We provide full details of these trials in the Characteristics of ongoing studies section. We note that one of these trials has subsequently been terminated owing to the resignation of key clinical staff and a lack of ongoing funding (Agyapong 2013).
+Studies awaiting classification
+Four potentially relevant trials are currently awaiting assessment (see Characteristics of studies awaiting classification table), two of DBT (Andreasson 2016; Linehan 2015), one of a mixed multimodal intervention combining face-to-face psychosocial therapy with a remote contact intervention (Gysin-Maillart 2016) and one trial testing the effectiveness of implementation intentions (volitional help sheet) in reducing suicidal ideation and behaviour (Armitage 2016).
+Risk of bias in included studies
+We present summaries of the overall risk of bias for the included trials in Figure 2 and Figure 3. Risk of bias for each included trial is also considered within the text of the review.
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+Allocation
+Sequence generation
+All of the 55 independent trials used random allocation. We considered the majority (k = 40; 72.7%) to have a low risk of bias for this item. In most trials a computer-generated randomisation sequence was used to allocate adults to the experimental and control groups (Beautrais 2010; Brown 2005; Carter 2005; Guthrie 2001; Hatcher 2016a; Hatcher 2015; Hatcher 2011; Husain 2014; Kapur 2013a; Kawanishi 2014; Linehan 1991; Linehan 2006; McAuliffe 2014; Morthorst 2012; Priebe 2012; Slee 2008; Tyrer 2003; Vaiva 2006; Van der Sande 1997a; Wei 2013). In two trials a minimisation algorithm was used to allocate participants to the experimental and control groups (Bateman 2009; Harned 2014) whilst in one a pre-generated block randomisation procedure was used (McMain 2009). In the remaining trials a variety of other randomisation procedures were used, including: a random numbers table (Bennewith 2002; Clarke 2002; Crawford 2010; Davidson 2014; Fleischmann 2008; Hassanian-Moghaddam 2011; Hawton 1981; Hawton 1987a; Welu 1977), shuffled envelopes (Gibbons 1978; Morgan 1993; Salkovskis 1990; Waterhouse 1990; Weinberg 2006), numbers drawn from a hat (Stewart 2009), and coin tossing (Tapolaa 2010). In one trial details on the method used to allocate participants to the invention and control groups were not provided, but the authors undertook post hoc analyses to investigate the distribution of various pre-treatment factors and found no significant difference in the distribution of these factors between the two groups, suggesting that the randomisation procedure used was unbiased (Turner 2000). We therefore also rated this trial as having low risk of bias for this item.
+We rated 13 trials (23.6%) as having unclear risk of bias for sequence generation, as study authors provided no information on the method used to allocate participants to the experimental and control groups (Cedereke 2002; Dubois 1999; Gratz 2006; Gratz 2014; Hvid 2011; Liberman 1981; Marasinghe 2012; Patsiokas 1985; Torhorst 1987; Torhorst 1988). In an additional three trials opaque, sealed envelopes were used, but it was unclear whether these were shuffled to ensure random sequence generation (Allard 1992; Evans 1999a; Evans 1999b). We rated the two remaining trials as having high risk of bias for this item, as investigators used an open numbers table to allocate participants to the experimental and control groups (McLeavey 1994; Van Heeringen 1995).
+Allocation concealment (selection bias)
+We considered just over half of the included trials to be at low risk of bias for allocation concealment (k = 31; 56.4%). A
+third-party researcher working independently of the trial team handled allocation in nine trials (Beautrais 2010; Bennewith 2002; Clarke 2002; Guthrie 2001; Harned 2014; Hassanian-Moghaddam 2011; Hvid 2011; Morthorst 2012; Vaiva 2006), 14 trials used opaque, sealed envelopes (Allard 1992; Cedereke 2002; Crawford 2010; Evans 1999a; Evans 1999b; Gibbons 1978; Hawton 1981; Hawton 1987a; McMain 2009; Morgan 1993; Salkovskis 1990; Waterhouse 1990; Weinberg 2006; Van der Sande 1997a), and a remote/offsite researcher allocated participants in six trials (Bateman 2009; Fleischmann 2008; Husain 2014; Kawanishi 2014; Slee 2008; Tyrer 2003). In the one remaining trial information was not provided on the method used to conceal allocation, but correspondence with authors confirmed that they had adequately concealed allocation (Linehan 1991).
+We rated a total of 18 trials (32.7%) as having unclear risk of bias for this item, as they provided no information on the method used to conceal allocation (Brown 2005; Davidson 2014; Dubois 1999; Gratz 2006; Gratz 2014; Kapur 2013a; Liberman 1981; Linehan 2006; Marasinghe 2012; Patsiokas 1985; Priebe 2012; Stewart 2009; Tapolaa 2010; Torhorst 1987; Torhorst 1988; Turner 2000; Wei 2013; Welu 1977). We also rated six trials as having a high risk of bias for this item. For four trials this was because the Zelen’s design, in which participant consent is obtained after randomisation, was used (Carter 2005; Hatcher 2016a; Hatcher 2015; Hatcher 2011), whilst for the remaining two trials, this was because randomisation was via an open numbers table (McLeavey 1994; Van Heeringen 1995).
+Blinding
+Blinding was assessed separately for participants, clinical personnel, and outcome assessors.
+Blinding of participants
+Overall, we classified the majority of trials (k = 53; 96.4%) as having high risk of bias for blinding of participants, as it is generally not possible to blind participants to psychological therapy. In Harned 2014, correspondence with study authors clarified that allocation was concealed from participants until their first therapy session, at which point the therapist informed participants as to which treatment condition they had been allocated, confirming that participants were not blind to treatment allocation. We rated one trial as having low risk of bias for this item, as the authors asserted that “[t]he subjects were blinded as to their assignment” (Fleischmann 2008, p. 704). We rated the remaining trial as having an unclear risk of bias for this item; although authors did not provide any information on participant blinding, treatments were so similar that participants might have been blind to which treatment they were receiving (Liberman 1981).
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+Blinding of personnel
+We classified the majority of trials (k = 52; 94.5%) as having high risk of bias for blinding of clinical personnel, as it is not possible to blind clinicians to the psychological therapy they are delivering. We rated one trial as having an unclear risk of bias for this item as, although GPs received a copy of the green (emergency) card given to participants randomised to the experimental group, it is unclear whether GPs were aware which of their patients received this card (Morgan 1993). We rated the two remaining trials as having a low risk of bias for this item, as clinicians were masked to allocation status (Beautrais 2010; Hassanian-Moghaddam 2011).
+Blinding of outcome assessors
+As outcome assessors were blind to treatment allocation in 30 (54.5%) of the trials included in this review, we rated the majority of trials as having low risk of bias for blinding of outcome assessment. We rated 10 trials as having a high risk of bias for this item, as outcome assessor blinding was not possible: in four trials this was due to reliance on self-reported information from participants who were not blind to treatment allocation (Guthrie 2001; Slee 2008; Torhorst 1987; Van Heeringen 1995), and in six it was due to issues related to feasibility, implementation, or both (Allard 1992; Brown 2005; Gratz 2006; Hassanian-Moghaddam 2011; Stewart 2009; Waterhouse 1990). We rated the remaining 15 trials as having an unclear risk of bias for this item as they did not provide information on outcome assessor blinding (Bennewith 2002; Cedereke 2002; Dubois 1999; Fleischmann 2008; Liberman 1981; Morgan 1993; Morthorst 2012; Patsiokas 1985; Salkovskis 1990; Tapolaa 2010; Torhorst 1988; Tyrer 2003; Van der Sande 1997a; Wei 2013; Welu 1977).
+Incomplete outcome data
+For most trials the authors reported having conducted analyses on an intention-to-treat basis, earning them a rating of low risk for this item (k = 33; 60.0%), although the method used to conduct these analyses was not clear for the majority of these trials (Allard 1992; Bateman 2009; Beautrais 2010; Carter 2005; Cedereke 2002; Crawford 2010; Davidson 2014; Evans 1999a; Guthrie 2001; Hassanian-Moghaddam 2011; Hawton 1987a; Husain 2014; Hvid 2011; Kapur 2013a; Kawanishi 2014; Marasinghe 2012; Morgan 1993; Morthorst 2012; Salkovskis 1990; Turner 2000; Vaiva 2006; Van der Sande 1997a; Wei 2013; Weinberg 2006). Three used regression methods (Bennewith 2002; Clarke 2002; Priebe 2012), one used longitudinal modelling (Brown 2005), and one used Bayesian Markov chain Monte Carlo simulation (Gratz 2014). The remaining three trials combined intention-to-treat with per protocol analyses (Harned 2014; Hatcher 2011; McMain 2009; Slee 2008), so we also rated them as having low risk of bias for this item. We rated seven trials as having unclear risk, as insufficient information was provided to confirm whether
+intention-to-treat or per protocol analyses had been undertaken (Hawton 1981; Patsiokas 1985; Torhorst 1987; Torhorst 1988; Tyrer 2003; Waterhouse 1990; Van Heeringen 1995). We classified the remaining 15 trials as having high risk of bias because per protocol analyses were undertaken (Dubois 1999; Evans 1999b; Fleischmann 2008; Gibbons 1978; Gratz 2006; Hatcher 2015; Hatcher 2016a; Liberman 1981; Linehan 1991; Linehan 2006; McAuliffe 2014; McLeavey 1994; Stewart 2009; Tapolaa 2010; Welu 1977).
+Selective reporting
+As the review authors did not have access to trial protocols for the trials included in this review, it is difficult to assess the extent to which selective outcome reporting could have occurred. Consequently, we classified the majority of trials as having an unclear risk of bias for this item (k = 52; 94.5%). We rated the remaining three trials as having high risk of bias for this outcome because data on pre-specified outcomes were not reported in the text (Kawanishi 2014; McLeavey 1994; Torhorst 1987).
+Other potential sources of bias
+We classified most trials as having low risk of bias for this item as no evidence of other bias was apparent (k = 47; 85.4%). We rated seven trials as at high risk of bias for this item. Three had used Ze-len’s post-consent randomisation procedure to allocate participants to the experimental and control groups (Hatcher 2011; Hatcher 2016a; Hatcher 2015). In an additional three there were substantial imbalances between the experimental and control groups on a number of putative risk factors for repetition of SH, despite randomisation (Beautrais 2010; Davidson 2014; Torhorst 1987). We rated the remaining trial as at high risk of other bias because a small number of participants in the control group (n = 20; 5.1%) mistakenly received the intervention treatment and yet were included in the control group for all subsequent analyses (Carter 2005). We rated one further trial as having unclear risk of bias for this item, as the participants were biased towards more compliant patients who were willing and able to attend a psycho-education session at the commencement of treatment and were able to attend hospital regularly for case management sessions and follow-up face-to-face interviews. Additionally, this trial excluded individuals who had engaged in non-suicidal SH from participation (Kawanishi 2014). Few trials used systematic means to investigate whether participants were able to guess if they had been allocated to the experimental or control arm.
+Thirteen trials did not indicate the source of funding (Allard 1992; Dubois 1999; Hatcher 2016a; Hatcher 2015; McLeavey 1994; Morgan 1993; Patsiokas 1985; Salkovskis 1990; Stewart 2009; Tapolaa 2010; Torhorst 1987; Turner 2000; Waterhouse 1990). Fifteen trials received funding from national medical associations, research organisations, or both (Brown 2005; Gratz
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+2014; Harned 2014; Hawton 1987a; Kapur 2013a; Liberman 1981; Linehan 1991; Linehan 2006; McMain 2009; Priebe 2012; Slee 2008; Tyrer 2003; Van Heeringen 1995; Wei 2013; Welu 1977). An additional nine trials received funding from a health organisation (Bennewith 2002; Carter 2005; Clarke 2002; Davidson 2014; Evans 1999b; Fleischmann 2008; Gratz 2006; Guthrie 2001; Vaiva 2006). The remaining trials received funding from a variety of sources, including: research organisations associated with specific diagnostic groups (Bateman 2009; Weinberg 2006), charitable trusts (Crawford 2010), private research foundations (Cedereke 2002), accident compensation organisations (Hatcher 2011), health insurance companies (Van der Sande 1997a), government departments (Evans 1999a; Hawton 1981; Kawanishi 2014; Torhorst 1988), and university sources (Marasinghe 2012). A number of trials also received joint funding, including from health and accident compensation organisations (Beautrais 2010), health organisations and legal charities (Hassanian-Moghaddam 2011), university sources and educational institutes (Husain 2014), government departments and health organisations (Gibbons 1978), government departments and health insurance companies (Hvid 2011 ), government departments and charitable trusts (Morthorst 2012), and health organisations and national research organisations (McAuliffe 2014).
+Effects of interventions
+See: Summaryof findings forthe main comparison Comparison 1: CBT-based psychotherapy vs treatment as usual; Summary of findings 2 Comparison 2: Interventions for multiple repetition of SH/probable personality disorder vs treatment as usual or other alternative forms of psychotherapy; Summary of findings 3 Comparison 3: Case management vs treatment as usual or other alternative forms of psychotherapy; Summary of findings 4 Comparison 4: Adherence enhancement approaches vs treatment as usual or other alternative forms of psychotherapy; Summary of findings 5 Comparison 5: Mixed multimodal interventions vs treatment as usual; Summary of findings 6 Comparison 6: Remote contact interventions vs treatment as usual; Summary of findings 7 Comparison7: Othermixed interventions vs treatment as usual or other alternative form of psychotherapy
+Comparison I: CBT-based psychotherapy vs treatment as usual (TAU)
+Eighteen trials assessed the effectiveness of CBT-based psychotherapy, in which participants in the experimental group were offered some form of specific psychological therapy, such as cognitive behavioural therapy or problem-solving therapy (Brown 2005, N = 120; Davidson 2014, N = 20; Dubois 1999, N = 102; Evans 1999b, N = 32; Gibbons 1978, N = 400; Guthrie 2001, N = 119; Hatcher 2011, N = 1094; Hawton 1987a, N = 80; Husain 2014, N = 221; McAuliffe 2014, N = 433; Patsiokas 1985, N = 15; Salkovskis 1990, N = 20; Slee 2008, N = 82; Stewart 2009, N =
+32; Tapolaa 2010, N = 16; Tyrer 2003, N = 480; Wei 2013, N = 162; Weinberg 2006; N = 30). One of these used Zelen’s design (Hatcher 2011). In most trials, therapy was typically very brief (i.e., less than 10 sessions), and it was delivered on an individual basis in all but one (McAuliffe 2014). One trial included only patients with borderline personality disorder (Weinberg 2006). In Stewart 2009, there were separate treatment arms for cognitive behavioural therapy and problem-solving therapy. We therefore combined data from these two conditions using the formula outlined in section 7.7.3.8 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).
+Primary outcome
+1.1	Repetition of SH
+There was no evidence of a significant treatment effect for CBT-based psychotherapy on repetition of SH by post-intervention in McAuliffe 2014 (23/171 vs 27/142; OR 0.66, 95% CI 0.36 to 1.21; k = 1; N = 313; GRADE: low quality).
+By the six-month follow-up assessment, however, on the basis of data from 12 trials there was evidence of a significant treatment effect for CBT-based psychotherapy on repetition of SH (Analysis 1.1;OR0.54,95%CI0.34 to0.85;k= 12;N=1317),with moderate quality of evidence (see Summary of findings for the main comparison). Omitting Weinberg 2006, which included only participants diagnosed with borderline personality disorder, did not materially affect this result. There was, however, evidence of a significant difference by modality (Analysis 1.1; test for subgroup differences: Chi2 = 7.32, degrees of freedom (df) = 1, P = 0.007, I2 = 86.3%). Specifically, although individual CBT-based psychotherapy was associated with a significant treatment effect on repetition of SH by the six-month follow-up assessment (OR 0.52, 95% CI 0.36 to 0.75; k = 11; N = 1083), a group-based approach was not associated with a significant treatment effect for this outcome in one trial (OR 1.35, 95% CI 0.75 to 2.41; k= 1; N = 234; McAuliffe 2014).
+There was also evidence of a significant treatment effect for CBT-based psychotherapy by the 12-month follow-up assessment in 10 trials (Analysis 1.2; OR 0.80, 95% CI 0.65 to 0.98; k = 10; N = 2142), again with a moderate quality of evidence (see Summary of findings for the main comparison). Once again, omitting Weinberg 2006 did not materially affect this result, nor did omitting Hatcher 2011, which used Zelen’s design. Hatcher 2011 also reported numbers of participants self-reporting an episode of SH rather than those admitted to hospital following an episode of SH. Using these data, however, did not materially affect this result. There was no evidence of a significant difference by modality for this outcome (Analysis 1.2; test for subgroup differences: Chi2 = 1.68, df= 1,P = 0.19, I2 = 40.6%).
+In two trials data on repetition of SH from 12 to 24 months were reported. A significant treatment effect for this outcome was
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+found (Analysis 1.3; OR 0.31, 95% CI 0.14 to 0.69; k = 2; N = 105; GRADE: moderate quality) (see Summary of findings for the main comparison).
+Including all 17 trials that reported information on repetition of SH suggested a significant treatment effect for CBT-based psychotherapy by the final follow-up assessment (i.e., including data for the last follow-up assessment available in each trial) (Analysis 1.4; OR0.70, 95% CI 0.55 to 0.88; k = 17;N = 2665). Excluding Hatcher 2011 or Weinberg 2006 did not materially affect these results. Once again, using data on self-reported incidents of SH for Hatcher 2011 did not materially affect this result, nor did using data for the randomised rather than consenting group. There was also no evidence of a significant difference by modality for this outcome (Analysis 1.4; test for subgroup differences: Chi2 = 3.08, df = 1, P = 0.08, I2 = 67.5%). However, quality of evidence, as assessed by the GRADE criteria, was low for this outcome (see Summary of findings for the main comparison).
+With respect to frequency of SH, data from six trials indicated no significant treatment effect for CBT-based psychotherapy on frequency of repetition of SH by final follow-up (Analysis 1.5; k = 6; N = 594). Excluding Weinberg 2006 did not materially affect this result. There was no evidence of a significant difference by modality (Analysis 1.5; test for subgroup differences: Chi2 = 1.17, df= 1, P = 0.28, I2 = 14.2%). However, this outcomewasassociated with low quality of evidence (see Summary of findings for the main comparison). One trial reported information on median, rather than mean, number of episodes of SH at six months. However, the authors found that although “[t]he rate of self-harm episodes was lower in the [experimental] group . . . [it was not] significantly so” (Evans 1999b, p.22). A further trial reported information on median number of episodes of SH at12 months’ follow-up, finding that “[t]he median number of self-harm episodes was two in both [the experimental and TAU] groups” (Tyrer 2003, p. 972).
+Secondary outcomes
+1.2	Treatment adherence
+Data on adherence was reported for both the experimental and control groups in one trial in which a significant treatment effect for CBT-based psychotherapy on the proportion of participants who completed all 12 sessions of therapy in addition to the three follow-up appointments was found (40/40 vs 33/42; OR 22.97, 95% CI 1.29 to 409.37; k = 1;N = 82; Slee 2008).
+Four trials reported adherence data for the experimental group only (Brown 2005; Evans 1999b; Husain 2014; McAuliffe 2014). Brown 2005 found that “participants in the cognitive therapy (CT) group participated in a mean (SD) of 8.92 (5.97) CT sessions (range 0-24). Thirty participants (50%) received ten or more CT sessions” (p. 568). In Evans 1999b, five participants in the experimental group did not have specific sessions of manual-assisted
+cognitive-behaviour therapy (MACT) and received almost all input from the booklet component of CBT alone. Overall, 17 of the 18 participants in the experimental group received the booklets. Husain 2014 found that “more than half of the (intervention) group attended all six sessions (n= 56)” (p. 466).
+McAuliffe 2014, in which a group-based approach was used, likewise found that “almost half of those assigned to [problem-solving therapy] (103, 46.4%) attended all 6 therapy sessions” (p. 4).
+1.3	Depression
+There was no evidence of a significant treatment effect for CBT-based psychotherapy on depression scores at the post-intervention assessment in McAuliffe 2014 (mean 18.20, SD 14.80, n = 171 vs mean 20.60, SD 16.0, n = 142; MD -2.40, 95% CI -5.84 to 1.04; k = 1; N = 313).
+Data on depression scores at six months’ follow-up suggested a significant treatment effect for CBT-based psychotherapy on depression scores (Analysis 1.6; SMD -0.30, 95% CI -0.50 to -0.10; k = 11; N = 1668). Omitting Hatcher 2011, which used Zelen’s design, did not materially affect this result. There was also no evidence of a significant difference by modality (Analysis 1.6; test for subgroup differences: Chi2 = 1.38, df = 1, P = 0.24, I2 = 27.3%). Seven trials reported data on depression scores at 12 months, suggesting evidence of a significant treatment effect for psychological therapy (Analysis 1.7; SMD -0.36, 95% CI -0.64 to -0.07; k = 7; N = 1130; I2 = 76%). This outcome was associated with substantial levels of heterogeneity (I2 = 76%). Omitting Hatcher 2011 did not materially affect the result nor the heterogeneity. Visual inspection of the forest plot did not clearly indicate which trial/s contributed to this substantial level of heterogeneity.
+Only two trials reported depression scores between 12 and 24 months’ follow-up; however, there was no evidence of a significant treatment effect for CBT-based psychotherapy (Analysis 1.8; k = 2; N = 225). One trial also reported data on depression scores at 6 and 12 months’ follow-up (Hawton 1987a); however, there was not enough information to enable calculation of the SD. Nevertheless, the authors reported that there were no significant differences between groups in BDI scores at any time point.
+Analysis of all 14 trials at final follow-up indicated a significant treatment effect for CBT-based psychotherapy on depression ( Analysis 1.9; SMD -0.31, 95% CI -0.48 to -0.14; k = 14; N = 1859). Omitting Hatcher 2011 did not materially affect this result.
+1.4	Hopelessness
+There was no evidence of a significant treatment effect for CBT-based psychotherapy on hopelessness at the post-intervention assessment in three trials, regardless of treatment modality (Analysis 1.10; test for subgroup differences: Chi2 = 2.06, df = 1, P = 0.15, I2 = 51.5%).
+However, by the six-month follow-up assessment, CBT-based psychotherapy was associated with a significant treatment effect in
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+four trials (Analysis 1.11; SMD -0.36, 95% CI -0.58 to -0.13; k = 4; N = 968). There was evidence of a difference by treatment modality for this outcome, however (Analysis 1.11; test for subgroup differences: Chi2 = 8.11, df = 1, P = 0.004, I2 = 87.7%). A group-based CBT-based psychotherapy approach was not associated with a significant treatment effect on hopelessness scores at six months in one trial (MD -0.30, 95% CI -1.89 to 1.29; k= 1; N = 234; McAuliffe 2014).
+Three trials reported data on hopelessness scores at 12 months, again showing evidence of a significant treatment effect for CBT-based psychological therapy (Analysis 1.12; MD -1.89, 95% CI -2.97 to -0.81; k = 3; N = 539). Omitting Hatcher 2011, which used Zelen’s design, did not materially affect these results.
+Analyses of all seven trials also suggested evidence of a significant treatment effect at final follow-up (Analysis 1.13, SMD -0.31, 95% CI -0.51 to -0.10; k = 7; N = 1017). Omitting Hatcher 2011 did not materially affect this result. There was also no evidence of a significant treatment difference by modality for this outcome (Analysis 1.13; test for subgroup differences: Chi2 = 3.65, df = 1, P = 0.06, I2 = 72.6%).
+1.5	Suicidal ideation
+There was no evidence of a significant treatment effect for CBT-based psychotherapy in three trials at the post-intervention assessment, regardless of treatment modality (Analysis 1.14; k = 3; N = 360; test for subgroup differences: Chi2 = 1.84, df = 1, P = 0.17, I2 = 45.8%). However, sensitivity analyses using the fixed-effect, rather than random-effects model, suggested evidence of a significant treatment effect for CBT-based psychotherapy on this outcome (fixed: MD -1.93, 95% CI -3.83 to -0.04).
+By the six-month follow-up assessment, CBT-based psychotherapy was associated with a significant treatment effect for suicidal ideation scores (Analysis 1.15; SMD -0.32, 95% CI -0.51 to -0.13; k = 6; N = 1011). Omitting Hatcher 2011, which used Ze-len’s design, did not materially affect this result, nor did omitting Weinberg 2006, in which participants had been diagnosed with personality disorders. There was evidence of a significant difference by treatment modality, however (Analysis 1.15; test for subgroup differences: Chi2 = 6.69, df = 1, P = 0.010, I2 = 85.1%), with a group-based CBT-based psychotherapy approach not appearing to be associated with a significant treatment effect for this outcome (Analysis 1.15; MD -0.20, 95% CI -2.49 to 2.09; McAuliffe 2014).
+Data from Hatcher 2011 suggested that CBT-based psychotherapy was not associated with a significant treatment effect for suicidal ideation scores at 12 months (mean 3.70, SD 6.70, n = 187 vs mean 4.80, SD 7.40, n = 231; MD -1.10, 95% CI -2.45 to 0.25; k = 1; N = 418).
+Including all eight trials suggested evidence for a significant treatment effect for psychological therapy on suicidal ideation scores at final follow-up (Analysis 1.16; SMD -0.32, 95% CI -0.53 to -0.11;
+k = 8; N = 1129). Omitting Hatcher 2011 or Weinberg 2006 did not materially affect this result. However, once again there was evidence of a significant difference by treatment modality (Analysis 1.16; test for subgroup differences: Chi2 = 4.61, df = 1, P = 0.03, I2 = 78.3%), with a group-based psychotherapy approach not associated with a significant treatment effect for this outcome (Analysis 1.16; MD -0.02, 95% CI -0.24 to 0.20; McAuliffe 2014).
+Wei 2013 recorded data on the proportion of participants selfreporting suicidal ideation. There was no evidence of a significant treatment effect for CBT-based psychotherapy in this trial at the six-month follow-up assessment (30/35 vs 32/40; OR 1.50, 95% CI 0.44 to 5.10; k = 1; N = 75). Although data were also available for the 12-month follow-up period, authors reported a greater number of participants in the CBT arm who self-reported suicidal ideation (n = 30) than were reported to have been followed-up by this point (n = 25). As we were unable to clarify these numbers with the authors, we have excluded this analysis from the review.
+1.6	Problem solving
+Two trials recorded dichotomous data on problem-solving as the proportion of participants reporting improvement in problems. There was evidence of a significant treatment effect for CBT-based psychotherapy on problem-solving in these trial trials at the six-month follow-up assessment (Analysis 1.17; OR 2.81, 95% CI 1.50 to 5.24; k = 2; N = 231). However, for the same dichotomous outcome, there was no indication of any apparent treatment effect at the 12-month follow-up assessment in Hawton 1987a (24/30 vs 26/35; OR1.38, 95% CI 0.43 to 4.47; k = 1;N = 65). Gibbons 1978 reported data for problem-solving at the 24-month followup assessment, with evidence of a significant treatment effect for CBT-based psychotherapy (64/73 vs 40/73; OR 5.87, 95% CI 2.54 to 13.54; k = 1; N = 146). Analysis of both these trials at the final follow-up assessment, however, suggested no overall evidence of a significant treatment effect for CBT-based psychotherapy on problem-solving (Analysis 1.18; k = 2; N = 211). On the other hand, there was a significant difference using the fixed-effect model (fixed: OR3.66, 95% CI 1.88 to7.09).
+Data on problem-solving scores at post-intervention indicated no evidence of a significant treatment effect for CBT-based psychotherapy with no evidence of a significant difference by treatment modality (Analysis 1.19; test for subgroup differences: Chi 2 = 0.07, df = 1, P = 0.79, I2 = 0%). By the six-month followup, however, there was evidence of a significant treatment effect for CBT-based psychotherapy for this outcome (Analysis 1.20; SMD 0.33, 95% CI 0.08 to 0.58; k = 4; N = 949). Omitting Hatcher 2011, which used Zelen’s design, caused this effect to become non-significant (MD 0.24, 95% CI -0.03 to 0.51). There was also evidence of a significant difference by treatment modality (Analysis 1.20; test for subgroup differences: Chi2 = 8.11, df = 1, P = 0.004, I2 = 87.7%), with a single trial of group-based psychotherapy indicating no evidence of a significant treatment effect
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+for this outcome (MD 0.30, 95% CI -3.55 to 4.15; McAuliffe 2014). There was no apparent benefit for CBT-based psychotherapy in a single trial at 12 months (mean 92.2, SD 18.1, n = 190 vs mean 90.5, SD 18.9, n = 233; MD 1.70, 95% CI -1.84 to 5.24; k = 1; N = 423; Hatcher 2011). Combining all five trials at final follow-up suggested evidence of a significant treatment effect for problem solving at the final follow-up assessment (Analysis 1.21; SMD 0.26, 95% CI 0.02 to 0.50; k = 5; N = 958). Omitting Hatcher 2011 caused the association to become non-significant (SMD 0.35, 95% CI -0.00. to 0.69; k = 4;N = 535).
+Finally, Salkovskis 1990 reported the severity of participants’ three main problems using the Personal Questionnaire Rapid Scaling Technique (PQRST) at one week, one month, three months, six months and one year following entry to treatment. The authors reported that “the problem-solving therapy group showed significantly better overall results on their three main problems when compared with the group who received ’treatment as usual’ ” (Salkovskis 1990, p. 873).
+1.7	Suicide
+Fifteen trials reported data on suicides during follow-up; however, there was no evidence of a significant treatment effect for CBT-based psychotherapy on suicides by final follow-up (Analysis 1.22; k = 15; N = 2354). In Tyrer 2003, there was one death in the experimental group that medical staff considered to be a suicide, although the coroner did not record a suicide verdict in this case. Including this death as a suicide did not materially change the overall result.
+Comparison 2: Interventions for multiple episodes of SH/probable personality disorder vs TAU or other alternative forms of psychotherapy
+A number of trials investigated provision of a specialised treatment for patients with multiple episodes of SH and/ or probable personality disorder, including: group-based emotion-regulation psychotherapy (two trials; Gratz 2006; Gratz 2014), mentalisation-based therapy (MBT; one trial; Bateman 2009), DBT-oriented therapy (one trial; Turner 2000), DBT (four trials; Linehan 1991; Linehan 2006; McMain 2009; Priebe 2012), and DBT prolonged exposure protocol (one trial; Harned 2014).
+Group-based emotion-regulation psychotherapy vs TAU
+Two trials assessed the effectiveness of group-based emotion-regulation psychotherapy in women diagnosed with borderline personality referred for outpatient treatment as a result of recurrent SH (Gratz 2006, N = 22; Gratz 2014, N = 61). Correspondence with authors suggested that this treatment did not require participants to abstain from SH behaviour. Instead, participants were encouraged to work on resisting urges to engage in SH and, when SH occurred, to learn from the reasons for it.
+Primary outcome
+2.1	Repetition of SH
+Group-based emotion-regulation psychotherapy was associated with a significant treatment effect by the post-intervention assessment (Analysis 2.1; OR 0.34, 95% CI 0.13 to 0.88; k = 2;N = 83). Quality of evidence for this outcome, however, was low (see Summary of findings 2).
+With respect to frequency of repetition of SH, there was no evidence of a significant treatment effect for group-based emotion-regulation psychotherapy by the post-intervention assessment (Analysis 2.1; k = 2; N = 83). Once again, this was associated with a low quality of evidence (see Summary of findings 2).
+Secondary outcomes
+2.2	Treatment adherence
+No data available.
+2.3	Depression
+There was evidence of a significant treatment effect for group-based emotion-regulation therapy on depression scores at the postintervention assessment (Analysis 2.8; MD -9.59, 95% CI -13.43 to-5.75;k = 2;N = 83).
+2.4	Hopelessness
+No data available.
+2.5	Suicidal ideation
+No data available.
+2.6	Problem solving
+No data available.
+2.7	Suicide
+There were no suicides in either group for either trial.
+Mentalisation vs TAU
+Bateman 2009 (N = 134) assessed the effectiveness of mentalisa-tion-based therapy in adults diagnosed with borderline personality disorder referred to a specialist personality disorder treatment service following an attempted suicide or an episode of life-threatening SH in the six months prior to trial entry.
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+Primary outcome
+2.8	Repetition of SH
+There was a significant treatment effect for mentalisation-based therapy by the conclusion of the 18-month treatment period, with fewer participants in the experimental group engaging in SH based on data obtained by correspondence (Analysis 2.1; 18/71 vs 31/ 63; OR 0.35, 95% CI 0.17 to 0.73; k = 1;N = 134). However, quality of evidence was moderate (see Summary of findings 2).
+There was also evidence of a significant treatment effect for men-talisation-based therapy on frequency of SH episodes by the postintervention assessment according to data obtained by correspondence (Analysis 2.5; mean 0.38, SD 0.83, n = 71 vs mean 1.66, SD 2.87, n = 63; MD -1.28, 95% CI -2.01 to -0.55; k = 1;N = 134). Once again, quality of evidence was moderate (see Summary of findings 2).
+Secondary outcomes
+2.9	Treatment adherence
+There was no evidence of a significant treatment effect for mental-isation-based therapy on the proportion of participants who completed the full course of treatment (Analysis 2.7; k = 1; N = 134).
+2.10	Depression
+Mentalisation-based therapy was associated with a significant treatment effect for depression at the post-intervention assessment (Analysis 2.8; mean 14.80, SD 8.55, n = 71 vs mean 18.68, SD 8.76, n = 63; MD -3.88, 95% CI -6.82 to -0.94; k = 1;N = 134).
+2.11	Hopelessness
+No data available.
+2.12	Suicidal ideation
+No data available.
+2.13	Problem solving
+No data available.
+2.14	Suicide
+There had been no suicides in either treatment arm by the time of the post-treatment assessment.
+Dialectical behaviour-oriented psychotherapy vs other alternative forms of psychotherapy
+One small trial in participants diagnosed with borderline personality disorder and referred to outpatient services following a suicide attempt assessed the effectiveness of a DBT-oriented therapy versus client-oriented therapy over a 12-month follow-up (Turner 2000, N = 24).
+Primary outcome
+2.15	Repetition of SH
+There was evidence of a significant treatment effect for DBT-oriented therapy for repetition of SH by post-treatment assessment (Analysis 2.1; 1/12vs 8/12; OR0.05, 95% CI 0.00 to 0.49; k = 1; N = 24). This result had low quality evidence (see Summary of findings 2).
+Data on number of repeat episodes of SH, obtained by correspondence, also suggest a significant treatment effect for DBT-oriented therapy by the post-treatment assessment (Analysis 2.5; mean 0.75, SD 1.23, n = 12vs mean 5.58, SD 5.28, n = 12; MD -4.83, 95% CI -7.90 to -1.76; k = 1; N = 24). Once again, however, a low quality of evidence was associated with this outcome (see Summary of findings 2).
+Secondary outcomes
+2.16	Treatment adherence
+There was no evidence of a significant treatment effect for DBT-oriented therapy on the number of participants who completed the full course of treatment (Analysis 2.7; k = 1; N = 24).
+2.17	Depression
+DBT-oriented therapy was associated with a significant treatment effect for depression scores according to both the BDI and the HRSD by the post-treatment assessment (BDI: Analysis 2.8; mean 14.92, SD 8.26, n = 12vs mean 24.08, SD 5.55, n = 12; MD -9.16, 95% CI -14.79 to -3.53; k = 1;N = 24; HRSD: mean 7.50, SD 5.96, n = 12 vs mean 12.58, SD 3.90, n = 12; MD -5.08, 95% CI-9.11 to-1.05; k = 1;N = 24).
+2.18	Hopelessness
+No data available.
+2.19	Suicidal ideation
+There was also evidence of a significant treatment effect for suicidal ideation at the post-treatment assessment (Analysis 2.11; mean 3.83, SD 8.03, n = 12vs mean 11.58, SD 9.21, n = 12; MD -7.75, 95% CI -14.66 to -0.84; k = 1;N = 24).
+2.20	Problem solving
+No data available.
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+2.21	Suicide
+No data available.
+Dialectical behaviour therapy (DBT) vs TAU
+Three trials investigated the effectiveness of dialectical behaviour therapy (DBT) in adults diagnosed with personality disorders, typically borderline personality disorder, referred to specialist DBT services owing to recurrent SH (Linehan 1991, N = 63; McMain 2009, N = 180; Priebe 2012, N = 80).
+Primary outcome
+2.22	Repetition of SH
+We obtained data on repetition of SH through correspondence for all three trials. There was no clear evidence of a significant treatment effect for DBT compared to TAU in terms of the proportion of patients repeating SH in three trials by the post-intervention assessment (Analysis 2.1; k = 3; N = 267). Similarly, there was no evidence of a significant treatment effect for DBT by the 12month follow-up assessment in two trials (Analysis 2.3; k = 2; N = 172). Combining data from all three trials by the final assessment period suggested no evidence of a significant treatment effect for DBT versus TAU (Analysis 2.4; k= 3; N = 247). Quality of evidence for these three outcomes was low, however (see Summary of findings 2).
+There was evidence of a significant treatment effect for DBT as compared to TAU on frequency of SH by the post-intervention assessment (Analysis 2.5; MD -18.82, 95% CI -36.68 to -0.95; k = 3; N = 292). Once again quality of evidence for this outcome was low (see Summary of findings 2).
+Following a “naturalistic” follow-up period, data from Linehan 1993a (N = 39) indicated that the effectiveness of DBT in the Linehan 1991 trial was maintained at 24 months; however, this outcome was only investigated for a proportion of the original participants (61.9%) who the researchers were able to contact at 24 months. Results have therefore not been reproduced in the present review.
+Secondary Outcomes
+2.23	Treatment adherence
+There was no evidence of a significant treatment effect for DBT for the number of participants completing the full course of treatment in one trial (McMain 2009) (55/90 vs 56/90; OR 0.95, 95% CI 0.52 to 1.74; k = 1; N = 180). Numbers completing treatment in the control group for Priebe 2012 were not provided. Therefore we could not incorporate the results of this trial in a meta-analysis.
+Although Linehan 1991 did not provide numerical data on treatment adherence, the authors did report that participants allocated to the DBT group were “. . . significantly more likely to start individual therapy . . . (100% versus 73%)” (Linehan 1991, p. 1062).
+2.24	Depression
+There was no evidence of a significant treatment effect for DBT as compared to TAU on depression scores at the post-intervention assessment (Analysis 2.8; k = 2; N = 198).
+Data from McMain 2012 furthermore suggested there was no evidence of a significant treatment effect for DBT on depression at the 24-month assessment (mean 22.24, SD 16.40, n = 90 vs mean 21.67, SD 14.82, n = 90; MD 0.57, 95% CI -4.00 to 5.14; k = 1; N = 180).
+2.25	Hopelessness
+We obtained data on hopelessness by correspondence for one trial (Linehan 1991). There was no evidence of a significant treatment effect for DBT at the 24-month follow-up assessment (mean 10.86, SD 6.04, n = 7vs mean 10.69, SD 6.18, n=11; MD 0.17, 95% CI -5.61 to 5.95; k = 1;N = 18).
+2.26	Suicidal ideation
+One trial reported data on suicidal ideation (Linehan 1991). Again, there was no significant treatment effect for DBT at the post-intervention assessment (mean 24.01, SD 19.80, n = 46 vs mean 31.92, SD 26.80, n = 35; MD -7.91, SD -18.47 to 2.65; k = 1; N = 81).
+2.27	Problem solving
+No data available.
+2.28	Suicide
+Although a suicide occurred in the DBT arm of Linehan 1991 before the post-intervention assessment, there were no suicides in Priebe 2012 or in McMain 2009. There was therefore no evidence of a significant treatment effect for this outcome (Analysis 2.13; k = 3; N = 317). There were no suicides in Priebe 2012 or in McMain 2009 by the 24-month follow-up assessment.
+Dialectical behaviour therapy vs other alternative forms of psychotherapy
+One trial compared the effectiveness of DBT versus psychological treatment by ’experts’ (CBT-E) for women diagnosed with borderline personality disorder and referred to a specialist DBT service owing to recurrent SH (Linehan 2006, N = 101). Community mental health leaders (such as heads of inpatient psychiatric units
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+31
+and clinical directors of mental health agencies) nominated professionals who they considered experts in treating difficult clients. These therapists described themselves as “eclectic but non behavioral” or “mostly psychodynamic” in their treatment approach. No therapists with experience of delivering cognitive behavioural therapy were included, however.
+Primary outcome
+2.29	Repetition of SH
+There was no evidence of a treatment effect for DBT versus treatment by expert on repetition of SH by either the post-intervention assessment (Analysis 2.1; k = 1; N = 97) or by the 12-month follow-up period (Analysis 2.3; k=1;N = 97). Qualityof evidence for this outcome at both time points, as assessed by the GRADE criteria, was very low (see Summary of findings 2).
+Study authors did, however, state that those allocated to the DBT group had “ . . . half the rate of suicide attempts compared with the CTB-E group (23.1% vs 46% . . . hazard ratio, 2.66, P = 0.005)” (Linehan 2006, p. 761). Nevertheless, correspondence with authors regarding the total number of parasuicidal acts across the 12-month follow-up period revealed no evidence of a significant treatment effect for DBT (mean 8.79, SD 10.81, n = 52 vs mean 23.64, SD 77.34, n = 45; MD -14.85, 95% CI -37.64 to 7.94; k = 1; N = 97). Quality of evidence for this outcome was also very low (see Summary of findings 2).
+Secondary outcomes
+2.30	Treatment adherence
+No data available.
+2.31	Depression
+There was no evidence of a significant treatment effect on depression scores either at the post-intervention assessment (Analysis 2.8; k = 1; N = 89) or at the 12-month follow-up assessment (Analysis 2.10; k = 1 ; N = 81) in this trial.
+2.32	Hopelessness
+No data available.
+2.33	Suicidal ideation
+There was also no evidence of a significant treatment effect for DBT for suicidal ideation scores at either the post-intervention (Analysis 2.11; k = 1; N = 89) or 12-month follow-up assessments (Analysis 2.12; k = 1; N = 81).
+2.34	Problem solving
+No data available.
+2.35	Suicide
+There were no suicides in either treatment arm by the end of the 12-month follow-up period.
+Dialectical behaviour therapy prolonged exposure vs other alternative forms of psychotherapy
+The effectiveness of two forms of DBT were compared over a three-month follow-up period in one small trial of women with comorbid borderline personality disorder and post-traumatic stress disorder referred to clinical services due to recurrent SH (Harned 2014; N = 26). In the experimental arm, participants received, in addition to the standard DBT protocol, additional weekly therapy sessions involving in vivo and imaginal exposure to previously traumatic experiences.
+Primary outcome
+2.36	Repetition of SH
+Data obtained by correspondence suggested there was no evidence of a significant treatment effect for the DBT prolonged protocol on repetition of SH either by the post-treatment assessment (Analysis 2.1; k = 1; N = 18) or by the three-month follow-up (Analysis 2.2; k = 1; N = 18). Quality of evidence for both these time points was low, however (see Summary of findings 2).
+Data on frequencyof SH, obtained following correspondence with authors, also suggested no apparent benefit of the DBT prolonged exposure protocol by either the post-treatment (Analysis 2.5; k = 1; N = 18) or three-month follow-up (Analysis 2.6; k = 1; N = 18) assessments. Quality of evidence, as assessed by the GRADE criteria, was low (see Summary of findings 2). Data on frequency of suicide re-attempts could not be analysed as there were no repeat suicide attempts in the control group by the final three-month follow-up assessment.
+Secondary outcomes
+2.37	Treatment adherence
+There was no significant difference between the experimental and control groups regarding the number of participants who attended the full one-year course of treatment (Analysis 2.7; k = 1; N = 26). According to the authors, “. . . one therapist . . . was not adherent to DBT and had a 100% dropout rate” (p. 12). Excluding the four participants treated by this therapist did not, however, materially affect this result.
+Psychosocial interventions for self-harm in adults (Review)
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+32
+2.38	Depression
+There was no evidence of a significant treatment effect for the DBT prolonged exposure protocol for depression scores at either the post-treatment (Analysis 2.8; k = 1; N = 18) or three-month follow-up (Analysis 2.9; k = 1; N = 18) assessments.
+2.39	Hopelessness
+No data available.
+2.40	Suicidal ideation
+No data available.
+2.41	Problem solving
+No data available.
+2.42	Suicide
+There was no evidence of a significant treatment effect on death by suicide by the three-month follow-up assessment (Analysis 2.14: 0/17 vs 1/9; OR 0.16, 95% CI 0.01 to 4.41; k = 1;N = 26) in this trial.
+Comparison 3: Case management vs TAU
+Four trials investigated the provision of case management for the prevention of SH either compared to either treatment as usual (TAU; Clarke 2002, N = 467; Hvid 2011,N=133; Morthorst 2012, N = 243) or to enhanced usual care (EUC; Kawanishi 2014, N = 914). Although the intervention in Hvid 2011 and Morthorst 2012 also included aspects of problem-solving psychotherapy, this component was not the primary or only element of the case management strategy adopted in these trials, so we felt these trials were sufficiently similar to justify pooling within a meta-analysis.
+Primary outcome
+3.1	Repetition of SH
+There was no evidence of a significant treatment effect for case management on repetition of SH by the post-intervention assessment (Analysis 3.1; k = 4; N = 1608). Supplementing hospital-recorded episodes of SH with self-reported data for Morthorst 2012 did not materially affect this result. There was also no indication of a significant difference by comparator condition (i.e., TAU vs EUC) for this outcome (Analysis 3.1; test for subgroup differences: Chi2 = 0.20, df = 1, P = 0.66, I2 = 0%). Quality, as assessed using the GRADE criteria, was moderate for this outcome (see Summary of findings 3).
+One trial disaggregated data on repetition of SH by sex (Hvid 2011). Although there was no evidence of a significant treatment effect for males in this trial (4/20 vs 4/18; OR 0.88, 95% CI 0.18 to 4.17; k = 1; N = 38), case management was associated with a significant reduction in repetition of SH in females (2/49 vs 10/ 46; OR0.15, 95% CI 0.03 to 0.74;k= 1;N = 95).
+Multiple readmissions for SH were, however, significantly more common in the case management group than in the control group over the treatment period in one trial (Clarke 2002: 9/220 vs 2/ 247; OR5.23, 95% CI1.12to 24.45; k = 1;N = 467). Qualityof evidence for this outcome was moderate (see Summary of findings 3).
+Secondary outcomes
+3.2	Treatment adherence
+The authors of one trial reported that “11 participants in the assertive case management group did not receive the intervention” (Kawanishi 2014, p. 197). However, as corresponding numbers for the enhanced usual care group were not reported, we were unable to analyse the effect of assertive case management on treatment adherence for this trial.
+3.3	Depression
+No data available.
+3.4	Hopelessness
+Although the Beck Hopelessness Scale was administered to participants throughout the follow-up period in one trial (Kawanishi 2014), the authors did not report data on this outcome. Correspondence, however, revealed that they are currently analysing these data and will present them in a future report.
+3.5	Suicidal ideation
+No data available.
+3.6	Problem solving
+No data available.
+3.7	Suicide
+There was no evidence of a significant treatment effect on suicide by the post-intervention assessment (Analysis 3.2; k = 4; N = 1757), nor was there evidence of a significant difference by comparator condition (i.e., TAU vs EUC) for this outcome (Analysis 3.2; test for subgroup differences: Chi2 = 0.67, df = 1, P = 0.41, I 2 = 0%).
+Psychosocial interventions for self-harm in adults (Review)
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+33
+Comparison 4: Treatment adherence enhancement approaches vs TAU or other alternative forms of psychotherapy
+Two trials investigated the effectiveness of treatment adherence enhancement approaches compared either to TAU (Van Heeringen 1995) or to other alternative forms of psychotherapy (Torhorst 1987) in patients admitted to hospital following an episode ofSH.
+Treatment adherence enhancement vs TAU
+Van Heeringen 1995 (N = 516) investigated the effectiveness of adherence enhancement, involving home visits by a nurse for those patients who failed to attend outpatient appointments, over a 12month follow-up period in patients referred to accident and emergency departments following an episode of SH, irrespective of suicidal intent.
+Primary outcome
+4.1	Repetition of SH
+There was no evidence of a significant treatment effect on repetition of SH by the 12-month follow-up assessment, although the difference in repetition between groups was fairly marked (Analysis 4.1; k = 1; N = 391). Quality of evidence for this outcome, as assessed by the GRADE criteria, was low (see Summary of findings 4).
+Secondary outcomes
+4.2	Treatment adherence
+There was, however, a significant treatment effect for adherence with outpatient aftercare appointments in this trial (129/252 vs 102/256; OR1.58, 95% CI 1.11 to 2.25; k = 1;N = 508).
+4.3	Depression
+No data available.
+4.4	Hopelessness
+No data available.
+4.5	Suicidal ideation
+No data available.
+4.6	Problem solving
+No data available.
+4.7	Suicide
+There was no evidence of a significant treatment effect for the number of participants who died by suicide over the 12-month follow-up period (Analysis 4.3; k = 1; N = 391) in this trial.
+Continuity of care by the same therapist vs other alternative forms of psychotherapy
+One trial investigated the effectiveness of continuing aftercare with the same therapist (defined as continued therapeutic contact with the original hospital therapist in an outpatient setting) versus changing to a different therapist (defined as receiving therapy in a specialised suicide prevention centre, which involved changing both therapist and institution) over a 12-month follow-up period in adults admitted to hospital following an episode of self-poisoning (Torhorst 1987, N = 141).
+Primary outcome
+4.8	Repetition of SH
+There was no evidence of a significant treatment effect for receiving continued therapeutic contact with the original hospital therapist on repetition of SH by the 12-month follow-up assessment (Analysis 4.1; k = 1; N = 136). A very low quality of evidence was associated with this outcome (see Summary of findings 4).
+Secondary outcomes
+4.9	Treatment adherence
+There was evidence of a significant treatment effect for treatment adherence, favouring the same-therapist group (49/68 vs 36/73; OR2.65, 95% CI 1.32 to 5.34; k = 1;N = 141).
+4.10	Depression
+Depression scores did not differ significantly between groups at the 12-month follow-up assessment (Analysis 4.2: mean 6.20, SD 6.90, n = 65 vs mean 7.60, SD 9.20, n = 62; MD -1.40, 95% CI -4.24 to 1.44; k = 1;N = 127).
+4.11	Hopelessness
+No data available.
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+34
+4.12	Suicidal ideation
+No data available.
+4.13	Problem solving
+No data available.
+4.14	Suicide
+There was no evidence of a significant treatment effect for receiving continued therapeutic contact with the original hospital therapist on suicide by the 12-month follow-up assessment (Analysis 4.3; k = 1; N = 136).
+Comparison 5: Mixed multimodal interventions vs TAU
+Two trials investigated the effectiveness of a package of interventions, including problem-solving psychotherapy, postcards, and a GP voucher entitling participants to one free visit to their GP in adults admitted to emergency departments following an episode of SH, irrespective of intent (Hatcher 2016a: Hatcher 2015).
+Mixed multimodal interventions vs TAU
+One large trial using Zelen’s post-randomisation consent design investigated the effectiveness of a package of mixed multimodal interventions in adults admitted to emergency departments following an episode of SH irrespective of intent over a 12-month period (Hatcher 2015; N = 1474).
+Primary outcome
+5.1	Repetition of SH
+There was no evidence of a significant treatment effect for this package of interventions in terms of hospital-recorded episodes of SH by 12-month post-intervention assessment (66/327 vs 73/ 357; OR 0.98, 95% CI 0.68 to 1.43; k = 1;N = 684). This outcome had a low quality of evidence (see Summary of findings 5). Using data from the randomised (including both patients who, following treatment allocation, subsequently consented to participation and those who did not), rather than consenting, sample did not materially affect these results.
+Investigators also presented data on repetition of SH by repeater status at trial entry. However, there was no evidence of a significant treatment effect for those with no history of SH prior to the index attempt compared to those with a history of multiple SH episodes by the post-intervention assessment (those with a history of multiple episodes of SH: 47/176 vs 56/194; OR 0.90, 95% CI 0.57 to 1.42; k = 1; N = 370; those without a history of multiple
+episodes of SH: 19/151 vs 17/163; OR 1.24, 95% CI 0.62 to 2.48; k = 1; N = 314).
+With respect to frequency of SH, the authors reported that “[a]lthough there were 20% fewer episodes in the intervention group, the difference was not statistically significant” (Hatcher 2015,p. 17).
+Secondary outcomes
+5.2	Treatment adherence
+No data available.
+5.3	Depression
+There was no significant treatment effect for this intervention on depression scores at the 12-month post-intervention assessment (mean 6.8, SD 4.9, n = 211 vs mean 6.5, SD 5.1, n = 234; MD 0.30, 95% CI -0.63 to 1.23; k = 1;N = 445).
+5.4	Hopelessness
+There was also no apparent treatment effect for this intervention on hopelessness scores at the 12-month post-intervention assessment (mean 8.3, SD 6.3, n = 210 vs mean 8.4, SD 6.4, n = 233; MD -0.10, 95% CI -1.28 to 1.08; k=1;N = 443).
+5.5	Suicidal ideation
+No data available.
+5.6	Problem solving
+No data available.
+5.7	Suicide
+Correspondence with authors revealed there was no significant treatment effect for this intervention package on suicides by the 12month post-intervention assessment (1/327 vs 2/357; OR 0.54, 95% CI 0.05 to 6.03; k = 1; N = 684). One death in the experimental and one in the control group were due to uncertain causes as they had yet to be investigated by the Coroner. However, assuming these deaths were attributable to suicide did not materially affect this result.
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+35
+Culturally adapted multi-model interventions vs TAU
+The effectiveness of a culturally-adapted mixed multimodal intervention was investigated over a 12-month follow-up period in one trial using Zelen’s post-randomisation consent design in adults admitted to emergency departments following SH and who identify
+themselves as of Ma ori ethnicity (Hatcher 2016a; N = 365).
+Primary outcome
+5.8	Repetition of SH
+There was no evidence of a significant treatment effect for this intervention on re-presentation to hospital following an episode of SH by the time of the post-intervention assessment (34/95 vs 29/72; OR 0.83, 95% CI 0.44 to 1.55; k = 1;N = 167). Using data from the randomised sample only (including both patients who, following treatment allocation, subsequently consented to participation and those who did not) did not materially affect these results. Both outcomes had a low quality of evidence, as assessed using the GRADE criteria (see Summary of findings 5).
+Investigators presented information on repetition of SH by repeater status; however, there was no significant difference in repetition of SH between groups for either those with a history of multiple episodes of SH (24/60 vs 21/40; OR 0.60, 95% CI 0.27 to1.35; k = 1;N = 100) or for those without a history of multiple episodes of SH (10/35 vs 8/32; OR 1.20, 95% CI 0.41 to 3.55; k = 1;N = 67).
+Secondary outcomes
+5.9	Treatment adherence
+No data available.
+5.10	Depression
+There was no treatment effect for this intervention on depression scores at the 12-month post-intervention assessment (mean 5.80, SD 4.50, n = 66 vs mean 6.30, SD 4.30, n = 48; MD -0.50, 95% CI -2.13to 1.13; k = 1;N = 114).
+5.10	Hopelessness
+There was also no apparent treatment effect for this intervention on hopelessness scores at the post-intervention assessment (mean 5.00, SD 4.40, n = 66vs mean 5.70, SD 4.80, n = 47; MD -0.70, 95% CI -2.43 to 1.03; k = 1;N = 113).
+However, the authors note that “whilst there was a greater change in [hopelessness] scores at . . . 12 months [i.e., the post-intervention assessment] in the intervention group, the group had a
+significantly lower baseline score . . . because of the significant differences in baseline scores and missing follow up data we . . . used a mixed linear model to estimate the differences in scores at [the post-intervention assessment].” Using this model the authors found “there was a decrease in [h]opelessness scores in the treatment group compared to the usual care group but this was statistically non-significant” (Hatcher 2016a, ePub version, p. 5).
+5.11	Suicide ideation
+No data available.
+5.12	Problem solving
+No data available.
+5.13	Suicide
+Correspondence with authors revealed there was no significant treatment effect for this intervention on suicides by the time of the post-intervention assessment (0/72 vs 1/95; OR 0.43, 95% CI 0.02 to 10.82; k = 1;N = 167).
+Comparison 6: Remote contact interventions vs TAU
+A number of trials investigated the effectiveness of remote contact interventions, including, postcards (Beautrais 2010; Carter 2005; Hassanian-Moghaddam 2011; Kapur 2013a), emergency cards (Evans 1999a; Morgan 1993), general practitioner’s (GP) letter (Bennewith 2002), telephone contact (Cedereke 2002; Wei 2013; Vaiva 2006), or mobile telephone-based psychotherapy (Marasinghe 2012) for the prevention of repetition of SH.
+Postcards vs TAU
+Four trials assessed the effectiveness of sending postcards to patients on a regular basis over a 12-month follow-up period (Beautrais 2010, N = 327; Hassanian-Moghaddam 2011, N = 2113; Kapur 2013a, N = 66), including one that used Zelen’s postrandomisation consent design and reported data only for the randomised sample (Carter 2005, N = 772). The trials of Beautrais 2010, Carter 2005, and Kapur 2013a took place in three high-income countries, whilst the Hassanian-Moghaddam 2011 trial was in a low income country (i.e., Iran).
+Primary outcome
+6.1	Repetition of SH
+Due to the definition of SH used in the report for one trial of postcards, we obtained data for this trial on repetition of SH through correspondence (Hassanian-Moghaddam 2011).
+36
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+Overall, there was no evidence of a significant treatment effect on the proportion of patients repeating SH by the post-intervention assessment (Analysis 5.1; k= 4; N = 3277). Excluding Carter 2005, which used Zelen’s design, did not materially affect this result. Quality of evidence, as assessed using the GRADE criteria, was very low for this outcome (see Summary of findings 6). Visual examination of the forest plot suggested that the result for Kapur 2013a may have been an outlier. Removing this trial reduced heterogeneity to 0% and suggested a significant treatment effect for postcards on repetition of SH (OR 0.78, 95% CI 0.62 to 0.97; k = 3 ;N = 3212).
+There was no evidence of a significant treatment effect for postcards on repetition of SH by the 12-month follow-up assessment in two trials (Analysis 5.2; k = 2; N = 2885). The quality of evidence for this outcome was moderate (see Summary of findings 6). Excluding Carter 2005, however, caused this result to become significant (OR 0.67, 95% CI 0.52 to 0.86; k = 1; N = 2113) as did a sensitivity analysis using the fixed-effect rather than randomeffects model (fixed: OR 0.75, 95% CI 0.61 to 0.91).
+Combining data from both time points indicated no overall significant effect for postcards by the final follow-up assessment (Analysis 5.3; k = 4; N = 3277). Excluding Carter 2005 did not materially affect this result. However, as before, excluding Kapur 2013a caused this result to become significant (OR 0.77, 95% CI 0.63 to 0.95) as did a sensitivity analysis using the fixed-effect model (fixed: OR 0.79, 95% CI 0.66 to 0.95), quality of evidence was again very low for this outcome (see Summary of findings 6).
+Data on repetition of SH by the post-intervention assessment and 12-month follow-up were available by sex in one trial (Carter 2005; Carter 2007); however, there was no evidence of a significant treatment effect for postcards in either sex by either time point (post-intervention: males 20/145 vs 16/102; OR 0.86, 95% CI 0.42 to 1.75; k = 1; N = 247 versus females 37/233 vs 51/291; OR 0.89, 95% CI 0.56 to 1.41; k = 1; N = 524; 12 months’ followup: males 26/145 vs 19/102; OR0.95, 95% CI 0.50;k= 1;N = 247 versus females 54/233 vs 59/291; OR 1.19, 95% CI 0.78 to 1.80; k = 1;N = 524).
+With respect to frequency of SH, we obtained data on mean number of repeat SH episodes by correspondence for three of the four trials of postcards (Carter 2005; Hassanian-Moghaddam 2011; Kapur 2013a). For the one remaining trial (Beautrais 2010), the available data indicated a reduced mean number of SH episodes for the experimental group (0.57 vs 0.78); however, as no information on SDs, t-test or F statistics were reported, we were unable to impute SDs using the method outlined in Townsend 2001 to calculate the mean difference in frequency ofSH episodes between groups. Overall, there was no evidence of a significant treatment effect for postcards on frequency of repetition of SH by the postintervention assessment (Analysis 5.4; k = 3; N = 1,097). Quality of evidence for this outcome was very low (see Summary of findings 6).
+By the 12-month follow-up assessment, there was similarly no
+evidence of a significant treatment effect for postcards in two trials (Analysis 5.5; k = 2; N = 984). One trial also provided data for the 24-month follow-up period; however, no evidence of a significant treatment effect was apparent (mean 0.21, SD 0.75, n = 217 vs mean 0.24, SD 0.68, n = 255; MD -0.03, 95% CI -0.16to 0.10; k = 1; N = 472; Hassanian-Moghaddam 2011). Quality of evidence was very low to moderate for these outcomes (see Summary of findings 6).
+Through correspondence, we were also able to obtain post hoc data on frequency of repetition of SH by the post-intervention assessment by both sex and repeater status for three trials (Carter 2005; Hassanian-Moghaddam 2011; Kapur 2013a). There was no evidence of a significant treatment effect for postcards on frequency of repetition by the post-intervention assessment for either sex (males: Analysis 5.4; k = 3; N = 401; females: Analysis 5.4; k = 3; N = 695), those with a history of multiple episodes of SH (Analysis 5.4; k = 3; N = 339), or those without a history of multiple episodes ofSH (Analysis 5.4; k= 3; N = 758). There was also no evidence of a significant treatment effect for postcards on frequency of repetition for males (Analysis 5.5; k = 2; N = 336), females (Analysis 5.5; k = 2; N = 647), those with a history of multiple episodes of SH (Analysis 5.5; k = 2; N = 296), or those without a history of multiple episodes of SH (Analysis 5.5; k = 2; N = 688) by the 12-month follow-up assessment in two trials. Correspondence with study authors revealed no significant treatment effect for postcards on frequency of repetition in either males (mean 0.33, SD 1.07, n = 116 vs mean 0.29, SD 0.82, n = 104; MD 0.04, 95% CI -0.21 to 0.29; k = 1; N = 220), females (mean 0.14, SD 0.52, n = 101 vs mean 0.21, SD 0.59, n = 151; MD -0.07, 95% CI -0.21 to 0.07, k = 1; N = 252), those with a history of multiple episodes of SH (mean 0.42, SD 1.06, n = 155 vs mean 0.51, SD 0.96, n = 183; MD -0.09,95%CI -0.31 to0.13;k = 1; N = 338), or those without a history of multiple episodes of SH (mean 0.09, SD 0.47, n = 62 vs mean 0.10, SD 0.41, n = 72; MD -0.01, 95% CI -0.16 to 0.14; k = 1; N = 134) by the 24-month follow-up assessment in one of these trials (Hassanian-Moghaddam 2011).
+Secondary outcomes
+6.2	Treatment adherence
+No data available.
+6.3	Depression
+No data available.
+6.4	Hopelessness
+No data available.
+37
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+6.5	Suicidal ideation
+One trial recorded information on suicidal ideation at both the post-intervention assessment and 12-months’ follow-up ( Hassanian-Moghaddam 2011; Hassanian-Moghaddam 2015). There was a significant treatment effect for the number of people reporting suicidal ideation at the post-intervention assessment (302/1043 vs 446/1070; OR0.57, 95% CI 0.48 to 0.68; k = 1; N = 2113). Data reported by the trial authors in a subsequent follow-up paper suggested that this effect was maintained at the 12-month follow-up assessment (465/997 vs 588/1004; OR 0.62, 95% CI 0.52 to 0.74; k = 1; N = 2001; Hassanian-Moghaddam 2015).
+6.6	Problem solving
+No data available.
+6.7	Suicide
+There was no evidence of a significant treatment effect for postcards on suicide by the post-intervention assessment (Analysis 5.6; k = 4; N = 3464). Excluding Carter 2005, however, suggested a harmful effect of postcards on suicides (OR 3.74, 95% CI 1.04 to 13.51; k = 3; N = 2692).
+Data on suicides by the 12-month follow-up assessment were available for one trial (i.e., Carter 2005); however, no significant treatment effect was found (Analysis 5.7; k = 1; N = 772).
+Emergency cards vs TAU
+Two trials investigated the effectiveness of providing an emergency contact card (’green card’) providing 24-hour access to emergency advice from a psychiatrist in addition to TAU in adults admitted to general hospitals following an episode of SH, most frequently self-poisoning (Evans 1999a, N = 827; Morgan 1993, N = 212). Evans 1999a reported data on repetition of SH in a secondary trial publication (Evans 2005).
+Primary outcome
+6.8	Repetition of SH
+There was no evidence of a significant treatment effect for emergency cards on repetition of SH by the post-intervention assessment (Analysis 5.1; k = 2; N = 1039). Quality of evidence for this outcome was low (see Summary of findings 6). There was also no evidence of a significant treatment effect for emergency cards by the time of the 12-month follow-up assessment in Evans 1999a (Analysis 5.2; k = 1; N = 827). For this outcome, quality
+of evidence as assessed by the GRADE criteria was moderate (see Summary of findings 6).
+Evans 1999a disaggregated data on repetition of SH by repeater status (i.e., those without a history of multiple episodes of SH versus those with a history of multiple episodes of SH) in post hoc analyses. Whilst there was no evidence of a significant treatment effect for emergency cards on repetition of SH in those without a history of multiple episodes of SH (18/221 vs 25/206; OR 0.64, 95% CI 0.34 to 1.22; k = 1; N = 427), emergency cards were associated with a significantly increased risk of repetition of SH in those with a history of multiple episodes of SH (52/194 vs 33/ 200; OR1.85, 95% CI 1.14 to 3.03; k=1;N = 394) in this trial. Evans 1999a also reported data on frequency of repetition of SH as the proportion with no episodes at follow-up, the proportion with a single episode at follow-up, and the proportion of two or more repeat episodes of SH by the 12-month follow-up assessment. There was no significant difference between groups in the number of participants who had none (347/417 vs 351/410; OR 0.83, 95% CI 0.57 to 1.21; k = 1;N = 827), one (46/417 vs 32/410; OR1.46, 95% CI 0.91 to 2.35; k = 1;N = 827), or two or more (24/417 vs 27/410; OR0.87, 95% CI 0.49 to 1.53; k = 1;N = 827) episodes of SH over the six-month follow-up period.
+This authors also presented data on frequency of repetition of SH by repeater status in post hoc analyses. For those without a history of multiple episodes of SH, there was no significant difference between groups in the number of participants who had none (203/ 221 vs 181/206; OR 1.56, 95% CI 0.82 to 2.95; k=1;N = 427), one (13/221 vs 16/206; OR 0.74, 95% CI 0.35 to 1.58; k=1; N = 427), or two or more (5/221 vs 9/206; OR 0.51, 95% CI 0.17 to 1.54; k = 1; N = 427) repeat episodes of SH. For those with a history of multiple episodes of SH, however, receipt of an emergency card was associated with a significant reduction in the number of participants with no further episodes of SH (142/194 vs 167/200; OR0.54, 95% CI 0.33 to 0.88;k= 1;N = 394) coupled with a significant increase in the number of participants with one repeat episode ofSH (33/194 vs 15/200; OR2.53, 95% CI 1.33 to 4.82; k = 1; N = 394). There was no significant difference between the experimental and control groups with respect to the number of participants with or two or more subsequent episodes of SH for those with a history of multiple episodes of SH, however (19/194 vs 18/200; OR 1.10, 95% CI 0.56 to 2.16;k = 1;N = 394).
+Secondary outcomes
+6.9	Treatment adherence
+No data available.
+6.10	Depression
+No data available.
+38
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+6.11	Hopelessness
+No data available.
+6.12	Suicidal ideation
+No data available.
+6.13	Problem solving
+No data available.
+6.14	Suicide
+Data on suicides were reported in only one trial (Evans 1999a). There was no evidence of a significant treatment effect for emergency cards on suicide by the time of the six-month follow-up assessment (2/417 vs 1/410; OR1.97, 95% CI 0.18 to 21.82; k = 1; N = 827).
+General practitioner’s letter vs TAU
+A single, cluster-randomised controlled trial compared the effectiveness of a letter from patients’ general practitioners following discharge from hospital care offering an appointment and advice on patient management versus TAU over a 12-month follow-up period (Bennewith 2002, clusters = 98 practices, N = 1932).
+We were unable to adjust for the effects of clustering in this analysis, as the study authors could not provide us with either the intercluster coefficient or the design effect. Therefore the effects we report for this intervention should be interpreted with caution.
+Primary outcome
+6.15	Repetition of SH
+There was no evidence of a significant treatment effect for a letter from patients’ general practitioners on repetition of SHby the 12month follow-up assessment (211/964 vs 189/968; OR 1.15, 95% CI 0.93to 1.44; k = 1; N = 1932). A moderate quality of evidence was associated with this outcome (see Summary of findings 6).
+A post hoc analysis by sex, however, suggested that whilst there was no significant treatment effect for males (82/383 vs 84/413; OR 1.07, 95% CI 0.76 to 1.50; k = 1; N = 796), a GP letter was associated with a significant treatment effect on repetition of SH forfemales (30/581 vs 105/555; OR0.23, 95% CI 0.15 to 0.36; k = 1; N = 1136).
+In a second post hoc analysis, the authors also analysed repetition of SHby repeater status at trial entry and concluded that “[t]he odds ratio for the effect of the intervention in patients with a history of self-harm was 0.57 (0.33 to 0.98), indicating a beneficial effect, and in those with no history was 1.32 (1.02 to 1.70), indicating
+a harmful effect” (Bennewith 2002, p. 1258). As the raw data on which these sub-group results were based is not reported, we were unable to reproduce these results in this review.
+Secondary outcomes
+6.16	Treatment adherence
+There was no significant treatment effect for the number of participants with at least one contact with treatment services by the time of the 12-month follow-up assessment (351/599 vs 387/681; OR 1.08, 95% CI 0.86 to 1.34; k = 1;N = 1280).
+6.17	Depression
+No data available.
+6.18	Hopelessness
+No data available.
+6.19	Suicidal ideation
+No data available.
+6.20	Problem solving
+No data available.
+6.21	Suicide
+No data available.
+Telephone contact vs TAU
+Three trials investigated the effectiveness of telephone contact in adults admitted to emergency departments following a ’suicide attempt’ (i.e., suggestive of suicidal intent) (Cedereke 2002, N = 216; Vaiva 2006, N = 605; Wei 2013, N = 157).
+Primary outcome
+6.22	Repetition of SH
+There was no evidence of a significant treatment effect for telephone contact on repetition of SH at the six-month follow-up assessment in Wei 2013 (1/41 vs 4/40; OR 0.23, 95% CI 0.02 to 2.11; k = 1; N = 81), by the 12-month follow-up period in Cedereke 2002(14/83 vs 15/89; OR 1.00, 95% CI 0.45 to 2.23; k = 1; N = 172), or by the 24-month follow-up period in Vaiva 2006 (44/293 vs 59/312; OR0.76, 95% CI 0.49 to 1.16;k= 1;
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+39
+N = 605). Combining data for these three time points indicated no significant treatment effect for telephone contact by the final follow-up point (Analysis 5.3;k=3;N = 840). Qualityof evidence for these three time points was very low to low (see Summary of findings 6).
+With respect to frequency of repetition of SH, the mean number of episodes of SH was similar between treatment groups in both Cedereke 2002 (0.31 vs 0.30) and Vaiva 2006 (0.15 vs 0.19). However, as study authors did not report information on SDs, t-test or F statistics, we were unable to impute SDs using the method outlined in Townsend 2001 to calculate the mean difference in the number of repeat episodes of SH between groups in these two trials.
+Secondary outcomes
+6.23	Treatment adherence
+There was no evidence of a significant treatment effect on the number of patients attending treatment at least once by 12-month follow-up assessment in one trial (60/83 vs 58/89, OR 1.39, 95% CI 0.73 to 2.67; k = 1;N = 172; Cedereke 2002).
+6.24	Depression
+There was no evidence of a significant treatment effect for telephone contact on depression at either the six-month (mean 6.01, SD 8.87, n = 41 vs mean 5.85, SD 8.16, n = 40; MD 0.16, 95% CI -3.55 to 3.87; k = 1; N = 81) or the 12-month (mean 5.73, SD 8.71, n = 36 vs mean 5.84, SD 8.23, n = 27; MD -0.11, 95% CI -4.32 to 4.10; k = 1; N = 63) follow-up assessments in the only trial of telephone contact to report data on depression scores (Wei 2013).
+6.25	Hopelessness
+No data available.
+6.26	Suicidal ideation
+Suicidal ideation was recorded continuously in Cedereke 2002, whereas Wei 2013 recorded data on suicidal ideation dichotomously as the proportion self-reporting an episode of suicidal ideation.
+In Cedereke 2002, telephone contact was not associated with a significant treatment effect on suicidal ideation scores by the 12month follow-up assessment (mean 5.80, SD 7.80, N = 5 vs mean 4.00, SD 6.20, N = 8;MD 1.80, 95% CI -6.27 to 9.87; k = 1;N = 13).
+Telephone contact was also not associated with a significant treatment effect on the proportion of participants reporting suicidal ideation by the six-month follow-up assessment in Wei 2013 (26/
+41 vs 24/40; OR1.16, 95% CI 0.47 to 2.83; k = 1;N = 81). Bythe 12-month follow-up assessment, however, telephone contact was associated with a significant treatment effect on the proportion of participants reporting suicidal ideation in this trial (24/36 vs 25/ 27; OR 0.16, 95% CI 0.03 to 0.79; k = 1;N = 63).
+6.27	Problem solving
+No data available.
+6.28	Suicide
+There was no evidence of a significant treatment effect for telephone contact on suicides by either the 12-month (1/107 vs 1/ 109; OR 1.02, 95% CI 0.06 to 16.50;k= 1;N = 216; Cedereke 2002) or 24-month (1/293 vs 2/312; OR 0.52, 95% CI 0.05 to 5.89; k = 1; N = 605; Vaiva 2006) follow-up assessments. Combining data from these trials suggested no evidence of a significant treatment effect by the time of the final follow-up assessment (Analysis 5.8; k = 2; N = 821).
+Mobile telephone-based psychotherapy vs TAU
+One trial assessed the effectiveness of psychotherapy, including elements of training in problem-solving therapy, meditation and social support, delivered by mobile telephone over a six-month follow-up period in adults admitted to general hospitals following an episode of SH with significant suicidal intent (Marasinghe 2012; N = 68). As this trial used a cross-over design, we report only data from the post-intervention assessment (i.e., prior to crossover) in this review.
+Primary outcome
+6.29	Repetition of SH
+Data obtained by correspondence from the authors indicated there were no repeat episodes of SH in either the treatment or control groups by the post-intervention assessment. It was therefore not possible to calculate the pooled odds ratio and accompanying 95% confidence interval owing to zero cell counts (Analysis 5.1; k = 1; N = 68). A low quality of evidence was apparent for this outcome (see Summary of findings 6).
+Secondary outcomes
+6.30	Treatment adherence
+No data available.
+40
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+6.31	Depression
+There was evidence of a significant treatment effect for mobile telephone-based psychotherapy for depression at the post-intervention assessment (mean 7.00, SD 5.00, n = 34 vs mean 14.60, SD 10.40, n = 34; MD -7.60, 95% CI -11.48 to -3.72; k = 1;N = 68).
+However, a priori analyses suggested that this effect varied by sex. Mobile telephone-based psychotherapy was associated with a significant treatment effect for depression at the post-intervention assessment in males (mean 5.90, SD 2.40, n = 17 vs mean 13.30, SD 6.10, n = 17; MD -7.40, 95% CI -10.52 to -4.28; k=1;N = 34) but not in females (mean 8.10, SD 6.30, n = 17 vs mean 11.60, SD 6.50, n= 17; MD -3.50, 95% CI -7.80to 0.80;k= 1; N= 34).
+6.32	Hopelessness
+No data available.
+6.34	Suicidal ideation
+There was evidence of a significant treatment effect for mobile telephone-based psychotherapy for suicidal ideation at the postintervention assessment (mean 3.60, SD 1.60, n = 34 vs mean 7.30, SD 5.50, n = 34; MD -3.70, 95% CI -5.63 to-1.77;k = 1; N = 68) in this trial.
+When we analysed results separately by sex, however, there was no evidence of a significant treatment effect for mobile telephonebased psychotherapy for suicidal ideation at the post-intervention assessment in males (mean 3.50, SD 1.80, n = 17 vs mean 6.20, SD 5.50, n = 17; MD -2.70, 95% CI -5.45 to 0.05; k=1;N = 34). There was, however, a significant treatment effect for females (mean 3.80, SD 1.40, n = 17 vs mean 8.90, SD 6.20, n = 17; MD -5.10, 95% CI -8.12 to -2.08; k=1;N = 34).
+6.35	Problem solving
+No data available.
+6.36	Suicide
+Information obtained by correspondence indicated that there was one suicide in the experimental group by the time of the postintervention assessment and none in the control group. Mobile telephone-based psychotherapy was not associated with a significant treatment effect for suicide by this time point (Analysis 5.6; k= 1;N = 68).
+A number of single, small trials investigated the effectiveness of other types of heterogeneous interventions, including: interpersonal problem-solving skills training (vs TAU; McLeavey 1994), behaviour therapy (vs other alternative forms of psychotherapy; Liberman 1981), provision of information and support (vs TAU; Fleischmann 2008), treatment for alcohol misuse (vs TAU; Crawford 2010), home-based problem-solving therapy (vs other alternative forms of psychotherapy; Hawton 1981), intensive inpatient and community treatment (vs TAU; Van der Sande 1997a, general hospital admission (vs other alternative forms of psychotherapy; Waterhouse 1990) , intensive outpatient treatment (vs TAU; Allard 1992; Welu 1977), and long-term therapy (vs other alternative forms of psychotherapy; Torhorst 1988).
+Interpersonal problem-solving skills training vs other alternative forms of psychotherapy
+One small trial compared the effectiveness of interpersonal problem-solving skills training (IPSST) with brief problem-oriented therapy in adults admitted to accident and emergency facilities following an episode of self-poisoning (McLeavey 1994; N = 39).
+Primary outcome
+7.1 Repetition of SH
+There was no evidence of a significant treatment effect for repetition of SH, defined as a ’self-poisoning act’, within the 12-month follow-up period (2/17 vs 4/16; OR 0.40, 95% CI 0.06 to 2.57; k = 1; N = 33) in this trial. A very low quality of evidence was associated with this outcome (see Summary of findings 7).
+Secondary outcomes
+7.2 Treatment adherence
+There was no evidence of a significant treatment effect on the number of participants who completed the full course of treatment (2/19 vs 3/20; OR = 0.67, 95% CI 0.10 to 4.51; k = 1; N = 39).There was, however, evidence for a significant treatment effect in terms of the number of treatment sessions attended (mean 5.30, SD 0.48, n= 17 vs mean 4.20, SD 1.32, n= 16; MD 1.10, 95% CI 0.41 to 1.79; k = 1;N = 33).
+Comparison 7: Other mixed interventions vs TAU or 7.3 Depression other alternative forms of psychotherapy
+No data available.
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+41
+7.4	Hopelessness
+There was no evidence of a significant treatment effect for hopelessness at the six-month follow-up assessment (mean 6.12, SD 4.61, n = 19 vs mean 4.35, SD 4.39, n = 20; MD 1.77, 95% CI -1.06 to 4.60; k = 1; N = 39).
+7.5	Suicidal ideation
+No data available.
+7.6	Problem solving
+Analysis of means estimated by the review authors from graphics in the original report suggests that, at the post-intervention assessment, participants allocated to the experimental group had scores within the normal range whilst those allocated to the control group remained impaired according to both the Means-Ends Problem-Solving Scale (estimated means 6.4 vs 2.9) and the SelfRated Problem Solving Scale (estimated means 89.8 vs 78.0). Additionally, participants in the experimental group reported feeling more confident in solving problems post-treatment according to scores on the Perceived Ability to Solve Current Problems scale (estimated means 0.9 vs 1.9). Lastly, both groups reported a reduction in self-reported number of problems (estimated means 1.1 vs 1.5).
+Results reported by the trial authors suggest an equal benefit of both treatments in reducing the “number of presenting problems . . .” (McLeavey 1994, p. 382). However, the authors conclude that IPSST was “significantly more effective . . . as determined by other outcome measures . . .” including measures of interpersonal cognitive problem-solving, self-rated personal problem-solving ability, and perceived ability to cope with ongoing problems (McLeavey 1994, p.382).
+7.7	Suicide
+There were no suicides in either group during the 12-month follow-up period.
+Behaviour therapy vs other alternative forms of psychotherapy
+One small trial compared the effectiveness of behaviour therapy versus insight-oriented therapy in adults referred for inpatient treatment following a suicide attempt (Liberman 1981; N = 24).
+Primary outcome
+7.8	Repetition of SH
+There was no evidence of a significant treatment effect with regards to the number of patients repeating SH by the 24-month follow
+up period (2/12vs 3/12; OR 0.60, 95% CI 0.08 to 4.45; k=1;N = 24). This outcome was associated with a low quality of evidence (see Summary of findings 7)
+Secondary outcomes
+7.9	Treatment adherence
+No data available.
+7.10	Depression
+Depression was measured using both the BDI and the ZSRDS in this trial. There was evidence of a significant treatment effect for behaviour therapy at the post-treatment assessment according to both measures (BDI: mean 4.00, SD 4.00, n = 12 vs mean 14.00, SD 12.00, n = 12; MD -10.00, 95% CI -17.16 to -2.84; k=1;N = 24; ZSRDS: mean 32.00, SD 8.00, n = 12 vs mean 43.00, SD 14.00, n = 12; MD-11.00, 95% CI -20.12 to -1.88;k = 1;N = 24).
+At the six-month (24-week) follow-up assessment, although there was no significant treatment effect for depression according to the ZSRDS (mean 34.00, SD 8.00, n = 12 vs mean 41.00, SD 13.00, n = 12; MD -7.00, 95% CI-15.64 to 1.64;k = 1;N = 24), BDI scores did show an effect (mean 4.00, SD 6.00, n = 12 vs mean 13.00, SD 11.00, n = 12; MD -9.00, 95% CI -16.09 to -1.91; k = 1; N = 24).
+7.11	Hopelessness
+No data available.
+7.12	Suicidal ideation
+There was no evidence of a significant effect for behaviour therapy on the number of patients reporting suicidal ideation at the 24month follow-up assessment (5/12 vs9/12; OR 0.24, 95% CI 0.04 to 1.36; k = 1; N = 24).
+7.13	Problem solving
+No data available.
+7.14	Suicide
+No data available.
+42
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+Provision of information and support vs TAU
+The effectiveness of providing a one-off hospital-based information session combined with regular home visits and/or telephone contact in addition to TAU over an18 month follow-up period was investigated in one multicentre trial (SUPRE-MISS) conducted in ten countries, although data from only five of these countries are reported in Bertolote 2010 (N = 1,663) and Fleischmann 2008 (N = 1,699). Data from three of the individual countries ( Hassanzadeh 2010, N = 632; Vijayakumar 2011, N = 680; Xu 2012, N = 111) were also included for some outcomes.
+Correspondence with authors indicted that the term ’attempted suicide’ in this trial was used to refer to SH both with and without suicidal intent.
+Primary outcome
+7.15	Repetition ofSH
+For the overall SUPRE-MISS cohort, data from Bertolote 2010 indicated there was no evidence for a significant treatment effect for information and support on repetition of SH by the 18-month follow-up assessment (66/863 vs 60/800; OR 1.02, 95% CI 0.71 to 1.47; k = 1; N = 1663). This outcome was associated with low quality of evidence according to the GRADE criteria (see Summary of findings 7).
+Data on repetition of SH were also available for males and females separately. Overall, across all five sites, there was no evidence of a significant treatment effect on repetition of SH by the 18-month follow-up assessment in either males (30/349 vs 27/340; OR 1.09, 95% CI 0.63 to 1.88; k = 1; N = 689) or females (36/514 vs 33/ 460; OR0.97, 95% CI 0.60 to 1.59;k = 1;N = 974).
+Data on repetition of SH by the 18-month follow-up assessment were also available for each of the five countries separately. Although there was no significant difference between groups for the individual sites in Campinas, Brazil (21/71 vs 10/64; OR 2.27, 95% CI 0.97 to 5.28; k=1;N = 135), Colombo, Sri Lanka (3/ 130 vs 5/121; OR 0.55, 95% CI 0.13 to 2.34; k=1;N = 251), Karaj, Iran (33/303 vs 28/298; OR 1.18, 95% CI 0.69 to 2.00; k = 1; N = 601), and Yuncheng, China (1/58 vs 0/38; OR 2.01, 95% CI 0.08 to 50.60; k = 1; N = 96), significantly fewer participants in the experimental group had repeated SH by the 18month follow-up period at the Chennai, India site (8/301 vs 17/ 260; OR 0.39, 95% CI 0.17 to 0.92; k=1; N = 561). Quality of evidence for these five sites varied from very low to low (see Summary of findings 7).
+Breaking results down by gender revealed no significant effect for information and support on repetition of SH by the 18-month follow-up assessment for either gender at either of the five study sites [Campinas, Brazil: males 4/21 vs. 3/25; OR 1.73, 95% CI 0.34 to 8.76; k = 1; N = 46 versus females 17/50 vs. 7/39; OR 2.35, 95% CI 0.86 to 6.44; k = 1; N = 89; Chennai, India: males
+5/148 vs. 7/125; OR0.59, 95% CI 0.18to 1.91;k = 1;N = 273 versus females 3/153 vs. 10/153; OR 0.29, 95% CI 0.08 to 1.06; k = 1; N = 306; Colombo, Sri Lanka: males 1/54 vs. 3/53; OR 0.31, 95% CI 0.03 to 3.12; k = 1; N = 107 versus females 2/76 vs. 2/68; OR 0.89, 95% CI 0.12 to 6.51; k= 1; N = 144; Karaj, Islamic Republic of Iran: males 19/109 vs. 14/118; OR 1.57, 95% CI 0.74 to 3.31; k = 1; N = 227 versus females 14/194 vs. 14/ 180; OR 0.92, 95% CI 0.43 to 1.99; k= 1; N = 374; Yuncheng, China: males: 1/17 vs. 0/19; OR 3.55, 95% CI 0.14 to 93.01; k = 1; N = 36 versus females 0/41 vs. 0/38; OR not calculable; k = 1;N = 79].
+Hassanzadeh 2010 reported data on frequency of SH for one subsample at the six-month follow-up assessment in Karaj, Iran. In this sample, there was evidence of a significant increase in frequency of repetition of SH in the information and support group relative to the TAU group (mean 1.63, SD 1.19, n = 319 vs mean 1.17, SD 0.38, n = 310; MD 0.46, 95% CI 0.32 to 0.60; k= 1; N= 629). Quality of evidence for this outcome was low (see Summary of findings 7).
+Secondary outcomes
+7.16	Treatment adherence
+No data available.
+7.17	Depression
+Correspondence with authors revealed that information on depression was recorded at one site only: Yuncheng, China (reported by Xu 2012). Information and support was associated with a significant treatment effect for depression scores at this site by the 18-month follow-up assessment (mean 2.51, SD 3.25, n = 57 vs mean 5.60, SD 9.25, n = 54; MD -3.09, 95% CI -5.70 to -0.48; k= 1;N= 111).
+7.18	Hopelessness
+No data available.
+7.19	Suicidal ideation
+No data available.
+7.20	Problem solving
+Correspondence with authors revealed that information on problem solving was reported for one site only: Yuncheng, China (reported in Xu 2012). There was evidence of a significant treatment effect for information and support at this site by the 18-month follow-up assessment (mean 0.64, SD 0.29, n = 57 vs mean 0.52, SD0.30,n=54;MD0.12, 95%CI0.01 to0.23;k= 1;N=111).
+43
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+7.21	Suicide
+In the overall SUPRE-MISS cohort, as reported in the primary study reference (Fleischmann 2008), there was evidence of a significant treatment effect for information and support on suicide by the 18-month follow-up period (2/872 vs 18/827; OR 0.10, 95% CI 0.02 to 0.45; k=1;N = 1699).
+Data on suicides were also available for three of the five study sites in related publications: Vijayakumar 2011 reported data from Chennai, India, Hassanzadeh 2010 from Karaj, Iran, and Xu 2012 from Yuncheng, China. There was evidence of a significant treatment effect for information and support on suicides by the 18-month follow-up assessment at the Chennai, India site (1/302 vs 9/320; OR 0.11, 95% CI 0.01 to 0.91; k=1;N = 622) but not at either the Karaj, Iran (2/319 vs 2/310; OR 0.97, 95% CI 0.14 to 6.94; k = 1; N = 629) or the Yuncheng, China (0/57 vs 2/54; OR 0.18, 95% CI 0.01 to 3.89; k= 1;N = 111)sites.
+Notably, the number of completed suicides in the experimental group reported for these three subsamples is greater than the number reported for the overall SUPRE-MISS cohort in the primary study reference (i.e., Fleischmann 2008). We were unable to confirm the correct number of completed suicides in the experimental group with the authors. Including the one additional suicide for the experimental group identified from the three subsample publications with the data reported in the primary study reference, however, did not materially affect the result obtained for the overall SUPRE-MISS cohort.
+Treatment for alcohol misuse vs TAU
+One trial investigated the effectiveness of a brief intervention for alcohol misuse on repetition of SH over a six-month follow-up period in adults who were misusing alcohol and were admitted to emergency departments following an episode of SH (Crawford 2010; N = 103).
+Primary outcome
+7.22	Repetition of SH
+There was no evidence of a significant treatment effect for treatment for alcohol misuse on repetition of SH by the six-month follow-up period (7/52 vs11/51; OR 0.57, 95% CI 0.20 to 1.60; k = 1; N = 103). This was associated with a moderate quality of evidence (see Summary of findings 7).
+Secondary outcomes
+7.23	Treatment adherence
+The study authors report that only 47.1% of those randomised to the experimental group attended the brief alcohol treatment session (Crawford 2010, p.1826). However, as corresponding numbers were not available for the control group, who did not receive an invitation to a brief alcohol treatment session, we could not calculate treatment effect sizes for this outcome.
+7.24	Depression
+No data available.
+7.25	Hopelessness
+No data available.
+7.26	Suicidal ideation
+No data available.
+7.27	Problem solving
+No data available.
+7.28	Suicide
+Correspondence with authors confirmed that no participants died by suicide in either group over the course of the six-month followup period. However, the authors warn that as they were unable to track participants via their National Health Service (NHS) identity numbers, they were unable to confirm numbers of suicides from national mortality data. Thus, there may have been suicides amongst those participants whom the authors were unable to contact by the six-month follow-up assessment.
+Home-based problem-solving therapy vs other alternative forms of psychotherapy
+Hawton 1981 (N = 96) investigated the effectiveness of brief problem-oriented counselling delivered in two different ways, namely as a flexibly-timed home-based therapy, combined with open access via telephone services to the general hospital psychiatric service, versus treatment in weekly outpatient clinics, in adults referred to the psychiatric department of a general hospital following admission for self-poisoning, irrespective of intent.
+Primary outcome
+7.29	Repetition of SH
+There was no evidence of a significant treatment effect for homebased problem-solving therapy on repetition of SH by the 12month follow-up assessment (5/48 vs 7/48; OR 0.68; 95% CI
+44
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+0.20 to 2.32; k = 1; N = 96). Quality of evidence, as assessed using the GRADE criteria, was moderate for this outcome (see Summary of findings 7).
+Secondary outcomes
+7.30	Treatment adherence
+There was, however, a significant treatment effect for home-based problem-solving therapy on the number of participants who attended at least one treatment session over the course of the 12month follow-up period (45/48 vs 35/48; OR 5.57, 95% CI 1.47 to 21.08; k = 1;N = 96).
+7.31	Depression
+Although this trial included data on depression, the authors modified the scale used (Lorr and McNair Mood Scale; McNair 1964; Lorr 1967), thereby precluding inclusion of this data in this review.
+7.32	Hopelessness
+No data available.
+7.33	Suicidal Ideation
+Data obtained by correspondence suggested there was no significant treatment effect for suicidal ideation at either the post-treatment assessment (Mann-Whitney U = 984, P = 0.29) or six-month follow-up (Mann-Whitney U = 726, P = 0.14). As only median, rather than mean, scores were available for this outcome, we were unable to reproduce the mean difference in suicidal ideation scores between the experimental and control groups in this review.
+7.34	Problem solving
+No data available.
+7.35	Suicide
+No data available.
+Intensive inpatient and community treatment vs TAU
+One trial compared the effectiveness of brief psychiatric inpatient admission followed by regular outpatient appointments and 24hour access to the psychiatric unit with TAU over a 12-month follow-up period in adults admitted to a general hospital following a ’suicide attempt’ (i.e., suggestive of suicidal intent) (Van der Sande 1997a, N = 274).
+Primary outcome
+7.36	Repetition of SH
+There was no evidence of a significant treatment effect for intensive inpatient and community treatment on repetition of SH by the 12-month follow-up (24/140 vs 20/134; OR 1.18, 95% CI 0.62 to 2.25; k = 1; N = 274). Quality of evidence, according to the GRADE criteria, was low for this outcome (see Summary of findings 7).
+With respect to frequency of repetition of SH, there was also no evidence of a significant treatment effect for intensive inpatient and community treatment (mean 0.23, SD 0.57, n = 140 vs mean 0.23, SD 0.81, n = 134; MD 0.00, 95% CI -0.17 to 0.17, k=1; N = 274). Quality of evidence for this outcome was also low (see Summary of findings 7).
+Secondary outcomes
+7.37	Treatment adherence
+There was a significant treatment effect for treatment adherence. More patients in the experimental group attended at least one outpatient treatment session by the 12-month follow-up assessment (119/140 vs 64/134; OR6.99, 95% CI 3.69 to 12.36;k= 1; N = 274). However, there was no difference in the total number of treatment sessions attended (mean 14.30, SD 24.20, n = 140 vs mean 11.40, SD 27.70, n=134; MD 2.90, 95% CI -3.27 to 9.07; k = 1;N = 274).
+7.38	Depression
+There was no significant treatment effect for intensive inpatient and community treatment on depression scores by the 12-month follow-up assessment (mean 30.80, SD 15.90, n = 94 vs mean 35.80, SD 16.20, n = 50; MD -5.00, 95% CI -10.52 to 0.52; k = 1;N= 144).
+7.39	Hopelessness
+There was no significant treatment effect for intensive inpatient and community treatment on hopelessness scores by the 12-month follow-up assessment (mean 6.10, SD 5.00, n = 94 vs mean 7.50, SD 5.90, n = 50; MD -1.40, 95% CI -3.32 to 0.52; k=1;N = 144).
+7.40	Suicidal ideation
+No data available.
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+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+45
+7.41	Problem solving
+No data available.
+7.42	Suicide
+There was also no evidence of a significant treatment effect for suicide by the 12-month follow-up assessment (1/140 vs 2/134; OR 0.47, 95% CI 0.04 to 5.30; k=1;N = 274).
+General hospital admission vs other alternative forms of psychotherapy
+One trial investigated the effectiveness of general hospital admission versus non-admission over a four-month follow-up period in a group of adults attending an emergency room following an episode of self-poisoning, who had no immediate medical or psychiatric treatment needs (Waterhouse 1990, N = 77). In this trial, admission was described as consisting of little more than admission to an inpatient bed. The investigators did not attempt to influence referral to psychiatric or other treatment services. The median length of admission for those allocated to the experimental group was 17 hours.
+Primary outcome
+7.43	Repetition of SH
+There was no evidence of a significant treatment effect for hospital admission on repetition of SHat the post-intervention assessment (2/38 vs 2/39; OR 1.03, 95% CI 0.14 to 7.69; k=1;N = 77) or by the four-month follow-up assessment (3/38 vs 4/39; OR 0.75, 95% CI0.16 to 3.60; k= 1; N = 77). Qualityof evidence forthese time points was low (see Summary of findings 7).
+Secondary outcomes
+7.44	Treatment adherence
+No data available.
+7.45	Depression
+No data available.
+7.46	Hopelessness
+The authors state that there was no significant difference in hopelessness scores at the post-intervention assessment (mean 10.29, SD 5.68 vs mean 10.21, SD 4.97); however, they did not provide the numbers of patients in each group, thus precluding calculation of the MD and its associated 95% CI.
+7.47	Suicidal ideation
+There was no evidence of a significant treatment effect for hospital admission on suicidal ideation scores by the four-month followup assessment (mean 0.22, SD 0.85, n = 27 vs mean 0.04, SD 0.20, n = 25; MD 0.18, 95% CI -0.15 to 0.51; k=1;N = 52).
+7.48	Problem solving
+No data available.
+7.49	Suicide
+No data available.
+Intensive outpatient treatment vs TAU
+Two trials compared the effectiveness of intensive outreach interventions with standard outpatient care in adults admitted to emergency departments following a ’suicide attempt’ (i.e., suggestive of suicidal intent) (Allard 1992, N = 150; Welu 1977, N = 119). Allard 1992 compared an intensive intervention, involving psychiatrists and a social worker, a schedule of visits including at least one home visit, therapy provided where needed, reminders (telephone or written), and home visits with treatment by regular personnel in the same hospital over a 12-month treatment period. Therapies in the experimental group varied, and drug therapy was also an option. Welu 1977 compared a specialist, intensive outreach programme in which a community mental health team contacted participants immediately after discharge and arranged home visits and weekly or bi-weekly contact with therapists alongside routine psychiatric consultation.
+Primary outcome
+7.50	Repetition of SH
+There was no evidence of a significant treatment effect for intensive intervention on repetition of SH by either the four-month (Welu 1977: 3/62 vs 9/57; OR 0.27, 95% CI 0.07 to 1.06; k=1;N = 119) or 24-month (Allard 1992: 22/63 vs 19/63; OR1.24, 95% CI 0.59 to 2.62; k = 1; N = 126) follow-up assessments. For both follow-up periods, quality of evidence was low(see Summary of findings 7). We combined the results of these two trials, and again there was no evidence of a significant treatment effect for intensive outpatient intervention by the final follow-up point (Analysis 6.1; k = 2; N = 245). The quality of evidence was very low for this outcome (see Summary of findings 7).
+In the one trial that reported information on frequency of SH over the course of the 24-month follow-up period (i.e., Allard 1992), “the experimental subjects did not make fewer attempts than the comparison subjects” (Allard 1992, p. 310).
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+46
+Secondary outcomes
+7.51	Treatment adherence
+Data on treatment adherence were only available for Allard 1992. However, as the authors did not report information on SDs, t-test or F statistics we were unable to impute SDs using the method outlined in Townsend 2001 to calculate the mean difference in the number of treatment sessions attended. Nevertheless, the authors themselves report that “[t]he mean numbers of encounters with psychiatrists were 12.35 versus 1.54 (P < 0.001) in the first year and 2.11 versus 0.64 (P = 0.071) in the secondyear” (Allard 1992, p. 311).
+7.52	Depression
+No data available.
+7.53	Hopelessness
+No data available.
+7.54	Suicidal ideation
+No data available.
+7.55	Problem solving
+No data available.
+7.56	Suicide
+Allard 1992 reported data on suicide during follow-up. There was no evidence of a significant treatment effect for the intensive outpatient intervention on suicides, however, by the 24-month follow-up assessment (3/76 vs 1/74; OR 3.00, 95% CI 0.30 to 29.52; k = 1; N = 150) in this trial.
+Long-term psychotherapy vs other alternative forms of psychotherapy
+One trial investigated the effectiveness of long-term (one session per month over 12 months) versus short-term (one session per week over 12 weeks) outpatient psychotherapy on repetition of SH over a 12-month follow-up period in adults admitted to hospital due to repeated episodes of self-poisoning (Torhorst 1988, N = 80). The content of therapy was not specified in this trial, however.
+Primary outcome
+7.57	Repetition of SH
+There was no evidence of a significant treatment effect for longterm therapy on repetition of SH by the post-treatment assessment (9/40 vs 9/40; OR 1.00, 95% CI 0.35 to 2.86; k=1;N = 80). A low quality of evidence was associated with this outcome (see Summary of findings 7).
+Secondary outcomes
+7.58	Treatment adherence
+The authors did not provide numerical data on treatment adherence, although they state, “[attendance at the first session was about equal for both groups (about 60%)” (Torhorst 1988, p. 420). However, the authors further state that “participation of the 12-month (long-term therapy) group dropped drastically by the second session to under 40%, while the participation of the patients in the 3-month (intensive short-term therapy) program remained higher” (Torhorst 1988, p. 420). It is unclear whether this difference was significant.
+Overall adherence also appears to have been very low in both groups as the “average number of sessions was 3.9 (out of a possible 12 sessions) in the three-month group and 2.6 (out of a possible 12 sessions) for the 12-month group” (Torhorst 1988, p.420). Again, it is unclear whether this difference is significant. Additionally, as neither SDs, nor t-test, nor F statistics were reported, we were unable to impute SDs using the method outlined in Townsend 2001 to calculate the mean difference in the number of treatment sessions attended by the experimental and control groups.
+7.59	Depression
+Although numerical data on depression scores were not available, means estimated by the review authors from a graph in the original report suggest there was little difference in depression scores between those allocated to long-term therapy and those allocated to short-term therapy by the 12-month follow-up assessment (estimated means 9.3 vs 6.7).
+The study authors, however, stated that “self-evaluated depressivity . . . improved considerably more for the patients of the three-month program than for those of the 12-month program” (Torhorst 1988, p. 421). This improvement was described by the authors as significant.
+7.60	Hopelessness
+No data provided.
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+47
+DISCUSSION
+This systematic review represents an update of previous versions (Hawton 1998; Hawton 1999; NICE 2011). Whilst those versions included psychosocial and pharmacological interventions as well as data for adults, children and adolescents who engage in SH, this update focused solely on psychosocial treatments for adults. In the previous versions of this review, we only focused on a limited number of clinical outcomes, namely repetition of SH and suicide. In this update we have considerably expanded the range of clinically relevant outcomes examined to include treatment adherence, depression, hopelessness, problem-solving, and suicidal ideation where available. We also reported frequency of SH where data on this outcome were available. For the primary outcome of SH, we have included SH episodes with any type of motivation, including suicidal. Where we clarified suicidal intent with study authors a number reported including all episodes of SH irrespective of suicidal intent despite inclusion criteria suggesting that only those indicating intent to die were eligible to participate, highlighting the problems in attempting to ascertain suicidal intent.
+Recently there has been a considerable increase in the number of trials conducted in this field and in the types of interventions evaluated, reflecting the international concern about self-harm, the increased attention to suicide prevention in particular, and the involvement of newer countries in this research, especially in Asia.
+Previously we commented on the fact that the majority of trials included either patients who had all taken overdoses, or samples where the majority had, reflecting the types of patients who present to general hospitals following SH (Hawton 2007). However, there are other important patient subgroups, especially those who engage in self-mutilation. Some of the more recent trials in this review included such participants, particularly those that focused on patients who had a history of multiple episodes of SH at trial entry (e.g., Gratz 2006; Gratz 2014; Harned 2014; Linehan 1991; Linehan 2006; McMain 2009; Priebe 2012; Weinberg 2006). It should be noted that people who repeat SH may change the methods they use (Owens 2015; Lilley 2008). It is also important to note that multiple repetition of SH is associated with increased suicide risk (Zahl 2004).
+None of the trials included information on adverse effects of these interventions, other than further suicidal behaviour.
+We have used the intention-to-treat method where data allowed. This was usually possible when examining the outcomes of repetition of SH and suicide. Where outcomes relied on patient interview, this was generally not possible and we have instead used all available case data.
+Summary of main results
+CBT-based psychotherapy
+There were 18 trials that compared CBT-based psychotherapy, comprising cognitive behavioural therapy, problem-solving therapy, or both, versus treatment as usual (TAU) (Brown 2005; Davidson 2014; Dubois 1999; Evans 1999b; Gibbons 1978; Guthrie 2001; Hatcher 2011; Hawton 1987a; Husain 2014; McAuliffe 2014; Patsiokas 1985; Salkovskis 1990; Slee 2008; Stewart 2009; Tapolaa 2010; Tyrer 2003; Wei 2013; Weinberg 2006). Meta-analysis of these trials provided evidence to suggest a reduction in repetition of SH at both 6 and 12 months after trial entry and at final follow-up. However, we did not find any significant treatment effect for CBT-based psychotherapy on the frequency of SH at final follow-up. On the basis of data from 15 trials, there was no evidence of a significant effect of psychological therapy on suicides, although relatively few events (i.e., 24) were recorded. We also found beneficial effects for depression and hopelessness at 6 and 12 months as well as at final follow-up. Few trials assessed suicidal ideation, although there was an apparent benefit for CBT-based psychotherapy at three months, six months, and at final follow-up. Relatively few trials reported findings for treatment adherence, and they did so in different ways, so we cannot draw firm conclusions for this outcome.
+Interventions for multiple repetition of SH/probable personality disorder
+Group-based emotion-regulation psychotherapy
+On the basis of two trials, conducted by the same research group, emotion-regulation therapy for patients with borderline personality disorder provided in a group-based setting was associated with a reduction in the proportion of patients repeating SH in the final two months of the initial treatment period, but not with an overall reduction in the frequency of SH over the whole treatment period (Gratz 2006; Gratz 2014). There was also no effect for this type of therapy on depression.
+Mentalisation
+In a single trial, mentalisation therapy for patients diagnosed with borderline personality disorder was associated with fewer participants repeating both SH and suicide attempts by the post-intervention assessment (Bateman 2009). There were also beneficial effects for frequency of repetition of suicide attempts and for depression scores.
+Dialectical behaviour therapy
+Three trials compared DBT with TAU in patients diagnosed with borderline personality disorder, with no apparent overall effect on the proportion of patients repeating SH at 12 and 24 months following trial entry (Linehan 1991; McMain 2009; Priebe 2012).
+83
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+There was, however, a significant treatment effect for DBT on frequency of repetition of SH.
+A single trial compared DBT versus psychological treatment by ’experts’ (CBT-E; Linehan 2006). There was no evidence of differences in outcomes for patients in the two groups in terms of the proportion repeating SH or in the total number for ’parasui-cidal’ acts, although the authors stated that there was a beneficial effect for DBT on suicide re-attempts. There were no differences between the DBT and CBT-E groups for depression and suicidal ideation, however.
+There was no difference in terms of repetition of SH, depression, or treatment adherence in a single small trial between two forms of DBT, an experimental one in which participants were given significantly longer cognitive exposure to stressful events coupled with the standard DBT protocol and a control one in which participants received the standard DBT protocol as devised by Linehan 1991 (Harned 2014).
+Dialectical behaviour-oriented therapy
+In a single small trial (Turner 2000), DBT-oriented therapy appeared to be more effective than client-oriented therapy in terms of the proportion of patients repeating SH and the frequency of SH. There were also benefits for depression and suicidal ideation.
+Case management
+In a single trial comparing case management versus TAU, there was no effect for the experimental treatment on the proportion of participants repeating SH, but it was associated with fewer multiple admissions (Clarke 2002).
+Two trials compared case management with added assertive outreach versus TAU (Hvid 2011; Morthorst 2012). There was no evidence of a significant treatment effect for repetition of SH, but there was a reduction in the proportion of females repeating SH in the experimental group in one of these trials (Hvid 2011).
+A single large trial compared case management plus assertive outreach versus enhanced usual care (Kawanishi 2014). There was no difference between groups for repetition of SH or for suicide by the 24-month follow-up period, although the study authors state that are undertaking further analyses and also data for hopelessness.
+Treatment adherence enhancement approaches
+Adherence enhancement
+In a single trial that made efforts to improve adherence with treatment by having nurses make home visits to participants who had not attended initial outpatient appointments, there were increased rates of attendance at the outpatient clinic in the group receiving
+the experimental intervention. However, in spite of a marked reduction in subsequent repetition of SH in this group, the difference was non-significant (Van Heeringen 1995).
+Continuity of care by the same therapist
+In a single trial, continuity of care (i.e., where the clinician who assessed each participant in hospital also provided aftercare for them) resulted in better treatment attendance than where different therapists treated participants (Torhorst 1987). However, there was no beneficial effect on repetition of SH.
+Mixed multimodal interventions
+Mixed multimodal interventions
+A single large trial compared a package of interventions including problem-solving therapy, postcards, and vouchers entitling participants to GP care versus TAU (Hatcher 2015). However, there was no beneficial effect for the experimental treatment on repetition of SH, depression, or hopelessness.
+Culturally-adapted mixed multimodal interventions
+Hatcher 2016a adapted the treatment package developed by
+Hatcher 2015 for participants self-identifying as ofMa ori ethnicity. There were no apparent benefits of this intervention in terms of repetition of SH (including in subgroups of those with only a single episode of SH prior to trial entry and those with a history of multiple episodes of SH), depression, or hopelessness.
+Remote contact interventions
+Postcards
+Four trials compared the effectiveness of postcards sent on a regular basis over a 12-month period with TAU (Beautrais 2010; Carter2005; Hassanian-Moghaddam 2011; Kapur2013a). However, there was no benefit for this intervention in terms of proportion of patients repeating SH. Substantial heterogeneity was associated with this analysis, and removing Kapur 2013a (a small pilot trial which may have been an outlier) resulted in a significant treatment effect. The single largest trial of this intervention did find fewer patients repeating SH in the experimental group (Hassanian-Moghaddam 2011). This result is notable because the control treatment used in this trial would have consisted of little more than discharge because of the paucity of psychiatric services in Iran as compared to Australia, New Zealand, and the UK, which have well-developed services. This raises the possibility that such an intervention may be more effective in such settings. Additionally, the postcards used in this trial included religious and philosophical messages in addition to general support, which may also explain their apparent efficacy in reducing suicidal behaviour.
+Psychosocial interventions for self-harm in adults (Review)
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+84
+There was also no evidence of a significant treatment effect for postcards on frequency of repetition of SH at either post-intervention or at12 or 24 months’ follow-up in three of the four trials of postcards (Carter 2005; Hassanian-Moghaddam 2011; Kapur 2013a). It should be noted that the positive effect on frequency of repetition reported in Carter 2005 was, according to the study’s author, largely accounted for by difference in repetition in a small subsample (less than 3% of the total sample) of women with a history of three or more episodes of SH prior to trial entry.
+Emergency cards
+On the basis of two trials, there was no evidence that provision of an emergency contact card allowing emergency access to a psychiatrist on demand had an impact on repetition of SH (Evans 1999a; Morgan 1993). In the original report, however, a post hoc subgroup analysis indicated that receipt of the emergency card was associated with an increased risk of repetition of SH in those with a history of multiple episodes of SH prior to the index episode (Evans 1999a).
+General practitioner’s letter
+In a single trial in which general practitioners sent a letter to participants following their discharge from hospital after SH offering an appointment coupled with specialist advice on the management of SH patients (Bennewith 2002), there was no apparent beneficial effect on repetition of SH, although there was evidence of a substantial beneficial effect in females. When analysed by repeater status, furthermore, there appeared to be a beneficial effect in those with a history of multiple episodes of SH at trial entry, but the reverse was true in those with only a single episode of SH at trial entry.
+Telephone contact
+In three trials telephone contact with patients after discharge from hospital did not produce any apparent benefits in terms of repetition of SH compared with standard care (Cedereke 2002; Vaiva 2006; Wei 2013). There was also no evidence of any impact of telephone contact on depression, suicidal ideation, or on the proportion of participants attending at least one treatment session during the 12-month follow-up period.
+Mobile telephone-based psychotherapy
+A single trial delivered psychotherapy, which included elements of problem-solving therapy, meditation, and social support, by mobile telephone (Marasinghe 2012), but there was no effect of this intervention on repetition of SH. In the mobile phone psychotherapy group, however, depression improved more in males compared to those assigned to the wait list control group, whereas suicidal ideation improved more in females.
+Other mixed interventions
+Interpersonal problem-solving skills training
+In a single trial, interpersonal problem-solving skills training (IPSST) was no better than brief problem-oriented therapy in terms of repetition of SH, suicide and hopelessness (McLeavey 1994), although patients in the IPSST group may have had better scores after treatment with regard to measures of problem-solving. Treatment adherence also did not differ between groups, although patients in the IPSST group did attend more treatment sessions.
+Behaviour therapy
+In a single trial, although behaviour therapy appeared to lead to significant reductions in depression at the 10-week post-treatment assessment, by the nine-month follow-up assessment there was no apparent benefit for behaviour therapy compared to insight-oriented therapy (Liberman 1981). Behaviour therapy was also associated with mixed findings with respect to suicidal ideation. There was no apparent effect of behaviour therapy in reducing the proportion of participants reporting suicidal ideation at the two-year follow-up period, although there was some evidence of a significant treatment effect for shorter follow-up periods. Behaviour therapy was also not associated with a significant treatment effect for repetition of SH over a two-year follow-up period.
+Provision of information and support
+In a single trial conducted in five countries, a hospital-based information service combined with regular home support, telephone support, or both, appeared to have no extra benefit compared to TAU in terms of repetition of SH (Bertolote 2010). However, when results were analysed separately by site, although no apparent benefit for information and support was found for four of the five sites, this intervention package was associated with a significant reduction in repetition of SH at the Chennai, India site. There was evidence of significant reduction in suicide for the overall cohort (Fleischmann 2008) and for the Chennai, India site (Vijayakumar 2011), but not for the remaining two sites for which data on suicides were available (Karaj, Iran: Hassanzadeh 2010 and Yuncheng, China: Xu 2012). We have noted that there is a discrepancy between the findings for the overall cohort and those reported from the individual sites, in that the number of suicides reported for the overall cohort for the experimental group is less than that presented in three local site reports (i.e., two vs three, respectively).
+Treatment for alcohol misuse
+Evaluation in a single trial of a brief intervention for alcohol misuse inSH patients showed no significant effect for this intervention on repetition of SH, although the proportion of participants repeating SH was somewhat lower in the experimental group (Crawford
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+85
+2010). However, only 47.1% of those randomised to the experimental group attended the alcohol treatment session. The original trial report did, however, observe a non-significant trend towards reduced alcohol consumption per drinking day in those allocated to the experimental arm.
+Home-based problem-solving psychotherapy
+In a single trial, home-based problem-solving psychotherapy resulted in better treatment adherence than outpatient problemsolving therapy, but there was no difference in repetition of SH, depression, or suicidal ideation between the groups (Hawton 1981).
+Intensive inpatient and community treatment
+A single trial compared an intervention involving brief psychiatric admission followed by regular outpatient appointments plus 24hour access to a treatment service versus TAU (Van der Sande 1997a). There was no difference between the two groups in repetition of SH, depression, or hopelessness, although more patients in the experimental groups attended at least one outpatient appointment.
+General hospital admission
+In a single trial, there was no beneficial effect of general hospital admission on repetition of SH or on hopelessness compared with discharge from hospital (Waterhouse 1990). However, as this trial was limited to low-risk participants, only around 15% of the presenting patients were included.
+Intensive outpatient intervention
+Two trials compared a combination of intensive therapies, including psychotherapy, behaviour therapy and family therapy, versus standard outpatient care (Allard 1992; Welu 1977). There was no effect of this treatment package on repetition of SH.
+Long-term psychotherapy
+In a single trial that compared long-term therapy (the nature of which was unspecified) versus short-term intensive therapy, there was no difference in repetition of SH (Torhorst 1988). Estimates of scores from graphs also suggests little difference in depression scores.
+Overall completeness and applicability of evidence
+Completeness of evidence
+There have now been a considerable number of trials of psychosocial interventions for adult SH patients (we identified 55 independent trials). There have been multiple trials of CBT-based psychotherapy, dialectical behaviour therapy, case management, and postcards. Investigators have also evaluated a wide range of other types of interventions, including attempts to increase adherence with treatment and specific aftercare interventions; however, many of these evaluations have been limited to single trials.
+It is important to note that we identified no trials of psychosocial interventions for older (> 60/65 years) adults.
+Most trials evaluated a range of relevant primary and secondary outcomes (e.g., repetition of SH, hopelessness, depression). However, they infrequently reported information on suicide, and we had to request it from many authors. In 11 trials, information on repetition of SH was based only on hospital re-presentations, whereas in a large number of trials this information came from self-reported data, which in some cases was supplemented by information from clinical and other sources. More episodes of SH will be identified through self-report compared with information from clinical records, as much SH occurs in the community and does not result in presentation to clinical services (Borges 2011). However, these differences in the recording of SH would not have affected the overall results, as whatever approach was used in the individual trials would have affected the experimental and control arms equally. Also, some trials only assessed repetition during the period in which participants received therapy (e.g., Bateman 2009), whereas for most trials there were further post-treatment follow-up assessments.
+Acceptability of evidence
+The proportions of participants from the two sexes in these trials appears to be in accord with SH patients more generally (Hawton 2007). A number of trials focused on those with a history of multiple episodes of SH, including patients diagnosed with borderline personality disorder; this focus is welcome given that a history of multiple episodes of SH is associated with a particularly high risk of subsequent suicide (Zahl 2004). A number of trials did not record information on suicidal intent of participants (e.g., Carter 2005; Crawford 2010; Dubois 1999; Evans 1999a; Evans 1999b; Gibbons 1978; Gratz 2006; Hassanian-Moghaddam 2011; Linehan 1991; McLeavey 1994; Morgan 1993; Priebe 2012; Weinberg 2006), which is surprising given the association of SH with future risk of suicide (Carroll 2014; Owens 2002).
+Compared to previous versions of this review (Hawton 1998; Hawton 1999; NICE 2011), there is now a greater representation of trials from low- to middle-income countries, including China (Wei 2013; Xu 2012), India (Vijayakumar 2011), Iran (Hassanian-Moghaddam 2011; Hassanzadeh 2010), Pakistan (Husain 2014), and Sri Lanka (Marasinghe 2012).
+It is worth noting that this review focused exclusively on patients who had previously engaged in SH. As a result, we excluded pa-
+86
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+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+tients with conditions such as borderline personality disorderwho had not engaged inSH and mixed trials of patients with either SH or suicidal ideation in the absence of actual SH.
+Quality of the evidence
+Apart from trials of CBT-based psychotherapy (18 trials), group-based emotion-regulation psychotherapy (two trials), dialectical behaviour therapy (three trials in which DBT was compared with TAU), case management (four trials) and postcards (four trials), all the included trials compared specific interventions, thus limiting the robustness of possible conclusions about their effectiveness compared with routine care (TAU). Also, many trials were too small to detect significant differences in proportions of patients experiencing the primary outcome, namely repetition of SH. Additionally, quality of evidence, as assessed using the GRADE approach, was generally low to moderate, suggesting that further research is likely to have an important impact on our confidence in the estimate of treatment effectiveness and may, in some cases, change the estimates.
+Limitations in design and implementation
+All 55 included trials were rated as at high risk of bias in relation to at least one aspect of trial design, especially with respect to blinding of both participants and clinical personnel. In part this may reflect the fact that the focus of the present review was on the effectiveness of psychological interventions, and we believe it is generally not possible to blind participants or clinical personnel to psychological therapy. Nevertheless, we cannot rule out performance or detection bias.
+Indirectness of evidence
+Repetition of SH was measured using either self-reported information, medical records, or re-presentation to hospital in all 55 trials included in this review. It is possible that self-reported information might over- or underestimate the real recurrence of SH. On the other hand, use of medical records, hospital presentations, or both may underestimate the real recurrence of SH, as many episodes of SH occur in the community and do not result in presentation to clinical services (Borges 2011). However, these differences in the recording of SH would not have affected the overall results,
+as whatever approach was used in the individual trials would have affected the experimental and control arms equally.
+Trials assessed secondary outcomes using widely validated psychometric measures (e.g., BDI, BHS), which authors did not typically modify in scoring.
+Unexplained heterogeneity or inconsistency of results
+One meta-analysis included in the review, effectiveness of CBT-based psychotherapy on depression scores at 12 months, was associated with substantial levels of heterogeneity (I2 = 76%). Excluding Hatcher 2011, which used Zelen’s method of randomisation, did not materially affect heterogeneity.
+We also conducted sensitivity analyses where visual inspection of the forest plot indicated that one or more trials may have been outliers. For this reason, we excluded the postcard trial by Kapur 2013a from meta-analyses for repetition of SH both at post-intervention and at 12-month follow-up. In both cases, exclusion of this trial caused the overall estimate of treatment effectiveness for postcard-based interventions to obtain significance. This could be due to the fact that Kapur 2013a was a small pilot investigation. We also undertook sensitivity analyses where one or more trials included adolescent participants. Excluding trials for this reason, however, did not appear to systematically explain heterogeneity.
+Imprecision of results
+Results of the individual trials included in this review were associated with a high level of imprecision as indicated by the wide confidence intervals around the effect size estimates for many of the outcomes reported in this review.
+Probability of publication bias
+We could only formally evaluate the presence of publication bias for CBT-based psychotherapy with respect to repetition of SH at six months (Figure 4), 12 months (Figure 5), and final followup (Figure 6), and for depression scores at final follow-up (Figure 7). In all four cases, some funnel plot asymmetry was evident and particularly seemed to affect the right side of the plot. It is therefore possible that there are unpublished trials in which experimental treatment was ineffective. However, it should also be noted that funnel plot asymmetry could also be due to high levels of heterogeneity.
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+87
+For one trial of provision of information and support, investigators have not published data from two of the seven study sites (Fleischmann 2008; Bertolote 2010). Therefore we cannot rule out publication bias for this intervention. We are also aware of one further unpublished trial of CBT-based psychotherapy for which we were unable to obtain the results from the authors. All other trials included in this review have been published in full in peer-reviewed journals. We therefore believe that publication bias is unlikely to have been a major cause of heterogeneity. Instead, we believe either poor methodological design or true heterogeneity between trials may be responsible for this result (Sterne 2011).
+Potential biases in the review process
+We have no reason to believe we have not identified all relevant published trials of psychosocial interventions for SH in adults. Nevertheless, by using the random-effects model in all analyses our results possess greater generalisability than if we had used the fixed-effect model (Erez 1996). However, because our review criteria included trials in people who had all engaged in SH and presented to clinical services in the preceding six months, we excluded trials
+where only some of the participants had engaged in SH and also trials where SH was an outcome measured in general psychosocial interventions for patients with psychiatric disorders. Data on repetition of SH were available for all but one of the included trials (Patsiokas 1985). Information on suicides was available for 43 (78.2%) of the trials included in this review, although for most trials this information had to be obtained via correspondence with authors.
+Owing to uncertainties about the impact of using Zelen’s design, for trials using this approach we analysed data for the primary outcome of repetition of SH using data for both the randomised sample (including both those patients who, following treatment allocation, subsequently consented to participation and those who did not) and the consenting sample only (see Unit of analysis issues section). This typically had little impact on the pattern of results observed.
+Due to the large number and varied nature of the interventions included in theearlierversions ofthis review (Hawton1998; Hawton 1999) we decided, with agreement of the editors, to divide the review into three (the others reviews being of pharmacological inter
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+91
+ventions in adults (Hawton 2015b) andboth pharmacological and psychosocial interventions for children and adolescents (Hawton 2015a)). Because of the fact that we approached this review with a view to identifying the types of psychosocial interventions that had been evaluated to date in this clinical population, we used a consensus approach to grouping the interventions. This process might have been subject to bias, but in general, there was very good agreement between members of the review group, who have considerable experience in research and clinical practice in relation to SH in adults.
+Risk of bias for selective outcome reporting was based on the analyses undertaken by the study authors. As we were unable to include data that had been statistically adjusted for missingness in the present review, we believe it would be unfair to rate trials that made use of statistical adjustments to account for missing data at follow-up as having high risk of bias for this outcome simply because of our choice of outcome.
+Agreements and disagreements with other studies or reviews
+There have been several reviews of the efficacy of psychosocial interventions for adult SH patients. None of those that used systematic review methodology to identify all relevant treatment interventions also present meta-analyses of treatment efficacy (Comtois 2006; Crawford 2007a; Daigle 2011; Hepp 2004; Van der Sande 1997b), aside from Inagaki 2015 and NICE 2011; however, this latter review was conducted using data supplied by our review team during a previous update of the present review. Of two further meta-analyses, one specifically focused on cognitive behavioural interventions (Tarrier 2008b) and one examined remote contactbased interventions (Milner 2015).
+Inagaki 2015 combined trials of case management, treatment adherence enhancement, and remote contact interventions into one category which the authors termed “active contact and follow-up” interventions. They concluded that these interventions show promise in reducing repetition at 12 months’ follow-up but not at 24 months’ follow-up. Combining trials of such different treatment approaches, however, is potentially misleading given their very different mechanisms of action. Crawford 2007a also combined different treatment approaches (e.g., CBT-based psychotherapy, DBT, and adherence enhancement), as well as interventions specifically developed for children and adolescents (e.g., Spirito 2002; Wood 2001). The authors concluded there was no evidence of a preventive effect of psychosocial interventions for the prevention of suicide. However, they only assessed efficacy with respect to completed suicide, which few trials are adequately powered to evaluate. Repetition of SH, on the other hand, is a much more common outcome for which a greater number of trials are powered to evaluate.
+There is general agreement amongst these reviews concerning the efficacy of CBT-based psychotherapy. Comtois 2006, for example,
+concluded there were positive effects for psychotherapy and outreach interventions. However, conclusions concerning the latter type of intervention in this review were particularly influenced by the findings of a single trial in which regular letters were mailed to suicidal patients discharged from psychiatric inpatient care (Motto 2001). As not all participants included in this trial had engaged in SH behaviour, this trial did not meet inclusion criteria for the present review. Tarrier 2008b, moreover, concluded there was evidence for the effectiveness of CBT-based psychotherapy but only when compared against TAU rather than another form of active psychosocial therapy. Benefits of psychological therapy have recently been reported by findings of a national, non-randomised naturalistic study in Denmark (Erlangsen 2015).
+A recent meta-analysis of contact-based interventions found no significant reduction in terms of repetition of SH (both proportion and number of episodes per person) for these interventions (Milner 2015). Surprisingly, however, the review pooled together several different types of contact-based intervention, including letters, emergency cards, and postcards. Additionally, despite the stated focus of the review being on interventions following SH, it also included trials in which not all participants had engaged in SH prior to inclusion (e.g., Motto 2001; Robinson 2012). Also, for one of the trials included in this review, our correspondence with the original trial authors revealed that data on non-fatal repetition of SH could not be disaggregated from information on completed suicide (e.g., Chen 2013).
+Other reviews have focused specifically on interventions for patients with personality disorder, particularly borderline personality disorder (McMain 2007b; Kliem 2010). They, along with the review by Comtois 2006, concluded that dialectical behaviour therapy was effective for the prevention of SH, although these reviews included some trials in which not all participants had engaged in SH prior to trial entry. Further reviews by Luxton 2013 and Kapur 2010 of postcard, telephone, emergency card, and face-to-face interventions concluded they may be effective in preventing suicidal behaviour, although again, these reviews included some trials in which not all participants had engaged in SH at trial entry. The inclusion of these participants means that the focus of the intervention in such trials, and hence the specificity of the findings for SH patients and planning of clinical services for these patients, will be unclear.
+AUTHORS’ CONCLUSIONS
+Implications for practice
+Evidence drawn from an earlier version of this review has been incorporated in guidance for commissioners of clinical services in the United Kingdom, which states that brief CBT-based psychotherapy should be available in self-harm services (NICE 2011). Our
+92
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
+updated findings would reinforce the view that there is some evidence to suggest that CBT-based psychotherapy is effective in reducing repetition ofSH compared with treatment as usual (TAU). There is a lack of evidence with respect to the prevention of suicide, however, although there was a relatively small number of such events in these trials, precluding a firm conclusion.
+In most trials CBT-based psychotherapy was typically very brief (i.e., less than 10 sessions). It was delivered on an individual basis in all trials except one (i.e., McAuliffe 2014).
+While dialectical behaviour therapy (DBT) did not reduce the proportion of participants with borderline personality disorder who engaged in a repeated episode of SH as compared with TAU, it did appear to reduce the frequency of repetition. Arguably, where patients are multiple repeaters of SH and much of their SH will have included acts such as superficial self-cutting, reduction in the frequency of SH could be viewed as a key outcome.
+Less intensive remote contact-based interventions, such as sending regular postcards to patients in the year following an episode of SH, did not appear to reduce the proportion of patients repeating SH. However, these interventions may hold promise in settings where there are very limited psychiatric services (where the alternative may be no provision of little or no aftercare). Case management approaches also did not appear to be effective in reducing the proportion of patients who repeat SH.
+Other interventions have mostly been evaluated in single trials. In a trial of DBT-oriented therapy, for example, there was a significant reduction in both the number of patients repeating SH and the total number of repeat episodes, as well as significant reductions in depression and suicidal ideation. Mentalisation therapy was associated with reduced repetition of SH and depression in the latter stages of follow-up in patients with borderline personality disorder. Group-based emotion-regulation psychotherapy was associated with a reduction in the proportion of patients repeating SH in the final two months of the treatment period, but there was no apparent effect on depression. Provision of information and support was associated with reduced numbers of completed suicides in a single multicentre trial conducted in five low- and middleincome countries, but not with reduced repetition of SH. There is, however, some inconsistency in the reporting of numbers of suicides in the experimental group for the overall cohort as reported in Fleischmann 2008 and those from three of the five individual study sites (in Karaj, Iran (Hassanzadeh 2010); Chennai, India (Vijayakumar 2011); and Yuncheng, China (Xu 2012)). Homebased problem-solving psychotherapy and continuity of treatment by the same therapist from assessment to aftercare appeared to improve treatment adherence, but without clear benefit in terms of repetition of SH.
+Where possible, we analysed results separately by sex. Whilst one trial indicated reduced benefit for the intervention on depression in females as compared to males (Marasinghe 2012), in the few
+trials where a subgroup analysis by sex was possible, the majority suggested a significant treatment effect of psychosocial interventions for females but not males.
+In terms of repeater status, it appears that some more limited interventions (emergency cards) may have negative effects in patients with a history of multiple episodes of SH. However, trials of some more intensive interventions (e.g., group-based emotion-regulation psychotherapy, mentalisation, DBT) appear to have positive benefits on repetition of SH, and particularly for participants who were multiple repeaters of SH at trial entry, which would mostly have included participants diagnosed with borderline personality disorder.
+Implicationsfor research
+Given the apparent positive benefits of CBT-based psychotherapy and some other treatment approaches, future trials should identify which types of patients are most likely to benefit from these interventions. Although we were only able to undertake subgroup analyses by sex in five trials (Bennewith 2002; Carter 2005; Fleischmann 2008; Hvid 2011; Marasinghe 2012), evidence from the present review would suggest that some psychosocial interventions, particularly remote contact-based interventions and case management, tend to be of greater benefit for female patients. There should therefore be a greater focus on sex-specific interventions, especially to identify treatments most likely to benefit male SH patients.
+We could only undertake subgroup analyses by repeater status (i.e., with or without a history of multiple episodes of SH) in five trials (Bennewith 2002; Evans 1999a; Hatcher 2011; Hatcher 2016a; Hatcher 2015). Nevertheless, there is limited evidence that whilst some forms of psychosocial interventions may be more effective in those with a history of multiple episodes of SH (e.g., CBT-based psychotherapy) other forms of contact-based interventions, such as emergency cards, may lead to an increased risk of repetition of SH in those with a history of multiple episodes of SH prior to trial entry. For these reasons, history of prior SH should be clearly identified in future trials, with stratified randomisation according to repeater status being desirable.
+Researchers investigating psychosocial treatments should endeavour to investigate whether the intervention results in changes in the psychological or social mechanisms that are the targets of treatment (e.g., problem-solving, emotion-regulation, interpersonal skills) and the extent to which such changes relate to positive outcomes (Arensman 2001). Such knowledge will help clarify the mediators of treatment efficacy and allow therapists to modify interventions so that they may be more effective.
+In view of the apparent effectiveness of CBT-based psychotherapy in reducing repetition of SH and the development of online therapy for a range of psychological problems (Andersson 2014; Griffiths 2006), and the introduction online means of provid-
+93
+Psychosocial interventions for self-harm in adults (Review)
+Copyright ©2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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+Although a large portion of care fob people with mental and substance use disorders occurs in the general health sector in the United States, the mental health specialty sector is the health care home for adults vith severe mental illnesses.1 The poor integration between these two health sys-ems poses major challenges to effectively meeting the complex health care needs f this population. In this paper we call attention to this problem and propose rec-mmendations to improve the integration of mental and general health care for eople with severe mental illnesses. For the purposes of this discussion, the term evere mental illness refers to schizophrenia and other psychoses, bipolar disorder, or evere depression compounded by persistent functional impairment.
+■	Prevalence of general medical conditions among adults with severe nental illnesses. Compared to people without mental disorders, adults with se
+vere mental illnesses have higher rates of chronic general medical conditions, including hypertension, HIV/AIDS, and diabetes; a higher frequency of multiple general medical conditions; and a higher rate of premature mortality resulting from these conditions.2 Further, general medical comorbidity has a detrimental effect on their mental health, compounding already high levels of functional impairment.3 Because general medical conditions in this population are frequently missed, and many of the published rates are based on recorded diagnoses, true prevalence rates are likely to be higher than reported.4
+Several factors have been implicated in the excess burden of general medical conditions among adults with severe mental illnesses. These include illness factors; socioeconomic disadvantage; substance abuse comorbidity; medication side effects; and unhealthy behavior and neglect of self-care.5 Inadequacies in the health care system, covered in greater detail below, also contribute.
+■	Is “care as usual” meeting the general health care needs of this population? Evidence suggests that people with severe mental illnesses have difficulties obtaining general health care. Not only are they less likely to have insurance than people without serious mental illnesses, but they also perceive more barriers to obtaining general health care services and report that providers of such care often dismiss their somatic complaints.6
+Further, several studies indicate that the quality of general health care is poorer for people with severe mental illnesses than for less impaired people.7 For example, a study of Medicare beneficiaries found that the increased rates of mortality in this population following hospitalization for acute myocardial infarction (AMI) were partially explained by differences in quality of care.8 Several studies have been conducted in the Veterans Health Administration (VHA) system, which provides an opportunity to evaluate potential disparities in a national setting with apparent equal access. It is therefore worth noting that these studies have also documented quality gaps in preventive services, diabetes care, and care for cardiovascular conditions.9 People with severe mental illnesses who are older and who abuse substances—two subpopulations with particularly high rates of general medical comorbidity—appear to be at even higher risk for substandard quality of general health care.10
+Although the observed disparities in access to and quality of care are likely to be determined by a variety of factors, the lack of clinical integration in the care of people with severe mental illnesses plays a key role.11 The Institute of Medicine (IOM) recently laid out a comprehensive strategy to improve the quality of care for people with mental and substance abuse disorders, and achieving greater clinical integration between service providers is a centerpiece of this strategy12 Provisions in the 1963 community mental health center (CMHC) legislation requiring that physical examinations be offered on site demonstrate that the need to better address the general health care needs of mentally ill people through integrating strategies has long been recognized.13 However, recent developments—including
+the above-mentioned IOM report and reports by the President’s New Freedom Commission on Mental Health and the Bazelon Center for Mental Health Law— suggest that the time is ripe to revisit this issue.14 These reports have also contributed to the discussion and recommendations that follow to effect progress in integrated care.
+Conceptual Framework For Integrated Care
+Our discussion of integrated care for people with severe mental illnesses pertains to Stephen Shortell’s construct of clinical integration, defined as “the extent to which patient care services are coordinated across people, functions, activities, and sites over time.”15 Because of the centrality of care coordination to this conceptualization, the terms have been used interchangeably, as we do in this discussion. Critical prerequisites to clinical integration are communication (sharing of needed information among providers); collaboration (trusting interaction among providers, defined by shared understanding of goals and roles, effective communication, and shared decision making); comprehensiveness (effectively meeting all of the health care needs of patients); and continuity of care (timely and uninterrupted delivery of appropriate services across providers and over time).16
+■	Organizational integration. Because clinical integration can be facilitated or hindered by nonclinical factors, we distinguish two other forms of integration here: organizational and financial. Organizational integration corresponds to the availability and functionality of linking structures that enable and sustain clinical integration. Its dimensions are structural integration, or the linking mechanisms that enable and promote clinical integration; functional integration, or the functionality of these mechanisms; and accountability, or clarity about who is responsible for the delivery of which service and where to target performance monitoring and accreditation standards.17
+Linking mechanisms to bridge the mental/general health divide include formal agreements with general practitioners with expertise in treating people with severe mental illnesses; integrated clinical information systems; case management services; and deployment of general practitioners at mental health programs (called “co-location”).18 Fully functional linking mechanisms can be regarded as a continuum of models of care coordination that culminates in clinical integration.19
+■	Financial integration. Financial integration—the degree to which financial incentives for the two care systems are aligned in the service of integrated care—can be achieved through contractual arrangements wherein risk is shared or integrated functionality is a part of the performance agreement.20
+Although these forms of integration facilitate clinical integration, they are not prerequisites—in other words, much progress can be achieved along the continuum of clinical integration even when organizational and financial integration are in short supply.
+Obstacles To Clinically Integrated Health Care
+In its landmark Crossing the Quality Chasm report, the IOM described the U.S. health care system as “a highly fragmented delivery system that largely lacks even rudimentary clinical information capabilities” and highlighted the poor outcomes that result from lack of coordination.21 Despite broad recognition of the need to promote the widespread adoption of interoperable information systems that support access by multiple providers to longitudinal patient information, progress has been slow and uneven.22 If successful, a recently launched public-private initiative that aims to develop a national health information infrastructure (NHII) will reap its benefits only in the next decade.23 As a result, the larger health care system remains poorly interconnected, and the care provided to the 125 million chronically ill Americans remains poorly integrated.24 However, the greater separation between general and mental health care compared with other specialty medical sectors and the inadequacy of existing linking mechanisms make coordinated health care even less likely for people with severe mental illnesses than for other populations.
+■	Mental health service delivery. Because the mental health sector is the health care home for the majority of people with severe mental illnesses, mental health care providers are uniquely positioned to improve their patients’ general health care outcomes. However, psychiatrists and nurses working with such patients do not routinely provide preventive counseling or screen for general health problems; nor do they monitor for adverse general health effects of medications frequently used in this population.25 Moreover, key information—including contact information and critical elements of the general health plan—is rarely known or recorded.
+Further, linking mechanisms that, like co-location of providers and case management, can facilitate clinically integrated care either do not exist or are functionally ineffective for the purposes of better coordination of care. For example, although mental health case management has been widely adopted by public mental health systems around the country, the predominant model is largely concerned with preventing psychiatric hospitalization.26 The small fraction of people with severe mental illnesses served by Assertive Community Treatment (ACT) programs—evidence-based interventions that deliver integrated mental health and social services through multidisciplinary case-manager teams that include nurses—are more fortunate in this regard.27 However, unlike the Chronic Care Model, where nurse case managers deliver an evidence-based intervention that explicitly aims to improve patients’ self-care and care coordination, the ACT model does not take full advantage of nurses to help improve the general health outcomes of severely mentally ill patients.28 Further, despite limited but compelling evidence of the beneficial effects of co-location on access, quality of care, and outcomes, few such programs exist in the real world.29 Other organizational supports that facilitate coordinated care—including structured opportunities for interdisciplinary treatment planning and time free of other clinical duties so that
+Downloaded from content.healthaffairs.org by Health Affairs on April 18, 2015
+by guest
+providers may actively engage in such collaborative activities—are infrequent in practices serving people with severe mental illnesses.
+■	Information exchange. As discussed above, the health care system has failed to fully embrace information technology (IT) and its potential for improving safety, efficiency, and care coordination. The mental health sector has lagged further behind in this regard: Most people with severe mental illnesses are cared for in practices whose information systems are underdeveloped or poorly integrated with general health practices.30 Moreover, despite existing public-sector efforts to develop an information infrastructure to support care for mental conditions, information and policy needs specific to people with these disorders have not been well addressed by the nascent NHII.31
+Information exchange is also constrained by a complicated array of privacy rules and overzealous attitudes by mental health care providers. Although the Health Insurance Portability and Accountability Act (HIPAA) of 1996 generally allows providers to exchange patient information other than psychotherapy notes without requiring patients’ consent, it might be superseded by more-stringent federal and state laws and regulations.32 Furthermore, mental health practices might have even more restrictive policies, and psychiatrists might eschew discussing clinical information with general practitioners as a result of the strong emphasis on confidentiality during their professional training.
+■	Health care workforce. Inadequacies in the mental health workforce persist despite past efforts to improve the education and training of this heterogeneous provider group.33 The disciplines involved in the care of people with severe mental illnesses require varying depth and breadth of education and training as well as varying acceptance of and familiarity with the medical model. Further, the insufficient attention paid by all disciplines to developing and sustaining the skills necessary for interdisciplinary practice at the professional, postgraduate, and continuing education stages renders mental health practitioners ill prepared to deliver clinically integrated care.34 Lastly, licensing and credentialing assessment of mental health care providers’ competencies are not standardized across disciplines, states, or health plans and rarely incorporate expectations in areas relevant to general health or care coordination. It is noteworthy that expectations for mental health care competencies among general practitioners are also inconsistent with the provision of high-quality care of people with severe mental illnesses.35
+■	Financing of care. The critical role played by the public sector in the financing of both mental and general health care for people with severe mental illnesses sets this population apart from the majority of nonelderly Americans whose health insurance is privately financed. Public-sector purchasing of care for this population occurs through insurance schemes—mainly Medicaid, the federal and state program that covers three out of four people with severe mental illnesses—and through direct purchasing by regional governments.36
+Medicaid. Upon embracing managed care, state agencies also moved to “carve
+out” mental health care from the benefit package by delegating the management and financing of mental health services to managed behavioral health care organizations (MBHOs). Arrangements where benefits are not separated out are commonly referred to as carve-in contracts. Because most managed care states have either payer carve-out contracts with MBHOs (that is, public agencies separate mental and general health care in their procurement processes) or health plan subcontracts with MBHOs, most people with severe mental illnesses who are covered by Medicaid have their care funded through separate plans or through traditional indemnity programs.37
+Although coordination is comparably low for carve-out and carve-in arrangements, the greater separation of mental and general health care inherent in carve-outs poses additional challenges to coordinated care for people with severe mental illnesses.38 These include lack of reimbursement for the provision of general health services by mental health care providers and vice versa (that is, cross-care), greater constraints to provider communication, and unclear accountability. Inadequacies in the procurement process have repercussions for quality of care in both kinds of contract arrangements. Because expectations for coordinated care are rarely built into requests for proposals and contracts, performance standards, monitoring capacity, and willingness to enforce penalties are typically absent from the process.39 Further, capitation rates are typically insufficient to finance adequate chronic illness care.40 Thus, health plans have few incentives to coordinate care, and in the case of separate plans, incentives could impede clinical inte-gration.41 Incentives are also misaligned for traditional indemnity programs, as demonstrated by the lack of reimbursement for activities such as interdisciplinary meetings and case management.
+Direct purchasing of mental health care. State and local governments finance mental health care for the uninsured through grants awarded to local agencies. Because this financing method lacks built-in incentives for clinical integration, yet volume of services is rewarded, uninsured people with severe mental illnesses are unlikely to receive coordinated care.42
+Lastly, although a growing number of federally funded community health centers offer “primary mental health care” in addition to general health care, centers’ clients with severe mental illnesses are unlikely to benefit from this inverse form of co-location, given the severity of their mental health care needs.43
+■	Quality oversight. The National Committee for Quality Assurance (NCQA) and the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) monitor quality of care at the health plan and practice levels, respectively. However, these accrediting bodies have largely failed to promote coordinated care for people with severe mental illnesses. The accreditation infrastructure has few standards pertaining to the mental/general health interface, and existing standards lack specificity and “real teeth.” Moreover, the NCQA does not hold all health plans to the same accreditation standards, because it assesses coordination-related performance
+only for carve-in health plans, exempting MBHOs from this requirement. Because standards are vague regarding responsibilities for patient care when patients belong to a general health plan and an MBHO, accountability for a frequent scenario among people with severe mental illnesses is inadequate. Oversight activities are further encumbered by the lack of performance measures suitable for assessing care processes and outcomes related to coordination of mental and general health care.
+Practice And Policy Recommendations
+■	Reorganization of mental health service delivery. The goal of this reorganization is for mental health care providers and practices to assume responsibility for all of the health care needs of their patients with severe mental illnesses. Effective communication and collaboration between mental health care and general practitioners are central to this strategy. As a result, care processes would be better aligned with the goal of clinical integration. Among other improvements, psychiatrists and nurses would screen for prevalent general health problems and, in collaboration with general practitioners, would help patients with severe mental illnesses develop self-management skills.44 Consistent with best practices for chronic illness management, mental health practices would be equipped with organizational supports and case management programs redesigned to better address patients’ general health care needs.45 Radical changes to mental health service delivery require sustained leadership and a coherent policy agenda. Until such an agenda is fully delineated, and given the critical role of MBHOs in the care of people with severe mental illnesses, it is reasonable to entrust these entities with the responsibility of promoting the proposed changes.
+■	Promotion of patient-sanctioned communication and collaboration among providers. Attaining the goal of improved information exchange will require that relevant federal agencies ensure that the NHII addresses mental health care as fully as it does general health care. Further, federal and state governments and purchasers need to provide incentives for the adoption of clinical information systems by mental health care providers. Strategies for the latter include grants for the development of model software that can support integrated information systems and purchasing decisions that take into account the quality and extent of use of IT to facilitate coordinated care. Until such a system is fully operational, both sets of providers should use common forms of communication to seek consent and relay clinical information. Easing regulatory constraints to information exchange will require the revision of “privacy laws, regulations, and administrative practices that create inappropriate barriers to the communication of information,” whereas over-zealous attitudes by mental health care providers might be modified through practice-level training and written standards.46
+■	Preparation of the health care workforce to provide coordinated care. Several improvements are needed for both types of providers to adequately address the general health care needs of patients with severe mental illnesses. While psychi
+atrists and nurses need to acquire competencies to screen for general health problems and help patients become more involved in self-care for all of their conditions, general practitioners need to familiarize themselves with the phenomenology, management, and prognosis of severe mental illnesses. Both sets of providers need to learn skills for interdisciplinary and collaborative work. Achieving these objectives will require short- and long-term strategies at multiple levels. Short-term progress will require continuing education initiatives and changes in licensing and credentialing requirements that could be promoted through accrediting incentives to health plans and practices. Long-term progress might be achieved by reorienting providers’ professional and postgraduate education toward the acquisition of knowledge, competencies, and attitudes necessary for coordinated care of people with severe mental illnesses—by, for example, providing a substantial amount of clinical education in settings that can model interdisciplinary teamwork.47 Changes in provider licensing and certification requirements as well as financial and accrediting incentives to training institutions will be necessary to promote this shift.
+■	Elimination of policies and practices that offer no incentives for or discourage integrated care. Because of purchasers’ extraordinary power, this is a critical component of the policy agenda. For carve-out contracts, policies are needed to attenuate the negative impact of the separation between mental and general health care associated with this arrangement. Examples include reimbursement of cross-care and building shared responsibility for patients with severe mental illnesses into the procurement and accountability processes. Regardless of the contract, these processes need to include expectations for information sharing and coordination-related performance. As an example, state Medicaid agencies could request that bidders provide information on health plans’ coordinating practices. Further, capitation rates need to be adjusted to facilitate coordinated care. Traditional indemnity programs need to reimburse collaborative work to provide incentives for clinically integrated care. Lastly, state and local governments need to modify their grant-based approach to financing care for uninsured people so that incentives can be used to promote care coordination.
+■	Strengthening of the accreditation process. Accreditation needs to be standardized so that all health plans seeking accreditation have the same coordination-related requirements. Moreover, standards for accountability pertaining to care coordination need to be built into the process and consistently applied. To improve quality monitoring, the Agency for Healthcare Research and Quality (AHRQ) may be asked to coordinate the development of measures and standards to assess structures, processes, and outcomes associated with coordinated care for people with severe mental illnesses. One such performance measure assesses mental health care providers on their performance regarding diabetes care (testing for the HbA1C glucose level in severely mentally ill patients with diabetes). Until better measures are developed, publicly funded health plans and practices serving people with severe mental illnesses should adhere to current performance standards, and data on their
+performance regarding coordinated care should be made publicly available.
+■	Development of federally sponsored coordination research and demonstrations. Research is needed to evaluate the feasibility and effectiveness of (1) quality improvement interventions that, like the Chronic Care Model, provide organizational supports for coordination; and (2) adaptations of evidence-based practices for people with severe mental illnesses that might be particularly well suited to improving this population’s general health outcomes. Candidates for such adaptations are ACT and illness management and recovery, an intervention that helps people with severe mental illnesses develop coping strategies and become more actively involved in their mental health care.48 Further, federally funded demonstration programs could be used to support the reorganization of mental health service delivery aimed at increasing care coordination in this population.
+Despite the benefits associated with clinically integrated care for people with severe mental illnesses, skepticism exists on the feasibility of making progress in the face of centuries of mind-body dualism and the separation between mental and general health care. In addition, concerns exist that efforts to integrate these systems might lead to loss of autonomy, resources, and specialization, hurting the very people this strategy aims to serve. Evidence indicating that carve-in plans shift funds from mental health to general health care points to potential unintended consequences of a method to organize the financing of services that may appear integrating yet, as above, has not been found to outperform carve-outs in terms of coordination.49 These findings, along with evidence that carve-outs benefit people with severe mental illnesses as a result of their better control of adverse selection and specialization of administrative and clinical expertise, belie the notion that persons with comorbid mental and general health problems are better served by carve-ins.50 To our knowledge, there is no evidence that coordinated care for people with severe mental illnesses has deleterious effects, yet it certainly has the potential for greatly improving their general health outcomes.
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+Introduction
+Over the past decade, the field of mental health has gained recognition on the global health agenda.1 This is demonstrated by several key milestones achieved in recent years, such as The Lancet’s 2007 series on global mental health, the subsequent establishment of the Movement for Global Mental Health and the launch of the Mental Health Gap Action Programme by the World Health Organization (WHO) in 2008.2-4 More recently, global mental health has garnered further international attention as evidenced by the inclusion of mental health and well-being in the Sustainable Development Goals and by the 2016 meeting on mental health as a global development priority, jointly hosted by the WHO and the World Bank Group.5,6 This growing attention to global mental health is coupled by an increasing burden of disease attributable to mental, neurological and substance use (MNS) conditions. Between 2006 and 2016, the global burden of MNS conditions increased by 14.9%, accounting for 9.7% of all disability-adjusted life years.7
+The increased international focus on mental health is expected to increase resources for mental health, especially in the form of aid from international donors. It is therefore essential to measure current development contributions for global mental health in order to monitor future increases in resource allocation. Since the present landscape of support for global
+mental health is not well documented, we sought to conduct a comprehensive review.
+In the past three years, four studies have performed analyses of official development assistance for mental health (DAMH). All of the studies reviewed demonstrated a low level of DAMH and highlighted the ongoing need for increased funding towards global mental health.8-11 While data sources and target populations varied between the aforementioned studies, no estimate of DAMH amounted to more than 1% of the total development assistance for health (DAH).8-11 With consistently low estimates of DAMH across previous studies, we sought to conduct a comprehensive study of DAMH that includes mental health projects from all Organisation for Economic Co-operation and Development (OECD) sectors over the course of a decade. Expanding on the efforts of previous studies, findings should be placed within the global context of total DAH and should demonstrate a detailed understanding of the distinctions within DAMH.
+This article systematically assesses development-related assistance for mental health over the past decade (2006-2016). We seek to provide a general overview of the status of contributions to global mental health, highlighting current progress. Moreover, the analysis offers a baseline measure of financial support for mental health over the past decade to which future contributions can be compared.
+Our article aligns with the WHO Mental Health Action Plan (2013-2020) objectives of strengthening effective leadership and governance for mental health and to strengthen information systems, evidence and research for mental health.12 The analysis is pertinent to the current global health development agenda and has also been performed against a growing mental health burden. MNS conditions are the greatest contributor to global years lived with disability.13 This is furthered by a documented increase in the burden of mental health globally over the years this analysis considers.
+This analysis demonstrates that increased disease burden attributable to MNS conditions has not been adequately supported by the international development and aid sectors. The financial burden to address mental health then falls on domestic actors, many of whom also contribute only very small
+amounts of funding, as demonstrated in the recent 2017 WHO Mental Health Atlas.12 Addressing the burden of MNS conditions will require increased financial commitments from multiple stakeholders, including international donors, multilateral agencies and national governments—particularly in low- and middleincome countries (LMICs).14
+Review criteria
+Total DAMH over the course of a decade (2006-2016) was investigated using the Creditor Reporting System (CRS) Microdata Aid Activities database from Qwery Wizard for International Development Statistics (QWIDS) of the OECD.15 Records were obtained in May 2018.
+All gross disbursements between 2006 and 2016 were analyzed in 2015 U.S. dollars (US$) (constant prices). Where required, dollar amounts provided in other values were converted to constant 2015 US$ using the OECD consumer price index.16
+Projects related to mental health were identified using keywords developed by Gilbert et al.8 We added several additional terms to broaden the range of MNS conditions comprising this analysis, including the term self-harm (see Supplemental Table 1). Each keyword search was conducted across all development sectors reported in the database. Results were compiled and duplicate records eliminated. Analysis was conducted using Excel software (Microsoft, Redmond, WA, USA). If information was provided in a language other than English, Google Translate was used.
+Due to the basic search functionality, which identifies any use of key terms in all completed fields, and the variation in detail provided for each project, particular care was needed to ensure that identified projects were relevant to the intended search. First, projects obtained due to alternate (unrelated to health) usages of keywords were eliminated. Second, retained projects were systematically assessed for appropriateness of inclusion using the ‘Project Title’, ‘Short Description’, and ‘Long Description’ categories included in the CRS database. Due to the variety of projects found, it was imperative to conduct the review project by project to establish the degree of mental health focus in each identified project.
+Projects were classified into two categories: development projects dedicated solely to mental health, i.e. projects that specifically pertain to mental health or services for individuals with MNS conditions, and development projects that mention mental health, i.e. projects that include some mental health-related objectives among other non-mental health-related objectives. Research focus was determined using the ‘Purpose Name’ category provided within the CRS database. Examples of projects and corresponding categorizations are presented in Table 1.
+To supplement our review of DAMH we performed a basic scoping review of major bilateral donor agencies and large multinational pharmaceutical companies. On the historical basis of large drug donations for areas such as neglected tropical diseases, we enquired if the area of mental health is receiving similar attention. Fifteen bilateral donor agencies and 10 pharmaceutical companies were assessed for a prioritization of mental health-related objectives as an area of focus through a brief investigation of company websites and corporate responsibility reports where available. Other areas of focus, e.g. human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) and non-communicable diseases (NCDs) excluding mental health, were also assessed.
+Results
+Official development assistance
+After removal of all duplicate records (i.e. any record identified in two different keyword searches) our search yielded 9205 projects —across all sectors of development assistance—reported between 2006 and 2016 whose project title or description included any of the search terms. However, after assessment for appropriateness of inclusion in the analysis, a significant number of records obtained were eliminated because their objectives
+were not pertinent to mental health (see examples in Table 1). In total, 1449 records totaling US$1263.0 million were eliminated; 7756 projects that involved mental health remained for analysis.
+As per the OECD, official sector expenditures for refugees in donor countries can be counted as official development assistance for the first 12 months of stay within the receiving donor country.17 Upon review of the 7756 projects, 22 projects categorized by the OECD as ‘Refugees in donor countries’ were removed from the core analysis, leaving 7734 projects for systematic analysis. Although this category of OECD expenditure has been in place since 1992, there has been a significant increase in funding targeting refugees in donor countries in recent years in response to the refugee crisis (P. Blanchard [OECD], personal communication, 21 February 2019).
+As described in the review criteria, two categories of projects were created to better characterize this analysis of global DAMH. Development projects that include mental health, but not as a primary or specific project objective (e.g. provision of food aid, primary health care services, etc.) were noted and categorized as ‘development projects that mention mental health’ (4813 projects), while projects with specific mental health objectives were categorized as ‘development projects dedicated solely to mental health’ (2921 projects).
+Between 2006 and 2016, US$2451.0 million was allocated across the 7734 projects involving mental health. Of that US $2451.0 million, only US$409.1 million (16.7%) was allocated to development projects dedicated solely to mental health; the remaining US$2041.9 million (83.3%) was allocated to development projects that mention mental health but were not exclusively focused on mental health or individuals with MNS conditions.
+Projects dedicated solely to mental health
+Of the US$409.1 million in DAMH allocated to development projects dedicated solely to mental health, the large majority of funding (96.0%) supported mental health programs and projects; only 4.0% was allocated to mental health research (the database does not accommodate basic research). The greatest single amount allocated was US$5.8 million for community psychosocial and mental health services in the West Bank and Gaza in 2009.
+Projects that mention mental health
+Of the US$2041.9 million allocated to projects that mention mental health, the greatest single amount allocated was US $36.4 million in 2006 for a reconstruction project following the 1999 Marmara earthquake in Turkey, which included mental health services for victims in addition to extensive reconstruction efforts and emergency preparation. Figure 1 shows yearly disbursements in global DAMH from 2006-2016. Disbursement of DAMH increased slightly over the course of the last four years; total DAMH disbursed per annum was greatest in 2016 at US $308.3 million. However, funding for projects dedicated solely to mental health remained low across the decade, ranging from US$21.7 million in 2006 to US$55.0 million in 2015.
+To establish the priority for mental health in the context of all sectoral DAH, mental health was compared with other subsectoral expenditures. The OECD separately categorizes expenditures for the health sector and expenditures for the population policies/
+programs and reproductive health sector. Both sectors were included to fully represent all health-related official development assistance.
+Figure 2 shows the health and population policies/pro-grammes and reproductive health disbursements by OECD-assigned subsectors for 2006-2016. Through our keyword
+364
+search and project-by-project analysis across all OECD sectors, any project that was determined to be DAMH was recategorized as mental health to avoid duplication.
+Development assistance dedicated solely to mental health accounts for the equivalent of just 0.3% of all official DAH. Including development projects that mention mental health, total DAMH is equivalent to 1.7% of all official DAH from 2006 to 2016.
+Figures 3a and 3b illustrate where all identified development projects that are dedicated to mental health or that mention mental health fall across OECD-defined subsectors. Of the projects that were dedicated solely to mental health, 44.8% by value came from the health sector. Projects that mention mental health present a different distribution across OECD-defined sectors, with just 22.6% by value coming from the health sector and the majority coming from the emergency response or population policies/programmes and reproductive health sectors.
+Trends within mental health development assistance were further examined and categorized based on the main area of project focus. Three key areas of focus were identified among development assistance from projects that mention (but are not solely dedicated to) mental health: provision of social services and other health care; emergency response, conflict and natural disaster response; and HIV/AIDS management. Of the US$2 billion (US$2041.9 million) DAMH for development projects that mention mental health, US$718.9 million (35.2%) was allocated to the provision of mental health services in conjunction with social welfare services (e.g. primary education, vocational training) or other medical services and US$611.0 million (29.9%) was
+allocated to emergency response, i.e. natural disasters, conflict or violence. DAMH for development projects that mention mental health also allocated US$459.8 million (22.5%) in HIV/AIDS projects that included psychosocial support or other mental health services. The remaining US$252.2 million (12.4%) of development projects that mention mental health included broad areas of focus such as reconstruction projects, human rights and gender-based violence advocacy as well as legal and legislative processes.
+Bilateral donor agencies and pharmaceutical companies
+Based on a review of leading bilateral donor agencies’ websites listing key areas of focus or priorities, only the Austrian Development Agency (ADA) and the Swiss Agency for Development and Corporation (SDC) mentioned mental health within their global health and development portfolio (Table 2).18,19 It should be noted that the CRS database findings of this analysis demonstrated that several bilateral donor agencies provided one-off funding for specific projects that involved mental health; however, only 2 of 15 bilateral donor agencies assessed specifically list mental health as an area of focus.18-33
+Five of ten pharmaceutical companies included in the ancillary review mention mental health in their corporate social responsibility work.34-43 However, two of these five companies focused their efforts specifically on mental health in a singular program in an upper-middle or high-income country: Abbott Laboratories in Russia and Bristol-Myers Squibb in the USA.34,35
+Discussion
+Between 2006 and 2016, US$2451.0 million was allocated across 7734 projects involving mental health to any degree, of which only US$409.1 million (16.7%) was allocated to projects dedicated to mental health. Total DAMH disbursed per annum was greatest in 2016. Yet allocations to mental health remained consistently low across each year of the last decade.
+Cumulative development assistance dedicated to mental health between 2006 and 2016 accounted for 0.3% of all DAH. This is low, particularly when compared with other sectoral priorities. For example, ‘STD control including HIV/AIDS’ accounted for almost 50% of development assistance for health between 2006 and 2016. HIV/AIDS accounted for 4.1% and 2.4% of global disability-adjusted life years in 2006 and 2016, respectively. (reference 7) Meanwhile MNS conditions accounted for 8.4% and 9.7% of global disability-adjusted life years in 2006 and 2016, respectively, yet total DAMH is less than any other OECD DAH category.7 While the allocation of official development assistance is not expected to be equivalent to the global disease
+burden, these figures serve as an indication of the priority placed on mental health in the development agenda.
+The distribution of projects dedicated to mental health and those that mention mental health across OECD-defined sectors presents further variation between the categories differentiated by this article. While Figure 3a shows nearly 45% of DAH dedicated to mental health comes from the health sector, Figure 3b shows the emergency response and the population policies/pro-grammes and reproductive health sectors as leading contributors to DAH that mentions mental health. Many of the projects in these sectors mention mental health services (such as psychosocial support) alongside the provision of food, shelter and hygiene or clinical HIV/AIDS services. It should also be noted that 97% of projects represented by the population policies/pro-grammes and reproductive health sector in Figure 3b were further categorized by OECD as ‘STD control including HIV/AIDS’.
+By including projects that were otherwise excluded from the core analysis due to OECD categorization as ‘Refugees in donor countries’, total DAMH would increase to 2.6% of all DAH. Yet being focused in European donor countries, this value was not
+considered appropriate to include in our results. All but one of these excluded projects involved some element of mental health alongside accommodation, language classes and social and legal services, thus being an upper estimate of DAMH.
+Furthermore, the estimate of total DAMH presented in our analysis should be considered an upper limit of actual contributions to global mental health. Many of the projects incorporated into total DAMH reported in this article included mental health as a small component of other health and humanitarian work projects. Thus the total amount presented for projects that mention mental health is an inflated reflection of true global financial contributions towards mental health.
+While this review focuses mainly on international contributions to mental health, domestic involvement must also be considered. The WHO Mental Health Atlas 2017 was consulted for a current picture of national support for mental health action across LMICs. In considering the WHO African, South-East Asia and Western Pacific regions, domestic government expenditure on mental health is estimated to be US$0.1-1.1 per capita.12 Although there has been a positive increase in mental health legislation across the WHO member states, demonstrating that progress is being made, there is an enormous need for increased funding of mental health at both the domestic and international levels.12
+Across the 15 bilateral donor agencies reviewed, only 2 specifically identify mental health as a funding or donation priority. The majority of bilateral donor agencies included in this analysis list communicable diseases—particularly HIV/AIDs, malaria and tuberculosis—alongside reproductive, maternal and child health as key areas of focus. These areas of focus may be ongoing priorities stemming from the Millennium Development Goals. In light of the inclusion of mental health as a priority in the Sustainable Development Goals (specifically targets 3.4 and 3.5),5 mental health may begin to see increased prioritization and funding from this sector over the coming decade. Of the 10 pharmaceutical companies included in the ancillary review, 5 list mental health as an area of focus.34-43 However, unlike a mental health focus for bilateral donor agencies, these were generally of a narrower application. This includes two companies’ mental health-related objectives being for a specific uppermiddle or high-income country only, therefore not development assistance related.34,35
+The strengths of this review include the project-by-project assessment of appropriateness for inclusion in the analysis, ensuring that only projects related to mental health contribute to the findings and that duplication across OECD categories is avoided. Furthermore, the use of an exhaustive list of search terms across all OECD sectors, as well as analysis over the most recent decade, provides a realistic baseline against which future investment in global mental health can be adequately compared.
+The limitations of the study are the use of only English search terms, as well as the single-source financial information provided in the OECD database. Moreover, the amount of information in the database is not consistent across projects—some provide a paragraph of detailed information while others contain only a few descriptive words. Therefore assumptions made regarding the relevance of projects to mental health, as well as the degree of mental health inclusion within projects, could have affected our categorization of disbursements for analysis. Another limitation is the amount of time taken to manually
+assess OECD projects individually, which may limit the ability of this review to be replicated or the ability of future reviews to consider longer periods of time for analysis. Assessment of websites maintained by bilateral donor agencies and the pharmaceutical industry may not give a complete picture of the organization’s work; however, the listed areas of focus do suggest the general priorities pursued by the organization. The analysis relies on OECD disbursements to present a picture of global mental health funding, thereby not taking into account possible additional funds from private philanthropic donors and international non-governmental organizations (NGOs).
+An earlier review of official DAMH by Gilbert et al.8 yielded different results than our findings. Our categories differentiated projects and found a lower level of funding for projects dedicated solely to mental health and a higher level of funding for projects that mention mental health.8 A further review of DAMH was performed by Charlson et al.9 in 2017 utilizing data from the Institute of Health Metrics and Evaluation. Their review found a larger amount of DAMH as a proportion of total DAH for the year 2015. However, their analysis was based on a data set inclusive of private philanthropy. With their finding of private philanthropy contributing 30% of all DAMH in 2015, there are significant differences due to data sources between reviews.9 The two other previously mentioned studies focused on DAMH among children and adolescents, a comparison outside the scope of this study.10,11 Regardless, all articles reviewed emphasize the ongoing need for increased global funding towards mental health.
+We conducted a systematic assessment of global development-related assistance for mental health and established categories that can serve as a baseline for future reviews and measurement of potential increases in funding.
+Conclusions
+Despite a renewed focus on mental health in the past decade by the WHO and development partners, financial assistance for mental health remains inadequate. Development assistance dedicated to mental health between 2006 and 2016 accounted for just 0.3% of all DAH.
+Mental health is not currently a priority area for development partners: across 15 bilateral donor agencies, only 2 specifically identify mental health as a funding priority.
+Given limited public expenditure on mental health care, particularly in LMICs, coordinated efforts from the international community and development partners—comprised of bilateral and multilateral donor agencies, NGOs, foundations and pharmaceutical companies—are critical to increasing services and care for the prevention and treatment of MNS conditions.
+Supplementary data
+Supplementary data are available at International Health online http://inthealth.oxfordjournals.org
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+1.	Introduction
+Restricting physical access to methods of suicide is an effective means of suicide prevention that forms a key component of national prevention strategies. A less considered aspect of access is the cognitive availability of suicide. This
+encompasses an individual's awareness of suicide as an option in response to distress and their knowledge of possible methods, including how these can be implemented and their apparent acceptability in terms of factors such as painfulness (Florentine and Crane, 2010). Cognitive availability may therefore influence choice of method, which is of particular importance where methods with high fatality rates, such as hanging, gain social popularity.
+Cognitive availability of suicide has been considered almost exclusively in relation to the media (Pirkis and Blood, 2001a, 2001b; Stack, 2005) and calls for restricted media
+reporting are widely incorporated into suicide prevention strategies (Department of Health, 2002). However, an imitation effect, whereby individuals act out behaviour they have seen modelled, is difficult to prove. Evidence is frequently based on natural experiments where official statistics have indicated increases in suicide corresponding with the time of a media portrayal and often it is not known whether the suicide victim had been exposed to the media in question (Schmidtke and Schaller, 2002). Some studies provide more direct evidence. Hawton et al. (1999), for example, demonstrated an increase in hospital presentations for selfpoisoning in England immediately following portrayal of an overdose on a television drama; 20% of suicide attempters surveyed said the broadcast had influenced their behaviour and 17% that it had influenced their drug choice. In a study of people who had attempted suicide by charcoal burning in Taiwan, 87% said their choice of method had been influenced by the media compared to just 8% of a comparison group who had ingested poisons (Tsai et al., 2011).
+Attention is currently focused on the Internet. Pro-suicide sites have been documented which provide details and images of others' suicide, advocate particular methods with information about lethal doses, degrees of certainty and estimated time to death, and host bulletin boards providing a forum for individuals to discuss plans (Biddle et al., 2008) (Alao et al., 1999; Becker and Schmidt, 2004; Thompson, 1999). These provoke concerns about opportunities for imitation amongst vulnerable individuals. Single case reports describe patients who have attempted suicide using a method they claim to have discovered on the Internet (Alao et al., 1999; Baume et al., 1997; Beatson et al., 2000; Becker and Schmidt, 2004; Prior, 2004), however, rigorous empirical evidence of the role of the Internet is absent, debate hinging mostly around anecdotal evidence and speculative assertion.
+It is important to identify the sources of information influencing choice of suicide method in order to engage in clinical conversations with vulnerable individuals and target interventions designed to alter cognitive availability. This paper describes results from a qualitative study of individuals who had made near-fatal suicide attempts. The study explored in detail decision-making surrounding choice of method (Biddle et al., 2010). As part of this, we enquired about the sources of information that respondents had drawn upon, how these shaped their understandings of possible methods, and the role they had in directing choice. These data are reported here.
+2.	Methods
+2.1.	Participants
+Semi-structured interviews were conducted with twenty-two individuals who had made a ‘near-fatal’ suicide attempt. Ethical approval was obtained. ‘Near-fatal’ was defined as where the individual was likely to have died had they not received emergency medical intervention (for instance, admission to intensive care for management of overdose), or unequivocally employed a method with high case fatality (for instance, jumping from height) and sustained injury (Douglas et al., 2004). The study of near-fatal cases thus makes it possible to access the accounts of individuals who
+can act as proxies for completed suicides. Individuals were identified prospectively from 2006 to 2009 and, where possible, also retrospectively (past two years) from the clinical records at nine collaborating centres in England. First, all eligible adults were contacted, except those deemed to be too unwell. Sampling then became purposeful to focus on attempts involving particular methods. This was to ensure that the sample represented a range of methods including those commonly used in completed suicide, such as hanging. Recruitment continued for as long as practical and until a diverse group had been interviewed and a consistent understanding of their accounts reached.
+2.2.	Data collection
+Interviews were in-depth and open-ended allowing respondents to offer full accounts in their own words. Most lasted 1 to 2 hours and were conducted by LB (n = 17). Others were conducted by DG, AOS and JD. A topic guide was used to ensure the main issues relating to the research question were covered by all respondents and to maintain consistency across interviewers. In relation to sources of knowledge, respondents were prompted to discuss where or how they had heard about possible methods of suicide, the extent to which such information was influential, and any resources they had consulted to research methods. Respondents' broader history of self-harm and attempted suicide was examined in addition to the index episode. The Suicide Intent Scale (Beck et al., 1974) was used to assess the degree of suicidal intent associated with the act.
+2.3.	Analysis
+Preliminary analysis of completed interviews took place alongside further data collection allowing an iterative and progressive process where early understandings and questions were checked and expanded through further interviewing. All interviews were audio-recorded with the respondents' consent and transcribed in full. Transcripts were examined in detail and coded for emerging themes by LB. A coding frame was generated and revised until a refined version had been arrived at which could be applied consistently to all the data. This involved a double-coding exercise whereby DG independently coded a sub-sample of transcripts and the two sets of coding were compared, checking for consistency, completeness and that interpretations corresponded with the data. All data relating to sources of knowledge were retrieved. Themes were identified using a constant comparison technique and descriptive accounts produced to summarise these (Glaser and Strauss, 1967).
+3.	Results
+Eighty-three individuals were contacted and 22 (26.5%) took part, including 12 men and 10 women aged 19 to 60 years. A range of suicide methods had been used. Seven attempts were described as impulsive but the remaining involved moderate or extensive planning. Suicide intent scores ranged from 8 to 27 (maximum score 30) with 13 respondents' scores demonstrating ‘very high’ intent. Over half reported previous attempts. Full details are published elsewhere (Biddle et al., 2010).
+A range of sources had informed respondents' choice of method (Table 1). Many were media sources. Experience from previous self-harm was also important. Additionally, a few respondents referred to fictional books and celebrity suicides but none reported having modelled a celebrity suicide. One described an advert for carbon monoxide smoke detectors in which this was described as ‘the silent killer’. Exposure was often haphazard, respondents having unintentionally seen stories or portrayals of suicide in the media, or gaining knowledge from information not intended to inform about suicide, such as the advert noted above. However, some respondents deliberately consulted sources to research a method.
+Most respondents also referenced a general stock of lay knowledge about suicide. This included notions of ‘popular’ methods and beliefs about these and on occasion ideas about implementation, for instance, the direction along the wrist that one should cut. These ideas were mostly unformed, of unknown origin and regarded as something “everyone knows” (R10 male, electrocution.) or “you've always heard” (R15 male, hanging.). Several respondents located ideas about suicide by hanging with knowledge of capital punishment: “It's [hanging] one of those things everyone knows about just because people used to be executed by being hanged” (R11 female, jumping.).
+3.1.	Television and film
+Seventeen of the twenty-two respondents discussed the potential role of Television and Film as a source of information about suicide methods. Over half gave specific examples of suicides they had seen portrayed, including ‘The Bridge’ (film about jumping), ‘Casualty’ (UK television hospital drama, various methods), ‘Shawshank Redemption’ (film featuring hanging), ‘CSI’ (American television crime drama, hanging) and others. Some said television or film was the only place they had seen a method of suicide depicted. The most commonly acknowledged effects of such portrayals were to introduce ideas of possible methods, sometimes also appearing to demonstrate the basics of how to implement these:
+R: TV — they show you how to do it.
+I:	Anything you can remember seeing?
+R: Full Monty. One of the blokes in the car, it's broken down. Got the hose going from the back of the exhaust into the car. (R2: female, overdose.)
+You see some on T.V... Casualty. I used to watch that. the ambulance gets called to where someone's tried com-
+Table 1
+Main sources of information about suicide.
+mitting suicide, or someone's put their car in the garage and put an exhaust pipe in, you know. (R22: male, hanging.)
+Television portrayals created impressions of particular methods, including images of the likely nature of death or outcomes of using a particular method, such as speed, certainty and ‘cleanliness’. While some argued that such portrayals did not directly ‘cause’ them to use a particular method, there was acknowledgement that these ideas may be drawn upon to evaluate methods. They could thus encourage or dissuade.
+There was a television programme called The Colonel. And it was going on about people's methods of dying and one of the methods was jump in front of a train. And that was a hundred miles an hour the train was going and they found bits of body half a mile up the line. (R6: male, attempted hanging describing this as a ‘clean’ method on the basis: “you don't get damaged like if you jump in front of a train”.)
+You see it [hanging] on like Clint Eastwood films — do you know what I mean? You see people in the films, you know and you realise how quick it is. (R15: male, attempted hanging on the basis: “I thought it would be quick — your neck breaks and you're done”.)
+Six respondents stated they had ‘picked things up’ or gained ‘ideas’ from television/film portrayals that had directed their own behaviour. Three had imitated a portrayal, in one instance using this to inform about dosage:
+I:	Can I ask where the idea of attempting suicide in the way you did came from?
+R: I can remember an old black and white film where somebody stuck an electric fire in the bath. an Alfred Hitchcock film. (R10: male, electrocution.)
+I took [drug name]. I watched an episode of ‘Casualty’ and this young kiddie took about 4 but I think I took about forty odd so I knew what I was doing. (R18: male, overdose.)
+They show it [hanging] on telly quite a lot. That's when it gets into my head — ‘oh that could be a good idea’. It's in a lot of like police programmes. (R16: female overdose and previous hanging attempt.)
+3.2.	The internet
+Thirteen respondents identified the Internet as a source of information about suicide and just over a third (n = 8) discussed its role in shaping their own attempt. It had a similar effect to television in exposing respondents to examples of suicide from which impressions and information could be derived. For instance, one respondent was introduced to the method they subsequently adopted via a web-based news report:
+I tried to kill myself with carbon monoxide [details method]. Why did I do that? I read on the Internet that people had succeeded in killing themselves [in that way]. Ifeltitwas going
+to work because I'd read that these people had successfully killed themselves... I thought here's a fairly strong, um a fairly potent way of killing yourself. I mean, I don't go seeking chat rooms, it's just the BBC website and trawling the news, this happened, that happened. I'm not really into negative surfing (R9: male, carbon monoxide poisoning).
+Impressions formed through online portrayals could also be preventative:
+Looking at the [online footage of Saddam Hussein execution], I can imagine an awful lot of potentially suicidal people would have watched that video and maybe thought twice about it thinking they've got to get it right..there was mention of, you know, if the executioner doesn't get it right the prisoner is strangled or decapitated. a lot of people would have seen that and thought you've got to make damn sure they get [it right] or actually just choose a different method (R8: male, overdose. Rejected hanging.).
+Five respondents deliberately used the Internet to find out about suicide methods. It was perceived as an obvious source of information that could be accessed easily and privately. For instance, one respondent had used a search engine to discover a recommended ‘best’ method after previous failed attempts:
+Last time I got tablets I got them from America because I researched it on the internet and I found what would actually work. I put in on [search engine] I think I put in ‘what is the best tablet to kill yourself’, I think I actually typed that in. or how, I think I put in ‘how's the best way to kill yourself something like that. it came up with that name of that [group of drugs]. then I looked on the list of tablets on the site which came up for selling them. I have taken [other drugs] before and stuff and I thought I really need to find out how to do this properly so I thought I'm sure I can find out how to do it properly if I look for information about it. You're not likely to just sort of ask about it to anybody so. it [the Internet] gives you all the information you need. (R20: female, carbon monoxide poisoning and previous near-fatal overdose.)
+Others sought further information about methods already known to them such as their lethality, likely effects and how to implement effectively.
+I've accessed it [the Internet] for information I've wanted in relation to this sort of thing. Looked up suicide by means of overdose of [states drug]. When I looked into carbon monoxide poisoning more deeply I decided that had a high risk of failure and that there were risks that you'd be found and you may be incapacitated for life. (R1: female, overdose.)
+I:	Where do you get your information from about methods of suicide?
+R: The Internet. You search hard enough there's some quite good stuff. I've used it to look up sort of dangerous levels of tablets, what's meant to be fatal and what's not. I've used it to read stories of other people's suicide attempts just to get an overall picture of what it's like. there was one [site]
+— it was something like mysuicide.com or something like that — and it was just about somebody's, all their different attempts that they'd made and it had links to where they'd got information from, and where you can get extra information from. That was really good. (R16: female, overdose.)
+The internet was thus used throughout various stages of planning an attempt from searching for possible methods through researching details of a chosen method to accessing necessary materials. Four respondents used the Internet to access poisons.
+R: It [planning] was a couple of weeks, may be three weeks. First I looked on the Internet for ways to do it. [Chosen method of poisoning] was what I thought would be like a good option I suppose and um yeah, I can't actually remember where I could get it from. I went to Google and just ordered it from somewhere.
+I:	Where did you find out the information about how to use it?
+R: That was Wikipedia. That was one of them anyway. That came through Google as well. like how it grows and stuff, um, different parts of it that are poisonous, the symptoms of after you've taken it. it said that the berries are poisonous and two berries is enough to be able to kill a child. I'd researched after a few days and I thought this sounds good so I ordered it. (R3: male, self-poisoning.)
+Notably, the sites accessed by these respondents tended to be those containing professional information and resources (including online chemists), general knowledge sites (e.g. Wikipedia), or news sites, including BBC news. Specific ‘suicide sites’ were accessed less frequently, one respondent (R1) having ‘briefly’ consulted these and another (R16) more extensively.
+3.3.	News stories
+Over half the respondents (n = 12) recalled hearing of suicide methods through stories of others' attempts in newspapers, magazines, and the Internet. Several respondents indicated a generalised effect on their thoughts about suicide and possible methods, and five described instances where a method they used had been determined by a specific story. Stories could introduce or popularise certain methods, create impressions about the likely mode of death, and facilitate attempts by offering information about implementation:
+I'd heard on the news of people that had done it [jump from local bridge] and I thought maybe I can do what they did. (R16: female, overdose and previous interrupted jumping attempt.)
+I swallowed [name] berries. I read [name] berries at a certain time of the year are lethal if you swallow the seeds in a magazine article about someone. Woman's Own or something (R18: male, overdose.)
+I've used an indoor barbeque and, well, I only realised that a disposable barbeque could be used by reading something in
+the [national daily] paper about a woman who'd killed herself in her house and her son with a disposable barbeque... it said she blocked the air, you know the doorway up and she'd just had this indoor barbeque smoking and then it sort of, it was, you know it was toxic, what is it, causes carbon monoxide and that is caused by a disposable barbeque, so that's what it said and that was enough to give the idea. I thought it seemed a nice way, um gentle way, 'cos you'd just drift off (R20: female, carbon monoxide poisoning).
+News stories also provided encouragement by giving ‘real’, personalised examples of ‘successes’ using particular methods:
+I:	When you put something around your neck, what made you think of doing that?
+R: Just hearing things about people like hanging themselves, thinking that they've killed themselves, just through the news and newspapers and things. If you see like a news report and you see that someone's managed to kill themselves it looks feasible. (R11: female, jumping with previous episode of strangulation.)
+Two respondents demonstrated that information obtained did not require immediate effect but could be stored and recalled in moments of crisis:
+I didn't use it [method from news story] soon after, might even have been 6-8 months after. when I started feeling low it came back to me, what she done [person in story], I could do (R20: female, carbon monoxide poisoning).
+3.4.	Respondents' past suicide attempts/self harm
+Half the respondents described how the planning of their more recent suicide attempts was shaped by experiential knowledge gained from previous episodes. Such experience allowed respondents to identify effective or ‘best’ methods while ‘ruling out’ those that had been unpleasant or, more commonly, which had ‘not worked’.
+It [history of past attempts] does change what you do, because you know that isn't a successful way. if I feel very low I will go back to [methods used in near-fatal attempts] and sometimes it's a comfort to know that it's there. (R20: female, carbon monoxide poisoning and previous near-fatal overdose.)
+It [overdosing] was just experimenting and giving myself that break that I wanted. And then it sort of, I took three overdoses of [quantity] each time, I took three in a week, and that was when I started to realise it could be quite serious. once I knew it could harm me, I just thought of it as a good way out one day. (R16: female, overdose.)
+Some respondents described fine-tuning a method through ‘trial and error’. This emerged most frequently in relation to self-poisoning where past attempts provided information about lethal dosages and effective drugs. It was also evident
+in relation to hanging where previous attempts informed choice of ligature and ligature point:
+You've got to make the knot so it doesn't give way. See [named ligatures] all give way. I've tried them out. (R6: male, hanging.)
+Perfecting a method could also incorporate the logistics of staging an attempt to overcome previously encountered barriers or failures of implementation, for instance, choices about timing, location, opportunities to call for help, and means of overcoming fear.
+3.5.	Professional knowledge
+Seven respondents drew upon professional knowledge to inform their attempt. These included two healthcare professionals, two police workers, a counsellor and two nonprofessionals accessing medical literature.
+Four respondents had become aware of others' suicide attempts in their professional capacity. This familiarised them with a range of possible methods creating impressions about their fatality rate, the possible experience of death and impact on the body, allowing these respondents to evaluate possible methods for themselves:
+The method chosen for me was accessibility and experience of the effects of that drug that it's had on patients when, you know, I know that they've been admitted pretty sick when they've buggered up their doses. You know, it happens, and that's kind of much smaller doses than I tried (R8: male, overdose).
+Professionally derived technical information (for example, medical training and literature) added to this evaluation and assisted with planning once a method had been selected:
+It was my own medical knowledge that brought me to come to the decision to take [drug]. I did know what was likely to work and what wasn't. I checked out doses, looked up in [medical book] and worked out what most people couldn't cope with. brought up [on Internet] research related to suicide by [method] and read research papers on it (R1: female, overdose).
+Two non-professionals also purposefully accessed medical literature:
+There's a book in central library that tells you about how many roughly to take and stuff ... I've read about it. A big red medical book. (R18: male, overdose.)
+3.6.	Personal knowledge of others' attempts
+Many respondents recalled stories of friends or acquaintances that had attempted or completed suicide but most said this had not influenced own decisions. There were a few exceptions. One respondent (R2) said she had ‘learnt’ about overdosing from her mother who had taken multiple overdoses. Another (R19) said she had selected her method
+(hanging) in part because of “knowing that it'd worked for somebody else”. Four respondents also reported sharing knowledge of methods with other inpatients during periods of hospitalisation, though none had directly imitated a method.
+3.7.	Healthcare professionals
+Three respondents described how healthcare professionals had imparted information to them which they had subsequently used to plan an attempt, though in one instance, the same information had originally acted as a deterrent.
+I knew it was lethal what I was taking because of the amount I took. I know how dangerous they are. I was told by one consultant I was never ever to take an overdose of them. He said “it'll take an army to bring you back”... it did ward me off for a good few years... but then things haven't been as bad as they've just got lately (R2: female, overdose).
+[Describes hanging attempt] I knew it was possible 'cos when I was in therapy we had to do a risk assessment of all potential suicide or self-harm things that people could do with all different structures and one of them we discovered was [ligature point]. it comes to the front of my mind when I'm not feeling great (R19: female, hanging).
+4.	Discussion
+Respondents derived information about methods of suicide from two main types of source. The first and most common was the media. Most respondents discussed the role of television, film, news stories, and the Internet in introducing possible methods, creating impressions about these and suggesting how they should be implemented. The effect could be implicit and pervasive, infiltrating individuals' consciousness and shaping public perceptions, or explicit, directing individuals to specific methods through example or detailed information. Media messages also had longevity, several respondents drawing upon portrayals they had been exposed to earlier when not actively suicidal. The Internet was commonly recognised as a resource that could be used purposefully to search for information about methods. Such use was evident in several accounts. The second main type of information source was experiential knowledge accumulated throughout an individual's ‘suicidal career’. This derived from past attempts and from information gleaned from other patients or inadvertently from healthcare professionals. Additionally, a small number of respondents drew upon their own professional knowledge or, as non-professionals, sought this from professional literature.
+Our study suggests a range of sources that may contribute towards the cognitive availability of suicide methods in society and confirms the primary importance of the media (Florentine and Crane, 2010) with several respondents providing examples of direct imitation. For instance, two respondents attempted suicide by charcoal burning - a method described as currently cognitively unavailable in UK society (Florentine and Crane, 2010) - after reading news reports
+of this method thus illustrating how media portrayal may bring about cognitive availability of a ‘new’ method.
+To date, evidence of media effects has mostly derived from ecological studies while individual-level data is anecdotal (Hawton and Williams, 2001; Schmidtke and Schaller, 2002). This study has provided qualitative evidence from systematic questioning of a sample of individuals who have made near-fatal suicide attempts. It includes examples of internet use, an area about which there is a particular lack of current empirical data (Boyce, 2010) despite being a topic of considerable contemporary interest. The study also highlights how, due to its pervasiveness, the media may subliminally generate a stock of lay knowledge and impressions about suicide methods. This observation may be overlooked where research focuses on testing an imitation effect but it is of crucial importance in relation to the concept of cognitive availability.
+This study sought to understand the factors influencing choice of suicide method rather than the actual decision to attempt suicide as a response to distress. The broader question of whether sources of information such as the media prompted respondents to attempt suicide where this may not otherwise have taken place was not directly explored. However, it seems likely that the decision to attempt suicide and the decision about how to attempt suicide are often concurrent or at least interrelated. Suicide may become cognitively available to an individual when there is a proposed and acceptable method just as it becomes physically available through access to means. Further, there was evidence that sources of information such as the Internet could be actively sought by suicidal individuals to enable implementation of a determined act but equally media information about suicide could be unintentionally encountered seeding ideas that otherwise may not exist. These broader issues about the suicidal process and the media's influencing role within this could be explored through further work with near-fatal attempters.
+The actual frequency with which sources are ‘used’ and their relative importance cannot be inferred from a qualitative sample of this nature. The emphasis on experiential knowledge probably relates to the high number of repeaters with complex histories of self-harm in this sample. Approximately 40-50% of people who die by suicide do so at their first attempt (Gunnell and Frankel, 1994). Internet use may differ by age. The ordering of sources in Table 1 therefore cannot be generalised and additional sources may emerge in other groups. Although recruitment was challenging, a diverse sample was achieved including some respondents who had deliberately researched a method and some who had not. There is no obvious reason to believe that self-selection would bias data in relation to the themes described. It is possible that those who complete suicide research methods more than those who survive.
+5.	Implications and conclusions
+This study demonstrates that the media can be a powerful source in introducing and informing people about methods of suicide. In particular, the Internet provides an instantaneous gateway for the communication of ideas about suicide, either inadvertently through everyday usage or where searched
+deliberately as a readily available resource for accessing professional papers, general knowledge and its own unique information via dedicated ‘suicide’ sites. The typical response to these problems has been to argue for restrictions on reporting of suicide (World Health Organisation, 2008) or regulatory guidelines about how suicide should be portrayed (for example, the UK Press Complaints Commission code of practice, 2011). The findings of this study lend some support to these approaches. In particular, there is a need to work with news editors to minimise information about methods contained in reports of suicide, especially those that are novel but accessible, such as charcoal burning, or which have a high fatality rate, such as hanging. Prioritisation of help sites and ‘pop-ups’ to support services in response to Internet searches using certain terms may also be effective (Boyce, 2010).
+However, restricting the media, especially the Internet, is inherently difficult and may deny a primary opportunity for intervention whereby the media is used to discourage suicide. This possibility is highlighted where the concept of cognitive availability is applied to debate about media influence. This concept moves beyond notions of direct cause and effect implied by imitation theory and instead encapsulates the pervasiveness of the media detected in this study and its ability to contribute to a generalised lay knowledge about suicide, which may more subtly influence individuals' responses to distress. The media could be used to manipulate the cognitive availability of suicide via strategic portrayals designed to depopularise common methods by generating negative lay perceptions about these. Key factors influencing choice of method include the certainty, ease, speed and ‘cleanness’ of a method, including its impact on others (Biddle et al., 2010). Portrayals would need to focus on these areas. This may be more effective than a lack of information brought about by media restriction, which could lead to erroneous beliefs about the ease and acceptability of some methods. Respondents' frequent references in this study to popular television dramas suggest these as a likely medium for such an intervention.
+The finding that some respondents obtained information from healthcare professionals should alert clinicians to the varied meanings that can be derived during clinical encounters. Clinicians may also usefully discuss with patients the impact of media portrayals and past attempts on their perceptions of methods and enquire about internet usage while conducting assessments.
+With evidence of the importance of the media in contributing to public awareness of suicide mounting, research must progress to exploring how this can be exploited for the purposes of suicide prevention either through public health or in clinical practice. The challenge is to deliver information of content and in form that most people would find dissuasive. Further research and extensive piloting is essential to explore how individuals interpret and make meanings from media information about suicide. There is also a recognised lack of knowledge about instances where media has deterred suicidal behaviour (Hawton and Williams, 2001). Studies such as ours inevitably identify negative accounts since the respondents were those who had attempted suicide. Nevertheless, even amongst this sample there were some examples of media portrayals being discouraging. Further examples should be sought through systematic research and analysed
+to identify their particular features. It is also necessary to scope the current cognitive availability of suicide methods across various groups to identify existing perceptions that could be addressed.
+A recent study suggested choice of suicide method might be more pliable in younger adults (Lin et al., 2010). Young adults may also be more susceptible to imitation effects (Pirkis and Blood, 2001b) and have higher rates of internet usage. Piloting could therefore begin with this age group. An alternative would be to target those who have made a previous near-fatal suicide attempt. Their sources of knowledge and views about methods could be elucidated in clinical appointments, with a view to discouraging further attempts. Whatever approach is taken, further research will be challenging methodologically.
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+Suicide is one of the most devastating and perplexing of all human behaviors. Whereas the mortality rate for many leading causes of death (eg, tuberculosis, pneumonia, and influenza) has declined over the past century, the suicide rate is virtu-g	ally identical to what it was
+100 years ago.1 Our lack of Related article page 51	y g
+progress in suicide prevention is in large part owing to our limited understanding of this problem. Suicidal thoughts and behaviors (STBs) rarely occur in a research laboratory where they can be carefully probed, and we have not had the technology to study them in situ. As a result, we lack a firm understanding of the fundamental properties of STBs, and when, why, and among whom they unfold.
+The study by Henson et al2 in this issue of JAMA Psychiatry helps to advance the understanding of suicide in several important ways. The authors examined the population of more than 4 million people in England who received a diagnosis of cancer between 1995 and 2015 and found that 2491 of the 3 078 843 individuals (0.08%) who died during the study period died by suicide. They observed a 20% increase in the risk of suicide in those with a cancer diagnosis compared with those in the general population. Suicide risk was especially high in the first 6 months after cancer diagnosis, as well as among those with one of several specific forms of cancer (including mesothelioma, pancreatic, esophageal, and lung cancer). The population-based nature of this study, the documentation of patient characteristics associated with increased risk, and the long study period all strengthen the inferences that can be drawn from this excellent study.
+This study2 adds to a growing body of research that has identified segments of the population at elevated risk for STBs, and in doing so, it also highlights important lacunae in our understanding. Some of the most consistent findings in studies of STBs are that 90% to 95% of those who die by suicide have a diagnosable mental disorder before their death3 and that the presence of certain types of physical conditions, such as multiple sclerosis and cancer (as in the study by Henson et al2), also are associated with increased risk.4 The fact that most people who die by suicide have one of these conditions has led some to suggest that the conditions offer an explanation of the suicide.5 However, the explanatory power of such an association is limited, given that most patients with mental and physical conditions never even consider suicide. We will achieve much greater progress toward understanding and preventing suicide when we answer several additional questions about people with such conditions.
+jamapsychiatry.com
+Who Among Those With Mental and Physical Disorders Are at Greatest Risk of Suicide?
+A recent meta-analysis examining all studies designed to test the prediction of STBs over the past 50 years revealed that (1) no one category of risk factors (eg, mental illness, physical illness) is substantially stronger than the others, (2) most studies have examined 1 risk factor at a time, and (3) the magnitude of effects for known risk factors has not grown over time.6 Taken together, these findings suggest that researchers will not make significant progress in this area if we continue to examine 1 broad risk factor at a time. Henson et al2 did not stop at 1 risk factor but rather tested whether the associations were stronger when considering additional patient characteristics (eg, sex, age at death, and type of cancer diagnosed). Several recent studies have shown that simultaneously examining dozens (or more) of putative risk factors using machine learning methods can significantly enhance the assessment of which patients with mental or physical illness are at greatest risk for suicide.7 The accuracy of such an approach is still far from perfect, and the rate of false positives is unacceptably high. This represents an important point of departure for future research.
+When Are People at Greatest Risk of Suicide?
+Clinicians are tasked not just with identifying who is at risk for suicide, but when. Unfortunately, research has been relatively silent on this issue. The mean time between the assessment of a putative risk factor and the measurement of subsequent STB in published studies is approximately 9 years,6 precluding the ability to examine temporal markers of increased risk. However, there are several notable exceptions in the literature. Risk of suicidal behavior has been shown to increase dramatically in the first year after initial onset of suicidal thinking8 and in the week immediately after discharge from a psychiatric hospitalization.9 Henson et al2 found that people receiving a cancer diagnosis are at highest risk for suicide in the first 6 months after receiving their diagnosis. Taken together with the findings from earlier studies, this suggests that those at high risk for STBs experience temporal transitions (eg, at onset of disease or release from hospitalization) during which they are at especially high risk. Future studies must zoom in on these periods to obtain an even better understanding of when and in which patients STBs emerge during these transitions. The development of new technologies such as smartphones, wearable biosensors, and social media platforms provide novel opportunities for continuous monitoring in situ that can be used to answer such questions,10 as well as 1 other important but so far elusive question.
+JAMA Psychiatry January 2019 Volume 76, Number 1	11
+Downloaded From: https://jamanetwork.com/ by a UFMG User on 12/26/2022
+How and Why Do Known Risk Factors Lead to Suicide?
+Underlying all of this is the basic yet stubbornly challenging question of how and why known risk factors lead to STBs. In the study by Henson et al,2 how and why do specific forms of cancer increase the risk of suicide? Answering this question could dramatically advance prevention efforts. There are myriad obvious possibilities, some of which were reviewed by the authors, and many of which are consistent with longstanding theories of suicide. Despite the poor performance of individual or small sets of risk factors for STBs, proposed theoretical models continue to be overly simple and largely overlapping, each containing some admixture of factors initially proposed many decades ago: social isolation or disconnection (perDurkheim), psychological pain (per Shneidman), and hopelessness (per Beck), which together lead to a desire to escape via suicide (per Baumeister).11 Each of these factors could plausibly explain how and why a person recently diagnosed with cancer could come to die by suicide-just as they could
+among those with a mental disorder. However, in both cases, whereas they are sensitive, they lack specificity, because most people with these psychological experiences do not die by suicide. Our current theoretical models are woefully incomplete and inadequate.
+Conclusions
+So where do we go from here? We advocate for researchers studying suicide to move beyond simple studies that test the role of mental disorders or small sets of sensitive but nonspecific sets of known risk factors. What is needed now are studies that provide data on novel constructs (ie, beyond the set ofusual suspects described in the prior paragraph), as they unfold in real-time, during periods of known heightened risk, that can be used to test the complex and dynamic interactions that likely lead to STBs. Given that theory-driven general models have not gotten us very far, now is the time for us to also be more inductive and idiographic in our research.12
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+2
+Health Policy and Planning, 2016, Vol. 0, No. 0
+Background
+In low- and middle-income countries (LMIC), the treatment gap for people with mental health problems, defined as the difference between the number of people needing care and those receiving such care, is especially pronounced (Demyttenaere et al. 2004). Global initiatives to scale up mental health services are under way, aimed at tackling this treatment gap (WHO 2008). These efforts require an adequate information system, which includes mental health metrics to estimate changes in the treatment coverage and to monitor quality of care (De Silva et al. 2014). The Mental Health Action Plan of the WHO includes a target for 80% of countries to be routinely collecting a core set of mental health indicators through national health and social information systems by 2020 (WHO 2013), progress towards this target is being assessed through a periodic global mental health survey. The present research study set out to complement such efforts by identifying indicators that can be used within routine health information systems to measure different aspects of the concept of effective coverage. The concept of effective coverage—which brings together the need for, uptake of and quality of care—is currently not well measured yet, along with financial coverage or protection, represents a critical component of broader international efforts in the health sector to move and track progress towards universal health coverage for a range of diseases and conditions including severe mental disorders. Better information assists coherent planning, informed decision making, accountability and ensuring quality of care (WHO 2005). Conversely, the lack of available and pertinent data has been demonstrated to hinder the process of improvement of mental health care (Kielstra 2014).
+In LMIC, routine healthcare monitoring systems [e.g. through Health Information Management Systems (HMIS)] do not yet include mental health metrics, or these are included only to a minimal extent (N Upadhaya et al. in preparation; Eaton et al. 2011; Jenkins et al. 2011; Ndetei and Jenkins 2009). When included, the indicators are generally limited to mental health out-patient department attendance rate or psychiatric in-patient or psychiatric in-patient bed
+occupancy rate. With increasing attention being paid to the integration of mental health into primary health care, there is a need to formulate a set of indicators to assess the adequacy of implementation and scale-up. At the same time, the scope for adding new indicators to existing HMIS is limited due to resource constraints and the limited mental healthcare infrastructure in many countries (Hanlon et al. 2014). The need to collect high-quality, informative data without overburdening clinicians and other healthcare staff is therefore imperative (Ryan 2013).
+The programme ‘Emerging mental health systems in low- and middle-income countries (Emerald)’, which this study was a part of, aims to improve mental health outcomes in six LMIC settings (Ethiopia, India, Nepal, Nigeria, South Africa and Uganda) by generating evidence and capacity to enhance health system performance, including the development, use and monitoring of indicators
+for mental health service coverage and system performance (Semrau et al. 2015). Indicators for scaling up services for mental disorders have already been proposed, yet these are mainly health system-level indicators such as the availability of a mental health policy or the level of government spending on mental health services (Lancet Mental Health Group 2007). There is also a need for a common framework for indicators at the service-provision level (Ndetei and Jenkins 2009). Within Emerald, an expert opinion survey using the Delphi methodology was therefore conducted with the aim of generating a consensus-based decision on the selection of indicators for the measurement of effective coverage of mental health treatments.
+Methods
+The Delphi method has been widely used to help promote agreement amongst a group of experts, whose responses to a questionnaire are aggregated and then anonymously fed back to all participants to elicit potential revisions of earlier responses in the light of inputs from the larger group (de Meyrick 2003; Mullen 2003). A Delphi study was chosen because it is well suited to questions related to health policy. The two-staged Delphi exercise involved the following steps.
+Determining the scope
+A four-member steering committee consisting of Emerald investigators (MJ, OG, MS and DC) determined the scope of the study. First, it was determined that indicators should be appropriate for the use on a routine basis in LMICs to assess different aspects of effective service coverage and also financial coverage (which between them capture the concept of universal health coverage). Coverage has been conceptualized as consisting of different embedded levels that include availability coverage (i.e. people for whom the service is available), accessibility coverage (i.e. people who can use services), acceptability coverage (i.e. people who are willing to use services), contact coverage (i.e. people who use services) and ultimately effective coverage (Tanahashi 1978). Effective coverage was defined as the proportion of people in need of a service who gain the intended health benefit from that service (De Silva et al. 2014); financial coverage was defined in terms of levels of financial protection (such as national health insurance) for those using the services. Second, we identified experts from academic, policy and planning (i.e. health information systems), clinical backgrounds and representatives of service user groups. The list of experts was based on (1) first authors from relevant articles generated through a literature review, (2) the Emerald programme investigator team and (3) a country-specific list of experts generated by each participating site based on in-country track record in mental health policy development (i.e. as government staff, service users, service providers and researcher). A group of 180 experts was invited to participate (Figure 1).
+Generating candidate indicators
+The first round of the study resulted in the generation of indicators to measure effective coverage in LMICs. Participants completed a survey, asking them to generate indicators following the four categories of effective coverage (Text Box 1). In addition, they were asked to provide feedback regarding the measurement of the suggested indicators, the need for separate indicators for children and the relevance of developing a set of indicators for use across LMIC. All responses from Round 1 were reviewed and consolidated into a categorized list of selected indicators to be used for Round 2. The categorization was done by listing all non-duplicate indicators and grouping those with the same or similar meaning and choosing the most frequently used phrasing as the indicator being carried forward (i.e. many of the indicators had the same meaning but phrasing was different). This initial grouping was then reviewed by the members of the steering group, based on which further changes and grouping was applied to come to the final set of indicators.
+Prioritizing amongst generated indicators
+In the second round, participants were administered a survey and asked to score each of the selected indicators (n — 52) using three criteria: significance, relevance and feasibility (Text Box 1) on a five-point Likert scale [1 — strongly disagree; 2 — disagree; 3 — neutral (neither agree nor disagree); 4 — agree and 5 — strongly agree], with a ‘no answer (insufficiently informed to answer)’ option. Participants were further invited to provide feedback on each of the generated items. Finally, participants were asked to weigh the relative importance of each of the three criteria by dividing 100 points between the options, and the same weighting was done for the four domains. Mean priority scores for each of the three indicators, as well as an overall priority score, were calculated per indicator (score range, 1-5). Mean scores were re-calculated with individual responses adjusted for the average criteria weight. All no-answer responses were left out of the calculation in both numerator and denominator. For both rounds, questionnaires were e-mailed to experts and when e-mail was not possible a hard copy questionnaire was shared and the responses were later entered electronically. Mean priority scores between groups of experts were compared by doing Analysis of Variance (ANOVA). Analyses were performed in SPSS 20.0 (SPSS 2010). See Supplementary data S1 for the instructions for both rounds of surveys.
+Role ofthe funding source
+The funder had no role in study design, data collection and analysis, decision to publish or preparation of the article.
+Findings
+Indicators to measure effective coverage were generated by 93 people. Participants were mostly men (73%), representing a range of relevant professions (52% mental health professionals, 10% researchers, 25% government officials and 4% service users), with an average of 15.2 years of work experience. A total of 93 respondents out of the 180 invited to participate and completed this round (response rate of 52%). The majority of participants resided in LMIC (84%), and 97% reported working predominantly in LMIC.
+Altogether, 876 possible indicators were generated, which were consolidated into a list of 52 indicators after omitting duplicates and categorization of remaining responses. This list included 9 indicators around need for services, 13 for utilization, 23 for quality and 7 for financial protection. In the second round, 105 people (93 experts who participated in Round 1, four members of the steering committee and eight experts unable to make the Round 1 deadline but who had indicated willingness to participate in Round 2) were invited to participate. The survey was completed by 93 people (89% response rate). There were no significant differences with regards to gender, profession and country of work between Round 1 and Round 2 participants (%2 comparisons, P — 0.92, 0.50 and 0.96, respectively).
+The 15 most highly ranked indicators are listed in Table 1. All 52 indicators had a weighted mean score that ranged from 3.20 for the lowest ranked (number of people with mental disorders who receive information about mental health treatment) to 4.27 for the highest ranked (with the top three highest rated indicators having nearly identical mean scores; Table 1) (higher scores indicating higher level of agreement). Adjusting for the average weight given per criteria did not have much influence on the overall mean scores or position within the highest ranked indicators. We have limited the presentation to 15 indicators for reasons of succinctness of reporting; see Supplementary data S1 for an overview of all 52 indicators included in the study. Moreover, as the large number of potential indicators is a major challenge for HMIS in low resource settings, it is important to collect only essential minimum data to improve efficiency and maximize clinical time for direct patient care.
+The indicators included in the list of15 highest ranked indicators were highly stable between the different groups of experts, as well as between the different participating countries. Exclusion of any one group only had a small influence on the position in the ranking and had no influence on which indicators were included amongst the highest ranked ones. The 10 highest rated indicators were especially consistent between groups, which were the only 10 items that had an average score of >4.0. Between-group analyses (ANOVA) showed that only one indicator of the top 15 (no. 13; number of people with financial protection or insurance) had significantly different mean scores between the different groups (six countries, global and Emerald investigators) (F = 2.579(7); P = 0.016).
+The mean scores on the feasibility criteria were lower for all items than those on the other criteria. Low perceived feasibility influenced the position in the ranking for some of the indicators that were seen as important (i.e. high scores in significance and relevance), e.g. suicide rates (no. 6 in list) and measuring clinical
+outcomes post treatment (no. 14 in list). See Supplementary data S2 for a breakdown of average scores per criterion. The highest rated indicators include indicators from each of the four domains, consistent with the similar average weights for each of the domains as assigned by the respondents (29.4% needs, 27.2% utilization, 24.6% quality and 20.2% financial coverage). Compared with this weighting, the quality of care indicators are over-represented (7/15), which may be explained by the higher number of quality of care indicators in the list of 52 items included in the second round of the study (Supplementary data S2).
+During both rounds of the Delphi study, the participants were asked to provide feedback on the indicators and process. A large majority (79%) was of the opinion that there is a need for separate indicators for child and adolescent mental health. With regards to the relevance of developing a set of cross-country indicators, 87% reported to be moderately or very much in favor of this, whereas 13% were only a little in favor or not at all. At the same time, a
+considerable number of challenges were raised, especially in relation to the methods of collection for the proposed indicators. Some of the frequently mentioned barriers were related to scarcity of human resources (n — 58), lack of political will or low priority given to mental health information (n — 37), cost involved (n — 28), lack of tools and infrastructure for collection, documentation and reporting (n — 28), complexity of indicators (n — 22), lack of a system of management and utilization of data in decision-making (n —11) and inadequate quality assurance mechanisms (n — 11).
+Discussion
+Within the broader context of moving towards universal health coverage for mental disorders, monitoring and evaluation of mental health programmes needs to incorporate the routine collection of measures of effective and financial coverage. This is needed to inform quality improvement and scale-up of services (De Silva et al. 2014). This Delphi study, with an expert panel consisting of mental health researchers, clinicians, service users and policy makers almost all working and residing in LMIC, has generated and ranked a set of indicators for routine measurement of mental health service coverage and system performance. First, there was broad support for the identification of a minimum set of indicators towards improving
+monitoring of mental health care in LMIC, as well as for the content of these indicators.
+Currently, only a minority of LMIC collect routine data on mental health from primary care (WHO 2011), and the lack of such data has been found to be amongst the greatest limitations of some existing community mental health programmes (Cohen et al. 2011). There is a notable overlap between the selected indicators in this study with those that have been proposed in other publications suggesting integrated indicators for monitoring mental health services (Lund and Flisher 2003; Patel et al. 2009). Some of the listed indicators will need to be broken down into sub-categories for actual use (e.g. different disorders for items 1 and 7; or different types of treatment for items 5 and 9), a process that is currently ongoing within each of the participating countries.
+Second, the findings show that the set of most highly prioritized indicators cover the different domains of coverage, i.e. accessibility coverage, acceptability coverage, contact coverage and finally effective coverage. Acceptability coverage is covered by the item asking for number of patients and caregivers expressing satisfaction with received services. Although the first round of the Delphi study did ask participants to generate indicators according to these four domains, the prioritization according to pre-defined criteria could have led to one or more of the domains not being included. This can be seen as a sign of support for going beyond the measurement of
+6
+Health Policyand Planning, 2016, Vol. 0, No. 0
+contact coverage (i.e. service utilization divided by the population in need of services). To date, only very few mental health programmes in LMIC have been able to do this, according to a recent review (De Silva et al. 2014). Also, this approach is compatible with the conceptualization of Universal Health Coverage, which stipulates that services should be utilized without suffering financial hardship when paying for them (World Health Organization 2001). The combined selected indicators covered the measurement of various aspects of the mental health system, including health care delivery and management indicators (5/15), and individual health status indicators (10/15).
+Third, the indicators with the highest level of agreement amongst the panel of experts were highly stable between different groups of participants, based on country or profession. The ranking of the indicators also did not vary significantly after adjusting for the weighting of the three criteria. This stability speaks to the reliability of the findings, especially for Delphi studies that involve measuring or ranking the level of consensus amongst a group of experts (de Meyrick 2003).
+Fourth, a number of potential challenges have been voiced, mostly concerned with the feasibility of integrating new indicators for mental health care within existing surveillance systems. Potential barriers include scarcity of resources and poor commitment or infrastructure. Such barriers do not reflect the content of the indicators but rather are a reflection of the health system at large, especially in regard to efforts to integrate mental health services, in general, into primary health care. Some other frequently mentioned barriers, such as lack of a valid set of indicators, are exactly the reason for conducting this study, and in fact the Emerald programme (Semrau et al. 2015).
+Limitations
+A few study limitations need to be pointed out. First, we had a response rate of 52% between invited experts and the first round of generating indicators. This is largely explained by the low representation from the group of experts (3/34) from high-income settings that had been identified through the literature review prior to this study. Although this may entail a loss of expertise with health information systems, it has resulted in a group of participants who almost exclusively working in LMIC, who arguably better understands the actual needs and possibilities within the health systems of these countries. The high response rate between the first and second rounds (89%) is a strength of the study. Second, to actually reach consensus on the indicators, further rounds of questionnaires, and feeding back the groups’ intermediate positions, would be needed. The type of Delphi study that we used aims to identify the widest possible range of valid solutions to a health policy problem (de Meyrick 2003). The weakness of this approach is that it cannot provide in-depth discussion on any of the proposed responses. For that reason, the results of this study will be further discussed within each of the Emerald countries, and as such it provides a precursor for incountry refinement and consensus building. Next steps will involve studying the process of integrating new indicators within district level health information systems in the six programme countries, and evaluating their use and outcomes (Semrau et al. 2015). Finally, the dominance of male participants (73% in Round 2) in the sample is important to note, as this may have introduced bias. The study was introduced with the perspective of identifying indicators for measuring mental health treatment coverage. As a result, indicators for prevention activities are not adequately represented in this study. This should be addressed in a separate study.
+Conclusion
+To evaluate the scaling up of mental health care in LMIC, there is an urgent need to have an adequate information system. Good quality data on a number of key indicators can support the process of making decisions and improvements related to mental health care. Currently, mental health indicators are under-represented or not present in most routine information systems. To respond to this information gap, the formulation of a set of indicators that are recommended by a group of experts can serve as an important first step. This study has resulted in a highly stable list of most frequently endorsed indicators that cover different domains of measuring mental healthcare coverage, including needs, utilization, quality and financial protection. Further research is needed to demonstrate the feasibility and performance of these indicators in real-life practice, especially given the constraints of scarce resources for (mental) healthcare systems.
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+Access to mental health care is expected to have an impact on the health outcomes of local residents. Among persons who complete suicide, from 70% to 90% have mental disorders, mostly affective disorders (1,2). Therefore, access to services, which increases the possibility of diagnosis and treatment of mental disorders, should have an impact on regional suicide rates. On average, 55% of persons who complete suicide have no contact with mental health or primary care providers in the month before suicide (3). Thus the availability of services may be relevant in explaining geographical variations in suicide incidence.
+It was recently proposed that the density of mental health professionals and the availability of primary health care providers can be used as core indicators of mental health service quality and that the distribution of such providers between urban and rural areas and the suicide rate qualify as indicators of service quality (4). Indeed, the availability and utilization of mental health services may vary substantially between and within countries (5); typically, psychiatrists (6-8) and other physicians are less available in rural areas than in urban areas (9-11). Suicide rates are generally higher in rural areas (12,13).
+Studies of the relation between suicide rates and availability of mental health services are rare. The seminal Gotland study in Sweden showed that
+educating general practitioners to recognize and treat depression was effective in reducing suicides (14). In a later study in Hungary, the density of physicians was shown to correlate with the rate of diagnosed depression and suicide rates (15). In the United States the unbalanced relationship between demand and availability of access to mental health services was shown to be associated with suicide rates (16), and a proposed proxy of the quality of local mental health care—county-level prescriptions for selective serotonin reuptake inhibitors—was inversely associated with suicide rates (17). A separate U.S. cross-state analysis showed an inverse relationship between state suicide rates and parameters of access to mental health care (such as the density of psychiatrists, number of uninsured persons, and federal aid for mental health care) (18). However, the most convincing evidence was recently found in a nationwide smallarea study in Finland (19). The study showed that several mental health indicators, such as the availability of 24hour emergency services and mobile outpatient services, number of hospitalized patients, and number of involuntary admissions, were associated with suicide rates. However, after adjustment for regional socioeconomic factors, only one indicator, a greater number of outpatient mental health services compared with inpatient services, remained weakly associated with suicide rates (19).
+Differences in regional suicide rates may be explained by the influence of regional socioeconomic factors on mental disorders and by a selective migration mechanism of the population (20-25). Regional economic factors may also effect the migration and availability of mental health providers (6,7,26). Thus regional contextual factors may confound the association between access to health services and suicide rates. Researchers who examine such associations should be aware that the extent and magnitude of such regional effects may depend on the conceptualization of place effects (27).
+The aim of this study was to examine in a small-area analysis whether the availability of professionals providing
+mental health treatment had an effect on regional suicide rates in Austria. The hypothesis was that districts in Austria in which mental health care providers were more available, as measured by the density of general practitioners, psychiatrists, and psychotherapists, would have lower suicide rates than districts with a low density of providers. The models accounted for socioeconomic covariates that have been shown to be associated with suicide rates, such as population density (12,13), average income and unemployment (28), and religion (29).
+Methods
+Data
+Statistics Austria provided the official mortality database for suicides in nine age groups and for both genders in all 99 districts of Austria for each year from 1991 to 2005. Population density, proportion of Roman Catholics, and average income were obtained from the official Austrian population census of 2001. The 1991-2005 unemployment rates for each district were obtained from the Public Employment Service of Austria. Numbers of general practitioners and psychiatrists for each district were available for 1997 and 2002 and were obtained from the Austrian Medical Chamber. The Austrian Institute of Health provided figures on the official numbers of nonphysician psychotherapists registered for 1997 and 2002 by the Austrian Ministry of Health and Women. There were no human subjects concerns in this study. Given the aggregated nature of the data, no institutional review board approval was necessary. The study complied with guidelines for good scientific practice of the Medical University of Vienna and followed the Declaration of Helsinki.
+Statistical analysis
+The statistical analysis strategy has been described in previous reports (19,30). The suicide mortality rate of the Austrian districts was analyzed on the basis of standardized mortality ratios (SMRs). A SMR is the ratio between the observed and expected number of suicides. We calculated overall suicide rates for both sexes and the nine age groups. The expect
+ed suicide rates are obtained by multiplying the overall subgroup-specific suicide rates by the size of the subgroups in the district. To increase the number of events, for each district we pooled suicide data from 1991 to 2005. To adjust for random variation, the SMRs were estimated with a hierarchical Bayesian model that accounted for spatially correlated random effects by using an intrinsic conditional autoregressive prior that incorporated the neighborhood structure of districts and assumed a Poisson distribution for the observed number of suicides. All estimates were derived with R 2.9.2 (31) and OpenBUGS 3.0.3 (32) by using MCMC (Markov chain Monte Carlo) simulations with 10,000 iterations and two chains to validate the results.
+To investigate confounding effects of socioeconomic covariables, we performed a principal components analysis for the following variables: logarithm of population density (2001), proportion of Roman Catholics (2001), local unemployment rate (averaged from 1991 to 2005), and average income (2001). From these variables we identified two principal components that accounted for 82% of the variance. The first component, accounting for 57% of the variance, was strongly associated with population density and average income and negatively associated with the proportion of Roman Catholics. It can be considered an indicator of districts that have a more urban population, a larger proportion of nondenomina-tional residents (no religious affiliation) and non-Catholic immigrants, and a population density and income that are higher than average. The second component, accounting for 25% of the variance, corresponds essentially to the local unemployment rate.
+To investigate the impact of the number of psychiatrists and psychotherapists on the SMR, we included the respective covariable in the Bayesian model and computed an estimate of the relative risk (RR) together with the Bayesian credible intervals (CIs). Absolute risk reduction (ARR) was computed for the Austrian annual suicide mortality rate of 18.5 per 100,000 population in 2001. The RR and the ARR are
+PSYCHIATRIC SERVICES ♦ ps.psychiatryonline.org ♦ December 2010 Vol. 61 No. 12
+1199
+reported for the change of the covariable from the first to the third quartile. In the Bayesian model, we transformed the covariables by taking the square root followed by standardization (subtraction of the mean and division by the standard deviation) to account for the extremely skewed distribution.
+We used an analogous model to estimate the impact of the number of general practitioners on the SMR. Because the distribution of this variable was less skewed, it was only standardized—that is, no square root was taken. To evaluate the impact of possible confounders, the analysis was repeated to include the two principal com
+Figure 1
+and 17.3 per 100,000 inhabitants. Table 1 presents descriptive statistics of the variables studied. The spatial distribution of the density of psychotherapists and psychiatrists is shown in Figures 1 and 2. Population density was found to be correlated with the density of psychiatrists (r=.42, p<.001) and psychotherapists (r=.6, p<.001) but not general practitioners.
+The mean±SD SMR according to the Bayesian estimates was 1.02±.17, with a range from .63 to 1.47, indicating a large heterogeneity across districts (Figure 3). In the analysis in which socioeconomic factors were not included, the density of psychotherapists per 10,000 population showed a significant effect (RR=.97, CI=.94-.99; ARR=-.62, CI=-1.20 to -.11). However, no significant effects were found for the density of psychiatrists or of general practitioners per 10,000 population. [Figures showing scatter plots of the SMR and the density of psychotherapists and psychiatrists are available in an online supplement to this article at ps.psychia-tryonline.org.]
+The same results were found for psychotherapists and for psychiatrists when the mean density from 1997 to 2002 was used instead of the density in 1997: unadjusted analysis for psychotherapists, RR=.97, CI=.94-.997, and ARR=-.56, CI=-1.14 to -.06; unadjusted analysis for psychiatrists, RR=.99, CI = .97-1.02, and ARR= -.12, CI=-.65 to .38.
+When the analysis adjusted for socioeconomic variables by including both principal components in the model, the association between density of psychotherapists and SMR was no longer significant. However, the first principal component showed a significant effect on the SMR in this model (RR=.94, CI = .88-.99, and ARR=-1.17, CI=-2.34 to -.05). The second component showed no significant effect. The density of psychotherapists and of psychiatrists were both positively correlated with the first principal component (Spearman r=.69 and r=.64, respectively).
+Discussion
+The hypothesis of this study was that regional access to mental health care providers has an impact on regional
+suicide rates. Neither the density of general practitioners nor the density of psychiatrists was associated with SMR. However, the density of psychotherapists had a weak association with SMR. A further finding of this study was that the first principal socioeconomic component was highly correlated with the density of psychiatrists and of psychotherapists. This component reflects an urban population with a higher proportion of non-denominational (no religious affiliation), non-Catholic inhabitants and higher-than-average income and population density.
+General practitioners
+In Austria 77%-79% of all psy-chopharmacological agents are prescribed by general practitioners (33). The density of general practitioners varied little across districts, which may explain the lack of association between access to general practitioners and suicide rates. The assumption of heterogeneity in exposure of a population is vital to determining relative risk (34). The relatively homogeneous distribution of general practitioners across districts may result from demand-oriented planning by the social insurance system of its contracts with general practitioners. In 1997 approximately 70% of general practitioner practices and 20% of psychiatric practices were contract based, according to the Austrian Medical Chamber; however, in 1997 and currently, no psychotherapist has a contract with the social insurance system (33).
+We found no correlation between the first principal component and the population density of general practitioners. However, this component was correlated with the density of psychiatrists and of psychotherapists. As previous studies have shown, most mental health services in rural areas are provided by general practitioners (4,6).
+Psychiatrists and psychotherapists The density of psychotherapists was found to vary considerably between districts. The average density of psychotherapists was 1.73 per 10,000, which is about ninefold higher than the density of psychiatrists in Austria. The higher density could account for the finding of a weak crude effect of
+psychotherapist density on suicide rates but no effect of psychiatrist density. In the United States, where the density of psychiatrists is considerably higher (1.4 per 10,000) (8) than in Austria (.2 per 10,000), a crude effect of the density of psychiatrists on suicide rates has been described (18). After adjustment for the first principal component of covariates in our study, the weak effect of psychotherapist density on suicide rates was lost. In addition, the first principal component was a significant predictor of suicide rates.
+Because psychotherapist density and the first principal component
+were correlated, the results should be considered with caution. Much evidence has been reported supporting the view that suicide rates are influenced by regional socioeconomic characteristics, such as per capita income, deprivation, unemployment rates, and population density (13,22, 35,36). Our finding that the density of psychotherapists had a crude effect on suicide rates is new. However, it is likely that psychotherapist density is a function of the urban component. This multicollinearity makes it difficult to understand the separate effects of each factor on suicide rates, especially when the question of con
+ceptualization of place effects is raised (27).
+Studies examining the influence of access to mental health care on suicide rates are rare. Only one small-area study from Finland has been reported that used a similar statistical method (19). Although the Finnish study was based on more mental health care access variables than ours, it was not adjusted for spatial correlation of districts. Such spatial correlation takes into account the argument that local access to care can have an impact on local suicide rates (27,30). This argument supports the appropriateness of our analysis, even though it did not quantify the availability of psychiatric inpatient facilities: such facilities serve large catchment areas that typically cover more than one district. In the Finnish study, adjustment for socioeconomic variables similarly attenuated most of the significant associations between mental health access proxies and suicide rates, and only a higher ratio of outpatient services to inpatient services remained significant.
+Urbanicity and mental health specialists
+It has been shown that a region’s economic situation determines physician supply and utilization (26). Our results show that access to psychiatrists and psychotherapists is better in urbanized and privileged districts than in deprived regions. On the other hand, the low availability of psychiatrists resulting from long waiting lists and the minimal reimbursement for psychotherapy from nonpsychiatrists increase barriers to the professional mental health care needed by suicidal patients. Poorer access to mental health professionals partly explains how socioeconomic conditions may influence suicide rates. A previous study noted an increasing gap between rural and urban suicide rates in Austria (13). At the beginning of the 1970s, urban regions experienced higher suicide rates than rural ones; however, this ratio changed by the mid-1980s, and the disparity between rural and urban suicides continues to grow, with suicide rates falling more notably in urban areas than in rural
+ones (13). Further studies should examine whether improvements in access to mental health care and socioeconomic changes in urban areas are in line with the greater reductions in urban suicide rates compared with rural rates.
+Barriers to psychotherapist and psychiatrist care
+Although social insurance expenditures for psychotherapy have grown in recent years in Austria, demandplanning authorities have recognized that the need for reimbursement of psychotherapy visits is far from met. Waiting periods of up to 30 weeks are common. Of a total of 1.4 million reimbursed visits for psychotherapy in 2007, 36% were only partially reimbursed—at approximately 30% of the cost per visit (33). Thus access to psychotherapy depends on patients’ socioeconomic situation. Similarly, because only 20% of all psychiatrist practices in Austria are contracted with social insurance, waiting periods of up to three months are typical for a first appointment covered by social insurance (33).
+Economic conditions also determine the behavior of psychiatrists and psychotherapists. It is recognized and has been shown in this study that psychotherapists and psychiatrists are not as likely to reside in more deprived rural areas with a low mean income as they are in more urban areas (6,7). In addition, psychiatrists who contract with the social insurance system in Austria receive lower fees compared with fees received from self-payers. A similar situation appears to exist in the United States, where psychiatrists who accept new patients have been shown to prefer self-payers rather than patients with private insurance, Medicare, or Medicaid (37).
+Limitations
+The naturalistic design of this study does not allow conclusions about the effects of individual access to mental health providers on suicidality or about causal relationships. Although the statistical model accounted for the most common covariates and for spatial correlation, other factors not included could affect the results.
+For example, the proportion of persons divorced in each district was not available, although divorce has been described as a factor in suicide rates (38). Similarly, it is not known to what extent the population density of psychiatrists, psychotherapists, and general practitioners reflects service utilization or how different waiting periods could affect this relationship. Finally, we had to pool suicide data from 1991 to 2005 because of low incidences in some districts; however, pooling probably had little effect because of the temporal stability of suicide patterns in a given district.
+Conclusions
+Pirkola and colleagues (19) pointed to a possible advantage of communitybased outpatient treatment over inpatient treatment in terms of suicide prevention. This analysis of Austrian data points to another aspect of outpatient-based suicide prevention. A remarkable nationwide increase in antidepressant prescriptions has occurred in Austria since the introduction of selective serotonin reuptake inhibitors. In addition, there was a simultaneous increase in the density of psychotherapists between 1991 and 2005. Both of these factors have been shown to correlate with an overall decrease in the suicide rate in Austria, which suggests an improvement in Austrian mental health care (39).
+Nevertheless, this small-area analysis has provided evidence that access to mental health care cannot be viewed as independent from regional socioeconomic factors: the density of psychiatrists and psychotherapists is higher in urbanized areas with higher income, where suicide rates are low. Thus access and demand are unbalanced in Austria. As recommended in the United States (40) and many other countries (41), reducing socioeconomic barriers to mental health care, such as by increasing reimbursement of psychotherapy and promoting settlement of psychiatrists and psychotherapists in rural areas, is a desirable goal for the Austrian mental health system.
+Acknowledgments and disclosures
+The authors report no competing interests.
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+Suicide is a leading cause of death worldwide, killing more than 800,000 people each year (World Health Organization [WHO], 2014a). A much larger number of people make suicide attempts, with some researchers estimating that approximately 25 attempts occur for every suicide death (Goldsmith, Pellmar, Kleinman, & Bunney, 2002). An even greater number of people consider suicide; a world-
+Address correspondence to E. David Klonsky, University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver, BC V6T 1Z4, Canada; E-mail: EDKlonsky@gmail.com
+114
+wide study found that for every person who attempts suicide, there are two to three who have seriously considered suicide without attempting it (Nock et al., 2008). Given this immense public health problem, suicide has been the focus of many research and prevention efforts, particularly in the past few decades. However, despite these efforts, there is no evidence of sustained reductions in suicide rates (WHO, 2014b). The development of more effective prevention and intervention strategies will very likely require a deeper understanding of the fundamental processes that cause suicide ideation, attempts, and deaths.
+TRADITIONAL THEORIES OF SUICIDE
+Many theorists have sought to explain suicide. For example, Shneidman (1985, 1993) explained suicide as a response to overwhelming pain (i.e., psychache), Durkheim (1897/1951) emphasized the role of social isolation, Baumeister (1990) described suicide as an escape from an aversive state of mind, and Beck and Abramson (Abramson et al., 2000; Beck, 1967) highlighted the role of hopelessness. These theories have been tremendously useful in guiding suicide research and prevention efforts. At the same time, these theories share a particular feature that may be limiting progress in understanding suicide: They fail to differentiate explanations for suicidal thoughts and suicidal behavior. This distinction is especially important when one considers that most people who develop suicidal ideation never go on to make a suicide attempt (Klonsky & May, 2014; Nock et al., 2008).
+A NEW GENERATION OF THEORIES OF SUICIDE
+A critical advance in suicide theory occurred 10 years ago when Thomas Joiner (2005) introduced his Interpersonal Theory of Suicide. Joiner introduced a framework by which (a) suicidal ideation and (b) the progression from ideation to attempts were treated as separate processes that come with separate sets of explanations and risk factors. Joiner proposed a specific application of the framework: Perceptions of low belongingness and high burdensomeness combine to bring about desire for suicide, whereas high capability for suicide facilitates potentially lethal suicide attempts. However, we believe that the framework itself is at least as important a contribution to the field as the particulars of his theory. Inspired by Joiner’s work, we recently proposed that an “ideation-to-action” framework should guide all suicide theory and research (Klonsky & May, 2014). That is, the (a) development of suicide ideation and (b) progression from ideation to suicide attempts should be viewed as distinct processes with distinct explanations.
+We view Joiner’s Interpersonal Theory as the first ideation-to-action theory of suicide, which has likely spawned a new generation of suicide theories. For example, another recent theory of suicide, Rory O’Connor’s (2011) Integrated Motivational-Volitional model, also proposes separate explanations for suicidal
+ideation and suicide attempts. O’Connor suggests that defeat and entrapment are the primary drivers of suicidal ideation, and that acquired capability along with others factors (e.g., access to lethal means, planning, impulsivity) explain the propensity to act on suicidal thoughts. We find it extremely encouraging that theorists appear to be embracing and utilizing an ideation-to-action framework.
+OUR PROPOSED THEORY OF SUICIDE
+The purpose of the present article is to describe and empirically evaluate our own theory of suicide positioned within an ideation-to-action framework. Like the theories of Joiner and O’Connor, our theory offers separate explanations for (a) the development of suicidal ideation and (b) the progression from suicidal ideation to attempts. The theory is relatively parsimonious in that suicide ideation and attempts are explained in terms of just four factors: pain, hopelessness, connectedness, and suicide capacity. The theory is illustrated in Figure 1 and explained here.
+STEP 1: DEVELOPMENT OF SUICIDAL IDEATION
+Regarding the development of suicidal ideation, we believe that the first step toward ideation begins with pain. Pain usually, but not necessarily, refers to psychological or emotional pain. Fundamentally, people are shaped by behavioral conditioning. We perform behaviors that are rewarded and avoid behaviors that are punished. If someone’s day-to-day experience of living is characterized by pain,
+this individual is essentially being punished for living, which may decrease the desire to live and, in turn, initiate thoughts about suicide.
+It is intentional that we do not specify the nature of the pain. Just as different sources of punishment can similarly shape behavior (e.g., electric shock [Alexander et al., 1973], loud noises [Watson & Rayner, 1920], noxious odors [Tanner & Zeiler, 1975], social exclusion [Williams, Cheung, & Choi, 2000], or any sufficiently aversive stimulus [Mazur, 2012]), we believe that different sources of pain can all lead to a decreased desire to live. These may include physical suffering (Ratcliffe, Enns, Belik, & Sareen, 2008), social isolation (Durkheim, 1897/1951), burdensomeness and low belongingness (Joiner, 2005), defeat and entrapment (O’Connor, 2011), negative self-perceptions (Baumeister, 1990), and myriad other aversive thoughts, emotions, sensations, and experiences. The first step toward suicidal ideation begins with pain, regardless of its source.
+However, pain alone is not sufficient to produce suicidal ideation. If someone living in pain has hope that the situation can improve, the individual likely will focus on obtaining a future with diminished pain rather than on the possibility of ending his or her life. For this reason, hopelessness is also required for the development of suicidal ideation. From our perspective, when someone’s day-to-day experience is characterized by pain, and the person feels hopeless that the pain will improve, he or she will consider suicide. In short, the combination of pain and hopelessness is what causes suicide ideation to develop. Our perspective is consistent with recent research finding that pain and hopelessness are the two most common motivations for suicide attempts (May & Klonsky, 2013).
+Importantly, we believe it is the combination of pain and hopelessness that is required to bring about suicidal ideation. Someone in pain but with hope for a better future will continue to engage with life. Similarly, someone who feels hopeless about the future but without day-to-day pain will not consider suicide. The latter situation may seem less intuitive than the former, so consider the following example. A young adult recently graduated from university and moved back in with her parents. If this young adult lacks a marketable degree, good grades, and a sense of her career interests, she may very well feel hopeless about the future. However, as long as her day-to-day experience remains comfortable with little or no pain—for example, if her food and shelter are provided, she has free time to spend with friends, and she enjoys participating in activities of her choosing—she is unlikely to consider suicide. Pain and hopelessness in combination are required for the development of suicidal ideation.
+STEP 2: STRONG VERSUS MODERATE IDEATION
+The second step toward potentially lethal suicidal behavior involves connectedness. Connectedness most often means connection to other people; however, we use the term more broadly. Connectedness can also refer to one’s attachment to a job, project, role, interest, or any sense of perceived purpose or meaning that keeps one invested in living. Connectedness matters, because even if someone feels pain and hopelessness and considers suicide, the suicidal ideation will remain moder
+ate (e.g., “sometimes I think I might be better off dead”) rather than strong (e.g., “I would kill myself if I had the chance”) as long as one’s connectedness to life is greater than one’s pain. Consider the example of a parent who experiences daily pain and hopelessness, but is invested in or connected to his or her children. If the parent’s connectedness is greater than the parent’s pain, this individual may still have passive ideation but will not progress to active desire for suicide. However, if both pain and hopelessness are present, and connectedness is absent or less than the pain, the individual will have strong suicidal ideation and an active desire to end his or her life.
+Disrupted connectedness is similar to low belongingness and burdensomeness as described in Joiner’s Interpersonal Theory. We therefore want to highlight a key difference between our theory and the Interpersonal Theory. In our theory, the primary role of connectedness is to protect against strong suicidal ideation in those at high risk due to pain and hopelessness. While we believe disrupted connectedness can contribute directly to pain and hopelessness, we do not view it as necessary for the development of pain or hopelessness, and thus the development of suicidal ideation. We believe that many people with disrupted connectedness do not have suicidal ideation, and that many people with suicidal ideation do not experience disrupted connectedness.
+To be clear, our emphasis on pain, hopelessness, and connectedness in explaining suicidal ideation is not meant to suggest that other traditional risk factors for suicide are irrelevant. Indeed, we believe that numerous disorders (e.g., depression), states of mind (e.g., self-criticism), personality traits (e.g., borderline personality), temperaments/dispositions (e.g., negative emotionality), and experiences (e.g., interpersonal loss) are highly relevant to suicidal ideation. However, we believe that they are relevant in a specific way, through their effects on pain, hopelessness, and/or connectedness. For example, we would expect depression to relate to suicidal ideation to the extent that it influences pain, hopelessness, and/or connectedness, but not beyond.
+STEP 3: PROGRESSION FROM IDEATION TO ATTEMPTS
+Once an individual has developed a desire to end his or her life, the next question is whether the person will act on that desire and make an attempt. We agree with Joiner (2005) that the key determinant is whether the individual has the capability to make a suicide attempt. As Joiner argues, people are biologically and evolution-arily wired to avoid pain, injury, and death. It is therefore very difficult for people to attempt suicide, even in the presence of strong suicidal ideation. However, we expand on Joiner’s notion of capability in two ways.
+Joiner emphasizes acquired capability. Acquired capability refers to an individual’s habituation to pain, fear, and death through exposure to life experiences such as physical abuse, nonsuicidal self-injury, the suicide of a family member or friend, combat training, or any other experience that subjects someone to painful and provocative events. We take a broader perspective and propose three specific
+categories of variables that contribute to suicide capacity: dispositional, acquired, and practical.
+Dispositional refers to relevant variables that are driven largely by genetics, such as pain sensitivity (Young, Lariviere, & Belfer, 2012) or blood phobia (Cza-jkowski, Kendler, Tambs, Roysamb, & Reichborn-Kjennerud, 2011). For example, someone born with low pain sensitivity will have a higher capacity to carry out a suicide attempt, whereas someone born with a squeamishness or even phobia of blood will have a lower capacity. Indeed, more recent work from Joiner and others has found that capability for suicide is largely genetic (Smith et al., 2012). Acquired refers to the same construct Joiner describes, that habituation to experiences associated with pain, injury, fear, and death can lead over time to higher capacity for a suicide attempt. Practical refers to concrete factors that make a suicide attempt easier. There are many kinds of practical factors. For example, someone with both knowledge of and access to lethal means, such as a firearm, will be more able to act on suicidal thoughts than someone who lacks knowledge of and access to lethal means. Another example is anesthesiologists and other medical professionals whose suicide rates are elevated (Swanson, Roberts, & Chapman, 2003). Our theory suggests that suicide rates are elevated because these individuals have both extensive knowledge of how to end one’s life painlessly and easy access to the necessary drugs. In summary, dispositional, acquired, and practical factors contribute to the capacity for attempted suicide, and an individual with strong suicidal ideation will only make a suicide attempt if and when they have the capacity to do so.
+THE PRESENT STUDY
+The present study conducts an initial empirical evaluation of our proposed theory’s key principles in a large, sociodemographically diverse sample of adults in the United States. The key hypotheses we examined were:
+(1)	Pain and hopelessness will interact to predict current suicidal ideation (and predict suicidal ideation better than a comparison model consisting of burdensomeness and low belongingness).
+(2)	Connectedness protects against escalation of suicidal ideation among those high on both pain and hopelessness.
+(3)	Dispositional, acquired, and practical contributors to suicide capacity will distinguish lifetime suicide attempters from those with histories of ideation but not attempts.
+METHODS
+PARTICIPANTS AND PROCEDURE
+The data for this article were taken from a larger study on suicide ideation and attempts. The study was advertised on Mechanical Turk, a crowdsourcing Internet
+marketplace in which participants see descriptions of tasks they may complete, estimated time required, and compensation amount. The study consisted of a screening questionnaire and a full battery of questionnaires, both completed online. All participants filled out the screening questionnaire, which included questions about suicide ideation and attempt history. In the first wave of data collection, all respondents were also invited to complete the full survey, regardless of suicidality history (n = 906). In the second phase of the study, participants were invited to complete the full survey only if they endorsed a history of suicide ideation or attempt on the screening questionnaire (n = 191). The full survey included all the measures described here, as well as four validity questions to ensure that participants were attending to the questionnaire appropriately. After we removed participants who failed to answer the validity questions correctly, completed the survey multiple times, or failed to complete the suicide ideation measure, 910 participants remained.
+Approximately half of the participants were male (53%) with a mean age of 31 years (SD = 10; range = 18-70). Regarding ethnicity, the sample was predominantly Caucasian (76%). The remaining participants were African American (6%), Asian American (8%), Latino/Hispanic (4%), multiple ethnicities (4%), and other (2%). Fifteen percent of the sample endorsed a minority sexual orientation (e.g., gay, bisexual, lesbian, questioning). Most participants reported that their highest education level was either graduating from college (38%) or taking some college classes (36%). Of the remaining participants, 12% reported a high school degree or less and 14% reported some graduate school or more. The sample was drawn from diverse regions of the United States: Southeast (25%), Far West (19%), Mid-Atlantic (17%), Great Lakes (16%), Southwest (10%), Plains (5%), New England (5%), and Rocky Mountains (4%).
+Regarding history of suicide, in the unselected sample, 71% reported no suicidality history, 19% reported a history of ideation or planning but no attempt, and 10% reported a previous suicide attempt. In the full sample, which oversampled for histories of suicidality and was the basis for all the analyses reported here, 59% reported no suicidality history, 27% reported a history of ideation or planning but no attempt, and 14% reported a previous suicide attempt.
+MEASURES
+Suicidality Screening. Three items from the Youth Risk Behavior Surveillance Survey (YRBS; Centers for Disease Control, 2009) were used to screen for a lifetime history of suicide ideation, planning, or attempt. These questions have good to excellent reliability and validity (Brener et al., 2002; May & Klonsky, 2011).
+Current Suicide Ideation. Current suicide ideation was assessed using the selfreport version of the Beck Scale for Suicide Ideation (BSS; Beck & Steer, 1991) and the suicide ideation item from the Beck Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979). The BSS is a widely used 19-item measure of suicide ideation. It covers active and passive ideation, including the presence of plans or preparation. Each item has three response options, which are scored from 0
+to 2. Thus, total BSS scores could range from 0 to 38. The BDI item has four response options ranging from no ideation to active ideation. If participants endorsed nonzero ideation on any of the first five BSS items or the BDI item, they completed the entire BSS and were assigned the summed BSS total as their score for suicide ideation. Otherwise, they were assigned an ideation score of 0. In our sample, reliability of the BSS was excellent (a = .86).
+Psychological Pain. The 13-item Scale of Psychache measured current emotional or mental pain as conceptualized by Edwin Shneidman (1993). Items are rated on a 5-point Likert scale and total scores can range from 13 to 65. The psychometric properties of this measure have been established (Holden, Mehta, Cunningham, & McLeod, 2001). In this sample, reliability was excellent (a = .97).
+Hopelessness. The Beck Hopelessness Scale-Short Form (BHS-SF; Beck, Weissman, Lester, & Trexler, 1974) assessed hopelessness within the past week. Items were rated as true or false and total scores can range from 0 to 4. The BHS-SF is a 4-item version of the widely used Beck Hopelessness Scale (Beck et al., 1974), and the short form has demonstrated good psychometric properties (Aish, Wasserman, & Renberg, 2001; Yip & Cheung, 2006). In this sample, reliability was excellent (a = .86).
+Burdensomeness and Belongingness. These constructs, which are part of the Interpersonal Psychological Theory, were measured with the 10-item version of the Interpersonal Needs Questionnaire (INQ; Bryan, 2011; Hill et al., 2014; Van Or-den, Witte, Gordon, Bender, & Joiner, 2008). The INQ was designed to measure participants’ beliefs about the extent to which they feel connected to others (i.e., belongingness) and the degree to which they feel they are a burden to others (i.e., burdensomeness). Participants indicate the degree to which each item is true for them on a 7-point Likert scale, and scores for each factor can range from 5 to 35.
+Suicide Capacity. Two measures were used to assess suicide capacity First, the 20-item Acquired Capability for Suicide Scale (ACSS; Van Orden et al., 2008) measured the extent to which individuals perceive themselves as able to perform dangerous tasks and their degree of comfort with potentially dangerous situations. Items were rated on a 5-point Likert scale, and scores can range from 0 to 80.
+Second, a six-item Suicide Capacity Scale (SCS-3) designed by our lab assessed three characteristics that we believe contribute to suicide capacity: Dispositional Capacity (i.e., long-standing pattern of low fear of pain or death), Acquired Capacity (i.e., fear of pain or death had decreased over time), and Practical Capacity (i.e., access to and knowledge of suicide methods). Items were rated on a 7-point Likert scale. Total scores for the SCS-3 could range from 0 to 36. The correlations of this measure and its three subscales with the ACSS are included in Table 1.
+RESULTS
+Descriptive statistics and intercorrelations for the key study variables are presented in Table 1. Twenty-one percent of the sample reported current ideation. Among
+THEORY OF SUICIDE
+123
+those participants reporting at least some ideation, the mean of the untransformed BSS score was 10.8 (SD = 6.9). Fourteen percent of participants reported at least one suicide attempt. Here we describe results addressing each of our key hypotheses regarding the development of suicide ideation and attempts.
+HYPOTHESIS 1: PAIN AND HOPELESSNESS INTERACT TO PREDICT SUICIDAL IDEATION
+Because many participants had scores of 0 on suicidal ideation, we transformed the variable by repeatedly talcing its square root (three times total) until skewness and kurtosis were reduced to acceptable levels. The transformed variable was used in all subsequent analyses. In addition, we centered the pain and hopelessness variables to facilitate interpretation of their interactive effect on suicidal ideation. These transformed/centered variables were utilized for subsequent regression analyses.
+First, we report the direct effects of pain and hopelessness on suicidal ideation. As expected, both pain (r = .55) and hopelessness (r = .57) exhibited robust correlations with suicidal ideation. Not surprisingly, pain and hopelessness also related strongly with one another (r = .63).
+Second, as a direct test of our first hypothesis, we examined whether pain and hopelessness interacted to predict suicidal ideation. The interaction term was significant (t = 6.35, p < .001), and the full model accounted for 41% of the variance in suicidal ideation. The interaction term itself explained an additional 3% of variance over and above the main effects. However, because the interaction term correlates strongly with both main effect terms, the 3% of unique variance added may not fully capture the interactive nature of pain and hopelessness in predicting suicidal ideation. Thus, to illustrate the potential clinical significance of this pattern, median splits were utilized to create low and high subgroups for pain and hopelessness. As can be seen in Figure 2, suicidal ideation is negligible in subgroups with (a) low pain and hopelessness or (b) either high pain or high hopelessness, but is substantially higher in the subgroup (c) reporting both high pain and high hopelessness. In addition, we examined the percentage of participants meeting an empirically derived cutoff for severe suicidal ideation (i.e., scores of 24 or above on the BSS; Cochrane-Brink, Lofchy, & Sakinofsky, 2000) who fell into each of these three subgroups. This high threshold for severe suicidal ideation was met by 12 participants in our sample; notably, all 12 fell into the subgroup high on both pain and hopelessness.
+Next, we examined whether the statistically significant interaction between pain and hopelessness would appear consistently across demographic subgroups. It did. The interaction was statistically reliable in both men (t = 4.28, p < .001) and women (t = 4.45,p < .001), as well as in participants ages 18-25 (t = 3.80, p < .001), 26-35 (t = 4.55,p < .001), and 36-70 (t = 2.31,p = .02).
+Finally, we wanted to evaluate our model of suicidal ideation in comparison to an established, well-researched model. We therefore also examined the variables hypothesized by the Interpersonal Theory of Suicide (Joiner, 2005; Van
+Orden et al., 2010) to explain suicidal ideation. This comparison model specifies that perceptions of low belongingness and high burdensomeness interact to explain suicidal ideation. In the current sample, belongingness and burdensomeness indeed interacted significantly to predict suicidal ideation (t = 4.6, p < .001); however, the full model including the interaction term accounted for only 30% of the variance in suicidal ideation—lower than the 41% reported earlier for our hypothesized model.
+HYPOTHESIS 2: CONNECTEDNESS PROTECTS AGAINST
+ESCALATION OF SUICIDAL IDEATION IN INDIVIDUALS WITH
+BOTH PAIN AND HOPELESSNESS
+Our theory hypothesizes that connectedness can foster a desire to live even among those with both pain and hopelessness, and thus protect against higher levels of ideation among this at-risk subgroup. We conducted two analyses to evaluate this hypothesis. First, we examined whether the relationship of connectedness to suicidal ideation is particularly strong in the subgroup of participants high on both pain and hopelessness. We utilized the belongingness scale of the INQ (Bryan, 2011; Van Orden et al., 2008) to assess connectedness. As predicted, there was a significant interaction between connectedness and pain-hopelessness status (t = 6.28, p < .001). In particular, the relationship of connectedness with suicidal ideation was r = .36 in the subgroup with both high pain and high hopelessness (n = 283), compared to just r = .14 in everyone else (n = 627).
+Second, we wanted to test the more specific hypothesis that suicidal ideation is buffered in individuals with combined pain and hopelessness when connect
+edness exceeds pain (i.e., when one’s connection to someone/something in life exceeds one’s pain). To do this, we standardized scores for pain and connectedness, and then subtracted connectedness scores from pain scores. Thus, positive scores indicated that pain exceeds connectedness, whereas negative scores indicated that connectedness exceeds pain. If our hypothesis is correct, this difference score should be a particularly powerful predictor of suicidal ideation in the combined pain and hopelessness subgroup, and less predictive of suicidal ideation in everyone else. As predicted, the interaction between the pain-connectedness difference score and group status (i.e., combined high pain and high hopelessness vs. everyone else) was statistically significant (t = 8.07,p < .001). In particular, the correlation of suicidal ideation with the pain-connectedness difference score was stronger (r = .47) for the high pain and high hopelessness subgroup compared to everyone else (r = .22).
+HYPOTHESIS 3: DISPOSITIONAL, ACQUIRED, AND PRACTICAL CONTRIBUTORS TO CAPACITY FOR SUICIDE PREDICT TRANSITION FROM IDEATION TO ATTEMPTS
+Because there was no preexisting measure of the three components of suicide capacity stipulated in our theory (dispositional, acquired, and practical), we developed our own measure for the purposes of this study: the Suicide Capacity Scale (SCS-3). To help verify the construct validity of this measure, we examined its correlation with the ACSS, an established measure of capability for suicide (Van Orden et al., 2008). The ACSS includes items relevant to the broad construct of suicide capability but does not specifically target or distinguish dispositional, acquired, and practical aspects. In support of our measure’s validity, its correlation with the ACSS was positive and large (r = .69, p < .001). In addition, all three subscales correlated positively with the ACSS: dispositional (r = .62), acquired (r = .59), and practical (r = .39) (all p s < .001).
+Next, as a direct test of the hypothesis, t tests were utilized to compare dispositional, acquired, and practical components of suicide capacity between (a) participants with histories of suicide ideation but not attempts (n = 246) and (b) participants with histories of suicide attempts (n = 127). All three contributors to suicide capacity differentiated the ideation and attempt subgroups: dispositional (Cohen’s d = .34,p < .001), acquired (d = .38,p < .001), and practical (d = .23, p < .02). In addition, total capacity (i.e., the sum of the three subscales) distinguished suicide ideators and attempters: d = .42, p < .001. Considering that the SCS-3 consists of just six items compared to 20 items for the ACSS, this effect size compares favorably with that produced when comparing the ACSS between ideators and attempters (d = .44). Finally, logistic regressions revealed that all three SCS-3 subscales maintain their associations with attempt history even when controlling for current suicidal ideation (p s < .001 for dispositional and acquired, p = .03 for practical).
+DISCUSSION
+Thomas Joiner’s Interpersonal Theory of Suicide spawned a new generation of suicide theories that utilize an ideation-to-action framework. These theories regard the development of suicidal ideation and the progression from ideation to attempts as distinct processes that require distinct explanations. The purpose of the present article was to describe a new ideation-to-action theory of suicide and report findings from an initial empirical evaluation of the theory. The primary tenets of the Three-Step Theory (3ST) are that (a) suicidal ideation develops due to a combination of pain and hopelessness, (b) connectedness is a key protective factor against escalating ideation in those high on both pain and hopelessness, and (c) progression from suicide ideation to attempts occurs when dispositional, acquired, and practical factors create sufficiently high capacity to face the pain and fear inherent in attempting to end one’s life. Findings from a large sample of United States adults provide relatively strong support for the theory, although it will be essential to replicate these findings and examine the theory in other populations.
+The first tenet, that suicide ideation is driven by the combination of pain and hopelessness, was strongly supported. Pain and hopelessness interacted to predict suicidal ideation, and this interaction replicated across different genders and age groups. Moreover, the model including pain, hopelessness, and their interaction explained more variability in suicidal ideation than a comparison model including belongingness, burdensomeness, and their interaction. Results are consistent not only with our theory, but also with recent research findings that suicide attempts are motivated by pain and hopelessness more than by any other factor (May & Klonsky, 2013). We also found evidence for the second tenet: that connectedness plays a particularly important protective role against suicidal ideation in those at greatest risk for ideation (i.e., in those high on both pain and hopelessness). In particular, connectedness, as well as the degree to which participants’ connectedness exceeded their pain, predicted lower suicidal ideation among those with combined pain and hopelessness, but relatedly minimally to suicidal ideation for everyone else.
+Taken together, the results described here support our theory of how pain, hopelessness, and disrupted connectedness work in concert to bring about suicidal ideation. It is useful to address how our theory is different from previous models emphasizing pain (Shneidman, 1993), hopelessness (Abramson et al., 2000), and connectedness (Joiner, 2005). One key difference from Shneidman and Abramson et al. is that we believe the combination of pain and hopelessness is what matters. The importance of the combination can be viewed in Figure 2, which shows that suicidal ideation is negligible both in subgroups with (a) neither pain nor hopelessness and (b) either pain or hopelessness; in contrast, the subgroup with combined pain and hopelessness is where elevated suicidal ideation can be found. A second key difference regards the role of connectedness, which is similar to the belongingness and burdensomeness constructs emphasized by Joiner (2005). Joiner suggests that suicidal ideation is driven by feelings of low belongingness and high burdensomeness, specifically when one feels hopeless that these feelings will change (Van
+Orden et al., 2010). While we believe that belongingness and burdensomeness can contribute to the pain and hopelessness that drive suicidal ideation, we do not believe they are necessary for pain, hopelessness, or suicidal ideation. Instead, we view connectedness as playing an important protective role in those at risk for strong/intense suicidal ideation due to pain and hopelessness. Finally, we recognize that hundreds of suicide correlates have been identified and that many have been conceptualized as important risk factors (American Association of Suicidology, 2013). Our theory does not dispute the relevance of these factors; rather, it offers a framework for understanding them. From our perspective, the relevance to suicide of any given correlate or risk factor can be understood in terms of its contributions to pain, hopelessness, connectedness, and/or suicide capacity.
+The third key tenet of our theory was also supported. This tenet proposes that dispositional, acquired, and practical contributors to suicide capacity explain the progression from suicide ideation to attempts. In the present study, subscales measuring each of these three contributors were found to be higher in those with a history of attempted suicide than in those with a history of suicide ideation only. Associations were similar in magnitude for each of the contributors to suicide capacity. This pattern is consistent with Joiner’s (2005) emphasis on acquired capability, but also supports the relevance of dispositional and practical contributors to suicide capacity. At the same time, it is worth noting that the associations between suicide capacity and suicide attempt history were relatively small, and thus explain only a small amount of variability in the progression from suicide ideation to attempts. It will be critical for future research to continue to clarify how and when suicidal ideation leads to suicidal behavior.
+Our theory has potentially important implications for suicide prevention and intervention. Our theory suggests specific targets for reducing suicide risk. If we want to reduce suicide risk, we can (a) reduce pain, (b) increase hope, (c) improve connectedness, and/or (d) reduce capacity. The manner in which these variables are targeted cannot be stipulated because prevention and intervention can look very different depending on setting (e.g., school vs. treatment center), age (e.g., adolescents vs. older adults), and level of intervention (e.g., individual psychotherapy vs. government policy) among many other factors. For example, all four might be useful targets at the level of the individual (e.g., psychotherapy), and the fourth might also be a useful target at the level of government policy (e.g., bridge barriers, increased controls on firearms) (see Miller, Azrael, & Barber, 2012). These considerations regarding context notwithstanding, we believe that any effort to prevent or treat suicidality should target one or more of these four factors and will succeed to the extent that one or more of these four factors is changed for the better.
+This article represents an initial description and empirical evaluation of the 3ST. There are several important limitations and future directions. First, we examined our theory in a nonclinical population. Even though we oversampled for participants with histories of suicide ideation and attempts, our sample likely had less severe psychopathology and suicidal ideation as compared to an outpatient or inpatient sample. Future research should evaluate our theory in clinical settings. Sec
+ond, our study focused on adults, and primarily on adults between the ages of 18 and 35. Future research should examine the relevance of our theory in adolescent and older adult populations. Third, our study relied on self-report measures. We utilized measures with good psychometric properties, but there are certain biases inherent in self-report methods, and future research should determine whether results replicate when using interview and behavioral measures. Fourth, the data we obtained are cross-sectional. Longitudinal data will be important for determining whether the variables we emphasize are best conceptualized as contributors to, rather than consequences of, suicide ideation and attempts. Longitudinal data will be especially useful for examining whether higher suicide capacity predicts transition to suicidal behavior among those with suicidal ideation. In addition, longitudinal and diary methodologies can help clarify the time scales at which the factors we emphasize lead to suicidal ideation and attempts. For example, must pain and hopelessness develop gradually over time before they lead to suicidal ideation, or can they develop and lead to suicidal ideation in time frames as short as a week, a day, or even an hour? As recently emphasized by Glenn and Nock (2014), it will be important to increase the methodological creativity and sophistication of our studies in order to obtain a more accurate, nuanced, and actionable understanding of suicide.
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+O’Connor, R. C. (2011). Towards an integrated motivational-volitional model of suicidal behaviour. In R. C. O’Connor, S. Platt, & J. Gordon (Eds.), International handbook of suicide prevention: Research, policy, and practice
+(pp. 181-198). Malden, MA: John Wiley & Sons.
+Ratcliffe, G. E., Enns, M. W., Belik, S. L., & Sa-reen, J. (2008). Chronic pain conditions and suicidal ideation and suicidal attempts: An epidemiological perspective. Clinical Journal of Pain, 24, 204-210.
+Shneidman, E. S. (1985). Definition of suicide. New York, NY: Wiley.
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+Smith, A. R., Ribeiro, J., Mikolajewski, A., Taylor, J., Joiner, T., & Iacono, W. G. (2012). An examination of environmental and genetic contributions to the determinants of suicidal behavior among male twins. Psychiatry Research, 197, 60-65.
+Swanson, S. P., Roberts, L. J., & Chapman, M. D. (2003). Are anaesthetists prone to suicide? A review of rates and risk factors. Anaesthesia and Intensive Care, 31, 434-445.
+Tanner, B. A., & Zeiler, M. (1975). Punishment of self-injurious behavior using aromatic ammonia as the aversive stimulus. Journal of Applied Behavior Analysis, 8, 53-57.
+Van Orden, K. A., Witte, T. K., Cukrowicz, K. C., Braithwaite, S. R., Selby, E. A., & Joiner, T. E. (2010). The interpersonal theory of suicide. Psychological Review, 117, 575-600.
+Van Orden, K. A., Witte, T. K., Gordon, K. H., Bender, T W, & Joiner, T E. (2008). Suicidal desire and the capability for suicide: Tests of the interpersonal psychological theory of suicidal behavior among adults. Journal of Consulting and Clinical Psychology, 76, 72-83.
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+1.	Background
+Suicide is one of the major causes of premature death worldwide. Up to a half of patients that die by suicide interact with the healthcare system during a month before their death, and one third do it during the last week (Ahmedani et al. 2019). Contacts with the healthcare system, therefore, represent precious opportunities for suicide prevention. In an attempt to identify and target individuals at the highest risk, many emergency departments (EDs) have implemented suicide risk screenings. However, their efficacy has been reported to be negligible (Carter et al. 2017). While major risk factors, such as early life adversity, depression, and psychological pain (Pompili et al. 2014; Ducasse et al. 2018), as well as specific
+neuropsychological phenomena, such as impulsivity or sensory processing deficits (Swann et al. 2005; Engel-Yeger et al. 2016), help to define at-risk individuals, their interaction with each other is complex, and we are currently unable to predict which patients and when will engage in SB again.
+As a general approach, patients in a suicidal crisis are hospitalised and receive non-specific interventions to alleviate general distress, and the treatment for psychiatric comorbidity, if present. However, medical admission has been repeatedly reported to fail to reduce the risk of subsequent SB (Hjorthojet al. 2014). Meanwhile, the treatment of comorbid depression is essential for long-term suicide prevention, but antidepressants are sometimes feared to increase the shortterm risk of suicide (Courtet and Lopez-Castroman
+2017). Some interventions, entailing care coordination, safety planning, brief therapeutic interventions, and brief contact interventions, reduce the long-term risk of subsequent SA risk, measured several months to a year, and increase care linkage in individuals at risk of suicide (Doupnik et al. 2020). However, many patients engage in SB shortly after their previous attempts (Rhodes et al. 2019), failing to benefit from interventions with long-term goals. Indeed, a major part of suicide attempters did it before the first call at one month in a study measuring the efficacy of a brief contact intervention (Messiah et al. 2019). Many patients also could not be contacted - a common shortcoming of long-term interventions.
+Since the systematic review published in 2016 that identified the treatment of depression as one of the few medical evidence-based strategies for suicide prevention (Zalsman et al. 2016), a number of studies have suggested the potential interest in other types of interventions that are focussed on suicidal ideation (SI) and could be immediately deliverable in a suicidal emergency context, which often represents a window of a certain degree of ambivalence regarding the suicide. The present review aims to assess what specific healthcare system-based SI-targeted interventions, irrespective of their nature (psychological, social or biological), mode of action, or the duration that produced long-term benefits, reduce the risk of suicidal behaviour (SB) during one week and one month after the interaction with the mental healthcare system, representing the periods of extreme suicide risk (Chung et al. 2019). Ultimately, such interventions could be proposed to an acutely suicidal patient presenting at the healthcare institution to keep him or her alive, while the full effects of interventions with long-term goals (Doupnik et al. 2020) kick in.
+2.	Materials and methods
+While conducting this review, we adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Moher et al. 2009) and prospectively registered the review protocol in the PROSPERO database (ID164950).
+2.1.	Studies, participants and interventions
+We retrieved randomised controlled trials (RCTs) reporting findings in individuals of any age or gender with documented baseline SI and/or SA within the past three months, irrespective of comorbidity. Study intervention had to be directed at the suicidal
+individual and initiated in healthcare settings (inpatient, outpatient or emergency department). Studies comparing active interventions among themselves or against placebo were included. Studies could measure the efficacy of interventions of any duration, but had to report on at least one suicide-related outcome (SI, SA, suicide, or suicidality) within one month after the initial assessment (this could be a final or an intermediate outcome). Trials defined as pilot or feasibility were included, but it was taken into consideration when assessing the evidence. Exclusion criteria were: (1) at least part of subjects was not suicidal at baseline; (2) self-harm outcomes without documented suicidal intent or ideation; (3) trials that targeted communities or clinicians.
+2.2.	Outcome measures
+While any suicide-related outcome was eligible for inclusion, the primary outcome was the difference in SI between the active intervention and the comparator in included studies. Any of the validated measures assessing SI or a closely related construct (suicidality) were eligible. Immediate (within one week since the baseline assessment) and short-term (between the eight-day and one month) outcomes are described.
+2.3.	Search strategy
+A comprehensive search of mental and general health electronic databases (MEDLINE (EBSCO), Academic Search Complete (EBSCO), PsycARTICLES (EBSCO), the Cochrane library, and PubMed) for studies published between 01 March 2000 and 01 March 2020 (to cover the most recent advancements) was conducted, with an update on 15 September 2020. Subject headings, such as MeSH and keyword terms, were used to identify relevant articles. In order to reach the maximum amount of relevant studies, two subsequent searches were conducted. Firstly, one reviewer (AL) performed a general survey of putative interventions with broad search terms suicid* and self-harm* combined with terms interv*, prevent*, treat*, therap*. All interventions, targeted at suicidal ideation (SI), or risk of suicidal acts (attempt or suicide) were noted. Subsequently, two reviewers (AL and RS) performed an extended search, combining the subject heading suicid*, general terms and the names of specific interventions identified in the first stage was carried out (see Supplement 1). The reference lists of the included studies and relevant review articles and opinion papers were hand-searched for relevant studies.
+Only RCTs published in peer-reviewed journals were included. No restrictions were imposed on study country or language. After screening via title and abstract, and duplicate removal, when studies were deemed as possibly eligible, full texts were retrieved. Finally, studies were included after the full-text review following the pre-determined inclusion criteria. Any disputes were solved through a discussion with the senior author (PC).
+2.4.	Data extraction and quality assessment
+The following data from the selected studies were recorded onto a purposely designed spreadsheet by one reviewer (AL): (1) study author, country and date; (2) randomised sample size; (3) participant characteristics (age range, gender balance, the status of baseline SI or SA history, study setting, the modality of recruitment, comorbidity); (4) intervention details (modality and duration, sponsorship); (5) comparison group; (6) outcome measures and assessment points; (7) results - direction and effect size, if reported; (8) quality assessment; (9) other relevant information. If reported, intention-to-treat data were used. Data extraction results were discussed between reviewers and confirmed by consensus from all authors. We verified that data in trials were not overlapping.
+Only RCTs were included in this review to meet the review goal of creating a list of evidence-based interventions, for which RCT-based data is necessary. The risk of bias was evaluated using the Cochrane Risk of Bias 2 tool for randomised trials (Sterne et al. 2019). The overall risk of bias was classified into three groups (low, some concerns or high) based on assessments in five domains: randomisation process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported results. Studies with some concerns in one or two domains were classified as having some concerns in their overall risk of bias assessment, and those with some concerns in at least three domains or a high risk of bias in at least one domain were classified as having a high overall risk of bias.
+2.5.	Data synthesis
+The considerable between-study heterogeneity in the intervention modality, sample sizes, and outcome measures, the population studied, and the statistics used hampered the ability to perform a meta-analysis. Therefore, a narrative synthesis of aggregated and individual study findings, focussing on SI, which
+emerged as the primary suicide-related outcome described in the included studies, is presented. The data synthesis describes patterns across the studies in terms of direction and size (where available) of the efficacy of different interventions.
+3.	Results
+3.1.	Search results
+The comprehensive electronic search strategy yielded 5093 potentially relevant studies, and 45 studies were added after the reference list hand-search (Figure 1, the PRISMA flow diagram). After removing duplicates and screening titles, abstracts, and full-texts, 34 nonoverlapping studies, published between 2000 and 2020, contributed to the final data synthesis.
+3.2.	Characteristics of included studies
+The vast majority of studies (24 out of 34) were based in the United States. Four studies were based on adolescent samples (Pineda and Dadds 2013; Kennard et al. 2018; Diamond et al. 2019; Wharff et al. 2019). Others recruited adults only, with an average age ranging from 25 to 45 years. Female participants constituted the majority of subjects in most studies. Nine male-dominant studies included an only-male study with patients with concurrent opioid addiction (Ahmadi et al. 2018), a study in patients hospitalised in trauma centre after SA (O’Connor et al. 2015), and seven studies based in military medical centres (George et al. 2014; Burger et al. 2016; Bryan et al. 2017; Bush et al. 2017; Jobes et al. 2017; Johnson et al. 2018; LaCroix et al. 2018), and two studies measuring the efficacy of ketamine in outpatients with treatment-resistant depression (TRD) (Ionescu et al. 2019; Phillips et al. 2019). Eleven studies were carried out explicitly in inpatient or emergency department settings, while in others, the treatment was delivered on an outpatient or mixed basis. In addition to SI or SA, 17 studies included only patients with comorbidities, represented by mood and anxiety disorders, posttraumatic stress disorder, and opioid dependence. See Supplement 2 for a more detailed description of the characteristics of the included studies.
+The studies reported the efficacy of pharmacological (n = 17), psychosocial (n = 16), and neuromodulation intervention (n = 1). Twenty-four studies (73%) measured the SI either with the clinician-rated Scale for Suicidal Ideation (Beck et al. 1979) or the self-rated Beck Scale for Suicide Ideation (Beck et al. 1988). Seven and 13 out of 34 included studies reported SI
+outcomes within one week and between the eight-day and one month after the initial assessment, respectively, while 13 studies reported SI outcomes within both time intervals. Immediate and short-term outcomes will be presented separately and later discussed together.
+3.3.	Risk of bias assessment
+The quality of the 34 included RCTs was moderate to low, with the vast majority of studies having at least some concerns in their risk of bias measurement (see Supplements 3 and 4). Difficulties in blinding in psychological or dissociation-inducing interventions contributed to the increased risk of bias. Problems emerged from high rates of attrition and failure to describe the missing data, as well as the failure to publish study protocols. Another major limitation was the fact that most studies were pilot or reported SI or SA as a non-primary outcome. Only six studies justified their sample size by performing power calculations to detect meaningful differences in SI (Yovell et al. 2016; Grunebaum et al. 2018; Diamond et al. 2019; McCall et al. 2019; Fu et al. 2020; Ionescu et al. 2020), and one study performed power calculations to detect
+differences in SA within six months (Bryan et al. 2017), probably yielding a sufficient sample size to detect SI differences at one month. One study performed power calculation for annual incidence of SA (Kawanishi et al. 2014), while we included the intermediate one-month outcome, for which the study was likely underpowered.
+3.4.	Antisuicidal efficacy
+From the 34 studies, 20 RCTs, including 1552 subjects, reported on SI outcome within one week from the baseline evaluation, and 27 RCTs, including 3108 subjects, reported on suicide-related outcomes starting from the eight-day up until 30days from the initial assessment. Results are summarised in Table 1.
+3.4.1.	Immediate antisuicidal efficacy
+Ten trials, including 735 subjects, measure the antisui-cidal efficacy of a subanaesthetic dose of ketamine or its enantiomer esketamine. They reported comparisons regarding SI from hours to a week. Only three studies were neither pilot nor secondary analyses (Grunebaum et al. 2018; Fu et al. 2020; Ionescu et al. 2020).
+Four studies measured SI within the first few hours after the administration of ketamine or a comparator. One study (n = 41) reported dramatic decreases in SI following a single ketamine 0.2mg/kg infusion in soldiers with SI within four hours, but insufficient sample size hindered the comparison with saline (Burger et al. 2016). A small (n = 18), prematurely terminated trial in adults with clinically significant SI found a higher reduction in SI 1.5-3 hours after a single infusion of 0.2 mg/kg ketamine vs saline, but not earlier or later (Domany et al. 2019). A secondary analysis of a crossover trial (n = 37), designed to measure ketamine anti-depressive effects, reported significantly lower SI at 2 hours after a single 0.5 mg/kg ketamine infusion compared to midazolam 30 । g/kg in patients with TRD and SI (Phillips et al. 2019). Meanwhile, a study in adult outpatients with TRD and chronic SI (n = 26) found no differences in SI between a single infusion of 0.2 mg/kg ketamine and sale groups 4 hours after any of the twice-per-week administrations (Ionescu et al. 2019).
+Five studies measured SI after 24 hours and later. A well-powered study in 80 patients with major depressive disorder (MDD) and SI found a higher SI reduction in the ketamine 0.5 mg/kg group compared to midazolam 20 । g/kg after 24 hours, with a moderate effect size (Grunebaum et al. 2018). However, this study recruited participants via the Internet or social media. Four other studies with outcome measurement at day one reported no difference between 0.5 mg/kg ketamine and 20-45 । g/kg midazolam (Murrough et al. 2015; Grunebaum et al. 2017; Phillips et al. 2019), or 0.2 mg/kg ketamine and saline (Domany et al. 2019). However, the secondary analysis in TRD patients found a significant superiority of 0.5 mg/kg ketamine single infusion over 30| g/kg midazolam on day seven postinfusion (Phillips et al. 2019), and one trial (n = 24) in adults with mood and anxiety disorders reported a significant difference at 48-hour, but not at earlier or later time points spread over one week (Murrough et al. 2015).
+Three studies compared SI reduction following the administration of 84 mg intranasal esketamine and a bitter placebo in adults with MDD, assessed as being at imminent risk of suicide. A second-phase trial reported a moderate effect size for esketamine in reducing SI at four hours, but not 24 hours after administration (Canuso et al. 2018). Two subsequent third-phase RCTs found no superiority esketamine over placebo nor at four, neither at 24 hours after the administration (Fu et al. 2020; Ionescu et al. 2020).
+Seven trials reported the one-week antisuicidal effects of other medications. A pilot RCT in adults with MDD and current SI or past SA found no overall difference between paroxetine (25 mg daily, with a dose increase to 37.5 mg after two weeks) and bupropion (150 mg and 300 mg accordingly), but paroxetine was more effective than bupropion in patients with the most severe baseline SI (Grunebaum et al. 2012). One inconclusive RCT in males with opioid dependence, MDD and SI found no difference in SI between different doses of buprenorphine (96 mg vs 64 mg or 32 mg) during opioid withdrawal (Ahmadi et al. 2018). A larger RCT found no difference between an ultralow dose of buprenorphine (0.1-0.8 mg/d) sublingually and placebo in adults with clinically significant SI after the first week of use (Yovell et al. 2016). Studies also reported no difference in SI after one week of 300 mg lithium carbonate augmentation to citalopram treatment (Khan et al. 2011), daily prazosin (1-10 mg/day, incremental titration) (McCall et al. 2018), 6.25 -12.5 mg/d zolpidem (McCall et al. 2019), and 0.25 - 2 mg/d risperidone (Reeves et al. 2008), compared to placebo.
+Among psychological intervention studies, only one RCT (Ducasse et al. 2019), comparing the efficacy of the daily gratitude diary and food diary in adult inpatients admitted for SI or SA, was deemed as having sufficient power to detect meaningful differences in SI. Another slightly underpowered study compared the efficacy of a single 60-90 min family-based crisis intervention session to treatment as usual in suicidal adolescents (Wharff et al. 2019). Both studies reported no difference between the groups. Finally, one pilot RCT reported no difference between the high dose left prefrontal repetitive transcranial magnetic stimulation (rTMS) and sham rTMS (nine sessions over three days) on SI immediately after the intervention in depressed adults with SI or a recent SA, and a comorbid post-traumatic stress disorder or head trauma (George et al. 2014).
+3.5.	Short-term antisuicidal efficacy
+Thirteen RCTs compared the two-to-four weeks efficacy of pharmacological interventions. None of ketamine or esketamine trials found a difference between ketamine and placebo regarding SI at later than one-week assessments after a single administration (Burger et al. 2016; Canuso et al. 2018; Domany et al. 2019) or the start of the twice-per-week administration schedule (Ionescu et al. 2019; Fu et al. 2020; Ionescu et al. 2020). One study reported no difference in SI after
+10 @ A. LENGVENYTE ET AL.
+two weeks from a single administration of different dosages of buprenorphine in suicidal males with opioid dependence and MDD (Ahmadi et al. 2018). Meanwhile, in a study in adults with clinically significant SI, a daily ultra-low dose (0.1-0.8 mg/d) of buprenorphine was more effective in reducing SI compared to placebo both at weeks two and four, but not at week three (Yovell et al. 2016). One-month results in RCTs, evaluating the efficacy lithium carbonate augmentation, prazosin, zolpidem, risperidone, and paroxetine remained consistent with the one-week results (Reeves et al. 2008; Khan et al. 2011; Grunebaum et al. 2012; McCall et al. 2018, 2019).
+Five studies evaluated the efficacy of psychosocial inpatient interventions, ranging from one to five sessions. A well-powered (N = 97) three-arm study in military members presenting at ED with SI or after SA found that a crisis response plan was more effective in reducing SI than a standard contract for safety, with moderate effect size (Bryan et al. 2017). Other studies found no effect of a single of brief teachable moment intervention (O’Connor et al. 2015), a single familybased crisis intervention for adolescents (Wharff et al. 2019), six cognitive behavioural therapy (CBT) sessions (LaCroix et al. 2018), and 2-5 sessions of ‘As Soon As Possible’, which includes emotional regulation intervention, safety planning, and an app (Kennard et al. 2018) interventions.
+Six studies reported one-month results from trials measuring the efficacy of weekly or twice-per-week outpatient psychotherapy sessions (Guthrie et al. 2001; Brown et al. 2005; Pineda and Dadds 2013; Sinniah et al. 2017; Diamond et al. 2019; Johnson et al. 2019). Two studies found no difference regarding SI between weekly CBT (Brown et al. 2005) and psychodynamic interpersonal therapy (Guthrie et al. 2001), compared to usual care. A study on the efficacy of the twice-a-week individual CBT in adults with MDD and SI (n = 69) reported a significant SI reduction in both active and usual care groups, with no apparent difference between them. However, a formal comparison between the two groups was not made at this timepoint (Sinniah et al. 2017). In a study in 134 veterans with a recent SA, a weekly Suicide Status Form-assessment group therapy was not superior to the standard assessment group therapy (Johnson et al. 2019). Two studies on the efficacy of weekly family-based psychotherapeutic interventions in suicidal adolescents reported no statistically significant differences between the active and the comparator groups (Pineda and Dadds 2013; Diamond et al. 2019), but there was a tendency for more improvement in the
+rating of a questionnaire that incorporates SI, SA, suicidal intent and self-harming behaviours, in one of the studies (Pineda and Dadds 2013).
+Four studies evaluated other suicide-specific therapeutic frameworks. An ad-hoc analysis from a large trial (n = 914) in adults with psychiatric diagnoses admitted to ED after SA reported a lower risk of the first recurrent suicidal behaviour (SA or suicide) during the first-month postdischarge in those that received assertive case management, as compared to usual care (Kawanishi et al. 2014). There was no evidence of a treatment effect for other suicide-specific programs, including a mobile crisis team in the community (Currier et al. 2010), Collaborative Assessment and Management (Jobes et al. 2017), and adding virtual enhancement to the usual care (Bush et al. 2017) on SI at two to four-week assessments.
+3.6.	External validity
+All pharmacological and neuromodulation intervention studies were carried out in adults. All patients in ketamine and esketamine trials had comorbid mood or anxiety disorders. Studies with significant positive results for ketamine included adults with MDD and clinically significant SI (Grunebaum et al. 2018; Domany et al. 2019; Phillips et al. 2019). Meanwhile, studies with negative results included patients with a wider range of diagnoses, including bipolar disorder and anxiety disorders. All esketamine trials included MDD patients with SI with intent, with no significant difference in patient populations between trials (Canuso et al. 2018; Fu et al. 2020; Ionescu et al. 2020). Buprenorphine was associated with SI reduction in a trial that included patients with SI without substance use problems (Yovell et al. 2016), while it was negative in a complex all-male sample with opioid dependence, MDD, and SI (Ahmadi et al. 2018). Among psychosocial interventions, four studies in adolescents reported negative results (Pineda and Dadds 2013; Kennard et al. 2018; Diamond et al. 2019; Wharff et al. 2019). In adults, positive results were reported in two studies in patients following a suicidal crisis for which they contacted a healthcare facility (Kawanishi et al. 2014; Bryan et al. 2017).
+4.	Discussion
+This review summarises the evidence regarding the immediate and short-term outcomes of SI ir SB-targeted interventions for with SI or a recent history of SA initiated in healthcare settings, covering the studies
+published over the past two decades. Most of the 34 included studies measured either clinician-rated or self-rated SI, mostly with clinician-rated (Beck et al. 1979) or self-rated (Beck et al. 1988) version the Scale for Suicide Ideation. This scale is one of the few tools with proven predictive value for suicide (Brown et al. 2000). However, SI tends to fluctuate and is regarded as a poor predictor of suicide (McHugh et al. 2019), raising the question of the long-term utility of interventions that reduce SI. Indeed, there was a dramatic decrease in SI irrespective of allocation to the active or comparator intervention in most studies. However, SI is a factor sine-qua-non of any SB, and the patient discharge from healthcare facilities after a crisis often depends on it. One possibility is that having effective interventions that surpass the fluctuations inherent to SI might increase the reliability of suicide prediction. Rapid SI reduction might also increase adherence to other interventions.
+4.	1. Ketamine and esketamine
+Five out of nine studies reporting significant superiority of the active intervention over the comparator evaluated the efficacy of ketamine or esketamine. The only sufficiently powered trial that reported large effect size at 24 hours post-infusion recruited patients via the Internet or social media (Grunebaum et al. 2018), raising concerns whether these results translate to real-world effectiveness. Variation in timepoints of reported efficacy in other studies, ranging from shortlived efficacy up to three hours (Domany et al. 2019) to statistically significant difference only at 48 hours (Murrough et al. 2015), further undermines the understanding of ketamine efficacy timeline. The only study reporting ketamine superiority at seven days was a secondary analysis of a cross-over study tailored to measure depressive symptoms instead of SI (Phillips et al. 2019), and there was a significant carryover of the decrease in SI in subjects that received ketamine before midazolam. Ketamine did not outperform placebo at any timepoint in one study (Ionescu et al. 2019), and one study was deemed too small to make meaningful comparisons (Burger et al. 2016). Noncomparative data seems more promising, as many studies reported a substantial SI reduction following ketamine administration (Ballard et al. 2018; Vidal et al. 2018; Zhan et al. 2019). However, SI tends to decrease dramatically irrespective of intervention, undermining the clinical value of the non-compara-tive studies.
+Notably, all ketamine trials included patients with comorbid psychiatric conditions. Most trials focussed on depression rather than SI or SB and recruited patients accordingly. A patient-level meta-analysis has reported the antisuicidal efficacy of a single dose of ketamine, lasting up to 7 days (Wilkinson et al. 2018). However, only one study (Murrough et al. 2015) was eligible for inclusion in the present review, as other studies either included patients irrespective of their baseline SI or included open-label data. To date, studies in patients with MDD irrespective of SI seem to provide more promising results than studies that include only acutely suicidal patients - thanks to either bias of selective reporting or truly more benefit for patients that are depressed, but not acutely suicidal.
+Results from a phase-two esketamine trial reporting lower SI within three hours after an intranasal administration (Canuso et al. 2018) were later not confirmed by two well-powered phase-three studies (Fu et al. 2020; Ionescu et al. 2020). In addition to random effects, increased patient heterogeneity and standard-of-care associated with larger studies contribute to such failures to replicate early trials’ findings. In line with antidepressant studies (Cipriani et al. 2018), excitement with new interventions should be measured due to the ‘novelty’ biases. Moreover, in the third-stage esketamine trial (Fu et al. 2020) authors reported no difference between groups on elaborate SI measurement tools, but did find lower SI score on a depression rating scale. In initial ketamine depression trials, this was frequently the only suicide-related outcome reported (Wilkinson et al. 2018).
+In sum, results from this review corroborate findings from a previous meta-analysis that ketamine, and not esketamine, might have a short-lived benefit for reducing SI in adults with psychiatric disorders (Witt et al. 2020), yet showing that these findings are less strong in patients with baseline SI or a recent SA history.
+4.2.	Other new-old drugs - undeservedly forgotten?
+Two out of seven studies measuring the efficacy of non-ketamine pharmacological agents reported positive results. One pilot study reported paroxetine benefit over bupropion only in subjects with the most severe SI (Grunebaum et al. 2012). Inline, a large RCT in depressed subjects irrespective of baseline SI (not included in the present review) reported that SI decreases within a few weeks of escitalopram use in most patients with severe baseline SI (Madsen et al.
+12 @ A. LENGVENYTE ET AL.
+2019). Though selective serotonin reabsorption inhibitors (SSRI) have been infamously associated with short-term suicide risk, this association seems to disappear after adjustment for confounding by indication (Dragioti et al. 2019). The present findings further support the claim that benefits might outweigh risks.
+One study reported the superiority of daily ultra-low-dose buprenorphine, which modulates opioid system, over placebo at two- and four-week assessments, but not earlier (Yovell et al. 2016). Buprenorphine was also associated with a greater reduction in psychological pain, which is associated with SB (Ducasse et al. 2018). Inversely, one preliminary study did not differentiate between different dosages of buprenorphine regarding SI within three days post-infusion (Ahmadi et al. 2018). However, this study included a highly complex only-male sample with opioid dependence. Chronic opioid use causes |-opioid receptor codegradation with d-opioid receptors, resulting in analgesic tolerance (Bao et al. 2018), which could have undermined the benefits of buprenorphine. Larger trials are needed to see whether targeting the delicate opioid system is plausible.
+4.2.1.	Short-term effects of psychosocial interventions
+None of six studies measuring the efficacy of psychotherapy demonstrated definite short-term efficacy (Guthrie et al. 2001; Brown et al. 2005; Pineda and Dadds 2013; Sinniah et al. 2017; Diamond et al. 2019; Johnson et al. 2019). While two studies suggested a tendency for the benefit of psychotherapy over usual care (Pineda and Dadds 2013; Sinniah et al. 2017), one study reported more severe SI in the CBT group (LaCroix et al. 2018). This failure to detect psychotherapy benefits might stem from the generally low adherence of suicidal patients to psychological RCTs and the fact that four-week assessment was generally an intermediate outcome in already underpowered studies. However, studies on short-term psychological interventions, including one well-powered RCT (Ducasse et al. 2019) and three preliminary studies (O’Connor et al. 2017; Kennard et al. 2018; Wharff et al. 2019) also failed to demonstrate the difference from the comparator. The psychological interventions’ failure to provide rapid relief might stem from a generally reduced brain plasticity of suicidal individuals (Dwivedi 2010). Though psychotherapy is itself associated with brain connectivity changes (Marwood et al. 2018) and has been demonstrated to reduce self-harm and SA risk in adolescents (Iyengar et al. 2018), the timeframe is generally much more protracted than for pharmacological interventions.
+Over the years, several interventions were explicitly created for suicide prevention after the crisis. In one RCT, a specific Collaborative Assessment and Management program, which incorporates assessment, treatment planning and tracking, was not effective at one-month assessment but showed some efficacy at three months (Jobes et al. 2017). Several RCTs not included in this review have also demonstrated longterm efficacy of the safety planning with active outreach (Stanley et al. 2018) and a volitional help sheet (Armitage et al. 2016). An RTC in military members, included in the current review, reported the superiority of a crisis response plan compared to a contract for safety for reducing SI at one-month follow-up (Bryan et al. 2017), suggesting that the benefit might be fast-onset. However, the difference lost its significance after controlling for the baseline SI (the study was underpowered to find small and moderate differences).
+There is compelling evidence for the long-term efficacy of the active non-intrusive contact with individuals after their initial assessment (Miller et al. 2017; Vaiva et al. 2018; Fossi Djembi et al. 2020). However, we found only two studies reporting on the shortterm efficacy of suicide-specific programs after the presentation to ED. One study found no benefit from the mobile crisis team over routine outpatient care (Currier et al. 2010). Meanwhile, a well-powered RCT reported that adults with axis I diagnosis included in the assertive outreach program after SA were less likely to engage in SB within the first month than those who received usual care (Kawanishi et al. 2014). Assertive outreach, however, demonstrated no benefit after 18 months on average, suggesting that it may provide initial support before long-term interventions are implemented.
+5.	Limitations
+The present review should be interpreted with several limitations in mind. Most RCTs were rated as having a moderate to high risk of bias, which could be partly inherent to the nature of the evaluated interventions. Only several studies reported on the blinding process evaluation and had detailed study protocols published. High rates of attrition also hinder the confidence in the presented results. We cannot entirely exclude detection, performance, publication, and ‘novelty’ biases. The vast majority of studies were pilot or preliminary. The heterogeneity in outcome measurements hindered generalisability. Most studies measured SI, which might not be the best indicator of
+future death by suicide. Moreover, most studies excluded non-adherent subjects that might have worse outcomes. We excluded studies with patients without baseline SI or recent SA history to avoid ‘data fishing’, possibly missing some good-quality evidence. Only studies where subjects had some contact with a healthcare institution were included. However, many people who die by suicide have either not been in contact with mental health care or have not disclosed their SI (van Spijker et al. 2010). Finally, interventions with short-term effects could fail to benefit patients long-term and vice versa. The present review focussed only on the short-term outcomes, and long-term efficacy patterns should not be assumed from the present review.
+6.	Conclusions and future directions
+This is the first review attempting to summarise the evidence regarding the immediate and short-term efficacy of the interventions for patients in suicidal crisis. Taken together, there is some preliminary evidence that ketamine reduces SI during the first week after the treatment initiation in depressed adult patients with baseline SI or after SA. Single studies report the one-month efficacy of the daily use of a sublingual ultra-low dose of buprenorphine, the crisis response plan, and assertive case management., as well as both one-week and one-month benefits of initiating a standard regimen of paroxetine.
+Well-designed replication studies are needed to draw final conclusions for all mentioned interventions. Studies on interventions with demonstrated long-term benefits are encouraged to report on more immediate results, as such reporting may offer evidence for clinicians managing patient care on-the-spot while also planning long-term care. Given the severity of the condition in question and the absence of evidencebased interventions with proven short-term efficacy, ketamine should be further evaluated as a putative ‘band-aid’ intervention to rapidly alleviate SI. A crisis response plan and assertive case management might be proposed due to their proposed relatively rapid onset of benefit compared to other psychosocial interventions, preferably in research settings that would report further benefits and risks. It is early to propose buprenorphine and paroxetine as evidence-based rapid antisuicidal agents, especially considering their putative acute side effects, such as agitation, fatigue or gastrointestinal effects (Grunebaum et al. 2012; Yovell et al. 2016). However, their potential short-term antisuicidal benefits should be considered when
+weighing their risk-benefit ratios. These results do not negate the need to rapidly initiate interventions (same or different) with proven long-term benefit.
+14 @ A. LENGVENYTE ET AL.
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+1.	Introduction
+Bipolar Disorder (BD) is a chronic and recurrent mental illness with a 3%—5% lifetime prevalence (Cerimele et al., 2014; Merikangas et al., 2007) and is considered one of the leading causes of years of disability adjusted life years (DALYs) as well as years lived with a disability (YLDs) in young adults (Murray and Lopez,
+1997). Alongside mood and cognitive deficits (Bora et al., 2009), BD is associated with a high incidence of medical conditions such as overweight/obesity, type 2 diabetes (T2D), cardiovascular disease (CVD), and stroke (Galvez et al., 2014; Mcintyre et al., 2005; McIntyre et al., 2004; Prieto et al., 2014; Sharma et al., 2014; Sylvia et al., 2015). Furthermore, these medical conditions might negatively impact the course of the illness. For example, comorbid T2D is associated with an increased risk of recurrent episodes, frequent hospitalizations, suicidality, and poor response to conventional mood stabilizers in BD (Calkin et al., 2009; Gomes et al., 2010; Kim et al., 2009). Obesity appears to be a risk factor for relapse to depression as shown by the high number of obese patients experiencing a depressive recurrence (Fagiolini et al., 2003). In BD, suboptimal dietary patterns (Kilbourne et al., 2007; Sylvia
+et al., 2014, 2011) and physical inactivity have been correlated with low self-efficacy and high incidence of medical comorbidities (Vancampfort et al., 2013).
+Unhealthy lifestyle habits such as smoking, substance/alcohol misuse, poor dietary choices, and sedentary life (Kemp et al., 2010; Sylvia et al., 2011) contribute to the development and severity of the physical ailments and clinical symptoms in BD (Sylvia et al., 2013a). It becomes apparent that these maladaptive behaviors contribute to poor health outcomes and reduce cost-effectiveness of therapeutic interventions in BD (Elmslie et al., 2001; Fagiolini et al., 2008; Hong et al., 2011; Sylvia et al., 2013b).
+Current literature suggests that a better understanding of the attitudes and beliefs of BD patients towards food and exercise would assist health professionals in developing better targeted psychosocial interventions that induce lifestyle changes (Ussher et al., 2007; Van Citters et al., 2010). Although research supporting the potential effectiveness of increased physical activity (Daumit et al., 2013) and optimal nutrition control (Davison and Kaplan, 2011, 2012) is growing (Sarris et al., 2015), the question remains as to whether combining interventions aiming to change lifestyle behaviors as part of a multimodal psychosocial treatment in BD is feasible. Thus, we systematically reviewed the existing literature on interventions targeting diet and exercise in patients with BD which collected information on the physical and emotional health of participants as the result of the intervention.
+2.	Systematic search
+To systematically identify and review studies investigating the notion of interventions in BD targeting dietary habits and physical activity which examined health-related outcomes such as mood and course of illness, we searched Pubmed, Ovid Medline and Scopus (all databases) with no language or year restrictions, up to June 2015, for articles containing the words bipolar, depression, mania, mood cross-referenced with nutrition, food, obesity, diet, hunger, eating and/or weight, physical activity, sedentary, exercise, dietary and lifestyle, functioning, trial, intervention, therapy, adjunct, psychosocial, longitudinal, prospective, follow-up, coping, outcome, and course of illness. Based on existing guidelines for conducting systematic reviews (including The Cochrane Guidelines for Systematic Reviews of Health Promotion and Public Health) (Armstrong et al., 2007; Booth et al., 2011; Greenhalgh and Peacock, 2005) we additionally hand searched content pages of key journals in psychiatry which usually publish research about treatments for BD (Bipolar Disorders and Journal of Affective Disorders) to yield articles that would not have been otherwise detected.
+Abstracts were used to screen papers, and in cases where this information was not provided in abstracts, full texts were obtained. Review papers and dissertations were excluded but we checked reference lists to identify potential additional literature. We did not include studies that did not focus on an intervention targeting nutrition and exercise. Studies assessing the broader effects of psychotherapy and medication on clinical outcomes in BD were discarded. Inclusion was restricted to studies with a) clinical populations with a diagnosis of BD (regardless of subtypes) and b) diagnoses were based on formal diagnostic criteria such as DSM (e.g. ideally using instruments such as the SCID or WMH-CIDI-10). We only included studies which had specified their samples as being individuals with BD. Studies were excluded if they focused on a) using animal models, b) clinical populations with neurological diseases; c) cardiovascular diseases when this was the primary inclusion criteria to take part in the lifestyle intervention study c) children aged <13 because there are additional factors to be considered in preadolescent samples, or d) pregnant or lactating mothers. During pregnancy and lactation, a
+number of physiological changes take place and this physiological state and the interventions targeting this specific group are likely to be different. Given our specific interest in nutrition and physical activity, we considered studies associated with circadian rhythms and interventions targeting sleep quality in BD as falling outside the scope of the review. All data were extracted by two reviewers (IB & TDM) to determine if studies met inclusion criteria. All papers identified were published in English. The details of the search strategy are depicted in Fig. 1.
+3.	Lifestyle intervention programs currently used in bipolar disorder
+3.1.	Characteristics of the included studies
+Studies retrieved as part of the current review are presented in Table 1. Sample sizes ranged from one to 116. Participants' mean ages ranged from 28.87 (SD = 7.86) to 60 (SD = 6.7) years. Overall, males outweighed females, with the number of women in the studies ranged from two to ten. Only three studies used a randomized controlled trial (RCT) design, and the duration of the interventions varied from 12 weeks to 24 months. Studies included a variety of outcome measures such as body mass index (BMI), mood, estimates of autonomic functioning (e.g. blood pressure), inflammation (e.g. C-reactive protein), glucose levels, sleep quality, quality of life, exercise, thoughts and beliefs towards food and weight loss.
+3.2.	Findings of the included studies
+In this section we will first present the results from the RCTs and proceed by describing less well controlled or treatment development studies.
+Gillhoff and colleagues tested the effects of a 5-month multimodal lifestyle intervention on BMI compared with standard care (Gillhoff et al., 2010). This intervention included three modules focusing on nutrition, motivation, and physical activity. Using a randomized controlled design the authors tested 50 participants with BD prior to and following the intervention. Participants were assessed again four months after the end of the intervention (month 11). The primary finding of this study was a significant decrease in BMI in the treatment group compared to the control group. Looking at potential predictors, it emerged that this decrease was only observed in females and not in males. The authors speculated that women might be more driven to lose weight compared to men and therefore may have been more motivated to adhere to the intervention. Furthermore, given that participants were medicated it is possible that some medications may have interfered with the efforts to lose weight. There were no changes in cardiovascular and metabolic measures over time. In sum, this study provided evidence of the feasibility and efficacy of a multimodal lifestyle intervention targeting BMI decrease in a medium-sized sample of individuals with BD over a period of 5 months. This seems to be the first RCT in this field.
+Kilbourne and colleagues also conducted a RCT in the field of lifestyle management in BD and reported positive effects of an intervention involving patient self-management on physical health called ‘Life Goals Collaborative Care’ (LGCC) compared to treatment as usual. This trial collected physiological measures such as blood pressure parameters and non-fasting total cholesterol, and wellness measures such as self-reported physical health-related quality of life in patients with BD at risk for CVD (Kilbourne et al., 2013). LGCC is based on a chronic care model that aims to help individuals set their personal wellness goals by enhancing collaboration among health care providers, patients and community services. The LGCC
+includes self-management sessions to assist patients with goal setting and management of mental health symptoms. Educational sessions focus on the individual's symptom profile; potential symptom triggers, the link between lifestyle and mental health, and helps individuals identify appropriate coping responses. These sessions address issues related to co-occurring illnesses affecting quality of life, e.g. CVD, diabetes, anxiety disorders, and substance use disorders, along with positive lifestyle changes in areas such as nutrition, exercise, and sleep to support wellness. In the maintenance sessions, a health specialist discusses with participants their progress toward goals and recurring challenges and provides ongoing motivation for goal attainment. As part of this intervention participants received a workbook to review or reinforce what was learned. In Kilbourne et al.’s study LGCC led to reduced systolic and diastolic blood pressure and decreased severity of manic symptoms, but did not lead to a reduction of primary metabolic parameters of interest such as cholesterol and triglycerides levels, and BMI. Nevertheless, in the LGCC condition the patients' overall health-related quality of life was rated as significantly higher compared to that reported in individuals receiving treatment as usual. The current findings provide first evidence of the feasibility of an intervention targeting comorbid ailments in patients with BD. The self-management component of the LGCC model may be crucial in helping patients to develop symptom coping strategies and reassert a sense of control over the illness. The interpretation of these findings is, however, limited by the lack of positive findings in terms of blood tests and BMI, the open-label nature of the design
+and the absence of monitoring of treatment adherence. Despite these limitations, the LGCC model is a promising candidate for integrated psychosocial interventions searching to improve general health outcomes in BD.
+Frank et al. recently published a RCT in a sample of BD-I patients with a BMI above 25. The authors aimed to compare the efficacy of an integrated risk reduction intervention (IRRI) to a control condition. The control condition included psychiatric care with medical monitoring (PCMM) for a 6-month period. Both IRRI and PCMM involved psychiatric treatment, assessment and symptom monitoring conducted by a psychiatric nurse. In addition the IRRI included lifestyle coaching. Psychiatric treatment was offered throughout the intervention and medication was continuously monitored and adjusted to reduce metabolic side effects. As part of the nursing management the nurse met with patients once a month, and mediated and coordinated the communication between the patient, the psychiatrist, the primary physician, and the lifestyle coach. The lifestyle coach helped patients to develop, implement and maintain behavioral changes pertaining to sleep, food intake, social rhythms, mood, and overall functioning. Further the coach encouraged patients to identify health risk and find strategies to optimize physical and mental health. Overall, participants were heavily involved in the therapy and could choose their primary therapy goals (e.g. weight loss or exercise). A mixed effect model was used to determine the effects of a range of physiological and clinical measures on the BMI following each intervention. After 6 months IRRI was associated
+with a decrease in BMI. Three physiological measures related to inflammation and neuroendocrinological functioning (C-reactive protein, total cholesterol, instability of total sleep time) led to a faster decrease in BMI (Frank et al., 2015). This study shows that an intervention focusing on developing relevant and individualized therapeutic goals leads to beneficial lifestyle changes positively affecting physiological measures and the BMI. This study also
+indicates that BD patients can learn strategies to counteract the effects of medication on weight and metabolism in general.
+Although not a controlled study, we felt that at this stage of the research it is important to also present data from treatment development studies. Goldstein et al. presented a case study focusing on the outcome of a brief motivational intervention for a medicated 15-year old boy suffering from BD I disorder and with a
+BMI of 24 (Goldstein et al., 2011). This manualized intervention involved five sessions over a 3 month period. Sessions 1, 3, and 5 were delivered in person at weeks 1, 4—5, and 8—10, while the other sessions were conducted over the (15-min phone consultations at weeks 2 and 6). Primary outcome measures were the BMI trajectory and behaviors in relation to the intake of sweet beverages and fast food, interactions during family meals, exposure to media, and frequency of physical activity. The relative importance and confidence of the patient in his ability to change his lifestyle behaviors were also recorded. By week 11 the patient showed a decrease in BMI, increased energy and exercise levels, lower levels of depression and improved sleep. This finding suggests that a short motivational intervention has the potential to reduce weight associated with psychotropic medication. Further, in this case the patient's father was involved in the treatment (e.g. patient walked with his dad). This suggests that the support of family members may increase the chances of success of such therapeutic interventions, at least in younger patients. In sum, making patients aware of the relationship between mood, medication, lifestyle and behavior may be necessary for the maintenance of the benefits observed during the intervention.
+Another study reported findings from a pilot intervention program adopting a Nutrition/weight loss, Exercise, and Wellness treatment (NEW Tx) in BD individuals. This treatment comprised three modules. The first module focuses on weight loss and nutritional health. The second module aims at engaging individuals in regular physical activities. The third module targets negative thinking styles associated with lifestyle changes in patients with BD by including cognitive restructuring techniques and problem solving strategies to support long-term adherence to healthy lifestyle choices in terms of nutrition, physical activity, sleep, substance and alcohol use. The NEW Tx was administered in twelve 60-min group sessions over 14 weeks (Sylvia et al., 2011). The authors initially administered the NEW Tx to four participants and then administered a revised version of their program to six more participants. Revisions included the addition of Cognitive Behavioral Therapy (CBT) skills to the wellness module, more cognitive restructuring and goal-setting tasks as a group activity. Overall the authors reported greater gains following the revised Tx intervention. However, retention was lower in the second group. While one could view the small sample size and revising the manual during the course of the intervention as methodological limitations, one has to keep in mind that the goal of the study was to describe the development of the treatment. It is however worth mentioning that the educational level of the participants was rather high and that a high percentage of individuals was employed (67% in Group II). Furthermore, only weight and waist circumference were assessed in this treatment development study. A second study by the same authors used the NEW Tx in five adults with BD. However, this time it was a 20week, individual CBT-based treatment comprising the three modules described in the previous study (Sylvia et al., 2013b). The authors reported that the overall attendance rate was high (85%), and that by the end of the program, participants made better food choices (e.g. higher vegetable and lower sugar intake), exercised more and reported lower depressive symptoms. Furthermore, their clinical profile in terms of blood pressure, cholesterol and glucose levels improved over the duration of the study. Based on the findings of these two trials, the NEW Tx appears to be a successful example of lifestyle intervention for patients with BD. More importantly this intervention was shown to be feasible and the majority of the participants rated it as highly satisfactory. Not surprisingly in this early stage of the research, methodological limitations were the small sample size, the lack of a control group, the assignment to interventions based on self-motivation, and the
+lack of a follow-up to determine whether acute changes were maintained over time.
+4.	Discussion
+To date, only a small number of published studies have focused on lifestyle interventions in BD. The papers reviewed here provide preliminary evidence that multimodal lifestyle interventions targeting diet, physical activity, self-motivation, and beliefs surrounding wellbeing are feasible and efficacious in individuals with BD. Furthermore, a successful psychosocial management of BD should challenge the individuals’ potential negative view towards their health and improve their knowledge about modifiable medical risk factors. Thus, seeking to control or ameliorate medical risk factors and change maladaptive or unhealthy behaviors appear to be core components of an effective lifestyle intervention (Depp et al., 2008).
+Several methodological challenges must be taken into account when trying to implement a multi-modal intervention in BD. First, there is a dearth of empirical data about the mechanisms underlying the establishment of maladaptive routines and disruption of dietary habits and physical activity in BD patients and whether they are the same or differ from those in the general population. Second, research has not yet elucidated how to initiate and maintain healthy levels of physical activity among BD patients presenting with maladaptive behavioral patterns. For instance, it has been hypothesized that one reason for the reluctance in initiating physical activity observed in patients with BD may be due to the focus of the health professionals on the health benefits of exercise rather than on the individuals' value of the benefits of physical activity (Vancampfort et al., 2015).
+An important research question that arose while reviewing the current literature was related to who should be leading such lifestyle intervention programs. It could be expected that a multidisciplinary team involving psychologists, psychiatrists, nutritionists, and experts in sports medicines, lifestyle coaches, dieticians and fitness trainers would maximize compliance and long-term outcomes of the program. However, feasibility and implementation of such a multidisciplinary treatment have yet to be established. It is also unclear whether group therapy would be more or less beneficial than individual therapy. While a group can provide encouragement, helpful information and non-judgmental support, some individuals may feel intimidated and overwhelmed as they would compare themselves to their peers and feel they are not able to change their lifestyles. A group setting also leaves less room to work on individual goals, especially if these are quite different (e.g. decreasing sedentary behavior versus eating more healthy). Further, it has yet to be established whether patients would prefer and/or benefit from programs which integrate specific BD related modules with changing lifestyles or which solely focus on improving health and global functioning. Another important debate relates to the advantages and disadvantages of heterogeneous (i.e. mixed groups) and homogeneous groups (i.e. only patients with BD). While the latter promotes social cohesion and increases feelings of safety because participants share the same mental illness, the former makes individuals realize that concerns about health and lifestyle are struggles of all people in all circumstances. This realization may help individuals to focus on their commitment to make changes to their lives.
+Future studies should consider using predictive and meditational analyses that would help to learn about variables that affect lifestyle interventions. For instance Jewell et al. found that adolescents with BD were less likely than their counterparts to exercise on a regular basis (Jewell et al., 2015). However, incidental exercise was as prevalent as in healthy individuals. These findings
+may suggest that individuals with BD may benefit from external planning or strategies to help them implement and maintain optimal exercise levels. Such approach would help personalize interventions and possibly improve long-term clinical outcomes.
+Notably, all studies, except those by Kilbourne et and Sylvia et al. (Kilbourne et al., 2013; Sylvia et al., 2011) considered weight or BMI as a primary outcome variable. As highlighted by Belanger et al.’s review (Bélanger et al., 2013) even a modest weight loss can reduce the risk of developing CVD as it can lead to reduced blood sugar level and heart rate fluctuations. However, even weight maintenance, with no weight loss, can be viewed as contributing to individuals' health. Thus, one should consider other factors such as blood pressure, lipid levels, blood glucose levels, and especially quality of life and psychological variables that may affect the individuals’ behavior in response to a weight loss program.
+Not surprisingly, given the stage of the research in this area, only three of the six studies included in this systematic review used a randomized control design, which is often considered the gold standard for effectiveness research. Future studies should therefore consider including quality assurance checklists to enable effectiveness claims (Nover and Jackson, 2013). However, other designs such as single case experimental designs are an option as well at this stage (Kazdin, 2011). Another limitation of the current studies is the absence of questionnaires that reliably assess dietary habits and physical activity possibly due to the lack of availably validated and standardized batteries collecting such measures (Janney et al., 2014). For instance, self-rated measures of physical activity are not fully reliable as patients may either forget to report or report wrong figures for frequency, duration, and/or intensity of physical activity (Soundy et al., 2014). Further, food frequency questionnaires may provide more accurate estimates of intake distributions but may not be designed to collect relevant information such as food perceptions and beliefs and social settings surrounding eating occasions. Thus, different types of assessment methods may be combined to improve accuracy and facilitate interpretation of the individual's lifestyle habits.
+In conclusion, considering the patients' expertise in relation to helpful and unhelpful strategies, there is a strong need for studies a) developing interventions which are informed by the patient's needs and shaped by their priorities; and b) examining the effectiveness of such interventions targeting general wellness (e.g. dietary lifestyle, physical activity, and setting of realistic goals). Health-related psychosocial interventions in BD should move beyond treatment adherence and adopt a multi-modal harm reduction strategy. Such a shift would help both patients and health professionals to jointly modify and decrease the occurrence of unhealthy behaviors. In this sense, a specific multi-modal intervention promoting a healthy lifestyle and focusing on behavioral changes in terms of physical activity and dietary habits may result in improved long-term management of BD. Challenging maladaptive patterns of behavior is a complex process that requires multiple steps including both effective techniques to induce healthy lifestyle changes, boost the patients' readiness for change and awareness of the risks associated with current behaviors and lifestyle. Such approach is likely to increase the likelihood that individuals with BD adopt and maintain healthy behaviors, thus reducing the risk of long-term medical consequences and ultimately improving quality of life and functional outcomes.
+Conflict of interest
+Drs. Bauer, Galvez, Hamilton, Meyer and Zunta-Soares do not have any relevant conflicts of interest to disclose with respect to this manuscript.
+Dr. Vicent Balanzá-Martínez has received research grants from
+and served as consultant, advisor or CME speaker during the last 5 years for the following entities: Almirall, Angelini, AstraZeneca, Bristol-Myers-Squibb, Janssen, Juste, Lundbeck, Otsuka, the Spanish Ministry of Science and Innovation (CIBERSAM), and ‘Fundacion Alicia Koplowitz’.
+Prof. Jair C. Soares has received research grants from Bristol-Myers Squibb, Forest, and Merck; and has received speakers’ fees from Pfizer and Abbott. However, none of these sources directly supported or influenced this project, and no authors received any financial support relevant to this project.
+I.E. Bauer et al. / Journal of Psychiatric Research 74 (2016) 1—7
+7
+Shared clinical associations between obesity and impulsivity in rapid cycling bipolar disorder: a systematic review. J. Affect. Disord. 168, 306—313.
+Gillhoff, K., Gaab, J., Emini, L., Maroni, C., Tholuck, J., Greil, W., 2010. Effects of a multimodal lifestyle intervention on body mass index in patients with bipolar disorder: a randomized controlled trial. Prim. Care Companion J. Clin. Psychiatry 12.
+Goldstein, T.R., Goldstein, B.I., Mantz, M.B., Bailey, B., Douaihy, A., 2011. A brief motivational intervention for preventing medication-associated weight gain among youth with bipolar disorder: treatment development and case report. J. Child Adolesc. Psychopharmacol. 21, 275—280.
+Gomes, F., Telo, D.F., Souza, H.P., Nicolau, J.C., Halpern, A., Serrano Jr., C.V., 2010. Obesity and coronary artery disease: role of vascular inflammation. Arq. Bras. Cardiol. 94, 273—279.
+Greenhalgh, T., Peacock, R., 2005. Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ 331, 1064—1065.
+Hong, J., Reed, C., Novick, D., Haro, J.M., Aguado, J., 2011. Clinical and economic consequences of medication non-adherence in the treatment of patients with a manic/mixed episode of bipolar disorder: results from the European Mania in Bipolar Longitudinal Evaluation of Medication (EMBLEM) Study. Psychiatry Res. 190,110—114.
+Janney, C.A., Fagiolini, A., Swartz, H.A., Jakicic, J.M., Holleman, R.G., Richardson, C.R., 2014. Are adults with bipolar disorder active? Objectively measured physical activity and sedentary behavior using accelerometry. J. Affect. Disord. 152, 498—504.
+Jewell, L., Abtan, R., Scavone, A., Timmins, V., Swampillai, B., Goldstein, B.I., 2015. Preliminary evidence of disparities in physical activity among adolescents with bipolar disorder. Ment. Health Phys. Activity 8, 62—67.
+Kazdin, A.E., 2011. Single-case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press.
+Kemp, D.E., Gao, K., Chan, P.K., Ganocy, S.J., Findling, R.L., Calabrese, J.R., 2010. Medical comorbidity in bipolar disorder: relationship between illnesses of the endocrine/metabolic system and treatment outcome. Bipolar Disord. 12, 404—413.
+Kilbourne, A.M., Goodrich, D.E., Lai, Z., Post, E.P., Schumacher, K., Nord, K.M., et al., 2013. Randomized controlled trial to reduce cardiovascular disease risk for patients with bipolar disorder: the self-management addressing heart risk trial (SMAHRT). J. Clin. Psychiatry 74, e655.
+Kilbourne, A.M., Rofey, D.L., McCarthy, J.F., Post, E.P., Welsh, D., Blow, F.C., 2007. Nutrition and exercise behavior among patients with bipolar disorder1. Bipolar Disord. 9, 443—452.
+Kim, B., Kim, S., McIntyre, R.S., Park, H.J., Kim, S.Y., Joo, Y.H., 2009. Correlates of metabolic abnormalities in bipolar I disorder at initiation of acute phase treatment. Psychiatry Investig. 6, 78—84.
+Mcintyre, R.S., Konarski, J.Z., Misener, V.L., Kennedy, S.H., 2005. Bipolar disorder and diabetes mellitus: epidemiology, etiology, and treatment implications. Ann. Clin. Psychiatry 17, 83—93.
+McIntyre, R.S., Konarski, J.Z., Yatham, L.N., 2004. Comorbidity in bipolar disorder: a framework for rational treatment selection. Hum. Psychopharmacol. Clin. Exp. 19, 369—386.
+Merikangas, K.R., Akiskal, H.S., Angst, J., Greenberg, P.E., Hirschfeld, R.M., Petukhova, M., et al., 2007. Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Archives
+General Psychiatry 64, 543—552.
+Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., 2009. The PRISMA group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097.
+Murray, C.J.L., Lopez, A.D., 1997. Alternative projections of mortality and disability by cause 1990—2020: global burden of disease study. Lancet 349, 1498—1504.
+Nover, C., Jackson, S.S., 2013. Primary care-based educational interventions to decrease risk factors for metabolic syndrome for adults with major psychotic and/or affective disorders: a systematic review. Syst. Rev. 2, 116.
+Prieto, M., Cuellar-Barboza, A., Bobo, W., Roger, V., Bellivier, F., Leboyer, M., et al., 2014. Risk of myocardial infarction and stroke in bipolar disorder: a systematic review and exploratory meta-analysis. Acta Psychiatr. Scand. 130, 342—353.
+Sarris, J., Logan, A.C., Akbaraly, T.N., Amminger, G.P., Balanza-Martínez, V., Freeman, M.P., et al., 2015. Nutritional medicine as mainstream in psychiatry. Lancet Psychiatry 2, 271—274.
+Sharma, A.N., Bauer, I.E., Sanches, M., Galvez, J.F., Zunta-Soares, G.B., Quevedo, J., et al., 2014. Common biological mechanisms between bipolar disorder and type 2 diabetes: focus on inflammation. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 54, 289—298.
+Soundy, A., Roskell, C., Stubbs, B., Vancampfort, D., 2014. Selection, use and psychometric properties of physical activity measures to assess individuals with severe mental illness: a narrative synthesis. Archives Psychiatric Nurs. 28, 135—151.
+Sylvia, L.G., Iosifescu, D., Friedman, E.S., Bernstein, E.E., Bowden, C.L., Ketter, T.A., et al., 2014. Use of Treatment Services in a Comparative Effectiveness Study of Bipolar Disorder.
+Sylvia, L.G., Nierenberg, A.A., Stange, J.P., Peckham, A.D., Deckersbach, T., 2011. Development of an integrated psychosocial treatment to address the medical burden associated with bipolar disorder. J. Psychiatric Pract. 17, 224.
+Sylvia, L.G., Peters, A.T., Deckersbach, T., Nierenberg, A.A., 2013a. Nutrient-based therapies for bipolar disorder: a systematic review. Psychother. Psychosom. 82, 10—19.
+Sylvia, L.G., Salcedo, S., Bernstein, E.E., Baek, J.H., Nierenberg, A.A., Deckersbach, T., 2013b. Nutrition, exercise, and wellness treatment in bipolar disorder: proof of concept for a consolidated intervention. Int. J. Bipolar Disord. 1, 1—7.
+Sylvia, L.G., Shelton, R.C., Kemp, D.E., Bernstein, E.E., Friedman, E.S., Brody, B.D., et al., 2015. Medical burden in bipolar disorder: findings from the clinical and health outcomes initiative in comparative effectiveness for bipolar disorder study (Bipolar CHOICE). Bipolar Disord. 17, 212—223.
+Ussher, M., Stanbury, L., Cheeseman, V., Faulkner, G., 2007. Physical activity preferences and perceived barriers to activity among persons with severe mental illness in the United Kingdom. Psychiatr. Serv. 58, 405—408.
+Van Citters, A.D., Pratt, S.I., Jue, K., Williams, G., Miller, P.T., Xie, H., et al., 2010. A pilot evaluation of the in SHAPE individualized health promotion intervention for adults with mental illness. Community Ment. Health J. 46, 540—552.
+Vancampfort, D., Correll, C.U., Probst, M., Sienaert, P., Wyckaert, S., De Herdt, A., et al., 2013. A review of physical activity correlates in patients with bipolar disorder. J. Affect. Disord. 145, 285—291.
+Vancampfort, D., Madou, T., Moens, H., De Backer, T., Vanhalst, P., Helon, C., et al., 2015. Could autonomous motivation hold the key to successfully implementing lifestyle changes in affective disorders? A multicentre cross sectional study. Psychiatry Res. 228 (1), 100—106.
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+seek medical or psychological help for their suicidal thoughts or behavior3; examples include media guidelines and means restriction.5
+Restricting access to lethal and common means (means restriction) can lower the likelihood of a suicide attempt, delay a suicide attempt, or lead to use of a less lethal method of attempting suicide.6 Any of these outcomes are associated with a better chance of survival and rehabilitation given that many suicide attempts are impulsive, and people are often discouraged if their preferred method is not readily available.6
+Means restrictions, such as bridge barriers and gun control laws, are associated with a reduced number of suicides by jumping and by firearms, respectively.7-9 Pesticides are responsible for upto 20% of global suicides, especially in low- and middle-income countries.10,11 Pesticide restrictions are also associated with decreased suicide rates, especially when the targeted pesticides were hazardous or frequently used.10 Suicide attempts by other poisonsand medicines are common, but these means have not been evaluated as extensively.11
+Although means restriction is usually associated with reduced incidence of method-specific suicide rates, a subsequent shift toward other methods of suicide is a possible outcome. This shift is known as method substitution or displacement and is often debated by epidemiologists and suicide prevention experts when considering the overall benefit of means restriction.3,12,13 Population-level suicide is inherently dynamic and affected by multiple factors, and changes in other methods of suicide may be coincidental or driven by changes in means restriction.
+In this study, we aimed to assess population-level means restriction policies for poisons. Specifically, the goal was to identify changes in suicide rate by the targeted poison and, if available, the corresponding changes in suicide by other methods. We tested whether an increased incidence of other methods (method substitution) was consistently observed in these studies and whether it was associated with the change in suicide by the targeted poison.
+Methods
+This study is registered with PROSPERO (CRD42020160734). Articles were extracted and screened for relevance according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.14
+Search Strategy and Inclusion Criteria
+We conducted a systematic review of international evidence to assess the association between means restriction and suicide by poisoning. We searched keywords and/or Medical Subject Headings in 5 databases: MEDLINE, Embase, PsycInfo, Scopus, and Web of Science. The search strategy combined 3 concepts: suicide, poison, and restriction (eMethods 1 in the Supplement). Poison included terms such as overdose, medicine, and domestic chemicals. Restriction included terms such as intervention, policy, access, and scheduling. We retrieved all studies that were published in the English language from inception until December 31,2019.
+We excluded studies that were retrieved from gray literature, which we defined as literature searches that were not replicable (eg, Google Scholar) and works that were not published in a peer-reviewed, academicjournal (eg, government reports). Literature reviews were also excluded; however, we read relevant reviews to check for additional studies. We searched for additional studies by citation chaining (assessing the references of relevant papers) and checking publications by prominent authors in the field of means restriction.
+We selected longitudinal studies that measured changes in suicide rate while means restriction was implemented. The inclusion criteria were national interventions at the country level, and the outcomes were suicide rates reported in the country or a smaller, representative region.
+Studies that discussed means restriction but did not report changes in suicide rates or that reported trends in suicide rates without a relevant intervention were excluded. We also excluded
+interventions that were conducted at a community or regional level, such as interventions limited to 1 province or city.
+Study Selection, Data Extraction, and Quality Assessment
+Studies were exported into EndNote (Clarivate) and deduplicated. Unique articles were exported into Covidence (Veritas Health Innovation). Two of us (J.S.L. and R.C.) independently performed title and abstract screening. An article was reviewed for relevance in full text if a decision could not be readily made from the title and abstract alone. Conflicts were discussed with a third author (N.A.B.), who helped us reach consensus.
+Two of us (J.S.L. and R.C.) also independently extracted data. Data extracted included study location, study design, years reported, years of intervention, details of intervention, and suicide rates by the poison of interest. We extracted data, when available, on overall suicide rates (suicide by any method) and suicide by other methods (not by the targeted poison). The double data extraction was checked for consistency, and a meeting was held to resolve any conflicts.
+The main findings from each article are presented in a narrative synthesis (Table).15-76 We assessed each studyfor quality and bias using the Risk Of Bias In Nonrandomized Studies of Interventions (ROBINS-I) tool for uncontrolled before and after studies (eTable 1 in the Supplement).77
+Statistical Analysis
+We extracted data, when available, on suicide rates before and after a clearly defined intervention for a standardized before-and-after comparison. Because each study had different periods, we used annual data from 1 year before the intervention compared with 5 (for liquid or solid poison) or 10 (for gases) years after the intervention. Gases were evaluated after a longer time to account for regional differences and slow changes. Natural gas generally replaced coal gas sources within 10 years, whereas most older cars that predated legislative requirements would retire within 10 years in high-income countries.44,60
+We used a preintervention time point 1 year before the intervention given that the suicide rate during the year of the intervention could be erratic depending on when and how the intervention was implemented. If data for these time points were unavailable, we used the closest years available. If multiple studies examined the same intervention, we used the study with the most detailed quantitative data.
+Incidence rate ratio (IRR) was calculated as the incidence rate of suicides (usually number of suicides per 100 000 people) after the intervention divided by incidence rate before the intervention. The SE was calculated by adding the reciprocal of annual cases before and after the intervention, and CIs were calculated using loge (IRR) and SE (eMethods 2 in the Supplement). If the number of annual cases of suicide was not available, we retrieved data from global websites, such as the World Bank,78 for a population estimate to calculate CIs. We used ImageJ, version 1.53e (National Institutes of Health) to quantify any data that were presented in graphical form only. We calculated IRRs for suicide by the restricted poison and by other methods of suicide (any method that was not the restricted poison), and then we graphed IRRs on forest plots on a log scale to show the relative change in suicide rates before and after the intervention.
+We combined the same incidence rate data to quantify the change in suicide rate per 100 000 people before and after the intervention. These data were graphed on a scatterplot with linear regression to examine the association between the change in suicide by targeted poison and the change in suicide by other methods. Evidence for method substitution would be shown if there was an inverse association (eg, a slope of -1 would indicate that decreases in suicide by the restricted poison was, on average, matched by an equal but opposite change in other methods of suicide). We also graphed a scatterplot with linear regression to examine changes in suicide by other methods over time based on the year of the intervention. In addition, we calculated the median change in suicide by the restricted poison and suicide by other methods.
+We repeated all analyses with overall suicide rates instead of suicide by other methods (eFigures 4-9 in the Supplement). We did not conduct a meta-analysis because the studies were heterogeneous with different interventions, dates, and locations. All graphs and analyses were produced using GraphPad Prism, version 9.1 (GraphPad).
+Results
+The database searches retrieved 12 557 studies, of which 4916 were duplicates. Following the PRISMA guidelines, we performed title and abstract screening on 7657 studies, leading to the exclusion of 7566 studies. We reviewed 91 full-text articles, and 62 studies12,15-75 from 26 countries met the eligibility criteria for inclusion (eFigure 1 in the Supplement). Restricted substances included pesticides (15 countries), domestic gas (14 countries), motor vehicle exhaust (9 countries), and pharmaceuticals (8 countries). Different strategies to restrict access to poison included banning or withdrawingthem from the market, reducing concentration, limitingthe quantity sold, and allowing access for only a specific occupation or medical condition.
+The studies investigated interventions in Europe, the US, Australia, and Asia, with multiple studies on all poison classes in the United Kingdom (eFigure 2 in the Supplement). We found no research from South America and Africa. Most studies were conducted in high-income countries,12,15-28,37-71,73-75 with only 9 studies on pesticide and gas restrictions in low- and middleincome countries.29-36,72
+We evaluated each study using the ROBINS-I tool and found that most studies were prone to at least some low to medium risk of bias. Studies with limited trend analysis or limited preintervention data were at higher risk of confounding bias.15-1719,2a2e6-29,42,44,466,4753,6667 These risks of bias are presented in a heat map (eTable 1 in the Supplement).
+Pesticide Restrictions
+A total of 23 studies reported pesticide interventions in 15 countries (Table).12,15-36 Occupational licensing for conditional access to pesticides generally occurred earlier (1960-1975) than complete bans (1984-2012). Specific pesticides and classes that were restricted included organophosphates, parathion, organochlorines, and paraquat.
+The incidence of suicides by pesticide decreased in most studies (19 of 23).12,17,18,21-36 Seven interventions in 7 countries were suitable for IRR calculations, with 6 countries showing reduced incidence of suicides by pesticide (Figure 1).17-19,21,30,34,36 Five interventions were associated with decreases in suicides by pesticide were reported in Germany, Finland, South Korea, inner Mongolia, Sri Lanka, and Bangladesh, with IRRs ranging from 0.37 to 0.69.1718,21,30,34 The IRRsfor suicide by other methods suggested a decrease in other methods in Germany (0.79; 95% CI, 0.78-0.81) and
+South Korea (0.93; 95% CI, 0.91-0.95). Suicide by other methods did not change in Finland, Crete (Greece), Inner Mongolia, and Sri Lanka.17-19,21,30,34 Suicide by other methods was not reported in Bangladesh, but there was a “25% decline in unnatural deaths."36(pp175,178)
+In Sri Lanka, earlier pesticide bans from 1984 to 1998 included parathion and endosulfan. The bans were associated with a decrease in suicides by poisoning, with 35 of 100 000 suicides in 1984 and 20 of 100 000 suicides in 20 00.31,33,34 In Taiwan, various pesticide formulations were banned from 1980. Suicides by pesticide decreased by 67% from 1987 to 2010, whereas the overall suicide rate and suicide by other methods gradually increased after 1993.12,27,28
+Other studies included occupational licensingfor paraquat in Ireland and the United Kingdom (England, Wales, and Scotland), and a paraquat ban in Marseille, France.15,16,20 The sample sizes in these studies were too small to enable us to calculate changes before and after the intervention. Studies in India and Hungary did not specify the date of pesticide interventions but reported a decline in suicides by pesticide over time.29,35 However, the incidence of hanging increased rapidly in India and was associated with an overall increase in suicide rates.35
+Gas Restrictions
+Detoxification of domestic gas refers to the gradual removal of carbon monoxide from gas lines or replacing coal gas with natural gas. This detoxification was implemented in 14 countries from the 1950s to the 1990s (Table).37-5172-75
+Incidence rate ratios were calculated for 13 countries, which all reported reduced incidence of suicide by domestic gas (Figure 2). The largest decreases in domestic gas IRR were reported in the
+Netherlands (0.03; 95% CI, 0.01-0.06) and West Germany (0.03; 95% CI, 0.02-0.04).40-46
+The incidence of suicide by other methods subsequently decreased in Switzerland (IRR, 0.89; 95% CI, 0.81-0.97) and Denmark (IRR, 0.78; 95% CI, 0.72-0.84)4873 but did not change in the US,50 Vienna (Austria),45 and Northern Ireland.44 Suicide by other methods increased in 8 countries: England, Wales, Scotland, the Netherlands, Belgium, West Germany, Japan, and Australia, with IRRs rangingfrom 1.25 to 1.66.40,46,4749,51
+Catalytic converters and other motor emission legislations reduced carbon monoxide concentration in motor vehicle exhaust. This change was associated with a gradual decrease in carbon monoxide exposure risk because new cars were manufactured and purchased and older cars were removed from service. Nine countries reported suicides by motor exhaust from 1968 to 1993 (Table).41,52-60,72-74 Most countries introduced mandatory catalytic converters in new cars (carbon monoxide <0.5%).53,57 Japan’s motor legislation was more limited (carbon monoxide <4.5%), and the US reduced carbon monoxide emissions before mandating catalytic converters.56,57
+Incidence rate ratio was calculated for 8 countries (Figure 2). Four interventions in 5 countries were associated with decreases in suicides by motor exhaust, with IRRs reported in England and Wales (0.42; 95% CI, 0.38-0.46), Scotland (0.27; 95% CI, 0.18-0.41), Switzerland (0.39; 95% CI, 0.29-0.54), and Denmark (0.43; 95% CI, 0.32-0.58).52,54,5573 The incidence of suicides by motor exhaust was unlikely to have changed in the US (IRR, 1.05) or increased in Australia (IRR, 1.36; 95% CI, 1.18-1.56) and Japan (IRR, 2.07; 95% CI, 1.89-2.27).56,58,60The incidence of suicide by other methods subsequently decreased in Switzerland, Denmark, and Japan, with IRRs rangingfrom 0.58 to 0.95, or increased slightly in England and Wales (IRR, 1.08; 95% CI, 1.03-1.12) and Scotland (IRR, 1.13; 95% CI, 1.02-1.25).
+The incidence of suicides by carbon monoxide and overall suicides declined overtime in Hradec Kralove, Czech Republic. Domestic gas detoxification appeared to correspond to this trend more closely than catalytic converters.72
+Pharmaceutical Restrictions
+Seven countries reported pharmaceutical interventions (Table).61-71,73-75 Targeted medicines included paracetamol, salicylates, dextropropoxyphene, barbiturates, and caffeine tablets. Strategies to limit access to medicine included limiting tablet pack size, withdrawal from market, therapeutic restriction by indication, and upscheduling (from over-the-counter to prescription drug only).
+Seven interventions in 6 countries were suitable for IRR calculations (Figure 3).62,65,68-70,73,74 Six interventions were followed by reduced incidence of suicides by medicine overdose, with the
+largest decrease after the dextropropoxyphene ban in England and Wales (IRR, 0.04; 95% CI, 0.02-0.08).65 Four barbiturate interventions were associated with decreased incidence of suicides by overdose, with IRRs reported in Denmark (0.29; 95% CI, 0.17-0.50), Australia (0.72; 95% CI, 0.65-0.80), Sweden (0.17; 95% CI, 0.10-0.29), and Japan (0.22; 95% CI, 0.21-0.23).68-7073 Paracetamol pack size limits were also associated with a decrease in suicides by paracetamol overdose in England and Wales (IRR, 0.60; 95% CI, 0.46-0.77).62 Prescribing restrictions on dextropropoxyphene in Denmark did not appear to change the incidence of suicide by analgesic.73,74 The incidence of suicide by other methods decreased after barbiturates restrictions in Sweden, Japan, and Denmark, with IRRs ranging from 0.86 to 0.90, as well as dextropropoxyphene restrictions in Denmark (IRR, 0.87; 95% CI, 0.80-0.94) and England and Wales (IRR, 0.92; 95% CI, 0.88-0.96). Suicide by other methods did not appear to change after pack size limits of paracetamol in England and Wales and of barbiturates in Australia.
+Other studies reported dextropropoxyphene withdrawal in Scotland and the US (Florida) as well as caffeine tablet pack size limits in Sweden. Suicides by dextropropoxyphene declined in Scotland after 2 years,66 whereas Florida reported a decreasing trend in suicides by dextropropoxyphene, and Sweden reported a decrease in suicides by caffeine intoxication.67,71
+Analysis for Method Substitution
+The median (IQR) change in method-specific suicide rates after 29 interventions was -1.18 (-2.03 to -0.46) per 100 000 people during the same follow-up period (5 years for pesticides and medicines, and 10 years for gases). The change in other methods was distributed normally and centered around a median (IQR) of -0.09 (-2.22 to 1.65) per 100 000 people.
+No significant association was found (linear regression slope, -0.06; 95% CI, -0.25 to 0.14) between changes in suicide by the restricted poison and suicide by other methods (Figure 4). This result suggests that random variations or other unrelated factors were associated with the observed changes in other methods of suicide. Increases in other methods were often associated with domestic gas detoxification and earlier periods (1960-1975) (eFigure 3 in the Supplement). A conservative estimate of approximately 57 355 poison-specific suicides occurred annually before the interventions were implemented (eTable 2 in the Supplement); these cases may have benefited from means restrictions.
+Discussion
+Overall, this synthesis of the literature suggests that means restriction of a range of poison was associated with lower incidences of suicide. We found no evidence to suggest that change in other suicide methods that coincided with bans and restrictions was anything other than coincidental. A
+decrease in specific poisoning did not promote the use of other equally lethal methods. An increase in other suicide methods tended to be partial, to be focused on a subgroup, or to occur over a longtime.10,13
+Individuals usually prefer a specific method during a suicidal crisis, and any decrease in methodspecific suicide rates is beneficial.8,13 Firearm ownership in the US has been associated with 4.8-times higher odds of suicide, whereas a 10% decrease in firearm ownership has been associated with a 4.2% decline in suicide by firearm and 2.5% decline in overall suicide.7,8 Bridge barriers have been associated with an 86% reduction in suicides by jumping at sites with barriers, 44% increase in suicides by jumping at sites without barriers, or a 28% net decrease of all suicides by jumping.9
+No significant association was found between changes in suicides by poisoning and suicides by other methods. It seems likely that background historical trends in other methods may explain the many cases in which investigators have concluded that no method substitution occurred. When coal gas was phased out, decreased incidences of suicides by domestic gas coincided with increased access to psychotropic medicines and increased car ownership.3,39,44,52,55 Psychotropic medicines (especially barbiturates and tricyclic antidepressants) and cars without catalytic converters were relatively toxic from the 1960s to the 1980s until they were replaced or restricted.39,44,52,55,58,73 Benzodiazepines and selective serotonin reuptake inhibitors that were much less toxic in overdose became the most common sedatives and antidepressants.39,68,73,75,79
+Pesticide restrictions were complex, with different chemicals and formulations available. Outcomes varied depending on what other pesticides were available or banned in the same time frame. Pesticide bans were associated with fewer suicides by pesticide more often than occupational licensing.12,15-18,21-28,30-36
+Domestic gas detoxification was associated with reduced suicides by carbon monoxide, whereas motor exhaust interventions were sometimes associated with increasing trends of suicides by carbon monoxide. This finding likely reflects the total coverage and relative speed with which household domestic gas could be detoxified. Catalytic converters were applied only to new cars and can lose effectiveness over time.57
+Most strategies to limit access to medicine were associated with decreases in suicides by drug overdose. Medicine restrictions can also be affected by changes in prescribing patterns.75 Sedative and hypnotic overdoses were common in the twentieth century, whereas analgesics and opioids are currently associated with medicine poisoning. Means restriction of opioids other than dextropropoxyphene has not been studied to date. Dextropropoxyphene has direct cardiac toxic effects and thus should not be considered a typical opioid. Research on opioid policies and opioid suicide rates should be a high priority given the current epidemic of prescription opioid misuse in high-income countries.65,67
+A total of 57 355 poison-specific suicides were reported before the interventions were implemented (eTable 2 in the Supplement). This population represents the cohort who may benefit the most from means restriction of poison, but this population does not account for studies that occur during different periods. Means restriction had the greatest public health benefit when the restricted suicide method was common and highly lethal. This result was seen in Sri Lanka, where multiple pesticide bans were associated with 19 800 fewer suicides (based on time-trend analysis of overall suicide rate) in 1996 to 2005 vs 1986 to 1995.31 In contrast, car exhaust in Japan remained lethal because carbon monoxide was only partially reduced and the increase in suicides by motor exhaust was associated with an increase in car ownership.56
+The choice of suicide method at a population level is complex but often varies by socioeconomic profile.35 Some violent self-harm methods (such as hanging, jumping, and sharp-object injury) tend to have lower cost than poisoning. Hanging remained a common method of suicide, and the incidence of hanging sometimes increased over time.18,24,30,31,35,3641,444752,74 Hanging is highly lethal and difficult to target with means restriction, although means restriction has a place in specific settings, such as ligature restrictions in prisons and hospitals.52
+Many interventions such as detoxification of domestic gas and withdrawal of dextropropoxyphene were likely to be widespread, but these interventions were evaluated only in a small number of countries. Cost may be a factor, such as low-income countries using older cars without working catalytic converters. Bans of hazardous pesticides in low- and middle-income countries are considered highly cost-effective and should remain a high priority.80
+We evaluated each study with the ROBINS-I tool and found that some studies were prone to selection and reporting bias or with poorly specified interventions. Methods varied considerably, with repeated cross-sectional studies and studies with limited follow-up periods providing less reliable estimates. Most studies considered preintervention trends, but only 4 studies conducted an interrupted time series analysis.62-65 Interrupted time series analysis is a robust method for estimating preintervention trends and changes in slope to ascertain the magnitude of the intervention. Ideally, this method would be used for future means restriction studies.
+Strengths and Limitations
+This study has some strengths. To our knowledge, no other systematic review has focused on all poisons, quantitatively comparing poison-specific suicide rates and suicide by other methods. We collected and analyzed data on method-specific suicide rates and suicide by other methods before and after 29 interventions. This method allowed a comparison of different interventions with the same unit (rate per 100 000 people), but it does not account for different periods, preintervention trends, and cointerventions. Preintervention trends can change the reported outcome, such as when physicians were encouraged to prescribe alternate analgesics ahead of the dextropropoxyphene withdrawal.65-67 Social and economic detriments to mental health or quality of life, such as poverty, employment, substance misuse, and conflict, also alter the rates of suicidality and are potential confounding factors that are difficult to explain. Means restriction of multiple substances simultaneously or means restriction combined with other suicide prevention policies could have a synergistic benefit for suicide rates. However, quantifying this advantage was beyond the scope of this systematic review.
+This study also has some limitations. First, this systematic review did not identify any studies from South America and Africa, which may affect the generalizability of the findings. Second, we found low representation of low- and middle-income countries, despite most suicides occurring in these countries.2 This low representation may be attributed to the search strategy, which included English studies only and excluded reports and other articles that were not published in academic journals. Low- and middle-income countries may be less likely to publish studies on suicide given that underreporting and stigma are common.35
+Conclusions
+Means restriction appeared to be associated with decreased suicide rates and had the greatest benefit when it restricted suicide methods that were common and highly lethal. Coincidental changes in other methods likely reflects the background trends in other methods, and the overall changes across all studies did not provide support for the speculation that restrictive interventions lead to method substitution. Suicide prevention strategies should be synergistic, targeting both individuals who are at risk and broad population-level policies, including means restriction.
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+SUICIDAL BEHAVIOR IS A BROAD term that includes death by suicide and intentional, nonfatal, self-injurious acts committed with or without intent to die. It is associated with several mental disorders, including depression, substance dependence, and schizophrenia. Borderline personality disorder (BPD) is 1 of only 2 DSM-IV diagnoses for which suicidal behavior is a criterion.1 Borderline personality disorder is a severe and persistent mental disorder experience of severe emotional distress and behavioral dyscontrol.1-3 Among patients with BPD, 69% to 80% engage in suicidal behav
+ior,4-9 with a suicide rate of up to 9%.10 Forty percent of the highest users of inpatient psychiatric services receive a diagnosis of BPD.11,12 Patients with BPD use more services than those with major de-pression13 and other personality disor-ders.14 Among patients with BPD seen for treatment, 72% have had at least 1 psychiatric hospitalization and 97% have received outpatient treatment from a mean of 6.1 previous therapists.15,16 Despite this high-use pattern, patients with BPD have high rates of treatment failure.17,18
+Outpatient dialectical behavior therapy (DBT)20,21 and mentalization-based treatment provided in a partial hospital pro-
+Figure 1. Subject flowchart. CTBE indicates community treatment by experts; DBT, dialectical behavior therapy. Those in DBT lost to follow-up and discontinued interventions were not subtracted from the allocation total in the DBT treatment arm.
+gram22,23 are the only 2 treatments (to our knowledge) for which randomized controlled trials have been published that demonstrate significantly better results than treatment as usual (TAU) for patients with BPD.24 Of the 2 treatments, DBT has the most empirical support, having been evaluated initially in a 1991 randomized trial25 and followup19,26,27 and assessed subsequently in 4 published randomized controlled trials28-31 across 3 separate research centers and in 1 additional randomized controlled trial of a DBT-oriented treatment.32 Evidence from these studies has shown DBT to be an efficacious treatment for BPD. However, as noted in the American Psychiatric Association Practice Guidelines, “[I]t is difficult to ascertain whether the improvement reported for patients receiving dialectical behavior therapy derived from specific ingredients of dialectical behavior therapy.”33(p32) The present study is the first in a program of research systematically examining DBT with the objective of identifying the specific elements of treatment that are necessary and sufficient for an efficacious outcome among individuals with BPD. As such, this is not a “horse race” study pitting one complex active treatment against another. Rather, it is a dismantling study designed to begin answering questions as to the unique effects of DBT.
+To rule out factors commonly believed to be effective across a variety of disorders, the control condition, community treatment by experts (CTBE), was specifically designed to maximize internal validity by controlling for the following: (1) availability of treatment; (2) assistance finding and getting to a first appointment with a therapist; (3) hours of individual psychotherapy offered; (4) therapist sex, training, clinical experience, and expertise; (5) availability of group clinical consultation; (6) allegiance to treatment approach; (7) institutional prestige associated with treatment; and (8) general factors associated with receiving any psychotherapy. This study was designed as a replication of the 1991 study25 comparing DBT with TAU. We predicted that we would find identical outcomes, namely, that DBT would perform significantly better than CTBE in reducing suicidal (suicide attempts and nonsuicidal self-injury) and therapy
+debilitating condition. All participants provided informed consent using protocols approved by the University of Washington Human Subjects Division.
+Using a computerized adaptive minimization randomization procedure, eligible subjects were matched to treatment condition on 5 primary prognostic variables: (1 and 2) the number of lifetime suicide attempts or nonsuicidal self-injuries combined and psychiatric hospitalizations; (3) a history of only suicide attempts, only nonsuicidal self-injury, or both; (4) age; and (5) a negative prognostic indicator of a Beck Depression Inventory34 score higher than 30 or a Global Assessment of Func-tioning35 score lower than 45 for a comorbid condition. This matching method has been shown to be superior to simple and stratified randomization in producing balance for separate prognostic variables, particularly when the number of strata is large compared with the number of subjects.36 Based on 0.8 power to detect significant differences between conditions (P = .05, 1-sided), this procedure was used to randomize 101 subjects to DBT (n=52) or to CTBE (n=49). The flow of subjects through the study is shown in Figure 1.
+Initial assessments were done before informing subjects of treatment assignment and at 4-month intervals during the treatment and follow-up periods. Outcome assessments were yoked across conditions by screening date. Assessments were conducted by blinded independent clinical assessors with master’s or doctoral degrees. Lead assessors (K.A.C. and A.M.M.) were trained on interview measures by the instrument developers or by an approved trainer, and then trained, supervised, and evaluated for reliability across assessors (k statistic or intraclass correlation coefficient for all ratings ranged from 0.74 to 1.00). The participant coordinator, who was not blinded to treatment condition, executed the randomization program and collected all the data related to treatment.
+Diagnostic Interviews
+The structured clinical interviews for Axis I and Axis II DSM-IV36,37 and the International Personality Disorder Examina-tion38 were used as screening and diagnostic instruments. The Peabody Picture Vocabulary Test-Revised39 was used to rule out mental retardation.
+Outcome Measures
+The Suicide Attempt Self-Injury Interview40 measured the topography, suicide intent, and medical severity of each suicide attempt and nonsuicidal self-injury. Interrater reliabilities were 0.88 for medical risk and 0.94 for suicide intent. The Suicidal Behaviors Questionnaire (M.M.L., unpublished work, 1981) was
+used to assess suicide ideation. The Reasons for Living Inventory41 assessed the importance of reasons for living. The TreatmentHistory Interview (M.M.L., unpublished work, 1987) measured subjects’ experience with professional psychotherapy, comprehensive treatment programs, case management, inpatient units, emergency treatment and other crisis services, and medication use. The Hamilton Rating Scale for Depression-17-Item42 was used to evaluate the severity of depressive symptoms. All outcome domains are identical to those reported by Linehan et al25 in 1991.
+STUDY TREATMENTS
+Therapists
+There were 41 therapists in the study (16 DBT therapists and 25 CTBE therapists). Characteristics of the therapists are given in Table 1.
+Dialectical Behavior Therapy
+Dialectical behavior therapy is a cognitive behavioral treatment program developed to treat suicidal clients meeting criteria for BPD.20-21 It directly targets (1) suicidal behavior, (2) behaviors that interfere with treatment delivery, and (3) other dangerous, severe, or destabilizing behaviors.
+Standard DBT addresses the following 5 functions: (1) increasing behavioral capabilities, (2) improving motivation for skillful behavior (through contingency management and reduction of interfering emotions and cognitions), (3) assuring generalization of gains to the natural environment, (4) structuring the treatment environment so that it reinforces functional rather than dysfunctional behaviors, and (5) enhancing therapist capabilities and motivation to treat patients effectively. These functions are divided among the following 4 modes of service delivery: (1) weekly individual psychotherapy (1 h/wk), (2) group skills training (2/ h/wk), (3) telephone consultation (as needed within the therapist’s limits to ensure generalization), and (4) weekly therapist consultation team meetings (to enhance therapist motivation and skills and to provide therapy for the therapists).
+Recruitment, Training, and Adherence of DBT Therapists
+Psychotherapists recommended by colleagues as potentially good DBT therapists were recruited for the study; 8 had no previous DBT exposure and 8 had experience that ranged from workshop attendance to applied practice. The sample included 3 graduate students and 2 postdoctoral trainees. Training consisted of a 45-hour DBT seminar followed by supervised prac
+tice. The treatment developer (M.M.L.) was not a study therapist and did not attend the weekly DBT team meetings.
+Individual therapists were hired once 6 of 8 consecutive training case sessions were rated as adherent to DBT. During the study, adherence was assessed by coding a random selection of sessions on the DBT Global Rating Scale (M.M.L., unpublished work, 2003), which codes DBT adherence on a 5-point scale (to 1 decimal point), with a score of 4.0 or higher denoting adherence. Therapists were blinded to which sessions were rated. Coders were trained to reliability with the treatment developer.
+Community Treatment by Experts
+The CTBE condition was developed specifically for this study to control for factors previously uncontrolled for in DBT studies. Similar to a TAU (treatment as usual) condition, the treatment provided was uncontrolled by the research team, an essential component of TAU conditions. It differs from TAU conditions in that characteristics of CTBE therapists are controlled by the study via selection of therapists and supervisory arrangements.
+Expertise. The CTBE therapists were nominated by community mental health leaders. These included heads of inpatient psychiatric units and clinical directors of mental health agencies, who nominated therapists whom they considered experts in treating difficult clients.
+Allegiance to Treatment Provided. The content of the treatment provided by the CTBE therapists was not prescribed by the research study or interfered with in any way. Therapists were asked to provide the type and dose of therapy that they believed was most suited to the patient, with a minimum of 1 scheduled individual session per week. Ancillary treatment could be prescribed as needed.
+Availability of Clinical Supervision Group. The CTBE therapists were paid at the same rate as that paid to DBT therapists. The CTBE therapists were not required to attend a weekly clinical supervision group.
+Institutional Prestige. The CTBE clinical supervision group met at the Seattle Psychoanalytic Society and Institute and was led by its training director. The institute’s prestige outside the field of behavior therapy in Seattle rivals that of the University of Washington within the field of behavior therapy.
+General Factors and Assistance Finding a Therapist. The participant coordinator established an independent relationship with subjects in both conditions. The participant coordinator also provided assistance in contacting the therapist and in getting the subject to the first session.
+Availability of Affordable and Sufficient Treatment Hours. The study paid for CTBE at the same rate as for DBT. All participants paid a small sliding fee for therapy as determined by the assessment team before condition assignment. No participants were dropped because of failure to pay.
+Sex, Training, and Clinical Experience. The randomization program assigned clients to DBT and CTBE therapists, matching on sex, doctoral vs master’s training, and years of clinical experience. Results indicated that therapists’ sex and training did not differ in the 2 conditions. The CTBE therapists, however, had more clinical experience, which was expected because they were selected for their expertise (Table 1).
+Recruiting of CTBE Therapists
+Ninety-four potential therapists were recruited. Based on their descriptions of the treatment that they usually provide to patients withBPD, therapists were categorized into 6 groups anchored by “behavior therapist” on one end and “very nonbehavioral” on the other end. The CTBE therapist pool consisted of 38 therapists selfdescribed as “eclectic but nonbehavioral” or “mostly psychodynamic.” No cognitive behavior therapists were selected for the CTBE condition. Of the potential 38 CTBE therapists, 25 were assigned patients (the remaining 13 potential therapists declined to accept a patient when one was assigned).
+STATISTICAL ANALYSIS
+Our primary method for analyzing repeated-measures data (pretreatment through the 24-month assessment) was mixed-effects models.43 Two versions of mixed-effects models were implemented: (1) random regression modeling (RRM), also known as hierarchical linear models and multilevel linear models,44-45 and (2) mixed-model analysis of variance (MMANOVA).46 Differences in rates of change (ie, slopes) were compared for the 2 treatment groups. Outcome variables with nonnormal distributions were recoded into discrete ordinal levels and were analyzed using RRM for ordinal data (eg, total nonsuicidal self-injurious acts were recoded into 5 ordinal levels).47-48
+Outcomes that were infrequent (and highly skewed) were analyzed by means of a generalized MMANOVA using all clients with any outcome data (n=97), with the pretreatment values as a covariate. The MMANOVA required that suicide attempts, emergency department visits, and inpatient admissions be recoded into binary variables (none vs any) per assessment period; therefore, absolute frequency of these outcomes could not be statistically compared.49
+Cox proportional hazards regression model survival analysis tested the time to first suicide attempt.50 For simple crosssectional comparisons (eg, pretreatment differences and manipulation checks), t tests were used for normally distributed variables, Mann-Whitney tests for nonnormal variables, and x2 tests for binary variables.
+To assess the potential effect of missing data (ie, ignorable vs informative missing data), a pattern-mixture analysis was implemented using 2-tailed tests.51 We defined patterns using a binary completer status variable, which was entered as a predictor in the RRM and MMANOVA. To determine if the slope differences depend on completer status, a 3-way interaction of completer status-X-treatment group-X-time was included in the hierarchical linear models. To determine if the mean difference between groups depends on completer status, a 2-way interaction of completer status-X-treatment group was included in the MMANOVA. The same analyses were conducted for treatment dropout status.
+With mixed-effects models, appropriate covariance structures were analytically determined. Determination of the appropriate covariance structure was based on a mixture of x2 tests in comparing nested models.52
+RESULTS
+SAMPLE CHARACTERISTICS AND BASELINE DIFFERENCES
+The treatment groups did not significantly differ on demographic characteristics, diagnoses, or pretreatment number of suicide attempts or nonsuicidal self-injuries. These results are summarized in Table 2.
+STUDY IMPLEMENTATION
+Dialectical behavior therapy adherence was rated for 51 client-therapist dyads across 571 therapy sessions. Adherence scores ranged from 2.5 to 4.8, with a mean ± SD score of 4.0±0.2.
+Compared with the CTBE group, the DBT group received significantly more therapy from study therapists, primarily because of their weekly group sessions and their greater treatment retention (Table 3). There were no significant differences in total hours of therapy, however, when all study-provided and non-study-provided treatment hours (group, individual, case management, day treatment, and inpatient treatment) were summed. There were no differences between conditions in the use or the types of psychotropic medications at pretreatment. During the treatment year, the use of psychotropic medications decreased significantly more in the DBT group than in the CTBE group (11=2.6, P<.01, RRM). There were no significant differences between conditions during the follow-up year (Figure 2). At the pretreatment and 4-month assessments, the DBT and CTBE groups did not significantly differ in their expectations of receiving help from the study therapy. The mean ± SD expectancy ratings were 5.52±0.95 and 5.42±1.02 at the pretreatment assessment and 5.46±0.98 and 5.29±1.37 at the 4-month assessment, for the DBT and CTBE groups, respectively.
+ANALYSIS OF THE EFFECT OF MISSING DATA PATTERNS
+Eight episodes were not included in the analyses of suicide attempts and nonsuicidal self-injury because the subjects did not provide enough information in the interviews to make that classification. The information was missing because the subjects refused to answer the question or said they could not remember, or the interviewer skipped needed questions because the subject was too emotionally distraught.
+Compared with subjects assigned to the DBT condition, subjects assigned to the CTBE condition were significantly more likely to drop out of study assessments during the treatment year (3.8% vs 20.4%, P =.01, Fisher exact test) and during the overall study (11.5% vs 28.6%, P = .03, Fisher exact test). We examined the effects of differential missing data and treatment dropout on each of our major outcome variables and found no evidence that the findings were biased by these differences.
+SUICIDAL BEHAVIORS
+There were no documented suicides in either condition during the 2-year study. The DBT group had half the rate of suicide attempts compared with the CTBE group (23.1% vs 46%, x2=5.98, P =.01; hazard ratio, 2.66, P = .005; and number needed to treat [NNT],4.24 [95% confidence in
+terval {CI}, 2.40-18.07]) (Figure3). The NNT of4.24in-dicates that, during 2 years of treatment plus follow-up, 4 patients would need to be treated with DBT to prevent 1 patient from attempting suicide. Similarly, halfas many subjects in the DBT group made nonambivalent suicide attempts (5.8% vs 13.3%, P= .18,FisherexacttestandNNT,13.3[95% CI, 5.28-25.41]). There were significantly fewer suicide at-
+tempts per period in the DBT group across the 2 years when controlling for the number of suicide attempts during the
+pretreatment year (F1,94=3.20, P=.04, MMANOVA). The mean proportions of suicide attempters per treatment group per period were6.2%(95% CI,3.1%-11.7%)and12.2%(95% CI, 7.1%-20.3%) for the DBT and CTBE groups, respectively. Across the 2 years, the median number of suicide attempts was 0 for the DBT group and for the CTBE group. The interquartile range for suicide attempts during the 2 years was 0 to 0 for the DBT group and 0 to 1 for the CTBE group. Similarly, both treatments were effective in reducing the number of nonsuicidal self-injuries (x2= 120.6, P<.001, ordinal RRM), but the difference in the rates of change was not significant (F1,99= 1.1, P=.15 [standardized effectsize, 0.47]) (Figure 4). The median number of nonsuicidal acts for the 2yearswas 3.0 (interquartile range, 1.0-7.8) for the DBT group and 3.0 (interquartile range, 0.0-8.0) for the CTBE group.
+As summarized in Table 4, among subjects with any suicide attempt or intentional self-injury during the treatment year, the highest medical risk was significantly lower for the DBT group than for the CTBE group (F1,156=3.2, P=.04). Both treatment groups made significant improvement in suicide ideation and in reasons for living (P<.001 for both), but the slope difference between conditions was not significant.
+USE OF CRISIS SERVICES
+Based on results of the MMANOVA, the DBT group used crisis services significantly less than the CTBE group through
+out the 2 years of the study (Table 5). Fewer DBT subjects went to the emergency department at least once for any psychiatric reason, including drug or alcohol problems (year 1: 43.1% of DBT subjects vs 57.8% of CTBE subjects, and year 2: 23.4% of DBT subjects vs 28.9% of CTBE subjects; F1>92=2.9,P= .04;NNT,9.09 [95% CI, 3.30-12.04]), orfor suicide ideation specifically (year 1:15.7% of DBT subjects had at least 1 visit vs 33.3% of CTBE subjects, and year 2: 10.6% of DBT subjects vs18.4% of CTBE subjects; F1>92=4.3, P=.02; NNT, 4.42 [95% CI, 2.49-19.76]).
+In addition, significantly fewer DBT subjects were admitted to hospitals for any psychiatric reason (year 1: 19.6% of DBT subjectshad at least 1 admission vs 48.9% of CTBE subjects, and year 2: 23.4% of DBT subjects vs 23.7% of CTBE subjects; F1>92=6.0, P = .007; NNT, 3.88 [95% CI, 2.26-13.71]) or for suicide ideation specifically (year 1: 9.8% of DBT subjects had at least 1 admission vs 35.6% of CTBE subjects, and year 2: 14.9% of DBT subjects vs 18.4% of CTBE subjects; F192=7.3, P = .004; NNT, 4.46 [95% CI, 2.53-19.17]).
+BEHAVIORS THAT INTERFERE WITH THERAPY
+More CTBE than DBT subjects dropped out of the study therapy (Table 3). Subjects could choose reassignment to up to 2 additional therapists for the same condition. Cox proportional hazards regression model survival analysis indicated that the risk of dropping out of therapy was 3 times higher for CTBE subjects for dropping the first therapist (hazard ratio, 3.2, P<.001; and NNT, 2.92 [95% CI, 1.91-6.21]) and for dropping therapy entirely (relative risk ratio, 2.7, P = .01; and NNT, 4.22 [95% CI, 2.4316.16]). In all, 29 CTBE subjects (59.2%) and 13 DBT subjects (25.0%) dropped their first study therapist. Twenty-one CTBE subjects (42.9%) and 10 DBT subjects (19.2%) dropped all study therapy (P = .005).
+BEHAVIORS REFLECTING QUALITY OF LIFE
+Both treatment groups had significant reductions in scores on the Hamilton Rating Scale for Depression-17 Item (P<.001 for both). However, the slope difference between the treatment groups was not significant (Table 4).
+COMMENT
+This study compared DBT with a rigorous comparison condition, nonbehavioral CTBE, to address whether the effectiveness of DBT in treating suicidal patients and patients with BPD can be accounted for by treatment factors common to most psychotherapy by experts. Results indicated that DBT was superior to CTBE in preventing suicide attempts, with a hazard ratio suggesting that suicide attempts can be reduced by half with DBT compared with non-behavioral therapy by experts. Dialectical behavior therapy was also more effective in reducing emergency department visits and inpatient psychiatric care for suicide ideation. In addition, DBT was more than twice as effective as non-behavioral therapy by experts in keeping subjects in treatment, as reflected by a 25% dropout rate from the first therapist in DBT compared with 59% in CTBE. The
+findings of this study indicate that the efficacy of DBT cannot reasonably be attributed solely to general factors associated with receiving expert psychotherapy.
+The hazard ratio for nonambivalent suicide attempts (ie, those with high intent and planning) was 2.2, almost identical to that for suicide attempts overall. (We cannot conclude that this is a statistically significant difference, as the low base rate of serious suicide attempts precludes adequate
+power in any single-site clinical trial.) This finding, however, combined with the significantly lower risk for any type of suicide attempt suggests that DBT may be uniquely effective in treating suicidal individuals. Similar to other DBT randomized trials,26,28-32,53 there was a lowmortality rate during the study. There were no documented suicides (other than 1 death in the CTBE group related to the cumulative effects of previous suicide attempts). The absence of any death by suicide may be due to any number of factors. Subjects were offered not only expert therapy but also extensive contact with an assessment team. They were mailed frequent nondemanding cards throughout the treatment and follow-up years. Such a regimen has been found to reduce completed suicides among individuals treated for suicidality.54
+In contrast to previous DBT randomized trials,25,28,31,32,54 there were no significant differences between conditions in the incidence or the frequency of nonsuicidal self-injury in our study. An examination of outcomes from the 1991 DBT trial ofsuicidal women withBPD suggests that non-behavioral CTBE may be more effective than TAU in reducing nonsuicidal self-injury. The mean ± SD number of nonsuicidal acts during the treatment year for subjects assigned to TAU in the 1991 study25 was 32.32±69.97, with half that many
+(16.82±60.81) for subjects assigned to CTBE in the present study; in contrast, DBT had outcomes in the present study almost identical to those found in the 1991 study (6.05±11.55 and 6.38±7.41 for the 1991 study and the present study, respectively). The differences between conditions in outcome variability across the 2 studies are remarkably similar, with DBT outcomes being consistently less variable across persons than those of either comparison condition. It is possible that the present study may have been underpowered to detect the 0.49 effect size of DBT vs CTBE.
+An alternate explanation of our findings maybe that subjects in CTBE underreported habitual self-injury to a greater extent than those in DBT. In a previous study30 among drug abusers, retrospective reports of opiate use at 4-month intervals to a blinded assessor were compared with contemporaneous urinalyses data. The correlation of DBT subjects’ self-reports with urinalyses data was 0.71, whereas the correlation in the nonbehavioral control condition was 0.02, despite a lack of any negative consequence for self-reporting drug use. Subjects in DBT self-reported using opiates more frequently even when thrice-weekly urinalyses indicated that they were actually using opiates less frequently.30 Daily diary keeping and weekly discussion of recorded behaviors
+most likely enhance memory of targeted behaviors in DBT.
+Subjects assigned to CTBE were also much more likely than those assigned to DBT to be hospitalized for suicide ideation, despite no apparent differences over time in suicide ideation for the 2 conditions. An important DBT strategy, at least as a first step, is to encourage outpatient use of behavioral skills over inpatient treatment even when suicidal risk is high. Consequently, we cannot determine from the present data whether the difference in hospitalization is due to a lesser tendency of DBT therapists to recommend inpatient treatment or to differences in actual need for hospitalization. There is no evidence that inpatient hospitalization is an effective treatment for suicidality. Therefore, it may be that inpatient admissions for suicide ideation were actually iatrogenic rather than therapeutic. These questions need to be addressed in future studies.
+Depression, suicide ideation, and reasons for living improved significantly in both conditions. The absence of significant differences on these measures is similar to our previous findings25 among severely suicidal patients with BPD and does not appear to be due to inadequate power. In contrast, a clinical trial among patients with less severe BPD found superiority of DBT compared with other treatments in reducing depression and suicide ideation.28 Both findings fit the 2-stage model of DBT that targets out-ofcontrol behaviors first (stage 1 target) among more severe patients (stage 1 DBT target) and targets “quiet desperation” such as depression (stage 2 DBT target) among less severe patients who demonstrate behavioral control.
+The overall dropout rate in CTBE was high. In addition, significantly more CTBE subjects were assigned a second or third therapist (24% of the CTBE group) compared with DBT subjects (10% of the DBT group). Because of this reassignment to new therapists and the greater tendency of CTBE subjects to see a therapist outside the study, the overall number of individual sessions was similar between conditions. Not surprisingly, DBT subjects had more group therapy hours. However, there were no significant differences between conditions in the total amount of therapy received when hours of individual, group, day treatment, and inpatient treatmentwere included. Therefore, the simple amount of therapeutic contact is not a viable explanation for differences in outcome between DBT and CTBE. A previous study20 comparing DBT group “skills training” combined with community psychotherapy vs community psychotherapy without group training skills found no significant benefit of the group component when added to non-DBT psychotherapy. However, we cannot rule out its effects when combined with individual DBT. A component analysis study is under way to tease out the effects of the DBT group component.
+Subjects assigned to DBT were less likely to continue taking psychotropic medications, including antidepressant drugs, during the treatment year. The use of psychotropic medications in general (and antidepressants specifically) resumed in the follow-up year, despite no apparent increase in depression or other dysfunctional behaviors. This suggests that medication use in this population may be influenced by psychotherapists’ treatment philosophy as much as by symptomatic need for medication.
+An innovative characteristic of this research was the development of the CTBE condition to control for nonspecific treatment factors of expert therapy. We could find no
+other published randomized trial that demonstrated the effectiveness of any other outpatient treatment for suicidal individuals with BPD. Therefore, there is no “standard” treatment with which to compare DBT. Indeed, with 6 previous randomized trials25,28-32,54 (more than any other treatment for suicidal behavior or BPD), DBT can be considered the current standard. Comparing DBT with an alternative, however, would provide little, if any, scientific information. Although knowing that one multicomponent treatment is more effective than another might be of clinical value in deciding which treatment to offer, it does not provide information about mechanisms of treatment and is of little help in developing more effective treatments.
+The major limitation of this study is that CTBE subjects dropped out of the entire study more often than DBT subjects, despite our best efforts to keep all participants enrolled. Careful statistical analyses, however, showed that this differential treatment dropout does not explain the differences in outcomes. Nonetheless, future studies must find a way to keep more suicidal patients with BPD enrolled. Although we attempted to control for allegiance and adherence to a coherent model by selecting expert therapists and by asking them to use the treatment that they believed to be most effective, results might be limited by the heterogeneity of the treatments used by the community experts compared with the more homogeneous DBT condition.
+Additional dismantling studies of DBT are also needed to clarify which components of DBT are essential and to what degree fidelity to the DBT manual is needed to achieve results comparable to those found herein and in other studies. This research is under way. Finally, further investigation is warranted on community-delivered psychotherapy by experts. Weekly non-behavioral CTBE may have much to recommend it as treatment for BPD. Nonetheless, findings from this study suggest that DBT may indeed be the superior intervention.
+Submitted for Publication: October 20,2004; final revision received December 1,2005; accepted December 8, 2005. Author Affiliations: Behavioral Research and Therapy Clinics and Department of Psychology (Drs Linehan, Brown, Heard, Korslund, and Reynolds, Ms Murray, and Messrs Tutek and Lindenboim) and Department of Psychiatry (Dr Comtois), University of Washington, Seattle; Department of Mathematics, West Chester University, West Chester, Pa (Dr Gallop), and Western Psychiatric Institute and Clinic, University of Pittsburgh, Pittsburgh, Pa (Dr Reynolds); British Isles DBT Training, St Louis, Mo (Dr Heard); and Arizona State Hospital, Phoenix (Mr Tutek).
+Correspondence: Marsha M. Linehan, PhD, Behavioral Research and Therapy Clinics, Department of Psychology, University of Washington, Campus Box 351525, Seattle, WA 98195-1525 (linehan@u.washington.edu). Funding/Support: This study was supported by grants MH34486 and MH01593 from the National Institute of Mental Health.
+Previous Presentation: This study was presented in part at the 36 th Annual Convention of the Association for Advancement of Behavior Therapy; November 14,2002; Reno, Nev. Acknowledgment: We thank the therapists and clients at the Behavioral Research and Therapy Clinics, with-
+out whom this research would not have been possible. We also thank Phillip Abrego, Audra Adelberger, Carol Barber, Marian Bauman, Susan Bland, Lynn Buell, Steve Clancy, Jim Crowe, Arthur Davis, Romalee Davis, Linda Dimeff, Brian Grant, Michael Gundle, Anna Harvey, Ruth Herman-Dunn, Marty Hoiness, Gerald Hover, Charlie Huffine, Claire Isaacson, Treg Isaacson, Susan Jenkins, Connie Kehrer-Sholl, Kelly Koerner, Glenn Leichman, Eric Levensky, Martha Lyttle, Michael Mandel, Joshua McDavid, Deb McGhee, Evelyn Mercier, Anna Meyer, Esther Neeser, Susan Radant, Shireen Rizvi, Barbara Sarda-rov, Jennifer Sayrs, Karen Schmaling, Henry Schmidt, John Schwab, Stacy Shaw-Welch, Stephen Sholl, Thao Truong, Sandra Walker, and Deborah Wothers for their tireless work. Gratitude is also extended to our colleagues in the Department of Psychology at the University of Washington and to the Office of Human Subjects for their continued support of our work.
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+Suicide is a major cause of premature death in the United States. In 2014, suicide was the second leading cause of death among people 10-34 years of age (1). Of the 42,773 suicides that year, 21,334 (49.9%) involved a firearm. Firearms accounted for 33,599 deaths in 2014 (suicides, homicides, accidental deaths, and undetermined), the third leading cause of violent death (1). The United States has the highest per capita rate of gun ownership in the developed world. About 38% of households own at least one gun, making firearms widely available to those at risk of suicidal or homicidal behaviors (2). This review focuses on prevention of firearm suicide but has relevance for other types of firearm deaths.
+Means restriction is a major method for reducing suicide rates (3). Examples include barbiturate restriction in Australia, pesticide regulations in Sri Lanka, and detoxification of domestic gas and packaging changes for analgesics in the United Kingdom (4-9). Reducing firearm access for those at risk of suicide may work for several reasons. Firearms are a highly lethal method for attempting suicide, with a mortality rate of 92%, compared with 67% for drowning, 78% for hanging, and 2% for intentional overdoses (10, 11). Because 70% of attempters take less than 1 hour between deciding to kill themselves and the actual attempt (12), they are more
+likely to use a method that is at hand. The transient nature of a suicidal crisis means that restricting access to more lethal means could allow the crisis to pass or result in an attempt with a less lethal method. About 90% of those who survive even an almost fatal suicide attempt do not go on to die by suicide later in life (13). Thus, surviving a suicide crisis offers a strong chance of long-term survival, and method substitution by a less lethal method often only modestly erodes the gains from restricting access to a more lethal method (14).
+In this review, we examine available evidence on the relationship between firearm ownership rates and firearm suicide rates and the potential effectiveness of policy-based strategies for preventing firearm suicides. Previous reviews are now dated (15-17) or only consider part of the available data, such as a meta-analysis of case-control studies (18). Unlike other recent reviews, we examine the relationship between firearm ownership and firearm suicide in casecontrol, ecological, and time-series studies based on data from the United States and other countries. We extrapolate from more definitive gun control measures implemented elsewhere in considering what can work in the United States given the social context and the constitutional limitations on firearm legislation.
+EVIDENCE REVIEW
+An electronic data search from 1980 to September 2015 was conducted using PubMed, PsycINFO, and MEDLINE to identify studies of the relationship between firearm availability and firearm suicide. No language or geographic limitations were employed. The terms suicide AND firearms OR guns were searched in the title, keywords, and abstract. After excluding 620 duplicates, we reviewed 1,687 articles, of which 60 were selected for inclusion because they directly examined the relationship between firearm suicide and firearm ownership, gun regulation, or prevention strategies. Excluded papers typically did not examine the relationship between firearm ownership and suicide risk. From the 60 selected articles, an additional 10 articles were identified, for a total of 70 studies (Figure 1). In addition, we searched more broadly using the top 100 hits on Google and Google Scholar for the period 1980-2016, which yielded one additional peer-reviewed article. We cite other sources in the text.
+Our review is based on 11 ecological or geographic area comparison studies of firearm ownership and suicide rates in the United States and elsewhere, five U.S. time-series studies of gun ownership and suicide rates, 15 case-control or ecological studies investigating the relationship between household firearm ownership and suicide risk, 13 targeted time-series studies examining the effects of firearm legislation or regulation, and seven studies involving geographic comparison of different legislative approaches across the United States. These studies are listed in Tables 1-5, along with summaries of their main findings. We also examined the relationship between firearm ownership and psychiatric illness and newer approaches to gun control and safety.
+FINDINGS
+Relationship of Firearm Ownership to Firearm Suicide Rates
+The United States has one of the highest firearm ownership rates of all developed nations (69), although precise gun ownership rates are not known because most U.S. states do not require firearm registration. Estimating household gun ownership through national and state-level surveys (21) means that data are not available on a regular or yearly basis. Therefore, proxies (i.e., hunting licenses, fraction of suicides that involve firearms, fraction of homicides that involve firearms, and gun magazine subscription rates in the era prior to online subscriptions) are also used to estimate firearm ownership (22, 70, 71). Approximately 57 million individuals own 283 million firearms (an average of five firearms per owner), and 38% of U.S. households own at least one gun (2). Although epidemiologic studies do not confirm causality, their findings can be consistent with a hypothetical causal model, such as one being examined here, in which firearm suicide rates are hypothesized to be partly driven by firearm availability. Studies in the United States show, at both ecological and individual levels, that greater firearm availability
+is associated with greater risk of firearm suicide (Tables 1 and 3). Globally, four studies of developed countries (Table 1) found that per capita gun ownership correlates with firearm suicide rates at the national level (26-29).
+Ecological Studies
+Time-series analyses in four studies examined the relationship between gun ownership and suicide rates in the United States (Table 2). From 1959 to 1979, there were 8,573 more suicides that appear to be attributable to the occurrence of 7,107 more firearm suicides (32, 34). From 1962 to 1975, the annual overall suicide rate rose from 10.9 to 12.9 per 100,000 (34). This rise in firearm suicides occurred in conjunction with the manufacture or importation of more than 6 million additional firearms in the United States (33).
+Beginning in the 1980s, this trend reversed (30). Over a 22-year period, household gun ownership dropped from ~46% in 1981 to ~36% in 2002. For every 10% decline in household firearm ownership rate, there was 4.2% decline in firearm suicide rate and a 2.5% decline in overall suicide rate. The reduction was more pronounced for children and adolescents, for whom every 10% decline in firearm ownership was accompanied by an 8.3% drop in firearm suicide rate and a 4.1% drop in overall suicide rate (30). This finding is consistent with studies showing that the relationship between firearm availability and firearm suicide is stronger for younger age groups than for midlife groups (31). During the same period, firearm homicide and overall homicide rates also declined comparably (72).
+The substantial geographic variability across the United States has allowed a time- and region-based examination of the firearm-suicide relationship (Table 1) (19-25). Over a 3-year period (2000-2002), the 15 states with the highest household firearm ownership (47%) had almost twice as many suicides (N=14,809) as the six states with the lowest firearm ownership (15%) (N=8,052). This difference in overall suicides is largely accounted for by the difference in firearm suicides (9,749 compared with 2,606). Non-firearm suicides (5,060 compared with 5,446) and the total populations of the two sets of states were comparable (21). During a more recent 2-year period (2008-2009), these findings persisted (19). If the relationship is causal, these findings suggest that a 1% decrease in household firearm ownership could reduce the firearm suicide rate by 3.5% and the overall suicide rate by 1.5%, with greater effects for adolescents (21). By extrapolation, reducing household firearm ownership by 5% could prevent about 3,000 suicides per year in the United States (1).
+Case-Control Studies
+The relationship between firearm ownership and suicide rates has been examined at the individual level (15 studies; Table 3). Many studies show a greater risk of firearm suicide in households with a gun (40-42, 44-49, 73). There are an estimated 37 firearm suicides for each gun use for self-defense (74). Firearms in the home are associated with a fivefold
+greater risk of suicide (42), and in firearm-owning households, 90% of suicides involve a firearm (42), compared with 50% of suicides in U.S. households as a whole (18). Firearm suicide rates in male and female veterans are elevated 81% and 188%, respectively, compared with demographically matched nonveterans, and this is attributed to higher gun ownership rates (43).
+Studies of household gun ownership and adolescent suicide rates (Table 3) (35-42) vary in sample size, with smaller studies being less reliable, but overall they indicate that a firearm in the home increases the risk of suicide (35-38, 42, 75). Guns are twice as common in homes of adolescent suicides compared with homes of nonfatal suicide attempters or of nonsuicidal psychiatric controls (35, 36). A loaded gun at home is associated with about a 10% higher risk of suicide (42). A locked gun closet, keeping guns unloaded, and locking ammunition or storing it in a different part of the house each reduces the risk of suicide by 55% — 73% (73). There is a trend for risk to increase with the number of handguns purchased (15).
+Firearm Ownership and Psychiatric Illness
+Firearm ownership rates correlate with firearm suicide rates, but not with non-firearm suicides or nonfatal suicide attempts (19), and rates of psychiatric illness in homes with a firearm are comparable with those in homes without a firearm (76-78), indicating that members of households with a firearm are not more suicidal than those without firearms (19, 22, 79, 80). Furthermore, nonfatal suicide attempt rates involve mostly means other than firearms and do not differ between high- and low-gun-ownership regions (19, 21). Firearm ownership rate, independently of
+underlying rate of nonfatal suicidal behavior, predicts suicide rate (19, 26).
+The Effect of Altering Firearm Availability and Ownership Rates on Firearm Suicide Rates Outside the United States
+Reducing the availability of firearms has lowered both firearm suicide rates and overall suicide rates outside the United States, but only where firearm suicides are prevalent (Table 4) (14, 27, 51, 52, 54-56, 59). Interrupted time-series analyses have been used to investigate the impact of gun control legislation on firearm-related outcomes. Legislation enacted in 2003-2004 in Switzerland halved the size of the civilian army (men 18-43 years old), instituted a fee to keep service firearms after discharge from the army, and introduced gun licenses (14). Before 2004, about half of all homes in Switzerland had a firearm, and firearms were used in 39% of suicides among men in the 18-43 age group. This legislation was followed by a steady decline in firearm and overall suicide rates in this demographic group. A decline in firearm suicides was not observed in other demographic groups, such as men in the 44-53 age group and women in the 18-44 age group. The Swiss firearm suicide rate and overall suicide rate declined at about the same rate during the period 2003-2008, and the only evidence for method substitution was a small increase in railway suicides that attenuated the decrease in suicide rate by only 22% over 5 years. In the United States, the rate of firearm suicides in 2014 among men in this age group was 11.04 per 100,000, for a total of 6,189 deaths. If the decrease in firearm suicide observed in Switzerland were to apply to this age group in the United States, it would potentially have saved 1,650 lives in 2014 (1).
+High firearm ownership rates, along with high rates of major depression, substance use disorders, and post-traumatic stress disorder, all conditions that carry a suicide risk, raise the risk of firearm suicide in veterans. The overall suicide rate is more than four times higher among active soldiers stationed in the United States compared with Europe, and the use of a non-military-issue firearm for suicide is 11/100,00 per year in the United States and zero in Europe (25). Firearm suicides while on leave in the United States involve personal guns, but on base or during deployment, they involve military-issue weapons (25). The lowering of suicide rates in the Israel Defense Forces by discontinuing the practice of soldiers taking their weapon home on leave (51) demonstrates the potential of firearm restriction for reducing suicide rates in the military, but it is likely to be less effective among U.S. service personnel on leave because of higher rates of personal gun ownership (43).
+Gun reform in Australia was enacted over several years but became much stricter in 1996 in response to a mass shooting that took 35 lives (56). The new laws required that firearms be registered to their licensed owners, prohibited private gun sales, and required a justificationbeyond just self-defense for firearm ownership. Police conducted large-scale gun buybacks that removed about one-fifth of the country’s guns
+from circulation. Starting before the 1996 reforms, and then perhaps accelerating after the 1996 reforms, there was a steady decline in national firearm suicide rates, but there is disagreement over means substitution by hanging (53,56, 81). Non-firearm suicide rates also declined, indicating that other factors were also operating (81). In the 18 years preceding the gun control reforms, there were 13 mass shootings in Australia, compared with none in the following 18 years, but the overall firearm homicide rate was not changed (56, 81).
+In New Zealand, less than 20% of suicides use firearms, less than half the rate in the United States. Legislation was introduced in 1992 in New Zealand in response to a lone gunman killing 13 people with a military-type assault weapon in 1990. This legislation required stricter licensing for gun ownership and locked storage for guns and ammunition. Police confirmed compliance by home visits. Before the legislation was enacted, firearm suicide accounted for 18% of all suicides in New Zealand, but by 2010, firearm suicide dropped to 8% of all suicides. The overall youth suicide rate declined transiently (1993-1996) (55). Strict gun control appears to reduce firearm suicides, but national rates are reduced only in those countries where gun ownership rates are higher and firearm suicides constitute a larger proportion of the overall suicide rate.
+Methods to Reduce Firearm Suicide Rates in the United States and Potential Effects on Overall Suicide Rates
+Successful gun control efforts in Switzerland, Australia, Canada, and New Zealand have proven impossible to duplicate in the United States. Because of Second Amendment rights, variations in state laws and regulations, and the opposition of gun rights advocates, such efforts have generally resulted in inaction or in a few states passing limited gun control or safety laws.
+Reducing access to firearms by at-risk individuals. The U.S. Supreme Court, in District of Columbia v. Heller, 554 U.S. 570 (2008), upheld an individual’s 2nd Amendment right to possess firearms, forcing gun control advocates to shift their legislative focus from broad-based to targeted restrictions. In response to mass killings, three states enacted legislation allowing temporary seizure and restriction of firearms for people deemed at risk of harming themselves or others, irrespective of psychiatric diagnosis (82-84). Although these policies were developed to prevent mass shootings, they also have the potential to reduce firearm suicides by restricting firearm access to those at risk of suicide. However, evaluation of their impact on suicide is needed (85). Media attention on mass shooters has exaggerated the appearance of a relationship between mental illness and firearm violence (86). Most individuals with psychiatric illness are never violent toward others, and psychiatric diagnosis alone is a poor predictor of future firearm violence (87). Policies focused on restricting firearm access based on dangerous behaviors, instead of psychiatric diagnosis, are more effective at reducing firearm violence (88, 89). Unlike interpersonal violence, suicide is a complication of psychiatric illness in almost 90% of cases (90, 91). However, it is difficult to develop policies to restrict firearm access for individuals at risk of suicide because of a psychiatric illness while respecting patient confidentiality and not discouraging psychiatric treatment.
+Targeted regulation, enacted in California in 2014 in response to the Isla Vista mass shooting, employed a “gun violence restraining order” (GVRO) (85). The GVRO models the legal process for domestic violence restraining orders, a strategy for preventing domestic violence adopted by all 50 U.S. states and the District of Columbia. The GVRO allows family members, significant others, and law enforcement personnel to formally request confiscation of firearms belonging to a person who may hurt him- or herself or others (85). A temporary or permanent GVRO is issued by the court, after which police are authorized to remove guns in the individual’s possession. If the order is permanent, it also prohibits the individual from purchasing firearms, through the National Instant Criminal Background Check System (NICS), for 1 year. After this 1-year period, affected individuals are eligible for a court review of risk, which may be followed by either continuation or cessation of the GVRO (85).
+Many individuals at risk of suicide have either undiagnosed or untreated psychiatric illness, and it is assumed that a family member or loved one may be best placed to notice alarming behaviors. Family members initiating a GVRO process could prevent firearm suicide and potentially lead to individuals receiving appropriate treatment. A limitation is that many families are unaware of the degree of psychopathology in at-risk individuals. Other potential gatekeepers are primary care physicians, who see almost half of all suicides within 30 days of their suicide, offering an opportunity for prevention (92). Guns used for suicide are bought a mean of 11 years before the suicide (15), so the implication for prevention is that it is more important for suicide prevention to restrict access to already owned guns by depressed suicidal persons than it is to prevent them from purchasing a gun (93).
+A similar policy in Indiana has an advantage over the California GVRO in that it permits police officers to seize firearms from individuals deemed dangerous, before receiving court approval (83). After a firearm seizure, the acting officer is required to submit evidence explaining why the individual was believed to be dangerous. Even if the firearm is returned, the seizure serves as a “cooling off” period, allowing the suicidal crisis to pass (83).
+The GVRO’s approach to restricting new gun purchases by at-risk individuals is also valuable, but it is limited by federal law to background checks via the NICS index, and reporting is mandatory only for firearm purchases made through licensed
+dealers (82). Because 30% — 40% of firearm sales take place in secondary markets, where buyers and sellers are unlicensed (94), extending mandatory reporting to all firearm sales and optimizing the completeness ofthe NICS national database is essential for maximizing the effectiveness of GVRO legislation background checks (82, 95).
+Strong local support from stakeholders, collaboration with experts in the field, and media engagement helped get the GVRO legislation passed in California (89). After firearm seizure legislation is passed, the public must be educated, with a special focus on families with a family history of suicide. In Indiana and Connecticut, ex post facto review of firearm seizure reports revealed that suicide risk was the
+most common reason for firearm seizure (83). The modest number of firearm seizures in both states indicates that these laws are not very effective (83). It is too soon to evaluate the GVRO in California. Enhancing awareness of such seizure laws through both suicide and firearm safety prevention groups may increase their impact on firearm suicide.
+Multifaceted or broader gun control legislation. Broader existing state firearm restrictions may reduce gun availability and thereby affect firearm suicide rates. States vary greatly in the stringency of such firearm laws. A review in 2005 concluded that there was insufficient evidence to support the effectiveness of state gun control legislation in reducing
+firearm-related deaths (17). However, recent studies in the United States (50, 62-67) and elsewhere (96) (Tables 4 and 5) are more encouraging about the benefits of multifaceted stricter firearm restrictions. Firearm restrictions, including permit to purchase (PTP), waiting periods, safe storage, background checks, and registration guidelines, are associated with both lower firearm suicide rates and overall suicide rates (62-68). Implementation of PTP laws in Connecticut was associated with a 15% reduction in the firearm suicide
+rate, and repeal of PTP laws in Missouri was associated with a 16% increase in the firearm suicide rate (50).
+Gun safety education. Nonlegislative strategies to prevent firearm suicides have been proposed. One approach uses education-based collaboration with family members of suicidal individuals, gun safety groups, and partners like firearm dealers (97,98). A commonly held misconception is that if one method of suicide is restricted, suicidal individuals will find
+another way to commit suicide (99). In fact, means restriction can save many lives because the suicide crisis resolves for many individuals before an alternative method is identified. Even in cases where an alternative method is identified, most methods other than firearms dramatically reduce the likelihood that the suicide attempt will be fatal (11). It is critical to educate people about the transient nature of suicidal crises and the potential lifesaving effects of restricting firearm access through safer storage or temporary off-site storage for individuals at risk of suicide and especially for adolescents (97). Means restriction counseling by gatekeepers like primary care physicians, mental health clinicians, and emergency department personnel for families with an adolescent at risk of suicide is against the law in Florida, demonstrating how challenging suicide prevention is in the United States (100, 101). Compliance has been inadequately studied, but one study found that only a quarter of the families of depressed adolescents heeded the clinician’s recommendations to remove firearms from the household for the duration of treatment (102).
+In response to a series of suicides in New Hampshire using newly purchased firearms, a recent initiative analyzed the potential of educating gun shop owners to prevent firearm suicides by learning to recognize suicidal customers and instead of selling them a gun, encouraging them to seek help, as well as by educating existing customers about the importance of safe or off-site gun storage if a member of the household is at risk of suicide (98). Roughly half of approached gun shops agreed to participate in the suicide prevention campaign. The willingness to participate was related to changing the gun
+retailers’ beliefs about the inevitability of suicide (98). Tennessee has initiated a similar program, but no results are available (http://tspn.org/gun-safety-project). Another public health approach involves a social media marketing campaign modeled on the Ad Council’s Drunk Driving Prevention campaign, “Friends Don’t Let Friends Drive Drunk.” This campaign would promote firearm safety as a social norm and urge friends and family of those at risk of suicide to temporarily store guns belonging to the individual in crisis, just as they would with a drunk friend’s car keys (98). While the effectiveness of such initiatives is unknown, efforts to change social values have contributed to the success of campaigns to encourage the use of seat belts, the practice of safe sex to prevent the spread of AIDS, and smoking cessation. Similar efforts in the domain of firearm safety emphasizing the value of safe storage and restricting firearm access for depressed and suicidal family and friends may lower firearm suicide rates when combined with referral for psychiatric help.
+Smart gun technology. Personalization or smart gun technology, such as fingerprint recognition, is a powerful tool that limits firearm access to the owner or permitted users (95). Although personalization technology is advocated for preventing criminals from using stolen firearms, it will potentially also reduce firearm suicides. About 40% of adolescents with psychiatric illness are living in a home with reported easy access to a firearm, and such technology can protect this large at-risk population (79). Legislation mandating personalization of firearms should mitigate concerns over allowing gun owners access to their own firearms and the
+impact of inadequate compliance with safe storage recommendations. One survey reported that 44% of Americans support government funding for research and development of personalized guns (86), and another survey found that 40% of gun owners would consider trading in old guns if safer technology became available (103). In reality, this approach has already met with such resistance that the one gun of this type currently on the U.S. market is sold only through the manufacturer’s web site because no gun stores would agree to carry it (103). Increased stakeholder support is needed for wider marketing of personalized guns, and this requires a change in social values regarding safe ownership and use of guns.
+Successful implementation of personalization technology for new guns needs to be extended to guns already in circulation by modifying them or trading them in for newer smart guns because of the huge number of firearms in circulation already. Restrictions on gun sales to at-risk individuals would have a limited immediate impact on firearm suicides in the United States relative to most countries because so many guns are already in circulation. Gun buyback programs have substantially reduced the number of firearms in other countries, but U.S. gun buyback programs have not, and consequently have failed to prevent firearm deaths (56, 104). The high firearm suicide mortality demands that current and new prevention approaches be implemented rapidly and outcomes measured (1). If personalization technology becomes common, it can prevent a household’s firearms from being used for suicide by family members other than the owner. Other strategies, such as educating gun shop staff about detection of suicide risk in their customers, are limited in their ability to protect gun owners, because less than 10% of the firearms used in suicides are purchased within 2 weeks of the suicide (15, 98), and the median interval between handgun purchase and suicide by the victim or another family member is 11 years, with the greatest risk in the first year after purchase (15). Reducing suicide risk for the actual gun owner will require broader GVRO-type approaches.
+CONCLUSIONS
+Shockingly, 21,334 lives were lost in 2014 to firearm suicide in the United States. Promising prevention methods must consider that firearm suicide overwhelmingly involves guns that are already purchased. Preventing at-risk individuals from having access to such guns requires targeted legislation like GVROs, safer storage, and smart-gun technology. Protecting gun owners requires better education of families and gun store owners. Effective implementation of such measures in U.S. society will require more legislative and social action than has been seen thus far. Society must come to value gun safety more in order to better protect depressed and suicidal individuals, and this requires public education and legislation requiring safer gun storage and smart-gun technology on all new firearms sold. All such initiatives must be accompanied by systematic evaluation of their effectiveness. Ultimately,
+such program evaluation and lifting of the ban on federal funding of research on firearm violence will help improve efforts to reduce firearm suicide mortality.
+PREVENTION OF FIREARM SUICIDE IN THE UNITED STATES
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+55.	Beautrais AL, Fergusson DM, Horwood LJ: Firearms legislation and reductions in firearm-related suicide deaths in New Zealand. Aust N Z J Psychiatry 2006; 40:253-259
+56.	Chapman S, Alpers P, Agho K, et al: Australia’s 1996 gun law reforms: faster falls in firearm deaths, firearm suicides, and a decade without mass shootings. Inj Prev 2006; 12:365-372
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+58.	Webster DW, Vernick JS, Zeoli AM, et al: Association between youth-focused firearm laws and youth suicides. JAMA 2004; 292: 594-601
+59.	Bridges FS: Gun control law (Bill C-17), suicide, and homicide in Canada. Psychol Rep 2004; 94:819-826
+60.	Ludwig J, Cook PJ: Homicide and suicide rates associated with implementation of the Brady Handgun Violence Prevention Act. JAMA 2000; 284:585-591
+61.	Loftin C, McDowall D, Wiersema B, et al: Effects of restrictive licensing of handguns on homicide and suicide in the District of Columbia. N Engl J Med 1991; 325:1615-1620
+62.	Anestis MD, Anestis JC: Suicide rates and state laws regulating access and exposure to handguns. Am J Public Health 2015; 105: 2049-2058
+63.	Anestis MD, Khazem LR, Law KC, et al: The association between state laws regulating handgun ownership and statewide suicide rates. Am J Public Health 2015; 105:2059-2067
+64.	Fleegler EW, Lee LK, Monuteaux MC, et al: Firearm legislation and firearm-related fatalities in the United States. JAMA Intern Med 2013; 173:732-740
+65.	Rodriguez Andres A, Hempstead K: Gun control and suicide: the impact of state firearm regulations in the United States, 1995-2004. Health Policy 2011; 101:95-103
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+MANN AND MICHEL
+66.	Price JH, Thompson AJ, Dake JA: Factors associated with state variations in homicide, suicide, and unintentional firearm deaths. J Community Health 2004; 29:271-283
+67.	Conner KR, Zhong Y: State firearm laws and rates of suicide in men and women. Am J Prev Med 2003; 25:320-324
+68.	Boor M, Bair JH: Suicide rates, handgun control laws, and sociodemographic variables. Psychol Rep 1990; 66:923-930
+69.	Graduate Institute of International Studies, Geneva (ed): Completing the count: civilian firearms, in Small Arms Survey 2007: Guns and the City. Cambridge, UK, Cambridge University Press, 2007, pp 39-71
+70.	Siegel M, Ross CS, King C 3rd: A new proxy measure for state-level gun ownership in studies of firearm injury prevention. Inj Prev 2014; 20:204-207
+71.	Duggan M: More guns, more crime. J Polit Econ 2001; 109:1086-1114
+72.	Siegel M, Ross CS, King C 3rd: The relationship between gun ownership and firearm homicide rates in the United States, 1981-2010. Am J Public Health 2013; 103:2098-2105
+73.	Grossman DC, Mueller BA, Riedy C, et al: Gun storage practices and risk of youth suicide and unintentional firearm injuries. JAMA 2005; 293:707-714
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+76.	Sorenson SB, Vittes KA: Mental health and firearms in communitybased surveys: implications for suicide prevention. Eval Rev 2008; 32:239-256
+77.	Miller M, Barber C, Azrael D, et al: Recent psychopathology, suicidal thoughts, and suicide attempts in households with and without firearms: findings from the National Comorbidity Study Replication. Inj Prev 2009; 15:183-187
+78.	Hemenway D, Miller M: Association of rates of household handgun ownership, lifetime major depression, and serious suicidal thoughts with rates of suicide across US census regions. Inj Prev 2002; 8:313-316
+79.	Simonetti JA, Mackelprang JL, Rowhani-Rahbar A, et al: Psychiatric comorbidity, suicidality, and in-home firearm access among a nationally representative sample of adolescents. JAMA Psychiatry 2015; 72:152-159
+80.	Betz ME, Barber C, Miller M: Suicidal behavior and firearm access: results from the second injury control and risk survey. Suicide Life Threat Behav 2011; 41:384-391
+81.	Baker J, McPhedran S: Gun laws and sudden death: did the Australian firearms legislation of 1996 make a difference? Br J Criminol 2006; 47:455-469
+82.	Swanson JW, Felthous AR: Guns, mental illness, and the law: introduction to this issue. Behav Sci Law 2015; 33:167-177
+83.	Parker GF: Circumstances and outcomes of a firearm seizure law: Marion County, Indiana, 2006-2013. Behav Sci Law 2015; 33: 308-322
+84.	Silver J, Fisher WH, Silver E: Preventing persons affected by serious mental illnesses from obtaining firearms: the evolution of law, policy, and practice in Massachusetts. Behav Sci Law 2015; 33:279-289
+85.	Frattaroli S, McGinty EE, Barnhorst A, et al: Gun violence restraining orders: alternative or adjunct to mental health-based restrictions on firearms? Behav Sci Law 2015; 33:290-307
+86.	Barry CL, McGinty EE, Vernick JS, et al: After Newtown: public opinion on gun policy and mental illness. N Engl J Med 2013; 368: 1077-1081
+87.	Swanson JW, McGinty EE, Fazel S, et al: Mental illness and reduction of gun violence and suicide: bringing epidemiologic research to policy. Ann Epidemiol 2015; 25:366-376.
+88.	McGinty EE, Frattaroli S, Appelbaum PS, et al: Using research evidence to reframe the policy debate around mental illness and guns: process and recommendations. Am J Public Health 2014; 104: e22-e26
+89.	Horwitz J, Grilley A, Kennedy O: Beyond the academic journal: unfreezing misconceptions about mental illness and gun violence through knowledge translation to decision-makers. Behav Sci Law 2015; 33:356-365
+90.	Arsenault-Lapierre G, Kim C, Turecki G: Psychiatric diagnoses in 3275 suicides: a meta-analysis. BMC Psychiatry 2004; 4:37
+91.	Isometsa ET: Psychological autopsy studies: a review. Eur Psychiatry 2001; 16:379-385
+92.	Luoma JB, Martin CE, Pearson JL: Contact with mental health and primary care providers before suicide: a review of the evidence. Am J Psychiatry 2002; 159:909-916
+93.	Gibbons RD, Brown CH, Hur K, et al: Suicidal thoughts and behavior with antidepressant treatment: reanalysis of the randomized placebo-controlled studies of fluoxetine and venlafaxine. Arch Gen Psychiatry 2012; 69:580-587
+94.	Siebel BJ: The case against the gun industry. Public Health Rep 2000; 115:410-418
+95.	Bloomberg MR, Webster DW, Vernick JS: Reducing Gun Violence in America: Informing Policy With Evidence and Analysis. Baltimore, Johns Hopkins University Press, 2013
+96.	Santaella-Tenorio J, Cerda M, Villaveces A, et al: What do we know about the association between firearm legislation and firearm-related injuries? Epidemiol Rev 2016; 38:140-157
+97.	Barber CW, Miller MJ: Reducing a suicidal person’s access to lethal means of suicide: a research agenda. Am J Prev Med 2014; 47(suppl 2):S264-S272
+98.	Vriniotis M, Barber C, Frank E, et al: A suicide prevention campaign for firearm dealers in New Hampshire. Suicide Life Threat Behav 2015; 45:157-163
+99.	Miller M, Azrael D, Hemenway D: Belief in the inevitability of suicide: results from a national survey. Suicide Life Threat Behav 2006; 36:1-11
+100.	Runyan CW, Brown TL, Brooks-Russell A: Preventing the invisible plague of firearm suicide. Am J Orthopsychiatry 2015; 85:221-224
+101.	Kuehn BM: Battle over Florida legislation casts a chill over gun inquiries. JAMA 2015; 313:1893-1895
+102.	Brent DA, Baugher M, Birmaher B, et al: Compliance with recommendations to remove firearms in families participating in a clinical trial for adolescent depression. J Am Acad Child Adolesc Psychiatry 2000; 39:1220-1226
+103.	Healy M: “Smart” guns may help prevent violence—if they can make it on the US market. Los Angeles Times, June 15, 2015
+104.	Braga AA, Wintemute GJ: Improving the potential effectiveness of gun buyback programs. Am J Prev Med 2013; 45:668-671
+ajp in Advance
+ajp.psychiatryonline.org 11
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+Introduction
+Since 8 December 2019, a cluster of acute respiratory illness, now known as Corona Virus Disease 2019 (COVID-19), occurred in Wuhan, Hubei Province, China (Paules et al., 2020; D Wang et al., 2020). Patients with COVID-19 have developed severe pneumonia, pulmonary oedema, or multiple organ failure and have died (Chen et al., 2020). As of 2 April 2020, a total of 82,735 COVID-19 cases in China have been confirmed and 3,327 Chinese died from the illness. Outside China, COVID-19 has affected more than 200 countries and regions worldwide (A total of 858,473 confirmed cases, and cumulative deaths reached 44,064) (World Health Organization, 2020a). On March 11, the WHO
+declared the COVID-19 outbreak could be characterized as a ‘Pandemic’ due to the virus spreads increasingly worldwide (World Health Organization, 2020b).
+Since Zhong Nanshan (the renowned Chinese respiratory expert who discovered the SARS virus) confirmed that the COVID-19 can be transmitted from human to human, more than 40,000 medical staff gave up the Spring Festival holiday and voluntarily applied to fight the outbreak in Hubei Province. In addition to causing physical damage, COVID-19 can also cause a serious psychological impact on the Chinese public. People have shown fear and anxiety behaviors, causing a significant shortage of masks and health equipment around the country. Besides, most frontline healthcare stiff worked more than 16 hours a day on average, causing them not to sleep enough. A 37-year-old Japanese government worker who was managing the isolated returnees from Wuhan, China was reported to have died from apparent suicide (The Japan Times, 2020).
+COVID-19 was found to be a distinct clade from the betacoronaviruses related to human severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) (Zhu et al., 2020). Previous research showed that psychological impact could occur in both healthcare workers and SARS survivors during the outbreak of SARS (Lee et al., 2007; Lu et al., 2006; McAlonan et al., 2007). Depressive disorders were showed to be one of the most profound and wide range of psychological conditions on the public (Mak et al., 2009). Similar results have also been reported in the previous findings of MERS (Lee et al., 2018). Currently, there is still no known information on the mental health burden of the general public during the COVID-19 outbreak. To our knowledge, most of the current studies related to this outbreak mainly focus on identifying the clinical or epidemiological characteristics of the infected cases, there is no relevant research examining the mental health burden on the COVID-19 on the general public in China. Based on the previous research evidence, we have reason to speculate that the mental health condition of the public may also be affected during the COVID-19 outbreak.
+Therefore, we conducted a web-based cross-sectional survey to examine the psychological impact on the COVID-19 on the Chinese public. We aimed to assess the mental health burden of the public during the COVID-19 outbreak and to identify the high-risk groups. Our findings may assist government advisors and healthcare professionals in providing targeted interventions for the public in the face of the COVID-19 outbreak expansion in China and the different parts of the world.
+Methods
+Study design and participants
+A web-based cross-sectional survey based on the Internet Survey on Emotional and Mental Health (ISEMH), an ongoing, online psychological-related behavior survey of Chinese public, was used to collect data from participants regarding their COVID-19 knowledge, psychological characteristics, and amount of time focusing on COVID-19. The use of this web-based approach to data collection avoided any risk of the spread of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). This web-based survey of the COVID-19 was broadcasted on the Internet through the WeChat public platform and the mainstream media. All Chinese public using WeChat, Weibo, or other
+social tools may see this survey, and answered the questionnaire by scanning the QR code of the questionnaire address or clicking the relevant link. This web-based questionnaire was completely voluntary and non-commercial.
+Ethics statement
+This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Huazhong University of Science and Technology Union Shenzhen Hospital. Electronic informed consent was obtained from each participant prior to starting the investigation. Participant could withdraw from the survey at any moment without providing any justification.
+Data collection and quality control
+Participants answered the questionnaires anonymously on the Internet from 3 February 2020 to 17 February 2020. All subjects reported their demographic data, COVID-19 related information, and three standardized questionnaires, which assessed their anxiety symptoms, depressive symptoms, and sleep quality. In order to ensure the quality of survey, we have set the response range of some items (e.g., some items needed to be answered in reverse) and encouraged participants to complete carefully through questionnaire explanations. In addition, we restricted each electronic device (by identifying the IP address) to complete the questionnaire once, so we can eliminate duplicate responses as much as possible. Finally, a total of 7,236 participants who completed the questionnaires were included in the analysis.
+Variable Measurement
+Demographic characteristics
+Demographic variables included gender, age, and individual occupations. Occupations including (a) Healthcare workers, including doctors, nurses, and health-related administrators; (b) Enterprise or institution workers, including enterprise employees, national/ provincial/municipal institution workers, and other relevant staff; (c) Teachers or students, including teachers or students from universities, middle schools, or elementary schools; and (d) Other occupations, including freelancers, retirees, and other relevant staff.
+Anxiety symptoms
+Chinese version of Generalized Anxiety Disorder-7 (GAD-7) scale was used to assess the subject’s anxiety symptoms. The GAD-7 has been previously used in Chinese populations, and found to have good reliability (Cronbach’s alpha = 0.90) (Tong et al., 2016; Wang et al., 2018). Total of seven items measured the frequency of anxiety symptoms over the past two weeks on a 4-point Likert-scale ranging from 0 (never) to 3 (nearly every day). The total score of GAD-7 ranged from 0 to 21, with increasing scores indicated more severe functional impairments of anxiety disorders (Spitzer et al., 2006). For the purpose of this study, we defined a GAD-total score of 9 points or greater as the presence of anxiety symptoms (Wang et al., 2018).
+4 @ Y. HUANG AND N. ZHAO
+Depressive symptoms
+We used the Center for Epidemiology Scale for Depression (CES-D) in Chinese version to identify whether participants had depressive symptoms (Zhang et al., 2010), and the Chinese version of this scale has been validated and extensively utilized in Chinese population (Zhang & Li, 2011; Zhang et al., 2010). Total of twenty items assess the frequency of depressive symptoms over the past two weeks on a 4-point Likert-scale ranging from 0 (rarely or none of the time) to 3 (most or all of the time). The score range of the CES-D is 0-60 points and higher scores indicated a more severe depressive symptom (Radloff, 1977). In this study, CES-D scores greater than 28 points indicated depressive symptoms.
+Sleep quality
+We used the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) scale to assess the subject’s sleep quality over the past two weeks (Liu et al., 1996). The Chinese version of PSQI has been demonstrated to be reliable and valid in Chinese population (Liu et al., 1996). The PSQI scale contains seven components including subjective sleep quality, sleep duration, sleep latency, habitual sleep efficiency, use of sleep medications, sleep disturbance, and daytime dysfunction, and the score for each component ranging from 0 to 3 points. The global PSQI score ranges from 0 to 21, with higher scores indicated a more severe sleep disorder (Buysse et al., 1989). In our study, a global PSQI score greater than 7 points indicated poor sleep quality.
+Knowledge of the COVID-19 outbreak
+The knowledge of the COVID-19 outbreak was evaluated by the following two items:
+(1) Times spent thinking about the COVID-19 outbreak, which measured the average time (in hours) the participants spent thinking about the COVID-19 outbreak information per day, by asking the participants: ‘How much time (in hours) do you spend thinking about the COVID-19 information per day on average? (Including reading outbreak information on the mobile or TV news, discussing the progress of the outbreak with family and friends, etc.)’;
+(2) Knowledge of the CO VID-19 outbreak, which assessed based on the following six judgment questions about COVID-19 outbreak-related knowledge: (1) The incubation period of the virus does not exceed 14 days; (2) Inhalation of droplets from sneezing, coughing, or talking of an infected person could cause infection; (3) Contact with something contaminated by an infected person could lead to infection; (4) Contact with an asymptomatic person might also lead to infection; (5) Taking ‘Shuanghuanglian Oral Liquid’ could prevent infection of this disease; (6) There are already targeted drugs that could cure the disease. Of the above six questions, 1 point was given for correct answers, and no points were given for incorrect or uncertain answers. Participants with scores >5 points, equal to 4 points, and <3 points were considered to be ‘Knowledgeable’, ‘Generally knowledgeable’, and ‘Not knowledgeable’.
+Statistical analysis
+All data were analyzed using SPSS version 24.0 (IBM Corp, Armonk, NY, USA). Descriptive statistics were conducted to describe the demographic characteristics and COVID-19-related knowledge in Chinese public. The prevalence of mental health burden stratified by gender, age, and occupations was reported in the general public and certain high-risk groups (such as healthcare workers and younger people), and Chisquare test (%2) was used to compare the differences between groups. P-values of less than 0.05 were considered statistically significant (2-sided tests). The alpha level for multiple comparisons were set by Bonferroni correction.
+Results
+Demographic characteristics
+The characteristics of the participants are shown in Table 1. Of the total 7,236 samples analyzed, 3,284 (45.4%) were males and 3,952 (54.6%) were females, and the mean (standard deviation) age of the participants was 35.3 ± 5.6 years. Among these samples, 2,250 (31.1%) of participants were healthcare workers, 3,155 (43.6%) participants focused on the COVID-19 for 3 hours or more every day, and 5,702 (78.8%) participants scored
+in the highest category, ‘Knowledgeable’, regarding COVID-19 knowledge. The overall prevalence of anxiety symptoms, depressive symptoms, and poor sleep quality was 35.1%, 20.1%, and 18.2%, respectively.
+Prevalence of mental health burden during COVID-19 outbreak stratified by gender, age, and occupations
+The prevalence of mental health burden stratified by gender, age, and occupations is shown in Figures 1-3, respectively. No statistically significant difference was found in the prevalence of mental health burden by gender (P > 0.05, as shown in Figure 1). The prevalence of anxiety symptoms and depressive symptoms was significantly higher in participants younger than 35 years than in participants aged 35 years or older (P < 0.001, as shown in Figure 2). Compared with other occupations, healthcare workers (23.6%) reported the highest rate of poor sleep quality (P < 0.001, as shown in Figure 3).
+Prevalence of mental health burden during COVID-19 outbreak in certain high-risk groups stratified by the times spent thinking about the COVID-19
+The prevalence of mental health burden in healthcare workers and young people (aged <35 years) stratified by the times spent thinking about COVID-19 is shown in
+Table 2. The prevalence of anxiety symptoms, depressive symptoms, and poor sleep quality was significantly higher in healthcare workers who spent a high level of time (>3 hours/day) on the COVID-19 than in those who spent less time (<1 hours/day and 1-2 hours/day) on the outbreak (P < 0.05). Compared with younger people who spent less time focusing on the COVID-19 information, younger people who spent >3 hours a day thinking about the outbreak had a significantly higher prevalence of anxiety symptoms (P < 0.001).
+Discussion
+In 2019, a cross-sectional epidemiological survey conduct by the China Mental Health Survey (CMHS) showed that 5.0% and 3.6% of Chinese adults have symptoms of anxiety and depressive disorder in the past 12-month, respectively (Huang et al., 2019). Compared to their findings, our web-based cross-sectional study identified a significantly higher prevalence of mental health burden in the Chinese public during the COVID-19 outbreak. Healthcare workers and younger people were at particular high-risk of anxiety disorder, especially when they spent too much time thinking about the outbreak. Our findings would provide data support for accurately understand the source of public’s panic during the COVID-19 outbreak.
+The data in this study suggested the public’s levels of anxiety-related symptoms increase when a major infectious disease occurred. Similar to the psychological burden caused by SARS (Su et al., 2007), we found that one in three participants showed anxiety disorders, and this mood was not different between male and female during COVID-19 outbreak, which was different from previous research that women were more likely to have anxiety than men (Gao et al., 2020; Guo et al., 2016). Besides, nearly one in five participants had depressive symptoms and sleep problems, indicating that the uncertainty of the outbreak progression would cause greater psychological stresses on the general public. The possible reason for these mental burdens may be related to the ‘hypochondriac concerns’ (worry about being infected) (Furer et al., 1997) and feared that the epidemic was hard to control. In addition, due to the long-term home isolation, the lack of sufficient sunlight might lead to a decrease in serotonin levels, which was associated with emotional disorders such as anxiety or depression (Lambert et al., 2002).
+Our findings showed that nearly one in four healthcare workers have sleep problems, which was significantly higher than other occupations. One possible reason is that many health workers are sent to the front to fight the sudden outbreak, the working intensity and time of healthcare workers will increase in the face of severe epidemic (e.g., SARS and MERS), resulting in them not having enough time to rest, and prone to chronic stress and psychological distress (Lee et al., 2018; Lu et al., 2006; McAlonan et al., 2007). In severe cases, PTSD symptoms may even occur, which is highly correlated with poor sleep (Kobayashi et al., 2007).
+In the study, we found that younger people (<35 years) were more likely to develop anxiety and depressive symptoms during the COVID-19 outbreak than older participants (>35 years). Our results were similar to those of a previous study in Taiwan during SARS outbreak (Su et al., 2007). More importantly, we found that times spent thinking about COVID-19 may be a potential risk factor for the psychological impact in certain high-risk groups. Healthcare workers who spent >3 hours a day thinking about the outbreak had a significantly higher prevalence of mental health burden than those who spent less time focusing on the outbreak. Similar results were also found in younger people. This may indicate that spending too much time focusing on the epidemic information during the COVID-19 outbreak may be harmful to the individual’s mental health and that these negative effects would be more pronounced for the high-risk groups (e.g., healthcare workers and young people) who are already vulnerable to the epidemic. The manifestation of this panic mood may be related to the body’s normal protective response to the stress caused by the epidemic (Maunder et al., 2003).
+The Chinese government has taken many strong national measures in time to avoid further spread of the COVID-19 outbreak, including requiring uninfected people to isolate themselves at home, prohibiting all gathering activities, and forcing everyone to wear medical masks to enter public places. However, there is still lack of relevant research on the targeted intervention of the public’s psychological burdens during the COVID-19 outbreak. We filled this research gap by analyzing the prevalence of psychological issue in the Chinese general public and examined the particular high-risk population. Some appropriate interventions are recommended as follows. First, the governmental organizations and various media outlets should further disseminate correct information to alleviate mental health burden among the people; Second, the national public health organization should establish an official, integrated, uniform mental health counseling platform to provide psychological counseling to people in need. Third, particular effect should be direct to vulnerable populations which include the suspected and diagnosed patients, younger people, and healthcare workers, especially physicians and nurses working directly with patients or quarantined people. Fourth, balance the spare time with other activities (e.g., exercise at home) and try to control the time of receiving outbreak information less than 2 hours a day, focus only on the necessary information (such as facts and data) and avoid receiving too many harmful rumors (Grein et al., 2000). Fifth, maintain normal work and rest as much as possible, exercise regularly to promote sleep quality, and do not pay too much attention to epidemic information before going to sleep.
+Several limitations should be noted when interpreting the findings of our study. First, the data and relevant analyses presented here were derived from a cross-sectional design, it is hard to make causal inferences. Second, the study was limited to the COVID-19 outbreak, leading to the sampling of our study was voluntary and conducted by online system. Therefore, the possibility of selection bias should be considered, which may be caused by overestimating the prevalence because people who voluntarily choose to participate in the survey may be more aware of their mental health issues than those who did not participate. Third, due to the web-based study design, the representativeness of the sample cannot be guaranteed, which may affect the external validity of the research results.
+Conclusion
+In conclusion, we identified a major psychological impact of the Chinese public during the COVID-19 outbreak, and younger people and healthcare workers were at an especially high-risk of displaying psychological issues when they spent too much time thinking about the outbreak. Psychological first aids could be delivered by someone who understands the basic principles, training community volunteers in the future might be an effective and sustainable way to alleviate the mental stress of the general public during times of crisis.
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+
+Introduction
+The role of immune activation in the development of psychiatric symptoms was first considered almost a century ago by Julius Wagner-Jauregg, who was awarded the Nobel Prize in 1927 for his discovery of the therapeutic use of malaria inoculation in the treatment of syphilitic psychosis. Our search of literature from the past few decades (for search terms, see appendix) has shown evidence of increased levels of peripheral inflammatory biomarkers in patients with psychosis or affective disorders; but it remains unclear whether this peripheral immune activation is associated with immune activation in the brain, and ultimately whether this peripheral activation is a causal or secondary pathological mechanism in these disorders. Since the 2000s, studies investigating the innate immune response in the CNS have started to emerge, focusing in particular on the activation of microglia, which act as resident macrophages of the CNS. The aim of this Review is to summarise and critically discuss studies that investigated central immune activation across psychiatric disorders. Throughout this Review, we will use the term neuroinflammation to denote the activation of immune cells in the CNS; therefore, our use of the term neuroinflammation does not include the attraction or recruitment processes of other cells into the CNS, as is the traditional meaning.
+Phenotype and function of microglia
+Microglia are the resident macrophages of the CNS and represent 5-10% of all CNS cells.1 One of the main recognised functions of microglia is to provide the brain’s first line of self-defence against tissue damage and infection by pathogen recognition, phagocytosis, and antigen presentation. In addition to this function, microglia also regulate a wider series of processes that are needed for CNS development and homoeostasis, including control of cell numbers, and the formation and refinement of neural circuits.1
+In the healthy brain, microglia are located relatively uniformly throughout the parenchyma. The morphology of microglia might reflect their functional capacity: specifically, in a healthy brain, microglia are usually in a
+quiescent state and present a downregulated phenotype with a ramified shape with short, fine, highly motile processes, which allow an increased surface area for tissue surveillance. When an insult occurs (eg, injury or inflammation), microglia morphology changes over time with shortening and extensive branching of processes, hypertrophy of the cell body, and the release of different inflammatory mediators, including cytokines. Classically, this process is referred to as microglial activation.2
+Since the 2000s, this basic idea of microglial activation has been refined and studies have tried to differentiate subtypes of reactive microglial phenotypes, depending on the nature of the immune-related signals to which they are exposed. Specifically, studies have described an M1 phenotype, induced by molecules such as lipopolysaccharide that release proinflammatory mediators;3 and an M2 phenotype, induced by anti-inflammatory molecules, such as interleukin 4 that release major anti-inflammatory cytokines to antagonise the proinflammatory responses.3 Following this differentiation, activated microglia could therefore be either cytotoxic or neuroprotective; therefore, although brain pathology is usually associated with the activation of microglia, it remains unclear whether, in the context of psychiatric disorders, this activation is beneficial or detrimental. Indeed, whether this represents real inflammation or simply a homoeostatic adaptive function is unclear.4 In 2016, with the increased body of research on microglia, the classification of the M1 and M2 phenotypes was challenged.5 This is because the M1 and M2 classification has been developed mainly from the study of peripheral macrophages rather than microglia, and because the definitions of the M1 and M2 phenotypes have been developed from the exposure of microglial cells to stimuli in vitro, whereas the M1 and M2 states consistently fail to emerge as isolated phenomena in vivo.5 The microglial M1 or M2 classification is therefore gradually disappearing, although another hypothesis is that the microglial phenotype might vary along a continuum.5
+A further important methodological problem is that morphological analysis alone is often used to report glial activation state. While microglia undergo cytoskeletal rearrangements that alter their morphology,6 these morphological changes might not accurately represent an
+active inflammatory profile. In particular, unlike macrophages, a recent study could not identify any morphological differences between M1 and M2 adult human microglia.7 Furthermore, one study in mice following peripheral lipopolysaccharide injection8 found a correlation between increase in cytokine mRNA in microglia and active sickness behaviour; however, this behaviour preceded any alteration in microglial morph-ology.8 In fact, morphological alterations in microglia following lipopolysaccharide stimulation were delayed and only evident during the resolution or recovery phase of the immune response to lipopolysaccharide.8
+Evidence of microglia activation in psychiatric disorders
+Increasing evidence has supported the role of activated microglia in the development of psychiatric disorders. However, some of the most compelling data have come from animal models of depression.9,10 We review the evidence from post-mortem studies and data from in-vivo PET studies. We also summarise indirect in-vivo neuroimaging evidence of central immune activation in patients with psychosis or affective disorders, tested using MRI techniques.
+Immunohistochemistry or post-mortem studies
+A recent meta-analysis of post-mortem studies in schizophrenia has identified 22 studies that investigated microglia.11 11 of these studies found increased levels of microglia markers, three studies reported a decrease, and eight studies did not detect any difference. Most of the studies focused on immunostaining or gene expression of HLAs, because the induction of major histocompatibility complex class II in microglia is considered a sensitive marker of neuroinflammation in histological studies. It has been suggested that inconsistencies across the studies might be partly due to the heterogeneity in study design, from the type of brain region analysed to the markers measured and the stage of the disorder. Furthermore, although some studies considered the effect of death by suicide on their measurements, many ofthe studies included in this metaanalysis did not report these data. Suicide has been linked to increased neuroinflammation and the absence of information on suicide as a possible cause of death is an important limitation in the interpretation of these data.
+In 2014, Torres-Platas and colleagues12 provided the first evidence of increased microglial activation in post-mortem brain samples from middle-aged people with depression who died by suicide. Previously in 2008, Steiner and colleagues13 found increased cerebral grey matter HLA-DR-immunoreactive microglial densities in people who died by suicide; however, this was not specific for patients with depression, because the study included patients suffering from schizophrenia or affective disorders. In 2014, Schnieder and colleagues14 investigated markers of neuroinflammation, including the density of microglia,
+in post-mortem brains of patients with affective disorders, patients with schizophrenia, and individuals with no psychiatric diagnosis. The authors did not find an effect of psychiatric diagnosis, but found a significant effect of suicide on density of perivascular cells in the dorsal white matter. The findings, which were more specific for suicide than for a psychiatric diagnosis, support the stress or diathesis model of suicide. According to this model, acute stress could activate an immune response in the brain that might put an individual at high risk for suicide.14 Finally, one post-mortem study using next-generation sequencing to quantify neuroimmune mRNA expression showed an increase in activation of inflammatory response pathways in the dorsolateral prefrontal cortex of patients with schizophrenia compared with healthy controls,15 and this increased expression was present in approximately 40% of patients with schizophrenia.
+PET and in-vivo studies
+Increased activation of microglia in vivo can now be investigated with PET using radioligands for the 18-kDa translocator protein. Translocator protein is a five-membrane domain protein localised mainly in the outer mitochondrial membrane of steroid-synthesising cells, including those in the CNS—endothelial and glial cells in particular.16 One of translocator protein’s main functions is the transport of cholesterol into mitochondria, a prerequisite for steroid synthesis.17 Peripheral lipopolysaccharide injection in human primates has been shown to increase translocator protein expression in a uniform manner across the brain.18 Increases in expression of translocator protein are associated with microglial activation and are robustly associated with expression of allograft inflammatory factor 1 (Aif1, commonly known as Iba1) in preclinical models and histological markers of brain activation in human post-mortem tissue.19-21
+nC-(R)-PK11195 is one of the main radiotracers that have been used to study brain translocator protein expression in vivo. However, given the increasing research interest in in-vivo neuroinflammation, few second-generation translocator protein radiotracers have been developed with increased affinity and possibly improved signal-to-noise ratios. The studies should be interpreted in view of the notable problems affecting the quantification of translocator protein density using PET.22 First, the affinity of second-generation radiotracers for the translocator protein depends on the recently discovered rs6971 nucleotide polymorphism in the translocator protein gene.23 Second, quantification is hampered by the cellular heterogeneity of translocator protein in brain tissue (present in microglia and astrocytes, but also endothelium), the presence of translocator protein (and its variability) in blood cells, and the high affinity of translocator protein radioligands for plasma proteins, and particularly for proteins upregulated during peripheral inflammatory events.22 Thus, peripheral inflammation is likely to increase the retention of these radiotracers in
+plasma, thereby reducing their uptake by the brain. No analytical methods are sensitive enough to correct for these effects, which might result in the underestimation of translocator protein density in the diseased cohorts. A summary of the translocator protein-PET studies conducted in patients with psychosis, depression, or bipolar disorder is shown in the table.
+To date, three studies have been published investigating microglial activity using translocator protein-PET brain imaging in patients with depression.24-26 Two of these studies reported increased translocator protein expression in patients with depression and one found no difference. The negative study investigated brain translocator protein expression in patients with mild-to-moderate depression and low peripheral inflammation (defined as blood C-reactive protein concentrations <5 mg/L); by contrast, the two studies reporting increased translocator protein expression in patients with depression focused on patients exhibiting more severe depression or significantly higher peripheral inflammation.25,26 The one study of patients with bipolar disorder showed an increase in 11C-PK11195 binding in the right hippocampus of patients with bipolar disorder compared with controls.27 Most patients were euthymic at the time of the imaging.27
+To date, nine studies have been published that investigated microglial activity using translocator proteinPET brain imaging in patients with psychosis: three of
+these studies found an increase in translocator protein binding, one found an increase in medicated patients but not in drug-naive patients, and the other five did not find significant differences between patients and matched controls.28-36 Some ofthe inconsistencies across the studies could partly be explained by the problems with using second-generation radiotracers. In particular, although 11C-PK11195 quantification uses only brain data and seeks to quantify microglial activity using an inflammation-free tissue in the parenchyma detected by automatic computing methods,37,38 the quantification of second-generation tracers, including 18F-FEPPA, uses plasma and brain radioactive concentrations. In our view, translocator protein reductions could thus be due to the anomalous retention of the tracer in blood because of increased plasma protein binding caused by peripheral immunity, as previously mentioned, which is not accounted for in the quantification.39
+In agreement with this view, the study by Bloomfield and colleagues32 showed an increase of 11C-PBR28 binding in patients with schizophrenia, obtained using plasma radioactivity as input for quantification; however, this increase became evident only when data were normalised to the whole brain uptake. In other studies,33,35 the findings remained negative even after the distribution volume was corrected for the whole brain grey matter translocator protein binding, highlighting that the discrepancies
+across studies might not all be ascribed to differences in methods.
+The most recently published study reported no differences in HC-PK11195 binding between antipsychotic-free patients and controls but found increased HC-PK11195 binding in medicated patients with schizophrenia compared with controls, which might suggest a possible effect ofantipsychotics on translocator protein expression.36 Another possible interpretation of the increased translocator protein binding in the medicated patients is that the increased translocator protein binding denotes a condition of treatment resistance, since the medicated group of patients in this study was significantly symptomatic despite treatment with antipsychotic drugs.
+Anatomical and diffusion MRI correlates of microglial activation
+Unlike PET, MRI does not have the specificity to detect microglial activation. However, MRI methods can be used to provide indirect measures of neuroinflammation, such as blood-brain barrier breakdown, interstitial microenvironment changes, cellular infiltration, and gliotic reaction.40
+The interpretation of the T2 relaxation time signal has been taken by some as providing indirect evidence of neuroinflammation. This is because T2-weighted images are sensitive to molecular motion and to interactions between neighbouring molecules. As such, they provide high contrast for cerebrospinal fluid, which has application in pathologies characterised by water accumulated within tissue. However, the T2 signal could be increased by other molecular changes that occur in tissue composition and in iron, so the exact interpretation of its contrast remains uncertain.
+Some studies have used magnetisation transfer imaging (MTI) and provided values for magnetisation transfer ratio (MTR), which should be sensitive to changes in the water content of tissue due to inflammation.41 However, the fact that MTR might also reflect demyelination complicates the evidence from studies of psychiatric disorders.
+Diffusion tensor imaging (DTI) is another approach used to investigate white matter microstructure. However, the two main measures provided by DTI, fractional anisotropy and mean diffusivity, are not specific, and can reflect alterations in myelination, fibre organisation, or membrane permeability. More recently, free-water fraction DTI has been proposed to reflect more accurately the presence of neuroinflammation in white matter.42
+Although some functional MRI studies of models of inflammation-induced depression have been published, we did not identify any anatomical MRI or DTI study that used the measures described above to investigate the presence of neuroinflammation in patients with depression or bipolar disorder.
+A few neuroimaging studies have assessed T2 signals in MRI scans of relatively small samples of patients with schizophrenia. These studies have mostly found an
+increase in the T2 signal in these patients, particularly in the frontal and temporal cortex white matter.43-45 These findings are suggestive of neuroinflammation;45 however, increases in T2 signals could reflect other types of changes in tissue composition.
+MTI studies have reported conflicting findings. Some have reported significantly higher MTR in the uncinate, arcuate, and inferior frontal occipital fasciculi of patients with schizophrenia than healthy controls, and interpreted this finding as evidence of neuroinflammation.4647 However, others have found decreased MTR in various areas, including frontal and temporal regions, and in the uncinate and superior occipitofrontal fasciculi, cingulum bundle, corpus callosum, internal capsule, and fornix, in patients with schizophrenia compared with healthy controls,48-51 and no evidence of MTR differences in the thalamus.52
+Studies that have used free-water corrected DTI have shown evidence that supports the presence of neuroinflammation at the time of the first episode of schizophrenia. For example, Pasternak and colleagues53 found that most of the differences seen at the first episode of schizophrenia could be explained by an increase in extracellular volume of grey and white matter. The same group found a smaller abnormal increase in the volume of the extracellular space in chronic schizophrenia, suggesting a less extensive neuroinflammatory response at this later stage.54 Still, in patients with chronic schizophrenia, the presence of active symptomatology (delusions) has been associated with extracellular free water in the left cingulum bundle, again suggestive of neuroinflammation in the more acute illness states.55
+In conclusion, MRI offers enormous potential for the in-vivo study of neuroinflammation, and it has several advantages: it is largely available; does not require the use of radioactive ligands and is therefore safer for repeated scanning studies; and is considerably less expensive than other techniques. The ongoing development of new MRI techniques will eventually help to dissect the various components of the neuroinflammatory processes that have become increasingly implicated in the pathophysiology of psychiatric disorders.
+Limitations of clinical studies
+Human post-mortem and in-vivo imaging studies of patients with psychiatric disorders have some notable limitations. The majority of the post-mortem studies have been conducted on tissue from patients with heterogeneous clinical presentations, often with a long duration of illness and decades of antipsychotic exposure, as well as other potential confounders, such as age-related incidental lesions. Therefore, definitively distinguishing drug-related changes from those related to disease is difficult.56 Clearly, clinical imaging findings using either PET or MRI offer different information from post-mortem neuropathology assessment, and do
+not provide information on the phenotype of microglia in vivo following antipsychotic treatment.57
+Perhaps most crucially, other risk factors for psychosis or depression might be hypothesised to drive the changes in neuroinflammation and microglia, but these cannot be easily tested in clinical studies. Of these, psychosocial stressors are a well described and documented risk factor for psychiatric disorders,58-60 and neuroinflammation, in particular elevated microglial activity, has been proposed to mediate this association.6162 This raises the question of whether the putative microglial activation and inflammation seen in the brains of these patients is linked to the neurobiology of their illness or to the effects of stress and other confounders, including alcohol intake, cigarette smoking, and other lifestyle or environmental factors.
+Microglia in animal models of stress
+Trying to disentangle whether microglia activation is associated with the neurobiology of the psychiatric illness or with other confounding factors is difficult in patient populations. Although rodent models cannot recapitulate all features of human psychiatric disorders, they allow invasive studies to probe the neurobiology associated with specific genetic or environmental risk factors linked to psychiatric disorders.6164 This can also be applied to elucidate the effects of other potential confounders, such as exposure to antipsychotic medication.57,65,66
+Much is known about the effects of stress on microglia from animal studies—which use a wide range of different psychosocial stressors (panel). In the context of microglial activation following stress exposure, Calcia and colleagues67 note that at least 11 published studies report increases in the expression of the microglia or macrophage cell surface marker Ibal in the rodent hippocampus, albeit over a wide range of magnitudes, depending on the type and duration of the exposure to stress.67 In the prefrontal cortex, at least ten published studies have reported that exposure to diverse psychosocial stressors results in increased concentrations of Ibal.67 Under stress conditions, microglial activation has also been reported in other brain areas, such as the nucleus accumbens, amygdala, and paraventricular nucleus.67 One might therefore conclude that, at least in rodents, exposure to psychosocial stressors has an effect on central Ibal-positive microglia in brain regions relevant to the neurobiology of psychiatric disorders.67
+However, to focus exclusively on stress exposure as an inducer of microglial activation in animals might be considered too reductionist. In fact, a wide range of factors relevant to or implicated in psychiatric disorders, including repeated exposure to noxious insults or direct proinflammatory stimuli, such as repeated exposure to infectious pathogens or perinatal malnutrition, can induce central microglial activation.68-71
+The morphology or number of microglia, or both, is only partially (sometimes poorly) linked to the true
+functional state of these cells following exposure to stressors. The precise nature of these microglial changes and their clinical implications are therefore difficult to establish. For example, whether they represent inflammation or homoeostatic changes and whether these are beneficial, or adverse, or even epiphenomena. Perhaps most relevant for the clinical studies described herein, the method by which exposure to stress or other immune stimuli in animals corresponds to changes in translocator protein within microglia in vivo is unknown. A study published in 20l7 of an infection-mediated neurodevelopmental mouse model shows that behavioural abnormalities relevant to schizophrenia and increased proinflammatory cytokine expression are associated with decreased translocator protein concentrations in the prefrontal cortex.72 In the same study, translocator protein concentrations were strongly increased in a mouse model of acute neurodegeneration and reactive gliosis induced by intrahippocampal injection of kainic acid.72 These findings suggest the need to quantify cytokines and other inflammatory biomarkers, microglial number, morphology, and transcriptional phenotype and determine how these relate to changes in translocator protein concentrations following stress.
+Notably, translocator protein is not a typical immune-related target, but a mitochondrial protein generally activated under conditions of mitochondrial stress. In the brain, translocator protein is mostly expressed by microglia and cells of the vasculature (eg, endothelial cells and perivascular macrophages) and is not expressed in neurons.73 Therefore, cellular mechanisms of mitochondrial regulation as a consequence of
+Panel: Definition of psychosocial stress in animal experiments
+In the context of preclinical research in animal models on the links between stress and central or peripheral inflammation, the term psychosocial stress could be defined as any experimental protocol that results in the expression of contextual distress in the model animal. Such protocols might include direct physical stimuli that are stressful for the animal, including restraint and foot shock, which could be applied to parent animals to induce maternal or perinatal stress. Alternatively, environmental stressors could be used, including tilted cages, soiled bedding, exposure to predator odours, altered lighting conditions, social isolation or overcrowding, short-term deprivation of water or food, or repeated exposure to social defeat by a larger, older intruder animal. These stresses could be applied singly or in a repeated, variable manner as in the widely used chronic mild stress paradigm. All of the above stressors have been suggested as valid preclinical models of different psychosocial stressors that have been linked to a higher incidence of psychiatric illness in humans.
+Figure: Proposed model of how psychosocial stress can increase microglial activation in a subsample of patients with psychiatric disorders across different diagnostic categories, possibly through increased peripheral inflammation
+The model proposes that patients with increased microglial activation have severe—rather than mild or moderate—psychiatric disorders, and include patients who are more resistant to treatment. Anti-inflammatory treatment targeting microglial activation could specifically be more effective in patients who present increased microglial activation.
+environmental stress are active only on the microglia and cells of the vasculature. From a molecular perspective, translocator protein upregulation in patients with psychiatric disorders could indicate that stress is the driving mechanism of microglial activation, not only through peripheral inflammation, but also through mitochondria-related mechanisms, rather than the disease per se.
+Discussion
+Increasing evidence points towards an activation of microglia in patients with psychiatric disorders; however, this activation is not consistently reported by all patients and appears not to be specific for any particular diagnostic category. Some post-mortem studies have shown that increased density of activated microglia, or increased density of perivascular cells, is associated with suicide rather than a specific psychiatric diagnosis. These findings suggest that neuroinflammation, and more specifically the activation of microglia, might play a role in more severe states of psychiatric disorders. This role might not be specific for patients at higher risk for suicide but could involve a reduced response to traditional psychotropic medications. Indeed, although direct evidence linking treatment response to microglial activation is not available, studies have shown that peripheral immune activation from blood samples is associated with poor treatment response both in patients with depression and in patients with psychosis.74,75 PET studies in patients with depression also found increased translocator protein binding, mainly in patients with severe depression rather than mild or moderate depression.25,27 We hypothesise that microglial activation is a marker for severe and untreatable psychiatric disorders.
+Why are microglia activated in some but not all patients with psychiatric disorders?
+To understand why only some patients with psychiatric disorders show activation of microglia, we need to understand the underlying physiology of microglial activation. Microglia are usually activated in the presence of harmful stimuli, which can be inflammatory stimuli, such as an infection, or psychosocial stressors. Psychosocial stress is one of the main risk factors for the development and relapse of psychiatric disorders, and various preclinical studies have shown that psychosocial stressors lead to the activation of microglia across different brain regions.67
+Although no direct evidence that stress leads to activation of microglia in human beings is available, our recent meta-analysis of clinical studies has shown that childhood traumatic events are associated with higher blood concentrations of inflammatory markers in adulthood.62 These observations support the hypothesis that microglial activation might represent a marker of stress rather than of the mental health problem. Therefore, the activation of microglia would work as a mediating mechanism between the stressful event and the development, increased severity, and resistance to treatment of the psychiatric condition (figure).
+Notwithstanding the clear evidence of increased peripheral inflammation in psychiatric disorders, the link between peripheral inflammation and microglia activation remains elusive. One study from 2015, using lipopolysaccharide injection in human beings in vivo, has shown that peripheral immune activation is associated with robust microglial activation measured through translocator protein binding,76 supporting the idea that the presence of peripheral immune activation in these patients might mirror a central immune activation. However, changes in peripheral immune-related proteins might confound these results; whether the peripheral immune activation precedes the central immune activation or vice versa in the patients is still unclear. Notably, the few studies that have tried to assess correlations between central (translocator protein binding) and peripheral measures of inflammation have so far not reported positive results.25,36 A recent review by Weber and colleagues77 discussed the evidence of a bidirectional communication between the CNS and peripheral immune system in response to stress and how this contributes to an increase in neuroinflammation. More specifically, the activation of the sympathetic nervous system in response to stress might contribute to a shift in haemopoiesis and a new generation of monocytes that appear less mature and more involved in inflammatory processes than maintaining homoeostasis; these monocytes are actively recruited to the brain to promote neuroinflammation.77 This shift provokes the question: is the degree of peripheral immune activation relevant to central inflammation? The evidence that only patients with
+peripheral inflammation above a specific threshold respond to anti-inflammatory treatment78 and do not respond to antidepressants and antipsychotics74,75 indicates that the degree of peripheral immune activation is important for detection of microglial activation.
+Implications for treatment
+Given the growing evidence of the role of microglial activation in psychiatric disorders, both academia and industry have unsurprisingly shown increasing interest in identifying and developing drugs that inhibit microglial activation.79
+One of the drugs proposed to have an effect in reducing microglia activation is minocycline, a broad-spectrum tetracycline antibiotic with broad anti-inflammatory actions. Various studies have shown that minocycline reduces microglial activation after stress or immune stimulation.80 In a study using chronic stress in rats, minocycline prevented both microglial activation and the depressive behaviour induced by chronic stress.81 This ability of minocycline to reduce microglial activation could be due to its broader spectrum of anti-inflammatory actions than other antibiotics; indeed, one review has challenged the suggestion that minocycline is a selective microglial inhibitor, because this drug affects peripheral immune cells as well as astrocytes, oligodendrocytes, and neurons.82 Clinical trials using minocycline in patients with schizophrenia or depression are showing some promising but inconsistent results.83,84 The inconsistent results could be due to the poor stratification of patients in these trials. Indeed, microglial activation appears to be present only in some patients, and trials testing antiinflammatory treatments in depression have shown them to be effective only in patients with increased baseline inflammation,78 further suggesting that only patients presenting microglial activation might benefit from drugs that specifically target microglia (figure).
+However, the effects of existing psychotropic medications on the immune system also require more detailed exploration,85 particularly given the suggestions that antipsychotics influence microglial activation in rodents65 and potentially in patients with schizophrenia.36 Again, whether these effects are beneficial or detrimental remain to be explored fully in rodent model systems, although studies are actively pursuing this question.
+Conclusions
+Although the role of immune activation in the development of psychiatric disorders was suggested almost a century ago, the renewed interest in this area has generated considerable new data, mainly over the past two decades. The emerging evidence of a role of microglial activation across different psychiatric disorders is contributing to the development of a new era for psychiatry. Notably, although microglial activation might not be a marker for a specific diagnostic category,
+www.thelancet.com/psychiatry Published online April 25, 2017 http://dx.doi.org/10.1016/S2215-0366(17)30101-3
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+Emerging adulthood, or the period of life between ages 18 and 25 (see Arnett, 2000), is a time of increased vulnerability to a number of mental health problems, including symptoms of depression (Kessler et al., 2003) and suicidal behavior (Kessler, Berglund, Borges, Nock, & Wang, 2005). Among racial and ethnic minorities, emerging adulthood is a time of particularly high risk for symptoms of depression and suicidal behavior, compared to later adulthood. For instance, the 12-month prevalence of major depression is higher among 18- to 29-year-old Asian, Black, and Latino individuals than among their older counterparts (Alegría et al., 2007; Kim & Choi, 2010; Williams et al., 2007). Between 38-47% of suicides that occur among Asian, Black, and Latino individuals
+(versus 24% of suicides among White individuals) happen between the ages of 15 and 34 (Centers for Disease Control and Prevention, 2011). Cognitive models have identified a number of risk factors for suicidal behavior (Ellis, 2006), with hopelessness being one of the strongest predictors of suicidal behavior (Abramson et al., 1998; Joiner & Rudd, 1996). However, very little research has examined whether culturally related experiences affect these cognitive factors, and no research of which we are aware has examined whether cognitive factors such as hopelessness may explain the association between culturally related stressors and suicidal ideation and attempts.
+Research has highlighted the importance of examining the cultural context of suicidal behavior and suggests that culturally related stressors, such as acculturative stress (Gomez, Miranda, & Polanco, 2011; Hovey & King, 1996; Hwang & Ting, 2008; Joiner & Walker, 2002), and perceived discrimination (Cheng et al., 2010; Gomez et al., 2011; Hwang & Goto, 2008) may increase vulnerability to suicidal thoughts and behavior. Within-group differences in depression and suicidal behavior have been reported among Latino (Fortuna, Perez, Canino, Sribney, & Alegría, 2007), Asian American (e.g., Duldulao, Takeuchi, & Hong, 2009), and Black individuals (Joe, Baser, Neighbors, Caldwell, & Jackson, 2009), with less acculturated or early-generation individuals at lower risk than more acculturated individuals. At the same time, there is evidence that ethnic identity buffers against the negative effects of culturally related stressors (Iturbide, Raffaelli, & Carlo, 2009; Greene, Way, & Pahl, 2006; Torres & Ong, 2010). Taken together, these findings allude to the cultural nuances associated with risk for suicidal behavior. The present study sought to examine the association between culturally related experiences (i.e., acculturative stress, perceived discrimination, and ethnic identity) and hopelessness, depressive symptoms, and suicidal ideation over time among emerging adults, given that hopelessness, depression, and suicidal ideation are well-known predictors of suicidal behavior (Abramson et al., 1998; Brown, Beck, Steer, & Grisham, 2000; Joiner & Rudd, 1996).
+CULTURALLY RELATED STRESS AS A RISK FACTOR FOR DEPRESSION AND SUICIDAL IDEATION Experiences of racial and ethnic discrimination are akin to culturally specific negative life events in that they are stressful events that occur based on an individual's ethnic group or racial affiliation (Landrine & Klonoff, 1996). Perceived discrimination has been linked to poor mental health outcomes across different racial/ethnic minority groups (Gee, Spencer, Chen, Yip, & Takeuchi, 2007; Greene et al., 2006; Kessler, Mickelson, & Williams, 1999; Sellers &
+Shelton, 2003), and also to increases in suicidal ideation and attempts (Cheng et al., 2010; Gomez et al., 2011; Hwang & Goto, 2008). For instance, a study with ethnically diverse 18- to 29-year-olds found that perceived discrimination statistically predicted endorsement of a lifetime suicide attempt history (Gomez et al.). Moreover, previous studies have found that emerging adults are more likely to experience perceived discrimination than older adults (Kessler et al., 1999; Pérez, Fortuna, & Alegría, 2008). Perceived discrimination may thus increase vulnerability to suicidal behavior among emerging adults from diverse backgrounds.
+Acculturative stress, or stress in response to acculturation, is another commonly studied culturally relevant stressor. Berry (1991) suggests that acculturation—or the way in which individuals adapt to interacting with a new culture—engenders cultural change through a novel set of experiences, such as racial/ethnic discrimination, acculturative stress, and development of an ethnic identity (Berry). Acculturative stress may arise in a variety of circumstances, such as when individuals are socially marginalized, experience negative attitudes toward their culture of origin, feel pressured to assimilate, or when conflicts arise between individuals’ expectations versus those of their family (Mena, Padilla, & Maldonado, 1987). Moreover, individuals who are ill equipped to confront such new challenges may develop maladaptive coping responses, which, in turn, may increase vulnerability to negative mental health outcomes, such as depression and suicidal ideation. Acculturative stress has been found to be associated with depression independently of general life stress (Hwang & Ting, 2008; Joiner & Walker, 2002). It has also been linked to symptoms of depression and suicidal ideation across different racial/ ethnic minority groups, including Asian Americans (Hwang & Ting), African Americans (Joiner & Walker), and Latinos (Hovey & King, 1996). Although typically examined among immigrants, acculturative stress is also relevant to nonimmigrants, as researchers have explored its impact on African American and European American college students (Joiner & Walker; Walker, Wingate, Obasi, & Joiner, 2008). Considering that ethnic identity becomes more salient among White American youth when they are in racially/ethnically heterogeneous environments (i.e., environments with a mix of races/ ethnicities versus environments in which individuals are primarily of European-American background; Phinney, Cantu, & Kurtz, 1997), acculturative stress may arise among U.S.-born Black and U.S.-born White emerging adults in a comparable setting, such as a college campus in which they are exposed to individuals from various cultural backgrounds.
+Acculturative stress may create additional vulnerabilities to depression and suicidal behavior among emerging adults (Gomez et al., 2011; Hovey & King, 1996; Iturbide et al., 2009; Walker et al., 2008). Walker and colleagues found that college students with depressive symptoms who reported experiencing higher acculturative stress reported more concurrent thoughts about suicide than students with depressive symptoms who reported less acculturative stress. Another study found that acculturative stress, specifically with regard to family relationships, was statistically associated with endorsement of a suicide attempt history, such that the odds of having made an attempt increased with higher acculturative stress (Gomez et al., 2011). A closer look at the effects of acculturative stress may provide additional insight into how it may impact risk for suicidal thoughts and behavior among emerging adults.
+culturally related stress, hopelessness, and risk for suicidal ideation
+Little research has examined the association between culturally related stress and hopelessness. Hopelessness is thought to arise when individuals believe that they will inevitably experience negative outcomes and that they will inevitably fail to experience positive future outcomes (Abramson et al., 1989). It is associated with risk for suicidal ideation and attempts (Abramson et al., 1998; Joiner & Rudd, 1996), along with completed suicide (Brown et al., 2000). Furthermore, there may be racial/ethnic variability in the relation between hopelessness and suicide risk. One study found that the association between hopelessness and a nearly lethal suicide attempt was stronger among Black emerging adults than among White emerging adults (Durant et al., 2006). Hovey (2000) reported that acculturative stress was the strongest predictor of depressive symptoms in Mexican immigrants, and while hopelessness was the strongest predictor of suicidal ideation, acculturative stress also emerged as an independent predictor. Later research found that Mexican immigrant women with suicidal ideation reported higher acculturative stress and higher hopelessness, compared to women with no suicidal ideation (Hovey & Magana, 2003), but the study did not directly examine the relation between acculturative stress and hopelessness. Perhaps culturally related stressors increase vulnerability to depression and suicidal ideation through increases in hopelessness. This idea is compatible with contemporary psychological models of suicide, such as Joiner's (2005) interpersonal-psychological theory, which suggests that hopelessness may arise from a thwarted need to belong, and such hopelessness may increase suicidal desire (Joiner, Brown, & Wingate, 2005). Negative social interactions that result in culturally
+related stress may decrease individuals’ sense of belonging, thereby increasing hopelessness and risk for suicidal behavior.
+ethnic identity as a potential buffer Ethnic identity is the meaning an individual ascribes to identifying with his or her ethnic group (Phinney, 1992). Phinney and Ong (2007) suggest that exploring this meaning and accompanying feelings of belonging represent the development of an ethnic identity, which is typically initiated during early adolescence and increases over time (see Quintana, 2007). This explorative process may persist through emerging adulthood (Arnett, 2000). Previous research has linked higher ethnic identity concurrently with lower symptoms of depression, thoughts of suicide, and a suicide attempt history (Cheng et al., 2010; Mossakowski, 2003). Hence, there may be protective properties attributable to identifying with a racial/ ethnic group. Cross (1995) proposed that the development of an ethnic identity is often triggered by encounters of racism and discrimination, and that such experiences may equip an individual with defenses to counteract the negative effects of future discriminatory events. Phinney extended this conceptual framework to describe ethnic identity development among a diverse group of adolescents that included White youth. A strong ethnic identity may thus protect emerging adults against the detrimental effects of acculturative stress and perceived discrimination.
+Ethnic identity may buffer against the effects of culturally related stress on concurrent symptoms of depression and suicidal ideation (Iturbide et al., 2009; Mossakowski, 2003; Torres & Ong, 2010; Walker et al., 2008). A recent study that recorded the daily experiences of ethnic discrimination and depressive symptoms among Latinos found that individuals with higher ethnic identity reported fewer depressive symptoms after discrimination experiences than did those with lower ethnic identity (Torres & Ong). Walker and colleagues found that a higher ethnic identity weakened the concurrent association between depression and suicidal ideation among African American college students. Ethnic identity may thus protect against the harmful effects of culturally related stressors on symptoms of depression, and consequently, may decrease risk for further depression and suicidal ideation.
+the present study
+Previous research suggests that acculturative stress and perceived discrimination may increase vulnerability to engaging in suicidal behavior, while ethnic identity may decrease risk, and perhaps even serve as a buffer against the impact of these culturally related stressors. However, this research has largely relied on
+cross-sectional data, providing limited insight into the nature of the relationship. The present study examined acculturative stress and perceived discrimination as predictors of hopelessness, depressive symptoms, and suicidal ideation at a 2- to 3-year follow-up point among a racially/ethnically diverse group of emerging adults, independently of general life stress. We hypothesized that acculturative stress and perceived discrimination would prospectively predict higher hopelessness, depressive symptoms, and suicidal ideation. We also expected that hopelessness would mediate the relation between these culturally related stressors and depression and also between these stressors and ideation, independently of depression. Lastly, we expected that ethnic identity would buffer against the impact of culturally related stressors on depressive symptoms and suicidal ideation. Specifically, we expected a weaker relation between culturally related stressors and these outcomes among individuals with a high (vs. low) ethnic identity.
+Method
+PARTICIPANTS
+Atotal of 143 emerging adults (80% female), ages 18 to 25, from a diverse public college in a Northeastern metropolitan area of the United States were selected from a larger sample of 1,011 college students who completed a survey between 2007 and 2008 as part of a study of cultural and cognitive correlates of suicidal behavior (see Chan, Miranda, & Surrence, 2009; Gomez et al., 2011). Participants completed this survey in partial fulfillment of a research requirement in their introductory psychology course. Participants were recruited from two groups—one (N=56) from a group of 96 participants who took part in a study of cognitive predictors of suicidality (see Rajappa, Gallagher, & Miranda, in press, for details on distribution of suicidal ideation and attempts among the 96 individuals), which was oversampled from the larger sample of 1,011 individuals for a history of suicidal ideation or attempts. It should be noted that all 96 of these individuals were contacted for potential participation, and 56 individuals agreed to take part in the follow-up study. The second group (N =87) was selected from the remaining 915 individuals by first compiling a database of individuals who matched the other group by age, sex, and race/ethnicity, and randomly selecting participants from that group of individuals. This procedure yielded a total of 32 out of 143 participants who had previously reported a suicide attempt history. At baseline, the mean age of this sample was 18.6years. The racial/ethnic composition was as follows: 34% Asian (51% non-U.S.-born), 29% White (38% non-U.S.-born), 17% Latino (38%
+non-U.S.-born), 11% Black (44% non-U.S.-born), 8% other (25% non-U.S.-born). The subsample was representative of the larger overall sample in terms of race/ethnicity, but individuals who were followed up were more often female (80% vs. 67%), x2(1)=9.19, p<.01, and younger at baseline (M=18.5 vs. 19.4), t(589.1) = 6.53, p < .01, than those not followed up. See Table 1 for sample characteristics.
+MATERIALS
+Demographic Information
+Participants reported on their age, gender, race/ ethnicity, year in college, and birthplace of self and parents.
+Depressive Symptoms
+The Patient Health Questionnaire-9 (PHQ-9; Spitzer, Kroenke, & Williams, 1999) is a 9-item self-report measure used to screen for the presence and intensity of symptoms of major depressive disorder. Participants are instructed to report the frequency with which they experienced each symptom in the previous two weeks, ranging from 0 (not at all) to 3 (nearly every day). The PHQ-9 was administered at baseline (a = .82) and follow-up (a = .83), with scores ranging from 0 to 21 at baseline and 0 to 22 at follow-up. The correlation between PHQ-9 scores at baseline and follow-up was .47.
+Hopelessness
+The Beck Hopelessness Scale (BHS; Beck & Steer, 1988), administered at follow-up, is a 20-item self-report questionnaire that measures overall hopelessness, including expectations, loss of motivation, and feelings about the future. It includes positively (e.g., I look forward to the future with hope and enthusiasm) and negatively worded items (e.g., My future seems dark to me) with a binary response option (true/false). Scores can range from 0 to 20, and in the present sample, they ranged from 0 to 19, with an internal consistency reliability of .91.
+Suicidal Ideation
+Suicidal ideation was assessed at follow-up with the Beck Scale for Suicidal Ideation (BSS; Beck & Steer, 1993), a 21-item self-report questionnaire that inquires about the presence and intensity of suicidal thoughts, planning, access to means, and intent in the previous week. Items were measured on a 0- to 2-point Likert scale, with higher scores representing active (vs. passive) thoughts about attempting suicide. In the present sample, scores ranged from 0 to 24, and the scale had good internal consistency reliability (a=.98).
+Suicide Attempts
+Suicide attempt history was assessed at baseline and follow-up by asking participants to respond to the question, “Have you ever, in your whole life, tried
+to kill yourself or made a suicide attempt?” This question was derived from the young adult version of the Computerized Diagnostic Interview Schedule for Children (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). At follow-up, participants were asked to also indicate whether they had made a suicide attempt, but were also asked whether they had made an attempt in the previous 2years (to assess suicide attempt within the follow-up period). Individuals who did not endorse a suicide attempt at baseline but who endorsed a suicide attempt at follow-up, but not within the previous 2 years, were also classified as having made an attempt within the follow-up period, provided that their follow-up period extended beyond 2 years. Agreement between endorsement of a lifetime suicide attempt at baseline and follow-up (excluding 5 participants who were classified as having made an attempt within the follow-up period) was high (k=.85). Suicide attempt history, as reported at baseline, was included as a covariate in the present study.
+Acculturative Stress
+Acculturative stress was assessed at baseline using the brief version of the Social, Attitudinal, Familial, and Environmental Acculturative Stress Scale (SAFE; Mena et al., 1987). This 24-item scale inquires about stress resulting from acculturation in the context of social interactions, attitudes toward one's culture of
+origin, family relationships, and environmental experiences. Participants were instructed to report the level of stress they experienced from each item on a Likert scale ranging from 1 (not stressful) to 5 (extremely stressful). The scale demonstrated high internal consistency reliability (a = .90) in the present sample, consistent with what has previously been reported by Mena and colleagues (a=.89), and total scores ranged from 24 to 96.
+Ethnic Identity
+The Multigroup Ethnic Identity Measure (MEIM; Phinney, 1992) was administered at baseline and follow-up to measure the degree to which individuals identified with an ethnic group. Thirty participants (21%) did not complete the MEIM at baseline. Thus, only scores at follow-up were included in the analyses. The correlation between total MEIM scores at baseline and follow-up for participants who completed the measure at both time points was .62. The measure contains two subscales: Affirmation and Belonging (e.g., I am happy that I am a member of the group I belong to), and Exploration and Achievement (e.g., I have a clear sense of my ethnic background and what it means to me). Participants were asked to report the ethnic group with which they most identified and to what extent they agreed or disagreed with several statements regarding that ethnic background, from 1 (strongly disagree) to 4 (strongly
+agree). Phinney demonstrated the scale's reliability, with Cronbach's alpha ranging from .69 to .86 for the two subscales across different ethnic groups. Good internal consistency reliability (a = .91) was observed in the present sample. Total scores, or the sum of both subscales, ranged from 14 to 48, with a higher sum of scores denoting a stronger ethnic identity.
+Perceived Discrimination
+The Schedule of Racist Events (SRE) is an 18-item self-report questionnaire used to measure perceived discrimination among African Americans (Landrine & Klonoff, 1996), but the scale was administered at baseline with the wording slightly modified to address all racial and ethnic groups (e.g., How many times have you been treated unfairly by teachers and professors because of your race/ethnicity?). Individuals were questioned only about discrimination experiences within the past year and not about lifetime discrimination. Frequency of discrimination was assessed on a Likert scale ranging from 1 (never) to 6 (almost all of the time), and stress appraisal was also assessed on a Likert scale ranging from 1 (not at all) to 6 (extremely). Landrine and Klonoff (1996) reported an internal reliability of .95 for frequency of racist events and of .94 for stress appraisal. An internal consistency reliability of .91 was obtained with the present sample for stress appraisal. The total scores for stress (rather than frequency), computed by summing items 1-17, were included in the analysis and ranged from 17 to 96.
+Negative Life Events
+The Life Experiences Survey (LES; Sarason, Johnson, & Siegel, 1978) was administered at baseline and follow-up to assess the impact of negative life events (INE) experienced within the previous year. Participants indicated whether any of 60 possible life events had happened to them in the previous 0 to 6 or 7 to 12 months, and rated the impact of the event on a 7-point Likert scale ranging from -3 (extremely negative) to +3 (extremely positive). Total INE score was calculated by summing the negatively rated events (i.e., between -3 and -1) and taking the absolute value of that score. INE at follow-up was included in the analyses to examine whether culturally related stressors would predict suicidal ideation beyond general negative life stress, which has been found to be associated with suicidal ideation (Chan et al., 2009).1
+1 Although negative life events were measured at both baseline and follow-up, only INE score at follow-up was included in the analyses, to adjust for the impact of the most recently experienced negative life events on symptoms of depression and suicidal ideation. In preliminary analyses that adjusted for INE score at both baseline and follow-up, INE score at baseline did not significantly contribute to explaining variability in the study outcomes. Thus, it was excluded from further analyses.
+PROCEDURE
+Participants completed self-report questionnaires at two time points, 2 to 3 years apart (with participants providing informed consent at each time point). A follow-up point of 2years was originally targeted, but because some participants were more difficult to reach than others, the follow-up spanned up to about 3years, on average (M=2.6years, SD = 0.5; range= 1.6 to 3.9). Research assistants with at least a bachelor's degree in psychology or a related discipline (e.g., social work) administered the questionnaires in a research lab. For the follow-up session, participants were contacted via email or phone and asked to return to the lab for another survey. Generally, individuals were contacted a minimum of two times by email (an initial contact, followed by a reminder), and for those who did not respond by e-mail, at least once by telephone.
+After each session, participants were debriefed and received credit toward partial course fulfillment at baseline and monetary compensation ($25) at follow-up. All participants were provided with a list of mental health resources. Individuals who reported a suicide attempt in the previous 2 weeks and current suicidal ideation (assessed by PHQ-9 Item 9 or the BSS) were seen by a licensed clinical psychologist or licensed clinical social worker, and if the clinician perceived a need for immediate assessment, they were escorted to see a counselor on campus. The study received full-board approval by an Institutional Review Board.
+Results
+Means, standard deviations, and correlations among the predictor and outcome variables examined are provided in Tables 1 and 2. There were no statistically significant omnibus race/ethnic differences in the majority of study variables, with the exception of acculturative stress, F(4, 138)=3.22, p <.05, and perceived discrimination, F(4, 138)=2.59, p<.05, with Asian participants reporting significantly higher levels than White participants, t(138) = 3.13, p<.05, and t(138)=2.86, p < .05, respectively (Bonferroni correction).2,3 Acculturative stress at baseline was significantly and positively correlated with hopelessness, symptoms of depression, and suicidal ideation
+2 One participant was missing a total score for perceived discrimination. This value was estimated using multiple imputation (see Schafer, 1999).
+3 We also examined whether there were differences in the study variables between participants who were born in the U.S. versus those who were not born in the U.S. and only found a statistically significant difference on acculturative stress, with non-U.S.-born individuals reporting higher levels of acculturative stress (M=54.25) than U.S.-born individuals (M=48.72), t(141)=2.02, p < .05.
+at follow-up, while perceived discrimination at baseline was significantly and positively correlated with hopelessness and depressive symptoms at follow-up but not with suicidal ideation at follow-up (p=.36). Ethnic identity at follow-up was significantly and negatively correlated with hopelessness, depressive symptoms, and suicidal ideation at follow-up. Hopelessness and depressive symptoms at follow-up were significantly and positively correlated with suicidal ideation. Baseline acculturative stress and perceived discrimination were not significantly correlated with ethnic identity at follow-up (p = .54 and .11, respectively).
+culturally related predictors of hopelessness
+We examined whether baseline acculturative stress and perceived discrimination would be associated with higher hopelessness at follow-up, and whether ethnic identity would moderate these relations, via a hierarchical linear regression analysis. To reduce multicollinearity and to account for the absence of a meaningful zero point for acculturative stress, perceived discrimination, and ethnic identity scales, total scores were centered around their means prior to computing interactions (Jacard, 2001). Age, gender, race/ethnicity, and nativity (i.e., whether individuals were born in the U.S.) were excluded from analyses because they did not significantly predict hopelessness, nor any of the other outcomes.
+Culturally related stress (baseline acculturative stress and baseline perceived discrimination) and ethnic identity (at follow-up) were entered as predictors of hopelessness in the first step of the analysis, adjusting for suicide attempt history (reported at baseline) and negative life events (reported at
+follow-up). The interaction between ethnic identity and each culturally related stressor was entered into the next step of the analysis (see Table 3). Both acculturative stress and perceived discrimination at baseline were associated with higher hopelessness at follow-up, while ethnic identity (at follow-up) was significantly associated with lower hopelessness. However, ethnic identity did not moderate the relation between culturally related stressors and hopelessness, as the interactions between ethnic identity and each culturally-related stressor were not statistically significant.
+hopelessness as a mediator of the relation between culturally related stressors, symptoms of depression, and suicidal ideation
+We examined both direct and indirect relations between culturally related stress and symptoms of depression and suicidal ideation, as per the recommendations of MacKinnon and colleagues (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). According to these recommendations, indirect relations should be tested when there is a relation between a predictor and mediator and between a mediator and an outcome variable, regardless of whether there is a significant direct predictor-outcome relation (MacKinnon et al., 2002). Previous analyses (see above) established a relation between culturally related stressors (i.e., the predictors) and hopelessness (i.e., the mediator). To examine the direct relations between our predictors (baseline acculturative stress and perceived discrimination) and outcomes
+(depressive symptoms and suicidal ideation at followup), and between the mediator (i.e., hopelessness) and outcomes, we conducted two sets of hierarchical linear regression analyses (one with depressive symptoms as the outcome and the other with suicidal ideation as the outcome). Predictor variables were entered in two steps. Sobel tests (Sobel, 1982) were used to calculate the significance of the indirect relations between culturally related stressors and symptom outcomes through hopelessness at different levels of ethnic identity, using the method suggested by Preacher and colleagues (Preacher, Rucker, & Hayes, 2007). This procedure enabled us to examine whether ethnic identity moderated the indirect relation between culturally related stressors and symptom outcomes through hopelessness.
+In the first regression predicting symptoms of depression at follow-up (see Table 4), baseline accul-turative stress and perceived discrimination were entered as predictors in the first step of the analysis, adjusting for suicide attempt history at baseline, symptoms of depression at baseline, ethnic identity at follow-up, and negative life events at follow-up. Hopelessness was added in the second step of the analysis. Neither acculturative stress nor perceived discrimination significantly predicted depressive symptoms at follow-up, adjusting for other variables in the model. However, ethnic identity and negative life events were negatively associated with depressive symptoms. Hopelessness significantly predicted symptoms of depression when entered in Step 2.
+Tests of the indirect relation between acculturative stress and depressive symptoms through hopelessness were calculated at low (1 SD below the mean), average, and high (1 SD above the mean) levels of ethnic identity, adjusting for baseline depressive symptoms, baseline suicide attempt history, baseline acculturative stress, and negative life events at follow-up. Hopelessness mediated the relation between acculturative stress and depressive symptoms at low, Zsobei=2.20, p < .05, but not at average, ZSobel =1.67, p = .10, or high levels of ethnic identity, Zsobei=0.30, p = .76.
+We also calculated the significance of the indirect relation between perceived discrimination and symptoms of depression through hopelessness at different levels of ethnic identity. The indirect relation between perceived discrimination and depressive symptoms through hopelessness was significant at low, ZSobel = 2.37, p< .05, but not at average, ZSobel =1.41, p = .16, or high levels of ethnic identity, ZSobel=-0.48, p = .63.
+Next, we examined the direct relation between culturally related stressors (acculturative stress, perceived discrimination) and suicidal ideation, and between hopelessness and suicidal ideation, adjusting for depressive symptoms, via a hierarchical linear regression (see Table 5). In the first step of the regression, baseline acculturative stress, baseline perceived discrimination, and ethnic identity at follow-up were entered as predictors of ideation at follow-up, adjusting for baseline suicide attempt history, baseline
+depressive symptoms, and negative life events at follow-up. Hopelessness was added in the second step of the analysis, along with depressive symptoms at follow-up (to examine whether hopelessness would mediate the relation independently of depressive symptoms). In Step 1, acculturative stress at baseline was associated with higher suicidal ideation at follow-up, adjusting for other predictors, while neither perceived discrimination nor ethnic identity was associated with suicidal ideation. Neither acculturative stress nor ethnic identity was significantly associated with ideation in Step 2, but perceived discrimination did significantly predict ideation in this step. Both hopelessness and depressive symptoms at follow-up were associated with higher ideation at follow-up.
+As in the analyses for depression, we also calculated Sobel tests of the indirect relation between accultur-ative stress and suicidal ideation through hopelessness at different levels of ethnic identity, adjusting for perceived discrimination (baseline), suicide attempt history (baseline), negative life events (follow-up) and depressive symptoms (both baseline and follow-up). Tests of the indirect relation between acculturative stress and suicidal ideation through hopelessness were calculated at low (1 SD below the mean), average, and high (1 SD above the mean) levels of ethnic identity. Hopelessness mediated the relation between accultur-ative stress and suicidal ideation at low, ZSobel=2.08, p< .05, but not at average, ZSobel =1.55, p = .12, or high levels of ethnic identity, ZSobel=0.21, p=.83.
+Finally, we calculated the significance of the indirect relation between perceived discrimination and suicidal ideation through hopelessness at different levels of ethnic identity, adjusting for acculturative stress (baseline), suicide attempt history (baseline), negative life events (follow-up), and depressive symptoms (both baseline and follow-up). The indirect relation between perceived discrimination and ideation through hopelessness was statistically significant at a low, ZSobel =1.94, p = .05, but not at average, ZSobel =0.95, p = .34, or high ethnic identity, ZSobel=-0.66, p = .51.
+Discussion
+There is limited research examining the cultural context in which suicidal behavior occurs among emerging adults of diverse racial/ethnic backgrounds, and more specifically, on the interplay between culturally related stress and cognitive vulnerability to suicidality. In addition, much of what is known has been obtained from cross-sectional data. The present study contributes to this literature by examining the association between culturally related stressors, cognitive vulnerability, and depressive symptoms and suicidal ideation at a 2- to 3-year follow-up point
+among a racially/ethnically diverse sample of emerging adults. In accordance with and as an extension of previous findings (Hovey & King, 1996; Hwang & Ting, 2008; Joiner & Walker, 2002), acculturative stress emerged as a significant prospective predictor of hopelessness and of suicidal ideation, independently of recent general life stress, and hopelessness mediated the relation between acculturative stress and ideation. Higher acculturative stress at baseline predicted higher levels of suicidal ideation 2-3 years later through greater hopelessness. At the same time, acculturative stress did not significantly directly predict depressive symptoms at follow-up but did have an indirect relation through hopelessness at low levels of ethnic identity. These findings suggest that acculturative stress may be a source of stress relevant to different racial/ethnic groups that increases vulnerability to hopelessness and suicidal thinking among emerging adults, but a strong ethnic identity may buffer against this process. Furthermore, among individuals low in ethnic identity, acculturative stress may increase vulnerability to depressive symptoms over time to the degree that it also increases hopelessness.
+Perceived discrimination at baseline was associated with higher levels of hopelessness but not depressive symptoms 2 to 3 years later. However, there was an indirect relation between perceived discrimination and depressive symptoms through hopelessness. This finding qualifies previous research demonstrating a link between experiences of racial/ethnic discrimination and depression (Gee et al., 2007; Kessler et al., 1999; Torres & Ong, 2010) by suggesting that increased levels of hopelessness may account for this relation. Perceived discrimination was not directly related to suicidal ideation at follow-up, contrary to recent reports that found discrimination to be concurrently associated with suicidal ideation (Cheng et al., 2010; Hwang & Goto, 2008). On the other hand, they are consistent with a previous study with African American emerging adults that found no link between racial discrimination and suicidal ideation and attempts (Castle, Conner, Kaukeinen, & Tu, 2011), although that was not a longitudinal study. Therefore, perceived discrimination may not directly increase vulnerability to depressive symptoms and suicidal ideation when accounting for accul-turative stress and other factors, such as negative life events. However, among individuals low in ethnic identity, it may indirectly increase risk for depression and suicidal ideation to the degree that is associated with higher levels of hopelessness.
+As expected, a higher ethnic identity was associated with lower hopelessness, fewer concurrent depressive symptoms, and less suicidal ideation, supporting previous findings of an association between higher ethnic identity and improved psychological well-being
+(Greene et al., 2006; Martinez & Dukes, 1997), as well as lower rates of suicidal ideation and attempts (Cheng et al., 2010). Ethnic identity moderated the indirect relations between culturally related stressors (acculturative stress, perceived discrimination) and both depressive symptoms and suicidal ideation through hopelessness. Together, these findings suggest that ethnic identity may buffer against the indirect relation between culturally related stress and symptoms of depression and suicidal ideation through hopelessness.
+This is the first study of which we are aware to examine the relation between acculturative stress, perceived discrimination, and vulnerability to depressive symptoms and suicidal ideation at a 2- to 3-year follow-up point. The findings suggest that closer examination of the experiences that result from acculturation may inform the assessment and treatment of at-risk individuals from diverse racial and ethnic backgrounds.
+STRENGTHS, LIMITATIONS, AND FUTURE DIRECTIONS
+While the present study examined the effects of culturally related experiences on depression and suicidal ideation in a heterogeneous sample, we did not examine between-group differences due to the relatively small number of participants per racial/ ethnic group. Ethnic identity may buffer against the effects of culturally related stressors on depressive symptoms and suicidal ideation in some racial/ethnic groups and not others. For instance, Walker and colleagues (2008) reported that ethnic identity moderated the relation between depressive symptoms and suicidal ideation among African American emerging adults, but not among their European American counterparts. This may explain why we did not observe a correlation between ethnic identity and perceived discrimination in the overall sample. In a related vein, the mediating effects of hopelessness observed may also vary across racial/ethnic groups. Future research should examine this relation by race/ ethnicity.
+Another limitation of the study design was the absence of a measure of hopelessness and suicidal ideation at baseline. However, it is noteworthy that culturally related predictors, measured at baseline, were associated with suicidal ideation at follow-up, even after adjusting for symptoms of depression and a suicide attempt history at baseline. This study is also limited by inclusion of only self-report measures. It is possible that some of the associations between variables are the result of shared method variance. In addition, the self-report measures of culturally related stressors may be capturing perceived, rather than actual, experiences, and these
+may be subject to recall bias. Future research should include non-self-report measures (e.g., interviews) or experience sampling. Moreover, sociocultural variables that were not accounted for in the present study, such as religion, socioeconomic status, and immigration status (documented vs. undocumented), may influence the relation between culturally related stress and suicidal ideation. Caution should also be heeded in generalizing the findings beyond a college-student population. Similarly, the sample was largely female and may not reflect the experiences of male emerging adults. We also intentionally oversampled for emerging adults with a suicide attempt history in order to conduct meaningful analyses about vulnerability to suicidal ideation, and findings may not generalize to a broader population of college students and emerging adults. Finally, similar research with a clinical sample might provide a more comprehensive view of the impact of culturally related stressors on vulnerability to depression and suicidal ideation in emerging adulthood, as this impact may differ based on the severity of risk for suicidal behavior.
+While this study examined the degree to which emerging adults identify with an ethnic group, some researchers argue that racial/ethnic identity is a multidimensional construct and have shown that different dimensions of ethnic identity may function separately from one another (Sellers & Shelton, 2003). Future research should investigate how the different dimensions of racial/ethnic identity may impact risk for depression and suicidal ideation and whether they buffer against the impact of culturally related stressors.
+Future research should further examine interactions between culturally related experiences and cognitive factors. For example, researchers have suggested that repeatedly dwelling on a negative mood in response to stressful life events may increase vulnerability to suicidal ideation (Chan et al., 2009), and such negative moods may be triggered by culturally related stressors. Hence, the impact of discrimination or acculturative stress on risk for suicidal thoughts and behavior may vary depending on an individual's cognitive response to such stressors. Furthermore, it would be useful to examine culturally related variables in light of prevailing cognitive models of suicidal behavior. For instance, Joiner suggests that suicidal desire is a function of the degree to which individuals perceive themselves as a burden to others and also the degree to which their need for belonging is thwarted (Joiner, 2005), and indeed, recent research with college students found that variation in suicidal ideation across academic semesters was partially explained by thwarted belonging (Van Orden et al., 2008). It may be that culturally related stressors increase vulnerability to
+suicidal behavior to the degree that they affect how much individuals feel a sense of belonging.
+Despite the aforementioned limitations, there are several strengths to the study that are noteworthy. Consideration of both cultural and cognitive factors provided information about the ability of cognitions such as hopelessness to impact the relation between culturally related experiences, depressive symptoms, and suicidal ideation. Another strength of the study is the racial/ethnic diversity of the sample, with non-U.S.-born individuals well represented across the various racial/ethnic groups, including White emerging adults. However, considering that the sample was obtained from a diverse metropolitan area, the findings from this study may not generalize to regions with more homogeneous populations. One study found that African American students in a predominantly Black college reported less accultur-ative stress than African Americans in a predominantly White college (Joiner & Walker, 2002), highlighting the importance of context in culturally related experiences.
+clinical implications
+Clinicians and researchers have urged the development of culturally sensitive services targeted at reducing risk for suicidal behavior in youth (Goldston et al., 2008). Understanding how acculturative stress, perceived discrimination, and ethnic identity impact risk for suicidal behavior may improve current assessments and treatments, particularly among emerging adults from diverse backgrounds, given the prevalence of such culturally related experiences among racial and ethnic minorities (Kessler et al., 1999; Pérez et al., 2008; Phinney, 1992). Clinical interventions with such emerging adults should address stress around the process of acculturation, along with stress related to discrimination and perhaps assist acculturating individuals in developing adaptive ways of coping with these stressors. For instance, rather than merely addressing hopelessness as a cognitive risk factor, professionals working with emerging adults at risk for suicidal behavior might inquire about the role that culturally related stressors play in the development of hopelessness. In other words, addressing culturally related stressors in addition to hopelessness may better inform risk assessment and treatment. Furthermore, clinicians should address ethnic identity development, as a strong ethnic identity may lessen the impact of hopelessness on the relation between culturally related stress and risk for depression and suicidal ideation. A strong ethnic identity may help generate adaptive coping responses, which clinicians might identify and promote as a way of reducing the negative effects of culturally related stress.
+conclusion
+In summary, the study findings suggest that accul-turative stress and perceived discrimination may increase, while ethnic identity may decrease, vulnerability to depression and suicidal ideation to the degree that they impact hopelessness. This study attempts to offer a better understanding of risk and protective factors for these symptoms in a cultural context by examining the interplay between culturally related experiences and cognitive vulnerability. Such an improved understanding may allow researchers and practitioners to better identify emerging adults at risk for suicidal behavior, including youths from racial/ethnic minority backgrounds, and to develop more culturally competent suicide prevention and intervention services for emerging adults of all racial/ethnic backgrounds. These steps would provide an avenue through which to address the racial/ethnic disparities in youth depression and suicidal behavior.
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+Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R. ... Wang, P. S. (2003). The epidemiology of Major Depressive Disorder: Results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association, 289, 3095-3105.
+Kessler, R. C., Mickelson, K., & Williams, D. (1999). The prevalence, distribution, and mental health correlates of perceived discrimination in the United States. Journal of Health and Social Behavior, 40, 208-230.
+Kim, J., & Choi, N. G. (2010). Twelve-month prevalence of DSM-IV mental disorders among older Asian Americans: Comparison with younger groups. Aging & Mental Health, 14, 90-99.
+Landrine, H., & Klonoff, E. A. (1996). The schedule of racist events: A measure of racial discrimination and a study of its negative physical and mental health consequences. Journal of Black Psychology, 22, 144-168.
+MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.
+Martinez, R. O., & Dukes, R. L. (1997). The effects of ethnic identity, ethnicity, and gender on adolescent well-being. Journal of Youth and Adolescence, 26, 503-516.
+Mena, F. J., Padilla, A. M., & Maldonado, M. (1987). Acculturative stress and specific coping strategies among immigrant and later generation college students. Hispanic Journal of Behavioral Sciences, 9, 207-225.
+Mossakowski, K. N. (2003). Coping with perceived discrimination: Does ethnic identity protect mental health? Journal of Health and Social Behavior, 44, 318-331.
+Pérez, D., Fortuna, L., & Alegría, M. (2008). Prevalence and correlates of everyday discrimination among U.S. Latinos. Journal of Community Psychology, 36, 421-433.
+Phinney, J. S. (1992). The multigroup ethnic identity measure: A new scale for use with diverse groups. Journal of Adolescent Research, 7, 156-176.
+Phinney, J. S., Cantu, C., & Kurtz, D. A. (1997). Ethnic and American identity as predictors of self-esteem among African American, Latino, and White adolescents. Journal of Youth and Adolescence, 26, 165-185.
+Phinney, J. S., & Ong, A. D. (2007). Conceptualization and measurement of ethnic identity: Current status and future directions. Journal of Counseling Psychology, 54, 271-281.
+Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theories, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227.
+Quintana, S. M. (2007). Racial and ethnic identity: Developmental perspectives and research. Journal of Counseling Psychology, 54, 259-270.
+cultural predictors of depression and suicidal ideation
+87
+Rajappa, K., Gallagher, M., & Miranda, R. (in press). Emotion dysregulation and vulnerability to suicidal ideation and attempts. Cognitive Therapy and Research.
+Sarason, I., Johnson, J., & Siegel, J. (1978). Assessing the impact of life changes: Development of the Life Experiences Survey. Journal of Consulting and Clinical Psychology, 46, 932-946.
+Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3-15.
+Sellers, R. M., & Shelton, J. N. (2003). The role of racial identity in perceived racial discrimination. Journal of Personality and Social Psychology, 84, 1079-1092.
+Shaffer, D., Fisher, P., Lucas, C. P., Dulcan, M. K., & Schwab-Stone, M. E. (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 28-38.
+Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology (pp. 290-312). San Francisco, CA: Jossey-Bass.
+Spitzer, R., Kroenke, K., & Williams, J. (1999). Validation and utility of a self-report version of PRIME-MD: The PHQ
+Primary Care Study. Journal of the American Medical Association, 282, 1737-1744.
+Torres, L., & Ong, A. D. (2010). A daily diary investigation of Latino ethnic identity, discrimination, and depression. Cultural Diversity and Ethnic Minority Psychology, 16,561-568.
+Van Orden, K. A., Witte, T. K., James, L. M., Castro, Y., Gordon, K., Braithwaite, S.,... Joiner, T. E. Jr., (2008). Suicidal ideation in college students varies across semesters: The mediating role of belongingness. Suicide and Life-Threatening Behavior, 38, 427-435.
+Walker, R. L., Wingate, L. R., Obasi, E. M., & Joiner, T. E. Jr., (2008). An empirical investigation of acculturative stress and ethnic identity as moderators for depression and suicidal ideation in college students. Cultural Diversity and Ethnic Minority Psychology, 14, 75-82.
+Williams, D. R., Gonzalez, H., Neighbors, H., Nesse, R., Abelson, J. M., Sweetman, J., & Jackson, J. S. (2007). Prevalence and distribution of major depressive disorder in African Americans, Caribbean Blacks, and Non-Hispanic Whites. Archives of General Psychiatry, 64, 305-315.
+Received: December 6, 2011
+Accepted: July 16, 2012
+Available online 23 July 2012
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+Mood: Promoting Access to Collaborative Treatment (IMPACT) trial3 enrolled 1801 depressed older primary care patients from 8 healthcare systems in a randomized controlled trial of a collaborative care management program for depression compared with care as usual. Participants from each organization were randomly assigned to collaborative care or to care as usual.
+Earlier findings from the IMPACT study3 reported that the collaborative care program was substantially more effective than care as usual in reducing depression and in improving physical and social function. Intervention patients continued to have significantly less depression than patients in usual care even at the 24-month follow-up, 12 months after the end of the intervention program.4 Analyses from the IMPACT trial5 found the collaborative care program to be substantially more cost-effective than care as usual. IMPACT participants experienced 107 more depression-free days during a 24-month period than patients assigned to care as usual. During the initial study year, total healthcare costs (including the costs of the IMPACT intervention) were slightly higher among the intervention group than among control subjects, but a slight decrease in costs among the intervention group compared with usual care patients was observed in the second year, suggesting that an initial investment in better depression care may result in long-term cost savings.5
+In this article, we report long-term (4-year) effects of collaborative care for late-life depression on total healthcare costs from a payer©s perspective. Our findings are based on cost data available from 2 participating group-model health maintenance organizations.
+METHODS
+Trial
+Detailed information about the methodology, clinical results, and 2-year cost-effectiveness outcomes from the IMPACT trial are reported elsewhere.3,5-7 The institutional review boards of all participating organizations and the study coordinating center approved all study procedures, and all patients provided written informed consent. A waiver of written informed consent for thce use of an additional 2 years of cost data was obtained from the institutional review boards of the 2 participating healthcare organizations (group-model health maintenance organizations in California and Washington) and the study coordinating center.
+Enrollment and Dropout
+Participants were identified by systematic depression screening or were referred by primary care physicians. Inclusion criteria included being aged 60 years or older, meeting criteria for current major depression or dysthymia on the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition),8 and planning to continue using 1 of the participating primary care medical offices during the next year. Exclusion criteria included current alcohol abuse, severe cognitive dysfunction, acute risk of suicide, and a history of bipolar disorder or psychosis. Patients were recruited into the study between July 1999 and August 2001. During this time, 2% to 3% of the older population served by the participating medical offices were enrolled in the IMPACT trial.3,6 Eligible patients who agreed to participate in the study were randomly assigned to the IMPACT intervention or to usual care.
+The number of dropouts because of death or disenrollment by the end of each year in the 2 centers participating in the present study were 17, 13, 57, and 27, for years 1 through 4, respectively, without significant differences in the number of dropouts between intervention and usual care patients. The resulting sample sizes were 534, 521, 464, and 437 in years 1
+through 4, respectively. All available cost information was included in the analyses. For example, if a patient died between year 2 and year 3, this patient©s year 1 and year 2 costs were included in the analyses.
+Intervention
+The IMPACT intervention was a 1-year stepped collaborative care program provided by a nurse or a psychologist care manager working in the participant©s primary care clinic to support the patient©s regular primary care clinician. The depression care manager completed an initial biopsychosocial history and provided education about antidepressant medication and psychotherapy treatment options. Care managers used behavioral activation with all patients; in addition, they offered a choice of treatment with an antidepressant medication or with problem-solving treatment in primary care.9-11 Problem-solving treatment in primary care is a 6- to 8-session psychotherapy program designed for primary care patients9-11 that has been found to be as effective as antidepressant medication for treating major depression.10 Depression care managers received training on pharmacotherapy and problemsolving treatment in primary care during a 2-day workshop, including didactic training using a treatment manual11 and role plays, and completed at least 5 videotaped training cases of problem-solving treatment in primary care supervised by a psychologist. The depression care manager participated in weekly supervision under a primary care physician with geriatric expertise and under a psychiatrist to monitor the progress of cases and to adjust treatment plans according to a stepped-care treatment algorithm.6 This algorithm guided acute and continuation therapy, as well as relapse prevention recommendations, during the 12-month treatment period. The depression care manager followed up patients in person or by telephone approximately every 2 weeks during the acute-phase treatment and approximately monthly during the continuation phase. At the end of the 12-month intervention period, care managers completed a relapse prevention plan with intervention participants, after which all patients continued in care as usual.
+Usual Care
+Patients randomly assigned to receive usual care were told that they met the criteria for major depression or dysthymia and were encouraged to follow up with their primary care provider for treatment. The primary care provider was also notified of the diagnosis. Patients with this condition were eligible to receive all treatments routinely provided for depression (antidepressant medication, supportive counseling by their physician, and self- or physicianreferral to specialty mental healthcare).
+Outcome Measures
+All cost outcome data used for these analyses were obtained from cost-accounting systems of the 2 participating healthcare organizations. These systems track costs of all healthcare delivered directly by the organizations (including overhead costs) and costs of all paid claims for services delivered outside of the organization. They cover costs for all outpatient and inpatient health services, as well as costs for prescription medications provided by or purchased under contract by the participating health maintenance organizations. Cost data cover the period from 1999, the initial year of enrollment, through 2006 (4 years after the last patient was enrolled in the trial). Data for each participant cover a 4-year period beginning with their enrollment in the trial.
+For purposes of our analyses, healthcare costs were summarized into the following categories: IMPACT intervention costs (only relevant for the intervention group in year 1), outpatient specialty mental health costs, pharmacy costs (all medications provided by health plan pharmacies), other outpatient costs (outpatient primary care and specialty medical and surgical visits, physical therapy, occupational therapy, urgent care, emergency department
+care, and other outpatient services), inpatient mental health and substance abuse treatment costs, and inpatient medical costs. Using the same methods as described in the original costeffectiveness article from the IMPACT trial,5 we estimated the costs of providing the IMPACT intervention based on the following factors: detailed study records of all patient contacts (in person and by telephone), mean salary and benefit costs of depression care managers plus 30% overhead costs to account for such costs as space and administrative support (the same approach used in a similar study12 and to account for the fact that such clinicians cannot be 100% efficient13), the cost of supervision and consultation from team psychiatrists and primary care experts at each site plus 30% overhead costs, and the cost of intervention materials.5
+Statistical Analysis
+For each cost category, we provide the mean cost during the 4-year period for the intervention and usual care groups. The difference in mean costs between these 2 groups and its 95% confidence interval are provided. We also generated 1000 bootstrap samples to estimate the probability that total health-care costs were lower in the intervention group than in the usual care group during the 4-year time frame. The probability was estimated by dividing the number of bootstrap samples in which the intervention group had higher total healthcare costs than the usual care group by 1000. The analyses were performed using SAS 9.1 (SAS Institute Inc, Cary, NC).
+RESULTS
+The IMPACT sample of depressed older adults included 551 patients enrolled in the 2 health plans with available long-term cost data. Study participants in this long-term cost analysis (Table 1) were clinically and sociodemographically diverse but were generally representative of the larger IMPACT sample,3 without significant differences between the intervention and control groups. The mean (SD) age of participants was 72.7 (7.8) years. Most (72%) were women, 9% reported belonging to a racial/ethnic minority group, 47% were married or living with a partner, and 89% were high school graduates. More than 80% had Medicare coverage and prescription drug coverage. All patients met Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) criteria for major depression or dysthymia, and 46% of the sample met criteria for both. Thirty percent showed some evidence of mild cognitive impairment on a 6-item screen. On average, patients reported a moderate amount of functional impairment on a composite index of the disability score by Sheehan et al,14 and 64% reported physical pain that caused functional impairment. Participants endorsed a mean of 3.1 chronic medical conditions (from a list of 10).
+Intervention patients had 4-year mean total healthcare costs of $29 422 (95% confidence interval, $26 479-$32 365), and usual care patients had mean total healthcare costs of $32 785 (95% confidence interval, $27 648-$37 921), representing a cost savings among intervention patients of $3363 (95% confidence interval, $±9282 to $2557) per patient on average during 4 years (Table 2). Intervention patients had lower healthcare costs than usual care patients in every cost category observed (outpatient and inpatient mental health specialty costs, outpatient and inpatient medical and surgical costs, pharmacy costs, and other outpatient costs). Intervention costs for IMPACT participants during the initial 12 months were $522 (95% confidence interval, $495-$550) per patient and are included in the total cost estimates.
+The Figure summarizes total healthcare costs among intervention and control patients during years 1 and 2 and during years 3 and 4 of follow-up, illustrating that the cost savings observed in the IMPACT group occur during the long term (in years 3 and 4) rather than
+during the short term. Results of a bootstrap analysis suggested an 87% probability that the IMPACT intervention was associated with lower total health-care costs compared with care as usual.
+DISCUSSION
+Long-term cost analyses from this trial suggest that the IMPACT program for late-life depression is associated with a high probability of cost savings during a 4-year period. Earlier cost-effectiveness analyses from this trial5 showed slightly higher costs for intervention patients compared with usual care patients in the initial year (the year that intervention services were provided) and somewhat lower costs in the second year of the study. The present long-term cost analysis (extending the earlier follow-up period by an additional 24 months) suggests that cost savings observed after the conclusion of the 12month intervention continued in subsequent years, resulting in a high probability of lower total healthcare costs among intervention patients than control patients during 4 years. Lower costs were observed for the intervention group in each cost category examined, a finding that is consistent with results of observational studies1,2 suggesting that depression is associated with higher healthcare costs across various cost categories.
+The confidence intervals for the estimates of differences in healthcare costs include 0, which means that observed cost differences are not significant at P <.05. We used bootstrapping, a procedure of generating random samples by resampling the observation from the study data (in this case 1000 times), to simulate the distribution of the data and to compute parameter estimates (eg, mean costs) based on estimates from the bootstrap samples. Given the wide variation in healthcare costs, bootstrapping is often used to estimate the probability of intervention vs control cost differences in studies with small samples. We recorded the results derived from the 1000 bootstrap samples and divided the number of cost-saving bootstrap samples by 1000 to estimate the probability that a similar study would yield a costsaving result. An 87% probability suggests that we have substantial but not absolute confidence that a study like ours would find the IMPACT intervention to be cost-saving compared with care as usual.
+To our knowledge, our study provides the longest-term follow-up data to date on healthcare costs associated with collaborative care programs for depression. Other studies that have provided long-term follow-up data include the study by Simon and colleagues,12 which reported lower total healthcare costs among patients with depression and diabetes mellitus receiving collaborative care during a 2-year follow-up period, and a long-term follow-up study by Katon and colleagues15 among primary care patients with depression, which had similar findings. Our findings of cost savings are consistent with a replication study16 (using a different sample of depressed patients) completed by 1 of 2 participating health plans after the IMPACT study ended. This study reported a cost savings of approximately $1117 associated with IMPACT care during a 12-month follow-up period.
+Limitations of this study include the fact that our long-term cost data are limited to 2 of the organizations participating in the original trial. We report long-term (4 year) cost findings on all study participants from the 2 participating health plans for whom such data were available. The sample is drawn from an insured, largely educated population of white race/ ethnicity, and further research should be performed among lower-income, underinsured populations predominantly of minority races/ethnicities to see if our findings are generalizable to such populations. However, the patients at the 2 participating study sites were generally representative of the overall IMPACT sample,3 and intervention effects on clinical outcomes and costs during 2 years of patient follow-up were consistent across each of the 8 participating healthcare organizations, including the 2 examined herein.3-5 Earlier
+analyses of the program's effectiveness suggest that participants of minority race/ethnicity benefited as much from the intervention as those of white race/ethnicity,17 and similar investigations in low-income countries show that collaborative care programs for depression are not only effective but also cost-effective in such settings.18 Study participants who died or disenrolled from the health plan during the 4-year follow-up period contributed cost data for the periods during which such data were available, but we do not have access to complete cost data for a small number of participants who left the health plan before the end of the 4-year follow-up period. Other limitations include the fact that we do not have longterm clinical follow-up data on depression outcomes with which to conduct costeffectiveness analyses, but findings from an earlier study4 that examined differences between intervention and control patients at 18 and 24 months (12 months after the end of the intervention) suggest that the clinical benefits of the intervention continue well beyond the original 1-year intervention period. Our cost analyses are from a payer's perspective and do not consider other societal costs, including costs of informal healthcare and other services, or other indirect costs.
+More than 35 randomized controlled trials have established that collaborative care for depression is more effective and more cost-effective than usual care.19 Findings from our study suggest that, for older adults with depression, collaborative care not only produces substantial clinical improvements but also carries a high likelihood of long-term cost savings compared with care as usual.
+Unutzer et al.
+Page 7
+NIH-PA Author Manuscript	NIH-PA Author Manuscript	NIH-PA Author Manuscript
+8.	First, MD.; Spitzer, RL.; Gibbon, M.; Williams, JBW. Structured Clinical Interview TorDSM-IV Axis I Disorders (SCID): Clinical Version.. American Psychiatric Press; Washington, DC: 1996.
+9.	Hegel MT, Imming J, Cyr-Provost M, Hitchcock-Noel P, Arean P, Unutzer J. Role of allied behavioral health professionals in a collaborative stepped care treatment model for depression in primary care: project IMPACT. Families Systems Health. 2002; 20:265-277.
+10.	Mynors-Wallis LM, Gath DH, Lloyd-Thomas AR, Tomlinson D. Randomised controlled trial comparing problem solving treatment with amitriptyline and placebo for major depression in primary care. BMJ. 1995; 310(6977):441-445. [PubMed: 7873952]
+11.	Hegel, M.; Arean, P. Problem-Solving Treatment of Primary Care (PSTPC): A Treatment Manual for Depression Project IMPACT. Dartmouth Medical School; Hanover, NH: 2003.
+12.	Simon GE, Katon WJ, Lin EH, et al. Cost-effectiveness of systematic depression treatment among people with diabetes mellitus. Arch Gen Psychiatry. 2007; 64(1):65-72. [PubMed: 17199056]
+13.	Harpole LH, Stechuchak KM, Saur CD, Steffens DC, Unutzer J, Oddone E. Implementing a disease management intervention for depression in primary care: a random work sampling study. Gen Hosp Psychiatry. 2003; 25(4):238-245. [PubMed: 12850655]
+14.	Sheehan DV, Harnett-Sheehan K, Raj BA. The measurement of disability. Int Clin Psychopharmacol. 1996; 11(suppl 3):89-95. [PubMed: 8923116]
+15.	Katon W, Russo J, Von Korff M, et al. Long-term effects of a collaborative care intervention in persistently depressed primary care patients. J Gen Intern Med. 2002; 17(10):741-748. [PubMed: 12390549]
+16.	Grypma L, Haverkamp R, Little S, Unutzer J. Taking an evidence-based model of depression care from research to practice: making lemonade out of depression. Gen Hosp Psychiatry. 2006; 28(2): 101-107. [PubMed: 16516059]
+17.	Arean PA, Ayalon L, Hunkeler E, et al. Improving depression care for older, minority patients in primary care. Med Care. 2005; 43(4):381-390. [PubMed: 15778641]
+18.	Patel V, Araya R, Chatterjee S, et al. Treatment and prevention of mental disorders in low-income and middle-income countries. Lancet. 2007; 370(9591):991-1005. [PubMed: 17804058]
+19.	Gilbody S, Bower P, Fletcher J, Richards D, Sutton AJ. Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. Arch Intern Med. 2006; 166(21): 2314-2321. [PubMed: 17130383]
+Am JManag Care. Author manuscript; available in PMC 2013 October 28.
+Unutzer et al.
+Page 8
+NIH-PA Author Manuscript	NIH-PA Author Manuscript	NIH-PA Author Manuscript
+Take-away Points
+Evidence-based collaborative care management programs for depression in primary care have been shown to be more effective and more cost-effective than care as usual.
+■	This study demonstrates that during a 4-year period older adults with depression and comorbid medical disorders who participated in a collaborative care program had lower total healthcare costs than those in care as usual.
+■	The findings add to the arguments for widely implementing such programs and for policies that facilitate coverage and reimbursement for such evidence-based care management programs.
+Am JManag Care. Author manuscript; available in PMC 2013 October 28.
+Unutzer et al.
+Page 9
+NIH-PA Author Manuscript	NIH-PA Author Manuscript	NIH-PA Author Manuscript
+Figure.
+Total Healthcare Costs
+□ Intervention
+Usual care
+Am JManag Care. Author manuscript; available in PMC 2013 October 28.
+Unutzer et al.
+Page 10
+NIH-PA Author Manuscript	NIH-PA Author Manuscript	NIH-PA Author Manuscript
+Table 1
+*
+Baseline Sociodemographic and Clinical Characteristics
+Randomized Group
+Characteristic	Overall	Intervention	Usual Care	P
+Sociodemographic				
+Referred by primary care provider	306 (56)	154(55)	152 (56)	.93
+Female sex	399 (72)	194 (70)	205 (75)	.13
+Age, mean (SD), y	72.7 (7.8)	72.7 (78)	72.6 (7.9)	.13
+Married or living with partner	256 (47)	132 (47)	124 (46)	.73
+Racial/ethnic minority	47 (9)	22 (8)	25 (9)	.65
+>High school graduate	489 (89)	247 (89)	242 (89)	.89
+Medicare coverage	440 (80)	223 (80)	217 (80)	>.99
+Insurance coverage for prescription medications	491 (89)	249 (89)	242 (89)	>.99
+Clinical				
+Depression status				
+Major depression	133 (24)	66 (24)	67 (25)	.55
+Dysthymia	164 (30)	89 (32)	75 (28)	
+Both	254(46)	124 (44)	130 (48)	
+>2 Prior episodes of depression	356 (65)	182 (65)	174 (64)	.79
+Depression severity score, mean (SD)	1.7 (0.5)	1.7 (0.5)	1.7 (0.6)	.79
+Cognitive impairment	167 (30)	79 (28)	88 (32)	.31
+Chronic disease count, mean (SD), from a list of 10	3.1 (1.7)	3.1 (1.8)	3.1 (1.7)	.31
+Overall functional impairment, mean (SD), score range of 0-10	4.3 (2.5)	4.2 (2.5)	4.3 (2.6)	.33
+Functional impairment from pain	355 (64)	174 (62)	181 (67)	.33
+*
+Data are given as number (percentage) unless otherwise indicated.
+^Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition).
+^Twenty-item Hopkins Symptom-Check List (score range, 0-4).
+Am JManag Care. Author manuscript; available in PMC 2013 October 28.
+Unutzer et al.
+Page 11
+NIH-PA Author Manuscript	NIH-PA Author Manuscript	NIH-PA Author Manuscript
+Table 2
+*
+4-Year Healthcare Costs
+Cost, $
+Randomized Group
+Cost Category	Overall Mean	Intervention	Usual Care	Difference
+Outpatient				
+IMPACT intervention	—	522 (495 to 550)	0 (0 to 0)	522 (495 to 550)
+Mental health	661	558 (362 to 753)	767 (561 to 974)	±209 (±494 to 75)
+Pharmacy	7284	6942 (6062 to 7822)	7636 (6287 to 8984)	±694 (±2304 to 916)
+Other	14 306	14 160 (12 899 to 15 421)	14 456 (12 909 to 16 002)	±296 (±2291 to 1700)
+Total ?	22 516	22 182 (20 368 to 23 996)	22 859 (20 470 to 25 247)	±677 (±3676 to 2323)
+Inpatient				
+Medical	8452	7179 (5450 to 8908)	9757 (6455 to 13 059)	±2578 (±6305 to 1149)
+Mental health and substance abuse	114	61 (±8 to 129)	169 (±2 to 340)	±108 (±292 to 76)
+Total Healthcare During 4 y				
+Overall Total	31 082	29 422 (26 479 to 32 365)	32 785 (27 648 to 37 921)	±3363 (±9282 to 2557)
+IMPACT indicates Improving Mood: Promoting Access to Collaborative Treatment.
+*
+Data are given as mean (95% confidence interval) unless otherwise indicated.
+^Total outpatient costs include IMPACT intervention costs which only apply in the intervention group.
+Am JManag Care. Author manuscript; available in PMC 2013 October 28.
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+the emphasis on individual achievement and independence in higher education.4-5’7-10 Higher education institutions have endeavored to address immigrant students’ financial and linguistic barriers,4,11 but attempts to address cultural stressors that deleteriously influence their academic and mental health outcomes have been more limited.
+Colleges serve students during emerging adulthood (ages 18-25) - a developmental period often associated with seeking autonomy from family and forging distinct identities.12 This period is one of increased risk for major depression and suicidal behavior.13-15 In fact, suicide is the second leading cause of death in emerging adulthood.15 Furthermore, emerging adulthood is a period of particularly heightened risk for suicidal thoughts and behaviors (STB) among racial and ethnic minorities,16-17 who comprise the majority of current US immigration.18 Understanding mechanisms that can influence cultural factors, which, in turn, influence STB among young immigrants, is important in addressing immigrant college students’ mental health needs.
+Cultural Factors and STB among Immigrants
+Acculturative Stress
+Several cultural factors frequently associated with immigration, such as acculturation and acculturative stress, have been found to influence psychiatric outcomes such as depression and STB.19-29 However, past studies report fewer suicide attempts19, 30 and less suicidal ideation and planning31 among immigrants versus non-immigrants. Acculturative stress -stress during the process of acculturation - has been found to be associated with suicidal ideation among immigrants,21-22 but the mechanisms surrounding its influence, and particularly among immigrant college students, are not fully understood.
+Acculturation occurs at the intersection of two or more cultural contexts32 and is an adaptation to a dominant cultural orientation by an individual from a non-dominant cultural orientation, that forms in opposition to developing an ethnic identity.33 Ethnic identity is an opposing process in which an individual shifts towards values of one’s heritage culture that are not part of the dominant cultural orientation.33 The tension between these two opposing processes is thought to result in acculturative stress,33 which arises when, during acculturation, individuals experience social marginalization, negativity towards their heritage origin, pressure to assimilate, and conflict between individual and familial cultural expectations.34
+Familial Acculturative Stress
+Prior research suggests the importance of understanding acculturative stress within a family context.10 Such conflict has been elaborated upon by acculturative family distancing (AFD) theory, which suggests that the pace of acculturation between ethnic minority parents and their children may vary, creating an acculturative discrepancy that may be associated with depressive symptoms.35 One study of children of Southeast Asian immigrant parents found that parent-child discrepancy in partiality for values of the culture of heritage, versus the culture at large, predicted depressive symptoms, and that parent-child cultural conflict mediated the effect of perceived parent-child acculturative discrepancy on depressive
+symptoms.28 Another study found that conflict between parents and their daughters mediated the relation between traditional female gender roles (marianismo) and depressive symptoms among Mexican American female college students.36 Stress from AFD and related family conflict have further been suggested to be positively related to adjustment difficulties among Asian American college students23 and depressive symptoms in second-generation Asian Indian women.26
+With regard to suicide and AFD theory, one prominent study that surveyed over 2,000 Asian American individuals from varied ethnic backgrounds found that culturally-based family conflict was positively associated with STB.20 Factors related to AFD have also been suggested to increase risk for suicidal behavior among Latina emerging adults. 19, 29, 37 Prior research has examined AFD for its influence on depressive symptoms and STB, but study of potential moderators of the AFD-STB relation has been limited and requires further inquiry.
+Ethnic Identity
+One potential buffer against AFD is ethnic identity, a fluid, multifaceted concept referring to one’s identification as belonging to an ethnic group.38 Ethnic identity has been reported to protect against depressive symptoms among both immigrant and non-immigrant high school students from diverse racial/ethnic minority backgrounds;25 and against acculturative stress, hopelessness, depressive symptoms, and suicidal ideation among urban college students.24 Among Asian American individuals, ethnic identity has been associated with reduced odds of a suicide attempt.20 Lower levels of ethnic identity affirmation and achievement have been associated with concurrent suicidal ideation27 and prior suicide attempts among African American individuals;39 and higher levels have been associated with reducing the influence of gendered racism (combined racism and sexism) on STB among African American women of low socioeconomic status.40 Other studies of racial and ethnic minority individuals, however, have reported no observable effect of ethnic identity on STB.41-42 Thus, whether ethnic identity buffers against STB remains an open question.
+Hopelessness, Depressive Symptoms, and STB
+Hopelessness
+Hopelessness, a prominent risk factor for both depression43 and STB,44-47 entails believing that undesired future ends will occur inevitably in lieu of desired ones.43 Family conflict has been reported to be positively associated with hopelessness among depressed youth.48 One study of Latino adolescent immigrants found that participating in cultural traditions protected against hopelessness.49 Another longitudinal study of an ethnically-diverse college-student sample reported that acculturative stress was positively related to hopelessness, which, in turn increased vulnerability to depression and suicidal ideation, though a strong ethnic identity protected against this effect.24 It is thus possible that AFD, via familial acculturative stress, may interact with hopelessness to exacerbate its severity. This possibility has yet to be examined among immigrants.
+Depressive Symptoms
+Depressive symptoms are positively associated with suicidal ideation and increase risk for suicide.45, 50, 51 At elevated levels of severity, depressive symptoms have been associated with wanting to die, with this being the foremost reason for a prior suicide attempt among repeat attempters in one prior study.52
+Depressive symptoms have been found to be significantly and positively associated with acculturative stress among Korean21 and Central American22 immigrants, as well as among a racially/ethnically diverse group of emerging adult college students.24 Familial acculturative stress has been found to be positively associated with depressive symptoms among second-generation Asian Indian women26 and children of Southeast Asian immigrants.28 Conflict between parents and daughters over traditional female gender roles has been reported to mediate a relation between cultural values espousing these roles and depressive symptoms among Mexican American female college students.36 In addition, ethnic identity has been found to be negatively associated with depressive symptoms among racially/ethnically diverse high school and college students.24-25 Given the prior associations of cultural factors such as AFD and familial acculturative stress with depressive symptoms, research is needed to assess the effects of these cultural factors among young immigrants.
+The Current Study
+Prior research has yet to examine the potential combined influence of AFD and associated familial acculturative stress on suicidal ideation, in conjunction with hopelessness, among a racially/ethnically diverse sample of immigrants that arrived in the US prior to 18 years of age. The current study attempts to address this gap. We hypothesized that among immigrants, compared to non-immigrants, (a) mean levels of familial acculturative stress would be greater; (b) familial acculturative stress would be more strongly associated with suicidal ideation; and (c) familial acculturative stress would moderate the relationship between hopelessness and suicidal ideation, while adjusting for depressive symptoms and ethnic identity.
+Method
+Participants
+One hundred fifty-two first and second-year college students (n = 117 women, 77%), ages 18-25 (M= 18.64, SD = 1.06), were recruited via an ongoing study of cognitive predictors of STB. These individuals were students from a New York City urban public university and other metropolitan area colleges. The sample included 42 (28%) immigrants of diverse racial, ethnic, and cultural origins (see Table 1).
+Racial/ethnic composition included Asian (n = 56, 37%), White (n = 41, 27%), Hispanic/ Latino/a (n = 22, 15%), Black (n = 11, 7%), biracial (n = 10, 6%), and other races/ethnicities (n = 12, 8%). Participants’ self-reported sexual orientation was heterosexual (n = 130; 86%), gay or lesbian (n = 4; 3%), bisexual (n = 11; 7%), or not reported (n = 7; 5%).
+Measures
+Demographic Information Questionnaire (DIQ)—Participants self-reported their race/ethnicity, age, gender, sexual orientation, country of origin, length of US residency, and parents’ countries of origin. Immigrant status was assigned to individuals who were born outside the US and whose length of US residency was less than their current age. All immigrants arrived in the US prior to age 18.
+Hopelessness—The Beck Hopelessness Scale (BHS) measures beliefs and expectations about the occurrence of desired or undesired future outcomes, with 9 positively and 11 negatively phrased true/false items.53 Total scores range from 0 to 20. Prior research suggests it is highly reliable (a = .82-.93).53 In the current study, its internal consistency reliability was .89.
+Depressive Symptoms—The Beck Depression Inventory (BDI-II) contains 21 items that assess symptoms of depression two weeks prior to study participation.54 Possible responses for each item ranged from 0 to 3. Summing all the items yields a total score that ranges from 0 to 63. The BDI-II has had strong internal consistency reliability (a = .93) in prior use among college students54 and also in the current study (a =.93).
+Suicidal Ideation—The Beck Scale for Suicide Ideation (BSS) is a 21-item self-report instrument used to rate active and passive suicidal ideation experienced during the week prior to study participation (on a 0-2 scale).55 Summing the first 19 items yields a total score that ranges from 0 to 38. The BSS is a reliable (a = .87) measure of suicidal ideation.55 In the current study, internal consistency reliability was .90.
+Familial Acculturative Stress—The Social, Attitudinal, Familial, and Environmental Acculturative Stress Scale (SAFE) is a reliable (a = .89) self-report measure that evaluates acculturative stress in the four named contexts.34 For the current study, we were interested in familial acculturative stress between parents and children, so only the familial context of this measure was used (SAFE-Family). The SAFE-Family consists of three items (e.g., “It bothers me that family members I am close to do not understand my new values”), with responses on a five-point scale ranging from “not stressful” (with a point value of 1), to “extremely stressful” (with a point value of 5). Summing these three items yields a total score that ranges from 3 to 15. Internal consistency reliability for this scale in the present study was .75.
+Ethnic Identity—The 12-item version of the Multigroup Ethnic Identity Measure (MEIM) measures an individual’s search for his or her specific ethnic identity in a developmental and cognitive context (e.g., “I have spent time trying to find out more about my ethnic group, such as its history, traditions, and customs”), and his or her level of ethnic identity affirmation, belonging, and commitment in an affective context (e.g., “I have a lot of pride in my ethnic group”).56 Responses are made on a four-point scale ranging from “Strongly Disagree” (with a point value of 1) to “Strongly Agree” (with a point value of 4). Total scores for the MEIM range from 1 to 4 and are computed by calculating the mean of the 12 responses. The original version of the MEIM included reverse-scored items, but the 12-item
+version does not.56 The MEIM has been found to be a reliable measure of ethnic identity among college students (a = .90).56 In the current study, its internal consistency reliability was .88.
+Procedure
+This study was approved by the institutional review board of the City University of New York. Participants provided informed consent and then completed self-report questionnaires (DIQ, BHS, BDI-II, and BSS) either alone in a room with a research assistant, or in small groups where they worked independently in the presence of each other and a research assistant. Participants were subsequently evaluated for suicide risk and debriefed. Those reporting suicidal ideation with a plan (on the BSS) were assessed by a clinical psychologist or social worker, and referred for further evaluation at a student counseling center, as needed.
+A subgroup of participants who had completed the previous measures was then randomly selected and invited to complete an online survey that included the SAFE-Family and MEIM. Participants were not scheduled in advance to complete this survey. Those who were unable to complete the survey immediately were given up to 48 hours to complete the measures online via Survey Monkey. Participants chose as compensation either a ten-dollar amazon.com gift card or one credit towards their introductory psychology research participation requirement.
+Data Analytical Plan
+T-tests were conducted to examine differences between immigrants and non-immigrants on study measures, and correlations were used to examine associations among study variables by immigrant and non-immigrant status. Racial/ethnic differences on continuous study variables were examined via one-way ANOVAs.
+Two-way (immigrant status X familial acculturative stress) and 3-way (immigrant status X familial acculturative stress X hopelessness) interactions were tested by entering interaction terms into a hierarchical linear regression. In block 1 of the regression, we adjusted our analysis for gender, LGB status (each individual was dichotomously coded as LGB or not LGB), ethnic identity, and depressive symptoms. In block 2, we entered the dummy coded dichotomous variable immigrant status. In block 3, we entered hopelessness and familial acculturative stress. In block 4, we entered the two-way interaction terms for immigrant status X hopelessness, immigrant status X familial acculturative stress, and hopelessness X familial acculturative stress. Hopelessness X familial acculturative stress was dropped from the analysis due to high multicollinearity and will not be further discussed. In block 5, we entered the three-way interaction term for immigrant status X hopelessness X familial acculturative stress.
+All continuous study variables (depressive symptoms, ethnic identity, hopelessness, familial acculturative stress) were centered around their means to reduce multicollinearity.57 In addition, we conducted our regression using bootstrapping, which has been recommended for regression models in small samples with heteroscedastic residuals, to ensure compliance with the assumptions of multiple linear regression.58
+The Johnson-Neyman technique was then used to probe significant interactions via the PROCESS script for SPSS.59 This technique identifies significant regions of the moderator for the conditional effect of the predictor on the dependent variable, providing greater precision in examining the moderator’s effect on the predictor-outcome relation than methods identifying arbitrary values of the moderator at the mean and at one standard deviation above and below the mean of the moderator.59
+Results
+Sample Characteristics
+Means, Standard Deviations, T-Tests, and Correlations—Table 2 displays means and standard deviations for continuous study variables (depressive symptoms, ethnic identity, hopelessness, familial acculturative stress), both for the overall sample and for the immigrant and non-immigrant groups. It also contains the results of independent samples t-tests between the immigrant and non-immigrant groups for each study measure. Table 3 displays correlations among continuous variables representing study measures, both for the overall sample and for the immigrant and non-immigrant groups.
+Differences by Immigration Status—Supporting our first hypothesis, immigrants (M= 9.45, SD = 3.08) reported significantly greater mean levels of familial acculturative stress than did non-immigrants (M= 7.73, SD = 3.12), Z(150) = 3.06, p = .003, d = .55. The effect size was moderate (see table 2).60 Immigrants also had significantly greater mean levels of depressive symptoms (M = 16.93, SD = 12.83) than did non-immigrants (M = 12.82, SD =10.50), 050) = 2.03, p = .045, d = .35. The effect size was small (see table 2).60 No additional differences in mean levels of continuous variables, based on immigration status, were found.
+Racial/ethnic differences—We used a one-way analysis of variance (ANOVA) to test racial/ethnic differences in continuous study variables, with post hoc t-tests with Bonferroni corrections used to examine specific group differences. There was an overall racial/ethnic difference in familial acculturative stress, F(5, 146) = 5.56, p < .01, with Asian individuals reporting significantly greater mean levels (M = 9.54, SD = 2.72) than both Hispanic/ Latino/a (M = 6.73, SD = 3.25) and White individuals (M = 6.85, SD = 2.61), t = 3.75 and 4.38, respectively, p < .05. No other significant racial/ethnic differences in continuous study variables were found.
+Predictors of Suicidal Ideation among Immigrants
+To test our hypothesized interactions (immigrant status X familial acculturative stress; immigrant status X hopelessness X familial acculturative stress), we conducted a hierarchical linear regression analysis in five blocks (see Table 4). In block 1, the control variables depressive symptoms, ethnic identity, lesbian; gay; or bisexual (LGB) status, and gender contributed significantly to the model, F(4, 147) = 26.78, p < .01, and accounted for 42% of the variance in suicidal ideation. Depressive symptoms and LGB status were significantly and positively associated with suicidal ideation, but ethnic identity and gender were not significantly associated with suicidal ideation.
+In block 2, immigrant status was added to the model, but was not significantly associated with suicidal ideation. In addition, ethnic identity remained non-significantly associated with suicidal ideation. However, depressive symptoms, LGB status, and gender became significantly associated with suicidal ideation. Block 2 did not contribute significantly to the model, F(1, 146) = .72, p = .40, and this change in R2 was not statistically significant, accounting for less than 1% of the variance in suicidal ideation.
+In block 3, hopelessness and familial acculturative stress were added to the model, of which only hopelessness was significantly associated with suicidal ideation. Depressive symptoms, LGB status, and gender remained significantly associated with suicidal ideation; and immigrant status and ethnic identity remained non-significantly associated with suicidal ideation. Block three contributed significantly to the model, F(2, 144) = 15.16, p < .01, and this change in R2 was significant, accounting for 10% additional variance in suicidal ideation.
+In block 4, neither the immigrant X hopelessness nor immigrant X familial acculturative stress interactions were significantly associated with suicidal ideation. Of the previously entered variables, LGB status and hopelessness were significantly and positively associated with suicidal ideation; but depressive symptoms, ethnic identity, gender, immigrant status, and familial acculturative stress were not. Block 4 contributed significantly to the model, F(2, 142) = 6.68, p < .01, and this change in R2 was statistically significant, accounting for 4% of the variance in suicidal ideation.
+In block 5, the hypothesized immigrant X hopelessness X familial acculturative stress (A = . 08, p = .01) three-way interaction was added to the model and was significantly associated with suicidal ideation (see Table 4). Block 5 contributed significantly to the model, F(1, 141) = 7.16, p < .01, and this change in R2 was statistically significant, accounting for 2% of the variance in suicidal ideation.
+Probing the Interaction among Immigrant Status, Hopelessness, and Familial Acculturative Stress
+Probing the statistically significant interaction among immigrant status, hopelessness, and familial acculturative stress with the Johnson-Neyman technique revealed two regions of significance for the conditional effect of immigrant status X hopelessness, at familial acculturative stress scores of 3.74 (11th percentile, b = -0.51, SE = 0.26, p = .05, 95% CI [-1.01, 0.00]) and 10.07 (72nd percentile, b = 0.21, SE = 0.11, p = .05, 95% CI[0.00, 0.42]). As Figure 1 indicates, at familial acculturative stress scores below 3.74 (11th percentile), immigrant status interacted with hopelessness to predict lower levels of suicidal ideation relative to non-immigrant status; and at familial acculturative stress scores above 10.07 (72nd percentile), immigrant status interacted with hopelessness to predict greater levels of suicidal ideation relative to non-immigrant status.
+Discussion
+Prior research suggests that the acculturative gap between immigrant parents and their children may arise due to a differential pace of acculturation, and may be positively
+associated with depressive symptoms35 and STB.19-20’29’37 The present study examined the interaction between hopelessness - a prominent risk factor for STB,44-47 - and familial acculturative stress in predicting suicidal thoughts among immigrant college students.
+As predicted by our first hypothesis, mean levels of familial acculturative stress were significantly and moderately greater among immigrant than among non-immigrant college students. This finding suggests that the experience of immigrating prior to age 18 may be characterized by some degree of AFD and related familial acculturative stress.
+Our second hypothesis was not supported. High levels of familial acculturative stress did not significantly predict greater levels of suicidal ideation among immigrant versus nonimmigrant college students (although the interaction was present as a non-significant trend). However, our third hypothesis of a three-way interaction with hopelessness was supported. Higher levels of familial acculturative stress, combined with higher levels of hopelessness, was associated with greater levels of suicidal ideation among immigrant versus nonimmigrant college students. This result helps to elucidate the process by which familial acculturative stress may influence hopelessness and suicidal ideation among immigrant college students, though the small effect size of this finding suggests it should be interpreted with caution.60
+Interestingly, the two-way interaction between immigrant status and hopelessness was not significantly associated with suicidal ideation, suggesting that the role of hopelessness in the relation between immigrant status and suicidal ideation may be explained by AFD and associated elevations in familial acculturative stress. However, this potential mediation relationship cannot be tested in the current study due to its cross-sectional nature. Thus, this idea requires further inquiry.
+Our findings should not be interpreted to suggest that immigrant college students are more vulnerable to suicidal ideation than non-immigrant peers. This was something we neither found nor hypothesized, given prior research that suggests more favorable outcomes regarding STB among immigrants, generally, than among later generation peers.19,30-31 Our findings do suggest, however, that the factors that influence vulnerability to suicidal ideation may vary between immigrant college students and their non-immigrant peers, and that familial acculturative stress may be part of a specific cultural pathway of vulnerability among emerging adult immigrant college students.
+Strengths, Limitations, and Future Directions
+The current study’s strengths include its examination of AFD among immigrant college students via familial acculturative stress and its relation with suicidal ideation and hopelessness. Immigrants constitute a sizable portion of the US population1 and of US college students.2 Familial acculturative stress and its effect on the mental health of immigrant college students is thus an important issue for future research, assessment, and treatment of STB among this group.
+Our study is also subject to certain limitations. First, it is based upon self-report measures and is subject to social desirability bias that could have led to the underreporting or over
+reporting of specific symptoms. Second, our study was cross-sectional, and we cannot draw conclusions about causality. Third, our sample consisted of mostly female college students. We did control for gender in our analyses; however, given our mostly female sample, our findings may not generalize well to men. Our findings may also not generalize well to clinical populations and to emerging adults who do/did not pursue a college education, as these individuals were not represented in our sample. Fifth, those not able to complete our survey immediately were given up to 48 hours after the initial administration to complete the ethnocultural measures online via Survey Monkey. Some shift in attitudes regarding ethnocultural factors may have occurred during this period. However, such a shift is unlikely, given that ethnocultural attitudes are longstanding perspectives formed through developmental processes and life experiences such as acculturation.38 In addition, though our participants always completed measures individually and in the presence of a research assistant, some of them completed their measures simultaneously while in the same room. Thus, it is possible that the presence of other individuals completing measures independently, while simultaneously nearby, may have influenced participants’ patterns of responses. Finally, our study may have benefited from recruiting additional participants for increased power.
+Future research can test if our findings generalize to men, clinical populations, and immigrants who have not and do not intend to attend college by replicating our study and recruiting individuals from these groups. Future studies should also pursue similar lines of inquiry to our own with data collected at multiple time points in order to conduct analyses that may establish mediation. Additionally, future research should test our findings by collecting data from sources other than self-report, including but not limited to parental and peer observations, and biological data such as heart rate and cortisol levels.
+Future research can also expand upon our study’s scope in a number of ways. These include: (1) Exploring potential group differences and relationships between factors such as hopelessness, familial acculturative stress, and suicidal ideation among different immigrant groups of specific races and ethnicities; (2) Examining the potential association of familial acculturative stress and hopelessness among immigrants with non-suicidal self-injury, prior suicide attempts, and completed suicides; and (3) Testing if our findings would generalize to immigrants who arrived in the US after 18 years of age, especially among those who lived apart from family members.
+Implications for Interventions with College Students
+It is vital that faculty, administration, academic advisement staff, medical staff, and counseling center staff are aware of cultural factors, such as familial acculturative stress, that can affect mental health among immigrant students. Given the multifaceted and unique needs of immigrant students, we propose that colleges may benefit from adopting an integrated care model,61 in which students expressing distress in academic or medical milieu (as students often receive their health care from on campus services) can meet with a mental health professional embedded within these service areas to address problems as they arise. This may serve to address issues in context and to circumvent cultural barriers that may exist regarding traditional mental health services.
+Immigrant students may also benefit from peer exposure. In settings where a sufficient number of individuals from similar cultural backgrounds require services, immigrants in treatment at counseling centers may benefit from a college based application of a group psychoeducational intervention such as the Family Coping Skills Program,62 an intervention designed for Latino youth that includes peers with similar experiences, as well as parents and siblings (if available in the area of the college campus). Such an intervention could be adapted to address the deleterious effects of AFD among a variety of racial/ethnic/cultural groups. In settings and situations where an immigrant student lacks peers of a similar cultural background, that student may still benefit from having access to trained immigrant peer mentors, who, despite a potentially different cultural background, may still share experiences of AFD and of being a child of immigrant parents while attending college.
+In addition, colleges should attempt to form partnerships with cultural organizations that represent immigrant communities, if they are locally available.63 Such groups may be able to provide cultural resources to help alleviate an immigrant student’s distress and may even be able to play a role in a coping skills program-style intervention.
+Awareness of cultural factors that affect the mental health of immigrants is crucial for informing suicide intervention and prevention among this growing segment of the US population. Our findings suggest that high levels of familial acculturative stress may strengthen the association between hopelessness and suicidal ideation among immigrant emerging adults, compared to non-immigrant emerging adults. Practitioners should thus take multifaceted treatment approaches to reduce hopelessness-related cognitions, familial acculturative stress, and AFD, among immigrant college students.
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+Introduction
+Suicide is among the leading causes of death worldwide.1,2 Although the etiology of suicide is not well understood, numerous studies have shown that the presence of mental disorders is one of the strongest risk factors for suicide attempts and suicide deaths.3,4 Indeed, psychological autopsy studies suggest that more than 90% of people who die by suicide have a diagnosable mental disorder,5 with similar figures reported among clinical samples of suicide attempters.6,7
+Although it is clear that mental disorders in general are associated with suicidal behavior, research has not yet revealed which disorders that uniquely predict these outcomes. This is because most studies have examined the associations between individual disorders and suicidal behavior.8-10 When examined
+in this manner, virtually all mental disorders are associated with suicidal behavior.3,11-13 However, because mental disorders are highly comorbid,14 these bivariate associations could be because of the true effects of only a small number of disorders. As an example, several early reports suggested that panic disorder predicts suicide attempts,8,15 even after controlling for comorbid depression and substance abuse.15,16 However, follow-up studies have reported that the association of panic disorder with suicide attempts is no longer significant when controls are introduced for a broader range of comorbid disor-ders.17-19 Effects of comorbidity need to be taken into consideration in a more rigorous manner to clarify the unique associations of mental disorders with suicidal behaviors. Knowledge of which disorders that are uniquely predictive of suicidal behaviors, and the manner in which comorbidity contributes to these outcomes, is needed to better understand the mechanisms through which mental disorders lead to suicidal behaviors.
+Another area of uncertainty is regarding which aspect of suicidal behavior mental disorders actually predict. Although decades of research have
+869
+documented a strong association between mental disorders and suicide, several recent epidemiological studies suggest that mental disorders predict the onset of suicide ideation, but may have weaker effects in predicting suicide plans or attempts among people with suicide ideation.12,20 Family and genetic studies have similarly suggested that the co-occurence of suicide ideation among family members is explained by the presence of mental disorders, but that the tendency to act on suicidal thoughts is not, and instead may be the result of a distinct genetic component, perhaps related to the presence of impulsive-aggressive traits.21-23 Despite these recent findings, surprisingly little is known regarding which mental disorders are uniquely related to suicide ideation, and which predict the progression to suicide attempts.
+This study was designed to address these limitations by carefully testing the associations between the presence of DSM-IV Axis I mental disorders and subsequent suicidal behavior in a nationally representative sample of the US household population. Axis II (personality) disorders and specific aspects of mental disorders, such as their severity and chronicity, are also likely to influence the occurrence of suicidal behavior; however, this study focused specifically on the presence of Axis I disorders as an initial step toward better understanding how and why mental disorders predict subsequent suicidal behavior. This study extends earlier research on this topic by simultaneously examining a wide range of DSM-IV Axis I disorders, by testing more complex statistical models of the effects of comorbidity than those carried out in earlier studies, and by carefully decomposing the associations between mental disorders and suicide attempts.
+Materials and methods
+Sample
+Data are from the National Comorbidity Survey Replication (NCS-R), a nationally representative face-to-face household survey of the US household population, conducted between February 2001 and April 2003. The NCS-R used a multi-stage clustered-area probability design to interview 9282 Englishspeaking adults (18 + years old) to assess the prevalence and correlates of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)24 mental disorders.25 Interviewer contacts were preceded by an advance letter that explained the purposes of the study and included a toll-free telephone number for respondents who wanted more information or who wanted to opt out. Interviewers then visited the homes of designated respondents and answered remaining questions regarding participation before obtaining informed consent. Respondents were paid $50 for participation. The NCS-R response rate was 70.9%. A probability sub-sample of initial non-respondents was asked to participate in a brief
+telephone non-respondent survey to check for systematic non-response bias. A $100 incentive was offered for participation in this survey. Data in the main survey were weighted using a propensity score weight.26 The university human subjects committee approved all procedures.
+The NCS-R interview was administered in two parts. Part I included a core diagnostic assessment of DSM-IV mental disorders along with questions on suicidal behaviors administered to all 9282 respondents. Part II included questions relating to correlates and additional disorders, and was administered to a probability sub-sample of 5692 Part I respondents that included 100% of those who met lifetime criteria for any Part I disorder and a probability sub-sample of other respondents. The Part II sample was weighted to adjust for differential probabilities of selection within households, oversampling of Part I respondents with a mental disorder, systematic non-response and residual differences between the sample and the 2000 census of the population on a variety of sociodemographic and geographical variables. Further details about the weighting procedures used are presented elsewhere.27
+Measures
+DSM-IV mental disorders. Mental disorders were assessed using the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) Version 3.0, a fully structured diagnostic interview administered by trained lay interviewers.28 The 16 mental disorders included in our analysis were anxiety disorders (panic disorder, generalized anxiety disorder, phobias, post-traumatic stress disorder and separation anxiety disorder), mood disorders (major depressive disorder, dysthymic disorder and bipolar disorder), impulse-control disorders (oppositional defiant disorder, conduct disorder, attention-deficit/ hyperactivity disorder and intermittent explosive disorder) and substance use disorders (alcohol and illicit drug abuse or dependence). A clinical reappraisal study revealed generally good concordance between CIDI diagnoses and those obtained using the research version of the Structured Clinical Interview for DSM-IV (SCID).29,30
+Suicidal behavior. Suicidal behaviors were assessed using the Suicidality Module of the CIDI.28 This module includes an assessment of the lifetime occurrence and age-of-onset of suicide ideation, plans and attempts. Consistent with our goal of examining relationships of mental disorders with a continuum of suicidal behaviors, we considered five dated lifetime history outcomes in a series of nested survival analyses (see below for analysis methods): (i) suicide ideation in the total sample; (ii) suicide attempt in the total sample; (iii) suicide plan among respondents with ideation; (iv) suicide attempt
+among those with a plan; and (v) suicide attempt among those with ideation in the absence of a plan (impulsive attempt).
+Analysis methods
+The prevalence of temporally prior mental disorders among respondents with each of the five outcomes was estimated using cross-tabulations. Temporal precedence of mental disorders was examined using individual-level retrospective age-of-onset reports. Predictive associations between temporally prior mental disorders and subsequent suicidal behaviors were estimated using discrete-time survival models with person-years as the unit of analysis.31 Mental disorders were treated as time-varying covariates in these models. Survival coefficients were exponentiated to generate odds ratios (ORs) and their standard error for ease of interpretation.
+We estimated survival models that were bivariate (in which only one disorder was considered at a time) as well as multivariate (in which all disorders were considered simultaneously) in predicting each suicidal behavior. We also estimated a series of models that allowed for multiplicative interactions among comorbid disorders. As 16 disorders were included in the analyses, the number of logically possible interactions (216-17 = 65,519) greatly exceeded the number of observations, which meant that we needed to impose some structure on the interactions to generate models with stable estimates. We began with a model that included summary dummy predictor variables for the total number of comorbid disorders experienced by each respondent (for example, separate dummy predictor variables to distinguish respondents with exactly two disorders, exactly three, ..., and so on). This model assumed that interactions were constant across types of disorders and were influenced only by number of disorders (that is, that the 120 ORs of the 16 x 15/2 logically possible twoway interactions could all be considered the same; that the 560 ORs of the 16 x 15 x 14/6 logically possible three-way interactions could all be considered the same; and so on). More complex models were then estimated, which allowed for separate interactions between each type of disorder and number of comorbid disorders. The simple model that assumed constant interactions was a good approximation of the data, so we focused on that model in subsequent analyses. Population attributable risk proportions (PARPs) due to each disorder and to all disorders combined were then calculated on the basis of the results of that simple interaction model. Standard errors of prevalence estimates and survival coefficients were estimated with the Taylor series method,32 using SUDAAN software33 to adjust for the weighting and clustering of the NCS-R sample design. Multivariate significance was evaluated with Wald /2-tests on the basis of design-corrected coefficient variance-covariance matrices. All significance tests were evaluated using 0.05 level two-sided tests.
+Results
+Prevalence of temporally prior disorders among those with suicidal behaviors
+Approximately two-thirds (66.0%) of people who have seriously considered killing themselves report having an earlier DSM-IV/CIDI disorder. The history of mental disorders is even higher among respondents who go on to make a suicide plan (77.5%) and a suicide attempt (79.6%). History of mental disorders is higher among respondents who make a planned attempt (83.4%) compared with those who make an unplanned attempt (74.1%), suggesting that the latter may be influenced to a greater extent by other factors such as stressful life events (more detailed results reported by each disorder and suicidal behavior are available on request).
+Associations of temporally primary disorders with suicide attempts
+Bivariate survival models show that each of the 16 lifetime DSM-IV/CIDI disorders examined is significantly associated with increased risk of the subsequent first onset of a suicide attempt, with ORs from a low of 2.7 (agoraphobia) to a high of 6.7 (bipolar disorder) (Table 1). The inter-quartile range (IQR) of ORs is 4.1-4.9. The bivariate ORs associated with broad classes of disorder are similar to each other: 4.6 for any anxiety disorder; 5.2 for any mood disorder; 4.8 for any impulse-control disorder; and 4.8 for any substance use disorder. The bivariate OR associated with having any disorder is even higher (7.0).
+As expected, the ORs all become much smaller in an additive multivariate model (IQR decreases to 1.31.9), although 14 of the 16 ORs remain greater than 1.0, and 10 of them are statistically significant. Next, a simple interactive multivariate model was estimated that included one dummy variable for each of the 16 disorders, plus additional dummy variables for each number of disorders (for example, exactly one earlier disorder, exactly two, etc.). The ORs for individual disorders in this model can be interpreted as the relative odds of a subsequent suicide attempt among respondents with a history of a pure disorder (that is, only one disorder) versus those with no disorders. Similar to the additive multivariate model, a number of pure disorders have statistically significant ORs that are comparable in magnitude (ORs = 1.5-2.3; IQR= 1.4-1.9), with all 16 ORs greater than 1.0, and 10 being statistically significant.
+Associations of number of comorbid disorders with suicide attempts
+Next, we examined the association between comorbidity and suicide attempts in isolation by estimating a model in which the only substantive predictors were dummy variables for number of disorders temporally prior to the first suicide attempt. A strong positive association was found, with ORs increasing from 3.7 for any one disorder, 6.8 for two, 12.1 for three, up through 29.0 for six or more (compared with respondents with no disorders)
+(Table 2). However, the ORs associated with having a large number (five or more) of disorders either do not increase, or increase at a decreasing rate, compared with ORs associated with fewer disorders. As a result, the ORs for high numbers of disorders in a more elaborate model that also included predictors for the
+16 types of disorders (as in Table 1) are lower than 1.0, indicating the existence of sub-additive effects of comorbidities involving large numbers of disorders. In other words, as the number of comorbid disorders increases, the relative odds of a suicide attempt increase at a decreasing rate.
+As noted above, the model that included as predictors both 16 dummy variables for types of disorders and dummy variables for number of disorders implicitly assumes that interactions are identical for all comorbid profiles involving the same number of disorders. We evaluated more elaborate models that considered distinct interactions for specific disorders with numbers of other disorders, but these models did not improve appreciably over the simpler interactive model (more detailed results are available on request). As a result, we based our subsequent analysis of intermediate outcomes (that is, suicide ideation and plans) on the simple interactive model.
+Interactive associations of comorbid disorders with intermediate outcomes
+To disaggregate the observed effects and to examine whether the association between mental disorders and suicide attempts is explained by the intermediate outcomes of suicide ideation and plan, we estimated models predicting each of the five suicidal behaviors studied (Table 3). Examining the model coefficients in parallel, we see that the most powerful and consistent associations are with suicide ideation (15 of the 16 ORs positive, 11 statistically significant, IQR=1.3-1.8). Conditional associations with suicide plans among ideators are both less consistent and less powerful (11 of the 16 ORs positive, only 2 statistically significant, IQR= 1.0-1.2). Conditional associations with planned attempts (14 of the 16 ORs positive, 5 statistically significant, IQR=1.2-1.6) and unplanned attempts (12 of the 16 ORs positive,
+4 statistically significant, IQR=1.1-1.9) are intermediate in strength and consistency.
+As in the model predicting suicide attempts in the total sample, ORs significantly less than 1.0 exist for comorbidities involving large numbers of disorders in predicting suicide ideation and planned attempts. These sub-additive comorbidities involving large numbers of disorders are not found in predicting plans among ideators, in which ORs for number of disorders are all close to 1.0. This suggests that the multivariate effects of comorbid disorders are additive. A non-significant trend pattern of sub-additive coefficients for high comorbidity is found in predicting unplanned attempts.
+An examination of the associations between individual disorders and each suicidal behavior reveals that, although major depression is among the strongest predictors of suicide ideation (OR=2.3), it does not significantly predict suicide plans or attempts among ideators (ORs= 1.0-1.3). Instead, suicide plans and attempts are predicted by anxiety, impulsecontrol and substance use disorders. The ORs for these disorders are generally stronger for unplanned (impulsive) suicide attempts (ORs = 2.1-2.9) than for the occurrence of suicide plans or planned attempts among ideators (ORs = 1.6-1.9).
+Population attributable risk proportions
+The results presented above focus on ORs describing individual-level associations without taking into consideration either the prevalence of the predictors or the distribution of comorbidity. We calculated PARPs to examine these population-level effects. PARPs represent the proportion of observed cases of
+outcome that would be prevented if specific predictor variables were prevented on the basis of the assumption that the ORs in the model accurately represent causal effects of the predictors. The PARP estimates show even more clearly than do the ORs that the predictive effects of the disorders considered here on suicide attempts are largely because of effects on ideation rather than on transitions from ideation to plans or attempts.
+Focusing first on PARPs for all disorders combined, we estimate that roughly three-fourths (76.5%) of all suicide attempts are associated with earlier DSM-IV/ CIDI disorders. However, this strong aggregate association is largely because of the prediction of suicide ideation (78.9%), with much smaller PARPs of disorders predicting the onset of a suicide plan among ideators (10.5%), attempts among ideators with a plan (25.4%), and attempts among ideators without a plan (36.9%) (Table 4). Mood and anxiety disorders play the largest roles in accounting for the onset of ideation, whereas anxiety disorders play the largest roles in accounting for attempts among ideators both with and without a plan.
+Discussion
+Three important limitations should be borne in mind when interpreting these results. First, the NCS-R data are based on retrospective self-reports of the occurrence and timing of mental disorders and suicidal behavior, and hence these reports may be subject to systematic recall bias.34 On balance, systematic reviews have suggested that people can recall past experiences with sufficient accuracy to provide valuable information,35 and that retrospective data are especially useful when, as in this case, prospective data are not available.36 Nonetheless, it is important to be cautious in interpreting the results, because recall bias almost certainly exists to some extent in the retrospective reports used to generate the data. Second, although NCS-R examined a wide range
+of mental disorders, several disorders known to be linked to suicidal behaviors were not included, most notably non-affective psychosis and personality disorders. Third, we did not examine the severity or chronicity of each disorder, which might have led to an underestimation in the strength of the associations between disorders and suicidal behavior.
+These limitations notwithstanding, these results advance our understanding of suicidal behavior in several important ways. Our study extends findings from psychological autopsy studies that show that people who die by suicide have very high rates of mental disorders,5 and the consistent finding of similarly high rates of mental disorders among suicide attempters in clinical settings.6,7 These results suggest that in the general population, the rate of temporally prior mental disorders, although elevated among people with each suicidal behavior, is slightly lower than that in psychological autopsy studies and in studies of clinical samples.
+The elevated rates of earlier mental disorders among people with suicidal behavior are most pronounced for suicide ideators compared with those for people who never considered suicide. It is important to note that differences in rates of earlier mental disorders between ideators who did versus those who did not make a plan or attempt are much more modest. This pattern of results is consistent across each disorder. These results indicate that, although the presence of mental disorders predicts the onset of suicide ideation, they are less useful in determining which people with suicide ideation will make a suicide attempt.
+The finding that every mental disorder measured predicts suicide attempts in bivariate analyses replicates results from earlier studies.11,12,20 Our multivariate analysis showed that the incremental predictive effects of individual disorders are much less powerful and that a general pattern of subadditive interactive effects exists for comorbidities involving a large number of disorders. One possible
+explanation for this pattern is that some disorders are correlated with suicide attempts because they are comorbid with disorders that are independently associated with suicide attempts. In this study, the disorders with the lowest ORs in multivariate analyses were generalized anxiety disorder, social phobia, agoraphobia, separation anxiety disorder and dysthymia—all of which are strongly comorbid with major depression.37,38 These disorders were also among those showing the largest decay in their association with suicide attempts in the presence of comorbid disorders, suggesting that they are not uniquely associated with suicide attempts. It is interesting that the significant bivariate association between panic disorder and subsequent suicide attempts, although decreased substantially in multivariate analyses, continued to be statistically significant and substantial in magnitude (OR= 1.9), arguing for a middle ground in the long-standing debate as to whether panic disorder is8 or is not17 uniquely related to suicide attempts. These results suggest that future studies should consider the effects of comorbid disorders when the examining predictive effects of mental disorders on suicide attempts.
+Another plausible interpretation of this pattern is that much of the association between mental disorders and suicide attempts is explained by some factor common to most disorders, such as the experience of distress or impairment. Such an interpretation is consistent with an escape model of suicide, which suggests that people attempt suicide in an effort to escape intolerable distress regardless of the specific source of that distress.39,40 Such a model may also help to explain the strong dose-response relationship observed between number of disorders and risk of subsequent suicide attempt. The presence of multiple disorders (that is, multi-morbidity38) is associated with higher levels of distress, impairment and disease burden,38,41 and these factors may be intolerable for some people when they occur at high levels. Taken together, these findings highlight one potential pathway through which mental disorders may lead to suicidal behaviors and provide a point of departure for future studies aimed at expanding our understanding of this pathway.
+The unique relations that emerged between specific disorders and the progression to suicide attempts are especially noteworthy. Results suggest that the onset of suicide ideation is best predicted by depression, but depression does not predict further progression to suicide attempt. Instead, disorders characterized by anxiety/agitation (for example, post-traumatic stress disorder) and poor impulse-control (for example, bipolar disorder, conduct disorder, substance disorders) emerged as the strongest predictors of which ideators make suicide plans and attempts. These results synthesize earlier findings on the association of depression,9 anxiety,42 and impulse-control dis-orders12,42,43 with suicide attempts, and suggest that some disorders may elevate suicide risk by increasing the desire for death or suicide, whereas others
+characterized by impulsiveness and poor behavioral control may elevate risk by increasing the likelihood of acting on suicidal thoughts.21-23,42-45 Studies examining genetic, biological and other risk factors for suicide should carefully consider which form of suicidal behavior is being examined in a given study, as these findings suggest that associations are likely to differ when predicting suicide ideation versus suicide attempts among ideators. Taken together, these results underscore the importance of carefully considering comorbidity in the study of suicidal behavior, illuminate the ways in which different types of mental disorders may contribute to the risk of suicidal behavior, and point toward modifiable targets for decreasing the morbidity and mortality associated with suicidal behavior.
+Mental disorders and suicidal behavior
+MK Nock et al
+876
+All correspondence can be sent to ncs@hcp.med. harvard.edu.
+References
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+2	Nock MK, Borges G, Bromet EJ, Cha CB, Kessler RC, Lee S. Suicide and suicidal behaviors. Epidemiol Rev 2008; 30: 133-154.
+3	Harris EC, Barraclough B. Suicide as an outcome for mental disorders. A meta-analysis. Br J Psychiatry 1997; 170: 205-228.
+4	Pokorny AD. Prediction of suicide in psychiatric patients. Report of a prospective study. Arch Gen Psychiatry 1983; 40: 249-257.
+5	Cavanagh JT, Carson AJ, Sharpe M, Lawrie SM. Psychological autopsy studies of suicide: a systematic review. Psychol Med 2003; 33: 395-405.
+6	Beautrais AL, Joyce PR, Mulder RT, Fergusson DM, Deavoll BJ, Nightengale SK. Prevalence and comorbidity of mental disorders in persons making serious suicide attempts: a case-control study. Am J Psychiatry 1996; 153: 1009-1014.
+7	Wunderlich U, Bronisch T, Wittchen HU. Comorbidity patterns in adolescents and young adults with suicide attempts. Eur Arch Psychiatry Clin Neurosci 1998; 248: 87-95.
+8	Weissman MM, Klerman GL, Markowitz JS, Ouellette R. Suicidal ideation and suicide attempts in panic disorder and attacks. N Engl J Med 1989; 321: 1209-1214.
+9	Bostwick JM, Pankratz VS. Affective disorders and suicide risk: a reexamination. Am J Psychiatry 2000; 157: 1925-1932.
+10	Phillips MR, Yang G, Li S, Li Y. Suicide and the unique prevalence pattern of schizophrenia in mainland China: a retrospective observational study. Lancet 2004; 364: 1062-1068.
+11	Kessler RC, Berglund P, Borges G, Nock MK, Wang PS. Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990-1992 to 2001-2003. JAMA 2005; 293: 2487-2495.
+12	Nock MK, Borges G, Bromet EJ, Alonso J, Angermeyer M, Beautrais A et al. Cross-national prevalence and risk factors for suicidal ideation, plans, and attempts in the WHO World Mental Health Surveys. Br J Psychiatry 2008; 192: 98-105.
+13	Kessler RC, Borges G, Walters EE. Prevalence of and risk factors for lifetime suicide attempts in the National Comorbidity Survey. Arch Gen Psychiatry 1999; 56: 617-626.
+14	Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005; 62: 617-627.
+15	Pilowsky DJ, Wu LT, Anthony JC. Panic attacks and suicide attempts in mid-adolescence. Am J Psychiatry 1999; 156: 1545-1549.
+16	Anthony JC, Petronis KR. Panic attacks and suicide attempts. Arch Gen Psychiatry 1991; 48: 1114.
+17	Vickers K, McNally RJ. Panic disorder and suicide attempt in the National Comorbidity Survey. J Abnorm Psychol 2004; 113: 582-591.
+18	Hornig CD, McNally RJ. Panic disorder and suicide attempt. A reanalysis of data from the Epidemiologic Catchment Area study. Br J Psychiatry 1995; 167: 76-79.
+19	Rudd MD, Dahm PF, Rajab MH. Diagnostic comorbidity in persons with suicidal ideation and behavior. Am J Psychiatry 1993; 150: 928-934.
+20	Borges G, Angst J, Nock MK, Ruscio AM, Kessler RC. Risk factors for the incidence and persistence of suicide-related outcomes: a 10-year follow-up study using the National Comorbidity Surveys. J Affect Disord 2008; 105: 25-33.
+21	Brent DA, Mann JJ. Family genetic studies, suicide, and suicidal behavior. Am J Med Genet C Semin Med Genet 2005; 133: 13-24.
+22	Brent DA, Mann JJ. Familial pathways to suicidal behavior— understanding and preventing suicide among adolescents. N Engl J Med 2006; 355: 2719-2721.
+23	Bondy B, Buettner A, Zill P. Genetics of suicide. Mol Psychiatry 2006; 11: 336-351.
+24	American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association: Washington, DC, 1994.
+25	Kessler RC, Merikangas KR. The National Comorbidity Survey Replication (NCS-R): Background and aims. Int J Methods Psychiatric Res 2004; 13: 60-68.
+26	Rubin DB. Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Serv Outcomes Res Methodol 2001; 2: 169-188.
+27	Kessler RC, Berglund P, Chiu WT, Demler O, Heeringa S, Hiripi E et al. The US National Comorbidity Survey Replication (NCS-R): Design and field procedures. Int J Methods Psychiatric Res 2004; 13: 69-92.
+28	Kessler RC, Ustun TB. The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatric Res 2004; 13: 93-121.
+29	First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, NonPatient Edition (SCID-I/NP). Biometrics Research, New York State Psychiatric Institute: New York, NY, 2002.
+30	Haro JM, Arbabzadeh-Bouchez S, Brugha TS, de Girolamo G, Guyer E, Jin R et al. Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health Surveys. Int J Methods Psychiatric Res 2006; 15: 167-180.
+31	Efron B. Logistic regression, survival analysis, and the Kaplan Meier curve. J Am Sociol Assoc 1988; 83: 414-425.
+32	Wolter KM. Introduction to Variance Estimation. Springer-Verlag: New York, NY, 1985.
+33	SUDAAN. Professional Software for Survey Data Analysis [computer program]. Research Triangle Institute: Research Triangle Park, NC, 2002.
+34	Angold A, Erkanli A, Costello EJ, Rutter M. Precision, reliability and accuracy in the dating of symptom onsets in child and adolescent psychopathology. J Child Psychol Psychiatry 1996; 37: 657-664.
+35	Hardt J, Rutter M. Validity of adult retrospective reports of adverse childhood experiences: review of the evidence. J Child Psychol Psychiatry 2004; 45: 260-273.
+36	Schlesselman JJ. Case-Control Studies: Design, Conduct, and Analysis. Oxford University Press: New York, NY, 1982.
+37	Moffitt TE, Harrington H, Caspi A, Kim-Cohen J, Goldberg D, Gregory AM et al. Depression and generalized anxiety disorder: cumulative and sequential comorbidity in a birth cohort followed prospectively to age 32 years. Arch Gen Psychiatry 2007; 64: 651-660.
+38	Angst J, Sellaro R, Ries Merikangas K. Multimorbidity of psychiatric disorders as an indicator of clinical severity. Eur Arch Psychiatry Clin Neurosci 2002; 252: 147-154.
+39	Shneidman ES. Suicide as psychache. J Nerv Ment Dis 1993; 181: 145-147.
+40	Baumeister RF. Suicide as escape from self. Psychol Rev 1990; 97: 90-113.
+41	Hawton K, Houston K, Haw C, Townsend E, Harriss L. Comorbidity of axis I and axis II disorders in patients who attempted suicide. Am J Psychiatry 2003; 160: 1494-1500.
+42	Fawcett J, Busch KA, Jacobs D, Kravitz HM, Fogg L. Suicide: a four-pathway clinical-biochemical model. Ann NYAcad Sci 1997; 836: 288-301.
+43	Mann JJ, Waternaux C, Haas GL, Malone KM. Toward a clinical model of suicidal behavior in psychiatric patients. Am J Psychiatry 1999; 156: 181-189.
+44	Fawcett J. Treating impulsivity and anxiety in the suicidal patient. Ann NY Acad Sci 2001; 932: 94-102.
+45	Joiner TE. Why People Die by Suicide. Harvard University Press: Cambridge, MA, 2005.
+Molecular Psychiatry
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+Introduction
+Almost 1 million people around the world die by suicide every year,1 constituting 2% of the global burden by the year 2020.2 Because suicide is partly preventable (eg, by changes in physicians’ education or by restricting access to lethal methods),3 several countries have adopted appropriate prevention strategies.1,4
+As in earlier economic downturns,5-8 the 2008 world economic crisis led to the re-emergence of the debate on the effect of the economy on suicide. Recessions, that is, decreases in the gross domestic product (GDP) over subsequent quarters, exhibit strong reductions in macroeconomic aggregates such as consumption, investment, industrial production, employment, exports, and imports.9 Accordingly, the 2008 economic crisis saw a 2% decline in world per capita GDP in 2009,9 and worldwide 31-8 million more people were unemployed in 2013 than in 2007.10 Whereas evidence suggests that all-cause mortality declines during recession, suicide does not.11 In line with evidence on former crises, current data from Europe,12-16 the USA,13,17 and Asia18 suggest an association between the 2008 economic crisis, rising unemployment rates, and
+increased rates of death by suicide. Men and those of working age seem to be particularly affected.5,8,12,13,19 The association between unemployment and poor health20 or suicide probably emerges from its direct effects on the individual via mechanisms such as increased probability of depressive illness, financial strain, decreased affordability of mental health care, or disruption of present treatment regimes.21 Accordingly, governmental interventions to keep and reintegrate people in jobs might be a promising solution.8
+However, the significance of the effect of unemployment on suicide has not been clearly shown.22 This absence of demonstration could be because of variability in research design or differing statistical models. Some studies have linked excess suicides to rising unemployment during the economic crisis by applying only a time indicator of 2007-09,13,16 although these studies considered linear long-term trends in the suicide rates. Previous evidence supports a time lag between the development of the economy and suicide related to awareness of job insecurity among other factors.6,14,18 Moreover, there are hints of a non-linear relationship between unemployment and suicide, and
+of an increased effect of rising unemployment in countries with low levels of unemployment before the crisis.13 Previous studies mainly examined one country7,12,18,19 or one world region5,8,14,16 rather than global data.13 Further, to better assess the long-term effect of the 2008 economic crisis, a statistical model should account for missing data of recent years.
+We aimed to enhance knowledge of the specific effect of unemployment on suicide by analysing global public data classified according to world regions. Besides unemployment, we also considered other basic economic factors and the above-mentioned methodological issues.
+Methods
+Suicide and population data
+In this longitudinal analysis, as was done in a recent study,13 we extracted data for annual sex-specific and age-specific suicide rates from the WHO mortality database. We then used International Classification of Diseases (ICD) codes (9th and 10th version) to extract suicide data (E950-E959 and X60-X84), selecting all countries with a mean number of more than 20 male suicides per year and covering at least the years 2004-09. Only Cuba had to be excluded, because it showed suicide data but no economic information. We replaced missing population data in the WHO mortality database with information from the UN population database. If population data were missing in both databases (which only concerned population data for 2011, for which 184 of 504 datapoints were not yet available), we replaced missing data within the age group and sex group of the respective country by linear extrapolation.
+The 63 countries selected were split into four world geographic regions according to UN classifications:23 Americas (Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Mexico, Nicaragua, Panama, Paraguay, Suriname, Uruguay, the USA; N=15, roughly 92% of the total population in this world region); northern and western Europe (Austria, Belgium, Denmark, Estonia, Finland, France, Germany, Ireland, Latvia, Lithuania, Luxembourg, The Netherlands, Norway, Sweden, Switzerland, the UK; N=16; roughly 99% of the total population); southern and eastern Europe (Belarus, Bulgaria, Croatia, Czech Republic, Greece, Hungary, Italy, Macedonia, Moldova, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Ukraine; N=18; roughly 98% of the total population); non-Americas and nonEurope (Australia, Egypt, Georgia, Hong Kong, Israel, Japan, Kazakhstan, Kuwait, Kyrgyzstan, Mauritius, New Zealand, Singapore, South Africa, South Korea; N=14; 25% of the whole population, 12% of the population of Africa, 54% of Asia, and 72% of Oceania).
+Similar to Chang and colleagues,13 we considered the number of suicides per 100 000 population (suicide rate) for the following four age categories by sex: 15-24 years, 25-44 years (reference category), 45-64 years, and 65 years and older.
+Economic data
+From the International Monetary Fund’s world economic outlook database, we extracted four economic indicators: the unemployment rate gives the percentage of unemployed people to the total labour force; the GDP based on purchasing-power-parity per capita is expressed in current international dollars; the growth rate describes year-on-year changes of annual percentages of constant price GDP; and inflation means year-on-year changes of the annual percentages of average consumer prices. The unemployment rate for Suriname was missing in 2011 and was taken from the corresponding public website.24
+Statistical analysis
+We analysed the annual number of suicides per 100 000 population in the 504 different age-specific and sex-specific groups, each with up to twelve timepoints. We applied a random coefficient model for non-Gaussian longitudinal data with a negative binomial distribution to account for overdispersion, using the procedure NLMIXED in SAS version 9.4 with log as link function. In table 1, K shows the estimated value of the parameter for overdispersion, adjusting the variance independently of the mean. The random coefficient model allowed us to specify intercepts and slopes for each ofthe 504 groups for the calendar years 2000-11 (rescaled to be zero in 2000). The variance of the intercept of subgroups (D11 in table 1), the variance of subgroup slopes (D22), and the covariance (D21) between intercepts and slopes were fitted as drawn from normal distributions. Although both variances must be positive, it is a common finding that the covariance is negative. The chosen random coefficient model estimated the number of suicides for the years with missing data.
+Visual inspection of the data pointed to a time lag of a few months between unemployment rate and suicide
+rate shifted forward. Because of the available annual data, we applied a 6 month time lag by calculating the mean of the unemployment rate in a current year and in the following year. To find an approximation of the time lag and possible non-linearity, we considered six variants of the unemployment rate: 1) linear; 2) linear, time lagged by 6 months; 3) linear, time lagged by 1 year; 4) nonlinear (logarithmised); 5) non-linear, time lagged by 6 months; and 6) non-linear, time lagged by 1 year. Other economic factors analysed were inflation, growth rate, and GDP per capita. GDP per capita was divided by 1000 to enhance estimation of the model and interpretation of effects, and also used as a non-linear predictor variable (logarithmised). We examined the model fit and model comparison with Bayesian information criterion (BIC) and checked the stability of the estimated effects of economic variables over world regions. We assumed a probable association between unemployment rate and
+suicide rate to be similar in different world regions as long as potential non-linearity is accounted for.
+Estimates of the final statistical model (table 1) were transformed (exponentiated) into relative risks (table 2, figure 1), and suicide rates and numbers (figure 2, table 3). 95% CIs shown in figure 2 and table 3 are based on predictions of the procedure NLMIXED applying the See Online for appendix A method.
+Role of the funding source
+The sponsors ofthe study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
+Results
+Of the four economic variables examined, the non-linear, 6 month time-lagged unemployment rate (table 1) led to the best model fit, while showing comparable estimates between world regions (appendix pp 1-15). Specifically, we noted higher effects for lower baseline unemployment rates, and an increase in the suicide rate preceding the increase in the unemployment rate by 6 months. Interaction effects with unemployment (appendix p 11) did not improve the model fit compared with the final model (table 1; appendix p 15) in any of the four world regions.
+In total, preliminary models of other economic variables led to a poorer model fit and less stable results. The inflation rate did not improve the model fit in any of the four world regions. Although the effect of the growth rate was significant in all regions except in the Americas, the estimated effect sizes of the growth rate were generally small. The GDP as a linear or non-linear predictor led to a better model fit in three world regions, but not in the non-Americas and non-Europe. Further, GDP led to unstable and inconsistent findings when considered together with the unemployment rate. Because GDP also showed very strong correlations with calendar year (r~0-96 within countries between 2000 and 2011; appendix p 16), we finally excluded GDP from statistical analysis because of multicollinearity.
+According to our final statistical models, the relative risk of suicide associated with unemployment was elevated by 20-30% in all world regions between 2000 and 2011 (figure 1). After the 2008 economic crisis, the unemployment rate increased most strongly in southern and eastern Europe. In general, the data shows that the unemployment rate decreased before the crisis in all world regions and after the crisis reached a level roughly similar level to that of 2005. However, northern and western Europe had higher unemployment rates after 2009 than in 2008 or before. By contrast to southern and eastern Europe, the unemployment rate decreased in other world regions after a previous short-term increase. The unemployment rates among the world regions differed more than the relative risk of suicides associated with unemployment between 2000 and 2011, reflecting non-linearity.
+An increase from 3% to 6% in the unemployment rate corresponds to a rise in the relative risk of suicide from 1-158 (95% CI 1-125-1-193) to 1-229 (1-179-1-282; table 2). This corresponds to a 6-1% increase in the suicide rate (1-229/1-158-1; appendix p 1). If the unemployment rate were to rise from 12% to 15%, the
+relative risk of suicide associated with unemployment would change from 1-313 (95% CI 1-243-1-387) to 1-342 (1-265-1-424). Here, the suicide rate increases only by 2-2% (1-342/1-313-1), and, therefore, less than the change in the unemployment rate. In these examples, the rise in the suicide rate is based on the assumption that no other time trends in the relative risk of suicide are present.
+The final overall model estimated a total of about 233 000 suicides each year and suicides associated with unemployment of about 45 000, making up about 20% of all suicides (table 3). Suicides associated with unemployment made up 41 148 (95% CI 39 552-42 744) in 2007 and 46 131 (44 292-47 970) in 2009, indicating an increase of 4983 compared with the precrisis year. Over all study countries, the suicide rate associated with unemployment per 100 000 population decreased from 3-53 (95% CI 3-36-3-69) in 2000, to 2-75 (2-65-2-86) in 2007, increased to 3-03 (2-91-3-15) in 2009, and decreased thereafter to 2-82 (2-71-2-94) in 2011 (table 3).
+Besides similar effects of unemployment, only the effect of sex was comparable among the four world regions. Women had a four-fold lower relative risk of suicide than men (overall model 0-251 [95% CI 0-212-0-297]; table 2). Men had the highest suicide rates in southern and eastern Europe; women had the highest rates in the non-Americas and non-Europe (figure 2).
+In the overall model, the relative risk of suicide decreased by 1-1% (95% CI 0-8-1-4%) per year between 2000 and 2011 (table 2). Notably, the time trends in the relative risk of suicide differed substantially. In the Americas, the relative risk of suicide increased by 0-7% (95% CI 0-1-1-4) per year, whereas in the non-Americas and non-Europe it remained stable (table 2). In northern and western Europe, the relative risk of suicide decreased by 2-4% (95% CI 2-1-2-7) per year and in southern and eastern Europe by 2-3% (1-9-2-7) per year between 2000 and 2011. The different time trends for each world region were also visible for sex (figure 2).
+Finally, age groups only differed significantly in Europe, but not in the Americas and non-Americas and non-Europe (table 2). The relative risk of suicide in Europe was increased among older age groups (reference group: 25-44 years; northern and western Europe, 45-64 years: 1-516 [95% CI 1-226-1-873); northern and western Europe, >65 years: 1-449 [1-176-1-786]; southern and eastern Europe, 45-64 years: 1-701 [1-270-2-277]; southern and eastern Europe, >65 years: 2-172 [1-627-2-901]). Those individuals aged between 15 and 24 years had a reduced relative risk of suicide (northern and western Europe: 0-643 [95% CI 0-520-0-796]; southern and eastern Europe: 0-571 [0-425-0-766]).
+Discussion
+We have shown that it is feasible to directly estimate the effect of non-linear and time-lagged unemployment rates on suicide rates in 63 countries while accounting for age and sex including specific time trends among subgroups. Of the economic variables tested, only unemployment is associated with similar effects in each of the four world regions. We noted that unemployment was related to an increased relative risk of suicide by 20-30% in all four world regions. Suicides associated with unemployment contributed 41 148 in 2007 and 46 131 in 2009, meaning an increase of4983 compared with the pre-crisis year. In the four world regions, a changing unemployment rate affected both sexes as well as different age groups equally.
+Generally, our results correspond with previous research indicating that a rise in unemployment is linked with an increase in suicides (panel).5-8,12-18 As suggested by previous studies, our model was able to corroborate that this association is non-linear13 and time-lagged.6,14,18 Therefore, by contrast with others8,12,25 who have described a linear increase between unemployment and suicide rate, the level of the unemployment rate has to be taken into account. The effect of a change in unemployment on suicide is stronger in countries with a lower rather than with a higher precrisis unemployment rate. Thus, in
+Articles
+Panel: Research in context
+Systematic review
+We searched PubMed for all studies published up to June 12, 2014, using the key words “suicide” and (“unemployment” or “economic crisis” or “prevention”). The resulting articles were selected manually. We included studies that, like our study, used official databases and conducted retrospective data analyses. We identified only one study13 that included several world regions in their analysis focusing on the economic downturn in 2008.
+Interpretation
+Similar to other studies, we noted a relationship between suicide and unemployment. Specifically, we identified a comparable effect size of unemployment on suicide in each of four world regions. Moreover, as shown in some previous studies, our final statistical model showed that the relative risk of suicide increased before the change in unemployment rates, pointing to a time-lag. Further, as stated by a recent worldwide study, the suicide risk associated with unemployment increased particularly in countries with lower unemployment rates, meaning that the effect of unemployment was non-linear. We noted that suicide due to unemployment might be severely underestimated if studies focus only the time period of the economic crisis. By contrast with other studies, we did not find that different age or sex groups were less affected by rising unemployment. As part of suicide prevention, governmental and specific therapeutic interventions are needed to prevent or reduce employment insecurity, thus helping the individuals affected.
+countries where unemployment is uncommon, an unexpected increase in the unemployment rate might trigger greater fears and insecurity than in countries with higher precrisis unemployment rates. With respect to the time lag, our final model showed that the suicide rate increased 6 months before the rise in the unemployment rate. Corporate downsizing and labour market restructuring during economic contraction could create additional work stress and a sense of job insecurity among the employed (eg, through an increasing work-load, change to part-time employment, or intermittent lay-offs), which might contribute to poor mental health.18,26 Besides such circumstances, however, particularly vulnerable individuals have an increased risk of dying by suicide.14,18
+We did not find a lower effect size of unemployment in the 65 years and older age group in any of the four world regions. Retired individuals might also be affected by changes in unemployment—eg, when their offspring are or become unemployed. Accordingly, job loss can affect the risk of suicide of other household or family members as well. 21 By contrast with our results, other studies have reported that men are more affected by unemployment than are women.5,8,12,13,19 One main reason for this reported difference could be separate analyses for men and women. To maintain statistical power and to directly assess sex and age differences, we preferred to construct a joint model.
+Our study has several limitations. Apart from problems with the definition of and measurement of unemployment, we hold the unemployment rate as a broad indicator of the level of economic stress and uncertainty faced by the whole population. Because of restricted availability, we could not control for agespecific and sex-specific unemployment rates. Even if
+there were differences in total suicide rates between world regions, which we accounted for, there are numerous unconsidered and difficult to obtain clinical and psychosocial factors associated with suicide.14 Without question, it is impossible that an analysis on a world region level can be interpreted causally in a narrow sense. Further, because of missing data, the Asian (eg, China and India) and also African countries included in this study are not representative. Therefore, suicide numbers and rates might be underestimated. As our applied model belongs to the group of random coefficient models, we assumed subgroup intercepts to be uncorrelated with predictors, thus effects could be overestimated because of unobserved heterogeneity.
+Corresponding with our results, other investigators13,17 have estimated excess suicide numbers of about 5000 cases associated with the economic crisis of 2008. Looking at our estimated suicide rates associated with unemployment, they had nearly reached pre-crisis level in 2011. Further, focusing on excess suicide alone leads to an underestimation of suicides. As we identified out from our statistical model, besides the additional 5000 cases, a nine-fold higher number of suicides (roughly 46 000) in 2009 can be associated with unemployment when considering all the years from 2000 to 2011 (figure 1).
+Despite certain minor differences between world regions, the relative risk of suicide associated with unemployment remained quite similar between world regions and with time. This finding means that there is a continuous need to focus on preventing suicides, even more so in economically prosperous, stable time periods than in times of lower prosperity, when resources are scarcer. These efforts are necessary and valuable not only in countries with high, but also in those with low, unemployment rates. In particular, the interaction of fiscal austerity with economic shocks and weak social protection seems to escalate health and social crises, at least in Europe.20 Sufficient investment of governments in active labour market policies that enhance the efficiency of labour markets can reduce the unemployment rate and therefore help to generate additional jobs.10
+Efforts at suicide prevention should be extended to professionals working with individuals at risk of unemployment, such as social workers and human resource management professionals. These professional groups should be informed about the specific risk and trained concerning the practical assessment of suicidality and possible interventions. Further, general programmes in medical education should support physicians to recognise, diagnose, and treat patients with depressions, thus leading to reductions in suicide.3 This also requires the particular attention of primary physicians to patients who are unemployed or at risk of job loss.
+Contributors
+CN compiled the data, designed and did the empirical analysis, and drafted the report. IW facilitated the interpretation of the findings, drafted the report, provided background information, and reviewed the
+literature. WK oversaw the design of the study. WK and ES facilitated the interpretation of the findings, provided background information, and critically revised the manuscript for important intellectual content. All authors have seen and approved the final version of the report. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
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+date. But, again, it also implicitly reinforces that fact that how one roadmaps must depend on context and goals as these play out over time, and, therefore, that it is generally beneficial to consider multiple perspectives. The authors describe a multi- scenario roadmapping methodology likely to be vital in cases involving major investment decisions in rapidly changing and/or volatile conditions of the sort becoming increasingly “normal.”
+The tool uses a PERT display of roadmap evolution rather than, or in addition to, the usual Gantt display method. In so doing, it points to alternative modalities for the roadmap user and the potential for breaking free from methodological and other assumptions that limit how he or she interacts with the world. The cost-effectiveness and organizational and strategic acceptability of such an approach would clearly depend on a firm’s clock speed and life cycles as well as on the patterns of potentially disruptive change with which it must deal.
+What We Have Learned
+Having presented the 11 individual papers contained in the two parts of this roadmapping special report, we can now identify a number of overall and cumulative messages:
+■	Roadmapping has evolved from, and appears to meet, the increasing need of firms (varying by size, type, etc.) to innovate with greater cost-effectiveness than in the past, with enhanced strategic focus, greater development and deployment speed, more coherent decision making, better cross-organizational integration and communication, and with discipline.
+■	Roadmapping methodologies are continually evolving. A number of different and useful ways to roadmap have already developed, and more are likely.
+■	Firms need to learn and adapt as they adopt roadmapping. Not only does what is needed and possible change over time, but so, too, may the drivers and constraints of the firm’s context and its management, with their varying operational philosophies, etc. Firms will differ in how they progress through an evolutionary pattern in which they establish the culture, trust and knowledge/data bases that can support internal roadmapping. This builds the needed foundation for involving other players (suppliers, customers and partners) and for adopting more programmed systems.
+■	Returning to and concluding with a message contained in the first part of this report, roadmapping must be recognized as a linchpin management tool that can help support integration with other needed strategic and operational management processes—it does not stand alone. And, bottom line, it is “roadmapping” that delivers results; the focus cannot be on just the “roadmaps.” ®
+CUSTOMIZING ROADMAPPING
+Technology roadmapping is a powerful and flexible technique for supporting strategic planning—provided it’s customized for the particular application context in both architecture and process.
+Robert Phaal, Clare Farrukh and David Probert
+The wide range of strategic contexts in which technology roadmapping can be applied makes the technique inherently flexible. The strategic context can be described in terms of a number of dimensions, including the nature of the organization, its goals, existing processes and procedures, and the available information and resources. But for roadmapping to be applied effectively, it is essential that the approach be adapted to fit the particular circumstances rather than limiting or distorting the strategic context to fit a particular roadmapping approach.
+A survey to establish technology planning practice in United Kingdom industry (1) revealed that only 10 percent of respondents had actually used technology roadmapping; an additional 9 percent had heard about the technique and considered it to be potentially useful, although in larger companies, to facilitate understanding and give structure to planning over a longer period. However, 16 percent of respondents had discounted the technique entirely, typically perceiving it as too complex, formal and administratively burdensome. We attribute this perception partly to a lack of recognition that roadmapping must generally be customized to suit a particular application and partly to the difficulty of accomplishing such customization.
+This article focuses on how the “fast-start” T-Plan method (2,3,4) can be customized to fit a wide range of organizational contexts, in terms of both roadmap architecture and process. The architecture must be designed to provide a framework for structuring relevant knowledge (Figure 1), together with the process for developing the roadmap. The capabilities of the roadmapping approach must be matched with the business issues being addressed, and this is achieved through a collaborative and iterative design activity. This can be complex, and a lack of specialized expertise is a barrier to its adoption.
+Following a brief overview of the T-Plan process, we illustrate the principles that enable roadmapping to be customized with six generic case study examples. The examples illustrate the output from the planning process in terms of architecture and process in several different applications, reflecting many of the issues we describe.
+The T-Plan guide focuses on how to initiate a roadmapping process in an organization as rapidly and economically as possible to develop a “first cut” roadmap. At its broadest level, the T-Plan process consists of three stages, illustrated in Figure 2: Planning, Workshops and Roll-out. The standard process has four workshops, as shown in Figure 3, while the customized process varies considerably. The customization takes place in the planning stage and is then implemented in the workshops and roll-out.
+Generic Types of Roadmap
+The T-Plan approach has been applied more than 40 times in a wide range of situations, including sector, techno lo gy, organization size, and strategic context. These applications can be grouped into the following six broad types (5,6), illustrated in Figure 4, which highlight the flexibility of the approach. All except producttechnology planning, which is the standard process, require customization of the approach.
+1.	Product-technology planning.—This is the standard T-Plan process. The roadmap is used to develop an integrated product-technology plan that meets the require
+ments of both the market and business, with the product feature and technology choices prioritized on the basis of customer needs and desires.
+2.	Strategic appraisal.—The roadmap template is used to capture, structure and share knowledge about the area of interest. This provides a resource for identifying and assessing strategic issues, leading to agreement on appropriate actions. The majority of T-Plan applications have been of this type.
+3.	Business reconfiguration.—The template is used to explore the implications of a particular strategic vision and to assess the current position. The migration paths that bridge the gap between the company’s current strategic position and future vision are then explored.
+4.	Process development.— The template is used to explore the flow of knowledge between commercial and technical functions, focused on improving and developing business processes, such as new product development.
+5.	Research network development.— The template is used to capture, structure and share knowledge from both the commercial and technical perspectives, and then to identify future application requirements and opportunities, together with current research and network priorities (see the UK Faraday Partnerships example below).
+6.	Sector foresight.—The template is used to capture, structure and share knowledge about industry and market trends and drivers, which are then used to define per-
+formance measures and targets for the system. Future technology evolution is then explored, and research challenges identified.
+While it is possible to broadly group roadmapping applications in this way, specific applications can differ considerably, and in most the roadmap architecture and the process for developing the roadmap will need to be customized to a greater or lesser extent.
+Principles of Customization
+The principles for customizing the roadmapping approach described below would be applied during planning discussions, before roadmapping begins. The two key elements of customization within a particular context are architecture and process. This is because at the heart of the planning phase is a design activity in
+which both the roadmap architecture and roadmapping process need to be considered in parallel. As with all design activities, the process is creative, iterativ e and non-linear. The following checklist can be used as a basis for focusing discussion, which continues until the parties agree on a plan that makes sense to all involved:
+1.	Context.— The nature of the issue that triggered interest in roadmapping needs to be explored and articulated, together with any constraints that will affect the approach adopted, including the following considerations:
+•	Ownership of the business problem: The roadmapping will not be successful unless a clear business purpose and business problem owner are established.
+•	Scope: Defining the boundaries of the domain of interest (i.e., what is being considered and what is not).
+•	Focus: The focal issue that is driving the need to roadmap.
+•	Aims: Those goals and objectives that the organization hopes to achieve with roadmapping in the long and short term. Also, the overall business aims and organizational goals are typically included, such as the desire to improve communication and to understand how the roadmapping approach can be used to support ongoing strategic activities in the firm.
+•	Resources: The level of resource that the organization is willing to contribute, in terms of people, effort and money.
+•	Participants: Typically, a multi-functional team is required, representing both commercial and technical perspectives and having the knowledge and expertise necessary to develop a well-founded and credible roadmap.
+•	Information sources: It is important that the roadmapping activity takes account of available information, although there is a practical limit to the quantity of data that can be accommodated in a workshop environment.
+2.	Architecture.— The structure of the roadmap, in terms of:
+•	Timeframe: The chronological aspects of the roadmap (horizontal axis), in terms of the planning horizon and key milestones, and also whether past events and activities should be included.
+•	Layers: The structure of the roadmap’s vertical axis, in terms of broad layers and sub-layers, which is closely related to how the business is structured and viewed (physically and conceptually).
+3.	Process.— The staged set of activities needed to build roadmap content, make decisions, identify and agree on actions, and maintain the roadmap in the future. The process includes a “macro” level, in terms of the broad steps needed in the short-, medium- and long-term, as well as a “micro” level associated with the short term and in particular the agenda that will guide the workshop(s).
+Designing the Architecture
+In the T-Plan standard process for product planning, the architecture emerges during the first three workshops, due to the use of the market-product and pro ducttechnology grids. For customized roadmaps, the architecture is usually agreed upon up front with a smaller group. Based on the roadmaps that have been built and observed during this research, the type of roadmap can be generalized, in terms of the following dimensions:
+1. Time.— The temporal dimension must be explicitly shown on the roadmap, as it is a key feature of the approach. The scale chosen depends on the nature or the organization and the purpose of the roadmap. The ability to map and link time-based knowledge about various aspects of the business is what makes roadmapping such a powerful tool for supporting strategic planning. Strategic intentions are much more likely to be implemented if the various dimensions of the business are laid out in this way. The importance of time, in terms of business value, rests mostly on the tangible value that can be attributed to “time-to-market,” in terms of both products and technology, and the level of synchronization that must exist across the business for this to occur. A further benefit of including time explicitly on the roadmap is that the architecture is compatible with the Gantt chart—the most widely used planning tool— thereby improving the likelihood that strategic plans are implemented effectively.
+2. Layers.— The vertical axis of a roadmap consists of several layers and sub-layers, which can be designed to meet the particular needs associated with the roadmapping activity. There are a large number of options (see Figure 4), but in general three broad kinds of layers are used:
+■	The top layer usually relates to the trends and drivers that go vern the overall goals or “purpose” associated with the roadmapping activity, including external market and industry trends and drivers (social, technological,
+environmental, economic, political, and infrastructural) and internal business trends and drivers, milestones, objectives, etc. Collectively, the type of information contained in the top layer can be thought of as representing the “know why” dimension of knowledge.
+■	The bottom layer usually relates to the resources needed to respond to the trends and drivers, including knowledge-based resources, such as technology, skills and competences, and other resources such as finance, partnerships and facilities. Collectiv ely, the type of information contained in the bottom layer can be thought of as representing the “know how” dimension of knowledge.
+■	The middle layer generally relates to the tangible systems that are developed to respond to the trends and drivers (top) layer. Often this relates directly to the evolution of products (features and performance), although the middle layer can also represent the development of service-based products or other mechanisms for integrating technology in a way that delivers such benefits to customers and other stakeholders (and hence value to the business) as engineering, systems and organizational capabilities. The essential point is that a middle layer is almost always required, separating (bridging) the purely technical and commercial perspectives. The middle layer represents the “meat” in the roadmap “sandwich,” providing common ground for discussing both commercial and technical issues. Collectively, the type of information contained in the middle layer can be thought of as representing the “know what” dimension of knowledge.
+Each of these layers will typically include a number of sub-layers, which depend on the specific nature of the business and issues being considered.
+The three dimensions of knowledge (know why, know what and know how) relate to the top, middle and bottom layers of the generic roadmap, respectively. There are three other similar dimensions of knowledge that can also be related to roadmapping: “know when” is associated with the time axis of the roadmap, while the other two dimensions—“know who” and “know where”—are typically embedded in the content of the roadmap. This emphasis on knowledge is a fundamental aspect of technology roadmapping, which is in fact a practical, action-oriented tool for supporting knowledge management in an organization, allowing knowledge from a range of perspectives to be captured, structured and shared, in turn enabling dialogue, decision-making and action.
+Technology often appears twice on a roadmap, within the top and lower layers. The top layer generally represents external factors over which the roadmapping group has little or no control, while the other layers represent areas where there is a greater degree of control and where decisions have to be made about the route forward. For
+example, a manufacturer of consumer electronics equipment might include Moore’s law in the top layer (as a key technology driver) but the selection of which chip technology to integrate into products in the lower layers.
+There are many ways to segment the vertical axis of the roadmap, in terms of the layers and sub-layers that are chosen to structure its content. The structure needs to be considered in parallel with the roadmapping process, in terms of the overall roadmap “logic.” Using a mixed metaphor, the roadmap can be thought of as both a “canvas” (upon which workshop participants are challenged to sketch out the “picture” that represents the topic and issues under consideration) and a “story,” which represents the logical connectivity between information displayed on the roadmap, using the linguistic framework provided by the roadmap architecture.
+In this way, it should be possible to “tell a story” about each layer of the roadmap (for example, how a particular product feature or technology area evolves over time), and for the roadmap overall (how activities within the various layers link to give “the full picture”).
+Other factors to consider when designing the roadmap architecture include:
+■	The layers should be reasonably independent of one another (for example, the screen and keyboard of a computer are largely independent but the pigment and resin in a paint are not).
+■	The definition of each layer should generally be consistent over time, so that development and evolution can be mapped over the full period of the ro admap. For example, display technology may be more appropriate than cathode ray tube technology for a computer, as the shift from CRT to flat screen technology can be accommodated with this broader definition.
+■	There may be benefit in ensuring that the structure of the roadmap integrates with other approaches and systems in the organization. For example, if the business is organized around a set of technical groups, it can make sense to build these into the roadmap, as existing information is likely to be availab le for these group s, and actions arising from the roadmapping activity are likely to be implemented by the same groups. However, such a strategy may not be the most appropriate in terms of the roadmap logic and there may be good reason to use the roadmap to create an alternative view (for example, if one aim is to stimulate cross-group activity).
+Designing the Process
+The roadmapping process is often said to be as important as the roadmap itself, owing to the benefits associated with the discussion and learning that are associated with the development of a roadmap. Careful consideration needs to be given to the process during the planning
+stage, in parallel with design of the roadmap architecture, to capture, structure and share knowledge within the framework provided by the roadmap architecture, to identify key issues, support discussion, and agree on actions. As mentioned above, the process design needs to be considered at both the macro and micro levels:
+1. Macro-process.—The issue at this level is how the overall process will work, in terms of a series of stages that lead toward the end goal. It is difficult to be prescriptive, as there are many potential paths that might be followed. However, the general principle of staging the process is sensible, breaking it into a set of semiindependent steps (e.g., individual workshops) that each deliver value in their own right but connect logically to enab le the end goal of developing a sustainable and ongoing process. In this way, the resource commitments can be managed and flexibility retained in what is an inherently exploratory process.
+The T-Plan fast-start approach is based on this concept, enabling the first step and generating buy-in for subsequent activity. An essential feature of the fast-start concept is that it should provide useful output while at the same time acting as a “design” activity for the longer-term roadmapping process. For this reason it is essential that the initial activity be completed, producing a “first-cut” roadmap, which is likely to include some information that is of dubious quality, reflecting gaps in knowledge (an important output in itself) and perhaps raising more questions than answers.
+2. Micro-process.— The issue at this level is how to design the detailed process or the first stage of the macroprocess, and in particular the agenda for the workshop(s).
+Again, the logical progression of the process needs to be considered in terms of a series of activities that lead toward satisfying the aims of the first phase. The scale needs to be considered carefully as there is a tradeoff between the level of resources committed (time and money) and the quality of the output (information content and decisions). However, the law of diminishing returns applies and it has been found that a one-day workshop is normally sufficient, which is generally an acceptable level of commitment to ensure good participation. Over time, it has been found that the most effective format is a three-stage process, starting with planning, leading up to the one-day workshop, followed by a post-workshop meeting to review outcomes and agree on the way forward. Case study 5 below follows this format.
+Case 1:	Product-Technology Planning
+A small UK software company was considering a major redevelopment of an older softw are product aimed at a niche market in the pharmaceuticals industry. Technology roadmapping provided support for the product planning to assess the viability of the proposed development.
+Aims
+•	Explore the attractiveness of a niche market within the pharmaceutical/clinical trial arena that can be serviced by existing core competences.
+•	Define the desired product evolution, in terms of version features, timing and associated effort.
+•	Assess whether it is possible to “leapfrog” existing company technology.
+•	Promote a greater understanding within the company of the benefits of communication between marketing, development and sales.
+•	Develop a technology roadmapping process tailored to the company that can then be used for all product development questions.
+Process
+The standard T-Plan process was applied with a group of 10 participants from across the company, including software developmentengineering, sales, marketing, and management. The case illustrates the application of the “standard” T-Plan process (2,3,4) based on four workshops:
+1.	Market: trends and drivers brainstormed and prioritized.
+2.	Product: features brainstormed; market/product grid used to rank ability of product features to meet market drivers.
+3.	Technology: solutions brainstormed; product/ technology grid used to rank ability of technology solutions to meet product features.
+4.	Roadmapping: evolution of market, productand technology charted against time, using grid headings as sublayers of the roadmap.
+Architecture
+The broad structure of the technology roadmap is shown in Figure 5. As the standard process was applied, the detailed structure of the roadmap, in terms of the sublayers, emerged from the process as a result of using the Market-Product and Product-Technology grids.
+Benefits
+The company decided that it would over-stretch its resources if it went ahead with the development. This was a useful outcome and the process was re-applied soon after to plan an up date of an existing software product.
+Case 2:	Strategic Appraisal
+A large automotive supplier developed a new roadmap within the technical services group to support a new service-based strategic initiative.
+Aims
+•	Develop a business case for investing in methods in the service area and the capability that can be offered both within the company and more widely.
+•	Build a picture of the “suite of services” the company should offer.
+•	Generate an action plan (short, medium and longterm) for working toward being a Centre of Excellence.
+•	Learn how “fast-start” roadmapping can be applied most effectively.
+Process
+The company overall was familiar with roadmapping for productand technology planning; however, this application required a tailored solution. In the planning session with the roadmapping owner and the internal facilitator, it was decided to customize the standard process to take advantage of market and technology survey work already carried out and focus on services. This resulted in three half-day workshops, each contributing directly to the roadmap:
+1.	Market and technology: brainstormed business drivers and potential service offerings as context; market and technologies were then considered in more detail.
+2.	Service: gathered internal customer perspectives on service ideas.
+3.	Links and action: pulled layers of roadmap together and developed the action plan.
+Architecture
+The broad structure of the technology roadmap is shown in Figure 6. The main layers were chosen during the planning meeting. Each layer included a number of sublayers, defined during the workshops.
+Benefits
+The process revealed that the market and technologies were not as well understood as first thought. In addition, involv ing the potential fu ture customers proved very useful in challenging the preconceptions of the core group. At the end of the process, the company had a clear action plan and a list of knowledge gaps to take forward into the ongoing planning.
+Case 3: Business Reconfiguration
+A medical device manufacturer wished to consider migrating toward being a more-technology-intensive company, with a significant increase in R&D investment.
+Aims
+•	Define a future vision for the company—primarily in terms of technology and products but also market, business and organization (skills, structure, culture).
+•	Assess the current business to establish strengths, weaknesses, gaps, and mismatches.
+•	Consider how the gaps could be bridged; identify options and migration paths.
+•	Evaluate the technology roadmapping approach.
+Process
+The planning meeting devised an agenda for a one-day workshop focused on strategy development. The agenda included a series of presentations and related discussions, each addressing a layer of the roadmap and simultaneously charted by the facilitators. The key steps were:
+1.	Capturing the vision: The first presentation was the proposed business strategy, and the discussion identified the characteristics of the company in this new context. A further presentation focusing on future technological developments and requirements was followed by a discussion to identify important technologies for the future.
+2.	Gap analysis: Further discussions considered markets and products now and in the future, and issues and future requirements concerning organization and skills.
+3.	Migration path: Strengths and weaknesses and actions were charted for each level.
+Architecture
+The broad structure of the technology roadmap, shown in Figure 7, was designed in the planning meeting. Each layer included a number of sub-layers, defined during the workshop.
+Benefits
+The company used the workshop outputs to develop a detailed plan of action. A year later, a new CEO was appointed and the strategic plan was reviewed in a two-day roadmapping workshop. Day 1 mapped the new strategic plan onto a roadmap (first cut). Day 2 developed detailed functional plans, using Day 1 output to provide integration.
+Case 4:	Process Development
+This small process company designed and manufactured durable coatings for heavy transport and external metal drum applications. The company had a 1-2 year planning horizon and was market-rather than technology-driven.
+Aims
+•	Support a new project to develop a high-performance paint.
+•	Support improved strategic management of technology within the firm.
+•	Integrate technology better with the overall business strategy.
+•	Assess the value of “fast-start” technology roadmapping as a tool, technique or framework to aid these improvements.
+Process
+The standard T-Plan process was followed initially but because of the linked nature of the coating constituents (e.g., pigment and resin) a process flow approach was deemed more appropriate. The four workshops were cut to three, more production representation was obtained, and the necessary information to support the newly created new product development (NPD) process was charted. The main steps to develop the roadmap were:
+1.	Defining the process: The NPD process was charted in terms of key milestones against development time.
+2.	Revealing the dual perspective: Key aspects of commercial and technical support of the project were charted.
+3.	Identifying the information flows: The required commercial and technical information for each milestone was charted.
+Architecture
+The broad structure of the technology roadmap is shown in Figure 8. The layers were determined in the third workshop by the decision to chart information flows around the stages of the NPD process.
+Benefits
+The company gained a map of required knowledge flows to support its new NPD process. This showed the communication that needed to occur between the business’ technical and commercial functions. This type of roadmap is useful for business process design and re-engineering.
+Case 5:	Research Network Development
+The case is derived from application of the T-Plan approach within several UK Faraday Partnerships concerned with the development of products, services and infrastructure. There are currently 24 of these government-funded industrial-academic networks, which generally have either a technology or sector focus, each needing to carry out a technology translation task between the academic and industrial members of the network. Examples include food processing and information & communications.
+Aims
+•	Develop a “first-cut” technology roadmap for the Faraday Partnership, including market/industry trends and drivers, applications and technology.
+•	Collect, structure and share knowledge about markets, applications and technology, focused on identifying gaps and opportunities to exploit technology and leading to priority research themes and actions.
+•	Consider how technology roadmapping can support ongoing activities of the Faraday Partnership.
+Process
+The overall process consisted of a planning phase, a one-day workshop and a follow-on meeting to review outcomes and affirm actions. The workshop agenda followed these three broad steps:
+1.	Knowledge capture: commercial and technical groups mapped knowledge of drivers and technology.
+2.	Application functionality and performance (current and future): the same groups mapped requirements and capabilities, followed by feedback and discussion to identify synergies and gaps.
+3.	Applications: each group undertook a strategic review of its area, assessed the roadmap content for relevance to the application area, identified research challenges and skills requirements, and prepared an
+outline communication roadmap for feedback and discussion to identify priorities.
+Architecture
+The broad structure of the technology roadmap is shown in Figure 9. Each layer included a number of sub-layers, defined prior to the workshop.
+Benefits
+The customized roadmapping approach has provided the Faraday Partnerships with a mechanism to capture and share knowledge and define a way forward.
+Case 6: Sector Foresight
+This was a collaborative activity between industry, academia and the UK government to identify and demonstrate technologies for sustainable road transport (7).
+Aims
+•	Identify and demonstrate technologies for sustainable road transport.
+•	Identify technology and research themes for road transport.
+•	Support UK industry in the competitive global market for transport products.
+•	Provide sustainable mobility for UK citizens.
+Process
+The process was focused on research needs, communication and network development. The main steps were:
+1.	Mapping the context: charting market and industry trends and drivers.
+2.	Evolution of performance: charting requirements and desired functional performance of road transport systems.
+3.	Consultation: presentation to a wider audience and addition of further material to chart.
+4.	Mapping the technology: technical subgroups convened to chart required and desired technological response, including research needs.
+5.	Synthesis: integration of the layers of the roadmap and identification of gaps and actions.
+Architecture
+The broad structure of the technology roadmap is shown in Figure 10. Each layer included a number of sub-layers, defined during the workshops.
+Benefits
+The roadmap helps provide a framework for technological innovation in road vehicle systems. The 20-year horizon ensures that investment in technology and research is linked to the trends and drivers that influence the road transport system in that time frame. The roadmap encourages communication as a forerunner of action within industry and wider networks, and it provides a map of future innovation paths in a number of key technology areas.
+Lessons Learned
+The technology roadmapping process is an extremely flexible and powerful technique for supporting strategic planning. This flexibility is both a strength and weakness in that it can be applied in many different contexts but must generally be customized to fit the particular application. If the customization steps that have emerged from the over 40 applications of the T-Plan fast-start method are followed, then an organization has a good chance of developing a useful process and a meaningful roadmap.
+The following factors contribute to a successful and sustainable roadmapping initiative:
+•	A clear business need, tangible benefits and appropriate timing of the activity.
+•	A good architecture that reflects the structure of the organization and the problem at hand.
+•	Strong commitment and ownership from senior management and a champion to drive the process forward, perhaps supported by external independent facilitation.
+•	An effective process that collects relevant information by getting the right people involved, with the expertise required to develop a well-founded and credible roadmap , which typically requires multifunctional/ multidisciplinary input.
+•	Effective communication to ensure that the roadmap influences decision-making.
+•	An appropriate culture for supporting collaboration and sharing in the business, combined with a desire to develop and implement business processes.
+The T-Plan fast-start approach provides a template and a set of customization principles that provide benefits in their own right, as well as initiating a longer-term process. The fast-start approach can be used to explore a particular strategic issue (for example, an opportunity or threat) without the commitment to continue to develop an ongoing and embedded process. However, the option to do so is one of the outcomes of the approach and can lead to a strong, well-grounded roadmapping process. ®
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+
+1 Introduction
+
+This paper focuses on ‘fast-start’ workshop techniques, including the ‘T-Plan’
+method for product-technology roadmapping, and the more general ‘S-Plan’
+approach for strategy and policy applications. These approaches use interactive
+workshops to bring together diverse groups of participants to capture and discuss
+perspectives, focus and explore options and opportunities, make decisions and
+agree actions, and to develop preliminary roadmaps (a typical workshop is shown
+in Fig. 1). The methods have been developed over a period of 10 years, involving
+more than 200 wide-ranging collaborative applications in industry, government
+and academia.
+
+There are two principal variants of the fast-start approach that apply similar
+concepts and techniques to address different application classes, although each
+case requires some degree of customisation depending on goals and context:
+
+e S-Plan focuses on general strategic challenges, typically at business,
+corporate, sector and policy levels. The process brings together large groups
+of diverse stakeholders in 1-2 day workshops to explore and _ prioritise
+strategic issues, develop and align innovation and research strategies, and to
+agree the way forward (Phaal et al., 2007).
+
+ 
+
+e T-Plan focuses on product-technology roadmapping, bringing together
+medium sized groups of cross-functional stakeholders in four half-day
+workshops to explore and plan a product-based innovation (Phaal et al.,
+2001). The T-Plan process is described in detail in Section 3, including a case
+example.
+
+The two methods may be combined, although either method can be used in
+isolation, including particular modules, or as part of other business processes. S-Plan
+has the capacity for covering a very broad scope (typically business, corporate or
+sector levels), rapidly capturing perspectives, identifying and prioritising key topics
+for exploration and action planning. If one of the topics relates to product, service or
+process level innovation, then T-Plan provides a more detailed method for
+developing aligned market, product, technology and resource strategies and plans. S-
+Plan can be used as a starting point, associated with the left-hand side of the funnel,
+with T-Plan more suited to a slightly later phase (second iteration), once there is
+confidence on where to focus innovation efforts.
+
+2 Fast-Start Workshop Methods
+
+The fast-start approach uses multi-functional and multi-organisational workshops
+as a means for the rapid initiation of roadmapping. The methods are designed to
+be agile, in the sense of being flexible, rapid, efficient and scaleable. Focusing on
+immediate issues of concern and interest delivers quick benefits, while taking the
+first step on what can be a long roadmapping journey. The main outputs of the
+first iteration in this rapid prototyping approach are decisions and agreed actions,
+Fast-Start Roadmapping Workshop Approaches 93
+
+together with process learning from a pilot application of roadmapping. The
+general fast-start workshop approach is summarised in this section, with more
+detail of the T-Plan approach provided in Section 3, including examples.
+
+2.1 Role of Fast-Start Workshops
+
+The way in which a roadmapping initiative supports strategy, policy or innovation
+in the organisation should be considered carefully during the design phase, as
+inputs and outputs to and from the roadmapping activity will often be linked to
+milestones within these business processes, as indicated in Fig. 2 (for example,
+review points in a new product development process). Individual workshops
+support the broader roadmapping initiative, as a microcosm of the overall process,
+requiring planning, implementation and follow-on, with inputs and outputs aligned
+with the overall roadmapping initiative. Support is required in terms of
+facilitation, steering and project management, and further work is typically needed
+before, between and after workshops to collect data, analyse results, develop
+roadmap representations and associated reports, and to ensure that actions are
+taken forward.
+
+There is no single universally applicable roadmapping method, with a need to
+adapt and customise the approach to suit the particular circumstances.
+Roadmapping initiatives can be separated into three broad elements: preparation,
+implementation and follow-on. Also shown in Fig. 3 are the key success factors
+identified by de Laat & McKibben (2003) for supra-company (network / sector)
+level applications, most of which also apply to company initiatives.
+
+Fast-start roadmapping workshop techniques enable key stakeholders to
+address strategic issues efficiently using the visual structure of roadmaps to
+capture, discuss, prioritise, explore and communicate issues. Focusing on current
+concerns, using the approach as a problem-solving tool, improves the likelihood of
+quick benefits, and provides a valuable learning opportunity and first step towards
+a sustainable process.
+
+Roadmapping used in this rapid prototyping way can act as a useful diagnostic
+tool — mapping available knowledge and views will very quickly identify gaps in
+knowledge, together with issues and risks that require action. This ‘agile’ approach
+avoids the danger of over complicating and bureaucratising the process, which is a
+common pitfall and a reason why many roadmapping processes flounder. Moving
+forward, individual roadmaps, and the roadmapping system overall, can be
+developed as appropriate for the organisation, in terms of approach and the degree of
+formality required, to fit with its structure, culture and business processes.
+
+When designing a roadmapping process it is important to consider the desired
+qualities of the output (the roadmap): what constitutes a ‘good’ roadmap? how
+should ‘quality’ be assessed? Clearly, confidence and accuracy of commercial and
+technological forecasts are helpful, and it is important to use the best available
+knowledge, information and expertise. But, recognising that such forecasts are
+often highly uncertain in the longer term and that there will be may gaps and
+questions associated with the first versions of the roadmaps produced, other
+outputs of the process provide a better measure of utility — primarily the decisions,
+consensus and actions that arise.
+
+Treating a roadmap as a fixed project plan is dangerous, unless the purpose is to
+govern a set of projects to implement a complex programme. Rather, a roadmap
+should be considered as a type of ‘radar’, looking forward in order to improve
+understanding, enhance communication, build networks, capture knowledge, make
+decisions, agree priorities and take actions, steering the organisation into the future.
+
+The S-Plan and T-Plan methods provide reference processes that can and
+should be adapted as required, based on a clear understanding of the issues being
+addressed. This can involve incorporation of other tools and frameworks, such as
+valuation techniques or scenario planning. Modules within the methods can be
+used independently to support other processes and workshops if helpful.
+
+2.2 Planning a Fast-Start Workshop
+
+It is important to start planning well in advance of the workshop. This includes a
+collaborative design process, as shown in Fig. 3, involving both the roadmap
+owner (the person or group who want to use roadmapping to address their
+strategic issues) and the process owner (the person or group who will manage and
+facilitate the process). Collaboration is important due to the need to customise the
+roadmapping approach to address the particular organisational context and goals.
+Management and governance of the process should be considered. In
+companies this will usually involve a small team of senior managers, together with
+the process facilitators, to steer and review progress and outcomes, while for
+sector-level foresight initiatives more formal mechanisms may be required.
+Fast-Start Roadmapping Workshop Approaches 95
+
+Business need Focus,
+scope, aims
+Roadmap Process
+owner owner
+(Business (Roadmapping
+knowledge) knowledge)
+Architecture Process
+
+“CO
+
+Defining the architecture of the roadmap is a key step, as this provides
+the structure that guides virtually all activities in the process and workshop,
+providing a common visual language. The roadmap structure captures (and
+constrains) boundaries and timescales, allows pre-population with existing
+information and enables efficient population of roadmap in the workshop and
+subsequent reporting. The following six fundamental questions can be useful for
+designing the roadmap architecture, which relate to the horizontal (time) and
+vertical (layered) axes, respectively:
+
+la) Where do we want to go? 1b) Where are we now? 1c) How can we get there?
+
+2a) Why do we need to act? 2b) What should we do? 2c) How should we do it?
+96 R. Phaal, C. Farrukh, and D.R. Probert
+
+For S-Plan the architecture of the roadmap is generally designed prior to the
+workshop, as part of the planning process, while for T-Plan the structure of the
+roadmap is developed during the workshops. The layers and sub-layers need to be
+defined, to represent all of the key perspectives and system elements
+appropriately, with the amount of space devoted to each layer reflecting the
+relative importance and likely density of information. It is sensible to ‘test drive’
+the architecture before use in a workshop, confirming that key content can be
+positioned within the structure, and that the logical connections between the layers
+can be expressed.
+
+Appropriate timeframes should also be considered, including the past, current,
+short-term, medium-term, long-term and vision. The actual time that is
+represented depends on the industry and associated rate of change. Roadmaps of
+between 2 years (software) and 100 years (national energy systems) have been
+observed, but for many businesses the following timeframes are appropriate:
+short-term 1-year budget horizon; medium-term 3-year strategy horizon; and long-
+term 10-year ‘radar’ horizon. A non-linear scale is often used to represent these
+time horizons, with more space devoted to the short and medium term, where the
+density of information is likely to be greatest.
+
+The quality of the roadmap is largely dictated by the breadth and depth of
+expertise and knowledge of the participants in the workshop, and so careful
+consideration should be given to this during the planning phase. It is important to
+have a healthy mix of commercial and technical representation, and external views
+are helpful. The process benefits from diversity of perspectives, and the facilitation
+techniques need to be able to deal with the potential conflicts that may arise.
+
+The structure of the roadmap provides an initial checklist for ensuring that all
+important perspectives are represented in the workshop. For company applications
+this will generally include participation from business, market, application and
+technology functions. Senior management support is vital if the process is to have
+impact, and to ensure participation is given appropriate priority. Workshop dates
+may be dictated by key participants’ diaries, often resulting in lead-times of 4-8
+weeks. Participants should be sent joining instructions, briefing material and any
+required pre-work approximately two weeks prior to the workshop.
+
+For sector level foresight applications a range of industrial, academic and
+government participation is typical. Recruitment of workshop participants is more
+of a challenge for these situations, and considerable effort may be required to
+ensure success. Suitable participants should be identified early in the process, and
+as a rule of thumb one can expect 30-50% recruitment success, with personal
+contacts and follow up communication improving the response. It can be expected
+that a few participants will not turn up on the day, and so it is prudent to plan for
+several additional participants for this contingency.
+
+A suitable venue is required, with the key requirement being sufficient space
+for activities, including breakout sessions. Plenty of floor and wall space is needed
+to accommodate the interactive nature of the workshops. For the plenary sessions
+the room should ideally be approximately double the size required for a traditional
+conference meeting involving the same number of participants. It is helpful if the
+room is reconfigurable, allowing tables and chairs to be moved for different
+activities. The room should be arranged with tables in either a ‘U’ shape or as
+‘islands’, to facilitate interaction. It is advisable to view the room arrangements
+Fast-Start Roadmapping Workshop Approaches 97
+
+prior to the workshop, and to book the venue well in advance. It is essential that
+charts can be stuck up on the walls — if the venue does not allow this, suitable
+poster-boards should be sourced.
+
+Roadmap-inspired structures are used to guide the overall process and most of
+the workshop activities, although other tools and frameworks can be used to
+enhance or supplement the method. For example, a portfolio selection matrix can
+support topic prioritisation if appropriate. Professional printers can be used to
+produce the workshop charts, in colour if useful — allow enough time for printing
+and checking templates, as these are critical for the workshop process.
+Alternatively, charts can be created using flip chart paper, sticky tape and pens,
+which ensures flexibility during workshops if there is a need to refocus or adapt
+the process.
+
+In addition to structured templates, a range of other stationery is required for
+roadmapping workshops:
+
+e A plentiful supply of sticky notes, in a range of sizes, shapes and colours.
+Colours can be used for specific purposes (for example, if different business
+units are represented); although the main reason for using a range of colours
+is to help participants to navigate the content on the charts more easily.
+Different shapes (for example, arrows) are helpful for highlighting specific
+topics and issues during workshop activities.
+
+e Sticky dots are useful for quick voting processes. Again, colour can be
+helpful, for example to rank options in terms of both reward and risk.
+
+e Sticky tape, to construct charts from flip chart paper if required, and to secure
+all sticky notes at the end of the workshop.
+
+Masking tape or other devices to stick charts on walls.
+
+e Miscellaneous stationery, such as flip chart paper, scissors and felt-tip pens
+for ease of writing on sticky notes.
+
+2.3 Running a Fast-Start Workshop
+
+The facilitation approach is fairly light, in the sense that the main focus of the
+workshop is on group-based activities, where it is the participants’ experience and
+knowledge that is key, with interaction supported by the provision of structured
+frameworks (charts), clear steps and the means to capture, share and organise
+perspectives (sticky notes). The overall workshop agenda needs to be designed to
+meet the agreed aims, with the time available broken down into logical steps.
+
+The ways in which sessions are facilitated, and what can be achieved within a
+given time period, depend on the size of the group:
+
+e Small groups (< 5) can generally self-organise, with fairly minimal
+facilitation support. There is a trade-off between coherence and diversity. One
+person can draw a very neat roadmap, but it is only their view. Incorporating
+additional perspectives is hugely valuable for addressing complex and open-
+ended topics, although it becomes much more of a challenge to develop a
+98 R. Phaal, C. Farrukh, and D.R. Probert
+
+coherent and agreed output. Small groups can be relied on to deliver a result,
+with periodic review and support if required. Groups of 5 or more will find it
+difficult to proceed without active facilitation or guidance.
+
+e Medium sized groups (5 ~ 10) require facilitation, supported by some
+knowledge of the topic of interest to enable active engagement. For this size
+of group the roadmap framework enables the topic to be tackled in a
+structured and logical way, with discussion guided by the facilitator and
+captured on wall charts.
+
+e Large groups (> 10) require more formal and specialised facilitation
+techniques, with the activities orchestrated and managed professionally, often
+by a team of facilitators for workshops with 15-20 participants or more. Large
+group activities can be somewhat mechanistic, and so should be balanced
+appropriately with smaller group activities.
+
+From a facilitator’s perspective, timing is a key aspect to manage. Such
+workshops are very interactive and intense, and owing to the complex issues being
+considered and the large number of participants it is very easy for sessions to
+overrun. Larger groups invariably take longer to perform activities. It is vital that
+tight control of timing is maintained, while at the same time allowing for some
+flexibility, due to the inherently exploratory nature of roadmapping. It is vital that
+a satisfactory conclusion is reached by the end of the workshop, and every effort
+should be made to finish on time.
+
+Considering these issues during the planning phase allows for contingencies to
+be built into the process. Breaks in the agenda provide natural review points to
+ensure that the process is on track, and if required, short unscheduled breaks can
+be used to address key issues that arise. Judgement must be exercised concerning
+how much time can be devoted to discussion of issues as they arise. Once the
+nature of the issue is clear, if it is not critical to the logic of the workshop process,
+then it is generally advisable to note the issue on a flip chart sheet maintained for
+this purpose, for review at a later stage. Be aware that the pace of activities tends
+to be slower at the start, as participants familiarise themselves with the process,
+and then to speed up.
+
+Having two facilitators available is very helpful, in terms of splitting and
+rotating roles, allowing more complex group activities to be managed. Having a
+small supporting team available is useful, to review progress during breaks
+and to deal with unanticipated issues that may arise. This team would typically
+include the organisational sponsors of the roadmapping process, and /or their
+representatives, together with the facilitation team.
+
+2.4 After a Fast-Start Workshop
+
+An immediate task after the workshop is to transcribe all of the outputs as a full
+record of the event, and to interpret and summarise the outputs to create a report
+for circulation to participants for comment. For companies, it is recommended that
+the group work be captured and summarised by the groups themselves, as an
+immediate action, using templates provided for consistency, while for sector level
+Fast-Start Roadmapping Workshop Approaches 99
+
+applications the facilitation team would normally undertake this task. It is
+recommended that presentation software (e.g. PowerPoint) be used for reporting
+in the first instance, as this encourages an appropriate level of synthesis and
+summary, content can be extracted for presentation purposes, and it can be used as
+a resource for more formal reporting if required. The report can also be circulated
+and presented to a wider group for validation and dissemination purposes.
+
+It should also be recognised that the workshop is one (critical) step within a
+wider process, even if it is being used as a one-off problem solving technique.
+Consideration should be given to the actions required to move forward and
+whether and how the roadmap will be updated as an element of this process (part
+of the final review discussion during workshops). For companies this is often
+linked to other core business processes, such as an annual strategy / budget cycle,
+or review points within research or new product development processes.
+
+3 Product Level Roadmapping (T-Plan)
+
+The T-Plan approach focuses on integrated product-technology strategic planning.
+The process brings together 8-12 participants from across the organisation to
+develop a draft roadmap for a product (or product family), in four half-day
+workshops:
+
+1) Market: external market and internal business drivers are identified,
+categorised and prioritised for key market segments. Business strategy is
+reviewed and knowledge gaps identified.
+
+2) Product: potential product features, functions and attributes are identified and
+prioritised with respect to how strongly they address the drivers. Product
+strategy is reviewed and knowledge gaps identified.
+
+3) Technology: potential technological solutions for developing the product
+features are identified and prioritised and knowledge gaps identified.
+
+4) Charting: based on the outputs from the first three workshops, the initial
+roadmap is developed, linking market, product and technology perspectives,
+decisions are made and actions agreed.
+
+The T-Plan process is summarised below, including two company examples to
+illustrate the approach. For detailed guidance, including facilitation aspects, the
+reader is referred to the T-Plan guide (Phaal et al., 2001).
+
+3.1 T-Plan Process
+
+The underlying roadmapping principles are common to both the S-Plan and T-Plan
+processes, although the focus is more specifically at the product level (including
+associated market and technology perspectives). The main differences are:
+100 R. Phaal, C. Farrukh, and D.R. Probert
+
+e The focus on a single product or closely related product family requires
+greater granularity than for S-Plan, particularly in terms of market drivers
+(customer purchase motivations) and product functionality, performance and
+features. For large-scale complex products an S-Plan type approach may be
+appropriate in the first instance.
+
+e Workshop participants should be selected for their expertise in relation to the
+product area of interest. Typically this might involve 8-12 experts, including
+commercial and technical perspectives, with participants taking part in all
+four workshops.
+
+e The process is separated into four half-day modules, covering market, product
+and technology aspects in turn, before developing the draft roadmap in the
+final workshop.
+
+e  =6The structure of the roadmap is developed as part of the workshop process,
+and so does not need to be defined during the planning stage.
+
+e Linkage analysis grids are used as a key part of the process, to explore the
+relationships between market, product and technology perspectives, and to
+prioritise efforts.
+
+The T-Plan approach is summarised in Fig. 4, with workshop agendas and
+activities described in more detail in Table 1.
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+   
+    
+
+ 
+
+ 
+
+ 
+Fast-Start Roadmapping Workshop Approaches 103
+
+3.2 T-Plan Case Example —- New Product Development
+(Software)
+
+A small software company based in the UK was considering a major re-development
+of an older software product, aimed at a new niche market in the pharmaceuticals
+industry (Phaal et al, 2001). Technology roadmapping provided a means for
+supporting product planning, to assess the viability of the proposed development.
+
+The standard T-Plan process was followed, with four half-day workshops
+attended by about 12 senior members of staff from both technical and commercial
+functions. Figure 5 shows the main outputs from the second (product) workshop —
+the market-product linkage grid. This summarises the customer and company
+drivers and the main product areas, and the relationships between these. A
+weighted scoring system enables product areas to be prioritised on the basis of
+their contribution to the market and business drivers. A similar grid is used to link
+product to technology areas.
+
+Figure 6 shows a transcription of the outputs from the final workshop, where
+the commercial and technical views are brought together to define the product
+strategy in detail, together with the associated technology developments. A key
+part of the debate within the workshop related to the timing of features in terms of
+software version release dates. This revolved around the tension between the
+desirability of a feature (market pull) and the capability and effort required to
+develop the feature (technology push). The outputs from the first three workshops
+provide a basis for addressing this issue, to help prioritise feature development,
+and achieve an alignment between market, product and technology strategy.
+
+On the basis of this roadmapping activity it was decided not to proceed with the
+product development, because the required investment in staff and facilities was
+
+ 
+
+ 
+
+ 
+
+ 
+
+too great a step change for the company given its size. This was considered to be a
+positive result, avoiding the large waste of effort and management attention that
+might have resulted if they had proceeded. The company subsequently applied the
+roadmapping approach again within another part to their business, to support the
+development of a core product.
+
+4 Summary
+
+Fast-start workshops provide an efficient means of using roadmapping thinking to
+facilitate strategic dialogue. Wall charts provide structure, used as templates to guide
+activities, capture perspectives, explore concepts and summarise outputs (see again
+Fig. 1). Participants are empowered to contribute and interact through the use of
+sticky notes within facilitated group work sessions. The process is tolerant to
+diversity, enabling rapid progress in complex business and organisational contexts.
+
+The S-Plan and T-Plan methods support initiation of roadmapping at strategic
+and product-technology levels, and can also be used as one-off problem solving
+tools. Fast-start workshop approaches are designed to be agile, in that they are
+flexible, rapid and problem-focused, suitable for application in both small and
+large organisations. A light-touch approach to roadmapping can provide an
+efficient and effective solution, aimed at strategic concerns of immediate
+relevance and encouraging a focus on the most important issues.
+
+The process learning from the first pilot and subsequent applications can be
+used to plan the way forward, in terms of both strategy implementation and the
+roadmapping process itself.
+
+
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+Background
+Despite the availability of effective treatments for mental illness, there is a huge gap between the number of individuals who need mental health care, and those who receive treatment. The recent World Health Organization (WHO) World Mental Health Survey initiative reported that 86.3% of people with anxiety, mood or substance disorders in low or middle-income countries (LMICs) received no treatment in the past 12 months [1]. Among people who do receive treatment, many receive inadequate treatment [2]. For example, a recent study conducted in 21 countries found that only 1-in-27 people living with major depressive disorder in LMICs receives minimally adequate treatment [3]. The most common barriers for mental health care utilization include low perceived needs, stigma and discrimination associated with mental illness, inability to afford the treatment cost, poor identification and referral system, and shortage of human resources [4-10].
+Over the past decade, several initiatives have been undertaken globally to minimize the treatment gap for people with mental disorders. One of the most widely used approaches is the task-sharing, which involves specialist mental health workers (e.g. psychiatrists and psychologists) in designing and managing mental health services, building clinical capacity of primary and community health care workers to provide direct mental health care services with regular supervision and quality control [11, 12]. In 2008, WHO launched the mental health Gap Action Programme (mhGAP) [13], and the Intervention Guides (IG) in 2010, with the aim of providing evidence-based guidance to primary health care workers for detection and treatment of MNS disorders in primary care [14]. The mhGAP-IG has been found to be effective in offering basic mental health services through primary health care systems [15, 16]. However, due to the scarcity of mental health specialists in many LMICs, this approach alone may not be adequate to close the substantial mental health treatment gap [17].
+In the recent years, a significant change has taken place in health care delivery system due to the availability of smartphones and smartphone applications [18]. The use of mobile technology can help to reduce a number of barriers for mental health care such as providers’ workload, lack of qualified personnel, geographical and attitudinal barriers to seek treatment [19]. The use of mobile devices in healthcare (also called m-Health) consists of interventions that use mobile phones and other digital devices (e.g. smartphones, tablets) for health care purposes that
+includes: (a) creating awareness about mental health problems; (b) screening and diagnosis of mental disorders; (c) supporting clinical care; (d) training and supervision of non-specialists; and (e) improving treatment adherence [20, 21]. In 2017, the WHO launched an electronic version (e-version) of the mhGAP-IG v2.0 to facilitate the use of technology in mental health care [22]. Studies conducted in high income settings demonstrated that mobile technologies can be applied effectively in clinical decision making by health care professionals [23]. These technologies have not yet been used widely in LMICs for mental health care delivery; however, the available data demonstrates that mobile app-based clinical guidelines help to improve protocol compliance [24-26] and can facilitate task-shifting, and increase health workers morale [25, 27]. Despite these benefits, there are many potential barriers to implement m-Health solution, including suitability and acceptability to both healthcare worker and service user [26]. Developing a mobile app-based clinical guide using Human Centered Design (HCD, or user-centered design) may result in more engaging interventions as HCD actively include concerned stakeholders in the process of intervention development using cutting-edge methods to optimize for end-users and provide a solution for key problems that are being addressed [28].
+In Nepal, the number of mobile device contracts (i.e. 27.85 million in 2020) is more than the total population of the country (i.e. 26.49 million) [29]. However, few studies have assessed the feasibility and effectiveness of the application of mobile technology in health care delivery within the country. These studies have attempted to test if mobile devices can support maternal and new born child healthcare, particularly for patient data collection to send reminders for follow-up and assess signs and symptoms of illness [30]; gather health surveillance data from rural areas [31]; SMS reporting of neonatal health information and malaria surveillance [32]; support health workers in the rural district hospitals, including provision of remote specialists services [33]; aiding community health volunteers to engage persons with mental illness in treatment initiation at primary care facilities [34]; and remote supervision for mental health services [35]. None of these studies has attempted to assess the feasibility of an app-based decision aid for mental health diagnoses in primary care.
+This study was conducted as a part of the E-mhGAP Intervention guide in Low and middle-income countries: proof-of-concept for Impact and Acceptability (Emilia)
+project, which aims to determine the feasibility of implementation of an adapted e-version of the mhGAP-IG in primary care in Nepal and Nigeria. This study builds on our previous work in Nepal where the mhGAP-IG was adapted, evaluated and scaled-up [36, 37]. Understanding of the end users’ (i.e. primary healthcare workers) interest, motivation and concerns on a mobile app-based clinical guide is important to support the development of the app and its implementation strategy. Therefore, this study assessed the perception of mhGAP-IG trained primary health care workers on feasibility, acceptability and benefits of using mobile app-based clinical guideline for detection and treatment of mental health problems in primary care setting, and the possible features and functions to be included in the mobile app.
+Methods
+Setting
+The study was conducted in Chitwan and Ramech-hap districts in the Bagmati province in central Nepal. Ramechhap district lies in the hilly region, which has total population of 202,646 with a population density of 131 per sq. km. Similarly, Chitwan district lies in the Terai region which has total population of 579,984 with a population density of 261 per sq.km. Chitwan has comparatively better literacy indicators than Ramechhap (76.98% in Chitwan and 62% in Ramechhap), and per capita income (Chitwan-$1537 and Ramechhap-$951) whereas life expectancy in Ramechap is higher Chitwan (Chitwan-69.78, and Ramechhap-72.90) [38]. The majority of primary healthcare facilities in Chitwan have good internet connectivity and mobile networks. There is a psychiatric outpatient department in the government hospital and two private medical colleges in Chitwan. In Ramechhap, internet connection is available only in a few primary healthcare facilities. The mobile network is also poor. No specialist mental health service is available in Ramechhap district. In Chitwan, all primary healthcare workers were trained on mhGAP-IG by the Transcul-tural Psychosocial Organization (TPO) Nepal through the PRIME project, which aimed to generate new evidence on implementation and scale-up of mental health services in primary care [39]. In Ramechhap, 46 primary healthcare workers from 23 health facilities (out of 56 of two Municipalities (i.e. Manthali and Khadadevi) were trained as part of TPO Nepal’s post-earthquake psychosocial support programme [40].
+Study design
+The study used a qualitative study design because this method helps to explore detailed information about why a primary healthcare worker might be interested or opposed in using technology, and facilitating factors and
+possible barriers when using an app-based clinical guideline in assessment and management of mental health problems in primary care. The data gathering included semi-structured Interviews (SSI) and Focus Group Discussions (FGDs) [41] with primary healthcare workers who were trained in the WHO mhGAP-IG.
+Recruitment
+The participants were purposively selected [42] to meet the pre-defined criteria: (a) primary healthcare workers working in Chitwan and Ramechhap districts (b) be medical officer (MO) or health assistant (HA), or senior auxiliary health worker (S-AHW) or auxiliary health worker (AHW) (c) trained on mhGAP-IG (d) currently engaged in mental health service delivery and (e) be able to provide informed consent for the study. In total, 28 SSIs (6 in Ramechhap and 22 in Chitwan) and 2 FGDs (n = 15) were conducted in Chitwan. Of the total 28 SSIs, 4 were conducted with medical officers, 15 with HAs, 6 with S-AHWs and 3 with AHWs, while both FGDs were conducted with Medical Officers, HAs and Sr-AHWs/ AHWs together.
+Interview topic guide
+The semi-structured interview guides were used for data collection incorporating the human-centered design principles [28] with a strong focus not just on questions of preferences, but also provide examples of actual use of digital solutions to explore some of the critical assumptions underlying the project (e.g. potential use of a m-Health solution, availability of internet). The interview guides included a range of topics such as: (1) health workers’ interest, attitudes and concerns in using technology in clinical setting; (2) health workers’ familiarity with technology; (3) experience of using technology in health care; (4) feasibility; (5) acceptability of using mobile app-based clinical guideline in the primary care (e.g. other existing app or programs); (6) perceived benefits of using a mobile app-based clinical guideline in assessment and management of mental health problems; (7) features and functions to be included in the app; and (8) potential barriers and risk factors for using a mobile app-based clinical guideline use to support decision making in routine healthcare setting, particularly in mental health care. The interview guides were pilot-tested with two primary healthcare workers in Chitwan prior to the main study. With the exceptions of language editing and question order, there was no other major revision for the interview after the pilot-test.
+Interview process
+The study was conducted between February and March 2019.Written and oral information about the objectives
+and process of the study was provided to each study participants before their enrolment in the study. Participants provided a written informed consent to confirm their participation in the study. Two qualified and experienced researchers carried out the interviews and FGDs. Both SSIs and FGDs were conducted in a private room within the primary healthcare facility in Nepali language. One of the researchers facilitated the interviews and FGDs while the other researcher ensured correct audio recording of the sessions and took field notes. On average, the SSIs lasted for approximately 60 min while FGDs took 90 min.
+Data analysis
+The audio-recorded SSIs and FGDs were first transcribed in the original language, (Nepali) by the interviewers immediately after the field work. The transcriptions were then translated into English for analysis. The data were analyzed thematically to identify and generate patterns of codes and emerging themes from the data [43]. An inductive analysis was carried out to allow unexpected themes to be identified. The transcripts were all printed and thoroughly read by two researchers independently to familiarize themselves with the data, and generate initial codes. The codes identified by each researcher were discussed further with other members of the research team and the differences in the codes of two researchers were finalized in consensus. The interviews were re-read line by line and were color coded to assign the content to the particular themes and then organized within the identified themes. Similarities and differences within the data were grouped and organized in sequence. Finally, the analysis and interpretation of the data were conducted by the same two researchers together.
+Results
+Characteristics of the participants
+Forty-three health workers (SSIs: 28 and FGDs: 15) participated in the study with the mean age of 36.3 years (range 28-45 years). Among them, 31 (72%) were male, 27 (63%) had experience using any m-Health approach in their clinical work, and two (4.65%) health care workers did not own a smartphone. The mean number of years of experience in mental health was 2.02 years (range 1.25-5.29 years).
+The qualitative data analysis generated an analytical framework with 5 key themes: (1) facilitating factors of using the app based clinical guideline; (2) perceived barriers for using the app based guideline; (3) perceived benefits of using the app-based guideline; (4) perceived risk factors of using the app in clinical practice; and (5) suggested functions and features by the health workers (Table 1).
+Facilitating factors for using app based guideline in clinical practice
+Several facilitating factors that enable the use of the app based clinical guideline were reported in the interviews and FGDs. These included health workers’ familiarity with smart phones and other technologies in health care, and availability of reliable internet connections in the health facility. Most (n = 41) health workers owned a smart phone for phone calls, browse the internet for entertainment and to search for information required for clinical case management whenever they experienced confusion (e.g. verifying diagnosis, treatment, medicine dosage, and side-effects).
+“I use mobile to make calls, use YouTube and Medscape app [a clinical decision support app] to see medication’s dose and when I have any confusion regarding the diagnosis” Medical Officer
+Due to the provision of online recording and reporting of the routine data such as Health Management Information System (HMIS), Electronic Immunization Reporting System (EIRS), and the Integrated Management of Newborn and Childhood Illness (IMNCI) through mobile app, internet connectivity was available in most of the health facilities, particularly in Chitwan district. Several health workers (n = 27) also reported that they use clinical decision support apps (e.g. UpToDate and Medscape) when they require information during clinical practice.
+“If we [health workers] have [an] app, it will be easy because nowadays we have internet facility and therefore, it will be easier to work by looking at the mobile phones rather than to turn to a book. If we use mobile phones, all information can be found there” Health Assistant
+Moreover, most of the health workers (n = 41) were familiar and experienced in using Wi-Fi or mobile data to access online reporting systems; app based clinical management guidelines, search information online, and social networking and communication-based apps.
+“We [health care workers] use UpToDate and Medscape app mostly. Those apps can be used offline as well to see the drugs’ dose and medical procedures. Similarly, we can also connect with other [health worker] and share our [medical] problems to get the solution on that [problem]. I mostly use it [apps] during office time and also when I am free [not working].
+Medical Officer
+Perceived benefits of using the app based clinical guideline
+A commonly reported benefit of the mobile app-based clinical guide was about its accessibility and ease of use.
+Health workers pointed (n = 7) out that having the app in the mobile phone makes it accessible from anywhere and anytime. This would remove the need to carry a cumbersome paper version of the guidance which could be easily misplaced.
+“It is difficult to keep the book in my pocket whereas;
+the app can be in my pocket. Whenever I am confused, I can easily use it [the app] in my phone” Medical Officer
+Another frequently reported benefit of the mobile app-based clinical guidelines was standardized diagnosis and management across all health workers and health facilities. Several participants (n = 4) also articulated that because the general public is also exposed to the latest technology, when used for treatment some patients may trust the treatment provided to them, which in turn may increase the chance of treatment adherence.
+“I think the most important benefit of an app [app based clinical guideline] could be in bringing the uniformity in the diagnosis among all healthcare workers”. Health Assistant
+“I use IMNCI [Integrated Management of Childhood Illness] app in front of patient to confirm diagnosis and treatment otherwise; I use it [app] after completing patient consultation and lab investigation report” Medical officer
+Some health workers (n = 4) stated that they would not need to memorize the protocols if included in the mobile phone app. They emphasized that there are many guidelines and protocols that are used in the clinical practice in general health care, and are difficult to remember if they are not used for a long time. In such cases the mobile phone app was reported as helpful tool, particularly in the clinical settings.
+Some participants (n = 3) also reported that using the app in front of the patient was more comfortable than using a book.
+We [health workers] cannot remember everything given in the protocol; therefore, we need to use books/ manuals during the consultation. However, it is difficult to use manuals or books in front of the patients because patients think that the health worker is not good if he/she uses books or manuals during consul-
+tation. Senior Auxiliary Health Worker
+Another benefit reported by the health care workers was to accessing patients’ data whenever they are needed. Health workers reported that many patients forget to bring their Out Patient Department (OPD) card (i.e. clinical notes) in the follow-up visits, or sometimes even lose their medical reports; app might be very helpful to access patients’ medical records.
+“App [e-mhGAP] is good for recording of patient information as patient may lose or forget the prescription slip when coming for follow up. So, even when they [patients] do not bring the prescription slip, we [health workers] will be able to see all the information of the patient in the app and provide service as needed”. Health Assistant
+Finally, health workers also considered the app-based clinical guide as a means to improve and update their mental health knowledge. They reported that they will go through the app, particularly, the reading materials included in the app whenever they have free time. Such an instant accessible resource is helpful to enhance their mental health knowledge.
+Whenever I want to learn and refresh my knowledge;
+I can use the app [e-mhGAP]. Even if a supervisor is not assigned to me, or refresher training is not provided, the app [e-mhGAP] can help me revise whatever I learned in the training. Medical Officer
+Perceived barriers of using the app based clinical guideline Lack of internet connection in the remote health facilities emerged as a major barrier for the use of mobile app-based clinical guides. Some health facilities in Chitwan, and most of the health facilities in Ramechhap districts are located in remote hilly regions where the internet connections, as well as the mobile connectivity are very poor.
+“It [computer] was used for reporting of immunization data and was provided by WHO Nepal office. Nowadays, there is no internet connection in this health facility. Municipality don’t provide budget for internet [infrastructure]. A computer is just installed in health facility but has not been used”. Health Assistant
+The type of health worker was also reported as one of the barriers that could affect the use of the mobile app-based clinical guide. For example, the majority of the participants shared that some older (aged) health workers may have limited experiences with smart phones or
+tablets, and therefore they may find it difficult to operate the mobile app based clinical guidelines.
+“Older health workers, who are less experienced in using smart phones, might not feel comfortable to use the app, but the newly recruited health workers will use the app, even by searching about it [in the internet] by themselves” Health Assistant
+A few participants (n = 5) also reported that the app might be much easier and appropriate for medical officers in comparison to paramedics (a healthcare worker who has undertaken 18 months to 3 years training) because of the higher level of education of medical officers. They further stated that medical officers are experienced in using technology in their pre-service training as well as in the clinical practices. These results were also validated by our observations during the data collection, where medical officers found the e-mhGAP prototype easy to use whereas the paramedics required lot of support and guidance.
+“Electronic devices are used by everyone; therefore, the app will be feasible and acceptable. However, the app could be more relevant and appropriate for medical officers because of their higher level of education compared to the paramedics”. Medical Officer
+Another possible barrier reported by the health workers in using the mobile app-based clinical guide was about patients mistrust towards health workers’ skills and competencies to provide services. Health workers reported that using an app or book during a patient consultation is not a common practice in primary care in Nepal, hence, some of them expressed their anxiety that if they use the app during a consultation, patients might think the health worker does not know anything about case management/treatment and is not capable to treat them.
+"They [patients] may think that we [health worker] know nothing;[instead] we are working by using the mobile [during the consultation].” Health Assistant
+Perceived risk factors of using the mobile app-based clinical guideline
+Despite the numerous benefits of the mobile app-based clinical guideline reported in the study, some of the health workers also pointed out few possible risk factors of using the app in the real world. The most commonly reported was about the possibility of distracting the clinical conversation of health workers and patients. They reported that patients may feel that health workers are not paying attention to their concerns if they use
+the app during consultation, which may obstruct rapport building: an important component in assessment and diagnosis of a mental health problem. The health workers suggested a way forward could be to inform the patients before a consultation why they are using the app.
+“I think that patients will have negative perception that we [health worker] are not concentrating on them or listening to them [patients]” Health Assistant
+“I used to think that the patient may think that I didn’t know anything if I use the phone during consultation. But after sometime, I started explaining to the patient and their family about why I was using the phone. Now, I am comfortable using the phone and patients are also happy when I use mobile during consultation” Medical officer
+Another risk factor reported in the study was about entering patients data twice i.e. in both patients register and in the app. In the existing health care system, a patient is first register in the health facility, and his/ her clinical information is entered into an OPD card. If a new system (i.e. a mobile app) is introduced, patients’ data may be needed to be entered in both OPD card and in the app until government adapt online reporting system. This may create extra burden among health care providers.
+“Using the app will increase the health worker’s work load instead of reducing it as they have to enter the patient’s information twice, once in the health facility’s OPD register and again in the app.” Health Assistant
+Another point mentioned in the study was about longer consultation time when using mobile app-based clinical guides. Health workers reported that entering patients’ data and clinical notes in the app during consultation might take longer time, and it also may not be possible during high number of patient clinic appointments. They also stated that due to limited consultation time, there might be a possibility of having incorrect patients data collected in the app during rushed time. Furthermore, a few health workers also put forward that when using the app in front of the patients, other patients’ record would also be visible to them, which may break the confidentiality.
+“They [patients] might feel that we [health workers] are not focusing on them [patients] if we use mobile phone during consultation. Similarly, patients may also think that confidentially would be broken because they may see the names of other
+persons in the mobile app”. Health Assistant
+Suggested features and functions in the app
+Health care workers have suggested several functions and features in developing the app which included suggestive diagnosis and treatment process and options, use of local language and script (Nepali), large font size, short and simple sentences, and ability of using offline mode. Almost every health worker reported that their pre-service training was taught in Nepali medium (Nepali language). They have suggested Nepali language and script in the app. As some of the remote health facilities lack reliable internet connection, they also recommended that the app need to be able to work in offline mode. Furthermore, the large font size and short sentences as these will make it easily readable and will not take up longer time for health care workers when reading the app based guideline in Outpatient Department (OPD).
+“The app [e-mhGAP] should be developed in Nepali language because not all health workers understand English”. Health Assistant
+“The app [e-mhGAP] should be able work in offline mode because not everyone who uses the app has access to internet connection all times”. Health
+Assistant
+Likewise, writing/typing into a mobile phone was not commonly practiced among the health care workers. They reported that they prefer making phone calls more often even though message service are much cheaper because making phone calls is easier than sending text messages. Therefore participants proposed that the app should have fewer typing features and that pre-defined responses would be helpful to reduce the time spent entering information and maintaining the accuracy of the patients’ data.
+“Instead of typing the symptoms the [list of] symptoms should be already given there [in the app] so that we can choose from the list. It will be time consuming to enter all the symptoms in the app by typing.” Health Assistant
+Most health care workers (n = 43) recommended incorporating a decision support feature in the app. They shared that they are often confident in making diagnosis for ‘straightforward cases’ of depression, but found it difficult to diagnose patients presenting with co-morbid symptoms, e.g. depression with psychotic symptoms. As a result, the step by step functionality of the original mhGAP-IG app was considered to be very helpful, especially for health care workers with limited experience in mental health case management.
+“It [e-mhGAP app] should contain all the contents of the mental health register (i.e. patients registration form at the health facility). For example, we [healthcare workers] should be able to enter patients’ information and it [the app] should contain all the symptoms of [mental] diseases. Similarly, if we [healthcare workers] choose the symptoms, let it [the app] suggest the diagnosis itself, based on the [inserted] symptoms. Similarly, let it [the app] suggest the treatment as well as the medicines’ dose. Moreover, it [the app] should be developed in Nepali so that everyone can understand [the content]” Medical Officer
+Compatibility of the app was highlighted as another important aspect of its incorporation into working practices. As health care workers often use their personal mobile phones to access apps for work, it is imperative that apps compatible with both Android and iOS operating systems. Additionally, health care workers also had a concern about the storage space that the app will take in the phone. Due to limited storage space of some mobile devices, health workers also recommended that the app should take minimal memory space.
+“The app should not use much space of the mobile phone because the apps, which consume large storage space, are not used [by health workers]. Even I deleted that app [e-mhGAP] because it took large storage space and I didn’t use it often and there was no sufficient space to download other apps” Health Assistant
+“I do not use IMNCI app [Integrated Management of Childhood Illness] because it is not compatible with my iPhone.” Medical Officer
+The next frequently recommended feature and function (n = 12) to be included in the app was a messaging feature for sending reminder to a patient for follow-up care. Health workers reported that dropout rate of patients receiving mental health treatment in primary care is relatively higher than general healthcare. Therefore, health workers recommended messaging feature through the app for improving treatment adherence among patients receiving mental health services.
+“It [IMNCI app] gives a ring or message to the parents’ mobile after entering the phone number and reminder message about the follow-up date is sent. It would be far better if we can do such [a feature] in this [e-mhGAP] app” Health Assistant
+Some of the health workers also endorsed including the pictures of Community Informants Detection Tools (CIDT), a vignette-based tool for pro-active case
+detection of people with priority mental disorders [44], for assessment, and videos of psychosocial intervention. They reported that the pictures and videos could be useful demonstration materials to deliver psychosocial interventions.
+“I think, it would be even easier to understand if the videos about the conversation between patient and health worker and the exercise of relaxation and deep breathing would be added” Auxiliary Health Worker
+In addition, some of the health workers also suggested a data sharing feature for sharing patients’ data to the supervisors in order to use in the remote supervision.
+“We [health workers] can make the app [e-mhGAP] in such a way that the app has a common conversation place where we would be able to upload our confusions [about case management] and our colleagues (health workers) will suggest us. Similarly, it will be more helpful if we would be able to send the information of the patient to the psychiatrist.” Medical Officer
+For supervision, the app [e-mhGAP] should have a feature that would allow us to enter the information of the case [patient situation] and the supervisor will review it [information], and suggests required steps [for case management] to be taken. Medical Officer
+Discussion
+This study assessed the perception of primary healthcare workers about the feasibility, acceptability, and benefits of using a mobile app-based mhGAP-IG in the detection and treatment of people with mental health disorders in primary care in Nepal. Most of the health workers who participated in the study reported high level of interest, positive attitude and motivation in using mobile app-based clinical guideline to support mental health treatment. As mobile technologies and internet facilities are increasingly expanded to the rural areas of Nepal; mobile app-based mhGAP-IG could be a viable and effective strategy in scaling -up of the primary care based mental health services throughout the country. A number of potential benefits have been reported by primary health care workers in using the app which included: (1) minimization of the barriers related to low detection of mental health disorders in primary care; (2) possibility of remote supervision of the trained health care workers; (3) increase in the treatment coverage of mental health services from primary care; (4) bringing uniformity in diagnosis and management of mental disorders across all health workers; (5) convenience and easy to use; and (6)
+validating health workers diagnosis and treatment process and options.
+Decision support options in the detection and treatment of mental disorder was one of the most commonly recommended functions in the app by all the health workers participating in the study. A similar advantage of m-health interventions was also reported in a study conducted in Uganda [27], and in a systematic review of the feasibility and effectiveness of m-health strategies in LMICs [45]. In our recent study among the same health care workers in Chitwan, the detection rates of depression and alcohol use disorder in primary care increased significantly (i.e.15.5 points for depression and 58.9 points for AUD) after the introduction of the mhGAP-based training; however, the rates reduced substantially (i.e. 10.2 points increase for depression and 12.1 points for AUD) 2 years after the training [36]. As the correct detection of mental health disorders is an important indicators for initiating a minimally accurate treatment; therefore, using mobile app-based clinical guide could be a viable and effective strategy to improve detection, diagnosis and treatment of mental health disorders in primary healthcare.
+However, use of such technology is not without barriers, as demonstrated by this study. These perceived risks and limitations highlighted the importance of a user centered approach to design of such interventions. For example, limited experience of using digital technology among older (aged) health care providers was highlighted as a potential barrier to uptake of the mobile app-based clinical guidelines. Similar challenges have previously been reported in other studies. For example, a study conducted to assess the acceptability and feasibility of using digital technology in mental health care in rural India also reported limited familiarity of health care providers’ with digital technology as a key challenge for delivering depression care [46]. Similarly, our results also support the findings of the review of available statistics and evidence of smartphone use among elderly people which demonstrated that they use a smartphone for making calls but rarely did they utilized downloaded apps [47].
+A systematic review on the feasibility and effectiveness of m-Health strategies for frontline health workers in LMICs reported that if adequate training is provided to the frontline workers, they can easily learn the technology for improving the services they provide [45]. Similarly, a study conducted to assess the feasibility of using smartphones in maternal health care in Ethiopia reported that the health care providers who did not have previous exposure to smart phones and electronic devices were able to use the phones effectively in day-to-day maternal health care service delivery [48]. A pilot programme in rural Nepal which tested the feasibility of using mobile
+phones for collecting health surveillance data reported that community health workers can collect health surveillance data accurately using mobile technology if they are trained adequately [31]. There have also been studies in Nepal that demonstrated limited uptake of mental illness detection strategies among community health volunteers [34]. However, the current study differs by focusing on primary care workers and using the app as a decision aid tool, rather than simply documentation and communication.
+Another important challenge in using mobile app-based clinical guide reported in the study was the additional burden for healthcare workers for entering patients’ data in both mobile app and paper-based patients register. Considering the high workload of primary health workers in Nepal, they might be less motivated in using mobile app if patients’ data and clinical notes are to be entered in both patients’ domains. If paper forms were to be replaced by electronic forms in the near future, health workers might be motivated to use electronic forms without reservation however this is not likely in the context of Nepal. These results are also supported by a pilot study of m-health application in multidrug-resistant tuberculosis (MDR-TB) program in South Africa, where a very poor uptake of m-health application was reported despite health workers’ positive response towards m-health [49].
+Other barriers and risk factors in using mobile app-based clinical guidelines identified in our study were potential distraction in the communication between patients and health workers, and breaking confidentiality of the patients’ data. Some of these challenges can be minimized by training health care workers adequately in the app, using the app only after the initial consultation, and using password protected mobile phones. If the privacy and confidentiality of patient data are not maintained appropriately patients may not be comfortable in sharing their personal information. About half of the study population in the US national survey had concerns about privacy and security of their medical records [50]. Similar results have also been reported in a separate study where 50% patients expressed concerns regarding privacy issues, and 40% felt uncomfortable about providing their personal information to clinicians [51].
+The findings of this study may have several implications to improve the reach and coverage, as well as the quality of the mental health services provided through primary health care settings. First, the results demonstrated that most of the health workers use android mobile, and are experienced in using technology in health care. They were highly motivated and positive in using mobile technology in mental health care. This approach, therefore, could be a viable and effective strategy in building
+clinical capacity of primary health care providers in mental health care in Nepal. The mobile app-based clinical guideline, which is currently being developed, is based on the second version of the mhGAP-IG [22], the first version has already been used in more than 100 countries [52]. The mhGAP-IG based district mental health care plan was also effective in improving treatment coverage, accurate detection of mental disorders and initiation of minimally adequate treatment in primary care, and positive clinical outcomes in Nepal [36].
+The mhGAP-IG version 1.0 has also been translated and adapted in Nepal, and hundreds of primary health care workers have already been trained in mhGAP-IG version 1.0. Moreover, the Ministry of Health in Nepal has developed treatment protocols and training manuals based on the mGAP-IG; therefore, the same materials and protocol could also be used in mobile app-based clinical guidelines. This strategic approach could be helpful for scaling up of primary care based mental health treatment throughout the country. Second, the government of Nepal has initiated telemedicine programs in several rural districts with the aim of providing specialists services from rural district hospitals. However, it has been reported that these services were not effective due to lack of information technology infrastructure, and competent technical person to operate the system in the rural hospital [33]. Although, there are no systematic data on cost-effectiveness of telemedicine system [53]; mobile app-based clinical guide may require less technical persons for its day-to-day operation compared to the telemedicine system. Therefore, m-health could be an appropriate and effective strategy to provide basic mental health services in the remote districts where both specialists’ mental health services and technical expertise are not present.
+Lastly, our previous studies found that one-time mental health training to primary healthcare workers was insufficient to improve performance [36] and regular supervision and mentoring from the mental health specialists was necessary to see improvement. Our previous project created an innovative approach of supervision, “psychiatric case conference”, where trained health care workers were invited to a specific venue to address their difficulties in assessment, detection and management of mental health disorder [54]. Although, the psychiatric case conferences were found to be effective and were also appreciated by the trained health care workers [37], this approach requires physical presence of both mental health specialists and primary health care workers. One of the features in the app-based clinical guide suggested by the health care workers is collection of patients’ socio-demographic and clinical data which can also be used by a mental health specialist or clinical mentoring
+and supervision of the trained health workers remotely. Finally, as suggested by health care workers, if the mobile app could include a feature of sending reminders to patients for follow-up visits, this could help to minimize the high dropout rate in mental health care [37]. This is the first study that has explored the perceptions of healthcare workers on feasibility, acceptability and benefits of using a mobile app based clinical guideline for mental health care in primary health care settings. This study will provide valuable insights and information for the development and use of mobile app based clinical guidelines in low resource settings. The study has a few limitations. First, the study was conducted in two districts namely Chitwan and Ramechhap; therefore, the results may not be generalizable to all health workers in Nepal. Second, the study was conducted with prescribing health workers (i.e. health assistants, auxiliary health workers and medical officers); therefore, the results may not represent the opinions of non-prescribing health workers (e.g. nurse and auxiliary nurse midwives).
+Conclusion
+This is, to the best of our knowledge, the first study assessing the perceptions of primary health care workers on the feasibility, acceptability and benefits of using mobile app-based clinical guide for detection and treatment of mental disorders in the primary healthcare setting in Nepal. Most of the health workers participated in the study found to be highly interested and motivated in using mobile app-based clinical guides for detection and treatment of mental disorders in primary care. The key features and functions suggested by the health workers in the mobile app-based clinical guides, i.e. suggestive diagnosis and treatment options, patients’ data recording system, messaging, and offline functions, are instrumental to inform the design of the app. The suggested functions and features as well as the potential risk factors highlighted by the health workers, will be considered when further developing the mobile app-based clinical guidelines, training modality and materials, and supervision system.
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+1.	Introduction
+The increased risk of premature death in people with severe mental illness (SMI) compared to the general population is well established (Brown et al., 2000; Hayes et al., 2017; Osborn et al., 2006). Studies to date have mostly been based in one setting, hospital (secondary care inpatient and/or outpatients) or community (primary care), rather than using linked population level data, and have consistently reported a two to three fold increase in all-cause mortality ratios. This corresponds to a reduction in life expectancy of 10-20 years for those with SMI - a clear health injustice. It is often assumed that studies based in the secondary healthcare setting are unlikely to bias findings on this mortality gap since most people with SMIs are likely to have accessed secondary
+care (Jayatilleke et al., 2017). However, this assumption is worth exploring. Mortality risk may be over or under estimated since more serious and vulnerable cases will be over-represented in hospital inpatient and outpatient settings (Amaddeo et al., 1995; Carney et al., 2006; Copeland et al., 2009; Crump et al., 2013; Jayatilleke et al., 2017; Kessler et al., 2005) and possibly under-represented in community/pri-mary care settings (Hayes et al., 2017; McDermott et al., 2005; Osborn et al., 2007; Osborn et al., 2006). Here, we report what is to our knowledge the first analysis of mortality risk using person level linked hospital and primary care data to explore the extent to which ascertainment bias has played a part in risk estimates from previous studies based in one setting alone (hospital or primary care).
+Some causes associated with this premature mortality are well documented. Suicide risk in those with SMI is highly associated with acute psychotic episodes and psychiatric hospitalisation, peaking during admission and shortly after discharge (Appleby et al., 2001; Nordentoft et al., 2013). People with SMI often have poor physical health and greater levels of obesity than the general population (Leucht et al., 2007). Deaths from cardiovascular, metabolic and respiratory diseases are common and account for most of the excess mortality from natural
+causes in both inpatient and community settings (Bushe et al., 2010; Copeland et al., 2009; Correll et al., 2017; Jayatilleke et al., 2017; Olfson et al., 2015; Osborn et al., 2007). A number of physical comorbidities were particularly over-represented in a recent study using a large primary care database (Olfson et al., 2015). These included Parkinson's disease and some disorders of the liver and the digestive system, with diseases such as epilepsy, diabetes, and respiratory disorders notably elevated in comparison to the general population. Many studies only report on common causes of natural deaths, while less common causes are under-reported, often due to insufficient sample sizes. Hence, in the present study we aimed to report a comprehensive assessment of mortality in people with SMI drawn from primary and secondary care populations compared to the general population.
+2.	Materials and methods
+2.1.	Design
+A retrospective electronic cohort study.
+2.2.	Study population and setting
+The whole population of 3.9 million individuals in Wales, 2004-2013, was included in the study. SMI diagnoses were divided into three groups based on the diagnostic categories in the International Classification of Diseases (ICD) version 10 and Read Codes version 2: 1) schizophrenia, schizotypal and delusional disorders; 2) bipolar disorder and other mood related disorders; 3) other severe mental illness (Read Codes only relating to ‘other non-organic psychoses’). Supplementary Table 1 lists study related ICD-10 codes.
+2.3.	Data source
+The Farr Institute's Secure Anonymised Information Linkage (SAIL) databank (www.saildatabank.com) was used in the study. SAIL is an expanding data repository (around 3 billion records) of anonymised person based linkable data from healthcare and public settings to support research. Policies, processes, structures and controls to manage and maintain this databank have been described elsewhere (Ford et al., 2009; Lyons et al., 2009). We utilised the following datasets in our study:
+1.	The Office for National Statistics (ONS) deaths register, which houses nationwide information on cause covering the whole population of Wales.
+2.	General Practice Database (GPD), which contains diagnoses, symptoms, investigations, prescribed medication, referrals, coded hospital contacts and test results. At time of analysis 71% (338/474) of all general practices in Wales were supplying SAIL with their data.
+3.	Patient Episode Database for Wales (PEDW), an NHS Wales hospital admissions dataset comprising clinical information of all hospital admissions (inpatient and day cases) covering the whole population of Wales.
+4.	Welsh Demographic Service (WDS) an administrative register of all individuals in Wales that use NHS services, which includes anonymised demographics and practice registration history.
+2.4.	Measures
+We used ICD-10 codes for SMI (Supplementary Table 1) to interrogate PEDW for both planned and emergency admissions identifying SMI cases as the principal discharge diagnosis for hospitalisation as done in previous studies assessing premature mortality (Ford et al., 2009). We used Read Code version 2 to identify cases of SMI in the GPD. Diagnostic codes used to identify people with SMI in the GPD have been previously validated (Economou et al., 2012; Lloyd et al., 2015). Diagnoses related to SMI, number of deaths and cause of deaths
+were extracted within the study period (01/01/2004-31/12/2013). ICD10 codes were used to classify cause of death and were grouped into main categories. We further categorised causes of death into natural and unnatural (accidents, assault, suicide, self-harm and undetermined intent). Supplementary Table 2 tabulates categories and sub-categories of causes of death as well as the corresponding ICD-10 codes used in the study.
+2.5.	Analysis
+We utilised the WDS to collect demographic information, such as week of birth and sex. The age of each individual was calculated at the midpoint of the follow-up period and described according to the following categories: under 45,45-64, 65-84 and 85 or above years in accordance with other studies of this type (Hoang et al., 2011).
+Standardised mortality ratios (SMRs) were calculated using the indirect method with the WDS population as the standard population. We estimated SMRs for four sets of patient cohorts identified as follows:
+a)	Primary care cohort: patients with a SMI diagnosis from the GPD within the study period using a previously validated algorithm (Economou et al., 2012; Lloyd et al., 2015);
+b)	Primary care cohort with no hospital admissions: patients with at least one GP diagnosis related to SMI but without subsequent inpatient diagnosis for the same disorder within the study period;
+c)	Secondary care cohort: patients with a principal diagnosis from PEDW (inpatient diagnosis) related to SMI within the study period; and
+d)	Combined cohort: combining the primary (a) and secondary (c) cohorts.
+The start date of the follow-up period was defined as the date of the first inpatient or GP SMI related diagnoses within the study period (01/ 01/2004-31/12/2013). An individual was included in the cohort once only. The end date was the death date, the end of the study period (31/12/2013) or the last end date of registration in the WDS within the study period, whichever came first. For the combined primary and secondary cohort (combined cohort), the start date of the follow-up period was the earlier date of either the GP or inpatient diagnosis. For individuals with no SMI related diagnosis, the start date of the follow-up period was the beginning of the study period (01/01/2004) and the end date was the death date or the end of the study period, whichever came first.
+All-cause and cause-specific mortality rates were calculated using the person years at risk corresponding to the follow-up periods (IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2010; Ressing et al., 2010). SMRs were calculated by dividing the observed numbers of deaths in the SMI cohorts (primary care, primary care without hospital admission, secondary care and combined cohort) by the expected number of deaths derived from the age (5-year age bands) and sex-matched all-cause and cause-specific mortality rates found in the whole of Wales. SMRs for causes with a small number of observed deaths (e.g., diseases of the eye and ear) were not reported (refer to Supplementary Table 2).
+We further characterised the four cohorts by age, sex, diagnostic category and stratified by underlying causes of death. To calculate SMRs for the cause of death of people within SMI categories, we used the rates from the corresponding specific causes within the population of Wales. We conducted further additional analyses to make comparisons between SMRs for specific causes of death between cohorts sourced from primary care and secondary care settings.
+Data in the SAIL databank were interrogated using structured query language (SQL DB2). All confidence intervals (CIs) of the SMRs were two-tailed mid-p exact CIs, assuming Poisson distribution for the observed deaths, and were calculated as previously described (Rothman
+and Boice Jr, 1979). CIs for proportions and percentages were estimated by the Wilson score method with continuity correction (Newcombe, 1998).
+2.6.	Ethical approval
+The use of data in the SAIL databank for this study have the required permissions from the SAILindependent Information Governance Review Panel, which includes representatives with a variety of expertise from different organisations: BMA, NRES, Involving People, NHS Wales Informatics Service and Public Health Wales NHS Trust. This study forms part of the “PsyCymru study” (Lloyd et al., 2015) ethically approved by Wales REC 3 (14/WA/1136).
+3.	Results
+3.1.	Participants, number of deaths and demographics
+The standard population was the whole population of Wales in WDS over the ten-year observation period. This consisted of 3.9 million people providing 35.3 million years of person time with 377,511 deaths observed. In the primary care cohort sourced from GPD, there were 2720 deaths over 112,316 person years, provided by 21,589 individuals with a SMI diagnosis. Of these, 37.8% (8163/21,589) also had hospital admissions and were present in the secondary care cohort. For the remaining patients within the primary care cohort (i.e., those seen in primary care and/or with recorded out-patient appointments where no hospitalisation for the same SMI occurred) we observed 1753 deaths out of 62,459 person years provided by 13,426 individuals. In the secondary care cohort, there were 2956 deaths within the 83,300 person years contributed by 16,371 individuals diagnosed with a SMI with hospital admissions.
+In the combined cohort, there were 4709 deaths out of 153,263 person years, contributed by 29,797 individuals who had received a diagnosis of SMI recorded in either primary care or secondary care. For this combined cohort, 27.5% (8208/29,797) were sourced from secondary care cohort (hospital admissions only), 45.1% (13,426/29,797) were from the primary care cohort (GP and recorded out-patients), and 27.4% (8163/29,797) were from both primary and secondary cohorts, i.e., had a SMI diagnosis recorded in primary care and also had a hospital admission.
+The distribution in terms of demographics (sex, age, deprivation) across each cohort is shown in Supplementary Table 3. Sex was evenly divided (50/50). Almost half of the cases were under 45 years (40.8%-47.3%) and 26.2%-26.8% resided in the most deprived quintile at the start of follow-up (ranges across all cohorts). Those in the hospital admissions secondary care cohort included lower percentages of under 45 year olds and those from the least deprived quintile compared to all the other cohorts. Over a third (>36%) had bipolar disorder, Schizophrenia ranged from 46.9% to 62.1% across settings with other SMI accounting for the remainder.
+3.2.	All-cause SMRs for different cohort ascertainment methods
+The mortality ratios (and CIs) for cohorts ascertained from the different settings are illustrated in the forest plot (Fig. 1). We found mortality ratios to be higher for the secondary care hospital admissions cohort (2.9; 95% CI: 2.8-3.0) than for the primary care cohort (2.2; 95% CI: 2.1-2.3), as well as the primary care cohort without hospital admission (2.3; 95% CI: 2.2-2.4) when compared to the general population of Wales. The all-cause SMR was 2.6 (95% CI: 2.5-2.6) for the combined cohort.
+3.3.	All-cause SMRs in the combined cohort
+The range of age at death in the combined cohort was 15-108 years old; with the mean age at death being 72.7 years old. The breakdown by age groups of age at death was: 9.8% (461/4709) were under 45 years; 21.2% (996/4709) aged 45-64 years; 47.1% (2216/4709) aged 65-84 years; and 22.0% (1036/4709) of deaths aged 85 years and over.
+The all-cause SMRs for all those with SMI and for SMI categories by age and sex in the combined cohort are summarised in Table 1. Males (SMR: 3.0; 95% CI: 2.9-3.2) had higher all-cause SMR than females (SMR: 2.3; 95% CI: 2.3-2.4). Higher ratios were observed in the younger age groups. Ratios for schizophrenia related disorders were higher (SMR: 2.7; 95% CI: 2.6-2.8) than for people with bipolar and other mood related psychotic disorders (SMR: 2.2; 95% CI: 2.1-2.3).
+3.4.	Cause-specific mortality in the combined cohort
+Fig. 2 illustrates the proportion of deaths by major underlying causes and the corresponding SMRs for the combined cohort. Nearly two-thirds of all the deaths were from circulatory, respiratory disorders
+and cancers (61.8%). Diseases of the circulatory system alone accounted for 28.4% (1339/4709) of all deaths; with ischaemic heart disease being the most common. Deaths from respiratory diseases accounted for 17.8% of deaths (840/4709); with just under half attributable to pneumonia alone (418/840). Cancers accounted for 15.6% (735/4709) of all deaths; the most common being lung and gastrointestinal cancer. 9.3% (440/4709) of deaths were due to suicides (self-harm and events of undetermined intent) and accidents. Deaths attributed to Alzheimer's, senility and dementia taken together across ICD-10 categories accounted for 11.8% (555/4709) of deaths.
+Overall SMRs associated with specific cause of deaths examined in the present study for the combined cohort are displayed in Table 2
+and SMRs for males, female and age groups are summarised in Table 3. SMRs associated with unnatural causes (SMR: 6.9; 95% CI: 6.2-7.5) were higher than with natural causes (SMR: 2.4; 95% CI: 2.3-2.5, see Table 2). In particular, males tended to have higher SMRs than females for unnatural causes of death (males: SMR: 7.3; 95% CI: 6.4-8.1; females: SMR: 5.9; 95% CI: 5.0-6.8, Table 3). The highest cause-specific SMR was for ill-defined or unknown causes (SMR: 13.3; 95% CI: 10.1-17.2) followed by suicide and events of undetermined intent (SMR: 12.1; 95% CI: 10.6-13.8). The SMR for the latter was especially elevated in females (SMR: 21.4; 95% CI: 17.1-26.4). We further compared suicide in the secondary care cohort with the primary care cohort. The SMR for suicide for the secondary care cohort was considerably
+higher (SMR: 16.5; 95% CI: 14.1-19.1) than for the primary care cohort (SMR: 8.9; 95% CI: 7.4-10.6) when compared to the general population.
+For natural causes, we found the highest differences in mortality to be deaths from Parkinson's(SMR: 5.4; 95% CI: 4.3-6.7). A cluster of cognitive decline related deaths was also particularly elevated in comparison to the general population: dementia (SMR: 5.2; 95% CI 4.7-5.7); Alzheimer's (SMR: 3.8; 95% CI: 3.0-4.6); and senility (SMR: 2.1; 95% CI: 1.6-2.7). The ratio of deaths from respiratory diseases was found to be high (SMR: 3.2; 95% CI: 3.0-3.4), particularly pneumonia (SMR: 3.8; 95% CI: 3.5-4.2). We also found the ratio of deaths from genitourinary causes to be elevated (SMR: 2.8; 95% CI: 2.3-3.4). The ratios of deaths attributable to diseases of the circulatory system (SMR: 2.2; 95% CI: 2.0-2.3), including cerebrovascular disease (SMR: 2.3; 95% CI 2.1-2.6) and ischaemic heart disease (SMR: 2.0; 95% CI: 1.8-2.1), were doubled compared to the general population. Deaths from diseases of the digestive system (SMR: 2.6; 95% CI: 2.2-2.9) and diabetes related deaths (SMR: 2.2; 95%CI: 1.7-2.8) were elevated. We found little difference in mortality from cancers/neoplasms (SMR: 1.5; 95% CI: 1.4-1.6).
+4.	Discussion
+We present what is to our knowledge the first study to assess mortality in people with SMI drawn from both hospital inpatient and primary care records in a linked dataset. We have been able to identify an ascertainment bias in previous mortality studies where patient inclusion was based on hospitalisation alone (Hoang et al., 2011) or outpatient and hospitalised secondary care cohorts (Jayatilleke et al., 2017), or primary care records alone (Hayes et al., 2017; Osborn et al., 2007). SMRs for people with SMI identified from hospital inpatient settings were more elevated than those identified in a primary care setting or a combination of both. The higher SMRs observed from the secondary care cohort might be due to either shorter duration of follow-up periods i.e., participants died sooner, or differences in disease severity between the primary and secondary care cohorts. SMRs for participants in the primary care cohort who were hospitalised were no different to those not hospitalised (the latter includes those seen in out-patients). This does not support the assumption of difference in disease severity between cohorts. It may reflect those within the secondary care cohort who do not access primary care but are admitted through emergency departments. It may also reflect differential ages between the cohorts with older members of the hospitalised cohort more likely to have late-onset psychotic disorders with potentially different aetiologies.
+For all-cause mortality across data sources, our results were similar to those reported in studies from secondary care settings. We found people with SMI had a two to threefold higher death rate than the general population. SMRs for individuals with schizophrenia related disorders were higher than those with bipolar disorders. We also found the mortality gap tended to decrease with age for all causes of death examined in the present study except for cerebrovascular disease and mental behavioural disorders (dementia and substance misuse). This agedependent decrease in mortality has also been reported elsewhere (Termorshuizen et al., 2013).
+Using a similar categorisation strategy adopted in a previous study (Termorshuizen et al., 2013), we computed SMRs associated with both natural and unnatural causes of death. SMRs associated with unnatural causes of death were found to be higher than those associated with natural ones and higher in males than in females. However, SMRs specifically associated with suicide, self-harm and undetermined intent were higher in females.
+In addition to all-cause mortality, we also report on a broad array of underlying causes of death in those with SMI using a cohort sourced from a combination of primary and secondary care settings. Some of these causes are not commonly described elsewhere. In those with SMI the highest SMRs were found from ill-defined and unknown causes of death, suicide and substance abuse. Ill-defined and unknown causes are often misclassifications of death from ischaemic heart disease and
+suicide (Lozano et al., 2001; Ohberg and Lonnqvist, 1998). Deaths from cardiac arrhythmias are not detectable even at autopsy. Suicide may also be misclassified due to the additional burden of proof i.e., beyond reasonable doubt at the coroner's court. Previous studies have shown that causes of death from ill-defined and unknown causes are more common in marginal populations, specifically the homeless and the elderly living alone, but this is likely to also apply to those with SMI (Andreev et al., 2008).
+The lifetime risk of suicide for this population has been estimated to be between 5%-13%(Hor and Taylor, 2010; Osborn etal., 2007; Pompili et al., 2007). We found the suicide ratios for this population to be disturbingly high, particularly for females. Death from suicide is three times higher for males than females in the general population. However, this disparity in rates does not follow for females with SMI. The psychosis may diminish any inhibition to suicide that is present in women generally (Caldwell and Gottesman, 1990). We found that the SMR for suicide was considerably higher in the cohort drawn from the hospital inpatient setting than in the primary care sourced cohort. This is most likely due to the difference in levels of severity between the cohorts. This finding confirms previous studies in which suicide risk is highly associated with psychiatric hospitalisations. Approximately a third of the suicides in people with schizophrenia occur during admission or the first week after discharge (Nordentoftetal., 2013). SMRs due to psychoactive substance abuse were unsurprisingly high in this population when compared to the general population. In a UK study, 44% of community mental health patients had reported problem drug use or harmful alcohol use in the previous year (Weaver et al., 2003).
+SMRs associated with age-related cognitive decline diseases, such as dementia and Alzheimer's were found to be particularly elevated. It is possible that cognitive decline in schizophrenia may be misattributed to dementia or it may be that those already in contact with psychiatric services are more likely to be appropriately diagnosed than the general population. Mortality ratios from pathogenic agents, including microorganisms leading to death from septicaemia and intestinal and peritoneal infections and peptic ulcers were also very high. The elevated risk for this population of developing peptic ulcers has been reported elsewhere (Liao et al., 2014). It may be that symptoms are under-reported or under- diagnosed by those with SMI so they present late to services. However, immune system dysfunction and the involvement of infectious agents in the pathophysiology of these psychiatric diseases, including antimicrobial defence, are being reported (Yolken et al., 2016).
+SMRs for respiratory diseases were also very high in comparison to the general population, particularly pneumonia. This can be perhaps explained by higher rates of smoking in this population (Olfson et al., 2015). We found a two and a half fold increased risk of death from cardiovascular disease in people with SMI, but this was lower than SMRs in neurological disorders, cognitive decline, infections and respiratory diseases. Despite this, given that cardiovascular disease is the most common cause of death in both general and the SMI populations in Western countries, much of the excess mortality in people with SMI will be attributable to this disease group (Osborn et al., 2007; Osborn et al., 2006). Similarly, we found death rates from cancers, which although slightly elevated, did not result in the highest disparities compared to the general population, which is in keeping with findings from many other studies (Kisely et al., 2013; Osborn et al., 2007).
+4.1.	Strengths and limitations
+We believe this is the first study to evaluate mortality for people with SMI drawn from both primary care and secondary inpatient care settings in linked data. This population-wide observational study thus encompasses the spectrum of severity of SMI of those engaged with services. However, some vulnerable groups, e.g., the homeless, may not be registered with a GP and would not be included. The combination of both settings balances some of the weaknesses of each alone. However, potential confounders may be that if an individual has a physical
+disorder and visits a GP they may be more likely to receive an SMI diagnosis. Two previous studies have made direct comparisons of mortality risks between inpatient and outpatients for this population to establish the over-estimation (Amaddeo et al., 1995; Crump et al., 2013) when compared to studies based solely on in-patients (Hoang et al., 2011). Ideally, we would have included those with SMI seen in out-patients. However in the United Kingdom, diagnostic coding (rather than specialty coding) in the out-patient dataset is too poor to utilise in studies of this type. Letters received from out-patients are Read coded in the primary care dataset. The diagnostic codes to create our cohorts have been previously validated (Economou et al., 2012; Lloyd et al., 2015).
+A further strength is that we have coverage of deaths for the whole general population. It is unclear, but likely, in other similar studies conducted in the UK that the linkage of the mortality dataset is to a subset of the population. If our study had been restricted to those present in the hospital episode statistics/admissions dataset with or without SMI, only 54.9% of those with an SMI diagnosis in the combined cohort would have been linked to the mortality dataset (16,371/29,797). Similarly most primary care databases are sampled i.e., studies based in The Health Improvement Network (Hayes et al., 2017), cover a sampled population of 6% of the United Kingdom population, whereas SAIL provides population level data of 74% of the Wales population allowing whole general population linkage of mortality. Also by calculating SMRs for underlying cause of death using the expected rates from the specific corresponding causes in the general population of Wales, we believe that the relative excess can be accurately determined.
+A further advantage is that we have included some underlying causes of death rarely reported elsewhere, for example, Hayes et al. (2017) report mortality from all causes, cardiovascular disorders and suicide only. Similarly in a study based in a large secondary care database in London, reduced life expectancy in those with SMI was accounted for by a wide range of major grouped causes of death but these were limited to circulatory, cancer, respiratory, digestive, suicide, other external and all other causes (Jayatilleke et al., 2017). Larger cohorts may identify further causes yielding even higher rates, e.g., epilepsy. We were restricted by the size of the cohort as to how many specific causes we could reasonably include. Other limitations include the usual caveats of the use of routine data (not collected primarily for research purposes, quality and completeness issues) and accuracy of death certification, particularly for suicide (Gunnell et al., 2011).
+Indirect standardisation was used for the calculation of SMRs, which means we cannot quantify the differences in premature mortality between our specified cohorts; only in relation to the general population since population structures within each cohort may vary.
+Some authors argue that life expectancy is a preferable measure of mortality to the SMR (Jayatilleke et al., 2017), primarily since it is easier to interpret and communicate, particularly for laypeople and clinicians working outside of academia. However, computation of this statistic has a number of difficulties. For example, its reliance on the construction of current life-tables makes it impractical for routine use, while for small populations computation of life expectancy may not be possible at all (Silcocks et al., 2001; Tsai et al., 1992).
+4(2. Implications for policy, practice and future research
+Methodological considerations and comprehensive characterisations of mortality in this patient population are important when informing policy initiatives that improve services and target interventions. By including people from both primary and secondary care in this study we highlight the need for better integration not only between specialty teams within hospitals, but also between primary and secondary care, particularly since secondary care mental health services are under increasing pressure to discharge people back to primary care earlier (Rethink Mental Illness, 2012). Therefore, the development of policies to: expand the remit of liaison psychiatry services from exclusively hospital based into community settings (Department of Health,
+2014); further develop health promotion interventions for unhealthy lifestyle behaviours; and improve screening opportunities for early detection and intervention is welcomed. It may be worth reflecting on investing, targeting and delivering lifestyle interventions to improve physical health care for people with SMI in secondary care settings, which is currently poor (Bailey et al., 2012; Crawford et al., 2014; Royal College of Psychiatrists, 2015).
+Previous studies may have over- or under-estimated premature mortality in people with SMI to some extent but, as we have demonstrated, SMRs are raised in all settings by an unacceptable ratio. Therefore previous literature has had appropriate major policy implications for improving the physical health of people with SMI. The significant emphasis on improving physical health care for people with SMI through screening for cardiovascular and metabolic disorders is important to address this health injustice, but our study suggests this could be broadened to, for example, peptic ulcers and early recognition and treatment of infection.
+Our results demonstrate that suicide prevention in this population is an important and appropriate focus of national policy. However, a greater emphasis on suicide prevention in community settings in this group should be considered. Suicide rates under crisis resolution and home treatment teams were found to be higher (14.6 per 10,000 episodes) than that under psychiatric hospital admission (8.8 per 10,000 admission) in a recent report (Hunt et al., 2014).
+Future studies should explore premature mortality in those with SMI using both primary and secondary care sources of cases. This includes whether the all-cause mortality gap has increased differentially over the last decade in those with hospital admissions compared to that demonstrated in primary care data (Hayes et al., 2017). Studies from secondary care demonstrate the excess mortality persists, but more recent comprehensive studies are essential (Chang et al., 2011). Long term studies are also needed to determine the extent and interaction between antipsychotic treatment, metabolic indicators, lifestyle choices, social deprivation, age and genetics that lead to premature mortality, particularly from certain diseases. In our whole population-based study we found some of the highest mortality ratios in causes not often commonly described elsewhere, such as deaths from unexplained causes, neurological disorders, cognitive decline, infections and respiratory diseases.
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+It is well established that persons with mental illness experience excess mortality compared with the general population (1-5). Studies that examine life expectancy or premature mortality have shown that persons with mental illness die much sooner than those without mental illness (5-7). The National Association of State Mental Health Program Direc
+tors’ (NASMHPD’s) report titled Morbidity and Mortality in People With Serious Mental Illness (7) reviewed the results of a number of state-level studies on premature mortality. Using data from inpatient and outpatient settings from 1997 to 2000, these studies reported that persons with serious mental illness die on average between 13.5 and 32.2 years earlier than the general
+population. The NASMHPD report also states, “People with serious mental illness die, on average, 25 years earlier than the general population. State studies document recent increases in death rates over those previously reported.” The report also states that “60% of premature deaths in persons with schizophrenia are due to medical conditions such as cardiovascular, pulmonary and infectious diseases.” Because the risk for these causes of death is associated with a number of modifiable risk factors, such as tobacco use and obesity, these deaths can be viewed as preventable (8,9).
+Premature mortality among adults with serious and persistent mental illness has important implications for the treatment system. These include the role that the fragmented health care system plays in reducing access to primary care for persons with serious and persistent mental disorders, the relative lack of attention that both the mental health and general medical health care systems have paid to wellness and prevention activities, and the contribution of treatment itself (for example, second-generation antipsychotic medications) to premature mortality. However, to really understand premature mortality among persons with serious and persistent mental illness, we need to better understand differences in premature mortality between those with and without mental illness, as well as the factors that may explain those differences.
+With the exception of one study (6), research on years of potential life lost (YPLL) among persons with serious
+mental illness is limited in that it typically does not take into account YPLL in the general population. By not examining differences in premature mortality, the results of these studies may have overestimated premature mortality in the population with serious mental illness. We seek to address this limitation through a comparison of YPLL in a sample of decedents with serious and persistent mental illness with a sample of decedents in the general population.
+Current research on premature mortality has also focused on inpatient populations or outpatient settings that include individuals with a wide range of diagnoses. Inpatient samples may overestimate premature mortality because those patients tend to be sicker, whereas differences in mental health diagnoses among outpatient samples may account for differences in premature mortality. All of the decedents with serious and persistent mental illness in this study were, at the time of death, receiving treatment in a community mental health center serving only clients with serious and persistent mental illness.
+We also took into account differences in social factors and cause of death that may account for differences in premature mortality. There are a number of studies that examine the relationship between social factors and mortality in the general population. They show that gender, race, education, and marital status are all associated with mortality (10-13). Additionally, social factors explain some of the difference between those with mental illness and the general population in terms of the rates at which people die. For example, one study (14) found that standardized mortality ratios were significantly reduced when the analyses adjusted for socioeconomic factors. However, studies using YPLL as a measure of premature mortality tend not to account for variation in social factors.
+The purpose of this study was to broaden the understanding of the relationship between serious mental illness and premature mortality. We compared decedents with serious and persistent mental illness who were treated in a community-based setting with decedents in the general population for the same period and in the same communi
+ty to examine whether there were significant differences in YPLL, whether social factors and cause of death explained any of the difference in YPLL, and whether there were particular causes of death that differentially affected YPLL among persons with serious and persistent mental illness.
+Methods
+Study setting
+The data for this study came from death records and clinical records. First, death records were obtained from the Ohio Department of Health for 1998 through 2004. Death records were then linked to clinical case management files from Community Support Services (CSS), a community mental health center in Akron, Ohio. CSS serves the majority of adults in the county who have serious and persistent mental disorders and is funded by the county behavioral health authority. These disorders include psychotic disorders (schizophrenia and schizoaffective disorder), mood disorders (dysthymic, major depressive, and bipolar disorders), and other disorders (anxiety and personality disorders). Consumers with a primary diagnosis of substance use disorder or dementia were excluded from analysis, because these diagnoses were deemed by the research team to fall outside the definition of serious and persistent mental illness. During the study period CSS served about 3,000 individuals annually, with an active caseload between 2,000 and 2,500. The CSS population was predominantly white or African American, with approximately 33.7% African Americans and 65.8% whites during the study period (the study sample was limited to African Americans and whites). The record link identified CSS consumers who died during the seven-year period and allowed us to determine axis I diagnoses for these consumers. A comparison group, composed of persons who were not CSS consumers and who died during this same period, was created by querying all of the deaths among adults 18 and older with addresses in Akron. Because the majority of CSS clients live in Akron (78.2%), city-level data (as opposed to state or county data) were used for the comparison group to provide a better sociodemographic match
+to the CSS population. This resulted in a total sample size of 16,164: 647 CSS cases and 15,517 non-CSS cases.
+Personal identifiers in clinical records were accessed for matching purposes only and were removed from the data file before analyses were conducted. The study was deemed exempt by the institutional review board at the Northeastern Ohio Universities Colleges of Medicine and Pharmacy.
+Cause of death
+Cause of death was determined using the underlying cause of death as documented by ICD-9 or ICD-10 codes on the death record. A variable was created for each cause of death and assigned a value of 1 if present and a value of 0 if not present.
+YPLL
+YPLL is a widely used measure of premature mortality that is based on the mean survival age for a living cohort at each age (4,6) and was determined for each case on the basis of U.S. life expectancy in the year of death (data from the Centers for Disease Control and Prevention [CDC], 1998-2004) (www.cdc.gov/nchs/fas tats/lifexpec.htm). For all years of the study combined, the mean YPLL was calculated for all causes of death.
+Social factors
+Information on gender, race, marital status, and level of education, all of which were obtained from the death record, were included in all multivariate models.
+Age adjustment
+Because the CSS and non-CSS cases had different age distributions, a weight was created to adjust for this difference following direct standardization procedures recommended by the CDC (15) and utilized in previous research (2,6). The formula used to compute the final weight was W=PGP/PCSS, where W is the final analysis weight for each CSS case by age, PGP is the proportion of Akron cases for each age, and PCSS is the proportion of CSS cases for each age. The final analysis weight was applied to the data and used in all subsequent analyses. After weighting, the CSS sample size was 649 and the nonCSS sample was 22,828.
+Analysis
+Chi square analyses and t tests were conducted to determine whether there were significant differences between CSS decedents and non-CSS decedents in YPLL, social factors, and causes of death. Analysis of covariance was used to estimate average YPLL with adjustment for social factors. Multivariate ordinary least-squares (OLS) regression models were estimated to determine whether differences in cause of death accounted for differences in YPLL. Interaction terms were then added to these models to test whether particular causes of death had a greater effect on YPLL for CSS decedents than for the community comparison group. SPSS, version 15.0, was used for all analyses.
+Results
+Table 1 presents descriptive information for the study sample, as well as the results of t tests for differences in means and proportions for all study variables. Mean age of decedents in the sample was 79.4±11.1 years. Fifty-six percent were female, and 20% were African American. The unadjusted mean YPLL for CSS decedents was 14.5±10.6, compared with 10.3± 6.7 for the non-CSS group. On average, CSS decedents died at the age of
+73.4±15.4, compared with 79.6±10.9 years for the non-CSS population. There were also significant differences in three of the sociodemographic variables used in this study. Although level of education was similar in both groups, there was a larger percentage of women and a smaller percentage of African Americans in the CSS group. Persons with serious and persistent mental illness were
+less likely to be married at the time of death. Most of the decedents with mental illness had a diagnosis of some psychotic disorder (47%), 36% had a mood disorder, and the remaining 17% had some other diagnosis.
+Table 2 presents leading causes of death for the U.S. population (16) and the two study groups. Heart disease was the top cause of death in all three groups. In contrast, although
+cancer was the second leading cause of death in the U.S and non-CSS populations, cancer was only the fifth leading cause of death in the CSS sample.
+The leading causes of death in all three groups include heart disease, cancer, cerebrovascular disease, chronic lower respiratory disease, diabetes, dementia, influenza or pneumonia, and nephritis. In addition, the CSS and non-CSS samples included liver disease in the ten leading causes of death. Septicemia was in the ten leading causes of death for the U.S. population, but it was not a leading cause for either the CSS or non-CSS samples. Compared with non-CSS decedents, decedents with serious and persistent mental illness were more likely to die from chronic lower respiratory disease (11.2% versus 9.0%; x2=4.0, df=1, p=.047), dementia (10.2% versus 6.7%; x2=8.3, df=1, p=.004), and influenza or pneumonia (6.8% versus 4.3%; x2=8.9, df=1, p=.003), but they were much less likely to die from cancer (9.9% versus 22.7%; x2=61.2, df=1, p<.001). Although suicide did not appear in the top ten causes of death for either study group, our results suggest that persons with mental illness were more likely to have deaths attributed to suicide (1.8% versus .4%; x2=22.3, df=1, p<.001).
+Table 3 shows that there were significant differences in YPLL between the
+CSS and non-CSS groups for the leading causes of death, after the analysis adjusted for gender, race, marital status, and education. For all causes combined, the CSS decedents died 12 years earlier than expected and lost over two more years of life, compared with persons in the general population. There were significant differences in YPLL for all causes after taking into account gender, race, education, and marital status. Causes of death for which the greatest difference in YPLL was found include accidents (13.1 years), suicide (9.7 years), cancers (5.0 years), septicemia (5.0 years), and liver disease (4.8 years).
+Next, multivariate OLS regression models were estimated to assess whether cause of death accounted for observed differences in YPLL. In addition, we added interaction terms to determine whether there were causes of death that had a differential effect on persons with serious and persistent mental illness. Results are presented in Table 4.
+Model 1 shows the effect of social factor variables. All of the social factors were related to YPLL and accounted for 6% of the variance in YPLL. Women and whites lost fewer years of life, compared with men and African Americans. In addition, decedents in the study who were married or had more education lost more years of life, compared with those who were not married and those with
+less education. Each cause of death was added in model 2. For these analyses, heart disease was used as the reference category because it was the highest ranked cause of death for both groups. Nephritis was the only cause of death for which premature mortality was not significantly different from heart disease. Model 3 assessed the effect of having a mental illness, after adjusting for gender, race, marriage, education, and causes of death. A diagnosis of mental illness significantly increased the YPLL, regardless of cause of death.
+As a final step, we entered interactions terms (together and separately) for each cause of death and having a mental illness diagnosis to determine whether there were particular causes of death that had a differential effect on YPLL for persons with serious and persistent mental illness. The significant interactions are presented in model 4 of Table 4. We used the coefficients in this model to predict YPLL for each of the significant interactions. These values are presented in Table 5. These findings suggest that accidents, suicide, septicemia, liver disease, and cancer have a greater impact on premature mortality for persons with serious and persistent mental illness.
+Discussion
+The purpose of this study was to broaden the current understanding of premature mortality among persons with serious mental illness. Consistent with previous studies (4,6,7) we found that for each of the leading causes of death, persons with serious and persistent mental illness lost more years of life, compared with those in the general population of Akron. Previous studies have also suggested that persons with serious mental illness are more likely to die from unnatural causes, such as suicide, accidents, and assault (4,6). This was true in the study presented here as well. However, most deaths among persons with mental illness in this sample were attributed to natural causes. The same causes of death appeared in the top ten for both CSS decedents and the general population. In addition, there were only four causes for which we found significant differences between the two study groups in the proportion of people dying: cancer,
+chronic lower respiratory disease, dementia, and pneumonia.
+Much previous work has been based on samples of consumers who have been hospitalized or on mixed inpatient and outpatient samples (5). The study presented here adds to previous work in this area by examining premature mortality in a sample of consumers with serious and persistent mental disorders who received treatment primarily in a communitybased treatment setting. The majority of individuals with serious and persistent mental illness live in the community; thus the sample in this study is more representative than state hospital samples. The results presented above suggest that although consumers in community settings experience a significant degree of premature mortality, they may experience it to a lesser degree than consumers who were recently hospitalized before their death.
+We found that a community-based sample of adults with severe and persistent mental illness lost 14.5 years of potential life, a difference of 4.2 years from the Akron sample. This gap is smaller than that reported by Dembling and colleagues (6), who reported an 8.8-year difference in YPLL. This difference may represent overall
+higher mortality in an urban sample, compared with that of a statewide population, especially because Akron is a city with a poverty rate nearly twice the national average and an unemployment rate near 10% (17).
+This study also makes a contribution by taking into account differences in social factors. Although we
+were limited to measures contained in the death record, our consideration of the role these factors play in premature mortality makes an important contribution to the literature. Our findings suggest that although gender, race, marital status, and education are importantly related to YPLL, premature mortality among persons with serious and persistent mental illness cannot be explained by sociodemographic differences.
+We also asked whether differences in cause of death accounted for any of the differences in premature mortality and whether the relationship between cause of death and YPLL is moderated by a diagnosis of mental illness. We found that differences in premature mortality are not explained by differences in cause of death. Moreover, our results show that when premature mortality for heart disease was used as the reference group, premature mortality related to suicide, cancer, accidents, liver disease, and septicemia was greater for persons with serious and persistent mental illness than for persons in the general population.
+Our finding that suicide and accidents are a source of premature mortality among persons with mental illness is consistent with previous research (4,6,18-20). However, our work adds to the growing body of literature that highlights the need for better preventive health care for persons with mental illness. Morbidity and mortality from the most common causes of death in this sample (heart disease, respiratory diseases, cerebrovascular disease, and cancer) may be reduced by effective medical care that attempts prevention, early detection, and chronic disease management.
+As noted above, one important limit of our study is that we were restricted to the measures contained in the death records. For example, we did not have access to better measures of socioeconomic status. Therefore, the results we report may be related to differences in income between the CSS and non-CSS groups.
+Conclusions
+We found that a community-based sample of individuals with serious and persistent mental illness died
+prematurely, compared with a demo-graphically similar general population sample. Social factors were related to premature mortality, but they did not account for the differences in YPLL that we observed. Although total YPLL and the difference in YPLL between the individuals with mental illness and the general population were smaller than those from hospital-based samples, deaths were largely from preventable causes. Heart disease was the top cause of death.
+Recent efforts to improve access and quality of primary care health services by integrating such services within the community mental health center appear to be a promising approach to addressing these preventable causes of death. The concept of the mental health care home (21) holds great promise. Integrating mental health care, primary health care, and wellness-promoting activities in a single center may ensure access to the interventions needed to reverse the causes of preventable early death.
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diff --git a/renewed agenda for global mental health.txt b/renewed agenda for global mental health.txt
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+4 years ago, The Lancet published a Series of articles highlighting the global health crisis due to an astonishingly large treatment gap: up to nine of ten people with a mental health problem do not receive even basic care in some countries. The Series showed that this gap was not due to insufficient evidence about the effect of mental health problems or their effective treatment, but to a range of barriers operating at all levels of the health system, from global policies through to local health-care provision. The Series ended with a call to action to scale up services for people with mental health problems, especially in low-income and middle-income countries where the gaps are the largest, and where some of the most serious human rights abuses against affected people are perpetrated.1 4 years on, we take stock of what progress has been made.
+The themes of this new Series were selected by the members of the Movement for Global Mental Health, a coalition of 95 institutions and over 1700 individuals from more than 100 countries, representing professionals and civil society, and working together to advocate for the necessary conditions for a better life for people affected by mental health problems. In this respect, the Series represents a unique example of agenda setting for scientific publications by a social movement. The Movement chose themes about tracking progress in achievement of the goals of the call to action, and filling in gaps in the knowledge synthesised in the first Series.
+There is cause for us to celebrate the emergence of global and national responses to mental health care. Several global initiatives have been launched in the past 4 years, notably: WHO's mhGAP intervention guidelines, which provide the symbolic bednets for priority mental, neurological, and substance misuse disorders for use by non-specialists in routine healthcare settings;2 the Grand Challenges in Global Mental Health that support a new generation of research;3 and the Movement for Global Mental Health itself.4 At national and local levels, we see concrete examples of countries making bold steps to develop mental health policies and plans to step up care;5 an impressive growth in the evidence base for treatments and delivery systems (eg, for children's mental health6
+and in humanitarian settings7); new programmes for building capacity;5 and an increasing presence of diverse stakeholder communities, particularly from low-income and middle-income countries, in leadership roles. In this context, we are delighted that 40% of the 52 authors in the Series are based in low-income and middle-income countries and another 15% are based in UN or international development agencies. In view of the need to involve diverse stakeholder communities, we are also pleased to note that more than a third of authors are drawn from outside academia, including representatives of user groups in low-income and middle-income countries. Exciting new evidence points to the effectiveness of task sharing with non-specialist and lay health workers to address the massive shortage of specialists.8 Scaling up such innovations will require a substantial redefinition of the role of specialist personnel which, in turn, will need the strong buy-in of the professional bodies that lead these specialists.5 In this context, we welcome the engagement of the World Psychiatric Association leaders with the challenges posed by the shortage of specialists.9
+However, there is still a long way to go, with many challenges to face. First and foremost, the issue of the human rights of people with mental health problems should be placed at the foreground of global health— the abuse of even basic entitlements, such as freedom and the denial of the right to care,10 constitute a global emergency on a par with the worst human rights scandals in the history of global health, one which has rightly been called a “failure of humanity”.11 People with mental health problems, particularly serious mental disorders and disabilities, who bear a disproportionate burden of human rights abuses, should be empowered to ensure a life of dignity. Second, health systems need increased resources to scale up care. Budgetary allocations for mental health care are still grotesquely out of proportion to the burden posed by mental health problems, resulting in slow progress in scaling up of care.5 Furthermore, there is a need to ensure that the increasing resources for developing mental health services account for the unique needs of people who are particularly vulnerable, notably children and those affected by serious mental disorders and disabilities. The mental health needs
+of children and adolescents, a demographic group comprising more than a third of the global population, have been neglected, even though addressing their needs might alleviate suffering, improve educational attainment in childhood, and potentially reduce the burden of mental disorders in adulthood.6 Third, much is still to be learned about how to deliver effective treatments in the real world. The mhGAP guidelines should become the standard approach for all countries and health sectors; irrational and inappropriate interventions should be discouraged and weeded out. Use of scarce resources for ineffective treatments and inefficient models of care is unacceptable. Delivery of effective treatments depends crucially on the development of human resources, especially among the frontline health workforce, often using innovative solutions.8 Furthermore, a review of research into interventions that can break the vicious cycle of poverty and mental health problems has shown evidence that effective mental health interventions can lead to a reduction in poverty.12 We need to ensure that all development assistance for global health specifically tracks mental health-related funding and assesses the effect of development activities on mental health problems in the population.12 Fourth, natural disasters and conflicts provide not only a high need but also a unique opportunity to scale up care to the affected population.7
+This new Series on global mental health reaffirms our conviction that the provision of appropriate mental health services is intrinsic to the development of prosperous, humane societies worldwide.
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diff --git a/risk factor for suicide.txt b/risk factor for suicide.txt
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+Suicidal thoughts and behaviors (STBs) are major public health problems that have devastating impacts on individuals, families, and communities. Suicide is among the leading causes of death worldwide, accounting for more than 40,000 annual deaths in America (Centers for Disease Control and Prevention [CDC], 2014) and an
+estimated one million annual deaths across the globe (World Health Organization [WHO], 2012). To put this in perspective, suicide accounts for more annual deaths than homicide, AIDS, car accidents, and war (CDC, 2014; WHO, 2012). These suicide deaths are in addition to an estimated 25 million annual suicide attempts (Crosby,
+Gfroerer, Han, Ortega, & Parks, 2011) and 140 million annual suicide ideators worldwide (Borges, Angst, Nock, Ruscio, & Kessler, 2008). Unfortunately, the STB problem does not seem to be improving. Suicide rates have not declined appreciably in many decades (CDC, 2014; McKeown, Cuffe, & Schulz, 2006; Nock et al., 2008) and large national surveys indicate a similar pattern for suicide ideation, plans, and attempts (Kessler, Berglund, Borges, Nock, & Wang, 2005). The scope, severity, and consistency of this problem has prompted decades of research devoted to understanding how STBs work, how to accurately predict STBs, and how to best treat and prevent STBs.
+Risk factors are a critical component of each of these branches of research. As noted by the World Health Organization (2012),
+The identification of risk and protective factors is a key component of a national suicide prevention strategy, and can help determine the nature of type of interventions required. Risk factors, in this context, are indicative of whether an individual, a community or a population is particularly vulnerable to suicide. (p. 13)
+The present meta-analysis of risk factors for STBs may accordingly inform suicide theory, prediction, and treatment.
+Risk Factors and Theories
+Many different theories of suicide have been proposed over the last century. These include biological (e.g., Oquendo et al., 2014) and sociological approaches (e.g., Durkheim, 1897), and psychological theories that conceptualize suicide as a phenomenon related to the following: psychache (Shneidman, 1993); escape from aversive self-awareness (Baumeister, 1990); hopelessness (e.g., Beck, Steer, Kovacs, & Garrison, 1985); emotion dysregulation (Linehan, 1993); perceived burdensomeness, thwarted belongingness, and capability for suicide (Joiner, 2005; Van Orden et al., 2010); defeat, entrapment, and low social support (Williams, 2001); various diathesis-stress models (e.g., Mann, Waternaux, Haas, & Malone, 1999; O’Connor, 2011; Wenzel & Beck, 2008); and “ideation to action” frameworks (see Nock, Kessler, & Franklin, 2016 for a discussion), among several others. Each of these approaches is actively researched, with several relevant publications each year.
+Such diversity is healthy for a young field, but may not be a good sign for the suicide research field, which has been around since at least Durkheim (1897). In many other fields, a broad set of early theories gives way to a dominant paradigm (or a small set of paradigms) that eventually shifts to new paradigm to account for anomalous findings (see Kuhn, 1962/2012). The current theoretical diversity of the suicide research field means that it is still in a preparadigmatic phase. Each STB theory specifies a unique set of risk factors (or specifies a unique relation among a set of risk factors) that drive STBs; each of these theories (and by extension, each set of risk factors) cannot completely explain STBs. It is therefore likely that some of these theories are largely inaccurate, others are partially accurate, and still others may only apply to specific populations or situations. For the field to progress to a paradigmatic phase, empirical data must be employed to winnow the accurate theories or accurate theory elements from the less accurate theories. The present meta-analysis can facilitate this winnowing process by: (a) determining whether the risk factor data necessary for theory evaluation exists; and (b) if so, testing the existing risk factor literature to ascertain which theories or theory elements are most promising.
+Risk Factors and Prediction
+As described in more detail in the next section, STB risk factors are essentially longitudinal predictors of STBs. Each day, thousands of health care professionals are tasked with predicting whether their patients will engage in STBs in the future (especially the near future). In addition, many nonprofessionals may encounter friends or family members who may seem to be at elevated risk for STBs. The primary aim of most studies in the STB risk factor literature is to improve the prediction of STBs by identifying a predictor (or set of predictors) that professionals and nonprofessionals can use to detect STB risk. Many studies conclude that they have identified strong STB risk factors that should inform STB prediction and treatment.
+For example, Beck, Brown, and Steer (1989) concluded that,
+The results of the present study . . . extend the evidence for the importance of clinical ratings of hopelessness for the prediction of eventual suicide. Furthermore, because hopelessness can be reduced fairly rapidly by specific therapeutic interventions, the assessment of hopelessness can potentially improve the prevention as well as the prediction of suicide. (p. 310)
+Likewise, Coryell and Schlesser (2001) suggested that, “In efforts to predict and prevent suicidal behavior in patients with major depressive disorder, HPA-axis hyperactivity, as reflected in DST results, may provide a tool that is considerably more powerful than the clinical predictors currently in use” (p. 748). Similarly, Chronis-Tuscano et al. (2010) noted,
+All subtypes of ADHD in young children robustly predict adolescent depression and/or suicide attempt 5 to 13 years later. . . . Identifying high-risk young children with ADHD sets the stage for early prevention trials to reduce risk for later depression and suicidal behavior. (p. 1044)
+To aid both professionals and nonprofessionals in accurately identifying STB risk, several organizations have summarized such information from the STB risk factor literature to produce risk factor guidelines (see Table 1). These guidelines typically include lists of both risk factors and “warning signs” for STBs, and usually conceptualize risk factors as more distal risk indicators and warning signs as more proximal risk indicators (cf. Rudd et al., 2006). However, there is a substantial overlap between these categories, with some organizations listing “multiple warning signs” as a risk factor (see Table 1). Consistent with more general taxonomies of risk (Kraemer et al., 1997; see next section), the present metaanalysis conceptualizes both “risk factors” and “warning signs” as risk factors.
+Although these guidelines are likely helpful, there is much room for improvement. Most of these guidelines are long lists of relatively nonspecific factors (see Table 1). Taken together, these guidelines indicate that any individual with nearly any type of mental illness (i.e., internalizing, externalizing, psychotic, or personality disorder symptoms), serious or chronic physical illness, life stress (e.g., social, occupational, or legal problem), special population status (e.g., migrant, prisoner, nonheterosexual), or access to lethal means (e.g., firearms, drugs, high places) may be at risk for STBs. A large proportion of the population possesses at least one of these factors at any given time, with many people possessing multiple factors. This lack of specificity makes it dif-
+ficult for professionals and nonprofessionals to accurately predict who will engage in future STBs. Moreover, there is some inconsistency across guidelines from different organizations, making it unclear which guidelines should be followed. For example, some organizations emphasize the importance of both depression and substance abuse, others only emphasize depression, and still others do not mention depression. Similarly, the World Health Organization (2015) advises that, “by far the strongest risk factor for suicide is a previous suicide attempt,” but no other organizations make a similar claim.
+All of these guidelines were developed primarily from expert recommendations based on qualitative reviews of the STB risk factor literature. Because of this literature’s size and many conflicting findings, there are likely to be many inconsistencies across qualitative expert reviews. The present meta-analysis may be able to provide a quantitative basis for STB guidelines. This could establish a standard set of guidelines, inform whether there should be separate guidelines for different populations (e.g., adults vs.
+youths), and provide information about how to best distinguish between distal and proximal predictors (i.e., “risk factors” vs. “warning signs”).
+Risk Factors and Treatment
+A large number of treatment approaches have been applied to STBs; unfortunately, few of these have been shown to consistently reduce STBs relative to a control group (for recent reviews, see: Brown & Jager-Hyman, 2014; Glenn, Franklin, & Nock, 2015; Ward-Ciesielski & Linehan, 2014). As with many mental illness treatments, STB treatments have not grown out of a large corpus of empirical data on the causal risk factors that drive these thoughts and behaviors. Rather, these treatments coalesced from clinical experiences, originated from a particular STB theory, or were developed for a separate phenomenon (e.g., depression) but applied to STBs. By determining which risk factors are most promising, the present meta-analysis may help to provide an em
+pirical foundation for STB treatments. In other words, extant STB treatment targets were derived from a top-down approach; the present meta-analysis may provide the basis for a bottom-up approach to identifying STB treatment targets.
+After 50 years of STB risk factor research and little progress toward the field’s most important goal—the reduction of STBs on a large scale—a comprehensive review of this literature would be helpful. Such a review would serve to summarize how this field has progressed, identify crucial gaps in knowledge, and establish the best way forward for future research. To address this need, the primary purpose of the present paper was to metaanalyze existing STB risk factor studies. To ensure clarity, we will start by defining what we mean by the term “risk factor.”
+What Are Risk Factors?
+As in many other fields (see Kraemer et al., 1997), the STB field has often been inconsistent and imprecise with how it has used terms such as correlate, risk, and risk factor. Confusion about these terms can lead to miscommunication between researchers, unfounded assumptions about how suicidal behavior works and how to best predict it, and misguided research, treatment, and policy decisions about suicide. To avoid such confusion in the present meta-analysis, we first note how we defined these terms.
+We subscribe to the risk factor typology described by Kraemer et al. (1997). According to this typology, a correlate is a factor that is associated with another factor. Correlates can have a range of associations with a factor, and specific types of research designs are necessary to determine the specific nature of the association. For example, if a study found that people who attempted suicide tended to display depression symptoms, this could indicate that depression symptoms are a correlate of suicide attempts; however, it would be unclear how or why depression symptoms and suicide attempts were correlated.
+A risk factor is a special type of correlate that precedes the outcome of interest and can be used to divide the population into high- and low-risk groups. Continuing the example, if people with depression symptoms (at Time 1) were more likely than others to attempt suicide (at Time 2), depression symptoms would be a risk factor for suicide attempts. Cross-sectional studies are sufficient to establish correlates or concomitants, but longitudinal studies are necessary to identify risk factors. Within our example, if thousands of cross-sectional studies showed that depression was a strong correlate of suicide attempts, it would be tempting to consider depression a powerful risk factor and to make this factor a centerpiece of theories, risk assessments, and treatments. But without evidence from longitudinal studies, there would be no empirical justification for considering depression a risk factor or for integrating this factor into theory and practice. As such, the present meta-analysis included only longitudinal studies.
+A causal risk factor is a special type of risk factor that is identified when the manipulation of a risk factor systematically changes the probability of the outcome of interest. Further continuing our example, if experimentally decreasing (or increasing) depression symptoms at Time 1 altered the probability of suicide attempt at Time 2, depression symptoms could be considered a causal risk factor. By this definition, there is very little existing research on the causal risk factors for STBs. As a result, the present meta-analysis will be most helpful for distinguishing STB risk factors from well-established STB
+concomitants. Promising risk factors may then be tested as possible causal risk factors in future studies. The distinction between these three terms is crucial because causal risk factors are predictors and valuable treatment targets; noncausal risk factors are predictors, but less effective treatment targets; and correlates/concomitants may be poor predictors and ineffective treatment targets. Mistaking one type for another can have major consequences for STB theory, research, and practice.
+Kraemer et al. (1997) also argued that it is important to evaluate risk factors based on clinical significance rather than statistical significance. Many studies in the STB risk factor literature include thousands or even millions of participants; in these instances, it is difficult not to detect a statistically significant effect. There are no widely accepted objective criteria for assessing the clinical significance of a risk factor, but one potentially helpful guideline is to estimate how the magnitude of a given risk factor might translate into population-level risk. For example, approximately 20 of every 100 people in the United States are annually infected with influenza (CDC, 2015). If a particular risk factor (e.g., age, location) tripled the risk of infection, many people may consider it clinically significant because it would signify that an individual with this characteristic would be more likely than not to develop the infection.
+Carrying this idea forward to suicide risk, approximately 0.013 of every 100 people in the United States died by suicide in 2013 (CDC, 2014). In contrast to the influenza example, many people may not consider a factor that tripled suicide risk to be clinically significant because it would only increase this rate to 0.039 per 100 people. In other words, even if someone possessed a characteristic associated with a 300% increase in the 1-year likelihood of suicide death, in absolute terms they would still have a near-zero risk of dying by suicide that year. Given the higher base rates for suicide attempts (approximately 0.33 per 100 people) and suicide ideation (approximately 2 per 100 people), a threefold risk would be more meaningful— but still weak—for these less severe outcomes.
+Any attempts to establish specific criteria for a clinical significance of STB risk factors would be highly subjective. In the present paper, we will estimate how the present findings might translate into 1-year population-level risk for STBs and leave it to the individual reader to determine subjective clinical significance. As very rough guidelines based on epidemiological data and large national studies (Borges et al., 2008; CDC, 2014; Crosby et al., 2011; Kessler et al., 2005), we estimate here that to increase the 1-year risk of a given outcome to 10%, a factor would need to approximate an odds ratio of 750 for suicide death, 30 for suicide attempt, and 5 for suicide ideation. Although it is important for clinicians to be aware of a ~10% risk of suicidal behavior over a long period of time (e.g., a year, decade, or lifetime), clinicians are often asked to determine whether a given individual has a greater than 50% chance of suicide death or attempt over the course of a day, week, or month. To meet such criteria, odds ratios in the present meta-analysis would need to be in the hundreds or thousands.
+The Present Meta-Analysis
+A meta-analysis of the STB literature is necessary because it is difficult for a single study to provide an accurate estimate of the true magnitude of a risk factor. There is often a wide variation in effect magnitude across studies. For instance, many studies have found that depression is a relatively strong STB risk factor (odds
+ratios > 10.0; e.g., Kishi & Robinson, 1996; Wolk, Weissman, & Puig, 1996) but several others have found that depression is not a risk factor (odds ratios < 1.0; e.g., Beck, Steer, & Trexler, 1989; May, Klonsky, & Klein, 2012). A meta-analysis can reconcile such divergent findings. Similarly, within a narrative review it is difficult to make sense of findings from studies with a wide range of methods and sample types. A meta-analysis can quantify the effects of these variations on risk factor magnitude. Accordingly, we conducted the present meta-analysis to address several broad questions about STB risk factors.
+First, what are the basic characteristics of the STB risk factor literature? We examined a range of characteristics, including the following: (a) the number of papers and risk factor tests across the decades of suicide research; (b) characteristics of study samples such as psychopathology severity, age, and number of STB participants; (c) the frequency of various broad risk factor categories; (d) the frequency of outcome types (i.e., ideation, plans, attempts, deaths); (e) variation in follow-up length across studies; (f) the overall predictive ability of the literature (measured in terms of odds ratios, hazard ratios, and diagnostic accuracy statistics); (g) potential publication bias; and (h) study quality based on factors like retention rates. This information may reveal patterns and gaps in the literature that help to establish important future directions for the field.
+Second, has predictive ability (i.e., risk factor magnitude and accuracy) improved across the decades of suicide research—why or why not? We hypothesized that suicide risk factor research has been progressive—later studies have built on earlier studies, knowledge has accrued over time, and the ability to predict STBs has steadily improved. If this hypothesis is supported, we will examine which factors appear to be most promising and attempt to gain insight into how the field might accelerate this rate of improvement. But if predictive ability has not improved, we will attempt to determine why this has been the case and what might be done to place the field on a more progressive path.
+Third, does predictive ability vary by outcome type? One possibility is that higher base rates translate into better prediction, with risk factor magnitude and accuracy improving from suicide death to suicide attempts to suicide ideation. Yet it is also possible that more severe outcomes (i.e., suicide death) are associated with more distinctive risk factors, making it easier to predict more severe outcomes. Related to this question, we were curious whether outcome characteristics such as definition and assessment strategy affected predictive ability. It is possible that more stringent definitions (e.g., explicit inclusion of “suicide intent” for suicide attempts) and more thorough assessments improve predictive ability. It could also be that such outcome characteristics improve the reliability and validity of results but have little bearing on the strength of prediction.
+Fourth, are longer follow-up intervals associated with improved predictive ability? Follow-up intervals in this literature range from several days (e.g., one week in Britton, Ilgen, Rudd, & Conner, 2012) to several decades (e.g., 76 years in Lahti et al., 2015). Given the low base rates of STBs, it is possible that longer follow-up intervals provide stronger prediction because they allow for more events to accumulate. But it is also possible that many factors only confer increased risk for STBs for a short period of time, such that prediction is more accurate over shorter intervals.
+For example, losing one’s job may sharply increase risk of STBs for a few days or weeks, but not for years or decades. Whatever the pattern of results, these findings may help to establish empirical guidelines for the follow-up intervals of future studies.
+Fifth, does predictive ability vary by sample characteristics? In the present meta-analysis, we analyzed sample types in three general ways: sample severity (general vs. clinical vs. prior STB sample reference groups); sample age (adolescent vs. mixed vs. adult samples); and sample size (number of STB participants). In terms of sample severity, we hypothesized that predictive ability would improve as the sample reference group became less severe. This is because differences between participants with STB outcomes and participants without those outcomes (i.e., reference participants) increase as samples become less severe. In effect, general community sample studies represent “extreme groups” designs that may generate larger effect sizes. We further examined the potential impact of clinical sample type (e.g., depression, psychosis), STB sample type (e.g., ideation, attempt), and clinical/ STB sample origin (e.g., inpatient, community) on predictive ability. Regarding sample age, it was unclear whether predictive ability varies across adult, mixed, and adolescent samples. Within sample size analyses, we hypothesized that a larger number of participants with a history of STBs would allow for a larger number of observed STBs and would produce more reliable prediction. But it was unclear whether this more reliable prediction would translate into stronger or weaker prediction.
+Sixth, we had several questions about broad STB risk factors categories (see Table 2 for a list of these categories). Some questions centered on the composition of these risk factors— has the literature tested a diverse array of risk factors or settled on a narrow set of factors? Have these risk factors become more heterogeneous or homogeneous over time, or have these factors clearly shifted along with the introduction of new theories? Other questions concerned the strength of risk factors— do any risk factors stand out as particularly strong or weak? Are the strongest risk factors the ones that have received the most attention from researchers, or has there been a discordance between risk factor strength and popularity? A final set of questions were related to the possibility that each STB outcome may be associated with a unique set of risk factors. Researchers have long been interested in such questions, particularly the possibility that certain factors may predict the transition from suicide ideation to suicide attempt or death (e.g., Kessler et al., 1999). Because of the methodological limitations of most existing studies, the present meta-analysis was unable to directly address questions about such transitions; however, we examined whether predictive ability differed by outcome and whether any broad categories or subcategories of risk factors were uniquely associated with an outcome.
+Seventh, is there any evidence for protective factors within this literature (i.e., broadly, factors that are associated with fewer STBs)? Many fewer studies have focused on this important complement to risk factors. The present meta-analysis examined the basic descriptive features of protective factors within this literature and estimated the magnitude of protective factor effects.
+Through addressing these questions, the present meta-analysis has the potential to provide new insight into STBs and to map out important future directions for research.
+Method
+Inclusion and Exclusion Criteria
+The aim of the present meta-analysis was to provide a broad overview of risk factors for suicidal thoughts and behaviors. All potentially eligible studies were examined for risk (or protective) factor tests (hereafter, “effect sizes”). We define an effect size as any instance where a particular factor was used to longitudinally predict a relevant outcome; as described below, most studies included multiple effect sizes (i.e., examined several risk or protective factors within the same study). The following criteria were established to select relevant effect sizes.
+Language. Only English language articles were included.
+Outcome specificity. Eligible effect sizes had to predict a specific form of suicidal thought or behavior; namely, suicide
+ideation, suicide plan, suicide attempt, or suicide death. This criterion was necessary because we aimed to provide a metaanalysis of all suicide-related outcomes (vs. potentially nonsui-cidal) and because there are important ontological and empirical distinctions among specific STBs (see Nock, 2010). Effect sizes predicting outcomes such as nonsuicidal self-injury, deliberate self-harm, and mixed outcomes (e.g., accidental and suicide death) were excluded. However, these latter factors were eligible to be included as predictors.
+Outcome specificity was complicated by the tendency for different researchers use different criteria to determine what did and did not qualify as a specific type of suicidal thought or behavior. These variations reflect the fact that, as with most other psychological constructs, there are no universally accepted definitions of the specific forms of suicidal thoughts and behaviors. To account for variations in definitions across studies, we coded several features of each outcome for each effect size (see below) and tested whether these variations impacted results. We excluded studies that clearly were not consistent with general definitions of suicidal thoughts and behaviors, which we operationalized in accordance with definitions of Nock et al. (2010, p. 342). Specifically, suicide ideation was defined as “thoughts of killing oneself”; suicide plan as “consideration of a specific method through which a person intends to kill oneself”; suicide attempt as “engagement in potentially self-injurious behavior in which there is some intent to die from the behavior”; and suicide death as “death that results from intentionally self-injurious behavior.”
+Slight variations on these definitions were allowed (but coded and tested for their impact on results); however, many definitions clearly assessed qualitatively different phenomena and were excluded. For example, several studies were excluded because they included deaths classified as “undetermined” as suicide deaths.
+Longitudinal prediction. Because risk factors must precede outcomes (Kraemer et al., 1997), only effect sizes that longitudinally predicted a relevant outcome were included. Most qualifying studies included longitudinal designs (i.e., measuring the risk factor before the outcome), but a few studies were able to establish longitudinal associations from cross-sectional designs (e.g., associations between serotonin transporter polymorphisms and suicide attempts; Bellivier et al., 2000).
+Nontreatment studies. Because we did not aim to provide a meta-analysis of STB treatments and because treatment effects may influence risk factor effects, we excluded effect sizes that occurred within studies where treatment was the primary aim. Predictors relevant to prior treatment history in a general sense (rather than within a specific study that tested a particular intervention) were included.
+Effect size independence. The present meta-analytic strategy assumes that all effect sizes are independent of one another. However, because many studies produced multiple effect sizes and some of the predictor variables for these effect sizes may be correlated (e.g., measures of depression and anxiety), it is likely that there is moderate dependence among some effect sizes in this meta-analysis. Simulation studies indicate that not accounting for dependence when it exists has a minimal impact on pooled effect estimates, but leads to slight underestimations of variance and confidence intervals, thereby increasing Type I error (e.g., Thompson & Becker, 2014). The best way to model dependence is to account for the covariance structure in each study; unfortunately,
+this level of information is rarely presented in research articles. Fortunately, several other strategies for handling this issue exist. In the present meta-analysis we took the following steps to reduce dependence and to model the potential effects of dependence.
+First, to reduce dependence and avoid redundancy, we excluded any effect sizes that were published two or more times. This occurred when the same effect sizes from the same dataset were reanalyzed across multiple publications. Such instances were easy to detect when data sets were from large studies such as the National Longitudinal Study of Adolescent Health (n = 12 included studies) and the Baltimore Epidemiologic Catchment Area study (n = 7 included studies). Repeated dataset use was more difficult to detect in smaller studies because these papers often did not note when prior papers had been published on the same or partially overlapping dataset. Notably, this exclusion criterion only applied to effect sizes reported multiple times across multiple publications. This means that the same dataset could produce multiple publications that met inclusion criteria for the present meta-analysis if each publication included unique effect sizes. Second, several studies also reported the same effect sizes across multiple time points. In these instances, we only included the effect sizes from the farthest time point because it was the most inclusive. We note here that patterns and statistical significance were the same when other time points or all time points were included in analyses.
+Third, to model the potential effects of dependence in this meta-analysis, we conducted analyses that assumed and accounted for complete dependence among effect sizes within each study. Specifically, we averaged all effect sizes within each study (see Scammacca, Roberts, & Stuebing, 2014). Although these analyses assume far greater dependence than likely exists in this metaanalysis, they are useful in that they provides an extreme upperbound of the potential effects of dependence on our results. Given space limitations and the minimal differences between estimates produced by this method and our primary method, we only present results for overall pooled estimates for each outcome.
+Main effects tests. We had hoped to include information about interactions among risk factors, but—precluding reliable meta-analyses—these tests were both rare and idiosyncratic (i.e., the same interaction was seldom examined in more than one study). As a result, effect sizes that directly tested interaction effects were not included in the present meta-analysis. We note here that these interaction effects typically produced magnitudes that were similar to main effects. We had also hoped to examine differences between static risk factors (i.e., factors assessed at one moment in time) and time-varying risk factors (i.e., factors that change during a study). However, only two qualifying studies included time-varying risk factors (n = 4 risk factor effect sizes; Keilp et al., 2010; Morrison & O’Connor, 2008), both of which examined change on a measure before and after an experimental manipulation. This low number of time-varying effect sizes is attributable in part to the fact that time-varying factors are usually most appropriately modeled with advanced statistics (e.g., latent growth curve modeling), especially across longer time periods. Unfortunately, as described below, these statistics could not be converted into one of the common metrics used in the present meta-analysis. This meant the exclusion of a few studies that examined time-varying risk factors. However, the major reason that the present meta-analysis does not include a large number of
+time-varying effect sizes, effect sizes from combinations of risk factor, and effect sizes from interactions is that the vast majority of existing STB risk studies have tested static factors in isolation.
+Necessary statistical and design information. The primary metrics for the present meta-analysis were odds ratios (i.e., relative odds of an event) and hazard ratios (i.e., relative frequency of an event over a certain time interval). Because most statistics can be converted into an odds ratio (see Meta-Analytic Methods section below), we were able to include effect sizes from a wide range of statistical tests. However, inclusion was not possible when an index of variance was not provided (e.g., beta weights with no additional information), certain statistical tests were used (e.g., standardized mortality ratio; latent growth curve modeling), or with certain design features (e.g., unclear or nonexistent comparison group or condition; qualitative outcome descriptions; unclear if STB outcome was assessed as a lifetime variable or since baseline variable). Effect sizes with these features were excluded. Another exception to odds ratio conversion was hazard ratios, but because 20.45% of effect sizes were reported as hazard ratios, we elected to include hazard ratio analyses in addition to odds ratio analyses.
+Published in print or online by January 1, 2015. We were primarily interested in the available scientific information on STB risk factors that researchers and clinicians use to inform theory and practice. As such, we only included published studies through January 1, 2015 (the earliest qualifying article was published in December 1965). We calculated several publication bias estimates to quantify and correct for this bias. As indicated by publication bias correction statistics (see Results below), the true risk factor effects were likely weaker than the published literature indicates. In other words, the present results should be regarded as slightly optimistic estimates of risk factor magnitude and accuracy.
+Literature Search, Papers, and Prediction Effect Sizes
+We conducted literature searches across PubMed, PsycInfo, Google Scholar, and the reference sections of all papers included in the present meta-analysis. Searches included permutations of several terms related to the words “longitudinal” and “suicidal thoughts and behavior.” These terms included: “longitudinal,” “longitudinally,” “predicts,” “prediction,” “prospective,” “prospectively,” “future,” “later,” “follow-up,” “followed,” “risk factor,” “risk,” “protective factor,” “protective,” “protect,” “genes,” “genetic,” “self-injury,” “suicidality,” “self-harm,” “suicide,” “suicidal behavior,” “suicide attempt,” “suicide death,” “suicide plan,” “suicidal thoughts,” “suicide ideation,” “suicide gesture,” “suicide threat,” “SITB,” “parasuicide,” “self-mutilation,” “self-cutting,” “cutting,” “self-burning,” “self-poisoning,” “deliberate self-harm” (DSH), “DSH,” “nonsuicidal self-injury” (NSSI), and “NSSI.” As shown in Figure 1, these searchers ultimately yielded 4,084 total effect sizes (after removal of outliers and redundant effect sizes) from 365 studies that were included in the present meta-analysis.
+Data Extraction and Coding
+Data extraction. Each paper was examined for relevant effect sizes. Each effect size that met inclusion/exclusion criteria was coded on all dimensions noted below. Codes were developed by the lead author in consultation with coauthors. Codes were com-
+pleted via an iterative process. The initial version of each code was completed by one of the authors with an advanced degree in psychology (i.e., masters- or doctoral-level). Each code was then independently checked for accuracy by two additional authors with advanced degrees in psychology. Any discrepancies were discussed and resolved such that all three authors agreed on the coding decision. All authors then reviewed each code; any discrepancies were discussed and resolved, with all authors agreeing on the final versions of each code for each effect size.
+Author, year, and era codes. The author and year of publication were recorded for each effect size. Era of research was coded in three separate ways: (a) actual year of publication; (b) 5-year intervals (starting with 2010-2014 and working backward to 2005-2009, 2000-2004, etc. until pre-1980 effect sizes, which were all included as a single interval due to few effect sizes in this era); and (c) 10-year intervals (starting with 2005-2014, and working backward to 1995-2004, 1985-1994, and pre-1985 effect sizes, which were coded as a single interval due the scarcity of effect sizes during this era). We ran analyses across all three coding schemes, but we elected to present only the 10-year interval findings because they provided the clearest depiction of results. We note here that patterns and statistical significance were the same across all three codes.
+Follow-up length codes. Follow-up length was coded in terms of the months of follow-up. When studies listed variable follow-ups, we included the longest-noted follow-up interval. Due to the skew of follow-up lengths, we divided these into six class intervals designed to categorize follow-up lengths into very short, short, medium, long, very long, and extremely long intervals. For ease of interpretation, these intervals are described in more detail in the Results section below. We conducted analyses across both raw and class interval coding schemes, but only present class interval code results. As with era of research analyses, there were no differences in terms of patterns or statistical significance across
+the two codes, and the class interval code permitted clearer presentation of the data.
+Sample severity code. Sample severity was coded based on the nature of the reference group for individuals with STB-related outcomes for a given effect size. If members of a reference group were selected on the basis of a prior self-injurious thought or behavior history, the sample was coded as “STB.” When at least some of the members of a reference group were selected on the basis of psychopathology (and the “STB” sample criterion was not met), the sample was coded as “clinical.” For instances where neither of these criteria was met, the sample was coded as “general.”
+Clinical sample type code. It is possible that the type of clinical sample influences risk factor magnitude. We accordingly coded each sample based on the primary type of diagnosis or symptoms for which participants were selected. Each sample with any participants selected for psychopathology were coded as one the following: (a) general psychiatric sample (i.e., multiple primary diagnoses or symptoms); (b) mood and anxiety disorders; (c) substance use disorders; (d) psychotic disorders; (e) eating disorders; (f) attention-deficit/hyperactivity disorder; (g) borderline personality disorder; or (h) other personality disorders.
+STB sample type code. It is also possible that the type of prior self-injurious thought and behavior influences risk factor magnitude. To test this possibility, we coded all samples with any participants selected for STBs as one of the following: (a) general suicidality and self-injury (e.g., parasuicide, deliberate self-harm); (b) prior suicide ideation; (c) prior suicide attempt; or (d) suicide death (for studies that selected suicide decedents for study post hoc).
+Clinical and STB sample origin. These two sample types were coded based on the locations from which participants were recruited. Specifically, samples were coded as inpatient, outpatient, mixed inpatient and outpatient, or community.
+Sample age code. Sample age was coded based on the age range of the sample at the baseline time point of the effect size. When all participants were below age 18, the sample was coded as “adolescent,” and when all participants were 18 and above, the sample was coded as “adult.” In cases where participants were both above and below 18, the sample was coded as “mixed.”
+STB sample size code. Sample size is a potentially important variable because larger studies typically record more STBs and accordingly provide more precise and reliable results. However, there were several population level studies (with millions of participants) that recorded a very low proportion of STB-related events and several smaller studies that recorded an extremely high proportion of STB-related events. In other words, the number of participants with STB-related events at any point during the study was the most relevant aspect of sample size for the present metaanalysis. We present analyses based on this STB-related sample size, but note here that analyses based on overall sample size provided very similar results.
+General outcome code. Outcomes were coded as one of the following categories: suicide ideation, suicide plan, suicide attempt, or suicide death. As described above, effect sizes that could not be classified cleanly into one of these categories were excluded. In addition to this code, we additionally included codes that assessed outcome characteristics including the nature of the outcome definition, outcome assessment type, and whether suicide attempt outcomes were initial or repeated attempts (see below).
+Outcome definition codes. The goal of these codes was to account for variations in acceptable outcome definitions. We employed a slightly different code for each outcome.
+For suicide ideation outcomes, we were particularly concerned about differentiating between passive and active ideation. Moreover, many common suicide ideation outcome measures include a mix of passive and active suicide ideation items. We coded suicide ideation outcomes as (a) definition not provided, (b) passive, (c) mixed passive and active, or (d) active. For suicide attempt outcomes, we were primarily interested in assessing suicide intent. As noted above, studies were excluded if their attempt definitions clearly indicated that intent was not necessary. For the included studies, suicide attempt outcomes were coded as (a) definition not provided, (b) intent implied but not explicitly stated, or (c) intent explicitly stated. For suicide death outcomes, we coded whether studies only included suicide deaths within their suicide death outcomes. As noted above, several studies (primarily large European studies) were excluded because they explicitly mixed suicide deaths and undetermined deaths (i.e., explicitly noted that outcomes were ICD codes for suicide death and death from acciden-tal/undetermined causes). Within included studies, we coded suicide death outcomes as (a) definition not provided; (b) did not explicitly include or exclude ambiguous deaths; and (c) explicitly excluded ambiguous deaths.
+Outcome assessment codes. There are many different ways to assess STBs, and it is currently unclear if the type of assessment impacts risk factor magnitude. For suicide ideation and attempt outcomes, assessments were coded as (a) assessment type not provided, (b) single-item assessment, or (c) multi-item question-naire/interview. For suicide death outcomes, assessments were coded as (a) assessment type not provided, (b) family report, or (c) official legal/medical report (e.g., death certificate).
+Suicide attempt repetition outcome code. It is possible that risk factors for an initial suicide attempt differ from risk factors for subsequent attempts. We accordingly coded suicide attempt outcomes as (a) unclear if attempts were initial or repeated; (b) repeated attempts; (c) mix of initial and repeated attempts; and (d) initial attempts.
+Risk and protective factor codes. The majority of predictors could be reasonably hypothesized to increase risk for future suicidal thoughts or behaviors (i.e., risk factors), but there were several exceptions and complications to this general rule that required attention. A smaller proportion of predictors met our broad definition of a protective factor. A few predictors met classic definitions of protection or resilience (e.g., high omega-3 intake), but many others simply indicated the absence of risk (e.g., “no psychopathology”), a relatively low degree of risk (e.g., rarely drinks alcohol vs. drinks alcohol daily), or potentially lower risk in certain circumstances (e.g., male gender). We included all of these types of effect sizes within a broadly defined category of protective factors.
+Categorization was complex for effect sizes that were associated with risk in certain circumstances and (broadly defined) protection in others. For example, suicide attempts are not associated with age in a clear linear pattern—attempts peak in late adolescence and early adulthood (Nock et al., 2008). Complicating matters, there was high heterogeneity among age grouping across studies, with some studies using age as a continuous variable and many others comparing idiosyncratic age group intervals (e.g., 18-24) to other idiosyncratic age group intervals (e.g., 55-64). In all such instances (not just with age), we drew on the best available information to determine whether the reference group would be expected to possess greater or lesser risk for a specific suicide-related outcome (e.g., CDC, 2014; Kessler et al., 2005; Nock et al., 2008). If the reference group was expected to have lesser risk, the effect size was coded as a risk factor; if the reference group was expected to display greater risk, the effect size was coded as a protective factor. For example, if an 18 -24 age group was the reference for a 55- 64 age group predictor, the effect size would be coded as a protective factor if suicide attempt was the outcome (fewer attempts would be expected in the 55- 64 age group) and a risk factor if suicide death was the outcome (more deaths would be expected in the 55- 64 age group).
+Broad risk/protective factor category codes. Across the 4,084 total effect sizes, there were thousands of predictor types— too many for the present paper to examine individually or even as moderately specific category types. To permit intelligible analyses, we classified each individual predictor into one of 16 broad categories. Table 2 lists these categories and provides general examples of the types of predictors included within each category. Effect sizes were also classified into much smaller subcategories, but space limitations precluded the full presentation of these analyses. Moreover, in terms of overall risk factor magnitude, these subcategory analyses did not provide much additional information. As described in the Results section below, no subcategory was substantially stronger than any broad risk factor category.
+Adjusted and unadjusted estimate code. We attempted to include zero-order effects (i.e., unadjusted effects) for each effect size to provide the purest estimation of effects. This was possible for most effect sizes, but not all. Effect sizes were coded based on whether their effects were adjusted or unadjusted, and analyses
+were conducted to examine the effects of statistical adjustment (see below).
+Predictor scale code. Odds and hazard ratios determine the change in odds/hazard for a given outcome per each unit of change in the predictor variable. For example, if the predictor variable is dichotomous (depression diagnosis vs. no depression diagnosis), there would be one unit of change. If the predictor variable was continuous (e.g., scores on the Beck Depression Inventory), there would be many units of change. These scale differences do not impact the statistical significance of results (because standard errors shift with this scaling), but they do affect the effect size magnitudes. Odds/hazard ratios tend to become smaller as the number of units of change increases because the differences between units becomes less extreme (e.g., the difference between Beck Depression Inventory scores of 16 and 17 vs. the difference between having/not having a depression diagnosis). To account for these statistical artifacts, we coded the scale of each predictor variable as (a) dichotomous or (b) continuous.
+Outlier code. Outliers were coded as any values above three standard deviations beyond the unweighted mean of the overall odds ratio magnitude (uwOR = 2.53, SD = 5.01, cutoff = 17.62; n = 43 outliers) or overall hazard ratio magnitude (uwHR = 2.26, SD = 2.78, cutoff = 10.60; n = 20 outliers). These 63 outliers accounted for just 1.54% of all effect sizes. We note here that inclusion of these effect sizes did not affect the general patterns or statistical significance of the reported results.
+Study quality. Meta-analyses often code for study quality when they include a high degree of methodological variability (e.g., wide range of study designs and outcomes) that may impact conclusions. Such codes are especially common among metaanalyses of treatments because factors like study design (e.g., single-group, case-control, double-blind), sample type (e.g., severity, representativeness), treatment provision (e.g., fidelity, compliance), and outcome type (e.g., anxiety symptoms, alcohol use) may powerfully influence results. Relative to many other metaanalyses, however, the present meta-analysis included a highly uniform set of studies. All were required to share a common core design (i.e., longitudinal) and all were required to include a specific STB outcome. Of course, smaller methodological differences existed among the present set of studies (e.g., follow-up length, sample severity, sample age, sample size), but there are no objective criteria for assessing how variations among these factors are related to study quality.
+Although there are no objective criteria for study quality in this literature, we took four steps to gauge the impact of how various methodological features may impact risk factor magnitude. First, we conducted moderation analyses with all codes noted above to examine how variations in these factors may affect effect sizes. Second, we additionally coded each study for the following: (a) whether the study reported a recruitment rate; (b) whether the study reported information necessary to calculate a retention rate; and (c) the calculated retention rate of the study. Third, we similarly tested the effect of the following codes on risk factor magnitude in a simultaneous multiple meta-regression analysis: (a) study recruitment rate reporting; (b) study retention rate reporting; (c) outcome assessment code (described above); and (d) outcome definition code (described above). Fourth, within a separate metaregression analyses we tested the effect of the calculated retention rate on effect magnitude. This variable was included in a separate
+meta-regression because it only included the subset of effect sizes that reported information sufficient to calculate a retention rate.
+Meta-Analytic Methods
+General strategy. All analyses were conducted in accordance with our research questions. Most of these questions pertained to the general effect of a particular variable (e.g., study length, sample severity, sample age, outcome definition, etc.) on overall risk factor strength. Relevant analyses accordingly tested the effect of a particular moderator (e.g., study length) on the overall pooled estimate across risk factor types. Other questions concerned the general effect of a group of variables related to a particular construct (e.g., study quality) on overall risk factor strength. Analyses for these questions examined the combined effect of multiple variables on the overall pooled estimate across risk factor types within simultaneous meta-regressions. Still other questions focused on variations in risk factor magnitude across broad risk factor categories (e.g., internalizing psychopathology) or specific risk factor types (e.g., depression). We examined these questions by calculating and comparing risk factor magnitudes across broad risk factor categories and specific risk factor types.
+Programs. Diagnostic accuracy analyses were conducted with Meta-Disc 1.4 (Zamora, Muriel, & Abraira, 2014), a program designed specifically for meta-analysis of diagnostic accuracy statistics. All other analyses were conducted with Comprehensive Meta-Analysis 3.0 (Borenstein, Hedges, Higgins, & Rothstein, 2014).
+Random effects models. The present meta-analysis only used random effects models (see Borenstein, Hedges, Higgin, & Rothstein, 2009). Fixed effects models assume that there is one true effect and that all effect sizes of a given association approximate this effect. This assumption is rarely valid in meta-analyses, leading to inaccurate effect size estimates because of a high degree of heterogeneity between effect sizes due to methodological variations. This heterogeneity is quantified with a statistic called I2. Guidelines suggest that I2 values of 0% to 30% indicate low heterogeneity, 31-60% indicate medium heterogeneity, and 61100% indicate high heterogeneity (Higgins, Thompson, Deeks, & Altman, 2003). To justify the use of random effects models in the present meta-analysis, we provided I2 for major analyses.
+Random effects models estimate a distribution of effects rather than a single true effect, and accomplish this by estimating both between- and within-effect size variance (vs. only within-effect size variance with fixed effects models). As a result, potential between-effect sizes heterogeneity (e.g., population, methods, etc.) is factored into the calculation and weighting of each effect size. Random effects models are recommended over fixed effects models for virtually all types of meta-analyses. Given the high degree of heterogeneity in the present meta-analysis (see Results section), random effects analyses were especially necessary.
+Odds and hazard ratios. Odds and hazard ratios were two of the three primary metrics in the present analysis (diagnostic accuracy statistics were the other, see below). These results are presented in terms of weighted odds ratios (wORs) or weighted hazard ratios (wHRs). Statistical significance occurs for both of these metrics when an accompanying 95% confidence interval does not include 1.0. The meta-analytic programs automatically calculated within- and between-effect size variance to weight each effect size
+(see Borenstein et al., 2009). In general, the lower the variance for a particular effect size (i.e., larger studies, studies with more precise or consistent results), the greater the weight it received.
+Odds ratios are a ratio of the odds of an event in one group compared with another. For example, the odds of a suicide attempt in a group of females compared with a group of males. Most effect sizes were either reported in terms of odds ratios or converted into odds ratios by the Comprehensive Meta-Analysis program (see Borenstein et al., 2009). Specifically, the following data structures were able to be converted into odds ratios: Pearson productmoment correlations; t tests and their variants; Cohen’s d and its variants; means and standard deviations; chi-squared analyses; and 2 X 2 tables with rates or raw information. Hazard ratios are similar to odds ratios except that hazard ratio calculations integrate information from time intervals within the study. Practically, hazard ratios and odds ratios generally produce similar numbers and interpretations; nevertheless, given the special time-based features of hazard ratios, they cannot be converted into odds ratios.
+Diagnostic accuracy analyses. Although the odds and hazard ratio analyses are necessarily relevant to prediction, diagnostic accuracy provides additional dimensions for assessing predictive accuracy. Compared with odds and hazard ratio analyses, these statistics are less vulnerable to misinterpretations due to low base rates. These statistics are most often used in medical science to estimate the accuracy of diagnosing a specific condition (e.g., lung cancer). In the present meta-analysis, diagnostic accuracy refers to the correct ‘diagnoses’ of STB-related outcomes rather than diagnoses of medical conditions or psychiatric disorders.
+Accuracy analyses can illuminate specific strengths or weaknesses in prediction (e.g., low false positive rate, high false negative rate), providing greater clarity for clinical decisions and future research directions. Whenever possible, we obtained the raw data necessary to calculate diagnostic accuracy statistics. A total of 926 effect sizes produced sufficient information for these analyses. Each of these effect sizes was also calculated in terms of odds ratios, so we emphasize that these analyses produced a different dimension of information rather than completely new information. A meta-analysis of this information was conducted with Meta-Disc 1.4, which uses random effects models and weighting procedures similar to Comprehensive Meta-Analysis, but is specialized for diagnostic accuracy statistics.
+All diagnostic accuracy analyses are constructed from a 2 X 2 table where the four cells represent true positive, false positive, true negative, and false negative events. From these cells, sensitivity (true positive rate) and specificity (true negative rate) were calculated (Simundic, 2008; Zamora et al., 2014). From these statistics, area under the curve (AUC) was calculated by plotting each effect size along dimensions of sensitivity and 1 minus specificity. An AUC of .5 indicates chance prediction and an AUC of 1.0 indicates perfect prediction. Guidelines suggest that AUCs above .90 indicate excellent prediction, .80 to .89 good prediction, .70 to .79 fair prediction, .60 to. 69 poor prediction, and .50 to .59 extremely poor prediction (Simundic, 2008). Weighted AUC (wAUC) was the primary metric of diagnostic accuracy evaluation in the present meta-analysis, and weighted sensitivity and specificity were provided for major analyses.
+Publication bias. Publication bias estimates were included for overall analyses. Several statistics were calculated to estimate
+different aspects of bias (see Borenstein et al., 2009 for more information on each of these indices).
+Two types of fail-safe Ns were calculated; these statistics estimate the robustness of an observed effect. First, classic fail-safe N analyses determine how many studies with a null effect size (i.e., odds and hazard ratios of 1.0) would be needed to make the meta-analytic effect nonsignificant. However, some have pointed out that this analysis is limited because it relies on statistical significance (which is often a function of power) and null effects (unpublished studies may have odds or hazard ratios lower than 1.0). Second, Orwin’s fail-safe N addresses these issues by allowing the researcher to set the level of a ‘trivial effect’ (rather than statistical significance) and the effect size of missing studies (rather than 1.0). There is no objective way to set these levels, but in the present meta-analysis we wanted to ascertain the robustness of results if the true effect had been 1.0 and unpublished findings were the inverse magnitude of published findings. Accordingly, we set the trivial effect size to odds and hazard ratios of 1.0 and the effect size of missing studies to 1.0 minus the effect size above 1.0 of the observed fixed effect (e.g., if the observed fixed effect was 1.05, the missing study effect size was set to .95).
+We also examined bias in relation to funnel plots, which chart standardized effect size against variance around the observed meta-analytic mean. Compared with large studies, small studies are more likely to obtain extremely positive and extremely negative results. Because of publication bias toward publishing positive findings—especially extreme positive findings—pub-lication bias tends to produce larger effects from smaller studies. This can be visually examined within funnel plots. In the absence of publication bias, the funnel plot is symmetrical, with studies equally likely to fall above and below the mean regardless of study size. Publication bias produces asymmetry in this plot, as small studies with positive findings (especially large positive findings) are more likely to be published than small studies with negative findings.
+Because interpretations of funnel plots can be subjective, we calculated three indices of this asymmetry. First, Begg and Mazumdar’s rank order correlation investigates the association between standardized effect sizes (log odds or hazard ratios) and the meta-analytic weighting factors (i.e., primarily sample size). A significant negative correlation indicates that smaller studies are associated with larger effects, which suggests publication bias. Second, Egger’s test of the intercept regresses standardized effect sizes onto study precision (i.e., inverse of standard error). A significant positive intercept indicates that less precise studies are associated with larger effects, again suggesting publication bias. Third, Duval and Tweedie’s trim and fill analysis determines how many studies would need to be included below the meta-analytic mean to make the funnel plot symmetrical. A higher number of studies denotes greater publication bias. This analysis also imputes these missing studies and calculates a meta-analytic effect size that adjusts for publication bias.
+Meta-regression. Meta-regression is similar to traditional regression, except that meta-regression includes standardized effect sizes as the dependent variable and weights each independent variable effect size differently (as in a meta-analysis). In the present meta-analysis, we used meta-regression to analyze associ
+ations between independent variables (i.e., moderators) and standardized odds or hazard ratios. For clarity of interpretation, we present these results as wORs, wHRs, or wAUCs and 95% confidence intervals at each level of the independent variable. For example, rather than a single linear coefficient for the effect of sample severity type on effect sizes, we present a weighted mean and confidence intervals for each sample type—general, clinical, and STB. The two methods generate identical results, but the latter provides much more detailed information about specific data patterns.
+Analytic Strategy
+Overview of analytic strategy. The analytic strategy included three different phases. First, we provided a descriptive account of the literature. Second, we analyzed the overall ability of all available effect sizes to predict a given outcome. For example, we combined all predictors from all effect sizes with a suicide attempt outcome to produce a single aggregated estimate of how well all extant predictors—as a whole—predict suicide attempts. Third, we examined several potential moderators of the association between these predictors and a given outcome. As described above, the aim of moderation analyses to address specific questions (e.g., the effect of sample age on effect sizes) rather than to account for all variation among effect sizes. To maximize the presentation of data patterns, we provided weighted means and 95% confidence intervals at each level of each moderator.
+Overall description of the literature. We first calculated the overall descriptive statistics for the literature as a whole, including the number of studies and effect sizes across the eras of STB risk factor research. For clarity, all other descriptive information is included within the appropriate subsections (e.g., descriptive information about follow-up intervals in the follow-up length moderator subsection).
+Overall effect size for each outcome. To provide a general estimate of risk factor magnitude and accuracy, we conducted nonmoderated analyses for each outcome (all moderation analyses were also organized by outcome). As noted above, odds ratio, hazard ratio, and diagnostic accuracy analyses were conducted separately. For odds and hazard ratio analyses, we calculated overall estimates, confidence intervals, and 12. For diagnostic accuracy analyses, we calculated estimates and standard errors for sensitivity, specificity, and wAUCs.
+Overall publication bias for each outcome. For each overall outcome analysis, we calculated publication bias statistics. These included classic and Orwin’s fail-safe Ns, Begg and Mazumdar’s rank-order correlation test, Egger’s test of the intercept, and Duval and Tweedie’s trim and fill analysis. This latter analysis provided estimates of overall effects corrected for publication bias. This correction was not available for diagnostic accuracy estimates.
+Overall dependence effects for each outcome. Within models that assumed and accounted for complete dependence among effect sizes (i.e., averaged effect sizes within each study to produce one effect size per study), we recalculated the overall pooled estimates and primary publication bias estimates for each outcome.
+Simulation of prediction with base rates. Although overall analyses provide an index of how well risk factors predict STB outcomes beyond chance prediction (i.e., odds/hazard ratios of 1.0, diagnostic accuracy statistics of 0.50), many clinicians may con
+sider the low STB base rates when estimating STB risk. To provide an index of how well risk factors predict STB outcomes compared with base rate guessing, we conducted simulation tests. For suicide death, we created a simulation where there were 13 instances of suicide death across 100,000 person-years (i.e., the United States suicide death rate in 2013; CDC, 2014). We then randomly selected 13 of these 100,000 person-years to see whether any of them were one of the 13 predetermined simulation suicide death instances. In other words, each simulation used base rate guessing to predict suicide death. We then repeated this simulation 10,000 times. We conducted similar simulations for suicide attempt (assuming a prevalence of 325 per 100,000) and suicide ideation (assuming a prevalence of 2,000 per 100,000). From these simulations, we calculated sensitivity and specificity statistics and compared them to prediction by risk factors.
+Moderation by outcome characteristics. The effects of outcome definition and assessment strategy were calculated for each outcome; suicide attempt repetition status was additionally examined for suicide attempt outcomes effect sizes.
+Moderation by adjusted versus unadjusted estimates. Adjusted and unadjusted estimates were compared for each outcome.
+Moderation by predictor scale. Dichotomous predictors were compared with continuous predictors for each outcome.
+Moderation by era of research. Dividing the eras of suicide risk factor research into 10 year intervals, we calculated effect estimates for each era for each outcome.
+Moderation by follow-up length. We calculated effect estimates for each follow-up length class interval for each outcome.
+Moderation by sample characteristics. The effects of sample severity, clinical sample type, STB inclusion type, clinical/STB sample origin, age, and STB sample size on effect estimates were calculated for each outcome.
+Moderation by study quality. Separate meta-regressions tested the effect of (a) recruitment rate reporting, retention rate reporting, outcome definition type, and outcome assessment type, and (b) calculated retention rates on effect estimates for each outcome.
+Moderation by broad risk factor categories. We calculated effect estimates for each broad risk factor category across each outcome. To demonstrate that no moderately specific predictor category substantially deviated from overall estimates, we also calculated the top five predictors (in terms of wORs) for subcategories with effect sizes from at least five studies.
+Protective factor analyses. Similar to risk factor analyses, for protective factor analyses we calculated descriptive information, overall protective factor magnitude and accuracy estimation across each outcome, and moderation by broad risk factor categories.
+Results
+Overall Description of the Literature
+Number of papers across time. A total of 365 papers met inclusion criteria. The earliest paper was Motto (1965), but less than one qualifying paper per year was published over the ensuing 20 years. Only 3.29% of papers meeting criteria were published before 1985. The number of papers meeting criteria nearly doubled during each 10-year period after 1984 (see Figure 2). In fact,
+60.73% of qualifying papers were published in the last decade and more than half were published after 2006.
+Number of effect sizes across time. These 365 papers produced a total of 4,084 effect sizes. Of these, 495 effect sizes (12.78% of all effect sizes) were categorized as pertaining to protective factors (broadly defined) rather than risk factors. Protective factor, outlier, and redundant effect sizes were not included in risk factor analyses (see below for a specific section on protective factors); the total number of risk factor effect sizes analyzed was 3,428. Following the paper-based pattern described above, only 1.93% of these effect sizes were published before 1985, 64.57% were published after 2004, and more than 50% were published after 2007 (see Figure 2). Overall, there was an average of 11.22 effect sizes per paper, with effect sizes per paper doubling in the mid-1990s and holding nearly constant since. In short, most of the empirical information on risk and protective factors for STBs was published in the last few years.
+Overall Analytic and Publication Bias Information for Risk Factors
+Overall suicide ideation prediction and publication bias. For odds ratio analyses, there were 572 total suicide ideation effect sizes. There was extremely high heterogeneity among these effect sizes (12 = 90.52%), and these effect sizes generated an overall weighted mean odds ratio of 1.50 (CI: 1.47 to 1.54). Although fail-safe N analyses indicated that this was a robust nonzero effect, a Begg and Mazumdar rank correlation test and Egger’s test of the intercept suggested significant publication bias (see Table 3). These tests show that smaller and less precise studies were more likely to produce larger odds ratios than larger and more precise studies. Similarly, the funnel plot was highly asymmetrical, with Duval and Tweedie’s trim and fill analysis determining that 106 effect sizes below the mean were missing. If these effect sizes had been published and factored into analyses, it is estimated that the weighted mean
+odds ratio would have dropped to 1.30 (CI: 1.27, 1.34). Unfortunately, there were not enough effect sizes to conduct reliable hazard ratio or diagnostic accuracy meta-analyses for suicide ideation.
+Within models that assumed and accounted for complete dependence among effect sizes (i.e., models that averaged all effect sizes within each study, producing one effect size per study), the overall weighted mean odds ratio was 1.51 (CI: 1.45, 1.57). Duval and Tweedie’s trim and fill analyses indicated that 22 studies below the mean were missing and, if included, would have lowered the overall weighted mean odds ratio to 1.30 (CI: 1.25, 1.35).
+Overall suicide attempt prediction and publication bias. There were 1,281 odds ratio effect sizes with suicide attempt as an outcome. As with suicide ideation, there was very high heterogeneity among these effect sizes (12 = 86.09%). These effect sizes produced a weighted mean odds ratio of 1.51 (CI: 1.49, 1.54). Fail-safe N analyses suggested that this was a robust nonzero effect, but a Begg and Mazumdar rank correlation test and an Egger’s test of the intercept both indicated substantial publication bias toward publishing large positive findings from small and imprecise studies (see Table 3). Consistent with these tests, the funnel plot was highly asymmetrical, with Duval and Tweedie’s trim and fill test finding that 344 effect sizes below the mean were missing. If these omitted findings had been published and included in analyses, it is estimated that the overall weighted mean odds ratio would have fallen to 1.26 (CI: 1.24, 1.29).
+Odds ratio models that assumed and accounted for complete dependence among effect sizes produced a weighted mean odds ratio of 1.49 (CI: 1.44, 1.53). According to Duval and Tweedie’s trim and fill analysis, 50 studies below the mean were missing. Inclusion of these studies would have produced a lowered estimate of 1.30 (CI: 1.26, 1.33).
+A total of 192 hazard ratio effect sizes included suicide attempt as an outcome. These effect sizes were highly heterogeneous (12 = 89.16%) and produced an overall weighted mean hazard ratio of
+1.23 (CI: 1.20, 1.26). Fail-safe N analyses found that this was a robust nonzero effect, and a Begg and Mazumdar rank correlation test did not detect significant publication bias (see Table 3). However, an Egger’s test of the intercept did detect significant publication bias (see Table 3), and the funnel plot was asymmetrical. A Duval and Tweedie trim and fill analysis indicated 70 effect sizes below the mean were missing, and that including these effect sizes would have reduced the overall weighted mean hazard ratio to 1.13 (CI: 1.11, 1.16).
+A total of 367 suicide attempt prediction effect sizes included information sufficient for diagnostic accuracy analyses. Overall accuracy for suicide attempt prediction was poor—with accuracy being only slightly better than chance (wAUC = 0.58, SE = 0.01). This appeared to be attributable to very poor sensitivity (i.e., true positive rate; weighted sensitivity = 0.26; CI: 0.25, 0.27), meaning that risk factors rarely correctly identified individuals who actually went on to make a suicide attempt. Specificity (i.e., true negative rate; weighted specificity = 0.75; CI: 0.74, 0.76) was much higher, but this may be a methodological artifact of crossing a low base rate phenomenon (suicide attempt) with low base rate risk predictors (e.g., prior suicide attempts).
+Overall suicide death prediction and publication bias. There were 912 odds ratio effect sizes that included suicide death as an outcome; as with suicide ideation and attempts, there was extremely high heterogeneity among these effect sizes (12 = 98.45%). These effect sizes generated an overall weighted mean odds ratio of 1.50 (CI: 1.46, 1.56). Fail-safe N analyses showed that this was a robust nonzero effect, but a Begg and Mazumdar rank correlation test and an Egger’s test of the intercept indicated significant publication bias (see Table 3). However, the funnel plot appeared to be symmetrical, with Duval and Tweedie’s trim and fill analysis finding no evidence of missing effect sizes.
+Assuming and accounting for complete dependence among effect sizes, the weighted mean odds ratio would have been slightly higher (wOR = 1.64; CI: 1.53, 1.76); however, this model also indicated greater publication bias. Duval and Tweedie’s trim and
+fill analysis identified 25 missing studies below the mean. If these had been included, the weighted mean odds ratio would have been reduced to 1.45 (CI: 1.36, 1.55).
+A total of 346 hazard ratio effect sizes included suicide death as an outcome. These effect sizes were very heterogeneous (12 = 90.01%) and produced an overall weighted mean hazard ratio of 1.66 (CI: 1.59, 1.74). Fail-safe N analyses once again indicated that this was a robust nonzero effect, but a Begg and Mazumdar rank correlation test and an Egger’s test of the intercept both detected significant publication bias (see Table 3). As with odds ratio analyses, however, the funnel plot appeared symmetrical as a Duval and Tweedie’s trim and fill analysis did not detect any evidence of missing effect sizes.
+There were 358 suicide death effect sizes that included information sufficient for diagnostic accuracy analyses. As with suicide attempts, diagnostic accuracy for suicide death prediction was poor (wAUC = 0.57, SE = 0.01). Echoing suicide attempt analyses, suicide death prediction followed a pattern of moderately strong specificity (weighted specificity = 0.81; CI: 0.80, 0.82) but very poor sensitivity (weighted sensitivity = 0.09; CI: 0.08, 0.10). In sum, current risk factors rarely correctly identify individuals who actually go on to die by suicide, and have a high true negative rate that may be a methodological artifact of a low base rate outcome crossed with low base rate predictors.
+Suicide plans analyses. There were too few effect sizes for suicide plans (n = 88 effect sizes; 2.57% of all risk effect sizes) to conduct reliable meta-analyses. There has been a recent increase in longitudinal studies of these behaviors, with 74% of suicide plan effect sizes coming from the last decade. Nevertheless, many more studies are needed to provide reliable information about risk factors for suicide plans.
+Simulation of base rate prediction accuracy. To compare the accuracy of STB prediction with risk factors (i.e., the preceding diagnostic accuracy analyses) to prediction with base rate guessing, we conducted simulation analyses. Across 10,000 simulations of 100,000 person-years, base rate guessing correctly identified
+401,419 of 20,000,000 suicide ideators and 960,401,419 of 980,000,000 nonideators. These simulations produced a mean sensitivity of 0.02 (SD = .003) and a mean specificity of 0.98 (SD < .001). Simulations for suicide attempt showed that base rate guessing correctly identified 10,361 of 3,250,000 suicide attempters and 993,510,361 of 996,750,000 nonattempters. Results indicated a mean sensitivity of 0.003 (SD = 0.003) and a mean specificity of 0.99 (SD < .001). For suicide death analyses, simulations for base rate guessing correctly identified 18 of 130,000 suicide deaths and 999,740,018 of 999,870,000 nondeaths. These simulations generated a mean sensitivity of 0.0001 (SD = 0.003) and a mean specificity of 0.99 (SD < .001). As with predictive accuracy based on risk factors (see above), base rate guessing rarely correctly identified instances of STBs (i.e., low sensitivity/true positive rate) but accurately identified non-STB instances (i.e., high specificity/ true negative rate). Compared with prediction with risk factors, base rate guessing produced moderate declines in sensitivity and large increases in specificity.
+Brief summary. These broad analyses show that overall prediction of STBs is poor in terms of odds ratios, hazard ratios, and diagnostic accuracy statistics. Specifically, odds and hazard ratio analyses indicate that existing factors do not substantially increase the risk of STBs and diagnostic accuracy analyses suggest that existing factors rarely correctly identify people who go on to engage in suicidal behavior. Models that assumed and accounted for complete dependence among effect sizes produced virtually identical results.
+Outcome Characteristics
+The preceding analyses assumed that all outcomes for a given STB were assessed and defined the same way. However, as in most other areas of psychology, many suicide researchers disagree about the best way to assess and define each STB. It is possible that heterogeneity in these outcome characteristics across effect sizes impacted results. To test this possibility, we examined the effect of various outcome characteristics on the prediction of each type of STB.
+Suicide ideation assessment type. Weighted mean odds ratio analyses showed a small but statistically significant reduction in magnitude for single-item assessments (n = 353; wOR = 1.43; CI: 1.39, 1.47) compared with questionnaire/interview-based assessments (n = 219; wOR = 1.68; CI: 1.60, 1.77). Only one effect size included an unclear/unstated suicide ideation assessment strategy.
+Suicide ideation definition type. Ideation definitions that were clearly passive produced significantly smaller weighted mean odds ratios (n = 65; wOR = 1.19; CI: 1.13, 1.26) compared with definitions that were unclear/unstated (n = 33; wOR = 1.56; CI: 1.30, 1.87), clearly active (n = 239; wOR = 1.52; CI: 1.46, 1.58), or clearly a mix of active and passive ideation (n = 236; wOR = 1.67; CI: 1.58, 1.76).
+Suicide attempt assessment type. Questionnaire/interview assessments (n = 401; wOR = 1.45; CI: 1.41, 1.50) and singleitem assessments (n = 551; wOR = 1.53; CI: 1.49, 1.57) produced similar weighted mean odds ratios. Interestingly, effect sizes that included an unclear/unstated suicide attempt assessment strategy generated a significantly higher weighted mean odds ratio (n = 329; wOR = 1.72; CI: 1.63, 1.81). Hazard ratio analyses indicated a slightly different pattern, with unclear/unstated (n = 71; wHR = 1.23;
+CI: 1.19, 1.28), single-item (n = 39; wHR = 1.28; CI: 1.20,1.37), and questionnaire/interview assessments (n = 82; wHR = 1.25; CI: 1.20; 1.31) producing nearly identical results. Diagnostic accuracy was uniformly poor across single-item (n = 103; wAUC = 0.58; SE = 0.01), questionnaire/interview (n = 132; wAUC = 0.60; SE = 0.01), and unclear/unstated assessment strategies (n = 132; wAUC = 0.58; SE = 0.01).
+Suicide attempt definition type. Suicide attempt definitions that explicitly included suicide intent produced significantly smaller weighted mean odds ratios (n = 486; wOR = 1.43; CI: 1.39, 1.47) compared with definitions where suicide intent could only be inferred (n = 510; wOR = 1.60; CI: 1.55, 1.65) and unclear/unstated definitions (n = 285; wOR = 1.59; CI: 1.53, 1.66). Hazard ratio analyses once again produced a slightly different pattern, with suicide definitions that were unclear/unstated (n = 13; wHR = 1.15; CI: 1.09, 1.21) or explicitly included suicide intent (n = 142; wHR = 1.21; CI: 1.18, 1.24) generating significantly smaller magnitudes than definitions where suicide intent could only be inferred (n = 37; wHR = 1.83; CI: 1.59, 2.12).
+There were no significant differences in diagnostic accuracy across suicide attempt definitions that were unclear/unstated (n = 111; wAUC = 0.59; SE = 0.02), explicitly included suicide intent (n = 173; wAUC = 0.57; SE = 0.01), or where suicide intent could only be inferred (n = 83; wAUC = 0.59; SE = 0.02).
+Initial or repeated suicide attempt. Weighted mean odds ratio analyses did not reveal any significant differences among attempt frequency types. Only two effect sizes included only first-time attempters (wOR = 1.25; CI: 0.53, 2.95) and these effect sizes were not significantly different from effect sizes that included only repeat attempters (n = 151; wOR = 1.67; CI: 1.51, 1.85), a mix of first-time and repeat attempters (n = 1,046; wOR = 1.51; CI: 1.48, 1.53), or participants with an unclear attempt status (n = 82; wOR = 1.56; CI: 1.41, 1.73). With hazard ratio analyses, there was only one effect size that included only first-time attempt-ers, precluding a meta-analysis. Effect sizes that included only repeat attempters (n = 9; wHR = 1.93; CI: 1.37, 2.71) generated significantly higher magnitudes than effect sizes that included a mix of first-time and repeat attempters (n = 132; wHR = 1.19; CI: 1.16, 1.23) and participants with an unclear attempt status (n = 50; wHR = 1.29; CI: 1.23, 1.35); however, the repeat attempter figure may not be reliable because it is only based on nine effect sizes. Diagnostic accuracy was similarly poor across effect sizes that included only repeat attempters (n = 83; wAUC = 0.60; SE = 0.02), a mix of first-time and repeat attempters (n = 266; wAUC = 0.57; SE = 0.01), and participants with an unclear attempt status (n = 16; wAUC = 0.65; SE = 0.04). There were too few effect sizes that included only first-time attempters to permit a diagnostic accuracy meta-analysis (n = 2).
+Suicide death assessment type. There were no significant differences among suicide death assessment types, with similar weighted mean odds ratios regardless of whether suicide death was assessed with legal/medical documentation (n = 723; wOR = 1.53; CI: 1.46, 1.59), family reports (n = 13; wOR = 0.77; CI: 0.38, 1.58), or unclear/unstated assessment strategies (n = 178; wOR = 1.42; CI: 1.27, 1.57). All hazard ratio effect sizes included legal/medical documentation, precluding moderation analyses. As with odds ratio analyses, diagnostic accuracy analyses did not detect any significant differences across legal/medical documentation (n = 276; wAUC = 0.55; SE = 0.01), family report (n = 3;
+wAUC = 0.31; SE = 0.30), and unclear assessment strategies (n = 79; wAUC = 0.62; SE = 0.02).
+Suicide death definition type. Weighted mean odds ratio analyses showed that all definition types generated similar magnitudes, with similar results for suicide death definitions that explicitly excluded ambiguous deaths (n = 332; wOR = 1.56; CI: 1.47, 1.66), did not explicitly include or exclude ambiguous deaths (n = 416; wOR = 1.48; CI: 1.39, 1.57), and unclear/unstated definitions (n = 164; wOR = 1.38; CI: 1.24, 1.53). As noted above, effect sizes with suicide death definitions that explicitly included ambiguous deaths were excluded; however, we note here that the weighted mean odds ratio was comparable for these excluded effect sizes (n = 206; wOR = 1.38; CI: 1.31, 1.47). Echoing these findings, hazard ratio analyses revealed nearly identical magnitudes for definitions that did not explicitly exclude or include ambiguous deaths (n = 84; wHR = 1.61; CI: 1.51, 1.72) and definitions that explicitly excluded ambiguous deaths (n = 262; wHR = 1.60; CI: 1.49, 1.72). Excluded effect sizes that explicitly included ambiguous deaths produced very similar effect estimates (n = 78; wHR = 1.67; CI: 1.45, 1.93).
+Diagnostic accuracy analyses revealed poor prediction across definitions that were unclear/unstated (n = 71; wAUC = 0.62; SE = 0.02), did not explicitly include or exclude ambiguous deaths (n = 175; wAUC = 0.56; SE = 0.02), and explicitly excluded ambiguous deaths (n = 112; wAUC = 0.55; SE = 0.02).
+Brief summary. Viewed as a whole, these outcome characteristic analyses show that factors such as assessment type, outcome definition, and attempt status had little effect on predictive ability within the present meta-analysis. In short, predictive ability was poor regardless of outcome characteristics.
+Adjusted Versus Nonadjusted Risk Factors
+Descriptive characteristics. Every effort was made to obtain unadjusted estimates for risk factor magnitudes to provide the cleanest possible estimates. This was possible for most effect sizes (82.06%), with most adjusted analyses controlling for basic demographic factors such as age and gender.
+Adjustment and suicide ideation prediction. The weighted mean odds ratio for adjusted suicide ideation analyses (n = 103; wOR = 1.28; CI: 1.23, 1.34) was significantly smaller than the weighted mean odds ratio for unadjusted analyses (n = 469; wOR = 1.59; CI: 1.54, 1.63).
+Adjustment and suicide attempt prediction. As with ideation, suicide attempt analyses indicated that weighted mean odds ratios were significantly smaller for adjusted (n = 155; wOR = 1.38; CI: 1.33, 1.44) compared with nonadjusted effect sizes (n = 1126; wOR = 1.55; CI: 1.52, 1.58). Hazard ratio analyses revealed that weighted means were statistically similar for adjusted (n = 15; wHR = 1.19; CI: 1.13, 1.26) compared with nonadjusted effect sizes (n = 177; wHR = 1.26; CI: 1.22, 1.29).
+Adjustment and suicide death prediction. Weighted mean odds ratio analyses showed that adjusted (n = 162; wOR = 1.71; CI: 1.51, 1.94) and nonadjusted effect sizes (n = 750; wOR = 1.47; CI: 1.42, 1.52) produced statistically similar magnitudes. Likewise, hazard ratio analyses indicated a statistically significant difference between adjusted (n = 148; wHR = 1.48; CI: 1.33, 1.65) and nonadjusted effect sizes (n = 198; wHR = 1.76; CI: 1.67, 1.85).
+Brief summary. These analyses indicate that adjusting for other potential risk factors (again, adjustments typically included one or two demographic factors) may slightly reduce effect size magnitude, but that magnitude is small regardless of adjustment.
+Predictor Scale
+Descriptive characteristics. The majority of risk factor effect sizes included a dichotomous predictor scale (n = 2,504; 73.05% of all effect sizes) rather than continuous predictor scales (n = 924; 26.95% of all effect sizes). This was expected as most demographic variables and psychiatric diagnoses (i.e., the majority of risk factors) are in a dichotomous format.
+Predictor scale and suicide ideation prediction. Odds ratio analyses revealed a small but statistically significant drop in risk factor magnitude from dichotomous (n = 414; wOR = 1.58; CI: 1.50, 1.66) to continuous predictor scales (n = 158; wOR = 1.32; CI: 1.29, 1.36).
+Predictor scale and suicide attempt prediction. Suicide attempt prediction analyses showed a similar drop in weighted mean odds ratios from dichotomous (n = 846; wOR = 1.80; CI: 1.72, 1.87) to continuous predictor scales (n = 435; wOR = 1.24; CI: 1.22, 1.27). Hazard ratio analyses also revealed a pattern of reduced magnitude from dichotomous (n = 115; wHR = 1.84; CI: 1.67, 2.02) to continuous predictor scales (n = 77; wHR = 1.11; CI: 1.09, 1.13).
+Predictor scale and suicide death prediction. Echoing findings for other outcomes, suicide death prediction analyses indicated that weighted mean odds ratios diminished from dichotomous (n = 694; wOR = 1.57; CI: 1.49, 1.65) to continuous predictor scales (n = 218; wOR = 1.22; CI: 1.17, 1.27). Despite including many fewer effect sizes, hazard ratio analyses showed a similar pattern, with magnitudes diminishing from dichotomous (n = 328; wHR = 1.71; CI: 1.60, 1.83) to continuous predictor scales (n = 18; wHR = 1.07; CI: 1.03, 1.10).
+Brief summary. These highly consistent findings show that risk factor magnitudes tend to be slightly larger when risk factors are in a dichotomous format. It is important to note that this does not mean that dichotomous risk factors are “better” predictors. This effect is explained by a mathematical feature of odds and hazard ratios: they describe the change in odds/hazard per unit of change in the predictor variable. Because dichotomous predictors only have one unit of change (i.e., from Group A to Group B), they tend to produce larger odds and hazard ratio magnitudes; however, the tradeoff is that estimates of dichotomous risk factor magnitudes are less accurate. The present analyses reflected this tradeoff: a greater number of effect sizes should produce tighter confidence intervals, but dichotomous effect sizes produced wider confidence intervals despite including a much larger number of effect sizes.
+Risk Factor Prediction Trends Across Eras of Research
+Trends for prediction of suicide ideation. The earliest qualifying suicide ideation effect size was from Petrie and Chamberlain (1985), but suicide ideation outcome studies did not become common until the late 1990s, with qualifying effect sizes increasing steadily from 1985 to 1994 (n = 16; 5.69% of all effect sizes in this era), to 1995 to 2005 (n = 167; 18.43% of effect sizes), to
+2005 to 2014 (n = 413; 19.87% of effect sizes). Although the number of effect sizes increased steadily over time, weighted mean odds ratios were very similar across eras of research (see Figure 3).
+Trends for prediction of suicide attempt. The earliest qualifying suicide attempt effect sizes were from Greer and Bagley (1971), with suicide attempt effect sizes increasing in nearexponential fashion across later eras, especially over the last decade. A total of seven suicide attempt outcome cases qualified during the pre-1985 era (9.45% of all effect sizes in this era), 67 during 1985 to 1994 (23.84% of effect sizes), 342 during 1995 to 2004 (37.75% of effect sizes), and 1,057 during 2005 to 2014 (50.84% of effect sizes). Despite this marked increase in research, weighted mean odds ratios have not improved over time (see Figure 3). Similar analyses for hazard ratio effect sizes are limited as there were relatively few overall effect sizes and only one qualifying study appeared before 2004. Magnitudes were nearly identical from 1995 to 2004 (n = 30; wHR = 1.23; CI: 1.17, 1.31) to 2005 to 2014 (n = 162; wHR = 1.24; CI: 1.21, 1.28). Echoing these patterns, diagnostic accuracy analyses showed no significant change in weighted AUCs for suicide attempt prediction across eras of research (see Figure 4).
+Trends for prediction of suicide death. The earliest qualifying suicide death effect sizes were reported in Motto (1965), with effect sizes increasing gradually from the pre-1985 era (n = 67; 90.54% of all effect sizes in this era), to 1985 to 1994 (n = 198; 70.46% of effect sizes), to 1995 to 2004 (n = 397; 43.81% of effect sizes), to 2005 to 2014 (n = 609; 29.29% of effect sizes). Weighted mean odds ratios were small in magnitude across each decade (see Figure 3). Time-based hazard ratio analyses are limited because of the low overall number of effect sizes and the fact that the first qualifying effect sizes were reported relatively recently (i.e., Brown, Beck, Steer, & Grisham, 2000). There was a small but statistically significant increase in effect magnitude from 2000 to 2004 (n = 35;
+wHR = 1.25; CI: 1.18, 1.34) to 2005 to 2014 (n = 311; wHR = 1.64; CI: 1.54, 1.75). Consistent with the preceding analyses, diagnostic accuracy analyses indicated poor accuracy across each era of suicide research (see Figure 4).
+Brief summary. Although the number of studies and effect sizes for all outcomes increased markedly across time, there was little change in predictive ability across time. This trend is shown clearly in Figure 5. This figure also shows that there is a highly restricted range of effect sizes in the existing literature, meaning that the poor overall predictive ability described above is unlikely to be meaningfully moderated by any methodological or psychological factor. In other words, because a cluster of abnormally large effect sizes does not exist in this literature, we are unlikely to find a moderator (e.g., outcome characteristics, time, and other moderators tested below) that separates abnormally large effect sizes from other effect sizes in this literature.
+Risk and Follow-Up Length
+Descriptive statistics. The overall mean follow-up length was 108.82 months (SD = 108.58 months), with a median of 72 months and a range of .50 to 912 months. To facilitate analyses, we grouped these heavily skewed follow-up lengths into six class intervals: 0 to 6 months (n = 149; 4.37% of cases); 7-12 months (n = 410; 12.02% of cases); 13-24 months (n = 382; 11.20% of cases); 25 to 60 months (n = 687; 20.13% of cases); 61 to 120 months (n = 785; 23.01% of cases); and 121+ months (n = 999; 29.28% of cases). Notably, 0.10% of effect sizes had follow-up lengths that were less than one month, and 0.90% of effect sizes had follow-up lengths that were one month or less; in stark contrast, nearly one third of effect sizes had follow-up lengths that were 10 years or longer.
+Follow-up lengths have shortened slightly across the decades of suicide research, with lengths being longest before 1985 (M =
+234.19 months; SD = 153.95 months), dropping during the 1985 to 1994 era (M = 127.80 months; SD = 103.67 months), remaining similar during the 1995 to 2004 era (M = 127.79 months; SD = 94.87 months), and decreasing slightly during the 2005 to
+2014 era (M = 94.24 months; SD = 108.51 months). Follow-up lengths for suicide death outcome effect sizes (M = 170.30 months; SD = 123.77 months) tended to be longer than lengths for suicide ideation (M = 80.72 months; SD = 88.39 months) and
+suicide attempt outcome effect sizes (M = 68.25 months; SD = 75.21 months).
+Follow-up length and suicide ideation prediction. Weighted mean odds ratios across follow-up class intervals indicated that odds ratio magnitude decreased slightly from 0-6 months to all other intervals (see Figure 6).
+Follow-up length and suicide attempt prediction. Across follow-up class intervals, there was an inconsistent pattern of weighted mean odds ratios (see Figure 6). Hazard ratio analyses showed a similar trend, with no valid effect sizes for the 0 to 6 month interval and an inconsistent pattern of effect sizes across intervals of 7 to 12 months (n = 10; wHR = 1.06; CI: 1.02, 1.10), 13 to 24 months (n = 73; wHR = 1.12; CI: 1.08, 1.15), 25 to 60 months (n = 26; wHR = 2.36; CI: 1.91, 2.92), 61 to 120 months (n = 59; wHR = 1.31; CI: 1.25, 1.37), and 121 + months (n = 24; wHR = 1.97; CI: 1.58, 2.46). Likewise, diagnostic accuracy analyses revealed an inconsistent pattern: 0 to 6 months (n = 8; wAUC = .78; SE = .08); 7 to 12 months (n = 61; wAUC = .57; SE = .02); 13 to 24 months (n = 76; wAUC = .57; SE = .02); 25 to 60 months (n = 104; wAUC = .58; SE = .01); 61 to 120 months (n = 90; wAUC = .59; SE = .02); and 121+ months (n = 19; wAUC = .65; SE = .04).
+Follow-up length and suicide death prediction. Weighted mean odds ratios across follow-up class intervals suggested a slight decline between intervals covering 0 to 12 months and all other intervals (see Figure 6). The only eligible effect sizes for hazard ratio analyses included long follow-up intervals: 25 to 60 months (n = 42; wHR = 1.87; CI: 1.55, 2.25); 61 to 120 months (n = 110; wHR = 1.86; CI: 1.66, 2.09); and 121 + months (n = 194; wHR = 1.43; CI: 1.36, 1.50). Diagnostic accuracy analyses also followed this general pattern, with accuracy diminishing from the 7 to 12 month interval (n = 13; wAUC = .68; SE = .05) to intervals of 25 to 60 months (n = 72; wAUC = .53; SE = .04), 61 to 120 months (n = 57; wAUC = .56; SE = .03), and 121 + months (n = 212; wAUC = .58; SE = .02).
+Brief summary. Predictive ability was poor across all follow-up intervals. Although a few statistically significant differences were detected, no consistent patterns emerged.
+Risk and Sample Severity
+Descriptive characteristics. Most effect sizes were tested within general samples (n = 1,400 effect sizes; 40.84% of all effect sizes) or clinical samples (n = 1,339 effect sizes; 39.06% of all effect sizes). Fewer were tested within samples where all participants had a history of some form of self-injurious thought or behavior (i.e., STB samples; n = 689 effect sizes; 20.10% of all effect sizes). General sample effect sizes accounted for a disproportionate amount of suicide ideation (72.48%) and death outcome effect sizes (40.44%), and clinical sample effect sizes were responsible for the majority of suicide attempt outcome effect sizes (52.82%). General sample effect sizes (M = 125.32 months; SD = 119.91 months) were longer than clinical sample effect sizes (M = 102.29 months; SD = 108.99 months), which in turn were longer than STB sample effect sizes (M = 88.14 months; SD = 73.05).
+Sample severity and suicide ideation prediction. Weighted mean odds ratios for suicide ideation dropped significantly from general to clinical to STB sample effect sizes (see Figure 7). However, it should be noted that effect sizes were small across all sample types and there were relatively few effect sizes from clinical (n = 133) and STB samples (n = 31).
+Sample severity and suicide attempt prediction. Weighted mean odds ratios for suicide attempt prediction were similar across all sample severity types, with slightly but significantly reduced magnitudes in clinical samples compared with general and STB samples (see Figure 7). Hazard ratio analyses showed a different pattern, with weighted means decreasing significantly from general (n = 13; wHR = 2.13; CI: 1.60, 2.85) to clinical (n = 142; wHR = 1.33; CI: 1.28, 1.37) and STB samples (n = 37; wHR = 1.05; CI: 1.03, 1.08). Similarly, diagnostic accuracy analyses indicated that AUCs were significantly larger in general samples
+(n = 40; wAUC = 0.60; SE = 0.02) compared with clinical (n = 234; wAUC = 0.58; SE = 0.01) and STB samples (n = 93; wAUC = 0.62; SE = 0.02), though accuracy was poor for all sample types.
+Sample severity and suicide death prediction. Similar to ideation and attempts, analyses showed that weighted odds ratios for suicide death declined from general samples to clinical and STB samples (see Figure 7). The pattern for hazard ratio analyses was slightly different, with weighted means being similar across general (n = 214; wHR = 1.61; CI: 1.48, 1.74) and clinical samples (n = 79; wHR = 1.44; CI: 1.35, 1.53), and slightly higher in STB samples (n = 53; wHR = 1.91; CI: 1.67, 2.19). Diagnostic accuracy analyses showed that accuracy was similarly poor for general (n = 70; wAUC = 0.58, SE = 0.02), clinical (n = 181; wAUC = 0.59, SE = 0.02), and STB samples (n = 107; wAUC = 0.51, SE = .02).
+Brief summary. Although there were a few exceptions, as a whole analyses indicated that general samples tended to produce slightly greater predictive accuracy than clinical or STB samples. As discussed below, this is consistent with the idea that clinical and STB samples represent more stringent reference groups.
+Risk and Subtypes of Clinical and Self-Injurious Samples
+Although the preceding sample severity analyses did not produce strong effects, it is possible that more specific aspects of clinical and self-injurious samples may influence STB prediction. Below, we test this possibility by examining the potential impacts of psychiatric issue type, clinical sample origin, and STB sample type. Because there were too few hazard ratio and diagnostic accuracy effect sizes for these subsample analyses, only weighted mean odds ratio results are reported below.
+Type of psychiatric issue. Odds ratio analyses showed that the type of psychiatric issue reported for a given sample had little effect
+on suicide ideation prediction, with only one significant difference across effect sizes from samples with general psychopathology (n = 54; wOR = 1.71; CI: 1.41, 2.07), mood or anxiety disorders (n = 55; wOR = 1.23; CI: 1.16, 1.30), substance use disorders (n = 7; wOR = 1.44; CI: 1.19, 1.74), and psychotic disorders (n = 11; wOR = 1.13; CI: 0.63, 2.05).
+There were likewise few significant differences in weighted mean odds ratio magnitude for the prediction of suicide attempts, with similar magnitudes for effect sizes from samples with general psychopathology (n = 112; wOR = 1.76; CI: 1.60, 1.94), mood and anxiety disorders (n = 298; wOR = 1.56; CI: 1.48, 1.64), substance use disorders (n = 89; wOR = 1.63; CI: 1.49, 1.78), psychotic disorders (n = 56; wOR = 1.16; CI: 1.08, 1.24), eating disorders (n = 25; wOR = 1.99; CI: 1.56, 2.54), attention-deficit/ hyperactivity disorder (n = 3; wOR = 3.56; CI: 1.76, 7.21), borderline personality disorder (n = 29; wOR = 1.22; CI: 1.17, 1.28), and other personality disorders (n = 23; wOR = 1.95; CI: 1.50, 2.53). Although samples with psychotic disorders and borderline personality disorders produced significantly smaller weighted mean odds ratios than other sample types, these magnitude differences were very small in an absolute sense.
+Similar to ideation and attempt findings, the type of psychiatric issue within a sample had little effect on suicide death prediction. There were no significant differences in weighted mean odds ratios across samples with general psychopathology (n = 120; wOR = 1.56; CI: 1.33, 1.82), mood and anxiety disorders (n = 150; wOR = 1.29; CI: 1.23, 1.34), substance use disorders (n = 36; wOR = 1.19; CI: 1.02, 1.39), or psychotic disorders (n = 38; wOR = 1.15; CI: 0.75, 1.77).
+Type of STB sample. Suicide ideation prediction was similar within self-injurious samples selected for prior suicide ideation (n = 16; wOR = 1.96; CI: 1.24, 3.10), prior suicide attempt (n = 10; wOR = 1.58; CI: 0.42, 5.92), and general self-injurious
+thoughts or behaviors (n = 17; wOR = 2.30; CI: 1.45, 3.63). A similar pattern emerged for suicide attempt prediction, with no significant differences across samples selected for prior ideation (n = 69; wOR = 1.33; CI: 1.26, 1.58), prior suicide attempt (n = 192; wOR = 1.73; CI: 1.57, 1.89), and general self-injurious thoughts or behaviors (n = 42; wOR = 1.76; CI: 1.46, 2.13). Echoing these findings, there were no significant differences in suicide death prediction across samples selected for prior suicide ideation (n = 5; wOR = 1.91; CI: 1.22, 3.00), prior suicide attempt (n = 211; wOR = 1.35; CI: 1.25, 1.46), general self-injurious thoughts or behaviors (n = 71; wOR = 1.31; CI: 1.20, 1.44), and suicide death (n = 139; wOR = 1.68; CI: 1.46, 1.59).
+Clinical and STB sample origin. Suicide ideation prediction was similar across community (n = 36; wOR = 1.47; CI: 1.28, 1.69), inpatient (n = 48; wOR = 1.38; CI: 0.94, 2.02), outpatient (n = 27; wOR = 1.14; CI: 1.04, 1.24), and mixed inpatient/ outpatient origins (n = 31; wOR = 1.35; CI: 1.24, 1.46). As with most other statistically significant differences in the present results, the significant differences between community and outpatient sample origins is small in an absolute terms. Analyses showed that suicide attempt prediction was slightly but significantly stronger in inpatient (n = 344; wOR = 1.47; CI: 1.43, 1.52) and mixed inpatient/outpatient samples (n = 362; wOR = 1.48; CI: 1.43, 1.54) compared with community (n = 55; wOR = 1.27; CI: 1.18, 1.37) and outpatient samples (n = 125; wOR = 1.32; CI: 1.21, 1.44). Suicide death analyses revealed the opposite pattern, with significantly stronger prediction in outpatient samples (n = 28; wOR = 2.60; CI: 1.91, 3.55) compared with inpatient (n = 485; wOR = 1.26; CI: 1.21, 1.31) and mixed inpatient/outpatient samples (n = 94; wOR = 1.21; CI: 1.16, 1.27). There were too few community sample effect sizes with a suicide death outcome (n = 1) to permit meta-analysis.
+Brief summary. As might be expected from the overall restricted range of effect sizes (see Figure 5) and the small effects within sample severity analyses, variations in clinical and STB sample types had little effect on results. A few significant effects were detected, but these effects were small and did not coalesce into a consistent pattern.
+Risk and Sample Age
+Descriptive statistics. The majority of effect sizes were from adult samples (n = 2,114 effect sizes; 61.67% of total effect sizes), with many fewer effect sizes from mixed (n = 539 effect sizes; 15.72% of total effect sizes) and adolescent samples (n = 775 effect sizes; 22.61% of total effect sizes). These proportions have held nearly constant since 1985, with adult effect sizes always accounting for nearly 60% of effect sizes and mixed and adolescent effect sizes each always accounting for nearly 20% of effect sizes. Follow-up lengths for mixed samples (M = 115.36 months; SD = 83.34 months) and adult samples (M = 112.19 months; SD = 118.69 months) tended to be longer than those of adolescent sample studies (M = 95.18 months; SD = 93.49 months). Compared with adult and mixed samples, adolescent samples included a high proportion of suicide ideation and suicide attempt outcomes; however, very few adolescent samples included suicide death as an outcome (see Figure 8). Adolescent effect sizes were primarily from general sample studies (70.71% of effect sizes), adult effect sizes included a high proportion of clinical sample studies (46.59% of effect sizes), and mixed samples were predominantly from STB sample studies (52.32% of effect sizes).
+Sample age and suicide ideation prediction. Analyses revealed that weighted mean odds ratios for suicide ideation were similar across all sample age types (see Figure 9).
+Sample age and suicide attempt prediction. Weighted mean odds ratios for suicide attempt were also very similar across all sample age types for suicide attempt outcomes (see Figure 9). Hazard ratio analyses detected a slightly different pattern, with a smaller effect size in adult samples (n = 141; wHR = 1.17; CI: 1.14, 1.19) compared with mixed (n = 26; wHR = 1.98; CI: 1.72, 2.27) and adolescent samples (n = 25; wHR = 1.66; CI: 1.39, 1.97). Diagnostic accuracy was poor for adult (n = 261; wAUC = 0.58, SE = 0.01), mixed (n = 24; wAUC = 0.59, SE = 0.01), and adolescent samples (n = 82; wAUC = 0.59, SE = 0.02).
+Sample age and suicide death prediction. As with other outcomes, analyses did not detect any significant differences
+among weighted mean odds ratios across sample age types for suicide death outcomes (see Figure 9). Hazard ratio analyses detected statistically smaller magnitudes in adult samples (n = 220; wHR = 1.50; CI: 1.43, 1.58) compared with mixed (n = 112; wHR = 1.79; CI: 1.61, 2.00) and adolescent samples (n = 14; wHR = 2.16; CI: 1.69, 2.76). As with attempts, diagnostic accuracy analyses showed that accuracy was poor across adult (n = 280; wAUC = 0.57, SE = 0.01), mixed (n = 74; wAUC = 0.55, SE = 0.03), and adolescent samples (n = 4; wAUC = 0.50, SE = 0.08).
+Brief summary. Sample age had little effect on predictive accuracy. Notably, however, results revealed a scarcity of risk factor studies with adolescent suicide death as an outcome.
+Risk and Number of STB Participants
+Descriptive statistics. Across all effect sizes, the mean number of STB participants was 166.53 (SD = 696.68; Mdn = 57; range = 27,095). To facilitate analyses, the number of STB participants for each effect size was categorized into one of four STB size classes: (a) 1 to 25 participants (i.e., very small; n = 891 effect sizes; 25.99% of all effect sizes); (b) 26 to 100 participants (i.e., moderately small; n = 1375 effect sizes; 40.11% of all effect sizes); (c) 101 to 500 participants (i.e., moderately large; n = 782 effect sizes; 22.81% of all effect sizes); and (d) 501 + participants (i.e., very large; n = 300 effect sizes; 8.75% of all effect sizes). Outcomes were mostly proportionately distributed across STB size classes, except that very large samples primarily included suicide death outcomes (67.00%). Follow-up intervals were not systematically related to the number of STB participants, with shorter follow-ups in very small (M = 100.89 months; SD = 89.24 months) and moderately large samples (M = 92.67 months; SD =
+92.48 months) compared with moderately small (M = 124.50 months; SD = 129.85 months) and very large samples (M = 119.37 months; SD = 86.90 months). The number of SITB participants was proportionately distributed across sample age types, except that adult samples accounted for a disproportionate share of very small samples (70.03%).
+Number of STB participants and suicide ideation prediction. Weighted mean odds ratio analyses for suicide ideation showed that magnitudes were similar across all STB sample size classes (see Figure 10).
+Number of STB participants and suicide attempt prediction. Similar to ideation analyses, weighted mean odds ratios for the prediction of suicide attempts was similar across all classes (see Figure 10). Deviating from this pattern slightly, hazard ratio analyses found that magnitudes were significantly smaller for very small (n = 25; wHR = 1.12; CI: 1.06, 1.18) than for moderately small (n = 109; wHR = 1.24; CI: 1.20, 1.29) and moderately large samples (n = 58; wHR = 1.31; CI: 1.25, 1.37). There were no eligible effect sizes for the very large sample size class. Diagnostic accuracy analyses found that accuracy was poor across all intervals, with low mean weighted AUCs for effect sizes across very small (n = 110; wAUC = 0.61, SE = 0.02), moderately small (n = 198; wAUC = 0.58, SE = 0.01), and moderately large classes (n = 59; wAUC = 0.57, SE = 0.01). There were no valid effect sizes for the very large class interval.
+Number of STB participants and suicide death prediction. Analyses showed that weighted mean odds ratios for suicide death were small in magnitude across all intervals (see Figure 10). Weighted mean hazard ratios produced a similar pattern of small magnitude effects for all class intervals: very small (n = 21; wHR = 1.43; CI: 1.10, 1.85), moderately small (n = 138; wHR = 1.44; CI:
+1.36, 1.52), moderately large (n = 146; wHR = 1.74; CI: 1.56, 1.94), and very large classes (n = 40; wHR = 1.64; CI: 1.39, 1.91). Echoing these findings, diagnostic accuracy analyses revealed little variation in weighted mean AUCs, with similar findings across very small (n = 141; wAUC = 0.61, SE = 0.02), moderately small (n = 175; wAUC = 0.56, SE = 0.01), moderately large (n = 27; wAUC = 0.50, SE = 0.05), and very large classes (n = 15; wAUC = 0.60, SE = 0.07).
+Brief summary. As with other potential moderators described above, the number of STB participants had little effect on predictive accuracy. Although there were a few significant effects across class intervals, no consistent patterns emerged—very small samples appeared to produce effects that were as strong as those from very large samples.
+Risk and Study Quality
+Descriptive characteristics. To examine the potential impact of study quality on STB prediction, we conducted a series of meta-regression analyses. As noted above, the relative homogeneity of the present set of studies (e.g., longitudinal designs) made it difficult to parse study quality. However, we employed two general types of variables to estimate study quality within the present meta-analysis: (a) variables related to assessment quality (described in Outcome Characteristic analyses above); (b) variables related to study recruitment/retention. Most studies did not report any information about recruitment rates (32.06% of all effect sizes, precluding reliable outcome-specific analyses on actual calculated recruitment rates), but most studies included information sufficient to calculate a retention rate (74.18% of all effect sizes). Across all studies, the average retention rate was moderate (M = 80.76%; Mdn = 86.53%; SD = 21.31%). Retention varied significantly across outcome types, F(2, 2,236) = 339.66, p < .001, increasing from suicide ideation (M = 69.94%; SD = 24.77%) to suicide attempt (M = 76.45%; SD = 19.99%) to suicide death outcomes
+(M = 95.32%; SD = 9.06%). Retention may be extremely high for suicide death outcomes because researchers typically relied on national death registries for follow-up information rather than continued direct contact with participants. Because only a subset of studies provided retention rates, we tested the effect of calculated retention rates on STB prediction in separate meta-regressions for each outcome. All results reported below are odds ratio analyses; there were too few hazard ratio effect sizes to permit reliable analyses.
+Study quality and suicide ideation prediction. In addition to codes for whether or not recruitment and retention rates were reported, study quality was assessed with suicide ideation assessment type (i.e., single-item vs. questionnaire/interview) and definition (i.e., unclear vs. passive, mixed passive/active, and active). Although no specific variable in the meta-regression analysis reached significance, the overall meta-regression model was significant (Q[6] = 25.31, p < .001). However, the model only accounted for a small proportion of the variance in weighted mean odds ratios for suicide ideation prediction (R2 = 0.04). In a separate meta-regression on the subset of effect sizes with retention rate data (n = 390), higher retention rates were not significantly associated with suicide ideation prediction (Q[1] = 0.45, p = .50, R2 < .01).
+Study quality and suicide attempt prediction. Study quality was assessed with codes for recruitment and retention rate reporting as well as the suicide attempt assessment (unclear vs. singleitem and questionnaire/interview) and definition codes (unclear vs. intent inferred and intent explicitly stated) described above. The meta-regression analysis showed that no specific variable reached significance and that the overall effect of the model was not significant (Q[6] = 9.23, p = .16, R2 < .01). A separate meta-regression on the subset of effect sizes with retention rate data (n = 1,045) showed that higher retention rates were significantly associated with stronger suicide attempt prediction
+(Q[1] = 5.93, p = .01); however, this was a very small effect (R2 = 0.01).
+Study quality and suicide death prediction. Similar to the preceding analyses, study quality was assessed with a combination of codes for recruitment and retention reporting and the suicide death assessment (unclear vs. legal/medical documentation and family report) and definition codes (unclear vs. ambiguous deaths likely excluded and explicitly excluded) described above. Analyses indicated that no specific variable within the meta-regression reached significance and that the overall model was not significant (Q [6] = 9.28, p = .16, R2 < .01). Within a separate metaregression on the subset of effect sizes with retention rate data (n = 510), higher retention rates were not significantly associated with suicide death prediction (Q [1] = 0.16, p = .69, R2 < .01).
+Brief summary. Taken together, these findings indicate that study quality variables generally had little to no impact on STB prediction strength in the present meta-analysis. As discussed above, this may be due to a combination of the homogeneity of research designs and the restricted range of effect sizes in the present meta-analysis. Additionally, it may be that “high quality” studies obtain more reliable and valid results, but this does not necessarily mean that these studies will obtain stronger results.
+Broad Risk Factor Categories and Prediction
+Risk factor categories. Space limitations preclude a full examination of the thousands of specific constructs that have been tested as risk factors. To provide a general index of the magnitude of various types of risk factors, however, we distilled all effect sizes into 16 broad risk factor categories (see Table 2). We note here that no risk factor subcategory (e.g., depression symptoms) substantially deviated from the risk magnitudes of these 16 broad risk factor categories (e.g., internalizing psychopathology). This is illustrated by Table 4, which shows the top five subcategory predictors for each outcome. None of these subcategory predictors stood out as particularly strong, especially in terms of absolute odds, and most were not significantly different from one another (see Table 4).
+Category popularity across time. The most popular broad risk factor categories were internalizing psychopathology, demographics, externalizing psychopathology, prior STBs, and social factors (see Table 5). These five categories accounted for 77.45% of all effect sizes, with internalizing psychopathology and demographics together accounting for 41.42% of effect sizes. Moreover, these five categories have been the top five most popular categories since the inception of longitudinal STB research, accounting for an increasing share of effect sizes each research era (see Table 5). This suggests that the field has primarily focused on the same risk factors for the past 50 years, with risk factors becoming increasingly homogenous over past five decades.
+Risk factor categories and suicide ideation. Weighted mean odds ratio analyses for suicide ideation revealed that prior STBs was the strongest broad risk factor category, though risk factor magnitude for even this category was weak in an absolute sense (see Figure 11). No other category exceeded a weighted mean odds ratio of 2.0. In terms of subcategories, prior suicide ideation was by far the strongest predictor, and hopelessness was the only other predictor to exceed a weighted odds ratio of 3.0 (see Table 4).
+Risk factor categories and suicide attempt. Suicide attempt analyses revealed that weighted mean odds ratios for all categories were weak in an absolute sense, with no broad risk factor category exceeding a weighted mean odds ratio of 2.37 (prior SITBs; Figure 12). A few categories clustered around a weighted mean odds ratio of 2.0 and several others clustered around 1.5 (see Figure 12). Despite being by far the most popular category of risk factors, internalizing psychopathology was a relatively weak category (wOR = 1.69). As shown in Table 4, subcategory analyses revealed that prior nonsuicidal self-injury was the strongest predictor, outpacing prior suicide attempts and screening instruments (which mostly consisted of questions about prior STBs); however, none of these top five subcategories were significantly different from one another.
+Hazard ratio analyses produced similar results, with most categories clustering around a weighted mean hazard ratio of 1.50 and
+only two categories exceeding 2.0 (see Table 6). The strongest overall category was prior psychiatric treatment, though it should be noted that this estimate may be unreliable as this category only included nine effect sizes for these analyses. Diagnostic accuracy analyses continued this pattern, with no category exceeding a weighted AUC of .61 (see Table 6). In short, no category improved predictive accuracy much beyond random guessing.
+Risk factor categories and suicide death. Weighted mean odds ratio analyses for suicide death found that only one category generated a magnitude above 2.0—prior psychiatric treatment history (see Figure 13). All other categories clustered around a weighted mean odds ratio of 1.50, including popular categories such as demographics, internalizing psychopathology, and prior STBs. Subcategory analyses showed that prior psychiatric hospitalization was the strongest overall predictor of suicide death, exceeding the magnitudes for prior suicide attempts and ideation
+(see Table 4); however, none of these top five subcategory predictors were significantly different from one another.
+Analyses for hazard ratios generated similar results, with most categories clustering around a weighted hazard ratio of 2.0 (see Table 6). Diagnostic accuracy analyses once again revealed poor prediction, with few categories reaching significance and only two statistically significant categories exceeding weighted AUCs of .60—social factors and prior psychiatric treatment history (see Table 6). No category exceeded a weighted AUC of .67 (see Table 6).
+Protective Factor Analyses
+Descriptive statistics. After removing outliers and redundant effect sizes, 495 effect sizes were categorized as protective factors. These effect sizes were accordingly analyzed separately from risk
+factor effect sizes. These protective factor effect sizes generally followed the same descriptive pattern as risk factor effect sizes, with comparable follow-up lengths (M = 112.82 months; SD = 121.96 months; Mdn = 72 months) and numbers of self-injury participants (M = 231.78 participants; SD = 903.13 participants;
+Mdn = 67 participants). Few protective factor effect sizes were published before 1985 (n = 3; 0.61% of effect sizes), with effect sizes increasing sharply between eras of 1985 to 1994 (n = 32; 6.46% of effect sizes), 1995 to 2004 (n = 109; 22.02% of effect sizes), and 2005 to 2014 (n = 351; 70.91% of effect sizes).
+Protective factor effect sizes included a high proportion of suicide attempt (42.63% of outcomes) compared with suicide death outcomes (27.47% of effect sizes) and suicide ideation outcomes (26.46% of effect sizes); very few effect sizes included suicide plan as an outcome (3.43% of effect sizes). Most protective factor effect sizes were examined in general (52.86% of effect sizes) or clinical samples (35.70% of effect sizes), with many fewer in STB samples (11.44% of effect sizes). Similarly, the majority of protective factor effect sizes were examined in adult samples (54.75% of effect sizes), with many fewer effect sizes from adolescent (10.51% of effect sizes) or mixed age samples (34.75% of effect sizes). In terms of broad categories, 55.56% of effect sizes were classified as demographic variables and 14.14% were classified as social factors; no other categories exceeded 4.84%. Because there were so few category-specific effect sizes across data types (i.e., odds/hazard ratios) and outcomes, reliable protective factor category analyses could not be performed.
+Protective factors and suicide ideation prediction. Odds ratio analyses for suicide ideation revealed a small but significant protective effect (n = 126; wOR = 0.93; CI: 0.90, 0.96; note: converse to risk factor effects, an OR/HR significantly below 1.0 indicates a significant protective factor effect). There were too few effect sizes to conduct hazard ratio or diagnostic accuracy analyses.
+Protective factors and suicide attempt prediction. Protective factor effect sizes generated a weighted mean odds ratio for suicide attempt that was slightly but significantly below 1.0 (n = 184; wOR = 0.93; CI: 0.91, 0.95). The weighted mean hazard ratio was similar in magnitude (n = 27; wHR = 0.93; CI: 0.89, 0.96). Diagnostic analyses also indicated a slight protective effect, but this did not reach significance (n = 36; wAUC = 0.48, SE = 0.03).
+Protective factors and suicide death prediction. Analyses revealed that protective factors did not produce a weighted mean odds ratio that was significantly below 1.0 (n = 76; wOR = 0.95; CI: 0.81, 1.11). In contrast, hazard ratio analyses did indicate a slight protective effect (n = 60; wHR = 0.94; CI: 0.91, 0.98). Echoing odds ratio analysis findings, protective factors did not produce a significant wAUC (n = 29; 0.45, SE = 0.03).
+Discussion
+Suicidal thoughts and behaviors are among the most common, deadly, and potentially preventable public health problems. Despite major advances in medical and psychological science, the devastating impact of this problem has remained constant for at least several decades (see CDC, 2014; Kessler et al., 2005; McKeown et al., 2006; Nock et al., 2008). Knowledge about STB risk factors is essential for crafting scientific theories, accurate risk assessments, and effective treatments. Each day, thousands of clinicians rely on a half century of risk factor research to inform critical decisions about suicide risk and treatment. The primary purpose of the present meta-analysis was to estimate the power and accuracy of these risk factors.
+These analyses produced several unexpected findings that may be surprising to many researchers and clinicians. Chief among them was the finding that, at least within the narrow methodological limits of the existing literature, existing risk factors are weak and inaccurate predictors of STBs. Analyses also revealed the following: predictive ability has not improved over the past 50 years; most studies included very long follow-up intervals (~5-10 years), but longer intervals were not associated with improved predictive ability; predictive ability was slightly better when a general
+sample reference group was used; risk factor categories have been homogenous and have become increasingly homogenous over time; no risk factor category or subcategory is substantially stronger than any other; there is no compelling evidence that any specific STB outcome is associated with a unique set of risk factors; and protective factors are rarely studied and are generally weak.
+These findings unfortunately have much greater implications for future research than for current suicide theory, prediction, and treatment. This meta-analysis was unable to establish the most promising future theoretical directions because, despite hundreds of studies across several decades, the available data were not sufficient to evaluate most hypotheses and theories about suicide. When relevant data were available for a particular approach (e.g., biological risk factors, hopelessness), study methods were usually too constrained to provide a helpful evaluation of a theory or hypothesis. Likewise, this meta-analysis was unable to provide clarity about risk factor and warning sign guidelines. As noted above, no broad or specific risk factors stood out as particularly strong and all risk factors have been evaluated within narrow methodological limits.
+It follows that this meta-analysis also cannot directly inform STB treatment and prevention efforts. Because the meta-analytic data could not winnow theories or risk factors, it cannot provide much useful information about treatment and prevention targets. The limited ability of this meta-analysis to inform suicide theory, prediction, and treatment emanates from one major source: the methodological limitations of the existing literature. This metaanalysis accordingly represents a clarion call to researchers to modify their methods in a way that allows STB risk factor research to have greater implications for suicide theory, prediction, and treatment. Below, we discuss specific meta-analytic findings in greater detail and provide recommendations for future research.
+Overall Risk Factor Effects
+Given the degree of difficulty involved in conducting a longitudinal STB study, this literature produced a surprisingly high number of qualifying studies (n = 365), which in turn produced 3,428 risk factor effect sizes. Unfortunately, this large amount of research did not produce strong overall risk factor effects. Weighted mean odds and hazard ratios were around 1.50 for all outcomes and dropped significantly when publication bias was taken into account. These small effects were not meaningfully moderated by outcome definition (e.g., type of ideation), outcome assessment strategy (e.g., single-item vs. questionnaire/interview), or study quality (e.g., retention rate). As discussed in the next section, these overall effects also were not meaningfully moderated by broad risk factor categories (e.g., internalizing psychopathology) or specific risk factors (e.g., depression). In terms of clinical significance, assuming that these weighted odds ratio figures would apply on a population level, these combined risk factor effects would increase the 1-year odds of suicide death from 0.013 to 0.019 per 100 people; suicide attempt from 0.33 to 0.49 per 100 people; and suicide ideation from 2 to 3 per 100 people. These may not represent clinically significant effects, especially when considering clinicians often are tasked with determining STB risk over the course of hours, days, or weeks rather than over an entire year.
+Diagnostic accuracy analyses were consistent with these findings. There were too few eligible effect sizes for suicide ideation analyses, but diagnostic accuracy was only slightly above chance for both suicide attempt prediction (wAUC = 0.58) and suicide death prediction (wAUC = 0.57). Sensitivity (i.e., true positive rate) analyses showed that risk factors rarely correctly identified instances of suicide attempt (26% correct) or suicide death (9% correct). At first glance, specificity (i.e., true negative rate) was much better, with acceptable levels for both suicide attempt (75% correct) and suicide death (81% correct). However, these rates appear to be inflated by a methodological artifact of crossing a low base rate predictor (e.g., prior suicide attempt) with a low base rate outcome (e.g., suicide death). In such scenarios, most participants will test negative for the predictor (e.g., will not have a suicide attempt history) and most also will test negative for the outcome (e.g., will not die by suicide). As a result, even if the predictor is not strongly related to the outcome, there will be a high true negative rate in these scenarios. It is notable that simulations showed that predicting solely according to base rates may be comparable to prediction with current risk factors. Compared with prediction with risk factors, prediction with base rates produced poorer sensitivity rates (~0-2% correct) and better specificity rates (~98-99% correct). In any case, both strategies produce poor STB prediction.
+These small effects across odds ratio, hazard ratio, and diagnostic accuracy analyses may be slightly optimistic estimates of effect size two reasons. First, we used unadjusted effect sizes whenever possible (~82% of effect sizes). Adjusted effect sizes, which control for various factors and more effectively isolate the contribution of a specific risk factor, were significantly smaller than unadjusted effect sizes. Second, we only included published studies and only calculated publication bias adjustments for overall odds and hazard ratio analyses (including these adjustments for all analyses would have been unwieldy and largely redundant with overall publication bias analyses). These publication bias adjustments substantially reduced the effect sizes of risk factors for suicide ideation (~40% reduction) and suicide attempt (~50% reduction).
+Specific Risk Factor Effects
+Composition of risk factors. Across our 16 broad risk factor categories, nearly 80% of risk factors fit into one of the top five most popular categories and nearly 95% of risk factors fit into one of the top nine most popular categories. Risk factors related to internalizing psychopathology accounted for nearly 25% of all effect sizes and risk factors related to demographics accounted for an additional 17%. This means that almost half of the 3,428 risk factor effect sizes fit into one of two categories. In short, STB risk factors have been very homogenous.
+This homogeneity has been present across all eras of STB risk factor research. The top five most popular broad risk factor categories were the same in each era (though in slightly different orders). These five categories have always accounted for at least 70% of all risk factors, with this proportion approaching 80% in the last decade. These patterns suggest that the STB risk factor literature has not become increasingly diverse, followed novel theoretical directions, or systematically built on or innovated beyond earlier work. Rather, the set of risk factors from pre-1985
+studies is virtually indistinguishable from the set of risk factors from 2014 studies, indicating that the field developed a narrow set of potential risk factors early on and stuck to them. There were, of course, some exceptions to this rule, but these have represented a small minority of effect sizes. By and large, the STB risk factor field appears to have conducted essentially the same studies over and over again throughout the last 50 years. In light of this pattern, it is not surprising that predictive ability has remained nearly constant over the last 50 years. Similarly, in light of these two patterns, it should not be surprising that STB rates have also remained nearly constant over the last 50 years.
+Strength of particular risk factors. Although overall risk factor magnitude and accuracy were weak (see above), we hypothesized that certain broad risk factor categories would stand out as much stronger than others. Results did not support this hypothesis. Even the strongest categories did not exceed weighted mean odds ratios of 2.5 for any outcome, and all categories clustered within a tight range of 1.0 to 2.5. Interestingly, the most popular risk factor categories tended to be among the weakest. Internalizing psychopathology, demographics, externalizing psychopathology, and social factors together accounted for 66% of all risk factors; however, they tended to be among the weakest half of categories for each outcome. The reason for the popularity-strength discordance is unclear. One possibility is that these categories remain popular because of tradition. Another possibility is that, because these categories are strong correlates of STBs, researchers have assumed that they are also strong risk factors. Regardless of the reason for this discordance, the present findings show a need for novel risk factors.
+After obtaining these category-level results, we speculated that these broad categories may have concealed a few powerful subcategories of risk factors. For example, although internalizing psychopathology as a whole is not a strong STB risk factor, it may be that depression, posttraumatic stress disorder, or similar subcategories stand out as especially powerful risk factors. Results did not support this hypothesis. Even the top five most powerful risk factor subcategories were weak in absolute sense (see Table 4). Across all outcomes, only four of these subcategories exceeded a weighted mean odds ratio of 3.0 (three were prior STBs, one was prior psychiatric hospitalization) and few were significantly different the rest of the top five. Taken together, these findings indicate that, at least within the narrow methodological limits of the existing literature, there is no evidence that any known risk factors—broad or specific—approach what many might define as clinical significance.
+Outcome-risk factor specificity. As noted above, researchers have long been interested in the possibility that each STB outcome may be associated with at least a partially unique set of risk factors. Although we tested this intriguing hypothesis in multiple ways, the present meta-analysis found no evidence for any such distinctions within the existing literature. First, the most general test of this hypothesis found that combined risk factor magnitudes and accuracies were almost identical across suicide ideation, attempt, and death outcomes. As risk factor types were similar across all outcomes, this indicated that, at least in a highly general sense, there were likely to be few risk factor differences across outcomes.
+Second, a more specific test of this hypothesis showed that broad risk factor categories tended to display similar magnitudes and accuracy across outcomes. In general, magnitudes rarely var
+ied more than 0.20 and confidence intervals almost always overlapped. For example, the demographics category displayed similar magnitudes across suicide ideation (wOR = 1.25; CI: 1.18, 1.33), attempt (wOR = 1.27; CI: 1.19, 1.36), and death outcomes (wOR = 1.39; CI: 1.24, 1.57). Third, an even more specific test of this hypothesis revealed that risk factor subcategories were also consistent across outcomes. Prior STBs tended to be the strongest subcategories, followed by factors that signified elevated levels of general psychopathology, prior STBs, and stress (e.g., prior psychiatric hospitalization, screening instruments, stressful life events; see Table 4).
+Considering all of these findings together, there appears to be no evidence of outcome-risk factor specificity. It may be tempting to interpret some of the small differences across outcomes as having meaningful implications. As emphasized above, however, we note here that all risk factors were weak in magnitude and that any differences across outcomes (or even categories) are not likely to be highly meaningful. Also as emphasized above, however, we do not take these results as proof that there are no meaningful differences between outcomes or categories. Rather, we strongly believe that there are many extremely important differences but that these differences have been obscured by the significant methodological limitations of the existing literature. Correspondingly, we stress that the present null outcome-risk factor specificity findings only apply to these associations within the narrow methodological limits of the existing literature.
+Moderator Effects
+We investigated several methodological features as potential moderators of general risk factor magnitude and accuracy. Although overall predictive ability and predictive ability across specific risk factors was weak, it was possible that certain methodological features were associated with improved prediction. Given the restricted range of effect sizes in the existing literature (see Figure 5), however, any moderation effects were likely to be modest.
+Risk factor effects across time. Since 1985, the number of qualifying papers and effect sizes nearly doubled every 10 years. More than half of all papers and effect sizes were published after 2007 and, if these growth trends hold, the 2015 to 2024 decade will produce more STB risk research than all pre-2015 years combined. The sharp increase in research was not accompanied by a sharp increase in predictive ability. Weighted odds ratios, hazard ratios, and diagnostic accuracy statistics were highly consistent across eras and, if anything, predictive ability diminished slightly over time. These findings suggest that, as a whole, STB risk factor research has not been progressive—later research has not built on earlier research, there has been little innovation, and knowledge has not steadily accrued. Only 3.29% of eligible papers and only 1.93% of eligible effect sizes were published before 1985; yet, if the present meta-analysis had been conducted in 1985, it would have yielded nearly identical findings. This fact signals the need for major changes in STB risk factor research.
+Follow-up length. Studies were very long on average. The mean follow-up length was nearly 10 years and the median follow-up length was five years. A much higher proportion of effect sizes had follow-up lengths of longer than 10 years (29%) than lengths of six month or shorter (5%). Less than 1% of effect
+sizes had follow-up lengths of one month or shorter. These findings show that there are very few studies on short-term or acute STB risk, which in turn means that there are very few longitudinal studies of “warning signs” for suicidal behavior. This is concerning given that clinicians are often asked to make decisions about acute risk rather than decisions about risk over months, years, or decades.
+Presumably, most studies have had long follow-up lengths because researchers aimed to capture as many STB events as possible and because they reasoned that longer lengths may translate into better predictive ability. Results did not support this possibility as weighted odds ratios, hazard ratios, and diagnostic accuracy statistics were consistent across all follow-up length intervals. These findings suggest that, at least in terms of predictive ability, there is no compelling reason to a conduct STB study with a long follow-up interval. On the other hand, new studies with very short follow-up lengths may yield tremendous theoretical and empirical advances. In particular, the present findings show that the existing literature can only speak to risk factor magnitude and accuracy over long intervals. It is possible that risk factors, especially time-varying risk factors (vs. stable or trait-like risk factors), are exponentially stronger over very short intervals. For example, an intense relationship breakup may confer high STB risk for minutes, hours, or days. If a study examines this factor over the course of months, years, or decades, this initial high-risk “signal” may get lost in an ever-increasing cacophony of “noise.”
+Sample characteristics. There were large variations in the severity, age ranges, and sizes of samples across STB risk factor studies. General and clinical sample studies were much more common than STB sample studies, and this gap has grown over the last 20 years. Prediction tended to be stronger in general samples compared with clinical and STB samples, but these differences were small in an absolute sense. Likewise, there was no meaningful moderation of these effects based on type of clinical sample (e.g., depression, psychosis), type of STB sample (e.g., ideators, attempters), or clinical/STB sample origin (e.g., inpatient, community). Stronger predictive ability in general samples may have occurred because risk factors related to psychopathology, prior STBs, and social factors tend to be correlated. Reference groups in clinical and STB samples likely acted as controls for these correlations, which slightly reduced effect sizes. It follows that, although general samples tended to produce better predictive ability, they also produced more ambiguity about the specificity of risk factor effects. For example, if depression was found to be a significant risk factor in a general community sample, it may be unclear whether this effect was due to depression or psychopathology more generally. If this same effect were found in a severe clinical sample, there could be greater confidence this effect was due to depression.
+Adolescent sample studies have increased in recent decades, but adult sample studies have increased more sharply and have always accounted for the majority of prediction effect sizes. There was a notable scarcity of adolescent sample effect sizes with suicide death as an outcome. Predictive ability was comparable across adult, mixed, and adolescent samples for all outcomes. This indicates that there are few meaningful age-related differences in risk factors from the existing literature. However, we again caution that this conclusion only apples to STB risk factors within the narrow methodological limits of the existing literature. Future studies that
+examine more innovative risk factors with improved methods (see below) may detect important risk factor differences among these age groups.
+The size of studies in terms of STB participants increased sharply in the mid-1990s and then leveled off. Over time, very small (1 to 25 participants) and very large (500+ participants) studies have become less common, and moderately sized studies (26 to 500 participants) have become more common. The median number of STB participants, 57, has remained relatively small across the eras of suicide risk factor research. Analyses revealed that risk factor magnitude and accuracy were comparable across all sample sizes for all outcomes. These findings suggest that extremely large samples (e.g., population-level samples or national survey samples) carry no benefit in terms of prediction strength. But because larger samples more accurately estimate true effects (even if those effects are small), future studies would benefit from including at least moderately large samples (e.g., 100 to 500 participants), especially for suicide attempt and death outcome studies.
+Protective Factors
+There were many fewer protective factor effect sizes than risk factor effect sizes, and studies rarely set out a priori to investigate protective factors. The majority of these effect sizes were demographic factors that we coded as protective factors based on their expected associations with each outcome according to epidemiological statistics on STBs (see Method section). Protective factor effect sizes generally mirrored risk factor effect sizes, nearly doubling every decade and producing weak overall effects. These findings make clear the need for studies specifically designed to evaluate protective factors, particularly studies with factors that are not simply the inverse of risk factors (e.g., “no psychopathology,” “no alcohol use vs. alcohol abuse,” and “male gender predicting suicide attempt”).
+Limitations
+This meta-analysis should be interpreted in light of its limitations. First, we were unable to accommodate the inclusion of studies that employed advanced statistical techniques, some of which included time-varying risk factors. Although these would have represented a small proportion of all studies, it is possible that their inclusion may have generated larger effect estimates. Second, many of the reported estimates may overestimate risk factor effects because we only included published studies and did not estimate publication bias within subanalyses (because this would have been unwieldy and largely redundant with overall analyses). Based on overall analyses, we estimate that effects for subanalyses are 40% to 50% weaker than reported for suicide ideation and attempt outcomes. Third, although we took multiple steps to reduce dependence among effect sizes (see Methods section), our primary analytic strategy treated each effect size within a study as if it were independent from all other effect sizes within that study (i.e., zero correlation among various types of risk factors in the same study). This analytic strategy slightly decreased confidence intervals and Type I error, increasing the likelihood of significant effects. Importantly, however, an analytic strategy that assumed and accounted for complete dependence among effect sizes within a
+given study produced nearly identical results. Given that data in the present study are likely much closer to complete independence than complete dependence, we estimate that our primary analyses produced very slight increases in Type I error (i.e., slightly more significant effects than truly exist).
+Fourth and most importantly, the overwhelming limitation of this meta-analysis reflects the overwhelming limitation of the existing STB risk factor literature: the methods of most existing studies have been extremely narrow and homogenous, and have not allowed for tests that approximate how STB risk may work in nature. This major limitation means that all of the present results should be interpreted with the caveat that these findings only apply to STB risk factors within the narrow methodological limits within which STB risk factors have been studied for the past 50 years. As a result, the present meta-analysis cannot support conclusions like “internalizing psychopathology is a weak STB risk factor.” Instead, it can only support conclusions like “internalizing psychopathology is a weak STB risk factor within particular methodological constraints, but it remains unknown if internalizing psychopathology is a strong or weak risk factor outside of these constraints.” Specifically, conclusions about the existing literature are constrained by at least three major methodological limits: (a) long follow-up intervals; (b) risk factors measured in isolation rather than in combination; and (c) risk factors measured in a static or trait-like fashion. That is, most existing studies have tested whether a single isolated factor measured at one moment in time predicts STBs over the course of years or even decades.
+The poor predictive ability produced by this modal research design is consistent with evidence that the vast majority of people who possess a specific risk factor never engage in suicidal behavior. For example, mood disorders and prior psychiatric treatment (especially inpatient) are widely considered to be major risk factors for suicide death. However, the lifetime probability of suicide death is low (in an absolute sense) among mood disorder outpatients (2%) and mood disorder inpatients (4%; Bostwick & Pankratz, 2000); for reference, approximately 1.6% of the general population dies by suicide (CDC, 2014). Because 96% to 98% of mood disorder patients will not die by suicide, a mood disorder diagnosis in isolation is inherently limited in its ability to accurately predict future suicide death: this prediction will be wrong 96% to 98% of the time. The present meta-analysis highlights the need for studies that overcome the limitations inherent in using a single, inaccurate factor to predict STBs.
+It is notable that the modal research design in the STB risk factor literature does not approximate most hypotheses or theories about STB risk. Although the specific content of these hypotheses and theories varies widely, most subscribe to a basic process whereby a subset of trait-like distal factors and highly volatile proximal factors combine in a complex way to raise STB risk for a few minutes, hours, or days. For example, few would expect hopelessness measured as an isolated trait-like factor to accurately predict suicide death over the course of a decade. But many might expect that, among older males who own a gun and have a prior history of self-injury and very little social support, a rapid elevation in hopelessness after the unexpected death of a spouse would greatly increase suicide death risk for a few hours or days. Yet, most of the existing literature has tested the former hypothesis rather than the latter. Viewed in this light, the present results may
+not be surprising: most hypotheses and theories are actually consistent with these weak meta-analytic findings.
+Summary
+This meta-analysis found that, based on the existing literature, all STB risk (and protective) factors are weak and inaccurate. This general pattern has not changed over the past 50 years and was not meaningfully moderated by study characteristics (e.g., length, sample severity) or type of risk factor (e.g., internalizing psychopathology, prior STBs). These results may be surprising and disappointing to many researchers and clinicians, and may be easily misinterpreted. To help facilitate the interpretation of the present findings, below we articulate what these findings do and do not mean for research and practice. Most of these points stem from the importance of distinguishing between (a) the actual nature of STB risk and (b) the narrow band of this nature examined by the existing literature.
+What the present findings do not mean. First, the present results do not mean that widely used STB risk guidelines (see Table 1) are invalid or useless. As emphasized above, the existing STB risk factor literature can only speak to STB risk within narrow methodological constraints. These constraints have greatly limited the ability of the existing literature to speak to the validity or utility of these guidelines. For example, suicide “warning signs” are widely used in clinical settings to gauge suicide risk. These warning signs are thought to indicate imminent suicide risk and often involve changes in factors (e.g., dramatic changes in mood, increased alcohol use, social withdrawal; see Rudd et al., 2006). Unfortunately, the existing literature has been unable to evaluate these warning signs because very few studies examined risk over a short time period (i.e., days or weeks) or examined changes in risk factors over time.
+Second and similarly, the present findings do not mean that traditional risk factors and theories based on these risk factors have little relevance to STBs. As noted above, the existing literature has been unable to test most STB hypotheses and theories about risk factors.
+Third, the present results do not mean that the field should lessen its focus on STB risk factors or prediction. Only a narrow band of the nature of STB risk has been examined. Failing to investigate the rest of this nature would be detrimental to the understanding, prediction, and prevent of STBs.
+What the present findings do mean. First, the present results mean that existing STB risk guidelines are rationally derived and have not been appropriately evaluated by the existing literature. As most of these guidelines were produced by expert consensus, there is reason to believe that they may be useful and effective. Accordingly, we recommend that these guidelines remain in use, but emphasize that there is an urgent need to empirically evaluate these guidelines within longitudinal studies.
+Second, traditional risk factors are poor predictors of STBs within the narrow methodological constraints of the existing literature, but it is unknown how these risk factors perform outside of these narrow methodological limits. Whatever the role of these risk factors, it is likely to be complex. The present results show that single risk factors (e.g., a depression diagnosis considered in isolation) are inherently limited in their ability to accurately predict future STBs. We speculate that the additive or interactive
+effects of a small number of risk factors would also produce inaccurate prediction. Consistent with recent research (Kessler et al., 2015) and many hypotheses and theories about suicide, accurate STB prediction will likely require a complex combination of a large number of factors (i.e., >50), many of which are timevarying. Moreover, it is likely that there are many different paths to STBs. This equifinality means that a single one-size-fits-all algorithm for STB prediction is unlikely; researchers will likely need to develop separate algorithms different populations (e.g., adolescents; the elderly; inpatients; soldiers/veterans; prisoners; mood disorder patients; psychotic disorder patients; etc.).
+Third, the present findings indicate a need for more STB risk factor studies, but clarify that these studies must overcome the methodological limitations of the existing literature. It may be helpful for future STB risk factors studies to be evaluated in part based on how they overcome these limitations. Similarly, it may be helpful for these future studies to be evaluated based on their clinical significance in addition to their statistical significance. In most fields, a factor that increases risk by 10% may be extremely important. But the low bases rates of STBs mean that even a statistically significant factor that increases suicide risk by 1,000% may not be clinically significant. In short, the present metaanalysis suggests that, to advance knowledge about STB risk, we must change our approach to conducting and evaluating studies.
+Future Directions
+The present findings show a clear need for studies that are capable of testing complex hypotheses about STB risk. We propose four suggestions for future directions that may facilitate such studies. First, studies should include short follow-up intervals—on the order of minutes, hours, or days. Second, studies would benefit from repeatedly or continuously measuring constructs. For example, it would be helpful to measure rapid increases or decreases in hopelessness rather than trait hopelessness. Third, there is a need for novel risk factors. Several such factors have recently been proposed, but they have rarely (e.g., implicit identification with death/suicide; Nock et al., 2010) or never been directly tested as risk factors (e.g., suicidal capability; Joiner, 2005). Fourth and finally, risk factors should be combined in a complex but replicable manner. In other words, we recommend that the field shift from a focus on risk factors to a focus on risk algorithms.
+Machine learning approaches are ideal for this latter recommendation (see Ribeiro et al., 2016). Traditional statistical techniques (e.g., regression) are helpful for testing relatively simple hypotheses about STBs (e.g., Diathesis X Stress models), and machine learning approaches confer little advantage for smaller data sets that include few predictors. As indicated by the present metaanalysis, however, accurate STB prediction may require models that consider complex relationships among hundreds of predictors. Within sufficiently large data sets, distinct advantages of the machine learning approach emerge, including the following: (a) the machine determines the optimal algorithm for prediction (vs. a human deciding which variables should be included and what the relationship among the variables should be); (b) many techniques can model highly complex relationships among predictors, going far beyond traditional additive, interactive, and linear models; and (c) these techniques are often applied with generalizability in mind, with most studies including a range of strategies to prevent
+overfitting (e.g., cross-validation, bootstrap aggregating). In short, these techniques have the potential to produce models that accurately reflect the complex nature of STB risk. Just as Internet search algorithms require the consideration of hundreds of variables to accurately answer a search query, complex machine learning algorithms are likely necessary for accurate STB prediction. Pioneering retrospective work showed the promise of this approach (e.g., Delgado-Gomez et al., 2012; Lopez-Castroman et al., 2011; Mann et al., 2008), with later prospective work confirming the utility of this approach for suicide prediction (Kessler et al., 2015).
+The four suggestions outlined above were nearly impossible to heed until recent advances in technology. These new technologies make it possible to repeatedly assess risk factors and STBs on the scale of minutes or hours rather than years or decades. Recent advances also make it easy to create mobile app versions of novel cognitive and affective tasks traditionally confined to laboratory studies (cf. Franklin et al., 2016). Similarly, it is now possible for most researchers—even those with no computer science knowledge—to employ machine learning algorithms to form complex (but robust and replicable) combinations of a large number of potential risk factors within large data sets. Perhaps most critically, the proliferation of powerful mobile technologies now makes it possible to quickly and economically recruit thousands of individuals from around the world who may be at elevated risk for STBs. This represents a democratization of STB risk factor research whereby most researchers can now conduct studies with the suggested methods without need of large grant funding, several research assistants, or connection to a large psychiatric hospital.
+This meta-analysis brought to light many concerning patterns. In doing so, however, it also provided a clear path toward progress. Studies that leverage recent technological advances and more closely approximate theories about STBs have the potential to produce rapid advances in knowledge. Such knowledge would bring about much needed advances in understanding, predicting, and preventing STBs.
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+Comment
+Dan Siskind and colleagues1 did a timely systematic review of randomised controlled trials that tested Food and Drug Administration approved medications— varenicline, bupropion, and nicotine replacement therapy—for smoking cessation in smokers with schizophrenia spectrum disorders. Pairwise and network analyses of 18 studies with 1437 participants allowed the authors to determine how these medications fared when compared with placebo and each other. All three medications were more effective than placebo, and those treated with varenicline showed the highest rates of smoking abstinence (48 [22%] of 215 vs ten [6%] of 179), followed by bupropion (26 [18%] of 146 vs seven [5%] of 146) and nicotine replacement therapy (25 [9%] of 272 vs five [2%] of 289). Across all studies, no medication was associated with negative neuropsychiatric effects, and all were determined to be safe.
+These results show that cessation medications are safe and effective in helping people with schizophrenia spectrum disorders to achieve short-term abstinence. All smokers with these disorders should be offered medication, alongside education and support, as part of standard clinical care. The fact that all three medication options are better than placebo allows for some discussion around patient preferences and offers alternatives if the first medication is ineffective. Research in the general population suggests that
+combined varenicline and nicotine replacement therapy and combination nicotine replacement therapy (ie, concurrent use of two different forms of nicotine replacement therapy, such as nicotine gum and nicotine patch) could be associated with higher quit rates than is monotherapy. One study included in the analyses1 reported a 33% abstinence rate in a combined bupropion and nicotine replacement therapy group, suggesting that combined therapies might produce promising results in those with schizophrenia. As noted by Siskind and colleagues, most of the studies included in the analyses were of low quality and brief duration. Because this population is at heightened risk for rapid relapse after medication discontinuation, higher quality studies with longer dosing and follow-up periods, as well different medication combinations, are needed to optimise pharmacotherapy strategies. Even so, the results reported here provide sufficient evidence to support widescale use of smoking cessation medications with these patients.
+Even with these promising results, many questions remain. The quit rates for all medications reported in this study1 are significantly lower than those reported in randomised controlled trials in the general population (eg, varenicline quit rates are consistently between 25-40% at the end of treatment). Why this degree of benefit is not found in smokers with schizophrenia is unclear. Additionally, smokers with schizophrenia
+consistently show comparatively low placebo quit rates (ranging from 2% to 6% in the present study1), suggesting cessation might be especially difficult in this population, possibly owing to biological influences associated with the disorder. Compared with people without schizophrenia, smokers and non-smokers with schizophrenia show differences in nicotinic acetylcholine receptor expression in various brain areas,2 which could affect response to both medication and placebo. These factors must be studied and addressed so that patients with schizophrenia can fully benefit from cessation treatments.
+Unfortunately, Siskind and colleagues were unable to analyse the effect of sex, counselling, and psychotherapy on medication response.1 In the general population, female smokers often show worse overall quit rates but better cessation outcomes with varenicline than do male smokers.3 Given the observed sex differences in disorder prevalence, symptom presentation, and smoking rates in schizophrenia, characterising sex differences in medication response in this population might be crucial to improving cessation outcomes. Furthermore, individual cessation counselling and psychotherapy are highly effective treatments in the general population,4 particularly when combined with medications. However, how such interventions can influence smoking pharmacotherapy responses in individuals with schizophrenia spectrum disorders remains unclear.
+Despite the need for treatment and the corresponding availability of treatments for smoking in schizophrenia, implementation of pharmacological interventions, as part of routine physical and mental health care, is insufficient. Fewer than 10% of these patients receive a prescription, even after being advised by their provider to quit.5 Despite sufficient evidence to the contrary, perceptions remain among health-care providers that cessation attempts will lead to a worsening of mental illness symptoms.6 Health-care providers should be informed that cessation medications are safe for these patients and that cessation contributes to reductions in mental illness symptoms.7 Psychiatrists continue to miss opportunities to offer cessation treatments,8 partly because of the belief that patients are not interested in quitting and partly because of time constraints.9 Studies have shown that psychiatrists can deliver evidence-based tobacco cessation treatment9 without adding a substantial amount of time to their patient
+encounters. Because they might see patients more regularly than primary care providers and are more likely to feel comfortable managing mental health issues surrounding cessation, psychiatrists could be in the best position to deliver this intervention.
+There is room for optimism that cigarette smoking in people with schizophrenia will soon receive the important attention it deserves. Research is pointing the way towards overcoming old barriers and has shown that patients with schizophrenia can quit smoking. Those who have quit identify advice from their prescriber and cessation medications as useful components of their success.10 Behavioural and motivational strategies can help patients prepare for cessation and learn new ways to cope with stress. Findings from Siskind and colleague's Article present some of the strongest evidence to date that these medications are safe and effective for people with schizophrenia and should help support increased prescribing by those who have been wary of such interventions in the past. These strategies will make a difference in the lives of people with schizophrenia.
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+The first time I spoke with Gail Levine, an internist whose practice at Boston’s Brigham and Women’s Hospital is devoted to patients with severe mental illness, she said something that still nags at me. I was caring for a patient of hers, a man in his mid-50s who had paranoid schizophrenia. Mr. D. had recently had a large myocardial infarction but refused revascularization. Though his condition had stabilized, that day he’d become visibly dyspneic, with hemodynamic evidence of early tamponade. Because he’d repeatedly expressed his unwillingness to have needles stuck in him, I feared he would refuse pericardiocentesis and was relieved when Levine called.
+My track record negotiating with patients with serious mental illness who refuse treatment wasn’t great. I’d recently cared for a relatively young woman with schizophrenia who had severe, symptomatic mitral regurgitation. She was an ideal surgical candidate, but I couldn’t convince her to get blood draws, much less surgery. She sat in bed, gently rocking, brushing her hair, not answering questions, and only occasionally allowing an exam. I soon gave up trying to persuade her and didn’t attempt to understand her reasoning. Instead, because she clearly lacked decision-making capacity, we initiated the months-long process of court-appointed guardianship. This step spared me a harder decision: Should we save her heart if doing so further harmed her already tortured soul?
+With Mr. D., there was no time to punt the tough decisions to someone else; if he went into tamponade, he could die. Though I wouldn’t have let that happen, Levine wasn’t so sure. “You realize,” she said, “that patients with severe mental illness die, on average, at age 53. This is one big reason why. Doctors assume that because he has mental illness, he has no quality of life. You can’t just let him refuse.”
+the m ortality gap
+In 1932, Benjamin Malzberg, a New York epidemiologist, published a study showing that people with mental illness died, on average, 14 to 18 years earlier than otherwise similar people in the general population.1 This mortality gap persists today and may even have widened: a 2006 U.S. study suggested that it ranged from 13 to 30 years.2 Indeed, the gap persists worldwide,3 mostly owing to medical conditions, such as cancer and cardiovascular disease, rather than “unnatural” causes, such as accidents and suicide.
+Until recently, this disparity was largely ignored, but its persistence reflects more than an attentional deficit. Some obvious contributing factors lack readily available solutions. Certain behaviors that are more common among people with serious mental illness, such as smoking, substance abuse, and physical inactivity, heighten the risk for chronic diseases. Treatment of such diseases depends on adherence to medications and behavior changes, which may be more difficult for patients with severe mental illness. The impaired insight resulting from some forms of mental illness can compromise adherence, as can low health literacy and poverty, which disproportionately affect this population. Finally, common medications that may be critical for psychiatric stabilization often cause obesity and diabetes, thereby contributing to the cardiovascular disease burden.
+These realities make it difficult to imagine narrowing the mortality gap anytime soon. But after observing Levine and other physicians who care for patients with serious mental illness, I wonder to what extent such pessimism is a selffulfilling prophecy. Might our attitudes toward mentally ill people contribute to this mortality gap, and if so, can we change them?
+Although there are few data on the effects of our attitudes on the health of mentally ill people, one relatively well studied phenomenon is diagnostic overshadowing, a tendency to attribute such patients’ physical symptoms to their mental illness. One study, for instance, showed that physicians’ awareness that a patient presenting with acute pain had a history of depression or somatic symptoms reduced their level of suspicion of serious illness and thus the likelihood of ordering further tests.4
+Yet there may be a fine line between biased interpretations and relevant information. For instance, when I’m trying to determine whether a patient with chest pain is having an acute coronary syndrome, a history of depression shouldn’t lower my suspicion, but a long history of somatic symptoms probably should. Clinical judgment, for better or for worse, comes down to judging.
+Another approach to studying bias is to assess whether rates of guideline-recommended care differ between patients with mental illness and those without it. And they seem to. Studies have identified disparities in various types of care, from some types of cancer screening5,6 to elective surgeries7 to bloodpressure monitoring8 — though such differences may also reflect patient preference or lack of follow-up.
+More telling disparities are those in rates of coronary angiography9 and revascularization10 among patients with acute myocardial infarction, a one-time event in an urgent setting. Still, bias may not be the cause. Patients with serious mental illness, particularly those with paranoia, may delay seeking care, which reduces the potential benefits of revascularization. Moreover, no one wants to put a stent in someone who’s unlikely to comply with dual-antiplatelet therapy. And, as Mr. D.’s case illustrates, care may be offered but refused. The question is whether a willingness to allow patients to refuse necessary care represents a bias in its own right.
+When Mr. D. presented with chest pain, Levine was notified early in the morning that he was in the emergency room. The cardiologist who saw him recommended coronary angiography. Mr. D. refused. Levine told the treating team that he had a guardian, a court-appointed attorney, who could give consent for the procedure and that if the guardian was unreachable, an ethics consult should be called. “He wasn’t competent to refuse,” she told me.
+Though Levine tried calling the treating team several times throughout the day, she learned that evening that the guardian had been reached at around 2 p.m. but got disconnected and never called back. The substantial delay in care meant that the proposed benefit of the procedure was uncertain, which made it harder to justify overriding Mr. D.’s wishes. But typically, when the benefit is clear, the stakes are high, and the patient lacks decision-making capacity, the law is straightforward: consent is “implied,” and physicians should override the patient’s wishes. Where the potential for bias comes in, then, is in determining whether the patient has capacity.
+As Nicholas Bagley, a health law professor at the University of Michigan, explains, capacity assessments represent the application not of rules (“Stop at a red light”) but of standards (“Stop if you think it’s unsafe”) — which helps explain why we don’t do them well. One study found that of 302 medical inpatients, 40% lacked capacity to consent to treatment, but the treating team recognized that lack in only about 25% of those cases.11 “You can’t just pull out your capacity-meter,” Bagley says, “and say that a patient lacks capacity.”
+One reason we may falsely assume that a patient has capacity is time: physicians may feel that they can’t afford to spend 20 minutes doing a proper assessment, and it’s much quicker to assume that patients have capacity to refuse care than to follow the long procedure to override their wishes. But that procedure consumes more than time: it also consumes us. There’s some
+thing inescapably brutal in sedating and restraining a floridly psychotic patient in order to impose undesired care, regardless of the medical need.
+The potential need for force also reminds us of our legal Catch-22. Although you could be sued for negligence for failing to provide necessary treatment for someone who lacked capacity to refuse care, you could also be sued for battery for overriding the wishes of a patient who you mistakenly thought lacked capacity. Though Bagley emphasizes that courts would be “pretty unlikely” to second-guess physicians, he concedes that the “residual legal risk may give a physician pause.”
+Moreover, often it’s simply unclear whether a patient has capacity. My own approach often entails a perfunctory exercise in repeat-after-me — for example, “Tell me you understand that if you leave the hospital with such a high potassium level, you could die.” If the patient says, “I understand I could die,” I document the interaction and rationalize: “What else can I do?” A lot, actually.
+The MacArthur test, the most widely used capacity-measurement tool, assesses four criteria.12 The patient must be able to communicate a choice, understand the relevant information, demonstrate appreciation for the situation and its consequences, and reason about the treatment options. Whereas “understanding” is reflected in an ability to relay the relevant medical facts, “appreciation” requires a sense of how those facts relate to oneself: a patient may understand that kidney failure may cause lethally high potassium levels while maintaining that his kidneys are not failing.
+Better training in capacity assessment is obviously necessary, but I suspect that no amount of training can bridge the gap where law and ethics divide. As the ethical mandate to share decisions and honor patient preferences mounts, invoking legal criteria to justify overriding such wishes becomes increasingly unacceptable. The law may distinguish between the competent woman with a recently controlled gastrointestinal bleed who refuses colonoscopy so she can attend her daughter’s wedding and the delusional woman
+who believes that the prep contains snakes; but as we strive to shed our paternalism, do we have a harder time distinguishing a preference that should be honored from an irrational decision that should be ignored?
+understanding the patient
+If coercion is what we find aversive, then the obvious question is whether there are better ways to convince patients to get needed care. But perhaps this question is precisely the problem. More than 30 years ago, research by Paul Appel-baum, a Columbia University psychiatrist who created the MacArthur test, showed that the reasons for treatment refusals were typically complex and related to poor communication, lack of trust, and underlying psychological factors such as depression or delusions. But he observed that “Little or no effort was made to ascertain, by talking with the patient, the factors underlying the refusal.”13
+Asked about the study now, Appelbaum emphasizes that just a little effort to understand can be lifesaving. One man with a massive lung tumor, for instance, refused surgical resection, without which he was likely to die. When Appel-baum spoke with the patient and his wife, he discovered that they’d heard that cancer spreads when exposed to air. In many cases, if a surgical team takes the time to elicit such misinformed beliefs, they can easily be corrected.
+But where does a misinformed belief end and delusion begin? A few months after Mr. D.’s heart attack, Levine saw him in the clinic. He’d stopped taking his medications, insisting that another physician had told him his heart was “beautiful.” Levine reviewed his electrocardiogram and told him it revealed the extent of his myocardial infarction. He fired her. Levine says she regrets her handling of the situation. “You should never directly contradict delusional people,” she says. I said I couldn’t understand how she could convince him to take his cardiac medications if she didn’t first convince him he had heart disease. She replied: “I should have said, ‘I want to keep your beautiful heart beautiful.’”
+The New England Journal of Medicine
+Downloaded from nejm.org on October 21, 2016. For personal use only. No other uses without permission.
+Copyright © 2016 Massachusetts Medical Society. All rights reserved.
+Jim O’Connell, the internist who cofounded the Boston Healthcare for the Homeless Program, once cared for a man with schizophrenia who had a small basal-cell cancer on his scalp. For 16 years, O’Connell urged him to have the lesion treated, but instead the man would pick it off and it would return, until finally it invaded his occiput and metastasized to his spine. He was hospitalized for surgery, and when he received medical and psychiatric treatment, his mental condition stabilized. Too late for a cure, his newfound mental clarity carried the harshness of hindsight. Several times before he died, he told O’Connell, “I can’t figure out why I didn’t just do what you said.”
+“We’re stuck in this situation a lot,” O’Connell says, “not knowing when the illness is deadly versus something we can work with.” Like many mentally ill people
+who live for years with chronic diseases, this man was in no imminent danger. Until he was.
+A tendency to accede to refusals of high-stakes care by patients who are near death may contribute to the mortality gap, but since most opportunities to extend life occur much earlier, attitudes informing our approach to chronic disease management may affect the disparity more. The challenge is to separate passivity driven by pernicious bias from appropriate respect for choice. Though capacity seems like a binary trait — either you have it or you don’t — in practice, capacity thresholds tend to be lower for less critical decisions: it’s generally inappropriate to force someone to take statins, get a mammogram, or even have a slow-growing basal cell removed.
+But though coercion is rarely justifiable, the rationalization that “there’s only so much you can do” risks misrepresenting laziness as respect. There’s usually plenty we can do short of coercion, but it’s infinitely easier to document that the “patient refused” care than to ensure that the care is provided.
+Consider the effort required to coordinate a routine colonoscopy for someone with serious
+mental illness. Several visits are often needed to convince the patient that the procedure is worthwhile, and physicians who succeed have often spent years building trust. If the patient consents, how do you orchestrate the preparation, particularly if the patient is homeless? You may admit the patient the previous night or work closely with group-home staff, but any little hiccup — difficulty rising for an early appointment, a breakfast eaten accidentally — can undo everything. One man, facing his second colonoscopy attempt, told me about being sent home before his first. “I even had the pajamas on,” he said, referring to the hospital gown. But he had no ride home, so the nurse sent him away.
+Levine told me many stories highlighting the challenges of getting patients with serious mental illness through our system. “‘It takes a village’ doesn’t even begin to describe it,” she said.
+our system, ourselves
+The need for intensive coordination explains the appeal of integrated care models, designed to combine medical and mental health care; Levine’s practice, which embeds primary care in the comprehensive mental health services at Massachusetts Mental Health Center, is piloting this approach. Although we don’t know whether these models will narrow the mortality gap, simply easing navigation through our system would be valuable. But purportedly transformative care models often fail to substantially improve the quality of care, perhaps in part because of behavior fostered by a subtle shift in responsibility: as we focus on “fixing the broken system,” do we stop seeking solutions from within ourselves?
+What do abstract concepts like integration mean to a specific patient who needs your help — say, a psychotic patient with poorly controlled diabetes who’s in the midst of detox, who’s blind in one eye from being pushed down the stairs at her group home, and who, after you spend 40 minutes clarifying her medications and arranging an urgent ophthalmology appointment, tells
+you she thinks she’s pregnant and wishes to focus the visit accordingly?
+Having watched Levine navigate such an encounter gracefully and compassionately as my own blood pressure rose, I realize it would be Pollyannaish to conclude that we should all just strive to be more like her. People with serious mental illness are often our toughest patients; feeling overwhelmed by the effort of addressing their needs within our current system is rational and precisely why system changes are imperative. Yet “the system” is made up of millions of people like us. To meaningfully improve care for the mentally ill, we must recognize that, as Massachusetts General Hospital psychiatrist Oliver Freudenreich puts it, “Care integration is an attitude.”
+No doubt, some attitudes that compromise care are easier to change than others. Negotiating more effectively about care refusals, more adeptly assessing capacity, and recognizing when our efforts to orchestrate care have been inadequate seem feasible. Far harder is overcoming any collective belief that what mentally ill people truly need is not something we can offer.
+I was reminded of this challenge a few weeks ago when, walking home late at night, I passed a disheveled woman crying in anguish: “How am I going to get home?” She looked familiar, and I assumed I’d seen her on the street before. But as I sped up in the opposite direction, I became increasingly certain that she’d been a patient of mine a few months earlier. At that time she was in heart failure, and I knew how to fix her. Now she was psychotic, seemingly homeless, and I didn’t. So I kept walking, telling myself we weren’t so much doctor and patient as two people on the street, late at night, both trying to get home.
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+1.	Introduction
+Suicide is a major public health concern with at least 800 000 people dying by suicide each year across the globe and at least 20 times that number attempting suicide [1]. The pathways to suicide are complex, with suicidebeing the end product of an interplay of biological, clinical, psychological, social, cultural risk and protective factors [2-4]. Although knowledge of risk factors for suicide has grown markedly in recent decades [4], our ability to predict suicide is no better now than it was 50 years ago [5]. There are many reasons why the field of suicide research has not enhanced its predictive ability; key candidates include the low base rate of suicidal behaviour, as well as the fact that risk factors are often assessed in isolation and in a static rather than in a dynamic fashion [5]. In addition, until relatively, recently, there was a paucity of comprehensive theoretical frameworks that have attempted to understand the emergence of suicidal ideation and the transition from thinking about suicide to attempting suicide/dying by suicide [6].
+In the present paper, we focus on one such predominant framework, the integrated motivational-volitional (IMV, [6]) model of suicidal behaviour; we describe its theoretical origins, the key premises underpinning the model, empirical tests of the model and future research directions. In brief, the IMV model is a tri-partite model that describes the biopsychosocial context in which suicidal ideation and behaviour may emerge ( pre-motivational phase), the factors that lead to the emergence of suicidal ideation (motivational phase) and the factors that govern the transition from suicidal ideation to suicide attempts/death by suicide (volitional phase). This is the most detailed
+© 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
+specification of the model to date, which includes some refinements since its original exposition in 2011 (figure 1).
+2.	Theoretical origins and conceptual rationale
+The guiding principle that led to the development of the IMV model was the desire to synthesize the extant evidence into a detailed theoretical framework that could make predictions about the factors that lead people to think about suicide and those factors which govern whether people act on their thoughts, i.e. attempt suicide/die by suicide. Until Joiner proposed his interpersonal theory of suicide (IPT) [7], for the most part, the theoretical literature [8-11] did not account for the distinction between the prediction of ideation versus enaction. In this regard, the IMV model is a second-generation model, which, alongside the IPT [7,12] and the three-step theory of suicide (3ST) [13], is a theoretical perspective which explains the suicidal process consistent with the ideation-to-action framework [14]. These more recent models specifically hypothesize that the factors leading to the development of suicidal thinking are distinct from those that govern behavioural enaction, i.e. attempting or dying by suicide.
+As detailed elsewhere [6,15], four distinct theoretical perspectives were particularly important in the IMV model's development [9,11,16,17]. First, the backdrop to the IMV model is the diathesis-stress model [9], which recognizes that individual vulnerabilities confer elevated risk for developing suicidal ideation when activated by the presence of stressors. Examples of these vulnerabilities are personality characteristics, such as high socially prescribed perfectionism, or socio-environmental factors, e.g. socio-economic deprivation [4,18]. Combined with acute or chronic life stressors, these vulnerability factors increase the likelihood that an individual will experience an adverse psychological reaction to stress. This forms the basis of the pre-motivational phase of the IMV model, which includes background vulnerability factors.
+Second, the theory of planned behaviour (TPB) [16] influenced the development of the IMV model as it contends that
+the strongest immediate predictor of behaviour is an individual's intention or motivation to carry out the behaviour. Crucially, the TPB delineates distinct phases of intention formation and behavioural engagement (enactment).
+Central to the motivational phase of the IMV model is the relationship between defeat and humiliation, and entrapment, leading to suicidal ideation; key variables within Williams' cry of pain theory of suicide [11]. These elements are drawn from a concept known as ‘arrested flight', which was adopted from evolutionary psychology and originally used to explain behavioural states observed in individuals with depression. Arrested flight describes the experience of feeling as though one has been brought down (defeated) and has no prospect of escape or rescue (entrapment) [19]. These concepts characterize well the ‘tunnel vision' often observed in individuals experiencing suicidal distress, whereby suicide becomes the only perceived escape route. Humiliation also features within the cry of pain theory, but has received little substantive attention relative to defeat and entrapment.
+The final theoretical perspective drawn upon within the IMV model is the differential activation hypothesis [20,21], which posits that when an individual experiences distress, an association is formed between the feeling of distress and, in this case, suicidal ideation. With each subsequent episode of distress, the pathway from distress to suicidal cognitions becomes more established and, therefore, more easily activated; negative mood also potentiates a bias towards negative information, termed ‘cognitive reactivity' [22]. Even once an individual is no longer acutely distressed, these pathways lie dormant until triggered by a negative mood state or stress.
+3.	Key premises underpinning the motivational-volitional model of suicidal behaviour
+The IMV model is a three-phase biopsychosocial framework (figure 1 and table 1) that delineates the final common pathway to suicidal ideation and behaviour. As noted above, the
+pre-motivational phase describes the biopsychosocial context, identifying vulnerability factors and triggering negative events. The motivational and volitional phases are operationalized at two different levels. From a higher-order perspective, the core constructs of defeat/humiliation, entrapment, suicidal ideation and suicidal behaviour form the backbone of the model and span both phases. These core constructs have the potential to be influenced by lower order moderators, with the latter defined as factors that facilitate or impede the transition within a phase (threat to self and motivational-phase moderators) or across the phases of the model (volitional-phase moderators). The key premises of the model are summarized in table 1.
+(a)	The pre-motivational phase: background factors and triggering events
+The pre-motivational phase is comprised of a diathesisenvironment-life events triad [2-4]. Diatheses take the form of biological, genetic or cognitive vulnerability factors or individual differences characteristics that increase risk of suicide. For example, decreased serotonergic neurotransmission is one such vulnerability factor for suicidal behaviour [23]. Socially prescribed perfectionism, defined as unrealistically high expectations that we believe significant others have of us [24], is another individual difference vulnerability factor that has been consistently associated with suicide risk [25,26]. According to the IMV model, socially prescribed perfectionism is hypothesized to increase the likelihood that an individual feels defeated when an interpersonal crisis occurs (heightened sensitivity to negative signals in the environment). Indeed, higher levels of perfectionism are also associated with sensitivity to emotional pain [27], another factor within the pre-motivational phase.
+Understanding the social and environmental context of suicide risk has a long history [28]. More recent evidence highlights the socio-economic inequality of suicide [18] and the impact of rapid societal changes, such as economic recessions [29]. Early life adversity is also an unequivocal suicide risk factor, with evidence that it is associated with epigenetic changes in genes, cortisol (dys)regulation as well as with the (disrupted) formation of attachment relationships [2,30]. However, negative life events experienced at any stage in life confer risk [31,32].
+The overarching premise of the IMV model is that the pre-motivational factors have their effect on suicide risk through their influence on the constructs within the motivational and volitional phases.
+(b)	The motivational phase: emergence of suicidal ideation
+Consistent with Williams' cry of pain hypothesis [11], in this phase, we focus on the psychological processes that lead to the emergence of suicidal ideation and intent. Although we acknowledge that suicidal ideation and intent are blurred but, arguably distinct constructs, at this stage there is insufficient evidence to specify whether it is useful to add another phase, which explains the movement from ideation to intent. In essence, we posit that appraisals of defeat and/ or humiliation from which there is no perceived escape—a sense of entrapment—are the proximal predictors of suicidal ideation. As introduced above, sensitivity to signals of defeat may be affected by a range of factors, including socially prescribed perfectionism, pessimism and negative affect. Entrapment can be internal or external in nature; the former is concerned with being trapped by pain triggered by internal thoughts and feelings, whereas external entrapment relates to the motivation to escape from events or experiences in the outside world [19]. Feelings of entrapment are likely to give rise to agitation. Entrapment is distinct from hopelessness which is a pervasive sense of pessimism for the future [33].
+The emergence of suicidal ideation is the outcome of a process beginning with feelings of defeat and humiliation. Defeat or humiliation may also be characterized by social rejection and loss, two frequently reported precipitants of suicidal distress [2,34 - 36]. However, entrapment is not an inevitable consequence of feeling defeated or humiliated. According to the IMV model, the presence or absence of threat to self-moderators (TSMs) renders it more or less likely that defeat leads to entrapment.
+Given their established relationships with suicidal ideation and behaviour, social problem-solving [37 - 39], autobiographical memory biases [39-41] and rumination [42,43] are included here as TSMs. Although these factors are likely to affect entrapment as well as defeat and humiliation, we hypothesize that they will have their strongest effect on the defeat - entrapment relationship because they are implicated in problem resolution. As brooding rumination [44] is more strongly associated with suicide risk than reflection [42,43], we hypothesized that brooding would be an important moderator of the defeat - entrapment relationship. Despite limited research into the relationship between coping and suicide risk [45], given the conceptual overlap with social problem-solving, we proposed coping to be a TSM; but depending on how it is operationalized, it is
+likely to also moderate the entrapment-suicidal ideation relationship [45].
+The final part of the motivational phase is the transition from entrapment to suicidal ideation. We posit that the presence of motivational moderators (MMs) will increase or decrease the likelihood that entrapment is translated into suicidal ideation. The MMs include factors that, when present and protective, allow the trapped individual to see alternatives, a more positive future and less pain. Reasons for living [46], attainable positive future thinking [47,48], adaptive goal pursuit [49], belongingness [12] or connectedness [50] are MMs as they are thought to buffer against the emergence of suicidal ideation and intent. Conversely, feeling a burden [51], having little or no social support [52] and depleted resilience [53] will each increase the likelihood that entrapment will be translated into suicidal ideation/intent. Consistent with the TPB, the IMV model also hypothesizes that individuals with less negative attitudes towards suicide/death are also more likely to consider suicide as an option when they are trapped [16,54]. As all human behaviour is influenced by reflective and automatic processes [55], the prediction of suicidal behaviour is no different; therefore, these attitudes are implicit as well as explicit [56,57].
+(c)	The volitional phase: from suicidal ideation to suicide attempts/suicide
+The final phase of the IMV model outlines the factors, entitled volitional moderators (VMs), that govern the transition from suicidal ideation/intent to enaction (the VMs are expanded upon in figure 2). Although factors such as entrapment may be associated with suicide attempts (largely due to entrapment's association with suicidal ideation), a central tenet of the IMV model is that VMs are vital for transition. Drawing from Joiner's IPT, the IMV model proposes that the components of the acquired capability for suicide (fearlessness about death and increased physical pain tolerance [12,51]) are VMs. We believe, however, that the factors that govern the transition from ideation to attempts are broader than capability. We posit that VMs can be environmental, psychological, social or physiological in nature.
+Having access to the means of suicide, an environmental VM, is an important risk factor for suicide [3,58]. Exposure to the suicidal behaviour of others ( family or friends) is a social VM with an established relationship with suicide risk [59,60]. There are a number of potential mechanisms that explain this relationship. For example, the suicidal behaviour of others may increase the likelihood that an individual models or imitates a loved one's suicidal behaviour. Exposure may also increase the salience and cognitive accessibility of suicide such that an individual is more likely to attempt suicide when they encounter stressors. Similarly, we hypothesize that exposure to inappropriate representations of suicide (e.g. glamorizing suicide) via traditional and new media channels may increase the likelihood that a vulnerable individual engages in a suicidal act (cf. suicidal contagion and suicide clusters, [61]).
+Although there is some debate about how best to operationalize impulsivity, and the extent to which impulsivity is associated with the individual versus the act, its relationship with suicidal behaviour is evident [2,62]. The model also predicts that those with detailed (if-then) plans for their suicide or suicide attempt are more likely to attempt suicide/die by
+suicide than those without plans. There is also growing interest in the role of mental imagery of suicide and suicidal ‘flash forwards' where an individual has a mental image of being dead or dying [63]. We hypothesize that mental imagery increases the likelihood of enactment as it acts as a form of cognitive rehearsal for the behaviour.
+A past history of self-harm or suicide attempts is a VM. If an individual engages in suicidal behaviour once, they are statistically more likely to do so again [3]. The dotted lines in figures 1 and 2 reflect the dynamic and ( for some) cyclical relationship between suicidal ideation and repeat suicide attempts. In addition, when at-risk individuals perceive themselves to have complete control over their suicidal behaviour, which may manifest itself as high capability, suicidal behaviour may be triggered directly, ostensibly bypassing the ideation/intention formation stage of the model. Although the model was developed originally to understand suicidal behaviour per se, the basic premises of the model also apply to self-harm, irrespective of motive. For example, volitional-phase moderators have been shown to distinguish between adolescents who have thought about self-harm and those who have self-harmed ( for a wide variety of motives) [64].
+4.	Empirical tests of the model and its components
+A growing number of studies have tested the IMV model or its components. As noted above, research has been supportive of the utility of VMs for differentiating between adolescents with self-harm ideation and those who enact the behaviour [64]. In another study of college students, those who reported suicidal ideation did not differ in motivational-phase variables from individuals who had attempted suicide in multivariate analyses, but they did differ on volitional-phase variables, as per the IMV model [65]. A recent study from a populationbased birth cohort of 4772 adolescents also found that exposure to the self-harm of others (alongside psychiatric disorder) was the factor that most clearly differentiated those who had attempted suicide from those who had thought about suicide without making an attempt [66].
+Prospective research has examined two of the central components from the motivational phase, defeat and entrapment, finding in one study that entrapment and past suicide attempts were the only multivariate predictors of readmission to hospital for self-harm at 4-year follow-up, even when controlling for depressive symptoms and hopelessness [67]. More recently, Owen et al. [68] found that defeat predicted suicidal ideation via entrapment at four-month follow-up in a sample of individuals with bipolar disorder. Wetherall et al. [69] also found that entrapment was a mediator of the relationship between defeat and suicidal ideation cross-sectionally, supporting the IMV model's prediction. Furthermore, when entrapment was high, resilience also moderated the relationship between defeat and suicidal ideation.
+Entrapment is also directly related to suicidal ideation in adolescents, but it also acts as a mediator of the relationship (along with psychosomatic symptoms, resilience and depression) between anger suppression and suicidal ideation [70]. Elevated defeat, entrapment and suicidal behaviour have also been found in individuals with trauma and a diagnosis of post-traumatic stress disorder (PTSD), relative to
+those with trauma but no PTSD diagnosis [71]. Furthermore, defeat and entrapment mediate the relationship between PTSD symptoms and suicidal behaviour [72]. The centrality of entrapment within the suicidal process was also evident in a study of 200 adult psychiatric patients who had been hospitalized following a suicide attempt or suicidal ideation. The authors found that entrapment fully mediated the relationship between ruminative flooding, panic-dissociation and fear of dying with suicidal ideation [73].
+Within the IMV model, pre-motivational factors such as socially prescribed perfectionism are posited to lead to the development of feelings of defeat, and in Wetherall et al.’s study [69], the relationship between socially prescribed perfectionism and defeat was partially mediated by negative social comparisons. This perception of being of a lower social rank and of making unfavourable comparisons between oneself and others is proposed to be associated with feelings of defeat and entrapment subsequently. The IMV model contends that individuals who are more sensitive to the ( perceived) social evaluation of others are more likely to experience feelings of defeat and entrapment, and Wetherall et al.’s study provides support for this.
+There have, however, been some inconsistencies in the findings between studies of defeat, entrapment and suicidal ideation. Tucker et al. [74] found that, in a sample of American college students, defeat was directly associated with suicidal ideation, but not indirectly via entrapment. While this is not consistent with the IMV model, central to this prediction is the temporal context of the transition from defeat/ humiliation to entrapment, such that defeat is expected to temporally precede feelings of entrapment. Here defeat and entrapment were measured contemporaneously [74], which may have impacted upon whether the relationship was observed. However, also in Tucker et al.’s study [74], as predicted by the IMV model, the relationship between defeat and entrapment was moderated by the presence of brooding
+rumination, supporting rumination as a threat to self-moderator, affecting the pathway from defeat to entrapment.
+Another recent study found that the rumination-suicidal ideation relationship was mediated by entrapment, but the reverse relationship whereby rumination mediated the pathway between entrapment and suicidal ideation was not significant, thus consistent with the sequential relationships outlined within the IMV model [75]. Additionally, a prospective study found baseline defeat, but not entrapment, predicted suicidal ideation at 12-month follow-up [76]. The same finding was also reported in a cross-sectional study of prisoners [77]. These findings may be due to low power, or because defeat and entrapment differ in their longitudinal relationship to suicidal ideation or the assessment of entrapment in prisoners requires closer inspection. For detailed discussion of the role of defeat and entrapment in suicide risk, see O’Connor & Portzky [78] and two recent reviews [79,80].
+Novel research using an online community sample also found some support for the IMV model. On the one hand, the authors found that entrapment (alongside burdensomeness) predicted suicidal ideation cross-sectionally [81]. However, they did not find support for the moderating role of thwarted belongingness and burdensomeness in the entrapment -suicidal ideation relationship. This may simply reflect the way in which this relationship was tested and how the variables were operationalized. Drawing from the IPT [12], the IMV model proposes that it is the interaction between thwarted belongingness and burdensomeness that acts as a moderator of the entrapment - suicidal ideation relationship, as opposed to either of these variables independently. Here belongingness and burdensomeness were tested separately as potential moderators [81]. In addition, the measure of suicidal ideation encapsulates a broad spectrum of suicide-related constructs including ideation, planning and impulsivity. How we assess suicidal ideation, in itself, may introduce unwanted variability, rendering it more difficult to investigate a priori hypotheses.
+In a new approach to understanding the relationship between risk factors, variables from the widely used Beck Scale for Suicide Ideation (SSI; [82]), which span the motivational and volitional phases of the IMV model, have been examined using network analysis in a sample of individuals who presented to hospital following a suicide attempt [83]. Results demonstrated that suicidal behaviour was more directly associated with volitional-phase variables, such as control over action and active planning, whereas factors such as reasons for living and wish to live (motivational phase factors) were more distal predictors. While innovative, this particular analysis was limited by focus on variables from the SSI, which was not designed to assess IMV model components. Future network analyses should assess all of the IMV model factors together.
+A few studies have also examined the IMV model in nonWestern settings. For example, Hye-Ji & Sung-Woo [84], in a sample of South Korean college students, found that entrapment mediated the relationship between defeat and suicidal ideation, as predicted by the IMV model. In sub-Saharan Africa, Atilola & Ayinde [85] applied the IMV model to examine the suicide of Samgd, a well-known figure in the culture of the Yoruba people, discussing how aspects from the narratives of his death map on to the IMV model. These studies provide some early evidence that the IMV model has utility for explaining suicidal behaviour in non-Western cultural settings, but this should be explored further.
+A number of studies have also indirectly tested components of the IMV model. For example, innovative work with adolescents using the Card Sort Task for Self-harm (CaTS) by Townsend et al. [86], found that individuals outlined a process whereby negative life stressors acted as a backdrop to their distress ( pre-motivational phase), leading to negative feelings and ideation about self-harm (motivational phase). Enacting self-harm behaviour was ultimately preceded by feelings of impulsivity and having the access to means for harming oneself (volitional phase). Townsend et al.’s work [86] supports the idea of a strong temporal component to the proposed pathways within the IMV model. In addition, work by Littlewood et al. [87] also found an indirect relationship between nightmares and suicidal behaviour via defeat and entrapment, supporting the idea that the combination of defeat and entrapment is particularly deleterious and leads to more severe suicidal ideation.
+5.	Key directions for future research
+The shift to ideation-to-action models of suicide represents vital progress in the way we conceptualize, research, and intervene to prevent suicidal behaviour. There is still much we have yet to accomplish, however, and here we discuss a number of key opportunities and challenges for the IMV model and also suicide research more generally. As is the case for the 3ST [13] and IPT models of suicide [12], the IMV model presents a linear picture of the suicidal process, from ideation and intention formation to enactment of suicidal behaviour. Although it is important to note that the potential cyclical nature of the suicidal ideation -attempts-ideation relationship is now acknowledged within the IMV model (see dotted lines in figures 1 and 2). Nonetheless, the linear model structure does not necessarily account for repeat suicidal behaviour; as noted above, if an individual
+has already made a suicide attempt, it is unlikely that the process of ideation and intention formation for a repeat suicide attempt will begin anew and manifest in the same way as for a first episode of suicidal behaviour. We expect individuals who have engaged in repeated suicidal behaviours to exhibit higher levels of distress than individuals with a single episode of suicidal behaviour, and as such we expect to see higher levels of motivational and volitional-phase variables among individuals repeating suicidal behaviour. Consistent with the differential activation hypothesis [22,88], we would expect that the process between ideation and enactment shortens with repeated engagement in suicidal behaviour, such that over time the transition between intention and behaviour becomes increasingly rapid.
+Given the complexity of the pathways to suicide, the model in its current form does not address the issue of whether or not particular combinations of variables from across the three phases of the model result in higher risk trajectories for suicidal behaviour. Identifying such ‘risk trajectories’ may represent important steps in generating more individually specific profiles or sub-types that may also aid our development of tailored interventions for particular groups.
+As is evident from emerging literature on variability in suicidal ideation [89], context and temporal fluctuations are pivotal to our understanding of the specific circumstances under which suicidal ideation and behaviour may occur. To understand the role of context in suicidal behaviour, traditional, retrospective self-report or laboratory measures are insufficient, being highly vulnerable to recall bias and lacking ecological validity [90]. The only way to truly capture such short-term variations in risk factors is to measure these at a momentary level using techniques such as ecological momentary assessment (EMA) methods, allowing data to be collected virtually in real-time, as participants go about their daily lives [91]. Despite its clear potential, however, EMA remains an underused methodology within suicide research [56,92,93] and requires rigorous evaluation.
+Since the IMV model was proposed in 2011, much progress has been made in empirically testing the model’s predictions but much remains to be done. First, consistent with suicide research more generally, there is a dearth of prospective studies. The issue of temporality returns when considering the proposed temporal pathway from defeat and humiliation to entrapment, then progressing onwards to suicidal ideation. Extant research examining these constructs within the context of the IMV model has consistently investigated these variables contemporaneously [81]. The concepts of defeat and entrapment may also exhibit further nuance, potentially having both state and trait components [94]. Stability of these constructs over time has received little to no attention within the field of suicide research.
+As well as new technological developments, the emergence of statistical techniques such as network analysis (e.g. [95]) provides new opportunities for addressing some of the key questions and challenges outlined above. By allowing us to compare the relative importance (centrality) of key variables associated with suicidal ideation and enactment, as well as the strength of these relationships, network analysis gives us new possibilities to investigate variations in risk trajectories in different populations. Other new methods, such as curtailment techniques, allow us to optimize the efficiency of the measures we use to assess suicidal ideation and behaviour, without compromising on their accuracy [96].
+Recent advances in machine learning techniques allow the computation of optimized risk algorithms, from hundreds of different individual variable pathways, to suicidal thoughts and behaviours [97,98]. The vast majority of tools to assess the likelihood of repeat engagement in suicidal behaviour rely on self-report. A burgeoning line of research investigates possibilities for detecting cognitive reactivity towards suicide-relevant content that is outside of individuals' conscious awareness, including implicit attitudes via the Death/ Life Implicit Attitudes Test [56,57,99]. Other approaches, such as the death evaluation Implicit Relational Assessment Procedure [99], have also found specific cognitive biases towards self-referent versus abstract death-related stimuli in individuals with current suicidal ideation. In short, given that behaviour is governed by reflective and automatic (e.g. implicit) processes [100], more suicide research needs to focus on these automatic (as well as reflective) processes.
+6.	Implications for intervention and suicide prevention
+A corollary of the IMV model is that intervention and suicide prevention activities should be tailored to the phase of the model that the person is presently within. If an individual is distressed and feeling trapped but they are not suicidal, then clearly interventions that reduce the likelihood that suicidal ideation emerges could offer benefit. To this end, targeting factors within the motivational phase of the model should be highlighted. For example, given that entrapment is a potentially modifiable predictor of suicide attempts over time [67], this is an important treatment target. It would also make sense to incorporate the assessment of entrapment into routine clinical care alongside depression and suicidal ideation. The challenge, though, is that there are not yet any evidence-based treatments to reduce entrapment. Nonetheless, there are effective, evidence-based psychological interventions for the management of selfharm that can be drawn from Hawton et al. [101]. If an individual is actively suicidal, in addition to trying to
+alleviate their suicidal distress, it is vital that interventions to reduce the likelihood that they act on their thoughts are prioritized. For example, safety planning [58] is one such promising intervention which targets VMs. Another is a volitional helpsheet (VHS) [102] that encourages an individual to make if-then plans to reduce the likelihood that their suicidal thoughts trigger a suicide attempt. Recent evidence suggests that a VHS may offer promise (as an adjunct to usual care), especially among those with a past history of self-harm [102,103]. More generally though, theoretical models such as the IMV model should be a starting point for the development of interventions, because they specify the potential mechanisms that should be targeted, thereby increasing the likelihood of interventions being effective [104]. Finally, at the macro-level, suicide prevention efforts need to urgently tackle inequality, poverty and disadvantage [18,105], key drivers of suicide ( pre-motivational phase).
+7.	Summary and conclusion
+We have presented the IMV model, a contemporary ideation-to-action model of suicidal behaviour. The tri-partite IMV model contends that suicide is a behaviour, preceded by ideation and intention formation and, crucially, it seeks to explain the transition from suicidal ideation to behavioural enactment. Empirical support for the model is growing; however, there remain a number of challenges, as well as opportunities, to be addressed in future research; understanding the roles of temporality and complexity of variable interactions within the model is a priority.
+Data accessibilit This article has no additional data.
+Authors' contributions. Both authors contributed equally to the manuscript.
+Competing interests. We declare we have no competing interests.
+Funding. R.C.O.C. received funding support from MQ Research (MQ1PI100009) and US Department of Defense (W81XWH-12-1-0007). O.J.K. is supported by a fellowship from an FWO Odysseus grant (Myin-Germeys, FWO GOF8416N).
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+Although suicide is not a disease, suicidal behaviour, which includes suicide and suicide attempts, is an important public health problem. Thus, it has been the focus of increasing attention in research and public awareness campaigns over the past decade, as well as a major focus of the international public health community1. Despite a one-third reduction in the global suicide rate, suicide deaths remain frequent worldwide1-2. Each suicide death represents an individual tragedy and is estimated to indirectly affect many individuals, including family, friends and communities3.
+Advances in our understanding ofthe factors involved in suicide risk have enabled us to paint a more comprehensive picture of suicidal behaviour. Although the literature on suicidal behaviour has been growing, its interpretation and integration is at times complicated by the multiple phenotypes that may be considered across the suicide spectrum. These phenotypes include suicidal ideation, which is defined as thoughts about ending one’s own life (whether active (with a plan) or passive (with only a wish to die but no plan)), suicide attempt and death by suicide (BOX 1). The risk of acting on suicidal thoughts increases with the frequency, intent and content (such as presence of a plan, levels of ambivalence or hopelessness, among other factors) of suicidal ideation. A suicide attempt is defined as self-injurious behaviour with inferred or actual intent to die. Although detailed nomenclature has been proposed for the terminology
+related to suicide4,5, it has not been widely adopted and diverse terms are still frequently used to describe similar phenomena. As a consequence, this Primer uses the terms that are most consistent with the accepted nomenclature (BOX 1).
+Although suicidal ideation occurs in depressive states across different psychopathologies, the transition from suicidal ideation to a suicide attempt is facilitated by co-occurring psychiatric conditions that increase distress (such as panic disorder or post-traumatic stress disorder) or decrease restraint (such as substance abuse or cluster B personality disorders)6. Additional factors involved in this transition, such as capability for suicide, exposure to suicide, mental imagery and access to the means of suicide7-10, have been identified. In contrast to suicide attempts, non-suicidal self-injury most commonly involves self-cutting to relieve negative affect without suicidal intent and is classified separately from suicidal behaviour by North American clinicians. In Europe, non-suicidal self-injury is sometimes classified along with suicide attempts under the term self-harm because the risk factors for the two types of behaviour overlap and commonly co-occur11.
+Strategies for preventing suicide have been implemented in most countries, with varying degrees of success. Prevention strategies are typically grouped under universal (population-wide) strategies, selective strategies (targeting subpopulations or environments
+within the larger population that could be at increased risk) or indicated strategies (in at-risk individuals who have already exhibited some form of suicidal behaviour or ideation). For these individuals, treatment strategies relying on psychotherapy or pharmacotherapy may also be indicated alone or in combination, depending on the characteristics of the individual.
+This Primer discusses the epidemiology and global burden of suicide and suicidal behaviours, in addition to providing an overview of our current understanding of the mechanisms of suicide, including risk factors for suicidal ideation and the transition from ideation to suicide attempt. In addition, this Primer describes the prevention and treatment of suicide and quality of life issues faced by people with suicidal behaviours.
+Epidemiology
+Based on self-report survey data, the WHO has estimated that, for every death by suicide, ~20 people make suicide attempts1. This ratio varies from country to country depending on the lethality of commonly used suicide methods (FIG. 1). In many countries, the incidence of suicide attempts is highest in individuals 18-34 years of age12, though reliable, global comparative data on suicide attempts is not available for individuals <18 years of age. By comparison, the lowest rates of suicide deaths are observed amongst young people <15 years of age, with the highest generally observed in people >70 years of age1. However, the age groups with the highest incidence at other ages vary from region to region; for example, in a number of high-income countries (such as the USA and UK) a second peak occurs amongst middle-aged men and women (45-60 years of age)1. Rates of suicide attempt are generally higher in females than in males12,
+Box 1 | Definitions of terms commonly used in suicide research
+Suicide: Intentionally ending one’s own life.
+Suicidal behaviour: Behaviours that may result in ending one’s life, whether fatal or not. This term excludes suicidal ideation.
+Suicide attempt: Self-injurious, non-fatal behaviour with inferred or actual intent to die.
+Suicidal ideation: Any thoughts about ending one’s own life. May be active, with a clear plan for suicide, or passive, with thoughts about wishing to die.
+Self-harm: Self-injurious behaviours with or without intent to die. Does not distinguish between suicide attempt and non-suicidal self-injury.
+Non-suicidal self-injury: Self-injurious behaviours without any intent to die.
+as is the prevalence of anxiety and depression13. However, in most countries, the rates of suicide are 2-3 times higher in males than in females1, which could possibly be due to a male preference for higher lethality methods and the reluctance of males to seek help1 (FIG. 2).
+Suicidal ideations are considerably more frequent than suicidal behaviour, but incidence estimates vary depending on the definitions and wording used in the study — these definitions can range from thoughts that life is not worth living to making concrete plans to end one’s life. For example, in an analysis of World Mental Health Survey data from 17 countries, the reported lifetime prevalence of suicidal ideation was 9.2%, with a lifetime prevalence of suicidal plans of 3.1%12. A person’s willingness to report suicidal ideation and attempts may also depend on their religious beliefs, stigma and the methods used for collecting data (such as self-report questionnaire, interview administered survey or hospital admission data).
+Geographic variations in incidence
+The incidence of suicide varies more than ten-fold between countries (FIG. 3); incidence is relatively low in the Middle East and some South and Central American countries, and high in Eastern Europe, Russia, India and South Korea. Of note, the vast majority of suicides and suicide attempts worldwide occur in low-income and middle-income countries, where treatment options are limited1. Several factors are believed to account for these differences in suicide rates, including variations in the accuracy of suicide statistics. Indeed, several factors, such as inconsistent recording of suicides by coroners and cultural or religious acceptability of suicide, can affect the reporting of suicide. To this end, cultural factors (as well as possible genetic factors) might have a role, as a number of studies have shown that the between-country ranking of national suicide rates is maintained amongst first-generation migrants to a single country operating a consistent suicide recording process14. In addition, the availability and cultural preferences for high-lethality suicide methods can influence between-country suicide rates. For example, poisons commonly ingested in high-income countries, such as analgesics and psychotropic medications, are generally considerably less toxic than pesticides, which are commonly used in suicide attempts in many low-income countries15. Indeed, pesticides such as paraquat, which have a case fatality rate of >50%, are readily available in low-income countries in which many people are involved in subsistence farming — pesticide ingestion accounts for approximately one-fifth of global suicides16. Moreover, variations in the acceptability of suicide because of religious sanctions against suicidal behaviour (such as in predominantly Catholic or Muslim countries) as well as variations in levels of alcohol and drug misuse could contribute to the global differences in suicide rates. In terms of the latter point, high levels of alcohol misuse may account for the high incidence of suicide in Eastern Europe17. The effects of these various risk factors likely overlap and may be confounded by relative levels of prosperity and ethnic and religious diversity within individual countries.
+As well as global differences in suicide rates, several studies have reported marked (that is, two-fold to fivefold) variations in suicide rates within countries. For instance, one analysis of suicide in Taipei (Taiwan) documented five-fold variations in the incidence of suicide between areas18; the factor most strongly associated with area differences was average levels of household income.
+Temporal trends
+As mentioned above, the WHO estimates that there are ~785,000 suicides annually around the world, with an incidence of 10.6 per 100,000 population in 2016 (REF19). Global suicide rates have fallen by ~30% in the 21st century2, largely driven by marked falls in China and, to a lesser extent, India2. This overall trend has masked increases in other countries, such as the USA and Brazil, where rates rose by 35% between 2000 and 2016 (REF19) (FIG. 2). Explanations for these differing trends are unclear. Economic growth in China has been accompanied with reductions in suicide rates, whereas the opposite has been observed in South Korea, which experienced a 130% rise in suicide between 2000 and 2010, although its rate has declined somewhat in recent years19.
+In addition to geographical differences, time trends in suicide rates vary within different age groups. For example, in recent years in the UK, suicide rates have fallen in elderly men but risen in middle-aged men and women20,21. In China, suicide rates are falling in almost all age groups22, whereas in India they are falling in young people but rising in elderly individuals23.
+In general, key influences on secular trends in suicide include periods of economic recession (which are
+generally associated with increases)24,25, changes in access to commonly used, high-lethality suicide methods26,27, periods of war (which are generally associated with falls in suicide) and media reporting of celebrity suicides (which may lead to transient increases in suicide rates)28.
+Mechanisms/pathophysiology
+Risk of suicide is influenced by the interaction of a variety of biological, clinical, psychological, social, cultural and environmental factors. General models to understand suicide risk have been proposed9,10, and most models recognize that suicide risk is a result of the interplay between predisposing (also known as distal or diathesis) and precipitating (also known as proximal, triggering or stress) factors, with some models also specifying a role for developmental factors. One such example is the biopsychosocial model for suicide6,29,30 (FIG. 4), which describes the interactions of genetic, experiential, psychological, clinical, sociological and environmental factors in the development of suicide risk. Although any number of these factors might be involved, their relative association with suicide risk varies greatly between individuals and can be mediated by a variety of factors, such as anxious or impulsive personality traits31,32, and having social support and stable relationships, which lead to the aetiological heterogeneity of suicide and suicidal behaviour. The relative importance of these diverse factors also varies by age and sex33. This model is compatible with the multiple phenotypes along the suicide spectrum that are observed in practice, as described above. In the following sections, risk factors are classified as distal, developmental and proximal according to their temporal
+relationship to suicide. Although this classification is helpful to understand relationships between risk factors and to build a model of suicide risk, it is imperfect as some risk factors, such a socioeconomic status or genetic and epigenetic factors, can act simultaneously in different categories.
+Distal or predisposing factors
+Familial and genetic predisposition. Suicidal behaviour has been known to cluster in families for >30 years, but convincing evidence for the heritability of suicidal behaviour has been provided only in the past decade from large cohort studies, including evidence that suicide and suicide attempts are transmitted independently of psychopathologies31-32-34-35. National registry-based studies as well as twin and adoption studies all suggest a heritability of 30-50%36,37, although a more accurate estimate may be 17-36% once the heritability of comorbid psychiatric disorders is controlled for37. In addition, the offspring of individuals who have attempted suicide have a five-fold greater risk of attempting suicide than the general population38, and suicide risk is doubled among individuals who lost a parent to suicide compared with those who lost a parent suddenly due to other causes, although <5% of people who take their own life have a history of parental suicide39. These data showing the familial clustering of suicidal behaviour further support the concept of genetic transmission. However, despite intensive investigations over the past 20 years, no single gene or group of genes has been identified as responsible for suicidal ideation, suicide attempt or suicide across multiple studies30.
+Suicide deaths are a rare event and, therefore, genomewide studies (GWAS) generally investigate non-fatal suicidal behaviour; the findings of GWAS have been compiled and published elsewhere30,40. Of note, only a handful of GWAS have included suicide as a primary phenotype40-42. These studies identified a total of 32 single nucleotide polymorphisms (SNPs) of interest, of which multiple SNPs were associated with the TBX20 locus41,42. The gene product of TBX20 has been implicated in the central nervous system, although its specific roles are unknown41. Although these findings need replication, one study that used exome sequencing of brain tissue from individuals who died by suicide has identified a number of candidate gene variants associated with suicide, including variants of COL6A6 (encoding the alpha-6 chain of collagen type VI, which is involved in axon guidance), GNAL (associated with schizophrenia), BACE1 (potentially associated with Alzheimer disease),
+NREP (associated with neural regeneration) and CDC34 (a ubiquitin-conjugating enzyme involved in cell cycle control)43. Other studies using suicide attempt and suicidal ideation as primary phenotypes identified multiple SNPs of interest that were associated with genes involved in nervous system development and function, immunological disease, infectious disease and the inflammatory response, among other biological processes41.
+Despite a few reports confirming associations between genes identified in GWAS and suicide phenotypes, overall, candidate gene-based studies44,45 and GWAS30,40,46-49 have not produced consistent results, with genes of interest frequently failing to pass rigorous reproducibility and significance testing. Similar to other behavioural phenotypes, individual genetic variants contribute only a small portion of the total variation in the suicidal behaviour phenotype, and despite the relatively large samples used in GWAS, they often lack appropriate power to detect significant loci and replicate previous findings50. Efforts to describe the association between genetic variance and suicidal behaviour using polygenic approaches have yielded promising results50,51.
+Early-life adversity. The past decade has seen great advances in our understanding of the ways that experiences can be translated into altered gene expression through epigenetic mechanisms52,53. Although changes to gene expression in response to environmental cues are essential in neurodevelopment, negative social experiences during critical periods of development can also lead to altered gene expression, believed to contribute to psychopathology. Indeed, early life adversity (ELA), defined as neglect or physical or sexual abuse during childhood, is strongly associated with suicidal behaviour later in life29,54,55. Evidence for this association comes from several observational studies55-57, and has since been investigated by studying the biological imprinting of these negative early-life traumatic experiences through epigenetic changes, including DNA methylation and histone modifications58-60 (see Stress response and the hypothalamic-pituitary-adrenal (HPA) axis, below).
+Developmental factors
+Although distal factors are important contributors to suicide risk, their link with suicidal behaviour is at least partially mediated by other factors. Such developmental or mediating factors, which may result in part from predisposing factors, increase vulnerability to maladaptive responses to proximal factors, thereby increasing the risk of suicidal ideation or suicidal behaviour. Of note, behavioural changes are regulated by biological processes61, although these processes are often poorly understood.
+Personality traits associated with suicidal ideation and suicidal behaviour. Key mediating factors between distal factors and proximal (also known as precipitating) factors include personality traits62. Among these, those most robustly associated with suicidal behaviour are anxiety and impulsive-aggressive traits (for example, the latter are traits that are associated with externalizing disorders like attention-deficit/hyperactivity disorder,
+borderline personality disorder, oppositional defiant disorder and conduct disorder)32-57-63.
+Longitudinal trajectory studies have provided convincing evidence linking behavioural traits with longterm suicidal behaviour57-64. Although multiple studies have found an association between both childhood anxiety and stable anxiety in adulthood with suicidal behaviour65-66, some studies have reported only a weak or non-significant link with suicidal behaviour when anxiety is considered as an independent variable for statistical analyses66,67. Anxiety is likely to contribute to suicidal behaviour by interacting with other characteristics of individuals who already have an increased vulnerability to suicide. For example, in trajectory studies accounting for ELA, anxiety convincingly explained the link between ELA and suicidal behaviour later in life68, and some evidence suggests a stronger link between anxiety and suicidal behaviour in girls than in boys63.
+In studies examining the familial transmission of suicidal behaviour, the transmission of impulsive-aggressive traits has at least partially explained familial aggregation of suicidal behaviour31,34,38. Indeed, relatives of individuals who died by suicide are more likely to have increased impulsivity and aggression, and have an increased risk of suicidal behaviour compared with individuals without first-degree relatives who died by suicide32,35.
+The familial transmission of behavioural traits is likely explained by both genetics69 and by lived experiences, as growing evidence has implicated epigenetic mechanisms in the regulation of aggression in adults70,71. The socially prescribed aspects of perfectionism (real or perceived social expectations of performance) are also associated with suicidal ideation and suicide attempts in clinical and population samples72, and likely increase individual sensitivity to social rejection, defeat and entrapment9. The analysis of population samples might provide a relevant clinical perspective on the
+Fig. 3 | Suicide rates in selected countries from 2000 to 2015. Suicide rates over the 21st century have increased in some countries (such as Brazil, Mexico and the USA) but have decreased in others (such as China, Russia and France). Data here include both sexes combined. Data from World Health Statistics, WHO 2016 (REF.19).
+contributions of personality traits associated with suicide risk73, including negative affect, which is a tendency to have negative emotions such as low self-esteem.
+Link between ELA and personality traits. As discussed above, ELA is strongly associated with suicidal behaviour and is associated with stable epigenetic changes. In animal models of ELA, negative experiences during postnatal development lead to behavioural changes in adulthood such as fearfulness74 or aggression75. Subsequent work also linked altered behaviour in ELA models to lasting epigenetic changes in stress regulation pathways (see Stress response and the HPA axis, below)76. In addition, individuals who have experienced ELA have a greater risk of developing pathological traits and emotional dysregulation, including both internalizing and externalizing behaviours, altered brain structure and impaired executive function77, all of which are traits that overlap with the suicidal phenotype57,78.
+Cognitive deficits. Diminished problem-solving ability, impaired memory and decreased positive future thinking have been associated with suicidal behaviour79. Additionally, hyperactive cortisol response to stress (see Stress response and the HPA axis, below), suicidal behaviour or having a first-degree relative with suicidal behaviour have all been associated with decreased cognitive function in stressful situations, suggesting that cognitive impairments are associated with risk factors for suicide80,81, and individuals with poor problem-solving skills are more likely to experience suicidal ideation in response to stress82. In addition, a five-fold increased risk of suicide attempts has been reported between high-achieving and low-achieving students in cohort studies, highlighting the importance of IQ and school performance in influencing suicide risk83.
+Cognitive deficits are also more frequent among individuals with experiences of ELA84. This association is likely due to the interaction between ELA and the still-developing brain throughout childhood and young adulthood85, which can lead to neuroanatomical and functional changes30. Together, these lines of evidence support a role for cognitive deficits as mediating risk factors for suicidal behaviour.
+Proximal or precipitating factors
+Beyond the factors that confer risk of suicidal behaviour, either distally to the suicidal episode or through development of traits associated with suicidal ideation and suicidal behaviour, there are identifiable factors that proximally associate with suicidal behaviour and that are commonly regarded as precipitating or facilitating it. Nonfatal suicidal behaviour is among the most robust predictors of future suicidal behaviour and suicide death. Indeed, ~40% of people dying by suicide have previously attempted suicide, and long-term follow-up studies have indicated that the risk of suicide amongst people who survived a suicide attempt is 1.6% within 12 months86 and ~4% at 5 years87. Despite these data, it is important to note that the vast majority of individuals who experience suicidal ideation or attempt suicide do not ultimately die by suicide, and certain risk factors might
+Fig. 4 | The biopsychosocial model of suicide risk. The biopsychosocial model for suicide risk presents a comprehensive view of the different types of factors that may contribute to suicide risk. Sociological, demographic, economic and environmental factors may influence any or all of the distal, developmental and proximal factors. The external, population or society-level factors, in addition to individual factors such as genetics, family history, personal history of abuse, behavioural dysregulation, substance abuse and psychopathology, may all interact to heighten the vulnerability to suicide through behavioural disinhibition and dysregulated mood, hopelessness and entrapment, which are in turn associated with suicidal ideation, suicide attempt and suicide.
+Data from REF.337 (global suicidal ideation and suicide attempt statistics) and REF19 (global suicide statistic).
+help to distinguish the likelihood of suicide attempt. In addition, some clinical factors that may influence the transition from suicidal ideation to suicide attempt include anxiety disorders, impulse-control disorders, post-traumatic stress disorder, eating disorders, previous self-harm, exposure to suicidal behaviour in others, and alcohol and drug abuse or dependence6,7,88. Additionally, epidemiological evidence supports a role for disorders that decrease restraint (such as substance abuse, oppositional defiant disorder and obsessivecompulsive disorder) or increase distress (panic disorder and post-traumatic stress disorder) in increasing the likelihood of a transition from suicidal ideation to
+suicide attempt, particularly in the context of mood disorders86. However, currently, it is not yet clear how to distinguish between individuals who may experience suicidal ideation but never progress to suicidal behaviour and those who may have one or multiple suicide attempts or who ultimately die by suicide.
+Psychiatric disorders associated with suicide. Mental illness is a key antecedent to suicide. Indeed, estimates of the proportion of people experiencing a mental illness at the time of their suicide range from ~90% in North America to 30-70% in East Asia89-92, with individual reports citing rates as low as 7% in some countries93. These disorders can be detected through retrospective interviews with family and friends of the deceased using psychological autopsy methods; however, they are not always diagnosed or treated before death, and likely represent one of the key modifiable aspects of an individual’s suicide risk. Surprisingly, even in countries with accessible, free-at-the-point-of-use, mental health services such as the UK, only ~25% of people dying by suicide are in current or recent contact with mental health services94.
+The most common psychiatric diagnoses in people who die by suicide are major depressive disorder (MDD), bipolar disorder, substance use disorders and schizophrenia89. Although suicide is not an inevitable outcome of any psychiatric disease, specific features of individual diseases are associated with suicide, such as certain demographics and the individual’s insight into their illness in schizophrenia95, early-stage illness, mixed-state episodes and irritable dysphoric states for bipolar disorder96, or number, duration and intensity of episodes for MDD97,98. It is likely that the co-occurrence of depressive symptoms and other factors that inhibit behavioural control may contribute to suicidal behaviour. In addition, one cross-national study has demonstrated that disorders characterized by anxiety and poor impulse control are strongly associated with the transition from suicidal ideation to a suicide attempt88. Furthermore, a birth cohort study identified substance use and personality traits as strong correlates of suicide attempt in both those who did or did not have previous suicidal ideation, with higher intellect/openness as a factor in those with suicidal ideation and lower extraversion as a factor in those without7.
+Substance use or misuse is found in a large proportion of people who die by suicide89, with alcohol misuse observed in up to 40% and use of illicit substances in up to 25%99. In individuals who die by suicide, those with multiple predisposing factors (considered to have high-risk histories) tend to show higher rates of substance disorders at the time of death100,101. Although the association of suicidal behaviour with alcohol use has been more extensively studied than with other substances, there are indications that, in addition to the use of alcohol, recent (within 6 months) use of sedative-hypnotic drugs and cannabis is more frequently associated with suicide attempt than other substances102,103. Impulsivity is likely to be a key mediator of substance use associated with suicidal ideation and suicidal behaviour99. Indeed, individuals with high impulsivity are more
+likely to use substances, and individuals with alcohol use disorder at the time of their suicide are more likely to also have high levels of impulsive-aggressive behaviour than those without alcohol use disorder at the time of suicide104. However, identifying causality is complex; animal studies have indicated that chronic substance use may drive increased impulsivity105 and human studies have suggested that the pattern of consumption may be equally important, with binge use of both legal and illegal substances being more closely associated with suicide attempt than non-bingeing substance use103-106.
+In addition, ample evidence supports the contributions of concurrent physical and mental illness (such as central nervous system and psychiatric disorders, inflammatory diseases, chronic obstructive pulmonary disorder and pain) to suicide risk107-108. Although impulsive-aggressive traits, substance abuse and conduct disorder make proportionally larger contributions to suicide risk in younger people7-97-109, depression, physical comorbidity, sleep and pain issues, and cognitive impairment are substantial contributors to suicidal behaviour in older people110,111. In terms of sleep problems, decreased sleep time, insomnia and nightmares have been related to risk for suicidal behaviour112. Chronic pain is also a predictor of suicide and suicidal behaviour, both independently as well as via the co-occurring difficulties of disability, sleep problems, reduced well-being and depression113.
+Psychological factors that contribute to suicide. From a psychological point of view, one of the key drivers of suicidal ideation and suicidal behaviour is the concept of unbearable psychological pain. Initial attempts to understand the components of psychological pain and their relationship with suicide attempts focused on a perceived lack of belonging, being an increased burden on others and acquired capability (that is, the ability to engage in suicidal behaviours)10,114. More recent efforts have identified a wider range of components, such as hopelessness and knowledge of or comfort with lethal means, and this approach has evolved to more specifically distinguish between factors that are associated with suicidal ideation and those that are associated with subsequent suicide attempt8,9,115,116. Indeed, the importance of other mediating factors, that, together with psychological pain, associate with subsequent suicidal ideation and suicidal behaviour, is increasingly acknowledged; these factors include psychological and cognitive traits (such as impaired problem-solving and memory biases and rumination) as well as proximally acting factors (such as feelings of defeat and entrapment, impulsivity, intentions/planning, implementation of a plan, access to means and imitation/exposure to suicidal behaviour of others). Of particular interest is work using population cohorts, in which volitional phase factors9 (such as acquired capability, mental imagery about death, impulsivity and exposure) distinguished suicide attempt from suicidal ideation, whereas motivational factors did not117. Such ways of understanding suicidal ideation and suicidal behaviour may be particularly informative in terms of selecting psychotherapeutic
+approaches that will be most suited to the individual’s needs.
+Socioeconomic, environmental and other contextualfac-tors associated with suicide risk. Important social and economic factors associated with suicide include proximal factors such as relationship breakdown, job loss, economic turmoil, being bullied and recent changes in socioeconomic position (having poor family connectedness, being single, having a low income and/or being in debt), as well as factors that may persist over time, such as poor social stability, stringent sociocultural norms, or being in a lesbian, gay, bisexual, transgender plus (LGBT+) group107,118-121. Some socioeconomic risk factors for suicide may operate differently in different social contexts; for example, the risk of suicide amongst individuals from minority ethnic groups is higher when they live in areas that have a low proportion of people from the same minority ethnic groups, compared with those living in areas that have a higher proportion of people from the same minority ethnic groups122. Social isolation, for example resulting from anxiety, bereavement or social exclusion107,118, is also a strong contributor to suicide risk, whether isolation is measured objectively (such as living alone) or through perceived loneliness123. Conversely, clusters of suicide deaths may occur, particularly in young people, through social contagion, accounting for up to 1-2% of suicides in children and young people124.
+Among the environmental-based risk factors for suicide, one of the most important is access to means. To this end, there is a large volume of work showing the effect of access to more-lethal means as an important predictor of suicide125. Of interest, these are modifiable risk factors and an important aspect of preventive efforts is based on decreasing access to lethal means (see Prevention strategies, below).
+Neurobiology of suicidal behaviour
+Suicidal behaviour is associated with widespread neuro-biological changes throughout the brain that affect a number of different functional pathways (FIG. 5). However, the extent to which these changes are specific for suicide and suicidal behaviour or are shared with depression and other psychopathologies is not always possible to determine given the intricate relationships between these phenotypes.
+Monoamine dysregulation in suicidal behaviour. The serotonin pathway is by far the best described pathway in depression and several alterations in this pathway have been associated with suicidal behaviour. Serotonin func-tion126-128, and particularly serotonergic neurons129, have been implicated in depression and suicide. For example, studies using post-mortem brain tissues of individuals who died by suicide have demonstrated decreased levels of 5-hydroxyindoleacetic acid126, which is a serotonin metabolite, as well as a compensatory increase in the raphe nuclei of serotonergic neurons130,131 and expression of tryptophan hydroxylase, a key enzyme in serotonin biogenesis132,133. In addition, dysregulated serotonin transmission has been demonstrated in both brain tissue134,135 and cerebrospinal fluid of individuals
+Fig. 5 | Neurobiological changes in suicidal behaviour. Suicidal behaviour is associated with widespread neurobiological alterations. Of these, alterations in the stress response pathways are among the best described in suicidal ideation, suicidal behaviour and suicide. Disruption of the hypothalamic-pituitary-adrenal (HPA) axis affects the coordination and signalling feedback mechanisms that control cortisol production, which is a key mediator of stress reactivity. Long-lasting changes to HPA axis function have been documented in individuals with histories of early-life adversity and epigenetic processes have been identified as key mediators of those changes. The polyamine system has been implicated in cellular responses to stress and may directly regulate mood through the antidepressant properties of some of its constituent proteins.
+In addition, multiple neurotrophic and neurotransmission pathways, primarily in the prefrontal cortex and the hippocampus as well as in the raphe nuclei of the brainstem, have been shown to be dysregulated in suicidal behaviour and constitute promising targets for therapy. More recent evidence has suggested coordination between signalling in the brain and in the gut, potentially through immune activation. GABA, y-aminobutyric acid.
+with suicidal behaviour136. An outstanding complication in the study of factors associated with suicidal behaviour is the high degree of comorbidity between depression and suicidal behaviour, yet some work has successfully shown discrete changes in expressions of both the serotonin transporter and serotonin receptor 1A in the midbrain in people with suicidal behaviour, compared with depressed individuals without suicidal behaviour134-137. Further investigation of alternative serotonin receptors138, including those resulting from mRNA editing of serotonin receptor transcripts139-142, may lead to new avenues of investigation of the role of serotonin in suicidal behaviour.
+Stress response and the HPA axis. In normal situations, cortisol release is triggered by stress; its release is controlled by a feedback loop in which glucocorticoid receptors in the hypothalamus and other brain regions, including the hippocampus, are negatively regulated by cortisol binding and lead to the inhibition of cortisol release29. Altered cortisol reactivity may increase the risk of suicidal behaviour29-80, although studies have provided contradictory results regarding the direction of association, which seems to be moderated by several factors such as age143, family history of suicidal behaviour80,144 and history of ELA145, among other variables. Indeed, the epigenetic regulation of genes associated with ELA is exemplified by alterations in the HPA axis29,60,146. Individuals who experienced severe abuse during
+childhood have increased methylation of a regulatory region of NR3C1 (encoding the glucocorticoid receptor) in the hippocampus58, compared with either people who were not abused or to psychiatrically healthy controls. Furthermore, some evidence suggests that various forms of severe adversity affect the methylation status of NR3C1 exons in central and peripheral tissues29,145. In addition, specific sequence variants of FKBP5, which encodes a protein that downregulates glucocorticoid receptor signalling, are associated with increased depressive symptoms and suicidal behaviour in people with ELA147,148, presumably due to increased cortisol secretion and the development of anxiety traits. Another candidate target of epigenetic regulation that might explain the link between ELA and altered cortisol control is SKA2, which encodes the spindle and kinetochore associated protein 2 (REFS149,150).
+In addition to alterations of the HPA axis, the polyamine stress response system seems to be differentially regulated in individuals with suicidal behaviour, although this pathway has been less thoroughly investigated than the HPA axis29. To this end, evidence that some polyamines exhibit antidepressant-like effects151,152 suggests that alterations to the polyamine pathway may be important contributors to suicidal behaviour.
+Neurotrophic pathways. Expression of neurotrophic genes, such as BDNF (encoding brain-derived neurotrophic factor (BDNF)) and NTRK2 (encoding the BDNF
+receptor tyrosine kinase B (TrkB)), are decreased in the brains of individuals who died by suicide compared with controls153,154. The link between BDNF and suicide has been studied intensively, and several studies have demonstrated altered BDNF expression in the sera of people who attempted suicide and in the brains of people who died by suicide155,156, although to what extent these results are accounted for by underlying psychopathology is unclear. This altered expression is at least partially due to the epigenetic control of BDNF expression through methylation of its promoter; indeed, altered methylation of the promoter or exon 4 has been demonstrated in the brains of people who died by suicide157 and in peripheral tissues of individuals who attempted suicide158. Additional evidence for the alteration of neurotrophic pathways in suicide is evidence of epigenetic regulation of TrkB-T1 (an astrocyte-specific variant of the BDNF receptor) in the brains of individuals who died by suicide, both through methylation of its promoter and through control by miRNA miR-185 (REFS159,160).
+Glutamatergic and GABAergic dysfunction. Evidence from neuroanatomical studies of individuals with depression and suicidal behaviour have demonstrated changes in the density of glutamatergic neurons and glial cells161,162 and in the volume163 of the hippocampus, which relies on excitatory neurotransmission via glutamatergic signalling and is involved in learning and memory164. In brain tissue from individuals with depression (most of whom died by suicide), glutamate transporter expression is decreased165, whereas N-methyl-D-aspartate (NMDA) receptor (a glutamate receptor) expression is increased166 in the locus coeruleus, suggesting dysregulation of glutamatergic signalling. Importantly, specific proteins in the glutamate pathway that have been associated with suicidal behaviour (such as glutamate-ammonia ligase, a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), NMDA and kainite glutamate receptors) have also been implicated in cognitive deficits167. GRIN2B, GRIK3 and GRM2 are among the glutamatergic genes that appear to be specifically upregulated in people with MDD who die by suicide, as opposed to those with MDD who die from other causes168, with a concomitant downregulation of the GLUL protein in astrocytes169. The glutamatergic pathway has become a particular point of interest in recent years owing to its probable involvement in mediating the potent antidepressant and antisuicidal activity of the NMDA receptor antagonist ketamine170-172, which not only leads to sustained effects in animals173 and humans172,174, but also appears to act through multiple mechanisms, including the inhibition of BDNF secretion by astrocytes175, upregulation of insulin-like growth factor 2 in the hippocampus176, and through regulation of AMPA and NMDA receptor expression levels177. In addition to the glutamate pathway, y-aminobutyric acid (GABA)-associated genes, including those encoding the GABA-A and GABA-B receptors, are disrupted in the brains of individuals who died by suicide178, particularly in regions linked to executive function179,180 and stress responses181, as well as in the integration of cognitive activity with affective experience.
+Immune function and inflammation. Inflammatory responses have drawn a great deal of attention in recent years, with several lines of evidence suggesting a link between inflammation and depression182,183. Indeed, in individuals with somatic disease, disorders with a strong inflammatory component, such as autoimmune disease184, and patients receiving cytokine therapy185 are more likely to develop depression than those who do not experience inflammation. The role of inflammation in suicidal behaviour has been explored through a number of studies that identified increased proinflammatory cytokines such as tumour necrosis factor, IL-6 (REF186), IL-8 (REF187) and IL-1p188 in individuals who attempted suicide or who died by suicide. In particular, increased plasma IL-6 levels have been associated with impulsivity and violent suicide attempts189, thereby linking proinflam-matory responses to the impulsive endophenotype for suicidal behaviour. High levels of inflammation may also be a tool to differentiate suicidal ideation from depression in adults190, and several studies have indicated that systemic inflammation in depression could be linked to a leaky gut (that is, abnormal permeability of the intestinal walls), which could expose bacterial molecular motifs to the immune system191. Other molecules that have been associated with suicidal behaviour include IL-2 (REF186) and vascular endothelial growth factor192, which are decreased in individuals with suicidal behaviour. Altered levels of quinolinic acid (an agonist of NMDA glutamate receptors) and kynurenic acid (an antagonist of NMDA, AMPA and kainate glutamate receptors) have also been reported in individuals with suicidal behaviour184,193. Of particular interest are the studies with Toxoplasma gondii, a brain-tropic nematode that may increase the risks of self-directed violence and of suicide attempt194,195. To this end, T. gondii infection is thought to trigger an immune response in the brain that could lead to decreased tryptophan availability, decreased serotonin biosynthesis and increased levels of kynurenic acid196,197. Of note, T. gondii infection may also amplify specific behavioural traits that are linked with suicidal behaviour such as aggression in women and impulsivity in men198,199.
+Brain-gut axis. A new avenue that has garnered much enthusiasm in psychiatry and beyond is the potential contribution of the gut microbiome to psychopatho-logy200. Evidence that intestinal flora may influence mood states has been mounting201, with some studies demonstrating that certain bacterial strains can produce molecules to mimic dopamine signalling and to counter inflammation202. Although no findings have suggested a direct influence of the gut microbiome on suicidal behaviour, evidence of its effect on mood has been growing. As this nascent field begins to produce more results, concrete treatment targets might emerge for depression, suicidal ideation and suicidal behaviour.
+Diagnosis, screening and prevention
+Suicide prevention draws on a wealth of complementary approaches that span individual and populational approaches, and include diverse strategies for the assessment of suicide risk and prevention of suicide and suicidal behaviour (FIG. 6).
+Fig. 6 | Approaches for preventing suicide. Prevention, risk assessment and diagnosis of suicidal behaviour are key to decreasing suicide rates. Prevention strategies range from universal interventions that target the general public, selective interventions that target subpopulations with a greater potential risk and indicated interventions that respond to individuals exhibiting suicidal ideation or suicidal behaviour. Risk assessment approaches can occur in a variety of settings and through a variety of methods, all aiming to detect individuals who may be at increased risk of suicidal behaviour to direct them to the most appropriate services. Diagnosis involves the diagnosis of psychiatric and psychological features associated with suicidal ideation and suicidal behaviour and includes psychopathologies and psychological traits that may contribute to suicide risk. BD, bipolar disorder; ED, emergency department; MDD, major depressive disorder.
+Assessment and determination of risk
+As previously mentioned, no single risk factor, whether previous suicidal ideation or behaviour, mental health disorder, or other psychological traits, is a strong predictor of suicidal behaviour203-204. Consequently, there is a nearly universal acknowledgement that prediction of suicide and suicidal behaviour can only improve substantially over chance by the examination of multiple risk factors simultaneously, with a specific focus on imminent suicide risk203-205. To this end, it is important to note that the factors that contribute to risk of suicidal ideation are different from those that contribute to the transition from suicidal ideation to suicidal behaviour. Broadly speaking, among psychiatric disorders that can contribute to suicidal behaviour, mood disorders are most closely associated with suicidal ideation, whereas disorders of distress (such as panic disorder or post-traumatic stress disorder) or of disrupted impulse control (such as substance abuse or behavioural disorders) can contribute to the transition from suicidal ideation to suicidal behaviour88. In addition, two important transdiagnostic risk factors for suicide and suicidal behaviour are sleep problems (including reduced sleep, insomnia and nightmares) and chronic pain.
+Several approaches have attempted to examine individuals at high risk of suicide by assessing multiple risk factors simultaneously using standard suicide risk scales, computerized adaptive tests (CATs) and machine learning of electronic health records. For example, one systematic review of screening instruments for predicting future episodes of deliberate self-harm (including
+suicide attempts), primarily in psychiatric populations, demonstrated that the SAD PERSONS scale had low sensitivity but high specificity, with the opposite for the Manchester Self-Harm Rule (MSHR)206. In addition, the MSHR had a fair accuracy at predicting deliberate self-harm in individuals who had engaged in previous self-harming behaviour (with an area under the curve of 0.72) in one prospective study, but none of the six rating scales examined performed better than a global clinical rating of risk (area under the curve of 0.74)207. Other studies, including a systematic review, confirmed that there is no evidence to support the use of risk assessment instruments as predictors of suicidal behaviour among patients with a psychiatric disorder208,209.
+The last clinical contact for the majority of people who die by suicide is either in an emergency department (ED) or in primary care210. Screening for suicide risk in EDs is justified because as many as one in five individuals who die by suicide have visited the ED within 4 weeks of their death210; similarly, a visit to the ED for a mental health reason is a predictor of suicidal behavi-our87. In addition, other reasons for visiting the ED can increase the risk for suicide (such as trauma or alcohol intoxication), and nearly half of individuals who screen positive for suicide risk in the ED do not spontaneously communicate suicidal ideation211,212.
+Although suicide risk screening, which aims to detect any signs of suicidal ideation or behaviour, in EDs is not routine, it is a recommended aspirational goal that is variably, albeit increasingly, being implemented in EDs in the USA (The Joint Commission: National Patient Safety Goals Effective 2019). The four-item Ask Suicide-Screening Questions (ASQ) tool213 for children and adolescents shows a high sensitivity (93%) but lower specificity (43%) for predicting future suicide-related visits to the ED in the subsequent 6 months. The Emergency Department Safety Assessment and Followup Evaluation (ED-SAFE)/Substance Abuse and Mental Health Services Administration (SAMSHA) decision support tool was prospectively evaluated in adult attendees in an ED. Two items — having thought of a suicidal plan and having a history of a past attempt — both showed very high sensitivity (86.2-94.5%) for predicting a suicide attempt within the subsequent 6 weeks, but with low sensitivity (15.0-29.7%). Taken together, these two items predicted 83% of all suicide attempts within 6 weeks214. However, screening in EDs without an associated follow-up intervention to bolster treatment adherence and adherence to a safety plan is no better than usual care, whereas screening coupled with followup calls can reduce suicide attempts relative to usual care in the ED215. In addition, screening in primary care settings is also important, as the last clinical contact of people who die by suicide is most often in primary care210. In addition, one study demonstrated that those who reported frequent or daily suicidal thoughts in item 9 of the Patient Health Questionnaire (PHQ-9; a screening tool for depression that is typically delivered in primary care settings) were at markedly increased risk for suicide and suicide attempts216. However, this item has a relatively low sensitivity and, indeed, 39% of those who made suicide attempts and 36% of those who died by
+suicide within 30 days of completing a PHQ-9 reported no suicidal ideation at the time216.
+Computerized adaptive tests. One challenge to screening for suicidal risk is that it is multidimensional and not easily or accurately captured in a scale that focuses on a single disorder such as depression. Moreover, as described above, approaches to screening for suicide risk have a high sensitivity but a lower specificity, which is problematic, for example, in ED settings213. To address this problem, CATs for suicidal risk have been developed, wherein the presentation of items or questions are personalized to an individual based on their responses to previous items or questions217. This technique allows for the assessment of a wide range of potentially relevant risk factors in a brief questionnaire of 6-10 items. A CAT for the detection of suicidal ideation and behaviour has been developed using a sample of adult outpatients with a pre-existing mental health condition, demonstrating strong discriminative validity, with both high sensitivity and specificity in the identification of individuals at moderate and high risk for suicidal behaviour in the ED218. Similarly, a CAT for assessing suicidal ideation or behaviour has been developed for children and adolescents, showing a high convergent and discriminative validity. However, predictive validity of both the adult and paediatric CATs for assessment of suicidal risk have yet to be reported218,219.
+Electronic health records. Machine learning is now being used to try and identify precursors of suicidal behaviour and suicide that can be detected from patients’ medical records. The advantages of this approach are that it provides a sample size large enough to detect effects on suicide and suicide attempts, it bypasses patient self-report, and it allows the testing of the effect of a combination of risk factors, each of which might only make a small individual contribution to suicidal risk. In addition to a review of diagnoses, medication and service use, some studies have included natural language processing of the clinician’s notes to try and identify phrases associated with risk or protective factors for suicide (such as social support or clinician optimism)220-221. Collectively, these approaches could alert clinicians that their patients are at increased risk of suicide, thereby facilitating referral to appropriate services.
+One study of machine learning of health records for the prediction of suicide and suicide attempts examined the medical and psychiatric records of ~3 million patients with at least one mental health diagnosis across seven different health care systems222. In this study, an algorithm was developed that could identify 5% of the population, accounting for 43-48% of the suicides and suicide attempts, and was able to predict suicide and suicide attempts in the next 90 days with a specificity of ~70% and a sensitivity of ~80% in both specialty mental health and primary care settings222. The listed predictor variables were primarily those that have a well-established association with suicide risk (such as depression, alcohol abuse, drug abuse or a history of a suicide attempt), but the power of this approach was in the ability to test the predictive power of the combination of
+many variables simultaneously. Although these results are promising, it will be important to prospectively test whether algorithms generated from the use of artificial intelligence improve clinical knowledge for assessing suicide risk and to carry out randomized controlled trials (RCTs) to assess the effect of algorithm use on clinical outcomes223.
+Prevention strategies
+One framework for suicide prevention that was proposed in the mid-1990s224-225 and that is still widely used today was built on other frameworks for the prevention of mental226 and physical227 disorders. The framework takes a risk factor-based approach to suicide prevention, classifying suicide prevention efforts as universal, selective or indicated on the basis of their target groups (FIG. 6). Each type of intervention is discussed below, along with examples. The examples are by no means exhaustive but are intended to give an overview of the types of preventive interventions that have been trialled.
+Universal interventions. Universal interventions aim to favourably shift risk and protective factors across the entire population, rather than in specific groups of individuals. In general, universal interventions usually either affect the social environment or promote resiliency within individuals226 and target risk factors without necessarily identifying individuals with those risk factors. For example, a number of universal interventions have targeted ‘access to means’ as a risk factor for suicide, without specifically identifying those whose access might place them at risk. Examples of these efforts include banning pesticides228, withdrawing medications associated with suicide (such as co-proxamol) from the market229, removing charcoal from retail out-lets230, equipping vehicles with catalytic converters231 and installing barriers on bridges and cliffs232,233.
+Other examples of universal interventions acknowledge the influential role of the media; to this end, many countries and organizations have produced guidelines for journalists to encourage the responsible reporting of suicide in the media in recognition of the fact that media presentations of suicide, for example, those glamorizing the decedent, can lead to ‘copycat’ acts234. More recently, efforts have shifted to adapting these guidelines for use in social media235, for example, with resources being developed to assist young people to have safe discussions about suicide online236 and reducing the availability of online information concerning lethal means237. In addition, suicide prevention media campaigns have also gained traction following the recognition of the potential of the media as a force for good in suicide prevention. These campaigns are delivered to the population as a whole, usually across multiple media platforms; additionally, most have brief community service announcements at their core and contain messages about offering or seeking help238,239.
+Other examples of universal interventions are delivered in specific settings and are usually designed to raise awareness about suicide and its prevention; often, these interventions target young people and take place in schools, universities and workplaces240.
+Selective interventions. Selective interventions target subgroups who exhibit risk factors that predispose them to suicidal thoughts or behaviours but who do not currently exhibit those behaviours226. Many selective interventions directly or indirectly target people with psychiatric disorders; for example, specific pharmacological treatments for mood disorders have been trialled (see below, Pharmacological interventions for suicidal behaviour). Similarly, interventions that aim to better equip general practitioners to detect, diagnose and manage depression (typically involving the provision of education and guidelines) have also been introduced, with the rationale that many people with depression will not see a specialist mental health care provider but will receive care from a general practitioner241. Other selective interventions target people with socioeconomic, environmental or contextual risk factors for suicide (see Mechanisms/pathophysiology, above), such as those experiencing bullying or social isolation. Selective interventions designed to reduce bullying and social isolation are often school based and tailored towards particular subgroups such as LGBT+ young people. For example, some schools have Gay-Straight Alliances that provide a safe and supportive environment to LGBT+ students, and there is emerging evidence that these interventions may have positive effects in reducing both suicidal ideation and suicide242.
+Indicated interventions. Indicated interventions are designed for people who are already starting to exhibit suicidal thoughts or behaviours, identified through screening programmes or by clinical presentation. As these interventions target people who already have suicidal ideation or suicidal behaviours, some individuals have argued that, unlike universal and selective intervention strategies that constitute true prevention, indicated intervention strategies fall into the realms of early intervention224. A key example of indicated intervention is the provision of psychosocial interventions to those who present to an ED or other health care setting following an episode of self-harm243. Such interventions include psychological therapies (see, Psychological interventions for suicidal behaviour, below) and social approaches designed to provide ongoing support (such as case management244 and regular communication through channels like postcards or text messages245). Telephone crisis services (sometimes called ‘hotlines’) constitute a further example of an indicated intervention246. These services have existed since the 1950s247, and many have now embraced newer technology like online chat and text-messaging services248.
+Multicomponent interventions. Universal, selective and indicated interventions are often delivered in combination through what is often termed a ‘systems-based’ approach249, sometimes delivered in local regions or communities. For example, the Alliance Against Depression model, initially implemented in Nuremberg (Germany), included a suicide prevention media campaign (universal intervention), education for general practitioners, gatekeeper training for frontline workers and other professionals (selective interventions), and an ‘emergency
+card’, which gives people who have attempted suicide ready access to professional help, as well as additional ‘self-help’ activities (indicated intervention). Following its demonstrated 24% reduction in suicidal behaviour250, the Alliance Against Depression model has since been adopted in a number of other regions in Europe, with the inclusion of an additional universal intervention (restricting access to means)250. Beyond regions and communities, multicomponent interventions have also been delivered in specific settings (such as health services251 and schools252) and to particular population groups (for example, armed forces personnel)253.
+Evaluation of suicide prevention interventions. One systematic review has synthesized the evidence around specific suicide prevention interventions254 and identified only a few interventions with unequivocal evidence of effectiveness in reducing suicide-related outcomes (suicides, suicide attempts and suicidal ideation) or which showed substantial promise. This review concluded that restricting access to means can prevent suicide and that school-based awareness programmes can reduce suicide attempts. Certain psychological treatments, particularly cognitive behavioural therapy (CBT), are also important in the suite of strategies to prevent suicide. Other interventions, such as pharmacological treatments (for example, lithium or antidepressants) also demonstrated benefits, although their effects might be specific for particular population groups.
+Although this review highlights the effectiveness of several interventions, it is striking that there are many interventions for which the evidence base is rudimentary. There are a number of reasons for this poor evidence base, not least is that RCTs are often difficult to mount in the area of suicide prevention. This difficulty is partly because of the low base rate of suicide (which has implications for the sample size needed to demonstrate the effect of an intervention) and partly because it is unethical to withhold a potentially life-saving intervention from the control group. These problems are compounded in the case of universal interventions, which typically target whole communities. Increasingly, suicide prevention researchers are looking to alternative or augmented evaluation designs to strengthen the evidence base255. In the field, there is a recognition that RCTs are not always possible and that well-designed ecological studies (observational studies in which groups or populations, rather than individuals, are studied to provide evidence of change associated with a particular intervention) will sometimes provide the best available evidence255. Indeed, the systematic review included ecological designs, and the authors of this review observed that these designs were particularly common in assessing the effect of population-level restriction of access to means254. In addition, suicide prevention researchers are borrowing methodological approaches from disciplines like programme evaluation to evaluate complex interventions (such as designing programme logic models and triangulating data from a range of sources)255. They are also using increasingly sophisticated analysis strategies to analyse evaluation data (for example, interrupted time series analyses across multiple sites)255.
+In addition, some researchers are also beginning to go beyond effectiveness to consider the cost-effectiveness of interventions256.
+Management
+In the past two decades, most efforts to develop suicidespecific psychosocial interventions have moved away from the view that treating the underlying psychiatric disorder would resolve suicidal urges and thoughts, to a perspective that suicide-specific treatments are necessary in addition to interventions for primary psychiatric disorders. Furthermore, in recognition that many people who become suicidal have limited access to behavioural treatments or do not wish to receive these treatments, brief suicide prevention approaches with limited or no health professional intervention have been developed (BOX 2).
+Longer-term psychosocial interventions
+Overall, psychosocial interventions, irrespective of type, have been shown to reduce suicide attempts. One metaanalysis demonstrated that, in adult and adolescent patients (hospitalized or not, and presenting with a variety of features such as borderline personality disorder, depression and schizophrenia-spectrum disorders) as well as in patients with or without a history of suicidal ideation or behaviour, psychosocial interventions were associated with significantly fewer suicide attempts during follow-up compared with those who received treatment as usual257. In addition, sensitivity analyses showed that psychosocial interventions are effective in outpatient settings among those with borderline personality disorder, irrespective of suicidal history257. The beneficial effect of psychosocial interventions demonstrated in this meta-analysis is consistent with a large-scale, register-based propensity score matching study which found that those who received a psychosocial intervention after self-harm were less likely to die by suicide at long-term follow-up258.
+Of all the longer-term psychosocial interventions, cognitive therapy (CT) and CBT have received the most research focus243,257,259. CT and CBT are types of psychological therapies that encourage an individual to
+manage their problems by changing the way in which they think and behave. Recent reviews confirm that CT and CBT that targets suicidal thoughts and behaviours in high-risk adults can reduce the incidence of self-harm at 6 months (OR 0.54, 95% CI: 0.34-0.85) and 12 months (OR 0.80, 95% CI: 0.65-0.98)243. In addition, in one RCT of active duty soldiers, brief CBT (12.5 h per patient) reduced the odds of suicide attempts in the 24-month follow-up compared with treatment as usual (HR 0.38, 95% CI: 0.16-0.87, NNT 3.88)260. However, it is important to note that there is considerable study heterogeneity in the CT and CBT field243,259 and one analysis of studies demonstrated that the favourable effect for CBT was strongest when treatment as usual was not clearly described261.
+Dialectical behaviour therapy (DBT) is a cognitive behavioural treatment (including individual psychotherapy and weekly skills groups) that combines the change-focused aspects of CT with acceptance-based work262. Many studies of DBT have focused on patients with borderline personality disorder243,263,264. Although individual studies have provided evidence that DBT reduces suicidal ideation and suicide attempts, one systematic review examining treatment of patients (regardless of diagnosis) who have self-harmed within 6 months prior to treatment initiation concluded that, although DBT was associated with a decreased frequency of self-harm, it did not reduce the number of patients who repeated self-harm during follow-up243.
+Other approaches include the collaborative assessment and management of suicidality265, a therapeutic framework to guide and manage suicide risk and inform treatment planning, mindfulness-based interventions, and acceptance and commitment therapy, a cognitive behavioural treatment that aims to promote psychological flexibility using six core processes, including acceptance and commitment to action266. Although collaborative assessment and management of suicidality has been shown to reduce suicidal ideation at 3 months267, whether this treatment is effective for reducing suicide attempts has not yet been established267,268. Although interest in mindfulness-based interventions has grown in recent years, it is not yet clear whether these interventions reduce suicidal ideation or suicidal behaviour269. There is also some preliminary evidence for acceptance and commitment therapy in reducing suicidal ideation, although further studies are required270.
+Evidence on the use of psychosocial treatments in children and adolescents, irrespective of type, remains weak257,271,272. An evidence update concluded that DBT was the only intervention to meet the threshold for ‘well established treatments’ for reducing suicidal ideation and suicidal behaviour in children and adolescents273. Where there was probable or possible evidence for efficacy, these interventions tended to come from single studies272,273. However, an updated systematic review found that, if DBT is treated as a type of CBT, then there is evidence for the positive effects of CBT on self-harm in adolescents274. The authors of this study also noted that CBT with a family systems component are promis-ing274. In addition, mentalization may also be effective in adolescents who self-harm275.
+Very few studies have focused on preventing suicidal ideation or behaviour in older adults276. However, one study demonstrated that problem-solving therapy was associated with a reduction in suicidal ideation at 12 and 36 weeks, compared with supportive therapy277. In addition, findings from a pilot study of a 16-session course of interpersonal psychotherapy adapted for older adults found reduced suicidal ideation post-treatment278.
+There is no evidence from RCTs to support that any of these psychosocial interventions reduce the risk of suicide279. However, it is important to note that absence of evidence does not mean that these interventions are not effective but rather, for the most part, that studies have been insufficiently powered to detect a reduction in suicide given the relatively low frequency of deaths by suicide. Three other challenges to the management of suicide risk are important to highlight, namely that men are less likely to seek help than women280, that men who die by suicide are less likely to be in contact with clinical services than women prior to death210, and that it is not clear whether psychosocial interventions are as effective for men than for women243.
+Brief psychosocial interventions
+Brief interventions to prevent suicidal behaviour are of great interest because they are easy to implement, inexpensive and require limited staff resources. These interventions are typically delivered in one session or via several brief contacts in person, by phone or mail. Typically, these interventions are often implemented with patients who had attended the ED for a suicidal crisis, most often a suicide attempt. Overall, the interventions target behaviour rather than symptoms associated with suicidal crises. The interventions, to varying degrees, focus on informing people about suicidal behaviour, helping people to become aware of problems, vulnerabilities and events linked to the behaviour, motivating people to engage in safety planning and help- seeking, problem solving, and developing practical strategies to manage future suicidal crises along with connecting to social and professional support.
+Caring contacts. This approach, which was first tested more than four decades ago281, refers to the routine sending of brief non-demanding messages (via, for example, postal mail, postcards, e-mail, texting and telephone) that express caring concern to suicidal patients following discharge from inpatient treatment. This intervention was shown to decrease suicide risk in one meta-analysis (OR 0.20, 95% CI: 0.09-0.42)279, although this finding needs replication. One caring contacts (delivered via text message) trial for military personnel yielded inconsistent findings on the effectiveness of messages between primary (current suicidal ideation and hospitalization or medical evacuation due to suicide risk) and secondary outcomes (worst-point suicidal ideation, ED visits and suicide attempts)282.
+No-suicide contracts. Another form of brief intervention usually takes the form of asking patients to promise not to engage in suicidal behaviour and to contact professionals during times of crisis. These interventions have
+been used in various contexts, including emergency helplines and inpatient units. The contracts usually include a statement of assent, details of the duration of the agreement and a contingency plan if the person feels unable to uphold the agreement. However, there is a lack of data supporting their effectiveness283 and they have fallen out of favour.
+Safety Planning Intervention or Crisis Response Planning. The Safety Planning Intervention (SPI)284 is a brief intervention that involves patients engaging in a suicide narrative aimed at identifying warning signs and provides individuals with a prioritized and specific set of coping strategies and sources of support that they can use if suicidal thoughts re-emerge. The intent of the SPI is to help individuals lower their imminent risk for suicidal behaviour by consulting a predetermined set of potential coping strategies and social supports. SPI has six steps: identify the warning signs; apply internal coping strategies; use people or social settings that could serve as a distraction; reach out to friends and family members who can provide support; contact professionals and agencies; and limit access to lethal means.
+SPI, in combination with follow-up calls (known as SPI+), is effective in reducing the incidence of suicidal behaviour and increasing treatment engagement in the 6 months after discharge from the ED285. Crisis response planning is an abbreviated form of safety planning that uses four of six elements of the SPI (without social interaction as a means of distracting from suicidal thoughts and counselling to limit access to lethal means). Compared with a contract for safety, crisis response planning is more effective in preventing suicide attempts, resolving suicidal ideation and reducing inpatient hospitalization among high-risk active duty military personnel286. In addition, the ED-SAFE intervention includes a safety planning aspect (self-administered), along with a secondary suicide risk screening designed for ED physicians to evaluate suicide risk following an initial positive screen, and a series of telephone calls with the optional involvement of a friend or relative of the individual for 52 weeks following an ED visit. The ED-SAFE intervention resulted in fewer suicide attempts over a year compared to treatment as usual215.
+Additional brief interventions. Several other types of brief interventions have been described. The Attempted Suicide Short Intervention Program (ASSIP)287 is a multi- session brief intervention that utilizes several elements of SPI and the crisis response plan in that it combines psychoeducation, a recorded patient-centred narrative description of a recent suicide crisis, safety planning and review of the recording to help continued long-term outreach contact. The ASSIP differs from other brief interventions in that it uses review of the recorded suicide narrative with the patient. ASSIP is administered in three weekly face-to-face therapy sessions supplemented by regular, personalized letters to the participants for 24 months. One recent study has demonstrated an 80% reduced risk of participants making at least one repeat suicide attempt when ASSIP was administered in addition to usual clinical treatment
+compared with usual treatment with a single assessment interview287. In addition, ASSIP led to fewer suicide attempts but had no effect on suicidal ideation287.
+Another type of brief intervention — the Volitional Help Sheet — consists of a table with two columns and is designed to enhance self-determination with respect to self-harm. One column lists theoretically derived critical situations (in which the individual might engage in self-harm) and the other lists alternative responses to self-harm. Although this intervention had no overall effect on the recurrence of self-harm, it has been shown to be effective in reducing the number of self-harm repetitions following a suicide attempt in those previously hospitalized for self-harm, compared to a control group receiving treatment as usual288.
+Similarly, in teens, two brief interventions — Teens Options for Change (TOC)289 and As Safe As Possible (ASAP)290 — yielded encouraging, yet non-significant results. These studies offered different interventions to increase emotional regulation and coping skills in patients at risk of suicide from ED and inpatient settings, respectively.
+Pharmacological interventions for suicidal behaviour Lithium. From the potentially antisuicidal pharmacological agents available (BOX 2), lithium has been used the longest, with its antisuicidal effects having been reported for decades. However, most of the data supporting its utility in preventing suicide-related outcomes derives from observational studies of patients followed clinically with lithium291,292 and some studies have reported conflicting data293. More recently, several methodologically rigorous pharmacoepidemiological studies, including >10,000 patients each294,295, have strongly suggested that patients treated with lithium are relatively protected from suicide attempts, death by suicide, hospitalization for suicide attempts and other suicide-related outcomes, compared with patients treated with valproate or other mood stabilizers. However, several RCTs that compared lithium to either placebo or other mood stabilizers have been unable to demonstrate this effect296,297, although some had very modest sample sizes298. Of interest, two of these studies identified antisuicidal effects based on secondary, post hoc analy-ses296,299. Although some meta-analyses that compared lithium to antiepileptic drugs and placebo found little effect279,300, another recent meta-analysis with >6,500 participants demonstrated that, despite little effect on suicidal behaviour, lithium treatment was effective at reducing suicide deaths compared to placebo in people with MDD300. Importantly, the risk of suicidal behaviour was much greater before starting treatment with antiepileptic drugs compared with after treatment initiation in one study301, whereas another study demonstrated no difference302; these findings are important, given the warning about possible increases in suicidal ideation in patients exposed to antiepileptic drugs by the US FDA.
+Proper interpretation of these contradictory results about the antisuicidal properties of lithium is essential given the toxicity of lithium in overdose and its relatively narrow therapeutic window. Observational studies,
+no matter how well controlled, may spuriously demonstrate the protective effects of lithium. For example, these studies cannot control for important variables that affect a clinician’s medication choice. Lithium toxicity and overdose risk may influence the likelihood of its prescription, particularly among individuals who have a greater risk of suicidal behaviour. Additionally, increased monitoring ofpatients who receive lithium may improve treatment efficiency and decrease suicide risk through repeated contact with clinical services.
+Clozapine. Clozapine was the first medication approved by the US FDA to prevent suicidal behaviour. It is indicated in schizophrenia, in which the lifetime rate of suicide deaths of patients is ~10%, with ~50% of individuals attempting suicide303. Evidence supporting the use of clozapine for suicide came from the InterSePT study, a multicentre RCT that demonstrated the superiority of clozapine in reducing the risk of suicide attempts and the other primary outcome variables in people with schizophrenia or schizoaffective disorder, compared with olanzapine303. Of note, this study reported no difference in the rates of suicide in the two treatment arms, although the total number of deaths was relatively small (five with clozapine and three with olanzapine). More recently, a meta-analysis of observational and trial data demonstrated that clozapine, when taken consistently, decreased not only suicides but other causes of mortality as well304, although this effect may be partially explained by closer monitoring of patients receiving clozapine treatment. Nonetheless, clozapine remains relatively underutilized for the prevention of suicidal behaviour in patients with psychosis, probably because ofthe required laboratory monitoring given the risk of neutropenia and agranulocytosis in patients treated with this drug305.
+Ketamine. The past decade has seen intense interest in the use of ketamine as a potential antisuicidal medication306 and its relatively novel mechanism of action has spurred a search for other drugs with similar mechanisms that might also be of use307. Several studies have examined the effect ofketamine on suicidal ideation, mostly in people with mood disorders308-310 and have demonstrated that ketamine decreases suicidal ideation independent of improvement in other mood symptoms174,308-310, although one small study did not find this to be the case311. Of interest, the effects of ketamine on suicidal ideation may be linked to improvements in insomnia312, a clinical variable that has been linked to suicidal behaviour313. Several additional studies are underway to examine the effects of intranasal ketamine (esketamine, the S-enantiomer of ketamine) on suicidal ideation in various populations, with some encouraging results314. Clearly, the intranasal administration route would facilitate the use of ketamine, if its therapeutic effects are verified. Some of the questions remaining include the duration of its effects and whether repeated administration would be a practical and efficient approach.
+Selective serotonin reuptake inhibitors. Selective serotonin reuptake inhibitors (SSRIs) are thought to have a more-pronounced effect on suicidal ideation than other
+antidepressants, based on >50 years of studies showing the importance of serotonergic dysfunction in suicidal behaviour61-128. Earlier studies were mostly secondary analyses of depression trials; in fact, of 10 such studies that compared the effects of serotonergic and noradrenergic antidepressants on suicidal ideation, five favoured serotonergic drugs315-319, one favoured a noradrenergic drug320 and four found no difference321-324. However, these studies used one item from a depression scale to measure suicidal ideation and did not specifically recruit individuals with suicidal behaviour. However, one small pilot study using a double-blind, randomized, controlled methodology demonstrated that paroxetine was more effective in reducing suicidal ideation, even after adjusting for depression symptoms, compared with bupropion in patients with depression who had past suicide attempts or active suicidal ideation325. Of note, benefit increased as baseline severity of suicidal ideation was higher. Clearly, more evidence is required to determine whether SSRIs are superior than other therapies for the treatment of people with depression who are suicidal, and trials would do well to specifically recruit those at risk for suicidal behaviour. There also appears to be an age-related effect of SSRIs on suicidal events — in one meta-analysis of clinical trials, SSRIs appeared to increase the risk of suicidal events in those <24 years of age, and protected against suicidal events in those >65 years of age326.
+Buprenorphine. The effects of buprenorphine have recently been studied for the treatment of suicidal ideation. A small RCT delivered promising results suggesting that patients with depression who received low-dose buprenorphine were more likely to show decreases in suicidal ideation after 2 weeks and 4 weeks of treat-ment327. Buprenorphine was well tolerated. Similarly, a 3-day study of men with depression and opioid use disorder also demonstrated decreases in suicidal ideation after a large dose of sub-lingual buprenorphine, although this trial was not placebo controlled328. Larger studies are needed to replicate the finding that opioids may improve suicidal ideation; this is an observation that comports with the hypothesis that psychic pain is an important determinant of suicide risk329.
+Quality of life
+Quality of life is a multidimensional concept that includes physical, mental and social aspects of subjective satisfaction with one’s own life. Studies investigating health-related quality of life typically focus on the physical and mental components in the context of either past suicidal ideation or past suicide attempt. For mental health outcomes, suicidal ideation and suicide attempt are associated with trajectories of decreasing mental health over time330. In addition, several factors are associated with reduced mental health components of quality of life assessments such as being female, having borderline personality disorder, or having high impulsivity, hopelessness or hostility331. Similarly, suicidal behaviour is associated with reduced physical health over time330,332, and depending on the method and severity of the suicide attempt, individuals might have physical disabilities that
+directly affect their quality of life333. Moreover, reduced quality of life can also affect those who are close to the individual attempting suicide. Indeed, scores for depression and anxiety are higher whereas scores for mental components of quality of life are lower in people who are related to an individual who died by suicide334.
+A continuing obstacle to suicide prevention and contributor to decreased quality of life in people who survive suicide attempts is the pervasive taboo surrounding suicidal ideation and suicidal behaviour. Although most countries have public health initiatives directed at breaking the stigma surrounding mental health issues, the perceived stigma associated with suicide is still an obstacle to help-seeking1,335 and, therefore, constitutes an important target for public health initiatives aiming to decrease suicide rates. Additionally, the stigma or perceived stigma associated with suicide and suicide attempts is associated with reduced disclosure of personal or familial histories of suicidal behaviour and are associated with decreased self-esteem335.
+As noted above, the prevalence of suicidal ideation and suicide attempt are many times that of suicide and, therefore, the potential effect of suicidal ideation and suicidal behaviour goes beyond the direct loss of the individual. Studies investigating the economic burden of suicide and suicide attempt placed the cost of suicide at US$58.4 billion in the USA in 2013 when considering only reported suicides and suicide attempts, and up to US$93.5 billion when numbers are adjusted for underreporting336. Of note, the costs were mainly due to lost productivity336. In addition, suicidal ideation and suicide attempt are associated with increased health care costs, with up to one-third of patient admissions in psychiatric hospitals attributed to suicidal ideation or suicide attempt331. These costs may be related to the direct treatment of injuries or to the psychological and social aspects of suicidal behaviour and their effect on others1. With an estimated per capita cost of suicide nearing US$300 in the USA336, investments in suicide prevention efforts are paramount.
+Outlook
+Suicide and suicidal behaviour continue to present a challenge both clinically and socially, and our theoretical understanding of the process involved in development of suicide risk continues to evolve as we obtain more evidence on the epidemiological, biological, clinical and psychological fronts.
+Several factors regarding the detection and treatment of suicide continue to be explored, including developing and validating clear assessment algorithms to assist clinicians in determining which of their patients are at greatest risk of suicide attempts or suicide, and assessing the practicality of their implementation in clinical practices. In addition, understanding which psychological or pharmacological treatments are the most effective, and for whom, requires further study. On this point, > 100 registered clinical trials for suicide are currently underway (Supplementary Table 1). These trials are investigating various interventions, including those based on psychological and behavioural approaches, pharmacological approaches, mixed approaches, use of medical devices,
+use of new media, screening, primary prevention and reducing access to lethal means.
+Similarly, what biological mechanisms underlie the cognitive and behavioural changes associated with suicide risk, and how public policies can be tailored to better prevent, detect and treat at-risk groups to continue to decrease suicide rates across the globe, are important questions to address. In addition, efforts should also be made into better understanding sex differences as they apply to pathology and interventions for suicidal behaviour. Given the important contribution of the easy access to pesticides and firearms to global suicide rates as well as the clear evidence that
+selective measures to reduce access to these means have an effect on suicide rates, single measures such as legislation for stricter regulation of firearm access in the USA or further regulation on access to pesticides in China and other countries in Asia may have a tremendous effect on global suicide rates125. With suicide rates continuing to rise at an alarming rate in some coun-tries1, it is evident that suicide will remain an important public health concern, and one that can only be addressed through concerted, multidisciplinary and multifaceted efforts.
+Published online: 24 October 2019
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+Key words: coronary artery disease/heart rate variability/QT interval/early repolarization/dilated cardi omyopathy/causality
+Introduction
+Schizophrenia is a serious mental disorder affecting up to 1% of the population.1 The life-expectancy of individuals diagnosed with schizophrenia is approximately 15-20 years shorter than that of the general population2—a major contributor being cardiovascular mortality.3 Both cardiovascular diseases (CVD), including coronary artery disease and heart failure, and CVD risk factors, including high blood pressure and abnormal electro-cardiogram (ECG) patterns, are prevalent amongst individuals with schizophrenia.4-8
+There are broadly 2, not mutually exclusive, explanations for this co-morbidity. First, there may be a shared etiology. Low birth weight, pre-term birth and maternal malnutrition during pregnancy are associated with an increased risk of both schizophrenia and CVD in off-spring.9,10 There is also evidence for shared genetic influences, but only for a limited number of cardio-metabolic traits and using considerably smaller samples than currently available.11 Second, there may be causal effects. The predominant hypothesis is that schizophrenia increases the risk of CVD. Schizophrenia is characterized by elevated cortisol levels, dysfunction of the autonomic nervous system, inflammation, lipid abnormalities, oxidative stress and increased platelet reactivity,4,9,10 all of which contribute to the development and progression of CVD.12 Reverse causal effects have also been proposed,
+with markers of CVD preceding and potentially inducing psychosis.10,13
+While systemic characteristics of schizophrenia may directly induce CVD, there are also potential mediators. Anti-psychotic medication use, which can cause central obesity, hypertension, and abnormal lipid patterns,14 may consequently increase CVD risk.15 This does not explain all excess cardiovascular mortality though, as patients who do not use anti-psychotics are also at increased risk of CVD.5,14 Other potential mediators are smoking, poor diet and lack of physical activity, all common in individuals with schizophrenia.16,17 While conducting a randomized controlled trial (RCT) to determine causal factors is not feasible, a powerful alternative is Mendelian Randomization (MR).18 MR mimics an RCT by using genetic variants as proxies, or “instrumental variables,” for the proposed risk factor.19 Because genetic variants are randomly passed on from parents to offspring, bias from confounders can be circumvented (provided core assumptions are met).20
+We capitalize on the availability of large genetic samples and sophisticated methods to elucidate the nature of the relationship between schizophrenia and CVD. Using summary-level data of genome wide association studies (GWAS), we: (1) compute genetic correlations to determine genome-wide overlap between schizophrenia and CVD risk, (2) perform univariable MR to test if liability to schizophrenia causally increases CVD risk, (3) perform univariable MR to test if liability to CVD causally increases schizophrenia risk, and (4) perform multivariable MR to test if key health behaviors mediate effects of liability to schizophrenia on CVD risk.
+Methods
+The analysis plan was pre-registered at https://osf.io/ fprew.
+Data
+We employed European-based estimates from the largest available GWAS on schizophrenia.21 Cases were individuals diagnosed with schizophrenia spectrum disorder based on DSM-IV criteria. For CVD, we selected 8 phenotypes often linked to schizophrenia, for which sufficiently large GWAS were available (supplement). These entailed 2 clinical endpoints: coronary artery disease22 and heart failure,23 and 6 markers of CVD risk: systolic blood pressure,24 diastolic blood pressure, 23 heart rate variability (HRV),25 QT interval,26 early repolarization ECG pattern,27 and dilated cardiomyopathy ECG pattern27 (phenotype definitions in table 1). For multivariable MR, we selected 5 potential mediators of effects of liability to schizophrenia on CVD. These capture health behaviors, or their downstream consequences, particularly prevalent among individuals with schizophrenia: smoking (initia-tion28 and lifetime smoking20), body mass index (BMI),29 464
+physical activity,30 lipid levels (total cholesterol and trigly-cerides31- may be increased as a result of anti-psychotic medication), and type-2-diabetes.32 For MR, overlap between some of the exposure and outcome samples was prevented by excluding overlapping UK-Biobank participants. For further explanation see Supplement.
+Genetic Correlations
+Genetic correlations were computed using linkage disequilibrium score (LDSC) regression.33 The genetic correlation is based on the estimated slope from the regression of the product of z-scores from 2 GWAS on the LD score and represents the covariation between 2 traits based on all polygenic effects captured by the included SNPs. We filtered GWAS summary statistics to only include the 1 290 028 million SNPs from the HapMap3 European reference panel, used to provide the genome-wide LD information.33,34
+Univariable Mendelian Randomization
+We conducted bi-directional MR to assess evidence for causal effects of liability to schizophrenia on CVD risk, and vice versa, of liability to CVD on schizophrenia risk. MR relies on 3 main assumptions (figure 1A). “Horizontal pleiotropy,” a variant associating with multiple traits, may violate assumptions 2 and 3, if the variant associates with the outcome directly or via a confounding factor.18
+The main method was inverse-variance weighted (IVW) regression. Independent SNPs that reached genome-wide significance (P < 5E-08) in the exposure GWAS were extracted to form instrumental variables. SNP-outcome effects were obtained from the outcome GWAS. A ratio estimate was obtained by dividing the effect a SNP has on the outcome by the effect it has on the exposure. Individual SNP-effects were weighted by the inverse of their variance and estimates of all SNPs combined. IVW provides the first indication of whether the exposure causes the outcome, assuming all assumptions are met. To verify its validity, we applied 6 sensitivity methods: First, weighted median regression which can provide a consistent causal estimate even when <50% of the weight of the instrument does not satisfy the MR assumptions.35 Second, weighted mode regression, the estimate of which is reliable as long as the most frequent SNP-effects are contributed by valid SNPs.36 Third, MR-Egger regression, which explicitly tests for horizontal pleiotropy.37 Instead of being fixed to zero, the intercept is freely estimated and can be interpreted as the average horizontally plei-otropic effect. Crucially, MR-Egger relies on 2 assumptions: the INstrument Strength Independent of Direct Effect (InSIDE; meaning that any pleiotropic effects should not be correlated with the instrument strength) assumption and the NO Measurement Error (NOME) assumption. An important limitation is that MR-Egger has markedly lower statistical power than IVW. Fourth,
+MR pleiotropy residual sum and outlier (MR-PRESSO) analysis,38 which consists of 3 steps: testing for horizontal pleiotropy (global test), correcting for horizontal pleiot-ropy using outlier removal (outlier test) and evaluating significant differences in the causal estimate before and after outlier removal (distortion test). Fifth, generalized summary data-based MR (GSMR).39 GSMR provides high statistical power by using low levels of LD between the SNPs and considering sampling variation in the estimated effects of all SNPs. Pleiotropic SNPs are removed with the HEIDI-outlier procedure. Sixth, Steiger filtering which corrects for reverse causality.40 The Steiger test was used to identify and then exclude SNPs that explained a larger amount of variance in the outcome, compared to the exposure.
+We also performed leave-one-out analyses, repeating IVW after removing each SNP, and computed Cochran’s Q to assess heterogeneity between SNP-estimates in each instrument. To assess instrument strength, we computed the F-statistic (F > 10 is sufficiently strong). To assess whether the NOME assumption was satisfied for MR-Egger we computed the 12GX statistic.41 If 12GX is higher than 0.9, bias due to violation of the NOME assumption is not likely. When 12GX = 0.6-0.9, bias can be corrected for with simulation extrapolation (SIMEX) MR-Egger.
+466
+An MR finding was considered robust when results were consistent across methods. Because sensitivity methods rely on stricter assumptions than IVW, they are less powerful. Their statistical evidence, but not effect size, will therefore be weaker, even for a true effect. We describe findings as showing no clear evidence, weak evidence, evidence or strong evidence for a causal effect, taking into account IVW and the sensitivity methods, adhering to the broad interpretation of P-values described by Sterne and Davey Smith (2001).42 We consider a finding to be consistent across sensitivity methods if the beta coefficient is in the same direction and of comparable size (or larger). Analyses were performed in R (3.6.3), using packages: “TwoSampleMR,” “GSMR,” “psych,” and “MR-PRESSO.”
+Multivariable Mendelian Randomization
+Using multivariable MR, health behaviors were added to see whether effects of liability to schizophrenia as the exposure on CVD as the outcome diminished, which could indicate mediation (figure 1B).43 For each univariable analysis we added each health behavior separately (we did not combine them to prevent violation of linearity and homogeneity assumptions44). We evaluated robustness with multivariable MR-Egger, computed the Sanderson-Windmeijer conditional F statistic for instrument strength, and computed an adaption of the Cochran’s Q statistic to detect heterogeneity (see Supplement for more information).
+Results
+Genetic Correlations
+Genetic correlations, reflecting overlap between genomewide liability to schizophrenia and CVD, were all close to zero (table 1).
+Univariable Mendelian Randomization
+Instruments for schizophrenia and CVD were sufficiently strong (supplementary table S1). Bi-directional univariable MR results are depicted in table 2.
+Liability to Schizophrenia on CVD. There was evidence that liability to schizophrenia causally increases heart failure risk (betaIVW = 0.027, 95% confidence intervals = 0.003-0.051, P = 0.027). Weighted median and weighted mode regression confirmed this with consistent effect sizes, but weaker statistical evidence. GSMR showed much stronger evidence for a positive effect, of greater magnitude (beta = 0.266, CIs = 0.25-0.281, P = 3.3E-251). While the MR-Egger intercept showed strong evidence for horizontal pleiotropy (-0.013, CIs = -0.019 to -0.007, P = 9.4E-05; supplementary table S2), its slope also indicated strong evidence for a positive effect (beta = 0.220,
+CIs = 0.129-0.312, P = 4.5E-06). There was strong evidence for heterogeneity across SNPs (Cochran’s Q P = 2.3E-04; supplementary table S3). MR-PRESSO detected one outlier, but eliminating it did not have a large impact (supplementary table S4). Steiger filtering did not find SNPs that explained more variance in the outcome than the exposure, suggesting no reverse causality (supplementary table S5). While leave-one-out analysis showed SNP rs13107325 to have a relatively large impact, the effect size and statistical evidence remained considerable after removal (supplementary figure S1). In sum, horizontal plei-otropy notwithstanding, there is consistent evidence for a causal, increasing effect of liability to schizophrenia on heart failure across all included (sensitivity) methods.
+There was evidence that liability to schizophrenia causally increases early repolarization pattern (betaIVW = 0.020, CIs = 0.001-0.038, P = .040). While weighted median and GSMR confirmed this, weighted mode and MR-Egger regression did not (beta = 0.005, CIs = -0.050-0.061, P = .850, and beta = 0.001, CIs = -0.066-0.067, P = .982, respectively). There was strong evidence for heterogeneity (P = 6.8E-09), but the MR-Egger intercept indicated that this was not due to horizontal pleiotropy (P = .580). MR-PRESSO detected one outlier, which did not impact the results. Steiger filtering excluded one SNP resulting in a slightly weaker effect.
+There was evidence that liability to schizophrenia causally increases HRV (betaIVW = 0.019, CIs = 2.5E-03 to 0.036, P = 0.024). Weighted median, weighted mode and GSMR were consistent, while MR-Egger was not. There was evidence for heterogeneity (P = 0.009), but this was likely not due to horizontal pleiotropy (MR-Egger intercept P = 0.499). Excluding one outlier with MR-PRESSO did not impact the results, while excluding 6 SNPs with Steiger filtering slightly attenuated the effect. No other analyses showed clear evidence for association (supplementary table 2).
+Liability to CVD on Schizophrenia Risk. There was weak evidence that increased systolic blood pressure causally increases schizophrenia risk (betaIVW = 0.008, CIs = -2.1E-04 to 0.017, P = .056), confirmed by all sensitivity methods. There was strong evidence for heterogeneity (P = 4.6E-68), but the MR-Egger intercept indicated that this was not due to horizontal pleiotropy (P = 0.491). MR-PRESSO detected 14 outliers, yet elimination of these SNPs did not change the results. Steiger filtering removed 16 SNPs, resulting in even stronger evidence (betaIVW = 0.010, CIs = 0.003-0.016, P = 0.006). Leave-one-out analysis showed that removing SNP rs11191548 considerably decreased the effect size and weakened statistical evidence (supplementary figure S2).
+There was very weak evidence that liability to increased diastolic blood pressure decreases schizophrenia risk (betaIVW = -0.008, CIs = -0.018-0.001, P = .088), but this was not corroborated by sensitivity analyses. No other analyses showed clear evidence for association (supplementary table 2).
+468
+Multivariable Mendelian Randomization
+For relationships with evidence of causality, multivariable results are shown in figure 2. Other multivariable results are in supplementary tables S6-S21. The conditional F-statistic indicated sufficient instrument strength, except for physical activity which ranged between ~8 and ~11 (table S15).45 Most health behaviors had a larger impact on heart failure than did liability to schizophrenia. When added in multivariable MR, only BMI had a noteworthy impact on the direct effect of liability to schizophrenia on heart failure. BMI increased the causal effect of liability to schizophrenia by ~70% (betaIVW = 0.046, CIs = 0.010.083, P = .013). MR-Egger confirmed the effect size and direction and the intercept indicated no horizontal pleiotropy (P = .537; supplementary table S16). BMI itself showed a positive association with heart failure (betaIVW = 0.447, CIs = 0.34-0.555, P = 3.4E-16), suggesting it is negatively associated with schizophrenia. We conducted post-hoc analyses with underweight (versus normal weight) and overweight (vs normal weight) as potential mediators (Supplement). Underweight did not change the main effect, while overweight increased it by ~30% (conditional F-statistic for underweight was low (2.28)). The effect of liability to schizophrenia on HRV remained consistent after adding health behaviors. The effect of liability to schizophrenia on early repolarization pattern decreased when adding BMI, total cholesterol and triglycerides, with 70% (betaIVW = 0.006, CIs = -0.017 to 0.028, P = .617), 65% (betaIVW = 0.007, CIs = -0.017-0.031, P = .549), and 60% (betaIVW = 0.008, CIs = -0.016-0.032, P = .538), respectively. MR-Egger results were consistent.
+Discussion
+We computed genome-wide genetic correlations between schizophrenia and eight important CVD phenotypes, all of which were nearly zero. Using MR, there was evidence that liability to schizophrenia causally increases heart failure risk. This effect was not mediated by health behaviors in multivariable MR. There was also evidence that liability to schizophrenia causally increases early repolarization pattern, largely mediated by BMI and lipid levels. There was evidence that liability to schizophrenia increases HRV, a direction of effect contrasting clinical observations. Finally, there was weak evidence that increased systolic blood pressure increases schizophrenia risk.
+The lack of evidence for genetic correlation between schizophrenia and CVD is striking, given consistent phenotypic correlations3 and the fact that a considerable amount of the risk variants for schizophrenia are located in genes relevant for cardiological functioning, such as calcium channels. In contrast, considerable positive genetic correlations between schizophrenia and immune-mediated diseases mirrored earlier epidemiological
+evidence.46 Our findings indicate that there is minimal shared genetic etiology, implying that phenotypic associations between schizophrenia and cardiovascular disease are due to other mechanisms. However, since a genetic correlation is computed on a genome-wide scale, it may also be that only a subset of variants overlaps, with the average shared signal being weak. Moreover, our analyses test the effects of liability to schizophrenia, and not a schizophrenia diagnosis per se. It may be that an actual diagnosis, and all detrimental factors associated with it, drive associations with cardiovascular disease and we were not able to capture that.
+With MR, we found evidence that liability to schizophrenia causally increases CVD. MR employs a selection of genetic variants, as opposed to the genome-wide approach for genetic correlations. MR might therefore find an association that was “cancelled out” by opposing effects in a genome-wide correlation.47 There was robust evidence that liability to schizophrenia increases heart failure risk, confirming its high prevalence among individuals with schizophrenia.48 MR’s powerful premise and the robustness across sensitivity analyses allows us to say with more certainty that this is due to causal effects. Important to note is that the MR-Egger intercept indicated horizontal pleiotropy, such that genetic variants for schizophrenia also exerted some effect on heart failure, independent of schizophrenia. The effect on heart failure was not mediated by health behaviors, which is particularly noteworthy for smoking - smoking rates among individuals with schizophrenia are high and smoking increases cardiovascular mortality.17 Our findings are in line with the notion that schizophrenia is characterized by a systemic dysregulation of the body, including inflammation and oxidative stress, which promotes cardiac alterations and ultimately heart failure.9,49 This implies that changing health behaviors—while useful to improve health—is not sufficient to reduce cardiovascular mortality among patients with schizophrenia. To prevent heart failure, priority should lie with optimal treatment and early-stage interventions, thereby preventing detrimental systemic effects. Finally, it should be noted that we did not find clear evidence for an effect of liability to schizophrenia on coronary artery disease (CAD), which is surprising as CAD can be an underlying cause of heart failure. This may indicate that causal effects on heart failure do not run through pathways typical to CAD (e.g. atherosclerosis), but rather through pathways related to the myocardium itself (e.g. contractility or electrical function).50 A post hoc multivariable MR analysis confirmed that the direct effect of liability to schizophrenia on heart failure remained the same after adding CAD (supplementary table S22).
+There was evidence that liability to schizophrenia increases early repolarization pattern, corroborating reports that early repolarization disproportionately affects patients with schizophrenia.51,52 Historically, this pattern
+470
+was considered normal,53 but recent evidence linked it to an increased risk of sudden cardiac death.54 Sudden cardiac death is particularly prevalent in patients with schizophrenia, making early repolarization an important risk marker. The association with early repolarization pattern declined when correcting for BMI, total cholesterol, and triglycerides. This helps elucidate the, currently poorly understood, etiology of early repolarization and suggests that among individuals with schizophrenia this pattern can be improved by lowering BMI and lipid levels.54
+Liability to schizophrenia was associated with higher HRV, while patients present with lower HRV in the clinic—partly due to anti-psychotic use.55,56 This discrepancy may be explained by the fact that we employed 2 separate GWAS (one for schizophrenia, a separate one for HRV). The number of individuals with a schizophrenia diagnosis - and thus antipsychotic use—was likely low in the HRV GWAS. Another consideration is that the HRV GWAS data were not corrected for heart rate, which correlates strongly with HRV and is usually higher in individuals with schizophrenia.8 Finally, lower HRV may result from the systemic presentation of schizophrenia, which our measure of liability did not capture. Studies tracking HRV before and after schizophrenia is diagnosed would help clarify our findings.
+There was evidence that higher systolic, but not diastolic, blood pressure increases schizophrenia risk. This corroborates a large cohort study reporting that, in men, higher blood pressure in adolescence predicts schizophrenia in adulthood.57 Higher blood pressure has also been reported in individuals who were at risk of, but had not yet developed, psychosis.58 Combined, this suggests that dysfunction of the autonomic nervous system precedes schizophrenia onset. A potentially important confounder is smoking, as it affects both blood pressure59 and schizophrenia.60 However, in a post-hoc multivariable MR analysis, we found no change in effect when adding smoking (supplementary table S23).
+Our study has some important strengths. We were able to conduct powerful analyses, investigating disorders with a low prevalence on the population level (especially schizophrenia). In addition to computing genetic correlations, we applied a wide range of rigorous MR sensitivity methods, which increases the robustness of our inferences, including multivariable MR which allowed us to investigate important mediators. Combined, this has kept the risk of bias from horizontal pleiotropy and reverse causality to a minimum. There are also limitations to consider. Schizophrenia is a severe illness and those who suffer most may not have been able to participate in research, causing selection bias.61,62 For early repolarization and dilated cardiomyopathy, it should be taken into consideration that these do not reflect the whole pattern, but rather a particular amplitude of the ECG that corresponds with the beginning of the respective patterns. For some relationships, there may have
+been temporality issues. Schizophrenia often arises in early adulthood, whereas heart failure and coronary artery disease develop later in life. Testing heart failure and coronary artery disease as exposures for schizophrenia is therefore imperfect. However, genetic risk to CVD can already have an impact early in life and therefore causal relationships are plausible.63 Assortative mating, dynastic effects (“genetic nurture’) and residual population stratification could not be accounted for - future within-family Mendelian randomization analyses may be able to reduce such bias.64 Another limitation that warrants attention is the fact that we based our primary analyses on European ancestry individuals only. When we attempted to replicate 2 of the main findings, increasing effects of liability to schizophrenia65 on heart failure66 and of blood pressure67 on schizophrenia risk, we found effects in the same direction, but without clear evidence for causality (supplementary table S24; GWAS were not available for heart rate variability and early repolarization pattern).
+In conclusion, we showed that a shared genetic etiology is not the most likely mechanism underlying associations between schizophrenia and CVD. There was, however, evidence that liability to schizophrenia causally increases the risk of heart failure and early repolarization pattern. While the effect on early repolarization pattern was largely mediated by BMI and lipid levels, the effect on heart failure remained stable after adding key health behaviors. This implies that effective treatment and intervention in early psychosis is important to decrease excess cardiovascular mortality. Tracking of physical health and screening for CVD is currently done less frequently than advised in clinical guidelines, and start of treatment is often delayed.68 More thorough screening throughout psychiatric treatment must become a priority, in order to decrease the stark mortality gap between schizophrenia patients and individuals from the general population.
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+Depressive symptoms are reported by up to one-half of patients after coronary artery bypass graft (CABG) surgery.1 These symptoms are associated with numerous adverse outcomes, including poorer health-related quality of life (HRQoL)
+and worse functional status,2 and higher risk of rehospitalizations.3 Given these adversities, Rollman et al.4,5 conducted a randomized controlled trial (RCT), which compared the outcomes of telephone-delivered collaborative care with a physician's usual
+care in treating depressed post-CABG patients. Patients randomized to the intervention versus patients randomized to usual care reported significantly greater improvements in their HRQoL, physical functioning, and mood symptoms at 8-month follow-up.5
+The findings by Rollman et al.5 are consistent with the extensive body of research that has found collaborative care to effectively treat depressive episodes,6,7 even among the old-old.8 Surprisingly, however, it remains unclear whether collaborative care achieves the same clinical outcomes with depressed mid-life and older patients and, equally critically, whether the two age groups benefit most from similar or age-specific elements of this multicomponent treatment.9 Several cautionary themes related to the interpretation of clinical studies conducted with older persons are emphasized by Scott and Guyatt.10 Of note in this regard is the possibility that depression and medical illness have bidirectional causality, with each contributing to the other, a phenomenon possibly more prevalent in older than younger persons.11
+The complex interactions between age, depression, and physical illness are of particular concern in treating depressed CABG patients, given the "doseresponse" relationship between medical comorbidity and treatment outcomes, that is, more serious physical illnesses, are associated with poorer treatment responses for depression.12 Since depressed older patients are more likely to be experiencing such medical disorders as arthritis and circulatory problems, which adversely affect the mood disorder's outcome,13 should collaborative care emphasize particular components of customized intensity in relation to the patient's age and medical status? Also pertinent to this concern is the indirect and fluctuating relationship between depressive symptoms and functional impairment.14-17 The latter may be associated with depression but also with other psychiatric and medical illnesses. Indeed, elderly depressed patients who also are medically fragile often suffer functional limitations for reasons substantially unrelated to their depression. One consequence of this indirect relationship is the time lag in response and remission rates for this mood disorder and functional limitations,16 that is, improved functioning often occurs at a later time point than remission of the core symptoms comprising the depressive episode.
+Few studies to date have considered the effectiveness of collaborative care in relation to a patient's age and medical status. The adaptation by Grypma et al.18 of the IMPACT model of collaborative care found this treatment effective for depressed primary care patients older than and younger than 60 years, but the authors provided no information about the severity of medical illnesses experienced by their subjects. This omission limits the generalizability of their findings to depressed post-CABG patients. In a study more directly pertinent to the treatment of depressed CABG patients, Ell et al.19 pooled data from three RCTs that tested collaborative care in oncology clinics, primary care settings, and home healthcare programs. They found this intervention equally effective for patients older than and younger than 60 years in reducing depressive symptoms and improving quality of life. However, their finding requires replication, given 6-month retention rates of only 65.5% across the three pooled RCTs.
+Given persisting uncertainties about the relationship between a depressed patient's age and the effectiveness of collaborative care's multiple components, an RCT of depressed CABG patients ranging in age from 35 to 87 years by Rollman et al.5 provides a unique opportunity for an exploratory post-hoc analysis of this issue, even though it was not initially powered for this purpose. The following report, therefore, describes treatments provided to patients older than and younger than 60 years (the chronologic dichotomy used by Unutzer et al.20 and Bruce et al.21 in their studies with older depressed primary care patients), age-specific response and remission rates, and functional outcomes at successive points in time for mid-life and older patients.
+METHODS
+The research design for testing the effectiveness of collaborative care interventions for depressive episodes after CABG surgery has been previously detailed.4,5 The following information, therefore, summarizes the major features of the RCT pertinent to ascertaining whether patient age affects treatment patterns and outcome by using a protocol that had been approved by the institutional review boards of the University of Pittsburgh, each of the participating
+hospitals, and the Data Safety Monitoring Board appointed by the National Heart, Lung, and Blood Institute, which funded the study.
+Participants
+Post-CABG patients were recruited before discharge at two university-affiliated and five community-based hospitals in the greater Pittsburgh, Pennsylvania, region between 2004 and 2007. Trained nurse recruiters were directed to medically stable post-CABG patients at each study site and obtained their signed informed consent to undergo screening with the two-item version of the Patient Health Questionnaire (PHQ-2).22 An affirmative answer to either the mood or the anhedonia question was considered a positive depression screen. Patients were required to score greater than 23 on the Folstein Mini-Mental State Exam23 as an evidence of their mental competence, to not exhibit indicators of severe mental illness or histories thereof, and to be discharged to home or to short-term rehabilitation. Patients meeting these and other eligibility criteria4 then signed informed consent statements, permitting a research assistant to contact them again after discharge. Since such mood symptoms as fatigue and sleeplessness commonly follow CABG surgery and possibly represent the routine sequelae of this surgery, patients were administered the full PHQ-924 2 weeks after discharge to confirm the earlier positive PHQ-2 depression screen. On this latter occasion, patients remained protocol eligible when scoring 10 or more, which signifies at least a moderate level of depressive distress.
+Assessment and Outcome Measures
+The nurse recruiters collected patients' sociodemographic information, including their self-reported race, and conducted a detailed chart review of needed medical information. Blinded telephone assessors administered the 36-Item Short Form Health Survey (SF-36)25 to determine mental (Mental Component Scale [MCS]) and physical (Physical Component Scale [PCS]) HRQoL, the 12-item Duke Activity Status Index (DASI)26 to determine disease-specific physical functioning, and the 17-item Hamilton Rating Scale for Depression (HRS-D)27 to track mood symptoms at 2, 4, and 8 months. At baseline, the
+AmJ Geriatr Psychiatry 19:10, October 2011
+Perceived Social Support Scale28 was administered to assess social support, and the Life Orientation Test29 was administered to assess generalized levels of optimism. Patients were mailed a $20 check as reimbursement for time spent in the research protocol after each of the four contacts for a total of $80.
+Randomization Procedure
+After the completion of the 2-week postdischarge assessment and confirmation of protocol eligibility, 150 patients were randomized to the collaborative care intervention and 152 to the usual care arm of the RCT in a 1:1 ratio in blocks of four at each site, with no stratification by age. Given our interest in whether age affects the delivery and effectiveness of collaborative care, this article focuses solely on the 150 of 302 protocol-eligible patients randomized to the intervention arm.
+Interventions
+Initial telephone contact. A nurse care manager telephoned patients to review their psychiatric history, provide psychoeducation about depression and its impact on cardiac disease, and describe treatment options. As was more fully described elsewhere,4 the treatment options included 1) a workbook to enhance the patient's ability to self-care for depression; 2) antidepressant pharmacotherapy prescribed by the primary care physician; 3) “watchful waiting" for mildly elevated mood symptoms; and 4) referral to a local mental health specialist.
+Case review. Nurse care managers presented clinical information regarding the patient to the clinical team consisting of study psychiatrist (CFR), internist (BLR), and psychologist (BHB). This included an overview of the patient's course of illness, medication usage, workbook chapters covered, and additional details to inform clinical decision making. On this basis, the clinical team formulated treatment recommendations consistent with the patient's needs, current preferences, and insurance coverage. The care manager then conveyed the clinical team's recommendations to the patient and his or her physician.
+Antidepressant pharmacotherapy. Selective serotonin reuptake inhibitors can be safely prescribed to cardiac patients,30,31 and none has superior efficacy in treatment-naive patients.32,33 The clinical team,
+873
+therefore, typically recommended that the primary care physician prescribe citalopram for the patient, since it is available in generic form, has limited drug-drug interactions, and requires few dosage adjustments. Primary care physicians had to approve and prescribe all adjustments to their patients' pharmacotherapy.
+Mental health referral. Referral to a mental health specialist was advised for patients displaying a poor treatment response, severe psychopathology, complex psychosocial problems, and for those expressing this treatment provider preference. When indicated, the care manager helped to facilitate the specialist referral and followed up to confirm that an appointment had been made and kept.
+Follow-up. Care manager telephone contacts of 15-45 minutes were conducted every other week during the acute phase of treatment to assess treatment response by administering the PHQ-9, review lesson plans, monitor pharmacotherapy where indicated, and discuss new treatment recommendations generated by the weekly case reviews. The acute phase typically lasted 2-4 months and, as the patient improved, telephone contacts with him or her diminished to a less-frequent schedule until the 8-month intervention was completed.
+Blinded assessments. Telephone assessors, blinded to the patient's status as an intervention or usual care patient, periodically contacted study subjects to administer the previously described instruments assessing clinical and functional status and explicitly cautioned patients at the start of each call not to divulge their treatment assignment. Given the nature of collaborative care for depression, neither patients randomized to this intervention nor their physicians were blinded to this treatment assignment.
+Statistical Analyses
+Baseline comparisons of the two age groups were performed with parametric t-tests for continuous variables and x2 tests for categoric ones. Eight-month response and remission outcomes (50% or greater improvement of HRS-D scores and HRS-D scores <7, respectively) were compared with the results of x2 test, and Cohen's h effect sizes were computed. Multiple imputation techniques were used to calculate the missing 8-month HRS-D scores that were used to determine remission outcome. Continuous
+874
+outcome measures were compared across time with repeated measures mixed-effects models, with time as a categoric variable. Time to first rehospitalization was determined with the Kaplan-Meier survival analytic methods, and the results were tested for significance of differences with a log-rank x2 statistic.
+RESULTS
+A depiction of the screening and assessment procedures through which patients were recruited for both the RCT's intervention and usual care arms is provided by Rollman et al.5 With regard to the subcohort of protocol-eligible patients subsequently randomized to collaborative care, Figure 1 traces the longitudinal course of the 58 such patients who were younger than 60 years and the 92 such patients who were age 60 years and older at baseline. Retention in the follow-up assessments was high for both age groups; 76%, 79%, and 81% of the younger patients participated in the 2-, 4-, and 8-month follow-ups, respectively. Similarly, 84%, 84%, and 86% of the older patients participated in the 2-, 4-, and 8-month follow-ups, respectively.
+Sample Characteristics
+The younger and older patients randomized to collaborative care differed, as would be expected, on the demographic characteristics of age and work status (Table 1). Furthermore, significantly fewer younger patients than older patients were white and fewer members of the latter group reported an education beyond high school. With regard to the 30 measures of psychosocial, psychiatric, and medical status assessed at baseline, the two age groups differed on only four of them. Compared with younger patients, older ones had significantly higher SF-36 Mental Component scores (44.7 versus 40.9, t-test = -2.00, df = 1, p = 0.05) and SF-36 Bodily Pain scores (41.0 versus 37.2, t-test = -2.40, df = 1, p = 0.02) Contrary to the concern that older patients experience more medical illnesses that can impede recovery from depressive episodes, this group experienced significantly more such morbidity with regard to only chronic obstructive pulmonary disease (28.3% versus 10.3%, x2 = 6.29, df = 1, p = 0.01) and congestive heart failure (26.1% versus 12.1%, x2 = 4.08, df = 1, p = 0.04).
+AmJ Geriatr Psychiatry 19:10, October 2011
+were assessed on the psychiatric indices of response and remission14 as well as related clinical and functional measures. With regard to the index of response, no significant difference was found between the two age groups (Table 3). After 8 months of collaborative care, a 50% or greater improvement of their mood symptoms was achieved by 56.5% and 43.1% of the older and younger age groups, respectively (x2 = 2.56, df = 1, p = 0.11). Remission rate was also similar for the two age groups; 53.3% and 41.4% of the older and younger patients, respectively, scored 7 or less on the HRS-D at the 8-month follow-up assessment (x2 = 2.01, df = 1, p = 0.16). Cohen's h effect sizes of 0.27 for response rate and 0.24 for remission were obtained for the two age groups (Table 3). The number needed to treat with collaborative care in contrast to usual care to achieve one additional response at 8 months was 4.3 (95% confidence interval: 2.5-14.6) for patients younger than 60 years and 4.6 (95% confidence interval: 2.8-13.0) for those age 60 years and older.
+Since factors associated with recovery at 8 months could differ among the study's younger and older patients, we analyzed the 14 baseline age-specific sociodemographic, psychosocial, and psychiatric variables for each age group that could possibly distinguish those who did and/or did not score 7 or less
+on the HRS-D at the 8-month follow-up assessment (Table 4). Among the younger patients, those who recovered and those who did not recover differed only on the SF-36 PCS, with the latter group scoring significantly lower. Older nonremitters differed significantly from older remitters in not being married, scoring lower on the SF-36 MCS, scoring lower on the Life Orientation Test's measure of optimism, and experiencing more depressive symptoms as assessed on the HRS-D.
+We additionally examined age-related outcome measures over time by using repeated measures mixed-effect models on each of four measures assessing domains of functioning (Table 5). As expected, an improvement in patient status is noted for all of these measures with a significant time effect. A significant age-group effect is also noted for the variables assessed by the HRS-D, SF-36 MCS, and DASI. Older subjects reported lower depressive distress on the HRS-D, better mental functioning on the SF-36 MCS, and better disease-specific physical functioning on the DASI throughout the 8-month assessment period. However, a significant age-group by time interaction was found only on the DASI. Scores rose from 7.9 to 29.9 during the 8-month follow-up period for younger patients but only from 6.6 to 23.1 (increments of 22.0 versus 16.5 points, F = 3.03,
+df = 3,379, p = 0.03) among older patients on this measure of disease-specific physical functioning.
+Finally, with regard to the critical outcome measure of time to initial rehospitalization for cardiac reasons, the Kaplan-Meier survival curves revealed no significant differences between the younger and older patients (likelihood ratio x2 = 0.01, df = 1, p = 0.99). The cumulative rehospitalization rate over 8 months was virtually identical for patients younger and older than 60 years, 14.5% and 14.8%, respectively.
+DISCUSSION
+Collaborative care's effectiveness in managing depressive disorders is now well established.6,7 Investigators and clinicians, therefore, are now considering whether and how this intervention can be delivered to patients of differing sociodemographic and clinical characteristics. The variables of patient age and health status are particularly significant in this regard since they profoundly influence the setting and manner in which depressed patients are treated. This article, then, is the first to our knowledge to find no significant age-related difference in the manner in which collaborative care is delivered and the outcomes that it attains for patients who are clinically depressed after CABG surgery. Thus, despite growing attention to the manner in which age distinctively affects the diagnosis of depression and should be considered in revisions of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition,34 developmental factors may not necessarily affect treatment processes and clinical outcomes across the age spectrum.
+The generalizability of our study findings is limited, given their derivation from a post-hoc exploratory analysis of data produced by an RCT.5 Furthermore, the RCT's initial statistical power was
+reduced in this post-hoc analysis, which focused solely on patients randomized to the larger study's intervention arm. Our findings regarding the lack of differences in the collaborative care provided and the clinical outcomes achieved by the two age groups, thus, potentially are of the false-negative type. Nevertheless, pending future evidence to the contrary, the present study is consistent with earlier findings that older depressed patients can be treated with interventions comparable with those provided to depressed younger adults and that both age groups will achieve clinical outcomes that do not significantly differ from each other.
+Our finding complements the recent report by Ell et al.19 that collaborative care is effective across the full age range in depressed patients experiencing noncardiac medical illnesses. In addition, despite earlier findings that CABG survival rates are poorer for older than for younger patients,35 collaborative care of depression produces benefits across the full age spectrum, as is evidenced by our findings about response and remission rates (Table 3) and risk of rehospitalization for cardiac reasons.
+Thus, while the OPTIMIZE-HF36 registry and performance-improvement program found persons older than 75 years experiencing heart failure to receive poorer quality of care than younger peers, age per se should not determine whether collaborative care is provided to persons experiencing post-CABG depression. Coryell et al.37 emphasize that depressive morbidity levels are influenced by age of onset rather than current age or the ageing process itself. We would also note that even when the patient's mood disorder results from vascular disease, collaborative care remains appropriate since this disease is strongly correlated with the level of disability rather than a specific depressive symptom pattern.38
+This study's finding that older depressed post-CABG patients achieve outcomes that do not significantly differ from those achieved by younger such patients (Table 3) extends earlier reports by Alexopoulos et al.39 and Reynolds et al.,40 which challenged the view that geriatric depression has a poor outcome. Indeed, the older post-CABG patients treated in the RCT by Rollman et al.5 achieved even better remission rates than the less medically ill, depressed primary care patients provided collaborative care in the IMPACT20 and PROSPECT21 clinical trials. Depressed post-CABG patients age 60 years and
+878
+older in the present study had 8-month remission rates of 53.3% (Table 3) compared with the 6-month remission rate of 30.1% in the IMPACT trial and the 8month remission rate of 36.8% in the PROSPECT trial.
+These salutary outcomes surely are commendable. We also acknowledge, however, that despite collaborative care's multipronged approach to treating the depressive episode, approximately half of patients so treated in the trial by Rollman et al.5 failed to remit when assessed at the 8-month follow-up and so remained at high risk for relapse and recurrence.14 Carney and Freedland41 emphasize that treatmentresistant depression may also be associated with a high risk of mortality or cardiac morbidity and that better pharmacologic and psychotherapeutic interventions are needed to treat more severe manifestations of the mood disorder. A complementary approach for minimizing poor outcomes is to identify patients at high risk for post-CABG depression and to initiate preventive strategies with them even before the surgery is performed. We identified five risk factors associated with nonrecovery among patients age 60 years and older but, strikingly, only one such factor for patients younger than 60 years (Table 4).
+The search for risk factors for incident depression, such as those identified by Lyness et al,42 opens the controversial question of whether routinely to screen all CABG candidates for this mood disorder. The American Heart Association's science advisory43 recommends that patients with coronary heart disease be screened for depression so as not to miss the opportunity to administer effective treatments. The US Preventive Services Task Force, however, recently reiterated its recommendation that such screening occur only when staff-assisted depression care supports are available.44 An even more restrictive approach is proposed by Thombs et al.,45 who note that we still lack adequate evidence that routine screening plus collaborative care improve cardiovascular outcomes. Whether and how often to screen patients with vascular disease for depression, thus, remains an open question requiring further evidence beyond that generated by the RCT by Rollman et al.5.
+Since the present study found patient age not significantly related to 8-month response and remission rates, cardiac-related rehospitalization rates, and number needed to treat, did collaborative care achieve these outcomes by expending similar or differing levels of effort in treating younger and older
+AmJ Geriatr Psychiatry 19:10, October 2011
+patients? Table 2 indicates that the median number of depression care manager contacts and the other components of collaborative care provided to patients were virtually the same across the age spectrum over the study's 8-month duration.
+Given this pattern, it is of interest that Grypma et al.18 found younger patients more likely than older ones to terminate collaborative care before attaining their protocol's 10-week minimum. The younger patients' outcomes, nevertheless, resembled those of older patients participating in the study's more extended follow-ups. The authors speculated that younger patients responded more quickly to collaborative care and, thus, required fewer care manager contacts. It remains to be determined, however, whether caregiving patterns pertinent to depressed primary care patients are equally applicable to more medically ill patients experiencing post-CABG depression.
+While this study found younger and older depressed patients to receive similar elements of collaborative care, the present data set does not permit us to replicate the finding of Oxman et al.46 that the intensity of collaborative care is positively related to clinical outcome. However, meta-analysis of earlier collaborative care research by Butler et al.7 found no correlation between the number of caregiving components provided to patients and various clinical outcomes. They, therefore, caution against “premature orthodoxy" in designating particular collaborative care programs as the “gold standard" for resolving depressive episodes.
+This work was supported by NIH grant P30 MH085943, G. Alexopoulos, PI (Schulberg); NIH grant R01 HL70000 (Rollman); NIH grant P30 MH71944 (Reynolds); and the UPMC Endowed Chair in Geriatric Psychiatry (Reynolds).
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+Introduction
+People with mental illness suffer higher rates of physical illness and are more likely to die prematurely as a result of physical illness than members of the general population [1-4]. For example, people with mental illness, and with schizophrenia in particular, are more likely to have diabetes [5-6], coronary heart disease, stroke or respiratory disease [4], HIV infection and hepatitis, osteoporosis, altered pain sensitivity, dental problems, and polydipsia [6], than members of the general public.
+People with severe mental disorders have between four to sevenfold higher mortality rates than the general population [7]. One study found that people with schizophrenia or bipolar disorder score twice as high (where high scores mean lower life expectancy) as persons without a record of psychiatric admission or outpatient contact, on the Charlson Comorbidity Index which predicts the ten-year mortality for a patient who may have a range of comorbid conditions [1]. A review of studies about life expectancy of persons suffering from schizophrenia, mainly from high income countries, have found that their lives are between 10 and 25 years shorter than those of the general population. The same review also
+identified lower life expectancy in people with other types of mental illness such as unipolar depressive disorder, bipolar affective disorder and schizoaffective disorder [8]. While suicide and other unnatural causes (such as accidents) account for some of this gap, at least 60 percent of premature deaths of people with mental illness are a result of natural causes, with the single most common cause being cardiovascular disease [9]. There is also a higher case fatality rate among people with severe mental illness suffering from cancer [10-12].
+Various explanations have been suggested for the higher rates of comorbidity and premature death from physical illness. Until recently, most of these explanations have focussed on the patient, the illness and social and economic impact of the disease upon other aspects of the patient’s life. More specifically, these explanations include side effects of medication, failure to seek early treatment or to seek it at all, difficulties in managing medication and complying with treatment more generally, unhealthy life-style and poor economic conditions affecting access to healthcare services [2], [6], [9].
+More recent studies have pointed to the contribution of systemic disparities in diagnosis and treatment to the disparity in physical
+health of people with mental illness and the rest of the population [13-14]. Such possible contributions can be indicated from service users’ complaints [15] and from evidence of disparities in treatment - for example, in hospitalisation and pathology tests for diabetes [5], [16-17], coronary re-vascularisation procedures and in basic health assessments such as blood pressure monitoring [18-19], as well as in screening for cancer [10].
+One form of systematic disparity to attract growing attention in recent years is a form of discrimination by healthcare professionals known as ‘diagnostic overshadowing’. In this context, ‘diagnostic overshadowing’ refers to the process by which a person with a mental illness receives inadequate or delayed treatment on account of the misattribution of their physical symptoms to their mental illness [14]. A similar problem has been investigated with respect to the attribution of physical illness and particularly symptoms of mental illness to the cognitive impairments of people with learning disabilities [20-22]. However, despite some evidence of diagnostic overshadowing provided by users of mental health services [15], very little research focused on the diagnostic overshadowing of people with mental illness until recently. Accordingly some have called for the development of a body of research in this area and pointed to Emergency Departments (EDs) as a setting that required special attention [14].
+Following this call, a group of researchers including some of the present authors, undertook a preliminary exploration of diagnostic overshadowing by conducting a qualitative study based on interviews with clinicians in a London general hospital [23]. The aim was to explore the views and experiences of ED doctors and nurses regarding diagnostic overshadowing: what factors made it more or less likely to occur; its frequency; and what might be done to reduce the likelihood and rate of its occurrence. The study identified eight determinants of differential outcomes related to three major problem categories: problems in eliciting a history, problems in the ED setting, and problems related to labelling and stigma.
+There were some limitations to these findings, however, given that the data were gathered in one hospital only and that only ED staff were interviewed. We therefore designed a qualitative study on a larger scale which explores diagnostic overshadowing in four hospitals from the perspectives of both ED staff and psychiatric liaison team members. Using the insight of psychiatric nurses and psychiatrists working with EDs, in addition to that of ED staff, the aim of this study is to establish a deeper and more generalisable understanding of the challenges involved in the diagnostic process and the dynamics that affect the risks of misdiagnosis or delayed treatment in this context.
+Materials and Methods
+This is a qualitative study based on semi-structured interviews with Emergency Medicine doctors and nurses as well as psychiatrists and psychiatric nurses in four hospitals in south London during 2012 and 2013.
+Ethics
+The study was fully approved by the Psychiatry, Nursing and Midwifery Research Ethics Committee of King’s College London. Access of researchers to participating hospitals was approved by the Research and Development departments of each of them. Written consent was obtained from each participant who expressed interest to take part in the study. The consent form was approved by the Psychiatry, Nursing and Midwifery Research Ethics Committee of King’s College London, as part of their full approval of the study.
+Sample and settings
+The four hospitals in the study are all located in London. They are among the capital’s biggest hospitals and their EDs are among the busiest in the country, with ED attendance level of up to 350 patients a day in the biggest and 180 in the smallest. In addition, all four hospitals have a high intake of about 3-4 percent of patients with presentations related to mental illness. In each hospital, psychiatric liaison team members, made of psychiatrists and psychiatric nurses, are available for referrals and consultation 24 hours a day, for seven days a week. According to the Guidance for Commissioners of Liaison Mental Health Services to Acute Hospitals [24], liaison teams may have various roles of mental health assessment, providing advice and training for acute staff and assisting acute staff with assessment and management of some group of patients. However, the main (and, for some, the only official) role of the psychiatric liaison units in EDs as understood by most of the interviewees in this study, is to assess the mental health of patients whose presentation is related to mental illness and to decide about further referral and treatment, which may include admission to a psychiatric hospital if necessary. On average 5-12 referrals from ED were made over a twenty-four-hour period in each of the four hospitals. The population attending these hospitals is diverse.
+We sought a purposive sample of ten participants from each ED and ten from each of the psychiatric liaison teams. Any nurse or doctor working in the ED for more than a month was considered eligible. Recruitment was administered initially by a member of staff in each department that agreed to act as a local collaborator and approached all staff in the department in general emails about their willingness to take part, providing them with an information sheet about the study. Staff members who were interested contacted the research team and interviews were set. Two participants who initially contacted the research team in order to be interviewed later declined because of heavy workload. None of the participants who were interviewed withdrew their consent during or after the interview. The data collection method used was semi-structured interviews. Interviews took place between October 2012 to October 2013 and each lasted about an hour, one interview only with each participant. Interviews were conducted in private offices in the Institute of Psychiatry psychology and neuroscience or in the hospitals, where only the interviewer and the interviewees were present in order to ensure privacy and confidentiality. There was no official pilot stage, beyond the preliminary study cited above [23], although issues that were raised by staff in the first five interviews were discussed at more length in the following interviews.
+Data collection
+All the interviews were audio-taped. All interviews were conducted by GS, a Post-Doctoral Researcher with extensive experience in conducting qualitative interviews. The interviewer did not know any of the interviewees beforehand and did not have any working relationships with any of the hospitals. Participants were asked about the nature of the diagnostic process for people with mental illness presenting in ED. They were asked how the diagnostic protocol for a physical illness differed, if at all, between patients with and without a mental illness. Participants were also asked to describe any emergency case they recalled where an existing psychiatric disorder interfered with the diagnosis of a physical illness. Staff were asked about the relationships between ED and the psychiatric liaison team, about different aspects of working together, and about their impact on the diagnostic process. In addition, all participants were asked for recommenda
+tions to optimise the diagnosis of people with mental illness who present with physical problems.
+(The interview script can be found in Text S1).
+Data analysis
+The interviews were fully transcribed verbatim. Thematic analysis was used to analyse the data. The transcriptions were coded using NVivo software. NVivo is a qualitative data analysis (QDA) computer software package which allows users to classify, sort and arrange information; examine relationships in the data; and combine analysis with linking, shaping, searching and modelling. Analysis involved the following stages: data management which included the following sub stages: (1) familiarization with the data and immersing in the data, including reading transcripts and notes and listening to the audio dialogue in order to extract main themes and ideas; (2) thematic framework development, identifying the key issues and concepts present in the data and creating a coding tree which is the organisation of set of headings in which people’s views experiences and behaviours can be organised in [25]. The coding tree was conducted both inductively, based on the data and deductively based on the research questions; (3) indexing the data - sorting all the parts of the data and are about the same thing and belong together [25]. The thematic framework included the categories ‘Actual cases/ consequences’ (with six sub-categories such as ‘death’; ‘irreversible damage to health’; and ‘near misses’) and ‘Views about the scope of diagnostic overshadowing’ - both these categories were later clustered into the head category ‘Scope and occurrences’. Three other categories were ‘Patients and presentations factors’ (with subcategories such as ‘complex presentations’; ‘difficulties in eliciting information’; ‘refusal to be examined’ and ‘other challenging behaviours’), Environmental issues, and Staff attitudes, these all formed the head category ‘Possible causes’. The data management was followed by the interpretation stage - non computerised reviewing and making sense of the data which is sorted and indexed to categories [25]. This included defining the main concepts and recurrent themes in the dialogues, mapping in what ways the different parts of the data are connected in order to try and explain the mechanisms that lead to misdiagnosis or delayed treatment in this context as well as creating typologies and hierarchies of factors and approaches responsible affecting and explaining diagnostic overshadowing [25]. The coding was conducted primarily by GS but verification and refinement of coding throughout the process was done in discussions with CH.
+Extending the focus of the research
+While our original aim was to explore the scope and nature of diagnostic overshadowing through the analysis of the clinicians’ accounts, during early interviews and data analysis it became clear we needed to extend our focus to embrace the overall diagnostic process of people with mental illness presenting in ED with physical symptoms. It became evident that diagnostic overshadowing and stigmatising views affected the diagnostic process for some patients, but this was typically only one factor and it is hard to discuss its impact on the diagnostic process without referring to other factors at work. Below we describe other challenges involved in this diagnostic process.
+Results
+Sample
+Thirty-nine clinicians were interviewed, 18 doctors (46%) and 21 nurses (54%). Nineteen participants were ED staff (49%) and 20 were psychiatric liaison team members (51%). Eighteen were male
+(46%, out of which 6 were nurses and 12 doctors) and 21 female (54%, out of which 6 were doctors and 15 were nurses). Twelve of the doctors (67%) had senior appointments (as registrars or consultants) and 6 (33%) were junior trainees. Three of the psychiatric nurses also had general nurse training. Length of professional experience varied with the range being between 6 and 24 years since qualification for the nurses (mean =12.8) and between 1 and 23 for the doctors (mean= 11.2). Thirteen of the doctors (68%) and all the nurses had more than 5 years of professional experience since qualification. All the nurses and the 12 senior doctors (67% of the doctors) have been working in their department for at least two years. The 6 trainee doctors (33%) have been working in their department for periods of between 318 months. There was at least one nurse and one doctor from each ED and from each psychiatric liaison team. Most interviewees were white British, although the sample included participants from other white, Asian and African Caribbean backgrounds.
+The scope and consequences of misdiagnosis
+We asked all interviewees to try and recall cases in which a psychiatric disorder interfered with the diagnosis of physical illness. Not all staff recalled such cases: six of the ED staff (15% of the whole sample) and three of the psychiatric liaison team participants (8% of the whole sample) did not recall any, although they were asked about and discussed other aspects of the diagnostic process of people with mental illness who present in EDs. All other participants (77%) reported one or more incidents in which psychiatric disorder led to misdiagnosis, or delayed examination or treatment with a varied degree of seriousness and with a range of consequences. The two most severe cases reported in this context involved the death of patients who refused to be examined and staff failed to conduct any assessment of their mental capacity to refuse treatment. There were two other cases of death that staff suspected might have been in consequence of psychiatric disorder interfering with the diagnosis of physical symptoms. However, their suspicions could not be verified as they did not have enough information about the case or it was not investigated. In these two cases, a patient with mental illness was sent home from the ED without the diagnosis of a risky or urgent condition, and died several days afterwards. There were also five cases in which delayed diagnosis led to irreversible, long-term damage to the patients’ health. The two most serious ones ended with the patient becoming paraplegic in one case and having some of his intestines removed in the second one.
+There were reports of more frequent ‘near misses’, a total of eleven specific cases. A typical ‘near miss’ happened when the ED staff ‘medically cleared’ a patient and referred him to the psychiatric liaison staff for mental health assessment, whereupon the latter group insisted upon further physical examinations during which an organic problem was diagnosed. In other eight cases reported by interviewees no lasting damage was caused by the delayed diagnosis but the patient suffered considerable discomfort, such as having to go back and forth between the psychiatric ward and ED, sometimes more than once.
+We also asked participants for their overall assessment of the scope of the risk of misdiagnosis or delayed treatment of people with mental illness presenting with physical symptoms. Despite the range of actual cases described above, 17 participants (44%, 5 of them were psychiatric staff members) did not think the risk of misdiagnosis or delayed treatment for physical illness was higher in their hospital for patients with history of mental illness than it was for patients with no mental illness.
+Fifteen participants (38%, 9 of whom were staff from the psychiatric liaison teams) thought that there were some specific
+risks of misdiagnosis or delayed treatment for people with mental illness presenting with physical symptoms in EDs but that those risks were mainly limited to complex and relatively uncommon presentations or to patients who refused examination or treatment.
+Seven participants (18%, 6 of whom were staff from psychiatric liaison teams in two out of the four hospitals) were more critical of the diagnostic process for patients with mental illness. One of the most critical staff members in one of the hospitals commented:
+The general problem that we have in the ED here, which we didn’t have with or I have not had with other EDs, is as soon as they [the patients] present with mental health, they [ttu- ED staff] are not interested. A psychiatric nurse
+Other critical interviewees did not go as far as arguing that ED staff were ‘not interested’ but also suggested that some ED staff in their hospital had an ingrained attitude problem and that the risk was therefore not limited to exceptional set of circumstances.
+Factors increasing the risk of misdiagnosis
+The determinants described by participants as contributing to the risk of misdiagnosis or delayed treatment of patients with mental illness presenting with physical symptoms can be divided into two groups. One group can be identified as ‘direct causes’ -mainly causes that have to do with the nature of the presentation or the behaviour of the patient. These factors make some presentations both more complex and more risky than others and these were present in most of the actual cases of misdiagnosis which staff reported to us. The other group of factors can be identified as ‘background factors’. These are not factors that stem from the presentation or from the patient’s behaviour but can either affect their behaviour externally (for example, the chaotic nature of the environment) or can affect the ability and motivation of the medical staff to dedicate the time and effort required for a thorough examination (for example, time pressures or staff stigmatising attitudes). The direct causes are discussed here first, followed by the background causes.
+Complex presentations and medical clearance
+When patients arrive in ED with a relatively easy-to-diagnose physical complaint such as a broken bone, and their behaviour is not challenging, the risk of misdiagnosis is small. In contrast, more complex presentations considerably increase the risk of misdiagnosis. A typical complex presentation is one that may look like episodes of mental illness because the patient is confused, disorientated or depressed, whereas in fact they are a result of organic cause (or a combination of an organic and psychiatric causes). Another form of presentation that can fall into the category of complex presentations is the one that is known as medically unexplained symptoms (MUS). When initial physical examinations provided no indication regarding what was the medical problem, it was ‘easier’ where patients with mental illness were involved, to assume that the problem was related to mental illness. This is how the following interviewee explained it when asked if there are any differences between the diagnostic process of people with and without history of mental illness:
+There is a difference when the doctors can’t diagnose any reason for the physical complaint. If somebody with some sort of psychiatric disorder comes in and they [ED staff] couldn’t identify what is the problem, they would just tell them, ‘‘we’ve done bloods, we’ve done check, we can’t find anything wrong
+to explain’’. There’s a high likelihood they’d then refer us, to us, to try and explain what is the problem. A psychiatric nurse
+The ED staff has responsibility for deciding whether there is any organic cause for the presentation, or whether the patient is ‘medically clear’, and has to be seen by the psychiatric staff for mental health assessment. Most of the psychiatric liaison team interviewees believed they were not supposed to be involved in this process as this is the ED staff expertise.
+Procedures related to medical clearance were one of the most common causes of tension and disagreement between ED staff and psychiatric liaison teams. Some psychiatric liaison team participants argued that ED staff were not always aware of possible organic causes for presentations that may appear as an episode of mental illness. ED staff insisted they were aware of the risk that presentations of confused or disoriented patients have organic causes and conducted tests to address such a risk. They opposed, however, conducting intrusive examinations where indications for such a risk were, in their view, very remote, emphasising they cannot reduce risk of misdiagnosis to zero. They also warned against a simplistic assumption that certain examinations such as blood tests would always provide a simple and rapid pointer to the nature of the presentation.
+Overall ED staff were more inclined to accept a request for further medical clearance if it named the specific procedure or examination to be conducted and explained why it may contribute to more accurate diagnosis. The chances that such a request would be accepted were higher if it came from a psychiatrist or from a nurse who had had a general nurse training in addition to the psychiatric one. Psychiatric nurses without dual training were less confident about making specific requests for further examinations, apart from the general request for medical clearance which was not receive that well by the ED staff. As one of the ED doctors explained: ‘‘Sometimes, when people have said, ‘Have you done bloods?’ I’ve replied ‘Looking for what?’ and we drew a blank’’.
+In most cases, when ED accepted the need for further examinations, no organic disorders were identified. Such cases might have reinforced ED staff views that psychiatric liaison teams are too quick to make such demands. However, as already noted, participants recalled eleven specific cases where a request for medical clearance ended with a positive result and required further medical examination. Among those were a patient with liver failure, which was diagnosed after the psychiatric nurse insisted on blood tests; and a patient with herpes encephalitis which was diagnosed after a psychiatrist noticed a rash on the patient’s face and insisted on a lumbar puncture. These two cases could have developed into a life-threatening situation if not identified early. Other cases included myxoedema, delirium, overdose of paracetamol (acetaminophen) and renal impairment complications that were all diagnosed thanks to the insistence by psychiatric liaison staff that patients referred to them for mental illness assessment were sent back for further physical examinations.
+Frequent attendees
+A particularly risky situation in the view of participants related to complex presentations involved patients with unexplained physical symptoms who attended EDs frequently. Each of the four EDs had a number of frequent attendees who showed up on a regular basis with a similar presentation of medically-unexplained symptoms. The risk was that the symptoms did have an explanation that could be diagnosed and treated. It was also possible that sooner or later they may arrive with a more urgent or even life-threatening problem which would be ignored because of the assumption that it was the same minor unexplained (or, for
+some staff, ‘imaginary’) symptoms that has caused the patient to attend on a previous occasion. Most interviewees from all hospitals were well aware of the possibility of the ‘boy who cried wolf syndrome. While there were some arrangements in place to address that risk, and while this risk could be minimised, it was widely viewed that it was unrealistic to expect staff to be as alert with a frequent attendee as with a patient who arrived at ED with a first presentation.
+In some cases, a lengthy and thorough examination, as well as commitment to communicate in spite of the difficulties, enabled ED staff to diagnose a physical problem that had been ignored by other staff and which could reduce the frequency of future visits, as in the following case described by one of the interviewees:
+One of our patients who had attended ED over fifty times this year, always under the influence of alcohol and always requesting strong analgesia, has underlying mental health problems such as depression and anxiety. Eventually I thought I needed to spend a bit more time with him and, actually, when I was able to put aside some time and spend some time with him, it turned out that, actually, he’d been getting a lot of sciatica and neurologic pain in his leg and the alcohol and the request for tramadol was, actually, just for pain relief for his back pain and his sciatica. So, we then addressed that and, actually, since then, his attendance has dramatically reduced. An ED staff member.
+Difficulties in communication and challenging behaviour
+As the above quotation suggests, in some cases complex presentations may become even more complex because of difficulties in communication or because of patients’ behaviour. Some patients who are mentally ill may have difficulties in communication and these can prevent or delay the diagnosis even with relatively straightforward, easy-to-diagnose physical symptoms:
+We had a man who was behaviourally disturbed and he was just screaming out in pain and they all thought he was psychotic, and I said “What’s wrong?’’ and I looked down at his foot and it was really, really swollen and nobody... Apparently, he had an accident and fractured his foot. Nobody had spotted it so they just referred him straight to me. They’d medically cleared him and I’m a general nurse, so I said ‘‘Hang on a minute. Something’s not right with his foot.’’ And they X-rayed it. It was completely shattered to bits. A psychiatric nurse
+In many cases, however, diagnosis required not just physical examination but a detailed description from the patient about the nature and history of the problem. With patients who suffered from disorientation, disorganised thinking or delusions, it could become difficult to elicit details. An example of difficulty in communication was provided by one of the nurse’s description of a patient who complained about being attacked by his legs. Because he described it in this delusional style it wasn’t initially taken seriously, but after a thorough examination it was found that the person suffered from insufficient blood-flow to the lower limbs, causing pain on walking. As these cases demonstrate, a patient and detailed investigation accompanied by a thorough examination could help in identifying what the physical problem might be.
+A more challenging and risky behaviour in this regard was refusal of the patient to permit certain medical procedures (especially those involving needles - for phlebotomy, for example). As noted above, the two cases resulting in death and two cases that ended with irreversible damage to health were caused, according to interviewees, because patients in critical but treatable conditions refused to be examined. In both death cases there were no records showing that staff conducted capacity assessment in order to decide whether they did not have capacity to decide about being examined or treated in order to save their lives.
+The following account points to a lack of clarity about whose role it is to conduct the capacity assessment and the heavy price paid for disagreement in one of the cases ending in death:
+This chap had taken quite a large overdose. We got our senior registrar to assess and his attitude was: ‘‘He’s a psychiatric patient. I’m not getting involved. Psych need to come and deal with him.’’ And the psychiatry were like, ‘‘Well, he’s not been medically cleared. We can’t get involved and any doctor can do a capacity assessment, so we’re not going to come and do your capacity assessment.’’ Unfortunately, the guy died on our clinical decisions unit. An ED staff member
+This quotation demonstrates the huge responsibility involved in conducting capacity assessments and also some lack of clarity and confusion about the roles of ED and the psychiatric liaison teams in this context. Some procedures have changed in this and some of the other hospitals following this incident and the interviews revealed consensus among all staff that it is the responsibility of ED to conduct these assessments. Several psychiatric liaison team interviewees remarked that senior doctors in the EDs were more skilled in conducting capacity assessments than junior doctors and those on the wards and that the main problem was the lack of experience of junior doctors in short term posts.
+A small minority of patients with mental health problems not only refused treatment but displayed other challenging behaviour. Such behaviour could seriously disrupt communication with the patient but also the work of the whole ED. In the opinion of some of the psychiatry liaison interviewees, this can lead to short-cuts in examinations and observations that might lead to diagnoses that overlook physical problems. On the other hand ED staff felt that the psychiatric liaison staff sometimes failed to understand the challenges involved in such situations and the detrimental impact on the whole ED if they are not dealt with urgently.
+Background factors
+While the nature of the presentation or the behaviour of the patient were typically regarded as the direct causes complicating the medical procedures and increasing the risk of misdiagnosis, there were also important background factors that either put extra pressure on staff or patients or hindered staff and reduced their ability to diagnose or provide effective treatment.
+The crowded nature of the ED environment
+There was a relative consensus among the participants that the ED is not an ideal environment for some patients with mental health problems. It is noisy, crowded, it is occupied by many people who are nervous or upset, and for people who do not work there it may seem chaotic. Being in such an environment can be particularly challenging for people with mental illness and exacerbate existing symptoms of delusions of reference, anxiety or agitation. As one of the staff members explains:
+It’s a terrible environment for people with mental illness. The waiting room is horrific for them to have to wait in that environment. Once they get into the department, it’s a very busy department and they are having to sit in very busy areas and people coming...it’s not good for somebody with a mental health problem to be in there. A psychiatric nurse
+This aspect of the diagnostic process can make some of the tasks to be carried out by ED staff, whether in communication or treatment, more difficult and it can impose extra pressure on staff to move patients to a quieter environment.
+Time pressure and targets
+The triage process in EDs together with relatively strict targets on the time patients should stay there - 4 hours in all the EDs in the UK - put constant pressure on staff to move patients on. These time pressures did not allow, according to some staff, the time required to conduct a thorough examination of a patient with mental illness:
+Time puts a lot of pressure on emergency staff. If they had more time, much of these conflicts can be addressed rather easily. Because there is a long waiting time, by the time a healthcare professional conducts a thorough assessment of a patient, some time would have passed so the clinician would be under pressure to make a quick decision. This means not sufficiently doing full scale of investigations, not ringing family or not exploring aspects in detail, purely because of time restraints. Sometimes, as a result of that, a case may be presented as a straightforward mental health problem but, in fact, when we, the psychiatry team, revisit the case, we identify from the history that there is a physical health concern and that person needed more investigations. A psychiatrist in the psychiatric liaison team.
+Stigmatising attitudes
+Most participants described the attitudes of emergency staff as non-stigmatising and suggested that stigmatising views were held be a very small minority of staff in some of the hospitals. Participants thought that most ED staff had very good communication skills, better than staff in the wards, and were able to communicate well with different groups of patients. Experienced staff also argued that attitudes had improved considerably over recent years and were better than in other hospitals they had worked in. Nevertheless, references to what can be perceived as stigmatising attitudes, some of them highly stigmatising, were made by some of the interviewees.
+In some cases, disrespectful comments were made directly to patients. Such comments, in addition to their offensive nature, could lead to short-cuts in diagnosis:
+There was one patient about whom the nurse said, ‘‘I don’t believe anything she’s saying. She’s one of the weirdest women I’ve ever met. She said that she’d come in with all of these problems and I don’t believe her.’’ And she didn’t do a lot of the observations.’’ She hadn’t taken an ECG and she didn’t want to take bloods without me being there. And so I went to speak to her, the patient, and she said that she’d had some horrible problems over at [another] hospital. I ended up calling [the olher] hospital who, actually, corroborated all of
+her stories and, actually, it was worse than what she’d made it seem and she had to go to [one of the units in the hospital] for a long time. A psychiatric nurse
+In some cases, the problem was not in expressing stigmatising views as such but in the way that the mental illness dominated the discussion between medical staff, even when the patient arrived with a presentation of physical symptoms:
+One patient told me [...] that every time he came, someone would say, ‘‘He is a known schizophrenic’’. That is how they would start presenting his case to each other. He didn’t like that [...] And he feared, and his fear was true, apparently, according to his experience [with regard to reoccurring abdominal pain], that by saying that there is a mental health problem, people overlook the physical health. A psychiatrist
+Other interviewees commented that some staff kept the interaction time with patients with mental illness, especially those more difficult or less pleasant to communicate with, to the minimum possible. This meant, for example, that some of these patients were subject to only one set of basic observations, while a person with no mental illness who spent the same time in the ED would undergo a second or third set of observations throughout their stay in the ED.
+What made this minimal interaction with some ‘difficult’ patients more acceptable was the notion of some ED staff, as reported by some of their colleagues, that people with mental illness histories ‘belonged’ to the psychiatric liaison team and were ‘their patients’, hence, their stay in the ED is only a temporary station before they move to the psychiatric liaison team where they belong. Several psychiatric nurses reported how ED staff, when talking to psychiatric staff about patients waiting in the ED, referred to them as ‘your patient’. A psychiatric nurse recalled how an ED staff member commented, ‘I just resent the fact that I’m dealing with your patients when I should be looking after my own’.
+Some ED staff suggested the pressure to move patients to the department where they ‘belong’ is not unique to patients with psychiatric history but part of the role of the ED as a triage unit. However, this pressure to move on patients to where they ‘belonged’ seemed to be particularly strong when it came to patients with a history of mental illness.
+The psychiatric team can often be seen as a dumping-ground for people who have a mental health problem. So, as soon as it looks like it’s a mental health problem then people, kind of, switch off and say ‘‘We have to do what we can so that they will take them away so that we can deal with other people.’’ And I think there is that mentality that goes on a bit.
+Mental health patients, they go to a special cubicle. So, already, there is a ‘section’: mental health. It’s not physical. So the staff are creating an idea about what the problem is without a full assessment. The barcode, as I call it, on the forehead is already booked. I can understand why it happens but I think, still, it’s wrong. An ED doctor.
+The risk with this approach, as the above quotation demonstrates, is that as a result of the presumption that these are psychiatric patients, the diagnostic process will be rushed and the focus will move to people who more ‘clearly’ belong in the ED.
+Again, many interviewees commented that such a ‘them and us’ division was much worse in the past, in other wards, or in other
+hospitals they have worked in before, and that overall relationships between the two departments in each of the four hospitals were good. However, a basic sense that these patients are part of a different group that belong elsewhere did not completely disappear.
+Discussion
+This study confirmed that diagnostic overshadowing can lead to misdiagnosis of people with mental illness who present in ED with physical symptoms. Interviewees reported a series of specific cases, with varied degree of seriousness, in which mental illness interfered with a diagnosis of physical problem. We also found evidence of a hierarchy of risk between the factors which may affect the diagnostic process in this context. We distinguished between the more direct causes of complex presentations and difficulties in communicating or challenging behaviour by patients, and the background factors such as time pressures and staff stigmas which may lead to wrong or delayed diagnosis and treatment in this context.
+The range of cases reported by participants in this study in which a psychiatric disorder interfered with the diagnosis of a physical problem, included two cases of death and five cases of irreversible long-term damage to people’s health, suggests that special challenges are involved in this process, and that failure to address them can lead to considerable number of misdiagnoses and delays in treatment, with severe and detrimental consequences to health.
+In a small number of cases, the stigmatising views of staff had been a serious factor in preventing the timely and accurate diagnosis of a physical problem. However, on the basis of views of most interviewees it seems that, more typically, a stigmatising attitude is only one of several contextual factors and that it exists in some but not all cases of the misdiagnosis of physical symptoms among patients with mental illness. The main risks for misdiagnosis are related either to the complicated nature of the presentation or to difficulties in communication or challenging behaviour by some patients. This finding is consistent with the prediction of researchers that ‘diagnostic overshadowing’ is unlikely to be simply the result of bias or of discriminatory attitudes and that a range of other factors related to the communication between doctor and patient may also be relevant [14].. .
+It is important to emphasise that by drawing attention to the contribution of complex presentations or challenging behaviour we do not shift the responsibility for misdiagnosis in such cases from the medical staff to the patients. It is still the full responsibility of ED staff to make correct diagnoses, even if the presentation is complex and also, in the case of people with mental illness, if the behaviour is challenging, as this is part of the medical disorder. Neither of these findings should undermine the importance of background factors and, in particular, of stigmatising opinions and time/target pressures.
+While most participants reported that stigmatising opinions were held only by a minority of staff in some hospitals, their existence is still worrying, especially since some of the more critical participants argued that it was enough that one or two dominant staff members held stigmatising views for the whole ED culture to become less mindful of the needs of people with mental illness. It should also be reiterated that stigmatising opinions towards people with mental illness in EDs can be upsetting and demoralising, sometimes even traumatic regardless of their impact on the misdiagnosis of physical illness.
+Psychiatric liaison team members have a key role in reducing the risk of misdiagnosis in these circumstances. They may be a last safety net, turning ‘real misses’ into ‘near misses’ by insisting on the further examination of patients referred to them after being medically cleared. The existence of the liaison team is a mitigating factor which does not exist in relation to other vulnerable populations, such as patients with learning disabilities. However, many of the psychiatric nurses lack a general nursing training and therefore their ability to contest misdiagnoses made by ED staff is limited. They do not have a formal role in the ED diagnostic process beyond conducting a mental health assessment. Further, not all hospitals have round the clock coverage by a psychiatric liaison team, as do the hospitals in the present study. Last, while their presence is useful in many ways, it can have adverse consequences, in particular in it creating a reduced sense of responsibility of emergency staff for patients they see as belonging to another team.
+Limitations, strengths and further research
+The study has been conducted in four hospitals with a high intake of people with mental illness attending the EDs and with round the clock coverage by psychiatric liaison teams. Although the sample is bigger than in previous studies [23], as with any qualitative study, caution is required before generalisation of conclusion, especially given that the experiences discussed here are based on staff recollection of the past and may be subject to memory bias and other cognitive distortions. Future studies may wish to add observational element in order to capture cases of diagnostic overshadowing when they take place. Also, some of the hospitals operate in close proximity to large psychiatric hospitals, which helps to create a higher level of awareness with respect to mental illness and the stigmatisation of people with mental illness. Additional research in hospitals with no such coverage might reveal even greater cause for concern.
+The reported cases of misdiagnosis can shed light on the dynamics and specific risks involved in the diagnostic process. In order to provide an accurate estimation of the frequency of such cases, a more quantitative study should be conducted, covering all cases in certain EDs within defined time-periods. The results of such a study could then be compared with findings of misdiagnosis in patients without mental illness presenting in ED, or of EDs in which psychiatric liaison teams operate only during normal working hours. While there are some limitations in the level of stigmatisation being self reported by ED staff, this limitation was mitigated in this study by interviewing also members of psychiatric liaison teams. Further research may focus also on experiences of patients with history of mental illness in this regard.
+Clinical implications
+From our exploration of complex cases it is clear that the procedures used by emergency and psychiatric liaison staff require fuller operationalization to reduce disagreement over where responsibilities lie, for example to reduce unnecessary delays in capacity assessments and in psychiatric admissions, and repeated transfers between the ED and psychiatric units. It also appears that there are simpler cases in which physical conditions such as fractures and other causes of pain are missed due to an assumption that people using psychiatric services ‘belong’ with the psychiatric services. These cases suggest training needs in addition to the need for agreed procedures for more complex cases. While the provision of psychiatric liaison teams may improve the cost-effectiveness of general hospital’s care of people with mental illness [26], an overall conclusion that can be drawn from the current study is that the full potential for improvement in quality of ED care represented by
+these teams is currently not being realised, for example due to unintended consequences in the form of problems between the psychiatric and emergency teams. In addition to a maximum availability and accessibility of psychiatric liaison team throughout the day and week, the study has some implication for training of ED staff, of procedures regarding parallel assessment by both teams, for the qualification and basic training of psychiatric nurses, for enhancing and structuring communication channels between the two departments and for using specially adjusted written pathway and of access of ED staff to electronic records of mental illness history of patients. In the future we aim to provide more specific list of recommendations and guidelines shaped by professionals interviewed for this study and members of Non Governmental Organisations advocating for better physical health care for people with mental illness and learning disabilities, about how to improve the diagnostic process of people with mental health problems presenting with physical complaints at the ED.
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+Introduction
+In most countries of the world, older adults kill themselves at higher rates than any other age group (Conwell, 2009). Worldwide demographic aging means that the absolute number of suicides by elderly people is likely to increase (O’Connell et al., 2004). Cross-national comparative study of elderly suicide rates in the European Union has revealed wide variations between European regions: elderly suicide rates have been found to be lowest in Western European and Mediterranean countries and highest in Central and Eastern Europe (Shah et al., 2009).
+Correspondence should be addressed to: Dr. Andriy Yur'yev, MD, MA, Estonian-Swedish Mental Health and Suicidology Institute, Oie 39, Tallinn 11615, Estonia. Phone/Fax: +3726516550. Email: aeyuryev@gmail.com; ayuryev@a-teleport.com. Received 6 May 2010; revision requested 10 Jun 2010; revised version received 2 Jul 2010; accepted 4 Jul 2010. First published online 14 September 2010.
+The World Health Organization explains the phenomenon of suicide using individual, relational, communal and societal models. At the individual level, characteristics like psychological or personality traits are considered. At relational and communal levels, the focus is on social relationships within people’s immediate surroundings (family members, communal contexts, workplaces). At the societal level, institutional or economic environments are considered (Wu and Bond, 2006).
+Shah and colleagues have conducted various studies in which they reviewed elderly suicide in socioeconomic and demographic contexts, considering the associations between elderly suicide and human development, life expectancy and elderly dependency (Shah, 2009; Shah et al., 2008a; 2008b). Psychosocial factors and stressful life events, such as declining social support, family discord and living alone, have been considered
+1338 A. Yur'yev etal.
+risk factors for elderly suicide (Lawrence et al., 2000; Rubenowitz et al., 2001; Yip et al., 2003; Duberstein et al., 2004b; O’Connell et al., 2004). The predictive role of low social interaction and loneliness for elderly suicide behavior has also been reported (Conwell et al., 1991; Rubenowitz et al., 2001; Chiu et al., 2004; Duberstein et al., 2004b). Similar results concerning family relations, emotional dependency and a mismatch of expectations between generations were reported by a study in Taiwan (Liu et al., 2006). Wu and Bond (2006) considered issues of social activity, dependency on family satisfaction and domestic integration, with the expectation that elderly suicide can be predicted better on the basis of cognitive and emotional factors and social support.
+The hypothesis of the present study is that disparities in elderly suicide mortality may be largely explained by society’s attitudes towards the elderly population. These attitudes include a number of cognitive and emotional factors, such as respect, friendliness, recognition, consideration, prejudice and contempt.
+The objectives of this study were to (1) identify elderly suicide rates in 26 European countries, (2) assess the relationship between elderly suicide mortality and national attitudes towards the elderly in these countries, and (3) compare patterns of elderly suicide mortality and attitudes towards the elderly in Eastern and Western Europe.
+Methods
+Twenty-six European countries participating in the European Social Survey (ESS) were included in the study. The ESS (http://www. europeansocialsurvey.org) is an academically driven social survey designed to chart and explain the interaction between Europe’s changing institutions and the attitudes, beliefs and behavior patterns of its diverse populations. Full ESS data are available on an open-access basis. Suicide rates in the 65+ age group, adjusted by gender, were evaluated in the context of attitudes towards the elderly in society. Age-aggregated elderly suicide rates were obtained from the WHO European Mortality Database, updated in January 2010 (http://data.euro.who.int/hfamdb/). For each country, mean annual elderly suicide rates were calculated, for males and females separately, for the last five years for which data had been available.
+The attitude data from the third edition of the European Social Survey Round 4, collected in 2008-9 in the “ageism” category, were selected. The term “ageism” was proposed by Robert Butler
+(Butler, 1987) to define a process of systematic stereotyping and discrimination against people because of their age. Seven questions that represent society’s attitudes towards the elderly and may correspond to the “group” dimension of social exclusion (Percy-Smith, 2000) were considered for the purpose of the study:
+A. How do most people view the status of people aged over 70 years? (Score: 0-10, where 0 means “extremely low status”’ and 10 means “extremely high status”.)
+B. All things considered, do you think people over 70 contribute very little or a great deal economically to [country] these days? (Score: 0-10, where 0 means “they contribute very little economically to [country]” and 10 means “they contribute a great deal”.)
+C. How likely it is that most people view those over 70 as friendly? (Score: 0-4, where 0 means “not at all likely to be viewed that way” and 4 means “very likely to be viewed that way”.)
+D. How likely it is that most people view those over 70 as having high moral standards? (Score: 0-4, where 0 means “not at all likely to be viewed that way” and 4 means “very likely to be viewed that way”.)
+E. How likely it is that most people view those over 70 with admiration? (Score: 0-4, where 0 means “not at all likely to be viewed that way” and 4 means “very likely to be viewed that way”.)
+F. Are any members of your family aged over 70? [INTERVIEWER NOTE: “Family includes any relative whether they are a blood relative or through marriage”] (1 means “yes”, 2 means “no”.).
+G. How much time of your working time have you spent with colleagues aged over 70 in the past month? (Score: 1-5, where 1 means “none of the time” and 5 means “almost all the time”.)
+For each country, mean scores for all responses were aggregated and correlated with average suicide rates over the last five years for which data were available. Correlations were analyzed using Pearson’s test (r).
+To arrive at mean scores for responses to the questions selected, the countries were divided into two groups: Western and Eastern Europe. The “Western European” group comprised 13 countries (Belgium, Denmark, Finland, France, Germany, Greece, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the U.K.) and Israel. The “Eastern European” group comprised 12 countries (Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Poland, Romania, the Russian Federation, Slovakia, Slovenia and Ukraine). Differences between mean scores for attitudes in Western and Eastern countries were presented as percentages of the highest scores on the appropriate scale. This approach was optimal to account for
+differences in the length (1, 4 or 10) and starting points (0 or 1) of the scales, especially since these scores are national averages, while individual respondents range throughout the scale.
+Data on labor-market exit age were taken from the EUROSTAT database (http://epp.eurostat. ec.europa.eu) and were not available for Russia, Ukraine or Israel. The pattern of covariance between suicide mortality and country-level attitudes was considered, using Pearson’s correlation coefficient.
+Results
+Mean annual suicide rates among the elderly were lowest in Greece for both males (9.4 per 100,000) and females (1.5 per 100,000). The highest male elderly suicide rate (95.0 per 100,000) was in Russia. The highest female elderly suicide rate (23.1 per 100,000) was in Switzerland (Table 1).
+The following associations were revealed between elderly suicide rates and society’s attitudes towards this age group (Table 1).
+Perception of elderly people as having higher status is inversely correlated with mortality from suicide in this age group. The male population shows a relatively strong negative association for this factor (-0.47), while for females it is weak (-0.27). Recognition of the elderly as contributing to the economy is also inversely correlated with suicide mortality. This correlation is higher among males (-0.56) than among females (-0.32). In both males and females, suicide mortality is inversely correlated with attitudes of friendliness (males -0.37, females -0.33) and admiration (males -0.58, females -0.53). A weak inverse correlation also emerged between rates of suicide mortality and the notion of elderly people as embodying higher moral standards (-0.31 among males, -0.34 among females). Working alongside elderly colleagues is inversely correlated with elderly suicide, and this association is weak in both sexes (males -0.19, females -0.17). Suicide rates are lower in countries where respondents more often have elderly members in their families. This correlation is stronger for males (0.49) than for the female population (0.21).
+In addition, an inverse correlation between suicide mortality among elderly people and their labor-market exit age was revealed. This correlation is higher for males (-0.46) than for females (-0.28).
+A more thorough study of attitudes towards elderly people shows several remarkable differences in attitudes between Eastern and Western Europe (Table 2). The elderly are accorded a lower status in Eastern Europe countries (difference 14.8%). People in Western Europe recognize the economic contribution of elderly people as 17.1% higher
+than in Eastern Europe. Western Europeans also regard the elderly with more admiration and as more friendly (the differences being 7.8% and 4%, respectively). Western Europeans have elderly members in their families more often than Eastern Europeans (difference 20.1%).
+Discussion
+Objective 1 of the Madrid International Plan of Action on Ageing, 2002 (United Nations, 2002) proclaims that “older persons should be treated fairly and with dignity, regardless of disability or other status, and should be valued independently of their economic contribution”. The latter part of life is often linked with declining social and economic status, as well as limited independence and significant lifestyle changes (Duberstein et al., 2004b). These changes result in an increased burden of stress. Many elderly persons feel lonely, empty and isolated, and their self-esteem suffers (Devons, 1996; Yip et al., 2003). Emotional and social aspects start to play a particularly important role in determining the risk for suicide behavior in the elderly age group (Conwell et al., 2002; Turvey etal., 2002). Industrial and post-industrial societies demand great mobility in families, and this has led to a dramatic shift in attitudes towards older people. Very often, elderly people are treated as second-class citizens with nothing to offer society, and the negative attitudes about aging that give rise to ageism tend to manifest themselves in subtle ways in the average older person’s daily life (Nelson, 2005).
+European social policy recognizes the elderly population as an at-risk group for social exclusion (Percy-Smith, 2000). In the current study, we attempted to evaluate how society’s attitudes towards the elderly affect suicide mortality in this age group. We assumed that the part of the European Social Survey module selected (the E1-E55 rotating module) corresponds to the “group” dimension of social exclusion and describes factors that affect the confidence, selfesteem and psychological well-being of elderly persons; nevertheless it may not be a direct measure of this concept. The results support the hypothesis that elderly people are prone to be affected by the level of social and family integration and societal attitudes towards them. Attitudes of society towards its elderly members play an important role and significantly influence suicide mortality. Seemingly trivial favorable behaviors, such as regarding elderly people as friendly, perceiving them as having higher status and moral standards, and experiencing feelings of admiration towards them may be considered protective factors that support elderly people and reduce suicide mortality among them. These positive feelings and cognitions are themselves derived from societal factors that predispose the population towards these psychological reactions (Wu and Bond, 2006).
+The results of our study support Durkheim’s theory, as well as some newer studies (Johnson, 1965) that consider a higher level of social and family integration to be protective. Whereas Durkheim suggested that both extremely high and extremely low levels of social integration and social regulation are associated with high suicide rates, Johnson (1965) argues that in modern societies only low levels of these social characteristics are associated with high suicide rates (Leenaars and Lester, 1999). According to Durkheim, traditional family life provides the best protection against selfdestructive behavior. The elderly in a country with an elevated level of family integration have more positive emotions (e.g. happiness) and sustaining cognitions (e.g. perceived health), and hence less probability of committing suicide (Wu and Bond, 2006). Conversely, elderly people living alone usually have the highest risk of suicide (Liu et al., 2006). According to our results, suicide rates are lower among elderly people in countries where people prefer to keep elderly relatives within the family. This outcome is in line with the results of several other studies that consider grandparentgrandchild relationships and argue that such contact reduces prejudice and increases positive attitudes (Silverstein and Parrott, 1997; Wright et al., 2002). Social and family support may play a buffering role, fending off functional and financial decline in the
+elderly population. The complex structure of social support and the moderating role it conceivably plays regarding suicide risk associated with stressful life events is mentioned by some authors (Conwell et al., 2002).
+Other outcomes of the study reveal that suicide rates are more strongly correlated with attitudes in the male population. There is greater divergence between male and female correlation strengths in attitude issues connected with elderly people’s economic contribution and the general status of the elderly in society. For males, the correlation between elderly suicide rates and labor-market exit age is also much stronger than it is for females. A greater concern among males about their socioeconomic contribution and career achievements and also higher expectations of males on the part of family members and society may be among the possible explanations why this factor shows a stronger association in the male population. Another finding is that male elderly suicide rates are related to the issue of family belonging much more strongly than female rates. Durkheim regards women as more socially integrated in daily life, which gives much stronger protection against suicide. Stronger self-reliance and control in daily life, as well as more independence in running the home, may also help to explain the weaker association with female elderly suicide (De Leo, 1998). In addition, the relationship between suicide rates and society’s attitudes concerning the moral standards of elderly people is revealed. Society’s perception of elderly people as having higher moral standards is inversely correlated with mortality from suicide among elderly males and females alike, but for females the correlation is slightly stronger.
+Differences in attitudes towards the elderly in Eastern and Western European countries also deserve consideration. Citizens of Eastern Europe place less value on the status of the elderly, who are seen as 14.8% less important than they are in Western Europe, and estimate their contribution to the economy as 17% lower. The emotional component of attitudes towards the elderly in Eastern Europe is less manifest than in Western European countries. Western Europeans regard elderly people with admiration and see them as more friendly. They also have elderly people as members of their families more often than Eastern Europeans. The results listed above support the conclusion about the role of a more positive emotional approach towards the elderly in preventing suicide mortality in Western European society. Enhancing elderly people’s social inclusion could be highly beneficial in terms of mental health. Eastern European countries seem to merit higher priority in terms of intervention deployment.
+1342 A. Yur'yev etal.
+One remarkable result in our study is the inverse correlation between elderly suicide mortality and labor-market exit age. Suicide mortality is lower in countries where elderly people can continue to participate longer in employment. There are several possible explanations for this phenomenon. Some studies link an elevated risk of suicide among elderly adults with lower incomes (Duberstein et al., 2004a). Yip and co-authors revealed that the ratio of economically inactive to active elderly suicide victims is about six to one (Yip et al., 2003). They also consider that jobs for the elderly are important not only to provide additional income but also to give them an active lifestyle, which is very important for their psychological well-being. Affording opportunities for the elderly to maintain an active lifestyle after retirement seems to be beneficial in terms of suicide prevention. This notion is suggested by another finding of our study: more time spent by people aged 70 years and over at work, the more suicide mortality might be reduced (a weak correlation is present). The beneficial influence of social interaction at work on elderly people’s adaptability may constitute a possible explanation for the relatively low suicide mortality among those elderly people who are employed. The positive influence of employment as a means of preventing suicide is more significant among elderly males. The hypothesis of our study is supported by a study of the social context of well-being based on the World Values Survey, U.S. Benchmark Survey and Canadian Survey (Helliwell and Putnam, 2004). Strong support from family and friends, frequent social interactions and civic participation, as well as higher incomes, exert a beneficial influence on wellbeing. The relationship between well-being and suicide is mentioned by many authors (Sisask et al., 2008; Voracek, 2009), although some of them do not support this notion (Lester, 1998). Revision of European countries’ employment strategies for the elderly towards broadening their employment options and creating favorable opportunities for them to remain employed for longer would not only encourage elderly people to maintain an active lifestyle but also reduce their suicide mortality. Finally, these outcomes are in line with the United Nations Program on Ageing, which calls for promotion of “a favorable attitude among employers regarding the productive capacity of older workers as being conducive to their continued employment and promotes awareness of their worth, including their self-awareness, in the labor market” (United Nations, 2002). We would advise governments to consider these results in policymaking when it comes to elderly employment.
+Nevertheless, it is important to keep in mind the limitations of our study design. As with
+most ecological studies, conclusions are prone to ecological fallacies in which we may have ascribed to group members characteristics that they do not, in fact, possess as individuals. Other macro-social indicators, such as religious affiliation, access to health care and social welfare facilities, and the baseline socioeconomic status of the nations (e.g. GDP per capita) studied, may be risk factors for suicide mortality in older adults.
+The higher proportion of elderly people in the population of Western Europe owing to longer life expectancy may influence the basis for conclusions regarding question F (“Are any members of your family aged over 70?”) in our study. This should therefore be considered among its possible limitations.
+Conclusions
+1	Society’s attitudes towards elderly people affect their suicide mortality.
+2	Attitudes towards elderly people are more favorable among Western European than Eastern European citizens.
+3	Extended labor-market participation by the elderly is a suicide-protective factor for this age group.
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+Introduction
+Suicide is currently the second leading cause of death among 15- to 29-year-olds globally (World Health Organization [WHO], 2018). In the United States, suicide is the third leading cause of death among youth ages 10 to 14, and the second leading cause of death among youth and young adults ages 15-34 (Centers for Disease Control and Prevention, 2016). Therefore, it is no surprise that suicidal ideation and suicide attempts are the most commonly reported mental health crises among youth (Miller, Esposito-Smythers, & Leichtweis, 2015).
+Historically, there has been a positive association between suicide and age such that the highest suicide rates were among the oldest populations (Cutler, Glaeser, & Norberg, 2001). More recently, however, youth suicide rates have reached an unprecedented high (Twenge, Cooper, Joiner, Duffy, & Binau, 2019). In fact, between 2007 and 2017, the suicide rate among 10- to 24-year-olds in the United States increased by 56% while homicide rates declined during this period (Curtin & Heron, 2019). Although the precise reasons for these increases in youth suicide are unclear, adolescence is a time of rapid psychological, biological, and social changes for youth contributing to what some have termed “developmental stress” (Aro, Marttunen, & Lonnqvist, 1993). Challenges such as mood instability, family adversity, and social alienation undoubtedly add to this stress, often
+contributing to suicidal ideation and behavior (Pelkonen & Marttunen, 2003).
+As these sobering statistics illustrate, suicide rates have increased despite additional research and prevention efforts. These increases are even more pronounced among youth and young adults. Unfortunately, current approaches in the field of suicide prevention are insufficient for combatting these trends. In particular, an overwhelming focus on intrapersonal psychological factors (e.g. Haw, Hawton, Niedzwiedz, & Platt, 2013) has contributed to the reduction of suicide to a purely pathological, individual-level event. Moreover, the neglect of intersecting social identities and a lack of focus on the extra-individual factors contributing to suicidality has resulted in a dearth of knowledge regarding the social and ecological factors impacting suicide among youth. To combat this trend, psychological approaches to suicide research must expand beyond its traditional boundaries. Such boundaries serve to “actively exclude from consideration approaches to understanding and preventing suicide that do not fit well with [our] orthodoxies” (White, Marsh, Kral, & Morris, 2016, p. 2). It is within this context that the subfield of critical suicidology has emerged as one effort to redress these concerns.
+Critical suicidology
+Although noble in their aims, these predominant approaches to suicide research and prevention have
+2 @ C. J. STANDLEY
+proven to be largely insufficient on their own and do not adequately attend to the complexities of suicide risk and prevention. As such, the recent emergence of critical suicidology has resulted in more discussion of the importance of social, historical, and cultural contexts in studying suicidality and its prevention (e.g. Hjelmeland, 2016; Hjelmeland & Knizek, 2016; Marsh, 2015; White, 2016; White & Kral, 2014). Critical suicidology examines and confronts the assumptions of psychological approaches to suicidology which are rooted in narrow epistemological and methodological approaches that (1) pathologize suicidality, (2) treat suicidology as an exclusively objective science, and (3) conceptualize suicidality as strictly individual (Abrutyn & Mueller, 2019; Hjelmeland & Knizek, 2019; Marsh, 2016). In short, critical suicidology aims to liberate the field from the domination of “an excessively individualistic and technical account of suicide, which serves to both de-contextualize the act and strip away its inherently relational, ethical, historical, and political nature” (White & Kral, 2014, p. 123). Recent critiques and defenses of the Interpersonal Theory of Suicide (Joiner, 2005) highlight this push for a critical suicidology, the need for increased attention on contextual factors (Hjelmeland & Knizek, 2019), and the importance of interdisciplinary approaches to suicidology (Abrutyn & Mueller, 2019).
+In the following pages, I propose two related approaches for answering these calls as they related to youth suicide in particular. First, I discuss social identities and their impact on risk, as well as the use of intersectionality theory in examining the intersections of social identities and their impact on suicide risk and prevention. Second, I echo previous calls to move beyond the individual level of analysis to look at suicide and the role of protective factors through the lens of a socioecological framework (e.g. Cramer & Kapusta, 2017).
+Social identity
+Identities and the meanings attached to them help to inform our social interactions and our place in society, as well as our behaviors, feelings, and self-concept (Stets & Serpe, 2013). Adolescence is a pivotal time for youth as they work toward the development of their identity. In fact, studies have found that adolescent development often differs as a function of gender (McLean & Breen, 2009), race and ethnicity (Crawford & Alaggia, 2008), and sexual identity (Cover, 2016) such that youth identifying with marginalized groups in these categories are often more
+conflicted with their identity and typically report more negative health outcomes, including suicidal ideation and behaviors (Bostwick et al., 2014). Often, these negative outcomes are the result of lower self-esteem and increased levels of distress and hopelessness influenced by discrimination, a lack of representation, and victimization (Cover, 2016; Crawford & Alaggia, 2008; McLean & Breen, 2009).
+In discussing risk for suicide among youth, it is important to note that these identities themselves are not necessarily risk factors for suicide. Rather, institutional and personally mediated discrimination and oppression on the basis of identity are risk factors for certain groups, particularly those who are marginalized (Jones, 2000). As discussed below, differential risk for suicide and suicidal behaviors among youth has been demonstrated based upon identities such as gender, race and ethnicity, and sexual identity (Miller, 2011; Sharaf, Thompson, & Walsh, 2009; Silenzio, Pena, Duberstein, Cerel, & Knox, 2007; Stone et al., 2018).
+Gender
+Within the extant literature, gender appears to have the strongest and most consistent association with suicidal behavior (Miller, 2011). Generally, males are at three times greater risk for death by suicide than females, whereas females are more likely to experience suicidal ideation and attempt suicide. In fact, in 2015, nearly 77% of those who died by suicide in the United States were males (Stone et al., 2018). This gender paradox is seen across ages and ethnicities and is largely due to the fact that males are more likely to use more lethal methods than females (e.g., firearms as opposed to poisoning, respectively; Curtin, Warner, & Hedegaard, 2016; Drapeau & McIntosh, 2017).
+Transgender and non-binary individuals are also at significantly increased risk for suicide. In a national study of transgender people in the United States, transgender adults were nine times more likely to have attempted suicide in their lifetime compared to the general U.S. population (40% compared to 4.6%; James et al., 2016). Moreover, seven percent of transgender adults attempted suicide in the previous year (James et al., 2016). This risk has been shown to be higher among transgender youth as well with over half reporting having seriously considered suicide and 29% having attempted suicide (The Trevor Project, 2019).
+Race and ethnicity
+Racial and ethnic groups differ significantly in their rates of suicide, and this same pattern is often borne
+out among youth (Miller, 2011). Whereas rates of suicide are generally lower among Hispanic and Latinx individuals than among white individuals, rates among Native American, Alaska Native, and other indigenous populations are higher than any nonwhite population (Drapeau & McIntosh, 2017; Miller, 2011; WHO, 2018). For example, in one study of highschool students in the Pacific Northwest and New Mexico, researchers found that, proportionally, American Indian youth had the highest average suicide risk in the sample (Sharaf et al., 2009). Moreover, while African American youth have historically had lower rates of suicide than white youth, the youth suicide rate increased by 60% among African American males and by 182% among African American females between 2001 and 2017 (Price & Khubchandani, 2019). Though research in such populations is scarce, one nationally representative sample of middle- and high-school youth found that mixed-race adolescents were at greater risk for considering suicide than their single-race peers (Udry, Li, & Hendrickson-Smith, 2003).
+Sexual identity
+Though research on the relationship between sexual minority youth (i.e. those identifying as lesbian, gay, or bisexual [LGB]) and suicide risk is increasing, there remains much to be learned in this area. Moreover, within-group differences among sexual minority youth—such as the potential differential risk for gay and lesbian youth as compared to bisexual youth—are scarcely examined as many studies dichotomize sexual identity as “heterosexual” and “other” (Miller, 2011).
+Despite this dearth of solid data, extant research suggests that LGB youth are at a significantly higher risk for suicidal ideation and suicide attempts than their heterosexual counterparts (Cover, 2016; Miller, 2011). For example, a study based on data from the National Longitudinal Study of Adolescent Health found that LGB youth were 2.7 times more likely to report experiencing suicidal ideation and over three times more likely to report attempting suicide than their non-LGB peers (Miller, 2011; Silenzio et al., 2007). Moreover, recent Michigan data from the Youth Risk Behavior Survey (YRBS) suggests that LGB students were significantly more likely to report experiencing suicidal ideation, having made a plan to attempt suicide, and having attempted suicide than were heterosexual students (Stone et al., 2018).
+Intersectionality
+Despite this knowledge base, there is little understanding within suicidology as to the compounding effects of multiple marginalized identities on suicide risk. The use of intersectionality theory provides a fitting lens through which to examine this issue. Broadly, intersectionality explores the dynamics between coexisting social identities (e.g., the intersection of gender and race) and systems of power and oppression (Crenshaw, 1989). It illuminates the relationships between individuals’ identities by recognizing the ways in which multiple cultural and social identities such as race, gender, disability, sexuality, and class combine to create unique experiences across domains and outcomes in every facet of one’s life (Crenshaw, 1989; Knudsen, 2006). Intersectionality recognizes that unique historical, social, cultural, and political factors inform the intersections of gender, race, and sexual identity and therefore differentially influence life experiences (Bostwick et al., 2014). The importance and salience of these intersections cannot be overstated. For example, research suggests that the categories of sexual identities differ greatly across ethnic groups, and thus LGB youth of varying ethnicities experience identity development differently (Sexual Minority Assessment Research Team [SMART], 2009).
+Intersectionality plays a vital role in the development of identity for youth. At the same time, discrimination and victimization based on one’s identity increases during adolescence (Garnett et al., 2014). When combined, these developmental and victimization factors can be fatal for youth.
+Overall, little research in suicidology has examined the role of intersectionality in suicide risk among youth. Emerging research on the topic, however, has found that youth with multiple marginalized identities are at increased risk for suicide (Bostwick et al., 2014; Garnett et al., 2014). For example, a 2006 survey of high school students in Boston public schools found that students who were identified as an “intersectional class” (characterized by an endorsement of a combination of racial, immigration status, sexual identity, and weight discrimination) were more likely to experience suicidal ideation than their counterparts, including those endorsing only one type of discrimination (Garnett et al., 2014).
+Despite this overarching theme, however, findings related to specific combinations of marginalized identities have been inconsistent. One mixed-methods study of LGBT youth in Chicago found that LGBT Latinx youth were more likely to report having attempted suicide while LGBT Black and white youth
+4 @ C. J. STANDLEY
+were more likely to report suicidal ideation (Mustanski, Garofalo, & Emerson, 2010). Conversely, a study of adults in New York City found that both Latinx and Black LGB respondents were more likely to report attempting suicide during adolescence than white respondents (O’Donnell, Meyer, & Schwartz, 2011). An analysis of National Longitudinal Study of Adolescent Health data found that same-sex-attracted Black and youth were more likely to report suicidal ideation than their heterosexual peers while Latinx and Asian/Pacific Islander youth did not differ in suicidality by sexual identity (Consolacion, Russell, & Sue, 2004). An analysis of YRBS data from multiple metropolitan regions of the United States found that Asian and Black LGB youth were less likely to report considering, planning, or attempting suicide than their white peers (Bostwick et al., 2014). However, when adding gender identity to the analysis, the researchers found that this pattern applied only to Asian and Black females, with the exception that Asian gay and bisexual males were less likely to report suicidal behavior than their White counterparts (Bostwick et al., 2014). Finally, although another study found that although White LGBT males and females and Hispanic gay males and bisexual females were more likely to report being bullied than White heterosexual teens, sexual minority youth were significantly more likely to report suicidal ideation regardless of race, ethnicity, gender, or having experienced bullying (Mueller et al., 2015).
+As these studies illustrate, the use of disaggregated data to examine the intersections of gender, race, ethnicity, and sexual identity has been increasing in recent years, but the findings from such studies have been mixed. These mixed findings highlight two tensions related to intersectionality and suicide. First, they speak to the varying definitions and measures used to capture intersectionality. As such, as a field, suicidologists must work to develop and test methodologies that authentically and accurately capture inter-sectionality and its impact on risk and prevention. Second, they highlight complexity of intersecting identities and “reaffirm that the consequences of possessing multiple marginalized identities are not simply additive,” but maybe multiplicative and/or interactive for youth (Bostwick et al., 2014, p. 1134). For example, Szlyk, Gulbas, and Zayas (2019) provide a powerful account of how gendered oppression (i.e. gendered scripts, performance expectations, and expected silence around oppressive experiences) can exacerbate suicide risk among Latinas. As such, it is imperative that suicidologists continue to examine the
+compounding effects of multiple marginalized identi-ties—and their relation to experiences of systematic oppression and power dynamics—on the complex relationship between protective factors and suicidal ideation among youth.
+Socioecological model
+If suicidology researchers agree on one thing, it is that suicide is complex. A multitude of individual factors (such as those mentioned above) as well as social and societal factors “attest to the fact that suicide cannot be easily understood in singular, static, or acontextual terms” (White, 2016, p. 1). Despite this complexity, however, much of suicide research has been focused at the individual level of analysis (Henry, Stephenson, Hanson, & Hargett, 1993; Marsh, 2015; White, 2016). Until relatively recently, research into the biological (e.g. biochemical precursors and physical illness) and psychological (e.g., psychiatric disorders, personality traits, and resilience) factors of suicide has dominated the field (Henry et al., 1993). This focus on the individual is necessary but insufficient. Moreover, although theories such as those outlined in Durkheim’s Suicide (1897; Durkheim, 1951) and family systems theory (e.g. Frances & Clarkin, 1985) have worked to investigate the ways in which social factors impact individuals’ suicidality, little has been done to integrate these levels of analysis to understand how these factors interact to contribute to suicidality and the role of the social, political, and historical contexts in which such interactions are embedded (Cramer & Kapusta, 2017; Henry et al., 1993; White, 2016).
+The contexts in which youth live, learn, and play are vital to their development and ability to thrive. Despite this, an overwhelming focus on risk factors for suicide at the individual level analysis has resulted in the neglect of the social contexts and ecological factors impacting youth (Cramer & Kapusta, 2017; Marsh, 2016). As Bearman and Moody (2004) state, “[a]dolescent well-being is largely the product of interactions among multiple contexts in which adolescents are embedded” (p. 89). Therefore, the use of socioecological theory is one method of better encapsulating this complexity by allowing researchers to explore the individual, social, and systemic factors impacting suicidality among youth.
+One such theory comes from Bronfenbrenner (1979). His ecological systems theory (EST) developed out of a belief that human behavior is a function of both the person and the settings in which they live. His ecology of human development posits that youth
+DEATH STUDIES 5
+development is a function of reciprocal interactions between the individual and their environments—interactions that make up a nested design of ecological levels. The first of these levels is the individual— characteristics and factors that make up the person themselves. The microsystem consists of interactions within settings directly experienced by the individual (e.g. school, family, or peer group). Links between microsystems (e.g. parent-teacher interactions) are known as mesosystems. The exosystem is made up of interactions that indirectly affect the individual, but do not actively include them (e.g. parents’ workplace policies). System- and societal-level policies and influences are known as the macrosystem (e.g. school funding policies). Finally, the chronosystem refers to the cumulative impact of developmental transitions over time (e.g. maturation, childhood trauma, etc.; Bronfenbrenner, 1979; Henry et al., 1993; Hong, Espelage, & Kral, 2011).
+Hong et al. (2011) provide a powerful, yet rare example of EST as applied to suicidality. In a review of empirical studies assessing suicidal behavior among sexual minority youth, the authors examined risk factors at multiple ecological levels to more holistically understand suicide risk and its implications for youth. Risk and protective factors at the microsystem level included youth demographic characteristics such as gender, racial identity, and sexual identity; the importance and salience of intersectionality; and one-on-one interactions with parents and peers. Mesosystemic factors included social support from teachers, school staff, and the mental health care system. Factors at the exosystem level included support for parents and its impact on youth, as well as parents’ workplace interactions and policies. Macrosystem level factors included larger societal influences such as homophobia, conservative values and beliefs, and religious beliefs. Finally, chronosystemic factors included changes over time such as normative youth development; physical, verbal, and sexual abuse; and other childhood trauma (Hong et al., 2011).
+Similarly, Duerden and Witt (2010) provide an excellent example of EST as applied to the presence of protective factors, specifically the role of social support. In describing the necessary components of effective programs for engaging youth, the authors describe key characteristics related to social support nested within ecological levels. Such characteristics included the hiring of supportive adults at the microsystem level; collaborative relationships, youth advocacy, and parental participation at the mesosystem level; workplace morale and employee
+encouragement at the exosystem level; and the public perception of youth at the macrosystem level (Duerden & Witt, 2010). Expanding on their framework, the emergence of social media as a method of communication is an example of an historical factor in the chronosystem level that may impact youths’ perception and incorporation of social support (e.g. Twenge, 2017).
+These are just a couple examples of socioecological theory applied in suicidology. Theoretical contributions also provide valuable insight in this area. For example, Cramer and Kapusta (2017) apply CDC’s ecological framework to their organization of risk and protective factors. Moreover, Abrutyn and Mueller (2018) supplement Durkheimian theory by proposing an increased focus on cultural coherence at the meso-systemic level. Broadly, the incorporation of socioeco-logical theories in suicidology provides a fitting lens through which to examine the complexities of suicide and its contributing factors. Specifically, it allows for further investigation into the individual, social, and environmental impacts on suicide and how these impacts are interrelated. Moreover, it allows for further investigation into how such factors might be mitigated within and between systems.
+Implications for research, practice, and policy
+In addition to their unique contributions, the integration of intersectionality and a socioecological model provides powerful opportunities to impact research, practice, and policy. Future research should build on the ideas presented here, as well as those presented by others regarding how identity is influenced by cultural and structural expectations, how such expectations contribute to distress and suicidal ideation and behavior, and how systems of power and oppression work to influence both. For example, Mueller and Abrutyn (2016) utilize socio-ecological ideas and expand on Durkheim’s concepts of integration and regulation by examining the how cultural and structural factors contribute to suicidality among adolescents.
+In addition, future research—both quantitative and qualitative—should aim to more purposefully and meaningfully define, measure, analyze, and interpret identity and context to more holistically and authentically capture the experiences of marginalized youth, how identity influences those experiences, and, in turn, how those experiences influence identity. Research methods must, therefore, consider these
+6 @ C. J. STANDLEY
+approaches from study concept all the way through dissemination.
+Clinically, the use of these approaches has implications for culturally informed and relevant treatment and prevention strategies, youth-friendly treatment environments, and risk and assessment tools that consider intersectional and social factors. Current screening tools such as the Columbia Suicide Severity Rating Scale (Posner et al., 2011) do not consider such factors, and doing so has the potential to save more lives. At the same time, the addition of such factors to these tools should be done ethically and implemented in collaboration with those with lived experience in order to avoid the potential of more psychiatric holds placed on those with marginalized identities. For example, incorporating the minority stress model for LGB populations (Meyer, 2003), and other relevant models for racial/ethnic minorities and gender non-binary individuals into risk assessments may provide a more context for providers by illustrating how stigma, prejudice, and discrimination contribute to suicidal ideation among youth.
+In policy development, intersectional and socioeco-logical approaches can lead to more holistic local, state, and federal policies aimed at improving familial, school, and community contexts for youth. In October of 2019, the United Nations Office of the High Commissioner for Human Rights (2019) published a statement on a rights-based approach to suicide prevention which said, in part, “[p]roviding holistic support for individuals and populations as a whole, particularly those who are most vulnerable, enables the attainment of the right to health by addressing the structural and psychosocial determinants of distress, such as childhood trauma and abuse, social inequality, and discrimination.” In the spirit of this statement, the application of intersectional theory and attention to multiple levels of the socioecological model have potential to increase the strength and reach of programs and interventions in a way that not only prevents death but creates livable lives.
+Conclusion
+Current research in suicidology is heavily focused at the individual level of analysis (Henry et al., 1993). Such a focus has hindered progress in the field and contributed to the neglect of intersecting social identities and context. As Marsh (2016) states, future research should be “founded on a different set of assumptions from those currently favoured in suicidology about the nature of suicide and how best to
+understand and respond to its prevalence and persistence” (p. 21).
+This article has proposed two such approaches: (1) paying attention to the unique experiences of youth through the study of intersectionality and context, and (2) exploring the role of protective factors through a socioecological model. Although these approaches are neither straightforward nor sufficient, they begin to challenge the predominant paradigms of our field thereby advancing our science. Moreover, they add to the extant literature regarding the social (e.g. Abrutyn, Mueller, & Osborne, 2019), societal (e.g. Hong et al., 2011; White, 2016), and systemic (e.g. Crenshaw, 1989) factors that contribute to youth suicide.
+With these approaches in mind and looking toward the future, I encourage us to critically examine our epistemological and methodological predilections, as well as the ways in which we disseminate our findings. More specifically, I invite researchers to (1) more authentically and meaningfully measure social identities and their intersections; (2) expand beyond the individual level of analysis to more holistically capture context; and (3) work with communities to design studies, analyze data, and interpret results in order to maximize authentic participation and voice. In short, we have an obligation to do “more than science” (Rappaport, 2005) and in doing so, can produce better science (Javdani, Singh, & Sichel, 2017).
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+1.	Introduction
+From 10 to 20% of the general population are significantly affected by chronic non-cancer pain (CNCP) (Von Korff et al., 2005). Following successful cancer pain initiatives, efforts have been made to liberalize the use of opioids for the treatment of these individuals. In a 1996 joint statement, the American Pain Society and the American Academy of Pain Medicine declared that concerns about addiction, respiratory depression, tolerance and diversion had been overstated and that “the use of opioids for the relief of chronic pain is a legitimate medical practice” (1997). In
+Great Britain, a similar consensus statement issued in 2004 recommends a trial of opioids as part of an overall rehabilitation strategy for patients with persistent pain (Royal Pain Society, 2004). These efforts are based on the belief that patients with CNCP deserve pain relief equally to those with cancer and that sustained pain relief is possible with stable doses of opioids.
+There is evidence in recent years of increasing use of prescription opioids for CNCP. Between 1980 and 2000, the rate of opioid prescribing at outpatient visits for chronic musculoskeletal pain doubled from 8 to 16% of visits (Caudill-Slosberg et al., 2004). Another study showed a marked increase in both use and abuse of prescribed opioids between 1997 and 2002 (Gilson et al., 2004). While some pain specialists have seen the growth in opioid prescribing
+as evidence of better attention to the problem of unrelieved pain (Portenoy, 2004), others have expressed concern that we have not had adequate trials to prove the “safety and effectiveness of long-term opioid therapy” in CNCP (Von Korff and Deyo, 2004). It has indeed been difficult to define standards of care in this area.
+Despite these increasing and variable rates of opioid prescribing, little is known about the clinical epidemiology of opioid use for CNCP. Although depression is very common in patients treated for CNCP in pain clinics (Banks and Kerns, 1996), the role of mental disorders in the general population treated with opioids for CNCP is not clear. Prospective surveys of primary care populations suggest that chronic pain promotes depression and that depression promotes chronic pain equally strongly (Gureje et al., 2001). Studies with small clinical samples suggest that patients with mental disorders may be more likely to receive opioids for CNCP (Breckenridge and Clark, 2003; Reid et al., 2002; Turk and Okifuji, 1997). This study further investigates the role of mental health and substance abuse disorders in the regular use of opioids with data from a large, nationally representative, population-based survey, Healthcare for Communities (HCC). On the basis of these earlier studies we hypothesized that individuals with common mental disorders (e.g. depression and anxiety) or substance abuse would be more likely to report regular use of prescribed opioids than those without common mental disorders, controlling for level of pain-related interference and other possible confounders.
+2.	Methods
+2.1.	Sample
+Data are from Healthcare for Communities (HCC), a part of the Robert Wood Johnson Foundation’s Health Tracking Initiative. HCC was a nationwide telephone survey designed to track the effects of the changing health care system on individuals at risk for alcohol, drug abuse, and mental health (ADM) disorders (Sturm et al., 1999). The use of a large, community-based sample such as HCC offers several distinct advantages for this type of analysis. First, it minimizes the bias that might occur when using data from only a limited number of practice settings or patient groups; this is especially important when studying a therapy whose use is thought to vary widely, such as opioids for CNCP. Further, the large sample size of HCC, and the resultant statistical power, allows meaningful subgroup analysis. Because this is a cross-sectional survey, it is not possible to assign causal priority to the psychiatric disorder, chronic pain condition, or opioid use.
+The HCC respondents were a stratified probability sample of participants in the Community Tracking Study (CTS), a nationally representative study of the US civilian population during 19961997 (Kemper et al., 1996). The CTS includes a sample clustered within 60 randomly selected US communities and a national sample. To improve power for analyses of individuals with ADM disorders and treatment utilization, HCC over-sampled the following four groups of CTS respondents: (1) the CTS national
+sample, (2) individuals with family income below $20,000, (3) individuals with high psychological distress on a scale consisting of two items from the 12-item Short Form Health Questionnaire (SF-12) (Ware et al., 1996) and (4) individuals with any ADM specialty care utilization during the prior year. HCC was conducted in 1997-1998. Of the 14,985 respondents selected for HCC, 9585 complete interviews were obtained, for a response rate of 64%. We use the sample weights designed by the statisticians on the HCC study staff to weight the data so that they would again be representative of the US population and adjusted for the probability of selection, non-response, and the number of households in the CTS survey that did not have a phone. We excluded respondents reporting cancer diagnoses other than skin cancer within the past 3 years (N=217) to isolate non-cancer pain. Those missing data for key variables (N=89) were also excluded, resulting in a sample size of 9279. Informed consent was obtained verbally before the interview, and the study was approved by the institutional review boards at UCLA and RAND.
+2.2.	Dependent variable
+Regular use of prescribed opioids. HCC respondents were asked about all prescribed medications which they had taken “at least several times a week for a month or more” in the past 12 months. Respondents were asked to read the names directly off of their pill bottles to the interviewers, for all of their medications. These medications were later grouped into therapeutic categories by study staff. In our sample, 96% of the 282 subjects reporting this level of opioid use specifically confirmed that the opioid was prescribed, and 79% confirmed opioid use for at least 2 months. (As a sensitivity analysis, we ran our models with the sample restricted to those participants who verified use of at least 2 months. The odds ratios derived for common mental disorders were similar but somewhat larger.) From this we constructed a dichotomous measure of regular prescription opioid use as our dependent variable, with 282 individuals reporting regular opioid use of at least one month.
+2.3.	Independent variables
+Our model specification is based on the work of Andersen (1995) who posited that access to medical treatment is a function of patient need, predisposing factors, and enabling factors. We used several complementary measures of physical health and chronic physical conditions as measures of patient need for opioid treatment. These included the Physical Component Summary-12 (PCS-12), an aggregate measure of physical health functioning, and a measure of self-reported health (excellent, very good, good, fair, or poor), both from the SF-12. The HCC contains information on 17 self-reported chronic medical conditions. Our final models included variables representing those medical conditions that were associated with opioid use (bivariate r>0.1): chronic lower back pain, chronic headaches, and other chronic pain. We also included a dichotomous measure of pain interference with the respondents’ daily activities derived from the SF-12 pain item (extremely/a lot vs. moderately/a little bit/not at all).
+The prevalence of common mental disorders in the past 12 months (major depression, dysthymia, generalized anxiety disorder (GAD), and panic disorder) was assessed using short-form versions (Kessler et al., 1998) of the World Health Organization’s
+Composite International Diagnostic Interview (CIDI, 1995), which is based upon the Diagnostic and Statistical Manual Third Edition-revised (DSM-III-R) (American Psychiatric Association, 1987). The CIDI (World Health Organization, 1995) is a fully structured interview designed to be administered by lay interviewers. The Mental Health Inventory-5 (MHI-5; McHorney and Ware, 1995) is a summary of psychological distress in the past month, which we use to indicate severity of psychological problems among those with common mental disorders. The scale is continuous and ranges from 0 to 100, with higher scores indicating better mental health. We divide individuals with common mental health diagnoses in the past year into four groups based on MHI-5 scores ranging from 0 to 24 (most distressed), 25-49, 50-74, and 75-100 (the least distressed). We also report mean MHI-5 scores for subgroups of respondents according to pain interference and mental health diagnoses.
+Problem alcohol and drug use were assessed with common assessment tools. However, these scales do not exactly replicate DSM-III-R diagnoses of substance abuse or dependence, so we use the terms problem alcohol use and problem drug use. Alcohol problems were assessed using the Alcohol Use Disorders Identification Test (AUDIT), with a score of 8 or more representing problem alcohol use (World Health Organization, 1992). The designation of problem drug use was based on answering yes to either of two items adapted from the CIDI—indications of tolerance and/or emotional or psychological problems from drug use (such as depression, anhedonia, paranoia, delusions, hallucinations). Note that problem use of prescription drugs is defined for the respondent as use ‘on your own’, meaning ‘without a doctor’s prescription, or in larger amounts than prescribed, or for a longer period than prescribed’. These questions are asked in a section of the survey completely separate from the questions on prescription medication use. Other covariates include: gender, income, marital status, insurance status, reporting disabled as employment status, race, age, education, and region of the country.
+2.4.	Analyses
+Demographic and clinical characteristic rates for opioid and non-opioid users were compared using chi-square analysis. In separate logistic regressions we investigated bivariate relationships between each psychiatric condition (major depression, dysthymia, GAD, panic disorder, problem alcohol use, and problem drug use) and prescription opioid use. We investigated the relationship between opioid use and a variable indicating the presence of any one of the four common mental disorders (major depression, dysthymia, GAD, panic disorder). We also examined the relationship between opioid use and the number of disorders (none vs. one or two vs. three or four). To proxy for current severity of distress among those with a mental disorder diagnosis in the past year, we used the MHI-5 score divided into four groups from highest distress to lowest and logistic regression to predict the likelihood of receiving opioids for those with mental disorders based on severity (McHorney and Ware, 1995). Using multivariate logistic regressions, we then investigated the effects of having any of the four common mental disorders on opioid use, adjusting for physical health, alcohol and drug problems, and sociodemographic factors. We chose to use a summary measure of any of the four common mental disorders rather than the individual disorders
+because they were highly correlated with each other, had similar effects in the unadjusted models, and simplified the model and the interpretation of interactions. We included an interaction term in the multivariate logistic regression model to investigate whether the association of mental disorders with opioid use varied by level of pain interference with daily activities, since high pain interference is the primary accepted indication for opioid use (Breivik, 2005). All analyses were conducted using STATA version 8.2 (College Station, TX), are weighted by the sample weights, and take into account the clustered survey design for estimates of standard errors.
+3.	Results
+Regular prescription opioid use (at least several times a week for a month or more) was reported by 282 (3%) of the sample. The descriptions of the HCC opioid use groups are shown in Table 1. Among demographic variables, age (46-60), Hispanic ethnicity, marital status, education (high school graduate), income, and disabled employment status showed a significant bivariate relationship (by chi-squared tests) with opioid use. Among physical health variables, pain interference, painful conditions, self-rated health and the PCS-12 index showed significant bivariate relationships with opioid use. Use of antidepressants as well as perceived need for mental health and substance abuse treatment were significantly related to opioid use in bivariate tests.
+In unadjusted logistic regression models, respondents with major depression, dysthymia, GAD, panic disorder, or any of the four disorders had higher rates of prescription opioid use than those without disorders (Fig. 1). These effects were highly significant (P! 0.001) and the odds-ratios (ORs) were large (4.13-8.46). Similarly, individuals with problem drug use (N=219) had higher rates of prescribed opioid use (OR = 4.75, 95% CI = 2.52, 8.94, P!0.001) than those with no drug problems (N=9060). Individuals with problem alcohol use (N=648) were somewhat more likely to use prescription opioids (OR= 1.89, 95% CI=1.03, 3.50, P=.04) than those without alcohol problems (N=8631).
+In unadjusted models, the number of common mental disorders was associated with the likelihood of receiving opioids. Those with one or two of these disorders (N=1480) had five times greater odds of opioid use (OR=5.63, 95% CI=3.52, 9.01, P!0.001) and those with 3-4 disorders (N=263) had nine times greater odds of opioid use (OR= 9.68,95% CI = 6.41,14.61, P!0.001) than individuals with no common mental disorders (N=7536). Similarly, if those with a common mental disorder in the past year are stratified by psychological distress in the past month using the MHI-5 score (McHorney and Ware, 1995), those with more psychological distress have greater likelihood of receiving opioids, with odds ratios increasing from the least distressed group (common mental disorder and MHI-5 score 75-100 vs. no disorder, OR = 4.49, 95% CI = 2.49, 8.11, P!0.001),
+to the most distressed group (common mental disorder and MHI-5 score 0-24 vs. no disorder, OR= 10.59, 95% CI = 6.31, 17.79, P<0.001).
+In multiple logistic regression models controlling for relevant demographic and clinical variables, including the interaction of common mental disorder and high pain interference, the following demographic variables were associated with a greater likelihood of prescribed opioid use: age under 30, living with an unmarried partner, finishing high school but not college, and work disability. Hispanics and females had lower likelihoods of prescribed opioid use (data not shown). Among clinical variables, back problems, other chronic pain disorder, problem drug use, pain interference, common mental disorder, and the interaction term for pain interference and common mental disorder were significantly associated with opioid use (Table 2). The statistical significance of the interaction term means there is a difference in the effect of common mental disorders between those reporting high levels of pain interference in their daily activities and those who do not report high pain interference. The odds ratios presented in Table 2 for common mental disorders (OR=3.15) and for high pain interference (OR=3.91) represent their effect when the other factor is NOT present. When both are present, the effect of each of these is calculated by multiplying these
+odds ratios by the odds ratio associated with the interaction term (OR=0.4).
+Common mental disorders remained a significant predictor of prescription opioid use among individuals with low pain interference (N=8307; OR = 3.15, 95% CI = 1.69, 5.88, P<0.001). However, among those with high pain interference, the presence of a common mental disorder was not a statistically significant predictor of prescription opioid use (N=972; OR= 1.27, n.s.). Similarly, high pain interference (‘a lot’ or ‘extreme’) was the strongest predictor of prescription opioid use for those without a common mental disorder (N=7556; OR=3.91, 95% CI= 2.08, 7.36, P<0.001). However, high pain interference was not significant among those with a common mental disorder (N=1723; OR=1.58, n.s.). Fig. 2 displays the unadjusted percent of the sample receiving opioids in each of the four groups (high/low pain interference vs. yes/no common mental disorder), as well as the mean MHI-5 score for each group.
+Other measures of physical health and pain, such as back problems (N= 1768; OR = 2.15, 95% CI =1.61, 2.86, P< 0.001), chronic pain disorder other than back or headache (N=904; OR= 1.76, 95% CI= 1.22, 2.56, P = 0.003), and work disability (N=755; OR =1.95, 95% CI =1.37, 2.75, P<0.001) were all significant predictors of prescription opioid use, while chronic headache was not (N=1323;
+OR= 1.59, n.s.). Table 2 also shows that, in the multivariate model, individuals with problem drug use (N=219) were significantly more likely to receive a prescription opioid over the past 12 months (OR = 2.28, 95% CI=1.10, 4.74, P<0.03), but those with alcohol problems were no more likely to be using prescription opioids than those without (OR=1.32, n.s.).
+4.	Discussion
+This study provides the first population-based data on the association of psychiatric comorbidity with the prescription of regular opioid medication. In unadjusted models, individuals with common depressive or anxiety disorders had six times higher odds of receiving opioids than those without these disorders. In adjusted models, having a common mental disorder remained a significant predictor of opioid use whose effect varied by the level of pain interference with daily activities reported by the respondent. In those with low pain interference, a common mental disorder increased the likelihood of opioid use threefold. In those with high pain interference, common mental disorder did not significantly increase the likelihood of opioid use. It is important to remember that it is not possible with this cross-sectional data to assign causal priority to our variables. For example, regular opioid use may be increasing the likelihood of psychiatric disorders in this population with chronic pain.
+The odds of using an opioid medication among those with a high level of pain interference with daily activities were almost four times greater in adjusted models than the odds for individuals not reporting a high level of pain interference. But the association of opioid use with pain interference was not significant among those with a depressive or anxiety disorder. This suggests that both high levels of pain interference and common mental health disorders are strongly associated with receiving regular prescribed opioids. High pain interference had a stronger association among a smaller group (OR=4.15, N=972) than common mental health disorder (OR = 3.32, N= 1723).
+In evaluating the strength of the relationship between pain interference and opioid use, it is important to note that self-reported pain interference is itself significantly
+associated with psychological distress. The MHI-5 is a purely psychological measure of distress, without a somatic component. It is nonetheless significantly associated with pain interference in our sample (|r| =0.37, P<0.001). The high pain interference groups are also more distressed compared to the corresponding low pain interference groups (lower MHI-5 scores signify more distress): for those with a common MH disorder: 46 (high pain) vs. 62 (low pain) and for those without a common MH disorder: 73 (high pain) vs. 85 (low pain). Thus, psychological distress may be playing a role in opioid prescription for both the common mental health disorder and high pain interference groups.
+There is evidence that unmet need for mental health care is associated with higher rates of substance use (Harris and Edlund, 2005). There is also a centuries’ long history of opioids being used by physicians to treat depression and other mental disorders (Jackson, 1986). Exogenous opioids elevate mood and endogenous opioids modulate basal emotional state (Narayanan et al., 2004). There are recent studies suggesting that treatment resistant depression (Bodkin et al., 1995) and obsessive-compulsive disorder (Koran et al., 2005) may respond to opioid treatment. It is well known that depression and other forms of psychological distress are associated with increased rates of both psychological and physical symptoms. Pain arising from chronic medical disorders is also rated as more severe in the presence of major depression (Katon et al., 2001). Therefore, we suggest that opioids are being used to treat this undifferentiated state of mental and physical pain. We report that 3% of the general population without cancer are regular users of prescribed opioids. We could find no other data published on opioid use in the general population, but this figure is compatible with opioids being used in 16% of musculoskeletal pain visits (Caudill-Slossberg).
+Individuals reporting problem drug use, but not problem alcohol use, had twice the odds of receiving opioids compared to those without problem drug use, after adjusting for demographic and other covariates. In another study of veterans receiving treatment for chronic low back pain, a substance abuse history was associated with a greater likelihood of receiving opioids rather than non-steroidal anti-inflammatory drugs (OR = 4.72) (Breckenridge and Clark, 2003). Other studies have examined a history of substance abuse as a risk factor for abuse of prescribed opioids. Most (Hermos et al., 2004; Miller and Greenfeld, 2004; Potter et al., 2004) but not all (Chabal et al., 1997) of these studies found increased rates of abuse of prescribed opioids in those with histories of substance abuse.
+The literature to date concerning the appropriateness of opioids for chronic pain has been dominated by “the need to be able to screen for and identify patients at risk for aberrant behavior and addiction” (Lussier and Pappagallo, 2004; Passik and Kirsh, 2003) when prescribed opioids. This caution is justified since illicit use of prescribed opioids has increased more than licit use in recent years (Zacny et al., 2003). However, among individuals in our study receiving
+prescribed opioids, the rate of common mental health disorders (45%) was four times higher than the rate of alcohol problems (11%) and almost six times higher than the rate of drug problems (8%). This implies that depressive and anxiety disorders are a much more significant clinical problem in candidates for chronic opioid therapy than substance use problems. While 45% of individuals receiving opioids in our study met criteria for at least one common mental health disorder, less than one-third of these were receiving any antidepressants (the most commonly used treatment indicated for these disorders). Furthermore, individuals receiving opioids were more likely to perceive a need for mental health treatment (30 vs. 10%) than those not receiving opioids. This implies significant undertreatment of depressive and anxiety disorders in the population receiving opioids. This group may be presenting pain rather than psychological distress to their physicians for which they receive ‘pain medication.’
+The guidelines and consensus statements published to date on the use of opioids for CNCP vary widely in their recommendations concerning depression and other psychiatric disorders. The recent Joint Commission on Accreditation of Health Care Organizations (JCAHO) (National Pharmaceutical Council, 1991) and British (The Pain Society, 2004) guidelines mention the importance of the assessment of psychiatric disorders. However, other guidelines (American Academy of Pain Medicine, 1997; Drug Enforcement Administration, 2002) and reviews (Ballantyne and Mao, 2003; Chodosh et al., 2004) make no mention of the psychiatric disorders or mental health care needs of patients who are candidates for chronic opioid therapy. We believe our data suggest that clinicians should carefully assess for common mental health disorders such as depression and anxiety in their CNCP patients being considered for chronic opioid therapy, as these individuals are at high risk for such mental health disorders. In the HCC sample that did not receive opioids, only 10% of those not taking antidepressants had a common mental health disorder, while for those on opioids, 39% of those not on antidepressants had a common mental health disorder. A recent trial of collaborative care for depression in older adults demonstrated that arthritis pain intensity and pain interference improved with improvement in depression (Lin et al., 2003). It is therefore important that clinicians treating patients with chronic pain should be comfortable with treating common mental health disorders or with referring to a mental health specialist.
+The strengths of our study include the use of a large nationally representative sample of the general population. This avoids the selection bias common in the clinical samples where opioid use has been studied to date and allows interpretation of our results in terms of the national population. The HCC also utilizes the systematic and well-validated WHO CIDI approach to psychiatric diagnosis. The HCC also provides extensive data on demographic and
+clinical covariates that could confound the true relationship between psychiatric disorders and opioid use.
+The primary limitation to our study results from the use of cross-sectional, survey data. The time frame for both opioid use and psychiatric disorders was the previous 12 months, so it is not possible to draw causal inferences. There was also no direct clinical observation of the study subjects; all assessments were conducted by phone and rely on selfreport. We do not know if subjects were taking their opioids as prescribed. Thus, our study only addresses the report of a prescription for opioids for at least a month’s supply, not abuse or non-adherence. The survey data was collected in 1997-1998, so it is now 8 years old. Replicating this study with more recent data is necessary given the increased rates of opioid prescribing during the time period since then.
+In summary, our study demonstrates that common psychiatric disorders (mood, anxiety and drug abuse) and psychological distress are associated with increased use of regular opioids in CNCP patients. While much attention has been given to the possibility of substance abuse in regular users of prescribed opioids, our data suggest that common depressive or anxiety disorders are as strongly associated with opioid use. These disorders may pose a more significant clinical problem in this population because they are so much more common. These depressive and anxiety disorders deserve careful evaluation and appropriate treatment as part of quality health care for CNCP. Further studies are needed to determine if improved recognition and treatment of these disorders would decrease the need for chronic opioid therapy.
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+Introduction
+More than 800 000 people die by suicide annually worldwide (World Health Organization, 2019). In Australia, suicide is the leading cause of death in the 15-44 years age group and the second leading cause of death in the 45-54 years age group. In 2018, it was estimated that there were 105 730 years of life lost to suicide (Australian Bureau of Statistics, 2019). In terms of suicide attempts, approximately 1 in 33 Australians (aged 16-85 years) make a suicide attempt during their lifetime and 1 in 13 young Australians (aged 12-17 years) had seriously considered attempting suicide in the previous 12 months (Lawrence et al., 2015; Slade et al., 2009). Suicide plans and attempts are associated with significant personal health care costs, lost productivity, emotional and psychosocial morbidity and additional burden and distress caused to family and friends (Johnston, Pirkis, & Burgess, 2009; Kinchin & Doran, 2017; Spillane, Matvienko-Sikar, Larkin, Corcoran, & Arensman, 2018). Rarely do suicide plans or attempts occur without the presence of suicidal thoughts and ideation, which may occur sometime before any suicidal plans or behaviours are exhibited, yet not all those with suicidal ideation go on to develop suicidal plans or attempts (Klonsky & May, 2014). As such, there is a critical need for new public health approaches to better assist the population with identifying and reducing suicidal ideation as well as recognising potentially differential risk factors associated with transitioning from ideation to plans and/or attempts as a mechanism of breaking down the path towards increased mortality.
+Whilst there are a myriad of studies examining factors associated with suicidal plans, attempts and ideation, there are relatively few studies that have examined the key transitional period from thoughts to action, as well as identifying predictive factors associated with that transition. Existing theories of suicidal behaviour have placed the transition between ideation and plans/attempts as a critical period that could potentially reduce high rates of suicide. In particular, the integrated motivational-volitional, three-step theory and interpersonal theory
+(IPT) identify a range of factors such as the capability for suicide, access to means, psychopathology, past suicidal behaviours, exposure and impulsivity as playing a key role in the transition from suicidal ideation to plans/attempts (Klonsky & May, 2015; O’Connor & Kirtley, 2018; Van Orden et al., 2010). Indeed, the IPT posits that suicidal ideation can develop with increased perceptions of thwarted belongingness (loneliness and absence of reciprocal care), high burdensomeness and hopelessness, which often presents as low social connectedness and increased rates of comorbid psychopathology such as depression, anxiety and substance use disorders.
+The role of psychopathology on this transition has been most extensively studied. Batterham, Calear, Christensen, Carragher, and Sunderland (2018) recently demonstrated multiple associations between mental and substance use disorders, particularly obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), attention deficit hyper-activity disorder and alcohol use disorder (AUD) and suicidal behaviours among individuals with suicidal ideation. However, in adjusted models that accounted for the presence of all other disorder, only OCD and PTSD remained significant and there was no significant effect of major depressive disorder on suicide attempts among those with ideation. Very few studies have identified independent effects associated with OCD and suicidal plans and attempts among those with ideation, given that mood disorders are commonly the focus. These findings potentially suggest that people who find it difficult to escape suicidal thoughts or those who feel compelled to act on those thoughts would be at increased risk of making suicidal plans and/or attempts (Batterham et al., 2018). Other population-based studies have similarly suggested that the presence of anxiety and impulse control disorders might potentially have greater effects than mood disorders on transitions to suicidal plans/attempts (May & Klonsky, 2016; Nock, Hwang, Sampson, & Kessler, 2010).
+While these studies suggest novel targets to prevent suicide attempts among those with suicidal ideation, there is a lack of evidence informing the specific length of time between first experiencing suicidal ideation and first making suicide plans or a suicide attempt. Such evidence may be crucial for optimising access to treatment services and prevention programs. In addition, the factors associated with increased risk of suicide attempts among those with suicidal ideation may differ with respect to factors that are associated with the speeding up or slowing down the time from first ideation to first plans/attempts. More nuanced evidence regarding transition from ideation to plans/attempts is likely to lead to more tailored and better targeted interventions, increasing the effectiveness and efficiency of programs. Yet, risk factor research on suicidal thoughts and actions requires large samples with data on multiple mental and substance use disorders as well as the longitudinal or developmental nature of these disorders over the lifespan.
+The current study utilises a large population-based survey of the Australian population with comprehensive data on multiple mental and substance use disorders assessed retrospectively over the lifespan. The study makes use of a novel cross-sectional design that obtained age of onset data from all individuals regarding suicidal ideation, plans and attempts as well as age of onset of multiple mental and substance use disorders. As such, this study uses discrete time survival analysis to model the estimated time (in years) from first experiencing suicidal ideation to first making a suicide plan and/or attempt. In addition, the study investigates important sociodemographic subgroups and social features that
+are related to either a faster or slower transition from first ideation to first suicidal behaviours. Previous studies have utilised similar data and techniques to determine associations between first use of alcohol and/or drugs and the onset of substance use disorders as well as the time taken to seek treatment (Chapman, Slade, Hunt, & Teesson, 2015; Marel et al., 2019). However, to the best of our knowledge, we are not aware of any study that has utilised these techniques to investigate time from suicidal ideation to plans and attempts in the general population.
+Methods
+Sample
+Data from the current study were from the 2007 Australian National Survey of Mental Health and Wellbeing (NSMHWB), a nationally representative population-based survey of Australians aged 16 to 85 years old. The survey comprised a stratified, multistage probability sample of households with one household member randomly selected to complete the survey. The survey oversampled (higher probability of selection) those who were younger or older to ensure representative estimates of these traditionally under-represented age groups. The survey was conducted by trained interviewers from the Australian Bureau of Statistics (the statutory body responsible for conducting Australian-based surveys and census taking using appropriate ethical standards and obtaining informed written consent). The total sample size is 8841 with an overall response rate of 60%, which is comparable to other national mental health surveys and reflective of declining global response rates to survey research (Slade, Johnston, Oakley Browne, Andrews, & Whiteford, 2009). Given the focus of the current study was on the transition from suicidal ideation to plans and/or attempts, this necessitated the selection of a subsample of those respondents who experienced suicidal ideation at some point in their lives. Similarly, the method required that each person was accurately able to recall the age they first experienced suicidal ideation. Those who could not recall the age they first experienced ideation were excluded from the analysis. The total analysed sample n = 1237 or 13% (s.e. = 0.46) of the total population. The data were weighted according to the sex and age characteristics of the Australian population.
+Measures
+The lifetime presence of suicidal thoughts and plans/attempts and age of onset information were assessed in the survey using several related questions. For the current study, suicidal ideation and suicidal plans were considered distinct and measured separately. The two were differentiated by multiple questions and skip logic with suicidal ideation referred to as seriously thoughts about suicide v. plans referred to as actively making a suicidal plan. The first question was designed to assess whether the respondent had ever experienced suicidal thoughts or ideation over their lifetime (e.g. ‘You seriously thought about committing suicide’). If they said ‘yes’ then the interviewer asked how old they were the first time this happened. Respondents who indicated that they had experienced suicidal ideation were then asked if they had ever experienced (1) making a suicide plan (e.g. ‘You made a plan for committing suicide’) and how old they were the first time this happened, and (2) attempting suicide (e.g. ‘You attempted suicide’) and how old they were the first time this happened. If the participant did not indicate that they have had suicidal thoughts,
+then they were skipped from answering questions about suicidal plans and attempts. For the current study, respondents were coded as having attempted suicide regardless of the intention, method or outcome (e.g. hospitalisation) of the suicide attempt.
+The lifetime occurrence of mental and substance use disorders was assessed using a modified version of the World Mental Health Composite International Diagnostic Interview (WMH-CIDI). This interview is considered a gold standard diagnostic instrument with strong psychometric properties and has been calibrated against a structured clinical interview (SCID-IV). The WMH-CIDI has been used extensively as part of the World Mental Health Survey initiative (Kessler, Ustun, & Ustun, 2004). All respondents were assessed according to the DSM-IV criteria for major depressive episode (MDE), generalised anxiety disorder (GAD), OCD, panic disorder, PTSD, social anxiety, AUD (abuse and/or dependence) and drug use disorder (abuse and/or dependence). Age of onset information for mental and substance use disorders was captured by asking the age they first experienced a core symptom set associated with each disorder. Using this information age of onset was then coded for those who met full diagnostic criteria for each disorder. To examine the impact of pre-existing mental and substance use disorders on transitioning from suicidal thoughts and behaviours, only mental and substance use disorders that specified an age of onset prior to the age of first experiencing suicidal ideation were coded as positive. If the age of onset occurred after the age of first experienced ideation, plans and attempts, the diagnosis was coded as negative.
+Additional demographic and social information related to suicidality obtained from the survey were limited given the secondary use of data for the current analysis. However, a range of sociodemographic and social variables that have been previously implicated with suicidal ideation, plans and attempts were identified and examined as correlates of transitioning from suicidal ideation to plans and/or attempts. These variables included: sex, age at first experience of suicidal ideation, age at the time of the survey, sexual minority status (homosexual or bisexual), location (major urban, other urban, non-urban), presence of one or more family members to confide in, and presence of one or more friends to confide in.
+Statistical analysis
+Descriptive statistics and univariable and multivariable logistic regression among those who ever experienced suicidal ideation was used to examine frequencies, odds ratios and statistical significance between the demographic and clinical variables with suicidal plans and attempts. The data were weighted based on the sex and age distribution of the Australian population. Unconditional and conditional discrete time survival models were estimated to model the time in years from first experiencing suicidal ideation to plans and attempts, separately. Given that almost all respondents who experienced plans or attempts did so within 20 years from first experiencing suicidal ideation, the current analysis right-censored the data beyond the 20-year point. Respondents who did not make plans or attempts were either censored at their current age or after 20 years since first experiencing ideation. The data were coded using binary person time indicators with a value of zero assigned if the respondent did not first experience plans or attempts within that time or a value of one if they did experience plans or attempts within that time. Once a respondent experienced plans or attempts, the subsequent indicators for the time intervals were coded as missing. Given the relative rarity of plans and attempts within this
+population, the binary time indicators represented 2-year intervals with dummy codes (a total of 10 values representing the 20 years since first experiencing suicidal ideation) beginning at within the first 2 years, followed by within the first 4 years, then within the first 6 years and so on.
+Model building commenced by first fitting unconditional discrete time survival models separately for plans and attempts. This was followed by fitting univariable models and testing the proportionality assumption for each covariate separately. Finally, multivariable models were estimated with all covariates included simultaneously (sex, age at first experience of suicidal ideation, age at the time of the survey, sexual minority status, location, presence of one or more family members to confide in, presence of one or more friends to confide in and psychopathology). Statistical significance was assumed by setting the alpha level at p < 0.05. Comparisons of model fit were achieved using Bayesian information criterion (BIC) where models with lower BIC values were deemed as providing better model fit. All models were estimated in Mplus version 8 using full information maximum likelihood with robust standard errors and a logit link.
+Results
+Descriptive statistics
+Of those with suicidal ideation, approximately 30% (s.e. = 1.99%) had reported making suicide plans, and 24% (s.e. = 1.15%) had reported a suicide attempt at some point in their life. Approximately 15% (s.e. = 1.25%) reported both making a suicide plan and suicide attempt, 15% (s.e. = 1.63%) reported making a plan but not attempt, 9% (s.e. = 1.14%) reported making a suicide attempt but no plans and 61% (s.e. = 2.22%) did not report making a plan or attempt despite experiencing suicidal ideation. The demographic and psychopathological characteristics of those with suicidal ideation are reported in Table 1. The sex distribution differed significantly between those with and without suicidal ideation with 57% male and 43% female in those with suicidal ideation v. 51% male and 49% female (f =10.1, p <0.01). The average age of those with suicidal ideation was 43 years (s.e. = 0.51), whereas the average age of first suicidal thoughts was 27 years (s.e. = 0.48). Among those with a suicide plan, the average age they first made a suicide plan was 27 years (s.e. = 0.87) and among those with a suicide attempt the average age they first made a suicide attempt was 24 years (s.e. = 0.97).
+Unconditional discrete time survival models
+The final sample for the discrete time survival model for first suicidal thoughts to first plan was n = 1182 (after removal of those who could not remember the age that they first made a suicide plan or indicated that the age they first made a suicide plan was prior to the age they first had suicidal thoughts, see Fig. 1). similarly, 46 respondents without suicide plans indicated that their current age was within 2 years from first having suicidal thoughts and were excluded. Of those with suicidal ideation, an estimated 31% made a suicide plan within 20 years from first experiencing ideation (the point at which the data was censored). An estimated 27% made a suicide plan within 2 years of first experiencing suicidal ideation (equating to approximately 87% of people who made a suicide plan within 20 years).
+The final sample for the discrete time survival model for first suicidal thoughts to first attempt was n = 1173 (after removal of
+cases who could not remember their age of first attempt or indicated their age was prior to the age they first had ideation, see Fig. 1). In addition, 47 respondents without attempts were removed given their current age was within 2 years of having first experienced suicidal thoughts. An estimated 25% of those with suicidal ideation experienced a suicide attempt within 20 years with the vast majority (80%) experiencing their first attempt within 2 years of first ideation. The estimated hazard functions for both suicide plans and attempts are provided in Fig. 2.
+Correlates of transitioning from first ideation to plans and/or attempts
+Tests of the proportionality assumption for all covariates indicated that models that assumed proportional effects of the covariate across all time points evidenced better model fit in comparison to non-proportional effects according to the BIC values. Given the presence of zero cells (no plans/attempts) in some of the categorical covariates by time in the non-proportional
+models, the estimates for these time points were fixed at the extreme value to avoid singularity of the information matrix.
+The results for the univariable and multivariable (inclusion of all other covariates) models for ideation to plans and ideation to attempts are provided in Tables 2 and 3, respectively. For suicidal ideation to first suicide plan, the univariable models indicated that AUD, GAD, MDE, OCD, PTSD, SMS and social anxiety were associated with a faster transition to first making a suicide plan. Having one or more family member to confide in, being male, and older age at the time of the interview were significantly associated with a slower transition. There was additional interest in examining the potential interaction between social connectedness ( friends and family support) and sexual minority status however additional models that included these interaction terms provided no evidence for a statistically significant difference. When adjusting for all other covariates, AUD, one or more family members to confide in, being male, MDE, OCD, SMS, non-urban and age remained as significant covariates. For suicidal ideation to first suicide attempt, the univariable models indicated that PTSD,
+GAD, OCD, SAD and drug use disorder were significantly associated with faster transition to attempts, whereas older age at first ideation, older current age and being male were significantly associated with a slower transition. In the multivariable model, sex and age of first ideation remained significant, with male sex and older age associated with significantly slower transition from ideation to attempts. The presence of drug use disorder also remained significantly associated with a faster transition from ideation to attempts in the multivariate model. Again, interaction terms between social connectedness and sexual minority status were tested but provided no evidence for a statistically significant difference.
+Discussion
+The current study utilised population-based age of onset data associated with suicidal ideation and suicidal plans and attempts
+to determine the time taken from first experiencing ideation to first experiencing plans and attempts. Moreover, conditional discrete time survival models examined a range of clinical and sociodemographic correlates of transitioning from suicidal thoughts to plans/attempts. The unconditional models indicate that the large majority of those who experience ideation and go on to experience plans or attempts will do so within 2 years from first experiencing ideation. These results provide an indication of the immediate seriousness of any suicidal ideation and the requirement that treatment and services should be sought rapidly rather than delayed for any significant period. Moreover, the findings indicate that there is a need for early recognition/detection and improved screening for suicidal ideation (including the use of novel highly efficient and accurate adaptive and static screening tools) particularly among high-risk subgroups (Calear, Batterham, Sunderland, & Carragher, 2020). For instance, the transition from suicidal ideation to suicide plans is particularly rapid for those with AUD, OCD or MDE, and those identifying as belonging to a sexual minority, whereas those who are older at the time of the survey, male or have one or more family member to confide in had a significantly slower transition from ideation to plans. In terms of transitioning from suicidal ideation to attempts, the results were less consistent with multivariable models indicating that only those with drug use disorder have a statistically significant increase in the transition from ideation to attempt, whereas the age of first ideation and being male are associated with statistically significant slower transition.
+The independent effects of mental disorders, such as major depression and OCD, on the transition from suicidal ideation to plans match results found in prior studies showing strong independent connections with suicidal behaviours (Batterham et al., 2018; May & Klonsky, 2016; Nock et al., 2010). Interestingly, none of these disorders were independently associated with more rapid transition to suicide attempts - this may be due to multicollinearity or insufficient power to detect effects for some specific disorders, but it may also suggest that psychopathology does not play a
+critical role in the speed of transition to attempt - rather, it is hypothesized that other psychosocial factors such as relationship breakdown or financial problems may drive the transition.
+With respect to substance use disorders, the results demonstrated an independent role of AUD on the speed of transition to suicidal plans, whereas drug use disorder was identified as
+significantly associated with the speed of transition to suicide attempts. These results offer an insight into the specificity of effects of different substances on the relationship with different suicidal behaviours. Indeed, a prior study that investigated the association between substance use patterns and attempted suicide in a cohort of people who inject drugs, found that chronic and occasional cocaine use, chronic amphetamine use and chronic sedative-hypnotics use were independently associated with suicide attempts, whereas no statistically significant effect was found for alcohol use (Artenie et al., 2015). These findings might be accounted for by evidence suggesting that poorer levels of impulse control are associated with cocaine and amphetamine use as well as an increased anhedonia state that often is associated with stimulant use leading to heightened vulnerability to suicide attempts (Artenie et al., 2015; Badiani, Belin, Epstein, Calu, & Shaham, 2011). Moreover, in line with the notion of increased capability accounting for suicidal behaviours posited by the IPT of suicidal behaviour, the findings may reflect increased access to means of attempting suicide as well as greater exposure to suicidal behaviours and traumatic events among those with drug use disorder (Darke et al., 2007; Khoury, Tang, Bradley, Cubells, & Ressler, 2010; Maloney, Degenhardt, Darke, Mattick, & Nelson, 2007; Mills, Teesson, Ross, & Peters, 2006). Unfortunately, the current study was not sufficiently powered to examine differences across substances among those with drug use disorder, but the significant independent association found here warrants further exploration. It should be noted that these results are predominately focused on suicidal plans and attempts and may not generalise to the strong links previously found between those who had died by suicide and AUD (Edwards, Ohlsson, Sundquist, Sundquist, & Kendler, 2020).
+The current study identified significant and independent associations between sex and the speed of transitioning from suicidal ideation to both suicide plans and attempts. Interestingly, the results demonstrate that males were slower to transition from ideation to plans and attempts in comparison to females. These findings align with the ‘gender paradox’ in suicide suggesting an overrepresentation of females in nonfatal suicidal behaviour relative to males (Schrijvers, Bollen, & Sabbe, 2012). Moreover, there are notable sex differences in the reporting of suicide ideation, plans and attempts, differences in psychopathology (females more likely to experience internalizing disorders and males more likely to experience externalizing disorders) and differences in responses to psychosocial life stressors and other vulnerabilities that might be driving these findings (Boyd et al., 2015). For example, cultural differences and attitudes about masculinity as well as perceived gender roles may be driving responses to questions regarding suicide in survey research (Hunt, Sweeting, Keoghan, & Platt, 2006). Additional research has also demonstrated that these gender gaps might be closing and, in line with other research on the closing gender gap in substance use (Chapman et al., 2017; Slade et al., 2016), could be attributed to recent changes in culture and gender roles at the population level, which have substantial impacts on individual outcomes such as suicidality (Milner et al., 2020). The current study did not examine rates of those who died by suicide, therefore it is unknown whether gender differences exist in the speed of transition from suicidal ideation to suicide or whether the direction is reversed.
+Finally, the current study identified significant and independent associations between sexual minority status and a more rapid transition from suicidal ideation to plans. These findings
+demonstrate the importance of issues such as social connectedness, inclusion, acceptance and the experience of discrimination in the association between suicidal ideation and suicidal behaviours (Skerrett, Kolves, & De Leo, 2017). As such, policy initiatives and social programs that aim to reduce discrimination and encourage equality and inclusion may have a significant impact of reducing suicidal plans and attempts among people who identify with a sexual minority. In addition, having a larger number of family members one can confide in was associated with a slower transition from suicidal ideation to plans. These findings speak to the importance of increasing social connectedness among those with suicidal ideation and facilitating help-seeking, communication and family support as a potential mechanism to prevent suicide (the Sources of Strength program is one key example) (Wyman et al., 2010).
+The strengths of the current study include a nationally representative sample, use of structured diagnostic interviews to generate diagnoses of mental and substance use disorders, as well as comprehensive data on age of onset to model the temporal order between suicidality and the experience of mental and substance use disorders. However, the study utilised a cross-sectional retrospective design and the survival curves were based on quasiperson year variables that could be subject to participant recall bias and it is difficult to ascertain the temporal order for disorders occurring during the same year of a person’s life. Moreover, the age of onset data could only differentiate between ideation and suicidal plans/attempts at yearly intervals and the study was unable to examine transitions that may have occurred at a more rapid rate, e.g. over weeks or months. Finally, the current study investigated a general population sample with relatively low rates of suicidal ideation, suicidal plans/attempts and additional psychopathology. The low number of people who reported suicide attempts may have restricted the power to detect independent effects of covariates in the multivariable models. Finally, whilst the sampling procedure of the survey was robust, the data are now over 10 years old and do not capture any potential changes that may have occurred at the population level in the time since. Additional replication of these results is required in more recent treatment seeking and clinical samples with higher rates of psychopathology.
+The current study demonstrated the rapid transition from first reporting suicidal ideation to reporting suicidal plans and attempts in a population sample of Australian adults aged 16-85 years with the majority of those who go on to experience suicidal plans and attempts doing so within 2 years of first experiencing ideation. The rapid speed of transitioning from suicidal ideation to plans and/or attempts further highlights the need for improved detection of suicidal ideation and the potential benefit of early intervention in reducing suicide attempts. The transition from ideation to suicidal plans is particularly rapid for females, those with AUD, MDE, OCD, those who are younger at the time of the survey and who identify as a sexual minority. Having one or more family member to confide in was associated with a slower transition from ideation to plans. The transition from ideation to suicide attempts was particularly rapid for females, those with a drug use disorder and those who were younger when first experiencing suicidal ideation. Further research is required to investigate the mechanisms driving this transition, particularly among the identified subgroups, with the aim of informing targeted prevention and early intervention programs that could reduce the sizeable burden associated with suicide plans and attempts experienced in the general population.
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+consultations for patients aged >60 years and after longer illness duration.
+Keywords Mental health • Schizophrenia • Somatic Health Care utilization
+Introduction
+An increasing body of evidence suggests that patients with non-affective psychotic disorder (NAPD), which involves schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder and psychosis not otherwise specified (Kendler et al. 1996; Kessler et al. 2005) have somatic problems in need of clinical attention. They have a higher risk of physical disorders and a mean life expectancy that is at least 15 years shorter compared to the general population (Altamura et al. 2011; Carney et al. 2006; De Hert et al. 2011a; Wahlbeck et al. 2011; Weber
+3	Division of Pharmacoepidemiology and Clinical
+Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, PO Box 80082, 3508 TB Utrecht, The Netherlands
+4	Altrecht Institute for Mental Health Care, Gedachtengang 1, 3705 WH Zeist, The Netherlands
+5	Achmea Health Insurance, PO Box 19, 3800 HA Amersfoort, The Netherlands
+6	Department of Clinical and Health Psychology, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands
+et al. 2009). Premature mortality is strongly associated with natural death causes and the increased relative risk of natural death is already present at a relatively young age (Hennekens et al. 2005; Capasso et al. 2008; Termorshuizen et al. 2013). Associated causes include higher somatic disease incidence (often cardiovascular and respiratory diseases), but a lower quality of somatic health care, underdiagnosis and undertreatment (Bradford et al. 2008; De Hert et al. 2011a; Mitchell et al. 2009; Nasrallah et al. 2006; Laursen et al. 2011). Patients with severe mental illness (SMI) also report more difficulties in accessing care than the general population (Bradford et al. 2008).
+Given the poor health condition of patients and increased risk of natural death causes (Laursen et al. 2009; Laursen et al. 2011), the guideline for schizophrenia and other psychotic disorders (NICE 2009) states that a high level of somatic health care is often needed. This guideline stresses the importance of early detection of cardio-vas-cular risk factors, also referred to as the ‘metabolic syndrome’. It recommends that regular somatic screening is done that covers family disease history and personal risks on diabetes mellitus, hypertension, cardiovascular diseases, cancer, smoking, diet, activity and other metabolic risk factors. Indicated treatment should start as early as possible. It also recommends that general practitioners (GPs) and mental healthcare professionals work closely together to meet the somatic needs of patients with psychosis, who often do not communicate their somatic complaints adequately (De Hert et al. 2011b, c).
+Studies have found divergent results with regard to somatic Health Care use (HCu) by individuals with NAPD and other forms of SMI. They typically focused on specific types of HCu. For instance, Carr et al. (2003) found higher patterns of overall psychiatric and non-psychiatric health service use for patients with psychotic disorders in Australia compared to patients with nonpsychotic disorders. Dickerson et al. (2003) reported mixed findings on HCu, such as higher medical care use and lower dental care use, among outpatients with SMI in Baltimore, Maryland (USA) compared to matched subsets of individuals from the general population. Oud et al. (2010) found that patients with NAPD had more frequent GP consultations and home visits in general primary care in The Netherlands, compared to patients with other mental disorders and patients without mental disorders. Studies have also reported similar or lower rates of specialist HCu for patients with psychoses or SMI (Laursen et al. 2009). Other research suggests underutilization in HCu, especially in preventive care and primary care, compared to controls with and without another psychiatric diagnosis (Druss et al. 2002; Hippisley-Cox et al. 2007; Folsom et al. 2002; Roberts et al. 2007) and in specialist services (Salsberry et al. 2005; Young and Foster 2000; Nasrallah et al. 2006).
+It should be noted that higher or similar levels of HCu do not preclude serious undertreatment. Domino et al. (2014) found that the quality of care metrics was generally lower among patients with depression or schizophrenia than for other patients in an adult Medicaid population with two or more out of eight chronic conditions (asthma, chronic obstructive pulmonary disease, diabetes, hypertension, hyperlipidemia, seizure disorder, depression, or schizophrenia). In addition, high utilization of emergency rooms for patients with SMI and a higher risk of hospitalization due to ambulatory care-sensitive medical conditions may point to poor access or inefficient use of the primary health care system (Salsberry et al. 2005; Muck-Jorgensen et al. 2000; Li et al. 2008). In fact some studies point to higher levels of undertreatment especially for the most vulnerable subsets of patients such as older or homeless persons with SMI (Young and Foster 2000; Folsom et al. 2002; McCarthy and Blow 2004). Parity in the access to care seems to be hindered by a mixture of patient, provider treatment and system factors. For instance, patients may choose not to seek help for physical problems due to symptoms of SMI, there is a focus on mental rather than physical health problems in mental health care, people with mental disorders are sometimes stigmatized by physicians, and the funding of somatic care throws up financial barriers (De Hert et al. 2011b).
+Few large studies so far have compared patients with NAPD to healthy controls and patients with other psychiatric disorders across the full spectrum of HCu (i.e., general and specialist somatic care). Yet, they are important in monitoring possible undertreatment and finding its correlates. Accordingly, the aim of the current study was to examine whether patients with NAPD have a lower somatic HCu level compared to controls without NAPD. We performed a retrospective observational study using linked registry and health plan data to compare HCu by patients with NAPD to HCu by non-psychiatric matched controls. For a sound interpretation, we also included patients with an anxiety, depressive or bipolar disorder as psychiatric references. Patients with these other psychiatric disorders are also at increased risk of premature natural death (Harris and Barraclough 1998; Laan et al. 2011), but may be less socially marginalized and may show less cognitive impairment, which relates to lower HCu (Carr et al. 2003). Consequently, the HCu of these other patient groups (also compared to controls) may more clearly point out the somatic care needs of patients with NAPD. By comparing NAPD patients to controls from the general population as well as other patient groups, we investigated whether undertreatment for somatic problems of psychosis patients was present and what factors determined this undertreatment.
+Methods
+Databases
+Since 1999, the Psychiatric Case Registry for the Central Netherlands (PCR-MN) has received anonymous information on patient characteristics and mental health care (MHC) of patients who use psychiatric services in the city of Utrecht and surrounding municipalities. In 2010, the area had over 760,000 inhabitants and formed a representative sample of 5 % of the Dutch population (Smeets et al. 2011). Under the Dutch system, health insurance is compulsory for all residents. Data from PCR-MN were anonymously linked to the Achmea Health Database (AHD), which records payments for medical care made by the largest insurance company in the region. This database includes payments for drug prescriptions delivered by community pharmacists, payments for GP consultations and for Diagnostic and Treatment Protocols (DTPs, in Dutch: ‘Diagnose Behandel Combinatie’) in all treatment settings (e.g. hospital, emergency department, outpatient clinic). A DTP is the insurance claim containing codes for diagnosis and treatment by medical specialists. Each DTP contains four codes: for a certain type of care, care demand, diagnosis, and treatment followed. We used this information to estimate HCu for physical disorders. Dutch privacy law allows use of these data for scientific research under strict conditions in relation to anonymity and storage, in which case informed consent is not needed. The research was approved by the institutional review board.
+Patients and Data Extraction
+Data were extracted for all 4770 adult patients (18 years) with NAPD with least one care contact between January 2007 and December 2009 at Altrecht MHC, which is the main mental health service in the Utrecht region. NAPD was defined conform DSM-IV subgrouping (codes 295.10, 295.20, 295.30, 295.40, 295.60, 295.90, 295.70, 297.1, 297.30, 293.81, 293.82, 298.80, 298.90). Half of these patients (2392) were insured with Achmea. This insurance company serves for a relatively large number of insured patients with lower social economic status, similar to Medicaid in the USA. We extracted data from the AHD on the HCu in the year before the last care contact at mental health care service Altrecht in the period 2007-2009 (‘index date’). Controls without NAPD were randomly selected from the AHD and were personally matched: for each individual patient six unique control persons with similar
+birth year, gender, and ethnic group (Western vs. Non Western) but without NAPD were selected from the AHD (Table 1). We extracted the data on HCu by the controls for the same year prior to the last care contact of the matched patient with a psychiatric diagnosis. We followed the same procedure for other groups with a psychiatric diagnoses: bipolar disorder (N = 700), unipolar depression (N = 5603), anxiety disorder (N = 1707). In the event of psychiatric comorbidity, patients were categorized according to their most severe disorder, which was NAPD, bipolar depression, major depression and anxiety disorder, respectively.
+Analysis
+Patients with a Psychiatric Diagnosis Versus Controls
+HCu by patients in the four mental health categories and by their matched controls was estimated in terms of percentages of patients with > 1 somatic medication prescription, >1 GP consultation, >1 somatic specialist consultation, and at least one of these sources of somatic HCu. Using a logistic regression model, we calculated the average marginal effect (AME; Williams 2012) and 95 %-confidence interval (95 % CI) to test the difference in percentages of somatic HCu between the groups of patients with a psychiatric diagnosis and the controls without a psychiatric diagnosis. We also differentiated the HCu in somatic disease category as diagnosed by the specialist (and registered in the DTPs): lung diseases, cardio-vascular diseases, cancer, diabetes or other physical disorders. Presence of a specific prescribed drug (e.g., antiarrhythmics, blood glucose lowering drug) and/or a specific diagnosis in a registered DTP (e.g. ischemic heart disease, diabetic retinopathy) was regarded as presence of somatic care for these physical disorders. Also the average costs reimbursed for the utilization of somatic health services were estimated and the difference (Delta) between patients and controls was tested using a t test. Appendix 1 lists somatic diagnoses and information on the Anatomical Therapeutic Chemical classification system used to specify somatic disease categories of HCu.
+Patients with NAPD versus Controls: Determinants of the Difference in HCu
+We analyzed the effects of age and duration since earliest registered psychiatric diagnosis and other covariates (gender, ethnic minority status, psychiatric comorbidity) on the difference in somatic HCu between patients with NAPD and their matched controls in a multiple logistic regression model. A significant {covariate x ‘patient vs. control’} interaction indicates that the differences in HCu between patients with NAPD and controls differs across levels of the covariate. To facilitate comparison with other studies on somatic HCu the calculations were also performed for a smaller subset of patients with schizophrenia, schizophreniform and schizoaffective disorder. For each somatic HCu outcome parameter (medication, GP, DTP, and ‘any HCu’) separately, the covariates and terms for interaction were included in one separate multiple logistic model. Finally, we analyzed the difference in somatic HCu between patients with NAPD and controls specifically for cardiovascular diseases and for diabetes.
+Data management, record linkage and crude estimations were performed in SPSS 20.0 and regression modeling in STATA 11.0.
+Results
+Study Population
+For patients with NAPD in our regional psychiatric case register, 50 % were insured with Achmea during the year prior to index date in 2006-2009. The Achmea insurance rate was lower for the other psychiatric diagnostic groups: 36.1 % for bipolar disorder, 37.0 % for major depression and 31.7 % for anxiety disorders. On average, included patients were older than excluded patients without Achmea database linkage (e.g., for NAPD: 47.8 vs. 43.7 years, p < 0.001). They also showed a longer period in care since the first registration of their most severe mental health diagnosis (i.e. the diagnosis used for categorization in this study) in the PCR-MN (e.g., for NAPD: 5.2 vs. 3.6 years, p < 0.001).
+Table 1 shows characteristics of the study population. More than half of the patients with NAPD were diagnosed with schizophrenia (59.0 %). Schizophreniform, schizoaffective or delusional disorder were also common diagnoses (14.6 %) and 26.4 % suffered from another NAPD, which was often ‘‘psychotic disorder not otherwise specified’’.
+Patients with a Psychiatric Diagnosis versus Controls: HCu (Univariable)
+No significant differences were found between patients with NAPD and their matched controls in the percentages of patients with at least one somatic medication prescription or with at least one GP contact during the year prior to the index date (somatic medication: 72.2 vs. 73.3 %; GP: 77.2 vs. 76.9 %, Table 2). Among patients with bipolar disorder, unipolar depression or anxiety disorder we did find significantly higher percentages of HCu compared to their matched controls. The percentage of patients with NAPD with specialist somatic treatment was even lower compared to their controls (44.3 vs. 47.2 %). Percentages of patients with bipolar, unipolar or anxiety disorders with specialist somatic treatment were all higher compared to their controls. The percentage of patients with NAPD with any somatic HCu (medication and/or GP consultation and/ or specialist care) was slightly higher compared to controls [87.3 vs. 85.1 %; AME 2.25 (0.80 to 3.71) p = 0.002]. For patients with bipolar disorder, unipolar depression or anxiety disorder, these percentages were markedly higher than for controls, which resulted in large, statistically significant marginal effects (Table 2).
+Slightly more pronounced results were found when the analysis for NAPD was restricted to schizophrenia, schizophreniform and schizoaffective disorder (no table included): e.g., the differences for GP contacts and specialist care became larger compared to controls [71.96 vs. 75.47 %; AME 3.52 (-5.85 to -1.18) p < 0.01] and [39.76 vs. 45.39 %; AME -5.61 (-8.19 to -3.05) p < 0.001], respectively.
+Patients with a Psychiatric Diagnosis Versus Controls: Associated Costs of HCu (Univariable)
+Figure 1 presents reimbursements for somatic HCu in number of euros per year. Obviously, the average difference in costs for all insurance claims of patients with NAPD compared to their matched controls (Difference, Delta D = €180.30) was lowerthan the average differences forthe other groups with a psychiatric diagnosis compared to their matched controls. Results were similar when we restricted the analysis for NAPD to schizophrenia, schizophreniform and schizoaffective disorder [D = €-94.40 (-327.0 to 137.1)].
+For the patients with bipolar disorder, this average difference in costs compared to their matched controls was higher than the difference for the patients with NAPD [D = €373.30 (-30.8 to 777.5) vs. D = €180.30 (-20.9 to 381.6)], but lower than the difference in costs among the patients with depression [D = €910.00 (778.2 to 1041.8)] and anxiety [D = €646.50 (430.6 to 862.4)]. Compared to
+controls, patients with NAPD did not have significantly higher reimbursements for claims for somatic medication prescriptions [D = €53.40 (-23.6 to 130.5)] or specialist treatment [(D = €90.80 (-77.3 to 259.1)].
+Patients with a Psychiatric Diagnosis Versus Controls: HCu Associated with Treatment by Medical Specialists, Classified into Somatic Diagnosis Categories (Univariable)
+Table 3 shows the HCu delivered by medical specialists, as shown in Table 2, but now broken down into somatic diagnostic categories. The relatively low HCu of specialist care by patients with NAPD is mainly explained by lower HCu compared to controls in the following categories: cardiovascular diseases [7.98 vs. 8.51 %; AME = -0.52 (-1.7 to 0.65)], oncology [4.77 vs. 5.66 %; AME = -0.89 (-1.82 to 0.04)] and diabetes [2.88 vs. 3.23 %; AME = -0.35 (-1.08 to 0.38)]. For respiratory disorders, a significantly higher percentage of NAPD patients with a HCu compared to controls was found [5.06 vs. 3.45 %, AME = 1.61 (0.68 to 2.53) p < 0.01]. Other diagnosis groups had significantly higher percentages HCu for respiratory disorders compared to controls (AMEs ranged from 2.96 to 3.80), but also for other categories of somatic disorders. Particularly patients with unipolar depression or an anxiety disorder had a higher HCu for cardiovascular diseases compared to their controls [AME = 4.92 (3.99 to 5.85) and 5.95 (4.30 to 7.59) respectively]. We found
+similar results when we restricted the analysis for NAPD to schizophrenia, schizophreniform and schizoaffective disorder (data available on request).
+Patients with NAPD Versus Controls: Determinants of the Difference in HCu (Multiple)
+The difference in somatic HCu between patients with NAPD and their matched controls differed significantly by strata defined on the basis of age and duration since earliest
+registration of NAPD in the PCR-MN (3 age categories x 3 duration categories give a factor with nine levels) (Tables 4, 5). Estimates were adjusted for gender, ethnic origin, presence of comorbid depressive and/or personality disorder and/or comorbid substance abuse/de-pendence. The effect of the interaction with df = 8 for each indicator of HCu refers to the differences between the nine different average marginal effects per stratum, simultaneously tested as one cluster of terms for the 1st order interaction between the above mentioned factor and
+‘patient versus control’ in one analysis step. The highly significant test for interaction (p < 0.001) does not point to undertreatment in one specific subgroup of patients compared to their matched controls, in the first place, but does show clear differences between categories defined on the basis of age and illness duration. The effect for the indicator ‘any somatic HCu’ presented in Table 5 was a 3.64 percentage point difference (-3.07 to 10.34) for those under 40 and for shorter durations (<2 years), and was substantially and significantly lower (i.e. larger in the negative direction) for older patients > 60 years) with a longer mental health care duration (>5 years): a -10.99 percentage point difference (-17.02 to -4.97) p < 0.001.
+Similar trends by age and the duration in mental health care were found and significant tests for interaction were obtained for the other indicators of HCu: ‘somatic medication’, ‘GP consultations’ and ‘medical specialist treatment’ separately. Again similar trends were found when we restricted these analyses to schizophrenia, schizophreniform and schizoaffective disorder (data available on request).
+Male gender, presence of comorbid depressive disorder, and comorbid alcohol abuse/dependence were associated with a higher somatic HCu among patients with NAPD versus their controls—percentages, AMEs and p values for interaction are respectively: 85.21 versus 80.57 %;
+AME = 5.14 (2.56 to 7.73) p < 0.001; 92.99 versus 85.49 %; AME = 6.80 (4.14 to 9.47) p = < 0.001; 92.10 versus 85.57 %; AME = 8.60 (4.65 to 12.54) p < 0.001. The percentage of patients with prescribed somatic medication tended to be lower for patients with NAPD versus matched controls (66.86 vs. 72.42 %) when there was a comorbid diagnosis of drug abuse/dependence but this difference tested as AME was non-significant.
+Patients with NAPD vs. Controls: Determinants of Difference in HCu for Cardiovascular Disorders and Diabetes (Multiple)
+The somatic HCu related to cardiovascular disorders (i.e. having received cardiovascular medication and/or specialist treatment with cardiovascular diagnosis) among patients with NAPD compared to their matched controls was lower for older patients (>60 years) with longer durations of their disorder (>5 years) (AME -23.95 [-31.17 to -16.73], p < 0.001). Higher AMEs were found for those patients under 40 with shorter durations (<2 years) compared to controls [AME 3.42 (-0.76 to 7.60)]; p value for 1st order interaction of age and duration <0.001).
+Effects of age and duration on the differences in percentages of somatic HCu related to diabetes showed a similar pattern (p value for 1st order interaction = 0.0020).
+Compared to their age-matched controls, patients over the age of 60 with a longer duration of their disorder received less anti-diabetic medication and/or less specialist treatment for diabetes [AME = -2.76 (-7.98 to 2.45)] whereas higher AMEs were found for younger patients (see Appendix 2).
+Discussion
+In this study, we found a slightly higher somatic HCu and associated costs among patients with NAPD compared to their matched controls. However, this difference in HCu was lower than the difference found for other groups with a psychiatric diagnosis, especially a depressive or an anxiety disorder. In addition, the somatic HCu of patients with NAPD was lower compared to their matched controls when the analysis was restricted to treatments delivered by a medical specialist. There was a clear and significant trend toward a negative difference in HCu with increasing age and longer duration since registered diagnosis.
+Younger patients with NAPD who spend less time in mental health care had a higher percentage of contacts with the GP compared to controls, but not for specialist care. The comparatively low HCu among patients with NAPD at higher ages was especially noteworthy for cardiovascular
+diseases and diabetes. Our findings contrast with the poorer health conditions and substantially higher risk of cardiovascular and other early natural death causes of patients with NAPD, as reported earlier. This study therefore strongly suggests underutilization of somatic HC by patients with NAPD.
+Comparison with Other Studies
+Our results are consistent with studies in various countries showing that patients with SMI or a psychotic disorder make less overall use of somatic health care than other patients with a psychiatric diagnosis and have a lower HCu of specialist services than patients in the general population. Our findings contrast with the studies from Dickerson et al. (2003) and Carr et al. (2003) who found higher HCu for patients with schizophrenia than for the general population. A possible explanation is that these studies concerned a selection of patients currently receiving outpatient care that may promote attention to physical health.
+In our study especially older patients with a longer duration in mental health care had a lower HCu which is also consistent with other research. Notably the research by Folsom et al. (2002) indicates that middle-aged and older homeless people with schizophrenia in California receive less primary and preventive health care and are treated for
+fewer chronic medical problems than a comparison group with major depression. In the Dutch study by Oud et al. (2010) patients with psychosis had more frequent GP consultations compared to other primary care patients, but similar or lower rates of GP contacts were found, especially among the elderly patients with psychosis, for diabetes, cardiovascular and respiratory diseases. This study was restricted to those with at least one GP consultation during the study period, which may imply that psychosis patients with comparatively high levels of HCu were slightly overrepresented. A higher level of undertreatment at higher ages has also been observed for cardiovascular procedures after myocardial infarction in patients with SMI, especially those with schizophrenia (Young and Foster 2000). Among 142,055 Veterans Affairs patients with SMI, McCarthy and Blow (2004) found that patients over 65 were substantially more negatively affected by distance barriers to use nonpsychiatric somatic care. Furthermore, in the study of Laursen et al. (2011) in Denmark, slightly higher rates of heart disease admissions were found among patients with schizophrenia and bipolar disorder, alongside an excess of cardiac mortality. Consistent with our results, they found an interaction between lower admission rates and higher ages. However our findings contrast with the study from Cradock-O’Leary et al. (2002) using data from the Department of Veterans Affairs. They found fewer medical visits and the lowest rates of diagnosed pulmonary disease, diabetes and hypertension among patients with a diagnosis of schizophrenia or bipolar disorder compared with other VA patients. In contrast to our findings, however, a slightly higher number of medical care visits were found at higher ages among those with schizophrenia while younger patients with schizophrenia had an especially high risk of not receiving general medical services. This need for somatic HCu among the partly institutionalized elderly with SMI may come to light more in a comprehensive system of both medical and mental health care such as the Department of Veterans Affairs.
+Strengths and Limitations
+Our study has several strengths. We reported HCu for a large group of patients with NAPD, other groups with a psychiatric diagnosis and their matched controls. We examined several aspects of HCu (somatic medication prescriptions, GP and somatic specialist consultations) broken down into specific somatic diagnoses and associated costs. Objective data from insurance claims were used, thus precluding the risk of recall bias. In addition, important covariates—age, gender, psychiatric diagnosis, ethnicity and alcohol and drug dependency—were taken into account. Therefore, this study can contribute to tracking patient groups at high risk of undertreatment.
+A number of limitations of the study needs to be addressed. First, the analyses were based on insurance payments for GP consultations and somatic health care treatments. No information was available on the actual presence or severity of somatic disorders, whether diagnosed and treated or not. Second, the data specified the distribution of crude categories of HCu (i.e. medication, GP or specialist care). Administrative database studies like this study can signal general health problems that should be followed by further research. Detailed information concerning duration of admissions and outpatient care, use of specific cardiovascular procedures and/or other preventive measures and degree of integration of the care system could provide more insight into the nature of the somatic underconsumption by patients with NAPD and ways to improve this.
+A third limitation is the cross-sectional study design, which was restricted to patients in an arbitrary year prior to the date of the last mental health care contact in 2007-2009. Selection of those who do relatively well and have survived the high mortality risks during the early phases of their treatment (‘healthy survivor’ effect) may lead to an underestimation of HCu (Oud et al. 2010; Laursen et al. 2009). In addition, people who receive mental health care for a longer period may have developed better self-management skills for somatic problems that tie in closely with their experience in managing their psychiatric disorder (Dixon et al. 2004). However these considerations do not fully explain why HCu by patients with NAPD is lower than that of other psychiatric patient groups, who probably experienced similar survival benefits and developed similar self-management skills.
+A final limitation is that the aim of the study was to examine whether patients with NAPD have lower levels of somatic HCu compared to psychiatric and healthy controls, but no explanation can be given for the outcomes based on the present results. The in comparison with controls higher percentages with at least one contact with the GP in younger patients with a shorter illness duration since the first registration of their diagnosis in the PCR-MN may point to the important function of the GP in the early diagnostic phases of a psychotic disorder. The observed trend towards lower percentages of contacts with the GP and somatic specialists (compared to age-matched controls) among the elderly NAPD patients with longer illness duration may be partly a result of the aforementioned selection of relatively fit people at higher ages (‘healthy survivor’ effect). However an additional explanation could be that finding your way to the somatic health care system is extra difficult for the most vulnerable patients (Hert, Cohen, et al. 2011b; Thornicroft et al. 2007). Doctors prescribing antipsychotics do not always accurately monitor somatic complications (Okkels et al. 2013), they may find this too stressful for patients with psychosis (Cahn et al. 2008). Reluctance to address patients and distance
+barriers as found by McCarthy and Blow (2004) could promote somatic undertreatment particularly in older patients. Differences in the distribution of NAPD sub-diagnoses among the age categories is a less likely explanation, as similar results were found when the analysis for Tables 4 and 5 was restricted to schizophrenia, schizophreniform and schizoaffective disorder.
+Conclusions
+Our data indicate undertreatment for physical disorders among patients with NAPD particularly for older patients with NAPD. This implies that the national context with compulsory insurance for all inhabitants in the Netherlands does not lead to parity in HCu. The lower HCu and lower costs incurred for medical specialist care among persons with NAPD are reasons for concern. Our data suggest that we have not yet achieved the goals of lowering the cardiovascular risk factors as formulated in the international guidelines on psychosis and schizophrenia. Improving awareness of the risks of undertreatment among health professionals and the public is an important starting point in bridging the health care gap between patients with SMI and the general population (Thornicroft et al. 2007; Ahire et al. 2013). We suggest a more assertive approach in the way somatic health care is delivered to these patients. By enhancing early detection of risk factors for diseases such as hypertension, weight gain and elevated cholesterol, it may be possible to decrease the rates of early deaths in patients with NAPD. Integration of medical and psychiatric care and preventive interventions may also result in improved quality of medical care (Nasrallah et al. 2006; Druss et al. 2001; Cahn et al. 2008; De Hert et al. 2011b, c). Further studies on somatic health care to patients with NAPD - from the appearance of initial symptoms to helpseeking, assessment, start of treatment and follow-up—are needed to find entry points for improving the match between needs and delivery of treatment.
+Acknowledgments We thank Achmea Health Insurance for kindly providing the data necessary for the present analysis. Furthermore, we thank the participating psychiatric hospitals and services for providing all data for the database of the Psychiatric Case Register. The authors thank Janneke Giele for data management and support in preparation of the analysis files and Ellen Mecking for preliminary analysis.
+Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://crea tivecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
+Appendix 1
+Information on the diagnosis by medical specialists and prescribed medications that were decisive for inclusion of patients in the categories respiratory disorders, cardiovascular disorders, oncology, diabetes or other physical disorders.
+Respiratory disorders
+Included are all respiratory disorders diagnosed by a medical lung specialist [airway diseases such as asthma, emphysema, bronchiectasis and chronic bronchitis, lung tissue diseases such as pulmonary fibrosis and sarcoidosis, unspecified problems (like chest pain, dyspnea, cough) and other diagnoses (such as hyperventilation syndrome, nicotine addiction)] and medications prescribed by specialists or GPs for respiratory disorders (WHO site http://www. whocc.no/, selection of all ATC codes starting with ‘R’).
+Cardiovascular disorders
+Included are all cardiovascular diseases diagnosed by a cardiologist (coronary artery disease, heart attack, abnormal heart rhythms or arrhythmias, heart failure, heart valve disease, congenital heart disease, vascular diseases) and medications prescribed by specialists or GPs for cardiovascular disorders (WHO site http://www.whocc.no/, selection of all ATC codes starting with ‘C’).
+Diabetes
+Included are the diagnosis of diabetes mellitus (I and II) diagnosed by a medical specialist and medications prescribed by specialists or GPs for diabetes (WHO site http:// www.whocc.no/, selection of all ATC codes starting with ‘A10’).
+Oncology
+Included are all oncological diagnoses by a medical specialist; breast cancer, skin cancer, lung cancer, colon cancer, prostate cancer, lymphoma and all other (>100 types of) cancers. No extra medication search in the health database was needed since patients never receive cancer medication without a registered diagnosis by a medical oncologist or receive this medication prescribed by their GP.
+Other
+All other somatic diagnoses by a medical specialist not included above.
\ No newline at end of file
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+1.	Introduction
+Non-suicidal self-injury (NSSI; deliberate destruction of body tissue without suicidal intent that is not socially sanctioned) represents a significant public health problem among adolescents (Muehlenkamp et al., 2012). In particular, vulnerable youth whose gender identity does not match their birth-assigned sex (transgender) and/or who are non-conforming to the gender they were assigned at birth (gender non-conforming) (TGNC) demonstrate greater risk of engaging in NSSI (Connolly et al., 2016). Findings from two populationbased surveys showed that between 45.5% and 54.8% of TGNC adolescents engaged in NSSI during the previous 12 months (Clark et al., 2014; Eisenberg et al., 2017). NSSI might increase risk of suicide (Joiner et al., 2012), especially among youth who engage in repetitive self-injury, defined as 10 or more acts over 12 months (Muehlenkamp and Brausch, 2016). Depression and bullying victimization represent important risk factors associated with NSSI (Borowsky et al., 2013), and are common among TGNC youth (Eisenberg et al., 2017).
+In contrast to a risk-focused perspective, resilience research focuses on positive features of development such as connections to parents, non-parental adults, friends, and school (Masten, 2009). Although researchers examined protective effects of different social connections for youth in general, they have lacked the ability to determine which connections provide vulnerable TGNC youth with the greatest
+protection against NSSI, including reducing the likelihood of engaging in repetitive self-injury. To address gaps in the literature, we sought to answer the research question: Which connectedness factors are related to and moderate risk for a) any NSSI and b) repetitive NSSI for those who self-injure, among TGNC youth? Based on previous research with LGBQ youth (Taliaferro and Muehlenkamp, 2017) and the Minority Stress Theory, which suggests connectedness to important individuals and/or institutions may buffer the impact of stressors experienced from identification with a sexual minority group (Meyer, 2003), we expected connections to parents and non-parental adults and school safety to emerge as important protective factors.
+2.	Methods
+2.1.	Design and sample
+Data came from the Minnesota Student Survey, an anonymous population-based survey conducted every three years with students in grades 5, 8, 9, and 11 (Minnesota Center for Health Statistics, 2017). In 2016, 85% of school districts participated statewide. Questions about gender identity were only included on the high school survey, which restricted the current analysis to students in grades 9 and 11 (N = 81,885). Student assent and passive parental consent were used. The University of Central Florida's IRB approved this secondary data analysis.
+The analytic sample included 2,168 TGNC students, which represented 2.7% of the total sample. Among the sample of TGNC students, 31.5% were biologically male and 67.2% were biologically female, while 1.2% declined to answer. Fifty-nine percent were in grade 9, and 58.0% identified as non-Hispanic White. Approximately 39.0% received free/reduced-price lunch at school, and 54.8% attended school in the Minneapolis/Saint Paul metropolitan area.
+2.2.	Measures
+Gender identity was determined by the question: “Do you consider yourself transgender, genderqueer, genderfluid, or unsure about your gender identity?” (yes/no). NSSI was assessed using the item: “During the past 12 months, how many times did you do something to purposely hurt or injure yourself without wanting to die, such as cutting, burning, or bruising yourself on purpose?” Students who responded one or more times were categorized as engaging in any NSSI and compared with those who responded never. Students who responded 10 or more times were categorized as engaging in repetitive NSSI and compared with those who responded 1 to 9 times, as recommended (Muehlenkamp and Brausch, 2016).
+We assessed five connectedness factors frequently used in previous research (Eisenberg et al., 2017; Gower et al., 2018; Taliaferro and Muehlenkamp, 2017). Three items were used to create a composite parent connectedness variable: how often students could talk with their mother and their father about problems they were having, and how much they believed their parents cared about them (a = 0.65). Tea-cher/school adult relationships were assessed with six items from a validated instrument (e.g., how much adults at their school treat students fairly, listen to students, care about students; a = 0.86) (Appleton et al., 2006). Perceptions of caring from friends was assessed with an item asking about how much students believed friends cared about them. Connectedness to other non-parental adults was assessed using two items asking how much students believed “other adult relatives” and “adults in your community” cared about them (a = 0.70). Finally, to assess school safety, students indicated how much they agreed/disagreed with the statement: “I feel safe at school.”
+Regarding risk factors, depressive symptoms were assessed with the PHQ-2 (e.g., “Over the past two weeks, how often have you been bothered by feeling down, depressed or hopeless?”). Students screened positive for depression with a suggested cut-off score of 3 or higher (Kroenke et al., 2003). Gender-based bullying victimization was assessed using two items that inquired about being harassed or bullied because of one's gender and/or gender expression. Finally, one item assessed physical bullying victimization with regard to being pushed, shoved, slapped, hit, or kicked by other students at school. We categorized students as victims of bullying if they responded once or more during the last 30 days to any of these questions. Demographic variables included biological sex (female vs. male), grade (9th vs. 11th), race/ethnicity (white vs. nonwhite), free/reduced-price lunch (a proxy measure of SES), and school location (Minneapolis/Saint Paul metropolitan area vs. elsewhere).
+2.3.	Statistical analyses
+For the primary analyses, we conducted logistic regression analyses using SPSS v.23. First, we examined relationships between each connectedness factor (separately) and each outcome (any NSSI and repetitive NSSI), controlling for demographic variables and risk factors. Second, connectedness factors that were significantly associated with either outcome (p < 0.05) were entered together into a model, with the demographic and risk factor variables. This two-step process allowed us to determine the connectedness factors most strongly associated with each outcome. Finally, we tested for interactions between all risk and connectedness factors, one at a time, in separate models, adjusting for all variables in the final models, to determine potential buffering effects
+of connectedness factors on relationships between identified risk factors and NSSI outcomes.
+3.	Results
+Overall, 54.8% (n = 1,076) of TGNC students reported any NSSI, and among those who engaged in self-injury, 40.3% (n = 434) reported repetitive NSSI during the previous year. Compared with biological males (38.1%) and 11th graders (50.9%), biological females (62.5%) and 9th graders (57.6%), respectively, were significantly more likely to report any NSSI (both p < 0.05). We did not find any other significant differences based on demographic characteristics.
+Fifty-eight percent of TGNC students screened positive for depression, 51.4% experienced gender-based bullying victimization, and 10.0% were physically bullied. This sample reported moderate levels of parent connectedness (M = 3.5, SD = 0.99; range: 1-5), teacher relationships (M = 2.8, SD = 0.62; range: 1-4), connections to other non-parental adults (M = 2.9, SD = 1.11; range: 1-5), school safety (M = 3.0, SD = 0.81; range: 1-4), and friend caring (M = 2.8, SD = 1.20; range: 1-4).
+Findings from Step 1 preliminary regression analyses are available upon request. Table 1 presents findings from the final logistic regression models. Among TGNC students, any past year NSSI was significantly associated with depression and gender-based or physical bullying victimization. Greater connections to parents and non-parental adults emerged as significant protective factors. Further, we found a significant interaction (OR = 1.28, p = 0.019) between connectedness to non-parental adults (simple effect OR = 0.70) and gender-based
+bullying victimization (simple effect OR = 0.93). Probing this interaction allowed us to see that among those who were bullied because of their gender/gender expression, youth who reported stronger connections to non-parental adults were significantly less likely to report NSSI.
+Among self-injurers, depression emerged as the most important risk factor associated with repetitive NSSI, and parent connectedness and school safety represented the most important factors to protect against repetitive self-injury. No significant interactions emerged in analyses used to examine repetitive NSSI among those who self-injured.
+4.	Discussion
+Over half of TGNC students reported NSSI during the previous year, a drastically higher rate than found in general populations of adolescents (i.e., 18%) (Muehlenkamp et al., 2012). Further, among TGNC youth who self-injured, 40% engaged in repetitive NSSI, indicating many adolescents may be at risk for an NSSI disorder (Muehlenkamp and Brausch, 2016). On a positive note, findings regarding the protective effects of connectedness factors for TGNC youth are consistent with the Minority Stress Theory and support previous resilience research showing that parent connectedness, connections to non-parental adults, and school safety represent important protective factors that might reduce risk of NSSI among vulnerable populations of LGBTQ youth (Taliaferro and Muehlenkamp, 2017). In particular, stronger connections to non-parental adults moderated the risk of NSSI conferred by gender-based bullying victimization.
+Study strengths were the large population-based sample of TGNC students and assessment of multiple measures of connectedness. Weaknesses included the cross-sectional design, item assessing biological sex rather than birth-assigned sex, variables measured using a single or few items, which result in lower reliability than multi-item scales, measures with different time references, and sample from only one state. Future research should use longitudinal designs, as well as assess NSSI and TGNC identity within national surveys.
+Prevention programming and clinical interventions to prevent NSSI, as well as the transition from infrequent to repetitive NSSI, should focus on fostering positive connections with parents, relatives, and community members. In particular, naturally occurring relationships with prosocial non-parental adults may represent especially important sources of support and mentoring for LGBTQ youth (American Psychological Association, 2012; Torres et al., 2012). Finally, policies and prevention programming should ensure schools remain safe places by addressing stereotypes and transphobic perceptions in bullying prevention programs, as well as promoting sociocultural norms of acceptance, tolerance, and positive identity development.
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+1.	Introduction
+The COVID-19 pandemic has had profound effects on health services. The UK implemented interpersonal distancing on 2020-03-16 (Prime Minister’s Office, 2020a). Its National Health Service (NHS) took steps to release inpatient and critical care capacity from 2020-03-17, including a shift towards remote (videoconferencing and telephone) patient consultations (NHS England and NHS Improvement, 2020). The
+UK moved to full “lockdown” on 2020-03-23 (Prime Minister’s Office, 2020b), legally enforceable thereafter (Hancock, 2020). Partial “unlocking” began in England on 2020-05-10 (Prime Minister’s Office, 2020c), and has continued.
+The impact of the pandemic on population mental health (MH) is of major interest. The SARS-CoV-2 coronavirus may have direct neurotropic effects in humans: in addition to anosmia/ageusia, COVID-19 has been associated with meningoencephalitis (von Weyhern et al., 2020;
+Varatharaj et al., 2020) and other neuropsychiatric complications, in which endothelial involvement and bradykinin dysregulation may be important (Varatharaj et al., 2020; Zubair et al., 2020; Romoli et al., 2020; Rogers et al., 2020; Garvin et al., 2020; Roche and Roche, 2020). A broader problem is that the societal response to COVID-19 has had effects with potential for major psychological and psychiatric consequences, including distress in COVID-19 sufferers and their loved ones (including those bereaved); confinement and dramatic reductions in the opportunities for social interaction leading to loneliness; worry about illness and death, which may have adverse psychiatric consequences (Vindegaard and Eriksen Benros, 2020); and changes in healthcare services that may have led to a reduction in support for those with pre-existing mental disorders. Some effects might have occurred in the opposite direction, such as beneficial effect on MH from a society uniting against a “common enemy” (Pridmore et al., 2018). However, to our knowledge, no previous observational study has reported the impact of the pandemic on clinical mental health services (PubMed search for “covid* AND ("mental health" OR psychiatr*) AND "observational study"[pt] ”, July 7, 2020). Likewise, no previous study has provided direct evidence of the impact of COVID-19 on mortality for those with severe mental illness (SMI; PubMed search for “covid* AND mortality AND ("serious mental illness" OR "severe mental illness" OR schizophrenia OR schizoaffective OR schizotypal OR delusional OR bipolar OR depression) ”, July 7, 2020).
+Our objectives were to measure changes in MH and community physical health (PH) service activity and mortality associated with the pandemic and lockdown. We examined population-level changes using data from the single public provider of specialist MH and community PH services to Cambridgeshire and Peterborough (C&P), UK. We examined presentations and referrals to services. We looked for evidence of redirection (e.g. was a reduction in face-to-face attendance associated with increased use of telephone crisis services?) and changes in the type of presentation to MH services (e.g. in anxiety, self-harm, or psychosis). We examined service activity, looking for changes in the nature of service provision (e.g. face-to-face versus remote consultation). Finally, we examined mortality, to establish if there was an excess of deaths in these patient groups, and whether those with severe mental illness (SMI) were at higher risk of dying during the pandemic.
+2.	METHODS
+2.1.	Service overview
+Cambridgeshire & Peterborough NHS Foundation Trust (CPFT) provides community PH services, psychological therapy services, and all secondary care MH services (including some embedded within primary care) to C&P, which has a population of ~0-86 million (Office for National Statistics, 2020a). It provides MH inpatient facilities in the cities of Cambridge and Peterborough, PH inpatient rehabilitation facilities in Cambridge, Peterborough, Ely, and Wisbech, and Minor Injury Units (MIUs) in Ely, Wisbech, and Doddington. See Supplementary Methods for more details of population demographics, geography, and services.
+2.2.	Data sources
+De-identified data was extracted from CPFT clinical records by CPFT’s Information & Performance team and via the CPFT Research Database (NHS research ethics 17/EE/0442; see Supplementary Methods). We obtained data from four clinical records systems (RiO, SystmOne, PCMIS, and Epic; see Supplementary Methods), representing all clinical records systems for CPFT plus one for services provided by CPFT within another Trust (Epic). Data from each system were analysed separately.
+2.3.	Variables
+For MH services, we extracted the following variables, per day:
+•	Referrals to CPFT teams embedded in primary care. We counted referrals to CPFT’s embedded primary care mental health service, and to its Improving Access to Psychological Therapies (IAPT) service, including self-referrals.
+•	Calls to 111 for MH crises. CPFT provides the NHS 111 MH crisis telephone service. We counted calls and triage psychiatric assessments.
+•	Referrals to secondary care CPFT teams. We classified teams as (a) child and adolescent mental health (CAMH) teams; (b) community MH teams (CMHTs) for adults; (c) crisis resolution/home treatment teams (CRHTs); (d) adult liaison psychiatry (LP) teams; (e) early intervention in psychosis (EIP) teams; (f) eating disorder teams; (g) other specialist services. See Supplementary Methods for detail.
+•	Liaison Psychiatry referrals and presenting problems. For CPFT’s LP service at Cambridge University Hospitals (CUH), we counted referrals, split as (a) from the Emergency Department (ED) with its associated Clinical Decision Unit (CDU), and (b) from other wards. When responding to a referral, LP clinicians record the primary reason(s) for referral. We counted presenting problems of (a) alcohol and/or drug use; (b) anxiety; (c) confusion, cognitive problems, requests for assistance with mental capacity assessment, and behavioural disturbance; (d) eating disorders; (e) low mood and suicidal ideation; (f) overdose and other forms of self-harm; (g) psychosis and mood elevation (hypomania/mania).
+•	Admissions. We counted admissions, discharges, inter-ward transfers, and inpatients per day across MH wards (excluding day-care facilities). We classified admission days as “voluntary” or “detained” according to the patient’s UK Mental Health Act (MHA) status for that day. We classified an admission as a readmission if it began < 30 calendar days after a previous discharge for that patient.
+•	MHA detention. We counted new episodes of detention under the MHA, of all kinds (not individual detentions, i.e. consecutive detention under different sections of the MHA counted as one episode). We counted assessments recorded following police use of section 136 (s136) of the MHA, a police power to take someone suspected of suffering from mental disorder to a place of safety in some circumstances. The data did not cover all detentions under s136 but included those where CPFT provided the place of safety (by far the majority in C&P).
+•	Documentation. We counted progress note entries by CPFT staff, and binary documents (written by staff or inbound from others), as general measures of activity.
+•	Appointments/contacts. We counted formally recorded contacts, excluding those marked cancelled, not attended, with a carer (rather than the patient), or where contact with the patient was not explicitly recorded. We split by contact type (telephone versus all other types). Videoconferencing was not recorded as a distinct contact type.
+•	Deaths. We counted deaths per day. We classified MH patients as having severe mental illness (SMI) or not. Based on the definition of SMI as schizophrenia, schizotypal and delusional disorders, bipolar affective disorder, and severe depression (UK National Institute for Health and Care Excellence, 2016), we looked for a lifetime coded diagnosis of ICD-10 codes F20*, F21*, F22*, F25*, F31*, F32.2, F32.3*, F33.2, and F33.3*. Any under-coding of diagnosis was unmeasured.
+For PH services, by day:
+•	MIU attendees.
+•	Referrals to children’s and adult services.
+•	Admissions. Historical daily admission data was not available, so we counted bed-days per month.
+•	Contacts. We counted contacts by age group (children’s and adult community services) and by contact method (face-to-face or other, e. g. telephone, videoconferencing).
+•	Deaths. We counted deaths of patients with an open referral to CPFT’s PH services at the time of death.
+COVID-19 infection:
+•	We obtained daily lab-confirmed COVID-19 case rates for Cambridgeshire and Peterborough (Public Health England and NHSX, 2020), by specimen date.
+•	We counted CUH patients testing positive for COVID-19 for the first time, from anonymised data, where tests were from inpatient wards or the ED.
+2.4.	Dates
+The first date of each available time series is shown in their respective Figures. We used 2020-03-23 (lockdown, week 13) as the event date for interrupted time series (ITS) analysis. We obtained data up to and including the following dates. RiO: 2020-08-09. SystmOne: 2020-05-18 (most data), 2020-05-22 (primary care MH). PCMIS: 2020-05-26. Epic: 2020-08-31 (LP), 2020-08-31 (COVID-19). National data: 2020-08-30.
+We truncated the last 7 days of RiO contact data, to allow for delays in recording appointment outcomes (lag estimated). We truncated the last 7 days of SystmOne death data, to allow for delays in death reporting to the NHS Spine (lag estimated), and 14 days of RiO death data, to allow for additional delays in Spine-to-RiO updates. Finally, we truncated all data to the last available full week.
+2.5.	Service reorganization
+A number of services reorganized during this period, influencing activity from the “supply” side (Supplementary Methods).
+2.6.	Analyses: general
+We used R v3.6.2 (R Core Team, 2019). We counted events per week according to the ISO-8601 standard, excluding week 53. For time series data, we conducted ITS analyses using a negative binomial generalized linear model (Ripley et al., 2020) (falling back to Poisson regression under nonconvergence suggesting lack of overdispersion) with augmented segmented regression. Predictors were: time (overall linear trend, with time = 0 at UK lockdown); event (0 before lockdown and 1 at/afterwards, measuring instantaneous change); time x event (0 before lockdown and time afterwards, measuring a change in slope after lockdown); week (a discrete predictor, to account for seasonal autocorrelation across years or other shorter-term phenomena) (Wagner et al., 2002); year (discrete predictor, to account for nonstationarity across years beyond a linear trend); and an intercept. We did not include a time lag. We show ITS model predictions and report the instantaneous and slope-change effects, with associated significance tests shown in the Figures (a = 0.05).
+2.7.	Analyses: mortality
+We examined age- and sex-standardized metrics for mortality. First, for each year, we calculated the mean number of deaths per week, by age at death in 10-year bands. In each age band, we performed ITS analysis using Gaussian regression with predictors year (linear time trend) and year_is_2020 (step change). This method does not account fully for the number of patients “exposed” to risk except via the linear trend, but was applicable to PH data (for which exposure data was unavailable) and MH data.
+Secondly, for MH data, we calculated standardized mortality ratios (SMRs). Within each year, a subject’s observation (exposure) period
+began on 1 January or at the subject’s registration (whichever was later), and ended on the date corresponding to the last data available for 2020 (to avoid comparing e.g. winter/spring 2020 to the whole of previous years). Only subjects registered and alive at some point during that period were considered. The observed death count was 1 if the subject died during that period and 0 otherwise. The expected number of deaths for that subject was taken from national mortality tables (unweighted mean of Cambridge, East Cambridgeshire, Fenland, Huntingdon, Peterborough, and South Cambridgeshire) (Office for National Statistics, 2020b), by sex and age (using the subject’s age on 1 January of each year, up to 90), weighted by the duration of the observation period. Across subjects, the total number of observed (O) and expected (E) deaths was calculated. The SMR is O/E, the standard deviation (SD) of ln(SMR) was taken as 1/V O, and the 95% confidence limits of the SMR as eln(SMR)±1-96/VO (Rothman et al., 2008).
+We calculated SMRs separately for those with and without SMI. We compared SMR pairs by taking the SD of ln(SMR ratio) as V(1/O 1 + 1/ O 2) (Rothman et al., 2008). We tested whether any change between 2020 and previous years was different for SMI and non-SMI groups via an ITS analysis across all years, using: negative binomial regression; deaths per thousand exposure years as the dependent variable; ln(ex-pected deaths per thousand exposure years) as an offset (Dalgaard, 2008); predictors of year (linear), year_is_2020 (binary), SMI (binary), SMI x year_is_2020, and an intercept.
+Since Black, Asian, and minority ethnic (BAME) groups are at disproportionate hazard compared to White ethnic groups of contracting COVID-19, hospital admission, critical care admission, and death from COVID-19 (UK Government Office for Science, 2020), we also included ethnicity as a predictor (White versus ethnic minorities or unrecorded ethnicity), and its interaction with all combinations of year_is_2020 and SMI.
+3.	RESULTS
+3.1.	Presentations and referrals to MH services
+There was a sharp reduction at lockdown in referrals to primary care MH services, psychological therapy, and all secondary care MH teams apart from EIP services (for which there was no significant change), with a subsequent increase towards normal levels for secondary care services (Fig. 1). There was a gradual decrease in telephone calls to the NHS 111 MH crisis service (Fig. 1C), and a small immediate drop in triage assessments by that service (p < 0-05) with no significant change thereafter (not shown). LP referrals from wards dropped sharply then gradually increased back to normal (Fig. 1D), likely reflecting the drop in acute hospital inpatient numbers. There was a substantial reduction in LP referrals from the ED followed by a slow increase (Fig. 1D). The changes in referrals to secondary care were proportionally less for patients with SMI than for those without SMI recorded; for example, referrals to crisis teams did not change for those with SMI, and referrals to CMHTs dropped less (Supplementary Results).
+This drop encompassed services accepting referrals from professionals (primary care MH service, IAPT, specialist MH teams). Service reorganization may therefore have contributed. However, the drop was also observed across services accepting patient self-referral, in part or exclusively (IAPT, 111, LP).
+3.2.	Presenting MH problems
+Presenting problems for LP referrals are shown in Fig. 2. All types of presentation dropped at lockdown and then increased towards normal levels, except presentation with psychosis/mood elevation (small sustained reduction) or eating disorders (no significant change), though these presentations are less common than the other reasons, potentially limiting power.
+3.3.	MH inpatient activity
+Prior to the pandemic, the proportion of detained patients (as opposed to voluntary or “informal” inpatients) was high compared to the local historical mean (Fig. 3A), suggesting higher acuity. Both
+detained and voluntary inpatient numbers dropped sharply at lockdown (Fig. 3A), reflecting fewer admissions (and a spike in discharges beginning just before lockdown; Fig. 3B). The reduction in inpatient numbers was deliberate (Supplementary Methods). Inter-ward transfers were reduced effectively (Fig. 3B), and readmission rates did not change
+(Fig. 3B). The same patterns were seen for patients with and without SMI, except that the admission rate did not drop significantly for those with SMI (Supplementary Results).
+3.4.	Mental Health Act
+There was no evidence for a change in the use of the MHA, either within the MH service or by police (Fig. 3C); neither was there a change
+for subgroups with/without SMI (Supplementary Results).
+3.5.	MH contacts and documentation
+Activity, as measured by clinical documentation, dropped (Fig. 3D) and contacts shifted towards telephone consultations (Fig. 3E) (see also Supplementary Results). These changes were substantial and sustained.
+3.6.	PH presentations, referrals, and contacts
+Attendance at the MIU that remained open dropped dramatically at lockdown and increased slightly thereafter, despite the closure of two other MIUs (Fig. 4A). Referrals to PH services dropped across age groups (Fig. 4B). PH ward occupancy decreased (Fig. 4C). Face-to-face contacts fell across age groups, with an increase in non-face-to-face contacts (Fig. 4D).
+3.7.	Deaths
+There was a clear and sustained increase in death amongst patients with open referrals to PH services at the time of lockdown (Fig. 5A), with the excess deaths occurring shortly after the rapid increase in COVID-19 infections in the region (Fig. 5E). The excess of deaths in 2020 was predominantly in the over-70s (Fig. 5B). Deaths in patients known to MH services increased at this time for those with and without SMI (Fig. 5C and D); again, this was primarily in the over-70s, but with additional increases in other age bands, notably for those with SMI (Fig. 5D). More recent data were available for MH services, and in
+patients without SMI (who were in the majority) a biphasic effect was seen, with a drop in mortality below the historical norm after the initial increase in deaths (Fig. 5C).
+Mortality, measured by SMR, increased significantly from 2019 to 2020 for patients with SMI, and increased significantly more for those with SMI than without (Fig. 5F; ITS analysis, SMI x 2020, p < 0-05). Across all data available from 2020 to date, the change in SMR for those without SMI was not significant (reflecting the biphasic pattern seen in Fig. 5C: an increase followed by a decrease). Across all years, SMI conferred a greater additional risk of mortality in the ethnic minority/ unrecorded ethnicity group (SMI x ethnicity, p < 0-05). There was no ethnicity-specific effect in 2020 (terms involving ethnicity x 2020, not significant) and the increased mortality amongst those with SMI in 2020 (as above) was not explicable by ethnicity.
+4.	Discussion
+4.1.	Summary
+Referrals and self-presentations to health services dropped substantially at lockdown across the MH and community PH system in our area.
+In some cases, these reflected service-led changes (e.g. closing some teams to non-urgent referrals; discharging patients to create inpatient capacity). For some services, though, activity is driven by patient selfpresentation (e.g. 111 crisis calls, attendance at EDs/MIUs) yet activity dropped nonetheless. There was no evidence that suppression of demand in some services led to “redistribution” to any other service, with no increase in demand for MH emergency services (111, crisis teams, ED), even for services permitting self-referral and remote consultation (111). There was no evidence of a compensatory “rebound” in demand beyond initial levels, though this may change in the future, and the trend for some services is now upwards. Service activity dropped less for those with SMI. There was clear evidence of the shift to remote consultation. Mortality increased substantially, with a much greater increase for patients with SMI than without (Fig. 5F).
+4.2.	Mental health presentations
+Our concerns a priori included an increase in MH presentations with affective disorders (anxiety, depression, self-harm, suicide attempts), other stress-exacerbated disorders (psychosis, eating disorders), and referrals for delirium associated with severe COVID-19. None of these
+effects were observed.
+Several authors have highlighted a risk that suicide increases as a result of the pandemic (Gunnell et al., 2020; Holmes et al., 2020), noting such effects after the 2003 severe acute respiratory syndrome (SARS) epidemic. In contrast, suicide rates fall during wars that increase social
+cohesion by uniting a population against a common foe (Pridmore et al., 2018). COVID-19 may have had some such “cohesion” effect, though a later increase in suicidality remains possible. Reduced presentations with self-harm/suicide attempts may also reflect reduced access to the means of suicide and/or reduced help-seeking; additionally, we do not
+have data on people who may have ended their life but were not known to our services. Population surveys of UK general mental health suggest a substantial deterioration following lockdown (Banks and Xu, 2020), but this has not translated (thus far) to an increase in presentation with mental disorder in our area. The increase in threshold for referral may be an important factor here.
+Lockdown may have had negative psychological effects for many but positive effects for some. For example, psychosocial stressors relating to school (e.g. bullying, examinations) commonly exacerbate MH problems in children (e.g. school avoidance, associated anxiety and depression); schools were closed and examinations cancelled. For some, there may have been beneficial effects of not having to leave the home environment. In contrast, there have been dramatic negative effects for others. The pandemic has brought uncertainty and anxiety both about the disease itself and associated consequences (Rettie and Daniels, 2020; McElroy et al., 2020). Our clinical experience of patients presenting in MH crisis has included people reporting COVID-19-related psychosocial stressors such as loss of employment and support networks (both formal and informal) and confinement (including in emotionally abusive situations), and people with severe mental disorders coming to the attention of the police and MH services having been unable to follow social distancing rules.
+4.3.	Physical health presentations
+The reduction in presentations to the open MIU was abrupt and dramatic, and occurred despite the closure of two others in the area. Whilst a reduction in help-seeking cannot be excluded (injuries still occurring but people not seeking medical attention or presenting to an ED instead), the degree suggests that lockdown has reduced minor injury.
+4.4.	Inpatient stays
+An effort to expedite discharge wherever safe had obvious results. The drop in the number of detained inpatients is particularly striking. Given the lack of change in new MHA detentions, we suggest that Responsible Clinicians perceived an increase in the risk of being admitted (relating to COVID-19 transmission) that shifted the risk/ benefit balance, near the end of a detained admission, towards earlier discharge. The sustained reduction in admissions, at least for those without SMI, suggests also a changed threshold for admission—though not for detention and by inference probably therefore for voluntary admission.
+4.5.	Mortality
+There was stark excess mortality in the over-70s in 2020 to date, likely due to COVID-19, but these data also provide strong (albeit observational) evidence that SMI is a major risk factor for death during the pandemic. The baseline SMR in patients known to MH services was high, likely reflecting CPFT’s provision of MH care to those with lifelimiting illnesses including dementia. The biphasic mortality effect observed in patients without recorded SMI—an increase in deaths when COVID-19 spread in Cambridgeshire and Peterborough, followed by a drop below historical norms—suggests a degree of temporal shift in mortality, i.e. that some patients died earlier within 2020 than they would otherwise have done. However, beyond this, there was a substantial and disproportionate increase in mortality amongst those with SMI during 2020. This was not explained by ethnicity. Our data do not identify the mechanisms conferring additional risk on those with SMI. These might include differences in interpersonal contact and viral transmission risk in those with SMI (Moore et al., 2020); higher rates of comorbidities such as obesity, diabetes, and cardiovascular disease (McGinty et al., 2016) that are associated in turn with greater severity of COVID-19 infection (Liu et al., 2020); and increased mortality via
+diseases other than COVID-19, potentially via differences in help-seeking or healthcare provision.
+4.6.	Strengths and weaknesses
+These data represent a relatively comprehensive view of NHS-provided MH services in one geographical area (because CPFT provides psychological therapy and all secondary MH care to the entire population of C&P), making it less likely that decreases were compensated for by help-seeking via another NHS service. For some services there were “unmeasured” alternatives (e.g. MH support from charities; support for children from the Local Authority). The data are less comprehensive for PH services, but sufficient to allow some comparison of health-seeking and mortality in the two domains. Changes in referrals to some services may have reflected changes to “demand” or “supply” or both, though activity in other services was more clearly led by patient help-seeking (demand). Widespread travel out of CPFT’s catchment area is not a plausible explanation for the changes, given the magnitude of change and national travel bans (Hancock, 2020). Mortality data was available via national reporting mechanisms for all MH patients, but only for PH patients with open referrals to CPFT, and mortality data may be incomplete if delays in national reporting exceeded 7 days. This was a retrospective observational study. Though the associations observed were strong via ITS analysis, a recommended approach for evaluating changes in routine data associated with a well-defined event (Bernal et al., 2017), they do not prove causation. Cambridgeshire and Peterborough have had COVID-19 infection rates spanning the “low middle” of the UK range (Public Health England and NHSX, 2020), which is high internationally (Our World in Data, 2020); our findings may not generalize to areas with different infection rates. The situation is changing rapidly and more recent data may change the overall picture (e.g. if a rebound in demand occurs).
+4.7.	Conclusions
+We show a reduction in demand as well as supply during the early phase of the pandemic for MH and community PH services covering a population of ~0-86 million. The changes in supply may have reduced access to mental health services for some. It remains to be seen whether the changes in demand reflect a reduction in MH morbidity or whether a hidden burden of mental illness will become apparent in months to come. However, activity in a number of services is now increasing; this suggests that there is patient need but that help-seeking has been transiently suppressed. Further research is required into the mechanisms by which SMI is associated with elevated mortality during this pandemic, but we suggest that patients with SMI be considered a high-risk group during COVID-19.
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+Impact and Implications
+•	Findings from this study offer a conceptual framework for understanding social support for persons living with serious mental illness (SMI) and lay the groundwork for the development of a SMI-specific measure of social support.
+•	Our research permits future researchers to investigate the conditions under which social support buffers the impact of SMI stressors, assisting service providers in more effective identification of individual support needs for clinical intervention. Given the exceedingly high rates of morbidity and mortality among persons with SMI that result from preventable medical conditions, identifying the interactions of SMI-specific support needs with SMI stressors has the potential to reduce health risks and increase wellness behaviors.
+•	Our study will add to the growing body of research on diseasespecific types of support to advance the clinical and research utility of social support.
+Introduction
+Serious mental illness is defined by federal regulations as any diagnosable mental, behavioral, or emotional disorder experienced
+by persons age 18 and older that is characterized by episodic, recurrent or persistent features that substantially interferes with or limits the ability to participate in one or more major life activities (U.S. Department of Health & Human Services, 1992). Five percent of the U.S. population experiences SMI and it is a leading cause of disability (National Institute of Mental Health, 2014). The SMI population experiences significant stressors such as illness management, isolation, homelessness and stigma—long understood to be barriers to functioning and societal participation— which elevate the risk of morbidity and mortality (Madianos, 2010; Narrow et al., 2000). In fact, the life expectancy of persons with SMI is an alarmingly 25-30 years less than that of the general population (Colton & Manderscheid, 2006). Studies showing that these stressors cannot be alleviated solely through psychopharma-cological and/or behavioral interventions have led to recommendations that psychosocial approaches such as social support interventions be added to the SMI treatment toolkit (Corrigan & Phelan, 2004; Madianos, 2010; Narrow et al., 2000; Whitley, Harris, Fallot, & Berley, 2008).
+Social support has been demonstrated to buffer the negative impact of stress among the general population (Cohen, 2001; Cohen, Doyle, Turner, Alper, & Skoner, 2003; Feldman, Cohen, Hamrick, & Lepore, 2004) and has achieved national attention as a key component of the mental health recovery paradigm for persons with SMI defined as “a process of change through which individuals improve their health and wellness, live a self-directed life, and strive to reach their full potential” (Substance Abuse and Mental Health Services Administration (SAMHSA), 2012). Specifically, community, or the relationships and social networks that provide support, friendship, love, and hope is identified as a major dimension of the recovery model, and three out of the 10 major principles guiding the recovery model address social support in some way. For example, recovery is described to include support from peers and allies, through relationships and social networks, and involving individuals, family, community strengths and weaknesses (Substance Abuse and Mental Health Services Administration (SAMHSA), 2012). In addition, clinical practice guidelines for health care settings now include social support assessment as an essential part of psychiatric evaluation (Lehman & Steinwachs, 2010).
+To date, little is known about how social support buffers the negative impact of stress among those with SMI. The limited body of research that exists suggests that social support is positively linked to quality of life (Bengtsson-Tops & Hansson, 2001; Sharir, Tanasescu, Turbow, & Mamam, 2007), yet, the findings in aggregate reveal small to moderate effect sizes, and some studies show no effect (Chou & Chronister, 2012). We believe these mixed findings are due, in part, to the use of nonpopulation specific measures that do not capture the unique types of support relevant for persons with SMI. These studies are based on measures of social support designed for the general population who experience “typical” life stressors (e.g., divorce, death, loss of job), whereas the SMI population experiences an additional distinctive set of chronic and often debilitating stressors related to stigma, isolation, homelessness, and illness management (Madianos, 2010; Narrow et al., 2000). Moreover, researchers who investigate social support with distinct clinical populations point out that general population measures are not sufficient in and of themselves (Broadhead & Kaplan, 1991; Wortman, 1984) and that population-specific support measures are necessary in order to help interpret the existing “morass of positive and negative studies [which are] of little value in the aggregate” (Broadhead & Kaplan, 1991, p. 67).
+Recently developed measures of social support for individuals with diabetes, cancer, and other specific health conditions have made important contributions in this area (e.g., McCormack et al., 2008; Taskila et al., 2006; Yanover & Sacco, 2008). For example, the diabetes-specific support scale Resources and Support for Self-Management (McCormack et al., 2008) measures five domains of social support unique to persons with diabetes and has been found to be both psychometrically sound and clinically useful. A limited number of studies have explored SMI-specific types of social support. Breier and Strauss (1984) conducted a qualitative study that identified SMI types of supports such as symptom monitoring, role modeling and reality testing. In addition, Walsh and Connelly (1996) observed that emotional support was more common than material and instrumental support among the SMI population. To date, research paralleling the scientific advancement made with other clinical populations has not oc
+curred with the SMI population; indeed, the types of social support relevant to persons with SMI differ from those of the general population and requires systematic investigation. Therefore, this study sought to better understand the population-specific types of social support relevant to adults living with SMI.
+Our study was exploratory and used a qualitative approach that centered on uncovering the types of social support meaningful and relevant to persons with SMI. Despite the phenomenological nature of our inquiry, the premise of our study was based on a broad conceptualization of social support defined as the emotional and tangible provisions by others that are perceived to be helpful by the receiver (Cohen & Syme, 1985). Our investigation was also guided by two overarching typologies of social support that have been consistently identified as the most salient and encompassing types of support: emotional support and instrumental support (Declercq, Vanheule, Markey, & Willemsen, 2007; Shakespeare-Finch & Obst, 2011). Emotional support involves the provision of caring, empathy, love and trust (House, 1981; Krause, 1986), an affective transaction which imparts liking, admiration, respect, and love (Kahn & Antonucci, 1980), or support that leads to the information that one is cared for and loved, is esteemed and valued, and belongs to a network of mutual obligation (Cobb, 1976). Instrumental support is the provision of tangible goods, aid, services, or concrete assistance that is intended to solve a problem or accomplish a task (Barrera, 1986; Cohen & McKay, 1984; Cutrona & Russell, 1990; Krause, 1986; Langford, Bowsher, Maloney, & Lillis, 1997).
+Method
+Participants
+The sample comprised of 52 adults receiving county mental health services in the San Francisco Bay Area. Table 1 summarizes the descriptive statistics for the sample participating in the study. The average age was 47.5 (SD = 10.2). Fifty-four percent identified as male, 29% female, 14% transgender, 2% genderqueer, and 2% other. Thirty-seven percent identified as White, 33% African American, 16% Asian American/Pacific Islander, and 14% other. Twenty-nine percent reported a primary diagnosis of major depression; 25% bipolar disorder; 15% schizophrenia; 11.5% post-traumatic stress disorder (PTDS); 10% schizoaffective disorder; 6% anxiety disorder; and 4% other. Sixty-six percent reported to be single and never married; 13.5% divorced; 8% living with partner; 5% married; 6% widowed; and 2% separated. Sixty percent lived independently; 12% in a residential/group home setting; 8% lived with family/friends; and 20% other. Sixty-nine percent were not-employed; 19% were employed part-time; and 12% were volunteering. Monthly average income was $836 (SD = $526). Participants reported that on average, they had three people (SD = 1.2) to which they could count on for support, reporting an average of three (SD = 3.6) friends, two family members/relatives (SD = 3.1), and three service providers (SD = 3.2).
+Researchers
+The primary research team consisted of two female associate professors (one White and one Taiwanese); one Chinese male associate professor, two graduate students (one African American/
+research reflexivity (Hill et al., 1997; Morrow, 2005). As suggested by Hill, Thompson, and Williams (1997), the research team openly discussed power issues throughout data collection and analysis. This process helped create a climate in which the student researchers had an equal voice in the data analysis.
+Data Sources
+Demographic form. Participants completed a brief demographic form immediately prior to the start of each focus group. Information about the participant’s age, gender, race/ethnicity, marital status, diagnosis, employment, education level, substance abuse history, housing, and income status data was collected. In addition, limited social network data was collected including questions related to participants’ number of friends, family members/ relatives, and formal/paid service providers perceived to be available for social support.
+Focus group protocol. The focus group protocol was designed to elicit discussion about participants’ experiences with social support in the recent past. Participants were asked to reflect on things that people have said or done that they believe were supportive, particularly within the context of living with a SMI. In addition, participants were asked to reflect on types of support they would like to receive that they were not getting. The following two questions were asked by the moderator at each focus group: (a) In the recent past (in the last couple weeks), what things have others (friends, family, service providers) done for you or said to you that was helpful or supportive to you in regards to the stressors associated with living with mental illness. We are particularly interested in specifics: things that others have said or done that you experienced to be supportive. Think behaviorally and specifically. (b) Now, think about what you would like from others with respect to support. What are you not getting that you would like? Or really want more of? When appropriate, follow-up probes were used for clarification or to request elaboration. The questions were presented orally and in written format. Participants were offered the option to write down additional responses on paper at the end of the focus group if they were unable to provide the information orally.
+Procedure
+Participants were recruited from community agencies in the San Francisco Bay Area from three county funded mental health programs. Participants were recruited through in-person presentations at community agencies by research team members and through posted and online flyers. Individuals interested in participating were asked to contact a research team member via phone or e-mail. All potential participants were screened by a research team member through a phone conversation or an in-person meeting. Inclusion criteria for the study were: (a) a primary diagnosis of SMI; (b) currently receiving county mental health services; (c) ability to speak and understand English; (d) willingness to participate in a focus group and be audio-recorded; and (e) not be actively experiencing psychosis. Participants were excluded if their primary diagnosis was a substance abuse related disorder. In addition, individuals whose spouse/partner/family member had already been screened into the same focus group were scheduled to join another group at a different time.
+Six focus groups were conducted, with six to eight participants in each group. The focus groups were held at partnering agencies, settings in which the participants could easily access. The composition of each focus group varied by which agency the participant was connected. Thus, participants were grouped with individuals to whom they were familiar, allowing for the most optimal context for open dialogue and discussion of their experience with social support (Kreuger, 1994). Prior to the start of each focus group, informed consent was obtained from each participant. All participants were informed of the voluntary nature of the research, their rights as a research participant, and the potential negative effects from participating in the study. Participants were reminded that participation in the study had no relationship to, or impact on, their services, and that all information shared in the focus group would be kept confidential by the researchers. One of the potential risks associated with participation was a breach of confidentiality by a group member. Given that the research team could not control information shared outside of the group and in recognition of the potential risk associated with this, participants were strongly encouraged to maintain confidentiality regarding the information shared in the meeting. In addition, the process and format of the focus group was explained. Recommended by Krueger (1998), one research team member served as the focus group facilitator and two graduate student researchers served as observer/note-takers. Focus group interviews lasted approximately 90 min and were audio-recorded. The audio-recordings were transcribed verbatim using NVivo10 and each transcript was checked for accuracy by the first author. Pseudonyms were used throughout the interviews and were included in the transcripts so that the researchers could distinguish among participants’ comments during data analysis. Food and beverages were provided during the focus group interviews and participants received $50 for their participation.
+The researchers enacted provisions to facilitate Guba’s (1981) four criteria for trustworthiness: credibility, transferability, dependability, and confirmability (Lincoln & Guba, 1985; Morrow, 2005; Shenton, 2004). Credibility, or how congruent are the findings with reality (Merriam, 1998), was ensured through in vivo member checks, whereby the moderator consistently checked-in with participants during the focus groups to ensure comments were accurately understood by the moderator. In addition, iterative questioning and probes were used to elicit additional data with the intention to uncover additional explanatory data, contradictions and discrepancies. The moderator enacted measures to ensure participant honesty through the establishment of rapport at the outset of the group. Specifically, participants were encouraged to be open and honest, were reminded that there were no “right answers,” that the research/researchers were independent from participants’ treatment/services, and that participants have the right to leave the group at any time. Participants were also reminded that while they were encouraged to respond, they were not required to disclose information; in particular, it was stressed that while the moderator may use probing questions, participants were free to state their response was complete. Finally, the moderator has experience working with adults with SMI within a group and clinical context and was therefore comfortable establishing group rapport and responding sensitively and appropriately.
+Transferability or the demonstration of findings to other situations (Lincoln & Guba, 1985; Shenton, 2004) was attended to by providing sufficient contextual information related to participants,
+data collection method and timeframes, and the location of the agencies participating in the study (Cole & Gardner, 1979; Mar-chionini & Teague, 1987; Pitts, 1994). The researchers worked to establish dependability, or the reliability of the findings, by documenting each step of the research design and continually evaluating the effectiveness of the process of inquiry used (Shenton, 2004; Morrow, 2005). Finally, confirmability, or the degree to which the findings reflect the experiences and ideas of the participants rather than the researchers, was ensured by an external auditor who reviewed the original transcripts and compared his interpretation with that of the primary research team. In addition, researcher assumptions about participants were explored regularly through debriefing sessions, and reasons for data analysis decisions were explained with acknowledgment of the limitations of this approach. Efforts to address researcher bias, predispositions, and “hopes” for the data were explored regularly through discussion and reflective commentary (Miles & Huberman, 1994; Patton, 1990; Shenton, 2004).
+Data Analysis
+Consensual Qualitative Research (CQR) was employed in this study (Hill et al., 2005; Hill et al., 1997). CQR involves the use of semistructured data collection and a team of researchers to analyze the data, offer various perspectives, and reach consensus on the meaning of data. Data analysis and coding involves synthesizing domains, core ideas, and cross analysis. CQR requires at least one external auditor to review the data to reduce the risk of “group-think” (Hill et al., 2005; Hill et al., 1997). The researchers employed a modified version of CQR for this study related to our data source. Specifically, although CQR was initially designed to analyze individual interview data, we examined focus group discussions. While we recognize the limitations associated with gathering data via focus groups versus individual interviews, focus groups have been identified as an effective approach for generating meaning in areas that have not been empirically investigated (Krueger, 1994). In addition, focus groups have been shown to facilitate spontaneous exchanges between participants which allow group members to build upon each other’s idea (Krueger, 1994). The use and effectiveness of focus groups for this study is consistent with recent investigations using focus groups to better understand issues related to living with a mental illness (e.g., Aschbrenner et al., 2013) and multicultural issues (Cruz-Santiago & Ramirez Garcia, 2011; Kordesh, Spanierman, & Neville, 2013; Sue, Lin, Torino, Capodilupo, & Rivera, 2009).
+In the first phase of data analysis, the research team members read one of the transcripts independently and generated a list of preliminary domains. Following the completion of this process for one transcript, each team member repeated the process with the additional five transcripts. The research team conducted multiple meetings to discuss preliminary domains until consensus occurred on eight domains. The researchers then independently linked data from each transcript to the eight domains. At times, data were coded into more than one domain and regular meetings occurred to allow the team to reach consensus on the most appropriate domains for the raw data. To control for researcher bias, groupthink, and identify content that may have been overlooked, our external auditor reviewed the transcripts, the domains, and then provided feedback. Modifications were made to some domains which re
+sulted in consolidating two domains into one, revising domain titles to more accurately reflect the data, and moving data into more appropriate domains. This process resulted in six final domains. Data that did not fit into these six domains were identified as not relevant to the study focus and were therefore omitted. The next phase involved the research team identifying core ideas associated with each domain by matching direct quotes to each domain. This phase of the analysis was completed individually and as a team, evaluating the data until consensus was achieved. The external auditor again reviewed our work and provided feedback, which was used to modify some of the core ideas to better represent the underlying meaning of the data. In the final phase, the team analyzed core ideas for each domain across all focus group transcripts and independently developed categories. Team meetings occurred to share findings and obtain consensus on final wording of categories and placement of core ideas into these categories. The external auditor again reviewed our cross analysis and feedback was incorporated based on team consensus.
+Results
+The researchers identified six domains which include: (a) supportive conditions, (b) day-to-day living, (c) illness management, (d) resources and information, (e) guidance and advice, and (f) community participation support. The content comprising the six domains appeared across all focus groups, and because the domains were interrelated, there was some content overlap. Below is a description of each domain and their associated categories (see Table 2).
+Domain 1: Supportive Conditions
+Supportive conditions reflect the knowledge, attitude, and behaviors perceived necessary for a supportive exchange to occur. Participants described five categories of supportive conditions: (a) mental illness knowledge; (b) enduring attitude of acceptance and affection; (c) enduring attitude of respect, worth, and value; and (d) instilling hope, growth, and recovery. In regards to mental
+illness knowledge, participants expressed that an understanding of the meaning and impact of mental illness on the individual was an important supportive condition. Specifically, participants expressed the need for support providers to understand that, at times, mental illness influence’s one’s ability to execute daily life activities (e.g., getting out of the house, going to work, school, and being with other people); that living with a mental illness can influence how one thinks, feels and behaves; and that some difficulties experienced by the participants are related to living with a mental illness. For instance, participants’ described:
+“I needed to understand partly understanding what I got from it, support, I had never really gotten the realistic, the technical things that a person goes through with a mental illness and how to deal with those things, you know, and it was like I finally got it and I didn’t want to be there, but I have to say I learned something for the last 3 months I was there that I didn’t want to be there, but in the process I was learning something.”
+“I’ve had numerous experiences where the police just talk to you like you’re retarded and, um, they seem to they seem to have a lack of understanding and, um, seems like they need to be trained more when it comes to people with mental health issues and, um, they act like there’s just a cookie cutter of what people with mental health issues deal with and who they are and I’ve been disrespected by the police on numerous occasions.”
+In regards to the supportive condition enduring attitude of acceptance and affection, participants expressed that those who are caring, warm and affectionate, and accepting of the individual and of their experience are perceived as supportive. For instance, participants described:
+“. . . I was too embarrassed about and some things I couldn’t even remember that I did so it just helps me a lot to know that I have somebody accept me and, you know just feel cared about and loved.”
+“. . . you know, she’s been there for me and really comforted me and made me, you know, just making me strong.”
+“my dad said, um, that, you know, maybe being an administrative assistant could be a career path for me, cause I’ve done that and had several jobs now as an administrative assistant and, but I’ve always felt embarrassed by it and he said, ‘Maybe it could be a good job for you,’ and maybe this could be, um, a really good thing. So I felt like, um, validated.”
+In regards to enduring attitude of respect, worth, and value, participants characterized this supportive condition as having one’s opinions and decisions respected and not judged, nor treated differently or “less than” because of the person’s mental illness. For example, participants described:
+“. . . and um, I do have one kind of professional person, um, I have a case manager who really is the only kind of professional person who doesn’t talk down to me. I have depression and anxiety but a lot of the professional people talk to me like I’m a child and she talks to me like an adult with problems but I only see her once a month, unfortunately but she’s very helpful.”
+“Well, she listens to me, she respects my opinions about what I have to . . . she trusted me and observed and you know, not kept tabs but, to make sure I didn’t lose myself, um, and she respected, she respected me, she respect, she I can tell when she has a difference of opinion but
+her concern, she respects my life choices, she respects, she cares enough to where um . . .”
+In regards to instilling hope, growth, and recovery, participants expressed the importance of support providers offering encouragement and inspiration, being positive, instilling hope, identifying progress and “meeting them where they are at.” For example, participants stated:
+“. . . and she’s like you can do anything you want, it doesn’t matter what your diagnosis is, pretty much, you can do anything as long as you put your mind to it.”
+“. . . she’ll tell me things like ‘You know there’s people I’ve known who’s bipolar and they’re doing good now,’ or people with jobs or people going to school and that would give me hope that I can do the same, you know, that that I can do just as good. I don’t have to feel down about myself or doubt myself.”
+“She makes me not feel bad about only taking one or two classes, you know, she just reminds me to do what you can do. I don’t have to take three classes or four, as long as I’m doing something productive whether it’s working or going to school and I chose to go to school. I’ve worked before but I really don’t like it too much I’d rather do work toward something that I’ll be happy with because I really want to get into social work myself and help others the way I’ve been helped.”
+“. . . one thing I really appreciate about my best friend and two best friends in life are that they both have been with me through all of my experiences and they have always been supportive of what I’m trying to do, rather than like telling me what I should be doing or saying, that your parents tell me that you should be doing this, they help me with what I’m actually working on and they might have ideas about the future but they’ll either keep it to themselves or they’ll share it when it’s appropriate when I’m in a place where I can accept the help.”
+Domain 2: Day-to-Day Living
+Day-to-day living supports are related to assisting persons with SMI with basic living needs related to food, clothing, money, transportation, and appointments. Participants described three categories in this domain: (a) ADL support, (b) appointment support, and (c) check-in support. In regards to ADL support, participants described support related to food, money, clothes, transportation, and so forth. For example, participants noted:
+“. . . first of all my case manager physically ran, I had nothing I had no shoes no clothes anything she brought me four sets of clothes out of her own time, she looked for clothes for me .. . and helped me with food stamp application . . . she showed me the way to go to St. Anthony’s to get more clothes at 7:30 in the morning. She gave me a list of free eats and underlying Glide Memorial Church . . .”
+“. . . he still take care of my groceries . . . and then he come over and we can see, he help me how to make breakfast in the morning and just him being around me and checking up on me is real supportive.”
+“As far as getting to work, she gives me a ride to work every day, takes me wherever I need to go and we not even together, she got her own but she still does what she does for me, as me being her daughter’s father so and I appreciate that.”
+With respect to appointment support, participants stated:
+“She rode my behind to make sure that I made appointments and so forth and ensured by making sure that I got, even though I can make it to places when she reminds me, ensured that somebody comes here to pick me up and take me to medical appointments because I have cardiac problems”
+“Making sure that I make regular appointments even, whatever, doctor’s appointments, paperwork appointments, blah, blah, blah. She’ll call me up the day of the appointment to remind me and if its scheduled a couple weeks in advance I’ll end up sitting around my house reading my book listening to music, didn’t, just blow it off like no big deal so she’s a great help in that way . . .”
+With respect to check-in support, participant’s described:
+“. . . you call him or you go up to the office and ‘I just need to check in for 10 minutes’ and then of course they’ll check in with you, at least my case manager does, like I could check in with her every day of the week if I want ...”
+“. . . you know, like, she calls me every day, every day. She never misses a day, and she calls me every day and talks to me and I explain to her what’s going on. What’s going on and, you know . . .”
+“. . . we’re in communication every day, even though they’re not getting paid every day through this system they’re there, they’re either calling, texting, or something, you know ‘How are you doing today?’ And they do come so many times a week too so.”
+Domain 3: Illness Management
+The illness management domain reflects support related to managing illness-related issues and included the following two categories: (a) medication adherence support and (b) symptom management support. With respect to medication adherence support, participants described support to include daily medication reminders, support related to medication reevaluation, and advocacy support related to psychopharmacological treatment. For example, participants noted:
+“I feel like I can never get better but my case manager did help me understand that, ‘No it’s important for you to stay on meds. Do you want to go back to hospital?’ At first I would feel like I’m better now, I don’t need meds, and then later down the line whether it’s a year or 3 months, cause sometimes I can go a long time without meds and I really feel I’m okay now and I would have another episode and I think it would be because I would get too overwhelmed or another incident would happen where I feel extremely sad or just overwhelmed.”
+“My, um, case worker, social worker here, has been helpful, um with my medication, um, and my appointments, um, I seem to have problems with remembering my appointments and, um, she’s been keeping up with that, um, I just found out I have a backlog of 2 months medications and it’s been here and I didn’t know that and I just found that out so um. Cause they keep changing my medication so, I’ve been just like going through it but she’s been helpful . . .”
+“. . . he sits down and he kind of gives me the ins and out on whether its medication that I’m getting ready to take or if I’m having a problem in my body or whatever the case may be or from dealing with something in my mind . . .”
+Participants described symptom-management support to include supportive behaviors and strategies intended to reduce the stress associated with illness-related symptoms. This type of support also
+involves supporting the individual in gaining awareness/skills to manage their symptoms. For example, participants described:
+“He says, you’re going to be better, take a deep breath . . . what’s like the worst thing that can happen, just let the feeling pass and move through you and it will go away so.”
+“They gave me feedback about how he saw my point of view and he gave me ideas about other ways to interpret things . . .”
+“I’ve asked for them to both tell me if they see any difference in me to where I’m going downhill. If they see ‘Oh Linda, I think you need to get help. Go talk to your therapist’ you know. Cause my last episode, they said they sensed it, but they didn’t know for sure you know, so I told them from now on to tell me so I can get help right away to talk it out or maybe I stopped my meds and I need to go back or maybe I need more meds so.”
+“. . . they can observe your reaction to help you get it out but sometimes you need a person that can say listen, you are doing this and it’s based on your illness or your symptoms.”
+“Just to respond to me as if, um, as if, I was talking normally, so not to react to the volume or the anger in my voice but just to ask the questions as you would normally ask and to be curious as to like why I was in that state . . . you say ‘Oh, I had a good day today, how was your day? Did you go out to eat, what did you have to eat?’ you know, try to calm them down sit and be nice to them, talk to them like you would talk to anyone ...”
+Domain 4: Resources and Information
+The resources and information domain reflects support related to obtaining community resources (e.g., housing, financial, medical, legal, vocational, and treatment-related). Participants described two categories of resources and information support: (a) knowledge of resources and (b) support accessing and receiving services. With respect to knowledge of resources, participants described:
+“She knows where all the resources are and which one’s would fit for me and which one’s wouldn’t.”
+“. . . he gives me information on things and he give me, if he don’t know nothing about it, he’ll point me to someone else that can like help me and stuff like that and I can just talk to him about what’s going on in my house, just talk to him about, you know anything.”
+Participants described support related to accessing and receiving resources to include such tasks as completing paperwork and making calls with or on behalf of the client, advocating for the individual to gain access, and assistance with navigating the system. For example, participants described:
+“. . . she brought me to DMV personally and helped me get my CA DL, she brought me GA personally and registered me for general assistance and helped me with food stamp application like EBT.”
+“She got me into the programs, I didn’t know nothing about no programs, they got me a psychiatrist and got me on some medication, and then the voices started going away, I stopped feeling so paranoid all the time.”
+“I’m very grateful that my case manager navigated for me these resources and then left it up to me after holding my hand for 2 weeks to go one step further.”
+“So finally he just woke me up, he said, today’s session will be us strolling down there and doing it and you know, we got it done ... so yeah, I mean, but that’s just one really concrete example of damn, the guy put $100 bucks a month in my pocket.”
+Of particular salience were participant descriptions of supports related to housing and vocational resources:
+“She got me into this place and I had to wait 10 months almost like 10 and half almost 11 months to get in here and then I got in here and I wasn’t really sure that I wanted to be here but. . . yeah she’s a good person . . .”
+“She helped me get from, once I got out of custody and the residential treatment program, then I moved in with the person I was seeing, and she, we were going through problems and she helped me get into an SRO faster.”
+“I have an employment specialist here that’s been working with me one on one about um, finding work ... a whole host of services for me, everything from paying for my transportation costs to um, the um Microsoft Office course that I’m taking here so to that end it’s very helpful um . . .”
+“I have an employment specialist here that’s been working with me one on one about um, finding work and then they referred me to the Department of Rehabilitation and now I have a one on one caseworker if you will, I guess he’s a client services representative at the Department of Rehab, they’re job is specifically to place people with disabilities in the job market so that’s sort of helpful . . .”
+“As far as getting to work, she gives me a ride to work every day . . . and I appreciate that.”
+Domain 5: Guidance and Advice
+The guidance and advice domain in this context is characterized by the provision of useful information that is requested by the recipient and is specific to a particular situation. More specifically, guidance and advice involves a type of support that involves the provider directly advising on how to manage a difficult situation or problem. Guidance and advice was characterized by the following two categories: (a) advice, guidance, and suggestions and (b) direct feedback and reality testing. For example, participants described the following examples of advice, guidance and suggestion support :
+“. . . she’s given me some great advice. She really has and sometimes the answer is well, that’s just the way it is, there’s nothing you can do about it, so, it’s nice to know that. I like it if it is like that she’ll just come right out and tell me.”
+“. . . and the stuff that she’s had experience with so she can help guide me and guide me through the woods. So that’s very helpful.”
+“. . . it’s about telling it directly, but maybe I just have to just tell them I need you to tell me what I need to do sometimes.”
+Participant descriptions of support related to direct feedback and reality testing include:
+““. . . letting you know when you’re acting inappropriate in a situation without getting on your case about it and that’s giving someone a little nudge in what you think is a right direction”
+“If I ask for it they give me helpfully confrontive feedback and they don’t try to fix me they just, if I say what do you think what would you do or how do you see this, they give feedback but otherwise they just listen to my experience um.”
+“. . . they saw things that even if I didn’t agree with them and even if I still don’t agree with some things but I know in the long period, in the big picture the decisions that were made . . . it’s been beneficial for me.”
+“Yeah, like, you need to know what you’re doing wrong before you can change it. You need to know and be told that you’re doing that wrong and you need to do some kind of contract . . .”
+Domain 6: Community Socialization Support
+The community socialization support domain refers to support that assists participants in locating and accessing social and recreational activities to which they may participate on their own, and/or with the support provider. Participants described two categories associated with this domain: (a) recreation, leisure, and other support and (b) relationship and friendship support. For recreation, leisure and other support, participants described:
+“. . . like one time she bought me concert tickets. I wanted to go see this heavy metal concert and she bought me the tickets and I did the contract and I got to go.”
+“[she] calls me a lot you know and he gets me out the house you know, gets me to go places you know and get away from the area in which I’m in you know, let’s go do something, just somebody to go to the store or go down to the church, let’s go do something, or go out to where everybody sits and do something else, you know. So, I feel that I’m grateful for that . . .”
+“My mom and the one in my board and care, they told us about the church nearby my board and care so I go there every Sunday, I go to the church every Sunday. And also sometimes on the weekend, my mom and my dad and my sister and my brothers, we go outside in the restaurant. Or we go to festival, like green festival or pride parade, gay parade, yeah we go out every weekend I’m out with my parents and my sisters and my brothers.”
+The category relationship and friendship support refers to support around facilitating social connections. For example, participants stated:
+“I mean even that just like in my life since I was 18 she’s always tried to encourage me to have more social support. Um, she’ll try to introduce me to people, doesn’t always work but you know, I appreciate the effort . . .”
+“[she] has always encouraged me to become involved in other groups and some women’s groups and through that and through my case manager where I live I’ve been able to do that and the greatest thing that happened to me was through a women’s group that I belong to
+Discussion
+Using a broad conceptualization of social support as our guide that acknowledges two main types of support—emotional and instrumental support—our exploratory study revealed six unique types of social support salient to adults living with SMI: (a) supportive conditions, (b) day-to-day living, (c) illness-management, (d) resources and in
+formation, (e) guidance and advice, and (f) community socialization support. While our support domains share some broad-based conceptual underpinnings with traditional models of emotional and instrumental support, the domains emerging from our study represent supports uniquely tied to the stressors associated with living with an SMI, and therefore hold some conceptual distinction from traditional types of support.
+Supportive Conditions
+Supportive conditions, which is most conceptually tied to Cohen’s (Cohen & McKay, 1984) concept of emotional support (i.e., provisions offered in an affirming manner, that relays being loved, cared for, esteemed, and valued), represents specific SMI-related emotional and/or attitudinal conditions needed for any act or provision to be perceived as supportive. Specifically, the supportive conditions of acceptance and affection, hope, growth, and recovery, and an understanding of mental illness were described to encompass areas unique to living with SMI. For example, acceptance within this context taps into a support providers’ attitude toward mental illness, which is particularly important given the stigma, discrimination, and intolerance that persons with SMI experience. Persons with SMI experience high levels of societal and internalized stigma leading to negative beliefs about themselves, their peers with SMI, and contributing to negative outcomes. Thus, a supportive condition of acceptance and respect has the potential to buffer the negative impact of stressors associated with stigma and discrimination, and may facilitate positive outcomes. Notably, these supportive conditions of acceptance and respect are well aligned with the recovery model’s principle of respect for persons with SMI through community, system, and societal acceptance and appreciation for people affected by mental health (Substance Abuse and Mental Health Services Administration, 2012).
+Acceptance in this context also taps into the provider’s ability to understand the person’s unique experience living with mental illness, empathizing with the person’s situation, while at the same, viewing the mental illness as one aspect of the individual. Paralleling Wright’s (1983) model of disability acceptance which suggests that persons who cope with their disability (vs. succumb to their disability) contain the effects of their condition, our findings suggest that to be supportive, providers also need to contain the effects of SMI, accepting mental illness as part of the individual, yet seeing the individual as more than their SMI. In this vein, an accepting condition involves understanding and validating the unique, personal experiences of the individual’s mental illness (e.g., what does living with a mental illness mean for the individual?), while at the same time interacting with the whole person. This condition is consistent with the recovery models message that recovery is holistic and that individuals are unique with distinct needs, strengths, preferences, goals, culture, and backgrounds (Substance Abuse and Mental Health Services Administration, 2012).
+Instilling hope, growth, and recovery were also identified as important supportive conditions. These conditions are well aligned with a fundamental assumption of the recovery model that states that recovery is engendered by people who believe in the person’s ability to recovery, who offer hope, support, and encouragement to overcome challenges and barriers (Substance Abuse and Mental
+Health Services Administration, 2012). Our findings reflect participants’ desire for supportive acts that are embedded in a message of hope and growth—a strength-based message that has the potential to buffer the negative emotions of hopelessness, loss of control, and low self-esteem that so often plague persons living with SMI.
+Day-to-Day Living Support
+Participants described a particular type of support, day-to-day living support, which represents tangible assistance with food, clothing, transportation, money, appointments as well as the provision of daily “check-ins.” Indeed, persons with SMI frequently have unmet basic living needs linked to various SMI-related stressors such as poverty, homelessness, and unemployment, and therefore supportive acts related to the provision of food, clothing, money, and transportation may buffer the negative impact of these stressors and facilitate more positive outcomes. Day-to-day living support also includes assistance with remembering and getting to appointments. Persons with SMI are often connected to numerous health, mental health, and allied social service agencies, and navigating appointments within a service-delivery system often characterized as large and fragmented, is a significant barrier to benefiting from available services (Hogan, 2003). Appointment support may increase the probability that the individual will access needed health, mental health, and other social services, while potentially reducing the negative impact of the stress associated with navigating a fragmented service delivery system. This type of support has the potential to facilitate a more integrated and coordinated service delivery experience for persons with SMI—a priority identified in the recovery model (Substance Abuse and Mental Health Services Administration, 2012). “Check-in” support was another category that emerged in the day-to-day living domain. Check-in support was described as regular (e.g., daily or weekly) home visits or phone contact in which the provider briefly inquired about the participant’s well-being. This type of support was not described to include lengthy meetings or counseling/psychother-apy sessions, but a brief greeting and inquiry into the participants’ well-being on a particular day. This type of support has the potential to buffer the well-documented stressors of loneliness and isolation common among persons with SMI by facilitating a consistent sense of connection.
+Illness Management
+Persons with SMI experience illness-related stressors ranging from mild symptom distress to suicidal ideation. Participants described the need for particular types of instrumental supports related to these stressors including medication support, crisis management support and illness-management behavioral support. Medication is a common intervention for persons with SMI—a front line treatment for SMI—and a typical intervention for co-morbid health conditions (e.g., diabetes) highly prevalent among this population (Parks et al., 2006; Sajatovic et al., 2011). Nonetheless, persons with SMI experience many stressors associated with daily medication regimens including unwelcome side effects, interfacing with providers of psychopharmacological treatments, and the practical and psychological adjustment associated with a proposed lifetime of prescribed medication adherence. These stres
+sors likely contribute to the appreciable number of persons with SMI who are nonadherent, which has been reported to be as high as 40%-50% (Medicaid Health Plan of America Center for Best Practices, 2013). Our participants spoke about the desire for daily medication reminders, support related to facilitating medication reevaluations, and advocacy support within the context of providers of psychopharmacological treatment. This finding is consistent with prior qualitative findings that described “helping with medication” to be an important type of instrumental support for persons with SMI (Walsh & Connelly, 1996). Our findings are also consistent with the larger body of medication adherence literature that links social support, particularly “practical support” to medication adherence (DiMatteo, 2004; Shumaker & Hill, 1991; Wallston et al., 1983).
+Participants also described the need for illness-management behavioral support. They voiced the desire for providers to support them through stressful mental health symptoms through such strategies as reality checking, facilitating stress management techniques, and offering a new perspective on their condition. These findings are consistent with the growing body of illnessmanagement research among persons with chronic illness and persons with SMI showing that the acquisition of new information and skills focused on managing symptoms produces positive outcomes (Cook et al., 2012; McCormack et al., 2008; Sajatovic et al., 2011). Specific to SMI, a number of studies have provided evidence that such illness management programs as the Illness Management and Recovery program (Gingerich & Mueser, 2005) and the Wellness Recovery Action Planning (Cook et al., 2012) produce positive outcomes (Cook et al., 2012; Mueser et al., 2002; Salyers et al., 2009). Our findings provide additional support for these interventions, emphasizing the need for support providers to offer direct illness-management behavioral support and support related to the development of illness-management skills.
+Resources and Information Support
+Resources and information support represents another type of instrumental support that was described by participants and involves support related to the knowledge of, and access to, community resources. Specifically, participants described persons who were knowledgeable of available mental health, housing, financial, medical, legal/criminal justice, substance abuse, and vocational resources as supportive. In addition, participants described tasks linked to accessing these resources and services (e.g., completing paperwork, making calls on behalf or with the client, advocating for access, and assistance navigating the system) as supportive. This type of instrumental support is particularly important given the challenges associated with navigating complex and fragmented service delivery systems. In addition, this type of support has the potential to reduce the stress associated with interfacing with service delivery systems, increasing the likelihood of service benefits and treatment adherence.
+One of the most salient areas discussed in the resources and information domain was housing. Indeed, persons with SMI are represented disproportionally among the chronically homeless (Weinstein et al., 2013) and this was consistently described as a significant stressor among our study participants. Support for accessing safe shelters, temporary housing, and gaining permanent housing was identified as vital, and is consistent with one of the
+major dimensions identified by Substance Abuse and Mental Health Services Administration, 2012 that support a recovery approach; namely, having a home—a stable and safe place to live. Our data also supports existing empirical research that links stable housing to better life satisfaction, empowerment, and emotional well-being (Lehman, Kernan, DeForge, & Dixon, 1995; Lehman, Slaughter, & Myers, 1991; Nelson, Hall, & Walsh-Bowers, 1999) and reduced admissions to hospitals, jails, and shelters (Culhane, Metraux, & Hadley, 2002; Gulcur, Stefancic, & Shin, 2003). Importantly, while housing was described to be a particularly important type of resource and information support, studies suggest that few psychosocial rehabilitation programs offer housing/ residential support services (Lucca & Allen, 2001).
+Guidance and Advice
+The guidance and advice domain emerging from our study supports the larger body of social support literature suggesting that informational support—a distinct type of support that is most often conceptualized in the literature as providing facts, advice, opinions, and information (Bambina, 2007; Braithwaite, Waldron, & Finn, 1999; Civan & Pratt, 2007; Coursaris & Liu, 2009; Cunningham, van Mierlo, & Fournier, 2008; Eichhorn, 2008; McCormack, 2010) to facilitate action toward assisting the support receiver in solving or eliminating problems causing stress (Cutrona & Suhr, 1992)—may buffer the negative impact of stress. Guidance and advice in this context was characterized by the supporter providing useful information that was requested by the recipient specific to a particular situation. To some degree, this reflects a type of problem-solving support, wherein persons with SMI describe the benefits of having a support provider tell them directly how to manage a difficult situation. This is consistent with the larger body of social support literature that suggests that advice offers ideas and suggests actions for coping with challenges by providing detailed information about the situation or skills needed to deal with the situation (Chuang & Yang, 2014). Indeed, a number of studies suggest that advice is a common way for individuals to respond to others’ problems and involves prescribing what a person should or should not do (Cowen, 1982; Cutrona & Suhr, 1994; Cutrona et al., 1990; D’Augelli & Levy, 1978; Knapp, Stohl, & Reardon, 1981; Reisman & Shorr, 1980)
+Participants also referenced the importance of the support provider’s experience or expertise when providing this type of support. In other words, participants described an important contextual element for this type of support; namely, the benefits of advice and guidance are contingent upon the expertise of the provider. This is consistent with prior research that suggests that the perceived helpfulness of advice depends on the expertise of the adviser with reference to a particular problem (Cutrona & Suhr, 1994; Dakof & Taylor, 1990). Thus, this domain is likely most beneficial when the person with SMI shares or confides a particular issue or problem with an individual who has expertise or experience in the area.
+Importantly, although advice and guidance is a common type of support, research suggests it is not always perceived as helpful (Cutrona & Suhr, 1994; Dunkel-Schetter, 1984; Dunkel-Schetter, Blasband, Feinstein, & Herbert, 1992; Goldsmith, 1994; Lehman, Ellard, & Wortman, 1986; Notarius & Herrick, 1988; Pearlin & McCall, 1990; Tripathi, Caplan, & Naidu, 1986), and there are
+many factors to consider with respect to insuring advice and guidance is supportive for persons with SMI. For example, does the supportive behavior promote autonomy or facilitate dependency? Is the behavior culturally appropriate (Philipsen, 1992)? Does it instill feelings of obligations (Goldsmith & Fitch, 1997)? Was it offered as an immediate response to the statement of a problem or followed after the supporter listened and responded sympathetically (Pearlin & McCall, 1990)? While advice may be appropriate at times, these contextual elements are important to consider when determining the supportive nature of advice and guidance for persons with SMI, particularly given the recovery model principles of consumer decision-making and choice.
+Community Socialization Support
+Community socialization support, in this context, was described as instrumental support to assist participants in locating and accessing social, leisure, and recreational activities to which they may participate on the their own, and/or with the support provider (e.g., assistance with purchasing tickets for an event, identifying local community events, and/or support that involves joining the individual in a social event). Importantly, this type of support is perceived as supportive to the extent that it involves facilitating enjoyable activities that are meaningful to the individual, supporting access to activities beyond agency-organized group activities. The need for the support provider to explore and validate the individual’s personal interests was described as a particularly important aspect of providing this type of support—which may be challenging due to the sociopolitical and psychological factors attached to SMI. Specifically, the stigmatizing perception that personal interests and desires do not exist for persons with SMI, coupled with the widespread view of social skill deficits among this population, may contribute to difficulties supporting and exploring social, recreation, and personal interests among persons with SMI.
+Davidson and Stayner (1997) poignantly describe three perspectives on the social lives of people with schizophrenia including an “empty shell” (clinical perspective), a “poker player” (family members), and a “caged panther” (persons with schizophrenia), revealing the need for support providers to look beyond the negative symptoms so often experienced by persons with SMI and explore with the individual, their personal interests and desires. This type of support has the potential to buffer the stress associated with isolation and loneliness and increase the opportunities for persons with SMI to be involved in activities and social roles alongside those without disabilities (Wong et al., 2009). In addition, this type of support may increase the size and nature of a person’s social network—which has been consistently identified as significantly smaller for those with SMI than the general public (Albert, Becker, McCrone, & Thornicroft, 1998; Baker, Jodrey, Intagliata, & Straus, 1993; Harris, Brown, & Robinson, 1999; Mueser & Tarrier, 1998) and comprised of a much lower number of social ties with nonfamily members (Meeks & Murrell, 1994; Wong et al., 2009).
+Limitations
+Our study was qualitative in nature and therefore our findings cannot be generalized. In addition, the sample was diagnostically
+heterogeneous and thus our findings can only be interpreted within the context of SMI. While we had a broad representation of gender categories in our sample, there were fewer women than men participating in the study, and the sexual orientation of our participants was unknown. Finally, our study was limited by the potential negative effect of not being able to guarantee confidentiality among the group members. Specifically, while we encouraged group members to keep shared information confidential, we could not prevent participants from sharing outside the group. This may have prevented some participants from disclosing sensitive information.
+Implications and Conclusion
+Findings from this study offer a conceptual framework for understanding social support for persons living with SMI and lay the groundwork for the development of a SMI-specific measure of social support. In addition, our research permits future researchers to investigate the conditions under which social support buffers the impact of SMI stressors, assisting service providers in more effective identification of individual support needs for clinical intervention. Given the exceedingly high rates of morbidity and mortality among persons with SMI that result from preventable medical conditions (e.g., cardiovascular disease, diabetes, respiratory disease, HIV/AIDS; Parks, Sveden, Singer, & Foti, 2006), identifying the interactions of SMI-specific support needs with SMI stressors has the potential to reduce health risks and increase wellness behaviors. Our study will also add to the growing body of research on disease-specific types of support to advance the clinical and research utility of such support.
+In conclusion, our findings have both clinical and theoretical significance. Our findings have the potential to provide researchers with the data necessary for developing and validating clinical interventions that extend beyond generic social network enhancements to target and enhance SMI-specific types of support. In addition, our findings advance research on the stress-buffering theory of social support by providing a vehicle for identifying the interactions between specific types of support and chronic SMI stressors (i.e., homelessness, unemployment, isolation, poverty, stigma, illness management)—information that will have significant clinical impact on improving quality of life, wellness, and recovery in a vulnerable population.
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+INTRODUCTION
+Globally, suicide is one of the three leading causes of death among people aged 15-44 years, and the second leading cause of death among young people aged 15-29 years (World Health Organization, 2014). There have been many advances in understanding which factors are associated with people’s vulnerability to suicidal thoughts and behaviors (Hawton,
+Saunders, & O’Connor, 2012; O’Connor & Nock, 2014; Turecki & Brent, 2016), including those related to family experiences (Brent et al., 1993; Cero & Sifers, 2013; Fergusson & Lynskey, 1995). Suicidal thoughts and behaviors (STB) is an umbrella term to describe suicide-related thoughts or ideation (including casual, transient, passive, active, and persistent thinking) and suicide-related behaviors— acts with some degree of suicidal intent,
+including suicide attempts with and without injuries (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007).
+STB affects people of all ages, ethnicities, sexual orientation, and genders, although the overwhelming majority of suicide deaths are by men (World Health Organization, 2014). Although most studies on STB focus on the role of risk factors (Franklin et al., 2017), another approach to improving knowledge and prevention of suicide is by understanding better the motivations for suicide attempts (Klonsky, May, & Saffer, 2016), as well as how psychological and contextual variables interact to develop vulnerability for STB. Recent research has suggested that key elements associated with developmental vulnerability for STB are having been exposed to adverse childhood experiences (Cleare et al., 2018), harmful parenting (Cero & Sifers, 2013), and early disrupted attachment relationships (Fergusson, Woodward, & Horwood, 2000). Although several researchers have proposed a range of theoretical frameworks to understand the etiology of STB (Barzilay & Apter, 2014), Adam (1994) was the first author to suggest a developmental model where the attachment system plays a central role in the emergence of suicidal behavior. According to this model (Adam, 1994), early adverse parenting experiences and other childhood negative experiences could lead to the development of insecure attachments which could act as a psychological vulnerability factor for suicide risk later in life. Adam’s model was based on Bowlby and Ainsworth’s work which suggests a permanent effect of early attachment experiences (Ainsworth & Bowlby, 1991).
+Through observational and experimental studies, Mary Ainsworth et al. (Ainsworth, Blehar, Waters, & Wall, 1978; Bretherton, 1992) proposed that the
+attachment system functioned through different orientations (i.e., attachment styles or attachment patterns), which reflects the way internal working models of the self and others work. A secure attachment orientation is characterized by a selfperception of worthiness (lovability) connected with an expectation that other people are generally accepting and responsive. Insecure attachment may develop different orientations, anxious or avoidant, characterizing specific ways of emotional bonding with attachment figures (mothers or the closest carers in infanthood, or partner and intimate relationships in adulthood). In general, anxious attachment (also known as preoccupied or ambivalent attachment) is characterized by an amalgamation of a sense of unworthiness (unlovability) and a positive appraisal of others, which leads the person to struggle for self-acceptance by gaining the acceptance of valued others. Finally, an avoidant attachment orientation includes behaviors that aim to produce and keep autonomy, control, and independence in their relationships, using “distancing” and “detaching” coping strategies, avoiding intimacy and emotional involvement. This is a result of having a negative view of their attachment figures and a negative view of the self (for those who present higher levels of attachment anxiety and attachment avoidance, “fearful-avoidant” or “disorganized”’ attachment patterns) or a negative view of their attachment figures and combined with positive view of the self (also known as “dismissive-avoidant” patterns) (Mikulincer & Shaver, 2008, 2016a; Simpson & Rholes, 2012).
+In trying to understand the role of attachment in STB, it is important to consider how attachment is assessed. Due to the conceptual diversity within attachment theory, several authors have developed
+different methods to measure adult attachment, including interviews and self-report measures (Ravitz, Maunder, Hunter, Sthankiya, & Lancee, 2010). These instruments are either based on typological approaches of attachment, which conceptualize secure, anxious, and avoidant relationships as categories (patterns or styles), or on dimensional approaches, which operationalize attachment across dimensions (levels) of anxiety and avoidance attachment. Although a typological approach seems to be useful in clinical settings (Ravitz et al., 2010), it has been criticized for losing individual variability, having poor reliability and validity properties, and lacking statistical power and precision. These measurement issues have been argued to be solved through dimensional approaches of assessing attachment orientations (Fraley, Heffernan, Vicary, & Brumbaugh, 2011; Fraley, Waller, & Brennan, 2000; Sibley, Fischer, & Liu, 2005).
+Although Adam’s (1994) theoretical framework suggests the existence of a relationship between attachment and STB, the nature of this relationship is still unclear, as is whether insecure attachment orientations (anxiety and avoidance) are specifically more associated with suicidal thoughts and/or suicidal attempts. Similarly, Adam’s model does not propose distinct psychological and contextual factors related to attachment that could differentiate those who experience suicidal thoughts from people who attempt suicide.
+Recent reviews have endeavored to summarize the available evidence between attachment and STB (Miniati, Callari, & Pini, 2017), and attachment and self-injurious behaviors (Wrath & Adams, 2018). Although the key message of the review by Miniati et al. (2017) is that insecure attachment style is associated with increased suicide risk, their review was not
+systematically conducted. Wrath and Adams’s (2018) review was systematic but its inclusion criteria were stricter, yielding only 17 studies in the final synthesis. For example, the latter review did not include studies assessing suicidal ideation. The present review not only extends the aims of the aforementioned reviews, but it also addresses some of methodological limitations of these reviews focusing on studies of STB (excluding non-suicidal self-injury and non-suicidal self-harm). In summary, the aims of this systematic review are to (1) investigate the extent of the relationship between attachment orientations and STB, including exploring potential mediators and moderators of the attachment and STB relationship, (2) investigate whether the relationship between attachment and STB is similar for men and women, and (3) provide a theoretical synthesis of the results presented by the included studies.
+METHOD
+Information Sources
+A literature search was conducted using the following psychological and medical databases: Web of Science (all years, 1900-2017); EBSCO Host Medical and Psychology-related resources (all years, 1934-2017, which includes CINAHL, Health Source:	Nursing/Academic
+Edition, Psychology and Behavioral Sciences Collection, and PsycINFO); and EMBASE (all years: 1946-2017).
+Search (Full Electronic Strategy)
+The search was conducted on March 6, 2017, with no date restriction. Keyword searches using the terms Attachment AND Suicid* were employed, which generated
+2,389 records (1,003 after duplicates were eliminated). The search was refined by document types (articles) and languages (English; Figure 1).
+Eligibility Criteria
+Given that there were only 1,003 records, we took the decision to read all abstracts to determine whether they met the following inclusion criteria:
+1.	The study should be an empirical article.
+2.	The study should report a relationship between attachment and suicidal ideation or behavior.
+3.	The study should assess general attachment	or attachment
+to parents.
+4.	The study	should measure
+adults’ or adolescents’ perceptions of attachment or attachment patterns.
+5.	The study should be written in English.
+6.	The study should not be a single case study.
+7.	The study should not be a qualitative study.
+Data Extraction and Quality Assessment
+A data-extraction sheet was completed for each article with relevant information for this systematic review (e.g., article identification, methodological aspects, main results, and authors’ interpretation of their data). After data extraction, all selected studies were assessed for methodological quality using a 9-item index based on O’Connor, Ferguson, Green, O’Carroll and O’Connor’s (2016) quality assessment tool. Total scores were computed ranging from 0-13, meaning that a lower score is indicative of a higher probability of methodological bias. Classifications of quality were set as follows: 0-2 = very low quality, 3-4 = low quality, 5-7 = reasonable/medium quality, 8-10 = good quality, and = 11-13 excellent/very good quality. The detailed quality-assessment tool can be found in the in the Supplementary material (Table A). The checklist of items to include when reporting a systematic review or meta-analysis (Moher et al., 2009) was completed and can be found in the Supplementary material available (Table B).
+Data Analysis and Synthesis
+A narrative synthesis was conducted. The studies were grouped according to study design. An appraisal of authors’ interpretations of their data was also conducted to identify psychological themes across the studies and compare these with predominant models of suicidal behavior. These models are the interpersonal theory
+of suicide (IPT; Joiner, 2005), the integrated motivational-volitional model of suicidal behavior (IMV; O’Connor, 2011; O’Connor & Kirtley, 2018), the three-step theory (3ST, Klonsky & May, 2015), and the developmental model of suicidal behavior and attachment (Adam, 1994).
+The first three contemporary models of suicidal behavior, known as ideation-to-action frameworks, propose that the factors associated with suicidal ideation (SI) are distinct from those association with suicide attempts (SA). The IPT suggests that the combination of perceived burdensomeness and thwarted belongingness leads to suicidal thinking but the presence of acquired capability (e.g., fearlessness of death) is the key factor that distinguishes SI from SA. On the other hand, the IMV model suggests that defeat and entrapment are the main psychological factors that lead to SI, in which entrapment mediates the defeat-SI relationship. A range of volitional moderators, however, are hypothesized to differentiate between SI and SA. Finally, the 3ST suggests that SI arises from the combination of pain and hopelessness, and the SI-SA transition is facilitated by dispositional, acquired, and practical contributors to the capability for SA. Although none of the three new theories includes attachment explicitly, Adam’s model (1994) suggests that attachment may act as a vulnerability or background variable acting as a general contributor to the development of STB.
+RESULTS
+Descriptive Overview of the Selected Studies, Quality Assessment, and Sample Characteristics
+Of the 1,003 records identified, 52 were included in the systematic review (see Figure 1): 35 of the studies were
+cross-sectional, 12 were prospective, three were case-control, one was a randomized controlled trial, and one used mixed methods (quantitative and qualitative analyses). Twenty-six (50%) were conducted in the United States; six in Canada; four in the United Kingdom; three in Portugal; two each in France, New Zealand, Turkey, and Italy; and one each in Israel, Iran, South Africa, South Korea, and Finland.
+Although study quality varied, most studies were classified as reasonable/ medium. The majority of the studies employed a cross-sectional design; therefore, it is not possible to comment about causality. The assessment of attachment and STB across the studies was heterogeneous, as the instruments were quite diverse (e.g., validated and non-validated self-report scales, interviews, single item/ question, hospital admissions/records; references of the measures employed by the studies cited in Tables 1-3 can be found in the Supplemental material). Although most studies (n = 43) employed validated measures of adult attachment, only eight out of the 52 studies assessed the construct via clinical/research interviews. For the assessment of STB, 33 studies used self-administered measures and 19 employed interviews. Furthermore, 19 studies assessed STB through a non-vali-dated scale or a single item/question; 10 used registers from hospital admission for suicide attempt or items from a validated diagnostic rating scale; and 23 assessed STB via clinical interview or a full validated scale.
+There were 46,476 participants in the 52 studies (21,777 males and 24,689 females). The mean age was 25.1 years old (SD = 11.8) and 74% of participants (n = 34,412) were from the United States. Of the participants, 4,752 reported suicidal ideation or a suicide attempt and
+25,893 did not report any STB. Fourteen studies did not provide details of the number of suicidal and non-suicidal participants. Two-thirds of participants (66.5%) were recruited from the general population, 16.1% were university students, 7.3% were clinical patients (inpatients, outpatients, and people looking for treatment or admitted to the hospital), 4.3% were a mix of clinical patients and participants from the general population, and 5.7% were participants from specific populations (those who are homeless, war veterans, or immigrants’ children). In 24 studies, those in the suicidal group were clinically diagnosed with a mental health condition (nonpsychotic) (n = 2,016), whereas in two studies members of the suicidal group did not have any psychiatric diagnosis (n = 406). Twenty-six studies did not state whether members of the suicidal group had been diagnosed with a mental health condition (n = 2,330).
+Associations Between Attachment and STB
+The associations between attachment and suicidal ideation and behaviors are described in detail according to attachment patterns: secure attachment, avoidant attachment; anxious attachment; and other classifications of attachment.
+Secure attachment. Thirty-three studies investigated the association between secure attachment and STB, and the vast majority of these associations were negative (i.e., higher levels of secure attachment were associated with lower levels of suicidal thoughts or suicide attempts). The following subsections show how secure attachment is related to suicidal ideation, suicide attempt, and STB.
+Suicidal ideation. Twenty studies reported statistically significant associations
+between secure attachment and suicidal ideation (studies 1, 10, 11, 13-15, 20, 23, 29, 31-34, 36, 39, 40, and 44-47; main results are in Tables 1-3). With the exception of Study 32, all studies found secure attachment to be a protective factor against suicidal ideation. Study 32, which explored suicidal ideation among Vietnam combat veterans in treatment for PTSD, found the secure attachment group had higher levels of current suicidal ideation than the insecure attachment group: however, given the relatively small sample size (N = 48) and specific population, these results should be interpreted cautiously. In nine studies, suicidal ideation was negatively correlated with secure attachment (studies 10, 11, 13, 15, 23, 29, 31, 34, and 40). Other studies conducted multivariate analyses controlling for a range of variables alongside secure attachment to explain the variance in suicidal ideation (10, 11, 13, 15, 20, 29, 33, 34, 36, 39, 40, and 44-47). Within these studies, secure attachment predicted a reduction in suicidal ideation even when confounders were controlled for.
+Suicide attempt. All 10 studies found a negative relationship between secure attachment and suicide attempt (studies 2, 3, 4, 19, 26, 36, 39, 40, 41, and 46). In particular: (a) high levels of secure attachment were negatively correlated with frequency of suicide attempts (study 40); (b) suicide-attempt survivors reported significantly lower levels of secure attachment compared with those who have not attempted suicide (studies 3 and 26); (c) high levels of secure attachment predicted lower frequencies of suicide attempts (studies 40, 41, 2, 4, and26); and (d) low levels of secure attachment predicted suicide attempt (studies 36, 39, and 46). The relationship appears robust as some studies (2, 4, 39, 40, 41, and 46) controlled for
+confounders (e.g., sociodemographic factors, depression, self-esteem, religiosity), and only one (study 40) reported a nonsignificant association between secure attachment and current suicide attempt (after adjusting for previous suicide attempt). Study 19 compared a “drug misuse problem” group with a “no drug misuse problem” group, with all participants having attempted suicide in the previous year; the former group showed lower levels of secure attachment. Study 3 included individuals with current and past experiences of self-harm recruited from an online community, and study 26 included adolescents from an inpatient psychiatric unit without previous self-harm.
+STB. Eight studies did not differentiate between suicidal ideation and suicide attempt, but referred to “suicidality” as a mixed concept (i.e., including STB). They all reported a negative relationship between secure attachment and STB (studies 3, 6, 7, 30, 37, 43, 48, and 50). In particular, (a) there was a negative correlation between secure attachment and STB (studies 3 and 37); (b) suicidal groups reported significantly lower levels of secure attachment (studies 7, 43, and 50); and (c) lower levels of secure attachment predicted STB alongside other psychosocial variables in multivariate analyses (studies 3, 6, 30, 37, and 48). In the three studies (studies 30, 37, and 43) that controlled for covariates, the association remained significant after statistical adjustment.
+The vast majority of the studies that investigated the relationship between secure attachment and STB found that secure attachment was protective against STB. Even when considering the methodological heterogeneity across the studies, this relationship appears to be robust.
+Anxious attachment. 18 studies assessed anxious attachment and how it is
+associated with STB. This section includes both anxious and preoccupied attachment because they address the same psychological characteristics of attachment anxiety. It is important to note that attachment anxiety and anxious attachment are used synonymously across the studies.
+Suicidal ideation. Seven studies investigated the relationship between anxious or preoccupied attachment and suicidal ideation (studies 12, 15, 23, 28, 31, 38, and 52). Higher levels of attachment anxiety were positively correlated with suicidal ideation (studies 12, 15, 23, and 31), and suicidal ideation groups reported higher levels of attachment anxiety when compared to controls (studies 23 and 52). One study (study 38) comparing levels of suicidal ideation across attachment pattern groups (secure, anxious and avoidant) reported that the anxious group had higher levels of suicide ideation than the other groups. Study 15 showed that preoccupied attachment (together with hopelessness and poor problem-solving skills) explained variance in suicidal ideation. Study 28 presented findings indicating that attachment anxiety predicted membership of the suicidal ideation group and this relationship was mediated by interpersonal problems. This was the only study to investigate the relationship between suicidal ideation and anxious attachment that controlled for age, ethnicity. and gender, and demonstrated significant associations after adjustment.
+Suicide attempt. Nine studies investigated the relationship between anxious attachment and suicide attempts (studies 3, 16, 21, 22, 24, 25, 27, 28, and 49). One study (study 24) found a positive correlation between the lethality of suicide attempts and the level of anxious attachment (the higher levels of anxious attachment, the more lethal a suicide attempt
+was). The remainder found that patients who had made a medically serious suicide attempt showed higher levels of anxious attachment than patients who were deemed non-medically serious (medically serious suicide attempts were defined as more than 24 hours of hospitalization in an intensive care unit or surgery under general anesthesia after a suicide attempt). Study 16 found that patients with two or more lifetime suicide attempt presented higher levels of anxious attachment when compared to other groups of patients (no suicidal ideation group, suicidal ideation with no attempts group, and one suicide attempt group) after controlling for traits of borderline personality disorder.
+Four studies using multivariate analyses reported that attachment was a significant predictor of suicide attempt: (a) study 21 found that depressive symptoms mediated the relationship between attachment anxiety and suicide attempt; (b) study 24 showed that attachment anxiety, alongside depression, hopelessness, and negative life events, predicted high levels of lethality in suicide attempts and that avoidant attachment explained the variance in medical lethality of suicide attempt mediated by self-disclosure and loneliness; (c) study 25 found that age and anxious attachment predicted the likelihood of having made a suicide attempt. Finally, (d) study 28 conducted a mediation analysis and demonstrated that attachment anxiety predicted suicide attempt group membership, but this relationship was mediated by lack of sociability. Other studies (16, 21, 27, and 28) controlled for covariates (e.g., gender, age, ethnicity, borderline personality disorder traits, depressive symptoms), and all reported significant associations between anxious attachment and suicide attempt.
+STB. Three studies (studies 8, 17, and 30) explored the association between
+anxious attachment pattern and STB. Study 8 found that anxious attachment explained the variance in STB through the mediation of dependency and self-criticism, after adjusting for gender. Study 17, assessing teenagers in psychiatric treatment, showed that those who were female, older than 15 years, and had unresolved-disorganized attachment-related trauma and an anxious attachment pattern were significantly more likely demonstrate STB. Finally, study 30 found that being older and having an anxious attachment pattern increased the likelihood of belonging to the STB group, after controlling for age and gender. Having an anxious (or preoccupied) relationship orientation seems to be a vulnerability factor for both suicidal ideation and suicide attempt even when other psychological variables are controlled for.
+Avoidant attachment. Seventeen studies looked at the relationship between avoidant attachment orientation and its association with suicidal thoughts and actions. This section includes studies reporting on avoidant attachment orientations in general, as well as the dismissive avoidant and fearful avoidant patterns. Attachment avoidance and avoidant attachment are used interchangeably across the studies.
+Suicidal ideation. Suicidal ideation was associated with (a) avoidant attachment (studies 4 and 42), (b) fearful avoidant attachment (studies 15, 23, and 31), and (c) dismissive avoidant attachment (studies 15 and 23). Specifically, avoidant attachment predicted variance in suicidal ideation at two time points (baseline and 3-month follow up; study 42), and suicidal ideation group membership in study 4 (when adjusted for several sociodemographic and mental health variables). Levels of fearful avoidant attachment were
+positively correlated with suicidal ideation (studies 15, 23, and 31), and participants within the suicidal ideation group reported higher levels of fearful avoidant attachment than controls (study 23). The picture in relation to dismissive avoidant attachment is less clear: Study 15 found a positive correlation between levels of dismissive avoidant attachment and suicidal ideation but only among female participants, and a negative correlation was found between dismissive avoidant attachment and suicidal ideation in study 23.
+Suicide attempt. Most of the studies that investigated the relationship between avoidant attachment and STB found it to be more strongly associated with suicide attempts than suicidal thoughts (studies 3, 4, 16, 19, 22, 24, 27, 28, 42, 49, and 51). In particular, (a) levels of avoidant attachment were positively correlated with levels of medical lethality of suicide attempts (study 24), and (b) higher levels of attachment avoidance among those who attempted suicide in the past compared to those who had never engaged in suicidal behavior or had suicidal ideation (study 27). Six studies (4, 24, 27, 28, 42, and 51) investigated avoidant attachment as part of a multivariate model predicting suicide attempt. Study 28 found that interpersonal sensitivity mediated the relationship between avoidant attachment and the risk of suicide attempt. Three studies (3, 19, and 22) investigating the relationship between dismissive and fearful avoidant attachments and suicide attempt showed that: (a) people with a history of suicide attempts reported higher levels of dismissive and fearful avoidant attachment than those with no history of suicide attempts (study 3); (b) women with a history of suicide attempt and drug misuse problems had significantly higher dismissive and fearful attachment scores than those with
+no history of drug misuse (study 19), and (c) participants with a history of suicide attempt were most likely to describe themselves as having a dismissive and avoidant attachment pattern in comparison with other attachment patterns (study 22). Study 49 found higher levels of fearful avoidant attachment in people with a history of suicide attempts compared with patients with major depression and health controls. Of the eleven studies that focused on the relationship between suicide attempt and avoidant attachment orientations, only six (4, 16, 27, 28, 42, and 51) controlled for confounders and found no post-adjustment changes in their results.
+STB. Four studies found associations between avoidant attachment orientations and STB (3, 8, 17, and 43). Study 8 found that avoidant attachment predicted variation in STB, and this relationship was mediated by self-criticism. Two studies (3 and 17) found dismissive avoidant attachment and STB to be associated but in different ways: Study 3 revealed a positive correlation between levels of dismissive avoidant attachment and STB, even after controlling for other psychological variables, but study 17 found that dismissive avoidant attachment was associated with the absence of suicidal ideation or attempt. Authors of this latter study suggested that it is unlikely that suicidal feelings were simply denied in the dismissive avoidant group and, therefore, future longitudinal research was needed. Studies 3 and 43 found that (a) levels of fearful avoidant attachment were positively correlated with levels of STB (study 3), and (b) participants with ultra-high levels of suicide risk reported significantly higher scores of fearful avoidant attachment after controlling for confounders (study 43).
+Consistent with the pattern of findings found in the studies reporting an anxious
+attachment-STB relationship, the literature also suggests that an avoidant orientation of attachment acts as a vulnerability factor for STB in interaction with other psychological variables. In most of the studies, this finding also appears to hold regardless of how the type of avoidant attachment is conceptualized (i.e., dismissive or fearful).
+Other Attachment Classifications. The conceptualization of attachment patterns may vary across theoretical views on attachment in the literature. Although Ainsworth’s original proposition established three basic attachment patterns (secure, anxious/ambivalent, or avoidant), attachment is used as a synonym for a diverse range of definitions across the studies, including emotional bonding (33), object relations (6), parental involvement (5), parental closeness (44), and separation anxiety (52). Therefore, some studies have assessed attachment differently from the more frequently used classifications (secure, anxious, and avoidant). In this section, we describe the results of those studies that adopt other conceptual attachment patterns and their associations with suicidal thoughts and actions.
+Suicidal ideation. Two studies (32 and 38) assessed unresolved/disorganized attachment. In Study 38, authors define unresolved attachment as a state of mind, “not an enduring, organized attachment strategy but rather a brief collapse of an adult’s mental organization during discussions of trauma” (p. 196). The authors found that unresolved adults (especially females) were significantly more likely than other attachment groups to report emotional distress and suicidal ideation. In study 32, those in the unresolved/disorgan-ized group demonstrated lower levels of current suicidal ideation when controlling for PTSD severity than those in the secure
+group. These results should be interpreted cautiously, though, given the relatively small sample size (N = 48). Moreover, the findings may not be generalized beyond the existing sample of Vietnam combat veterans in treatment for PTSD.
+In study 5, secure and insecure attachment were assessed as “parental positive stimuli” and “parental negative stimuli,” respectively, with parental negative stimuli being the only predictor of suicidal ideation group membership. Finally, in study 9, attachment patterns were assessed via “linkage prototypes” (defined as relationship proximity patterns). Using this operationalization of attachment, suicidal ideation was found to be associated with lower levels of “comfort with proximity” and “trust in others” and with higher levels of linkage anxiety.
+Suicide attempt. Study 18 assessed attachment through a projective test comprising seven black-and-white images of people illustrated in several circumstances eliciting themes of separation, loneliness, and loss. The vast majority of the participants with a history of suicide attempt were classified as having unresolved attachment (73.7%, n = 123). Eleven were classified with preoccupied attachment pattern (6.6%), 29 with dismissing attachment pattern (17.4%), and three (1.7%) were classified as secure. In this study, participants with a disorganized attachment pattern and five or more risk factors were 37 times more likely to belong to the suicidal group than those with zero risk factors. Study 41 employed a longitudinal design to assess attachment to parents among adolescents who had engaged in suicide attempt. It found that insecure maternal attachment independently predicted suicide attempt at 6-month follow up. Finally, study 35 evaluated general insecure attachment among women diagnosed
+with bulimia nervosa, with 27.5% of them reporting a suicide attempt history. These authors found that: (1) the lifetime suicide attempt group had significantly higher levels of insecure attachment than the no suicide attempt group; (2) insecure attachment predicted lifetime suicide attempt group membership (univariate logistical regression); and (3) insecure attachment alongside identity problems, depression, and anxiety disorder predicted the suicide attempt group membership (multiple logistic regression).
+STB. Only study 17 assessed the relationship between different classifications for insecure attachment and STB. The study reported that adolescents who were female, older than 15, and had unresolved-disorganized attachment-related trauma and preoccupied attachment pattern had a significant increase in the probability of membership in the STB group. By unresolved-disorganized	attachment-related
+trauma the authors meant a lack of resolution of traumatic events related to attachment such as the death of a parent or other close relationship figure or the lack of resolution of physical or sexual abuse. In this study, 77% of all participants classified as having unresolved-disorganized attachment-related trauma or preoccupied attachment pattern were in the suicidal group.
+Moderating and Mediating Factors
+Out of the 52 studies of the current review, only eight (8, 10, 13, 21, 24, 28, 29, and 45) investigated the effect of mediators or moderators of the attachment-STB relationship. Three studies (10, 24, 28, and 29) found significant associations for the mediating effect of interpersonal factors: self-disclosure and loneliness (24), interpersonal sensitivity, aggression, and
+lack of sociability (28), anomie (broken social bonds, study 10), and thwarted belongingness (29). Studies 8 and 10 reported evidence for the mediating effects of intrapersonal characteristics: self-criticism (8) and self-control (10). Two studies (13, 21) tested the role of psychiatric symptoms as mediators between attachment and STB: both studies found significant associations for depressive symptoms, and study 13 also found evidence for the mediating effect of defensive psychopathology, substance use, borderline features, and dissociative symptoms. Only one study (study 45) conducted a moderator analysis and found moderating roles for demographic characteristics (age and ethnicity).
+Gender Differences
+Only seven out of the 52 studies within this systematic review (14, 17, 18, 38, 44, 45, and 48) reported on gender differences or effects specific to males or females. Four studies (19, 22, 35, and 40) recruited only female participants. The studies that reported gender differences, with the exception of study 45, showed that insecure attachment increased suicide risk if the participants were female.
+Studies 14 and 44 reported that the association between secure attachment and suicidal ideation was significant only for female participants. Study 45 found that for girls, attachment to fathers independently predicted reduction of suicidal ideation over time and for boys, attachment to mothers independently predicted reduction of suicidal ideation over time. One study (study 48) reported a gender effect increasing suicide risk: Being female, perceiving poor attachment to parents, a high level of paternal rejection, a high degree of maternal control, and a low level of family
+cohesion increase the probability of membership in the self-destructive thoughts and behaviors group.
+Although there was some evidence of a potential gender effect for the attach-ment-STB relationship, the current evidence is inconclusive as there was no consistent pattern of findings across the studies and often the gender analyses appeared to be post hoc.
+Theoretical Explanations of the Relationship between Attachment and STB
+Although the studies yielded evidence of the existence of an association, an important question to be addressed is why attachment and STB are associated. Taking an overview of these studies, most authors argued that, in interaction with other personality factors, attachment moderates or mediates the relationship between current negative events and psychological states that, in turn, may lead to STB. More specifically, (1) current negative events interpreted as threatening contextual cues would (2) activate the attachment system which, through one’s models of the self and of others, would interact with other personality traits to act as coping strategies to deal with those negative events and with the emotional reactions to those events. This interaction would (3) trigger certain psychological states if those strategies fail to work, which would subsequently lead to (4) STB when a resolution of those current negative events is not met.
+An example could be: (1) in contexts of a high likelihood of being criticized by others, such as an experience of being bullied, (2) individuals with higher levels of anxious attachment generally develop a negative internal working model of the self and tend to engage in self-critical verbal
+behavior and harsh self-evaluation (and other traits of neuroticism and self-criticism) aimed at reducing the likelihood of being criticized by others. If this strategy fails to work, (3) other psychological states such as depression, hopelessness, and isolation may ensure and lead to (4) STB if the experiences of being bullied remain.
+DISCUSSION
+This systematic review set out to examine the relationship between attachment and STB in adolescents and adults. A key finding was that secure attachment functions as a protective factor against STB, and insecure attachment (variations of avoidant and anxious attachment orientations) seems to increase risk for suicidal thinking and behavior. Although the literature clearly shows the existence of an association between attachment and STB, the precise nature of the relationship between insecure attachment orientations and STB remains unclear; in the vast majority of the studies, all forms of insecure attachment orientations were found to be associated with both suicidal ideation and suicide attempt. This suggests that it is not possible to determine whether a specific orientation or style of insecure attachment differentiates between suicidal ideation and suicide attempt groups. As a consequence, it is difficult to identify a single explanation for why anxious attachment, for instance, would be positively associated with suicidal ideation but not with suicide attempts. Wrath and Adams’s (2018) review reported that attachment anxiety is a crucial factor for engaging in both forms of self-injurious behavior (non-suicidal self-harm and suicide attempt), but did not demonstrate whether specific forms of insecure attachment differentiated between
+those who engaged in self-injurious behavior from those who attempted suicide. Despite including a wider range of studies, our review yielded similar findings in that we found it difficult to determine whether anxious and avoidant attachment orientations were differentially associated with histories of suicidal ideation versus suicide attempt.
+A secondary aim was to investigate whether there are gender differences in the relationship between attachment and STB. Although gender differences regarding suicide are prominent and well documented in suicide research more generally, the same cannot be concluded for the attach-ment-STB literature. Few studies reported the effect of gender on the relationship between attachment and STB. Although six studies demonstrated that insecure attachment increased suicide risk in women, the generalizability of these findings should be interpreted with caution because the majority of the studies were cross-sectional and they represent only 21% of the total articles in this systematic review and 12.2% of the total number of participants. Moreover, it was often difficult to investigate the relationship in men because there were too few male participants in the majority of these studies. Thus, there is insufficient evidence to draw any conclusions on gender differences on the relationship between attachment and STB.
+Regarding factors that may elucidate our understanding of the relationship between adult attachment and STB, only eight out of the 52 studies (15.3%) tested the role of interpersonal, intrapersonal, psychopathological, and demographic factors. Although the inclusion of such factors was theoretically justified by the studies’ authors, only one study (29) tested a component of a contemporary theory of
+suicidal behavior (IPT; thwarted belongingness; Joiner, 2005). The attachmenttheory research field would benefit from investigating the extent to which psychological variables suggested by the ideation-to-action models of suicidal behavior help understand the attachment-STB relationship (Joiner, 2005; Klonsky & May, 2015; O’Connor,	2011; O’Connor &
+Kirtley, 2018).
+Nonetheless, it is possible to note that the relationship between attachment and STB is consistent with contemporary frameworks of suicidal behavior. For example, Adam’s (1994) model of suicidal behavior, focused on how the vulnerability for STB develops across the lifespan, posits that attachment insecurities in childhood may act as distal risk factors for STB later in life (e.g., studies 11, 36, and 37). Although the vast majority of the studies in this review are not longitudinal, their findings are consistent with Adam’s hypothesis of attachment serving as an intermediate risk factor connected to other psychological states and personality traits preceding STB.
+The theoretical synthesis also suggests that types of insecure attachment may act as a diathesis as described in the integrated motivational-volitional model of suicidal behavior (O’Connor, 2011). According to the latter, in conjunction with environmental influences, life events, and other personality traits (e.g., social perfectionism, loneliness), forms of insecure attachment may act as a psychological vulnerability for STB. Insecure attachment orientations may increase one’s sensitivity to feeling defeated and trapped (psychological states), thereby elevating the likelihood that suicidal thoughts emerge. Likewise, insecure attachment may also facilitate the development of thwarted belongingness and burdensomeness proposed in the interpersonal
+model of suicide (Joiner, 2005). In Study 29, for example, Venta et al. (2014) suggest that interpersonal risk factors for STB can potentially be targeted by addressing beliefs of thwarted belongingness, rather than treating attachment-related risk for STB as static. Moreover, activation of the attachment system could also increase emotional pain and weaken feelings of connectedness as outlined in the three-step theory (Klonsky & May, 2015), increasing the risk of developing STB. Although most authors mentioned the potential interaction of attachment with proximal psychological and interpersonal factors to increase suicide risk, no study has investigated the location of attachment within the ideation-to-action frameworks, with exception of study 29.
+Our findings highlight that both anxious and avoidant attachment may lead to STB, but the mechanisms involved in this vulnerability development for each attachment orientation are still unclear. Mikulincer and Shaver (2016b), for example, propose that a suicide attempt could be the result of an anxiety-mediated hyperactivation of the attachment system, communicating the need for help and indicating that the person is struggling to cope with adversities and emotional pain. Levi-Belz et al. (study 24) is another example that demonstrates how avoidant and anxious attachment may increase one’s vulnerability for a suicide attempt. These authors propose that the negative perception of others (internal working model of others), characteristic of high avoidant individuals, makes it precarious for them to disclose their emotions and thoughts to others. These attachment processes, in turn, reduce such individuals’ ability to approach someone and ask for help in an emotional crisis. As a consequence, they are more likely to become detached,
+lonely, and alienated, which increases the risk of suicidal behavior. Falgares et al. (study 8) offered a similar interpretation for their results finding that, having a negative view of others, individuals with higher levels of attachment avoidance may feel the need to be highly competent or nearly flawless at life tasks to maintain selfreliance rather than risk potential rejection. A suicide attempt could be a response to interpersonal rejection and/or to failing to reach these standards of perfectionism.
+There was no evidence that high-quality studies had a different relationship between attachment and STB compared to low-quality studies. When evaluating the quality of the studies, we considered how attachment and STB are assessed. It is known, for instance, that adult attachment can be assessed both through self-administered scales (e.g., Armsden and Greenberg, 1987; Griffin and Bartholomew, 1994) and clinical/research interviews (e.g., George et al., 1985; Hesse, 2008). These procedures are not only grounded in different approaches to attachment, they also can yield very different results which vary as a function of the nature of the assessment (e.g., an interview versus questionnaire, typological versus dimensional conceptualization). Although most studies assessed attachment and STB through selfadministered measures, STB were most often assessed via clinical/research interviews when the participants were patients, and most studies, irrespective of sample characteristics, used self-administered scales to assess attachment. Therefore, it is difficult to say whether the assessment method differentially impacted the findings.
+There are a number of clinical implications that arise from this review. Centrally, clinical assessment of patients should include an evaluation of their attachment relationships, their views of
+themselves and of others, and, thus, their potential approach to coping with a mental health crisis involving suicidal ideation and attempts. Clinicians should employ both self-report measures and clinical interviews when possible (see Ravitz et al., 2010 for an extensive review of adult attachment measures) to examine the patient’s beliefs and behaviors about close relationships. Such assessments may provide useful information about the patient’s vulnerability associated with attachment insecurities as well as their coping strategies to deal with difficult life events and mental health crises. For example, if a patient tends to engage in anxious hyperactivation strategies (Mikulincer & Shaver, 2016d) and does not find the support needed from their attachment figures, feelings of thwarted belongingness may emerge and increase the likelihood of suicidal thoughts (as suggested by study 29). Such information may be useful when establishing a safety-planning intervention (Stanley & Brown, 2012), for instance. By being aware of a patient’s attachment-related strategies (anxious hyperactivation or avoidant deactivating) during a crisis, both the clinician and the patient will be able to collaboratively plan a strategy that involves internal coping strategies and social support to mitigate against suicide risk. Such awareness will also provide clues about likely engagement with the therapeutic process. Some psychotherapeutic approaches that include attachment as a key construct, such as mentalization-based treatment (MBT; e.g., Bateman & Fonagy, 2013) and attachment-based family therapy (ABFT; e.g., Ewing, Diamond, & Levy, 2015) can provide clinicians a wide range of tools for assessment, formulation, and intervention.
+The results of the present systematic review should be interpreted within the
+context of its limitations. Conducting a meta-analysis of the studies included within this review was not feasible due to the level of heterogeneity across the studies regarding (1) the variety of conceptual frameworks of attachment and STB, (2) the diversity of instruments assessing attachment and suicidal thinking and behaviors, and (3) the heterogeneous populations participating in the studies. Therefore, we conducted a narrative review, which may be more susceptible to bias and subjectivity than a meta-analysis. We can also say little about whether the attachment-STB relationship differs across cultures because the vast majority (74%) of the participants in the 52 studies were from the United States. The attachment-STB relationship appears to hold irrespective of whether the samples were clinical or not. It is important to note, however, that we did not investigate publication and selection biases and we did not preregister a systematic review.
+A significant challenge in integrating the attachment-STB research literature is the inconsistent use of terminology used to describe different patterns of attachment. This is, in part, because attachment theory comprises different conceptual approaches including Bowlby and Ainsworth’s original conceptualization (e.g., Ainsworth, 1964; Ainsworth & Bowlby, 1991; Bowlby, 1988) and the contemporary frameworks on adult attachment (e.g., Bartholomew & Horowitz, 1991; Hazan & Shaver, 1987; Mikulincer & Shaver, 2016c). This inconsistency complicates the process of synthesizing the studies since the authors conceptualize attachment in such diverse ways, even though they are analyzing the same psychological phenomena. Equally, the term insecure attachment (e.g., study 35) limits our understanding of the specific attachment orientation that is being referred
+to, since insecure attachment encapsulates a whole range of patterns including being anxious, dismissive, preoccupied, fearful, or disorganized. A similar concern can be extended to the various definitions of STB. Suicide risk, suicide tendencies, suicidal triggers, and suicidality have all been employed to describe suicidal ideation or behavior in a non-uniform manner. Studies assessing suicidal ideation and behaviors as a mixed concept, i.e., suicidality, may restrict the understanding of their relationship with attachment further, as suicidal ideation and suicide attempts are different phenomena and have unique psychological characteristics (O’Connor & Kirtley, 2018). Terms such as suicidality and suicidal behaviors are often defined in a vague manner (Silverman, 2006). A similar criticism can also be applied to uses of self-harm or selfinjury to encompass both suicide attempt and non-suicidal self-injurious behaviors.
+This review generates key research questions, such as (1) whether attachment plays a role in the differentiation between those who suffer from suicidal ideation and those who have a history of suicide attempt and (2) whether and how specific attachment dimensions might play different functions in this differentiation. It highlights (3) the need for empirical testing of what role attachment play in the theoretical understanding of suicidal behavior (Joiner, 2005; Klonsky & May, 2015; O’Connor, 2011); and lastly asks (4) What is the role of gender in the attachment-STB relationship?
+CONCLUSION
+The findings of this systematic review reveal that secure attachment seems to act as a protective factor against suicidal ideation and suicide attempt, and insecure dimensions of
+attachment seem to function as vulnerability factors for STB. A synthesis of the studies’ explanations of how attachment may be related to suicidal ideation and suicide attempts indicates that, in interaction with other personality factors, attachment moderates or mediates the relationship between current negative events and psychological states that, in turn, may lead to STB. Implications for treatment include targeting attachment-related coping strategies to mitigate vulnerability for STB.
+AUTHOR NOTE
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+1.	Introduction
+Intimate partner relationships are an integral factor in the lives of many individuals, influencing the dynamic interplay between individual mental health and overall well-being (Whisman and Baucom, 2012). Around one-half of the Australian population, aged 15 years and older, are married (Australian Bureau of Statistics, 2012a) while the proportion of persons cohabiting has increased progressively from 6% in 1986 to 16% in 2011 (Australian Institute for Family Studies, 2014). The United States of America reported a marriage rate of 6.8 per 1000 and a divorce rate of 3.6 per 1000 in 2012 (National Centre for Health Statistics, 2012), with an increase of 14% in the rate of cohabitation since 1995 to 2010 (Copen et al., 2012). In comparison, demographic data for the European Union shows that there has been a decline in the crude marriage rate, while the number of divorces has increased (Eurostat, 2014). Further, the proportion of births outside of marriage has increased across the European Union with an approximate 12% increase in births since 2000 (Eurostat, 2014). Although it is clear that trends in couple formation have changed dramatically over the twentieth century, the constant nature of the formation and dissolution of intimate partner relationships is a perpetual feature in the community landscape.
+Suicide is currently the leading cause of death for Australians aged between 15 and 44 years (Australian Bureau of Statistics, 2012b) and is the second leading cause of death globally among 15 to 29 year olds (World Health Organisation, 2012). The purported association between suicidality and intimate partner relationships is grounded in empirical research that documents the influence of relationship factors on suicidality. For instance, studies have shown that low quality intimate partner relationships (Arcel et al., 1992), interpersonal conflict (Choi et al., 2013) and separation and/ or divorce (Wyder et al., 2009) are common precipitating factors to suicide. Furthermore, discord between two individuals in an intimate partner relationship is a prominent theme applicable to a range of cultural contexts.
+Researchers have increasingly found that spouse or partner negative life events contribute significantly to suicidality (Bagge et al., 2013). Specifically, the effect of a relationship separation has been shown to have a significant increase in suicidal ideation and plans and/or attempts in the two years following separation (Batterham et al., 2014), with the risk particularly high for males aged 15 to 24 years (Wyder et al., 2009). These results are significant, as the divorce rate in Australia is highest in the under 30 age group for both men and women (Australian Institute for Family Studies, 2011) and the average age for a first divorce in America is 30 years (National Centre for Health Statistics, 2012).
+The influence that a positive intimate partner relationship has on reciprocal mental health states can be significant. Studies have demonstrated that marital quality is positively associated with subjective well-being (Carr et al., 2014), and that positive romantic relationships influence physical health (Robles and Kiecolt-Glaser, 2003) and can be psychologically protective (Bookwala and Schulz, 1996; Markey et al., 2007). However, relationship discord is associated with the onset or maintenance of mental health problems, including depression (Whisman and Uebelacker, 2009) and subsequently poorer treatment outcomes (Whisman, 2013).
+Several systematic reviews have been conducted to investigate specific elements of intimate partner relationships that are associated with suicidality, including intimate partner violence and abuse (Devries et al., 2013; McLaughlin et al., 2012) and separation (Ide, Wyder, Kõlves, & De Leo, 2010). No review, however, has focused more broadly on intimate partner relationships as a whole and their influence on suicidality. The current systematic review aims to identify the factors within intimate partner relationships that influence suicidal ideation, attempts and completion.
+Potential differences in gender, age and relationship status will also be explored. This review will assist in identifying gaps in the research literature and isolate potential targets for suicide prevention research in this area.
+For the purpose of this review, a broad definition of intimate partner relationships will be adopted to capture the multifaceted nature of these relationships. As such, an intimate partner relationship is defined as: An interpersonal relationship between heterosexual and same-sex spouses, girlfriends or boyfriends (current and former) which involves physical and/or emotional intimacy.
+2.	Methods
+2.1.	Search and screening procedures
+A systematic review of the literature was conducted to identify published studies that explore intimate partner relationships in the context of suicidality. Scopus, PubMed and PsycINFO databases were electronically searched, up to October 2014, with one or more of the following sets of terms: (i) partner relationship or intimate relationship or personal relationship or romantic relationship or dating AND suicid*; (ii) marriage or marital or divorce or separt* or relationship breakup or spouse AND suicid*; (iii) love or relationship disruption or relationship difficult* or relationship problem* AND suicid*. The titles and abstracts of the 9321 articles initially identified by these searches were screened by the primary author to determine their relevance to the review. Completely irrelevant articles that were unrelated to the topic of this review (i.e. did not discuss intimate partner relationships or suicide) were excluded at this stage, while relevant studies and reviews were retained and the full-text article examined. Additional articles were obtained from reference list searches. Fig. 1 presents a flowchart detailing the review process.
+The inclusion criteria for the current review included: (i) measurement of intimate partner relationship factors (i.e. separation, conflict, and/or quality); (ii) measure of suicidal behaviours (ideation, plan, attempt and/or completion); (iii) tested the association between intimate partner relationships and suicidality; and (iv) the article was published in a peer-reviewed English language journal. Articles exploring intimate partner abuse and violence were excluded from the current review due to the recency of two systematic reviews (Devries et al., 2013; McLaughlin et al., 2012) explicitly exploring the relationship between these two factors. Articles were also excluded if intimate partner relationships were not distinguished from other general types of relationships (e.g. family, social or professional). Studies that fulfilled the inclusion criteria were coded by the primary author and one of three independent reviewers, with all relevant data collected and recorded.
+3.	Results
+Overall, 51 empirical studies were identified that fulfilled the inclusion criteria. Of these, 18 (35%) were retrospective studies, 15 (29%) were cross-sectional, five (10%) were qualitative, five (10%) were longitudinal or prospective and eight (16%) were case control or case crossover studies (see Table 1). Due to the heterogeneity among the included studies, analysis of the data was completed in the form of a narrative synthesis. This approach is considered appropriate for synthesising the results of studies with disparate study designs and aims (Hilari et al., 2012; Popay et al., 2006). The formation of themes were extracted according to key words used to describe the measure of relationship factors located within the
+reviewed articles. The themes were ordered to depict the progression of relationship factors found to contribute to suicidality. Tables 1-5
+3.1.	Quality
+From the outset of a relationship, the perceived level of relational quality will influence all facets of intimate interaction. Although the concept of ‘quality’ is difficult to define within the broad context of intimate partner relationships, the studies identified highlighted inequity, authenticity, communication and overall low quality as indicators for suicidal thoughts, behaviours and completion.
+Reporting on suicide death, Davis et al. (2009) found that people under 30 years were almost twice as likely to die by suicide in response to a perceived relationship inequity (i.e. the unequal contribution made to the relationship by each person), compared to those over 65 years who seldom mentioned relationship inequity as a reason for completing suicide. Canetto and Lester (2002) also found that love themes were significantly more common within suicide letters compared to achievement motives, independent of sex and age.
+Soller (2014) reported that perceived relationship authenticity (incongruence between thoughts, feelings and actions within relational contexts) was associated with greater ideation and attempts, but only for females. He argued that the verification of interpersonal relationships has a heightened salience within the female role-identity, significantly influencing mental health. Also analysing a younger cohort, Sandberg-Thoma and Kamp Dush (2014) reported that adolescent suicidal ideation was not significantly associated with the number of romantic relationships in adulthood. Interestingly, participants who had initially reported suicidal ideation had a significantly decreased likelihood of future dissolution of cohabitation (Sandberg-Thoma and Kamp Dush, 2014). However, this study did not prospectively examine the
+quality of relationships or separation on later suicidal behaviours.
+A specific factor pertinent to relationship quality was interpersonal communication between partners. Bonnar and McGee (1977) reported that the overall quality of interpersonal communication between spouses significantly contributed to the degree of suicidal behaviours. They observed that as the quality of interpersonal communication between spouses deteriorated (as perceived by the couple), the degree of suicidal behaviour increased (Bonnar and McGee, 1977). In support of this finding, Canetto et al. (1989) also reported ‘moderate to severely’ dysfunctional communication occurring in couples where a partner had identified high risk suicidal thoughts and/or behaviours. They argue that a lack of positive communication leads to an adoption of rigid defensive roles within the relationship, with suicidal behaviour occurring as a response to a series of interpersonal events (Canetto et al., 1989).
+Overall poor or low quality intimate partner relationships were identified as a predominant factor in the suicide attempts of women hospitalised in Greece and Denmark (Arcel et al., 1992). Low quality was characterised by a high degree of psychological and physical violence, creating a sense of entrapment fuelled by shame, and ultimately leading to a suicide attempt as a means of escape (Arcel et al., 1992).
+3.2.	Relationship problems
+Problems in the context of an intimate partner relationship can focus on an issue that is difficult to deal with and often a source of concern. Intrinsincly linked with poor quality relationships, tangible relationship problems (inclduing infidelity, rejection, abandonment etc.) appear to manifest over time and may be perceived as insurmountable by partners in the relationship.
+Problems in a marriage or relationship was a major risk factor associated with suicide death within a number of retrospective studies (Busuttil et al., 1994; Logan et al., 2011). With Kurtaç et al.
+(2012) observing that suicide letters written by men focused on financial problems, while for women interpersonal relationship problems with a spouse or boyfriend were more dominant. However, the contrary was reported by Lester et al. (2004) who found that men more frequently reported love or romantic problems, whereas women were more likely to have “escape from unbearable pain” as a motive in their suicides (p. 34). They also found that older people were more often motivated by ‘escape from pain’ than had love or romantic problems (Lester et al., 2004).
+In terms of age, consistent associations between relationship problems and suicidal behaviour were reported. Although nonintimate partner relationship problems (e.g. one or both parents or friends) tended to play a significant role in suicidal behaviours among 10-24 year olds (Chia et al., 2008; Karch et al., 2013), disruption in the partner relationship was frequently reported as a common contributory antecedent to suicide death among 15 to 24 year olds (Houston et al., 2001). This finding was also culturally reflected in a sample of Iranian youth (14 to 17 years) who also expressed “difficulties in love” as their primary reason for attempting suicide (Keyvanara and Haghshenas, 2011). Further, relationship problems with a partner (Haw, 2008) and chronic relationship difficulties with a spouse (Daradkeh and Al-Zayer, 1988) were reported as common contributory factors for individuals around the age of 25 years. Of the one study focusing on an allmale veteran sample, one out of every two younger (18-34 years) veteran suicide decedents experienced intimate partner relationship problems shortly before death (Kaplan et al., 2012).
+Osvath et al. (2003) reported that relationship problems and conflict with a partner was rated as the most common traumatic life event preceding a suicide attempt (94.1%), outnumbering death and experience of losses (79.2%) and mental (77.2%) or physical abuse (65.3%). Tavite and Tavite (2009) examined social factors contributing to suicidality on the small territory of Tokelau (a territory of New Zealand with significant suicide rates) and found that self-reported relationship problems including “marriage breakdown, affairs/betrayals and lack of spousal support” contributed to suicidal ideation and subsequent attempts (p. 72). Themes such as smothering love, infidelity, partner violence and denial of affection also contributed significantly to suicidal behaviour in Stephens (1985) study. Stephens (1985) also observed that younger participants tended to react to specific events by attempting suicide, whereas the older participants were more likely to be responding to long-term conflicts with their partners.
+Beautrais et al. (1997) reported that beyond antecedent social, family or personality factors, suicide attempts were elevated by interpersonal relationship problems experienced during the previous year. In fact, the study reported that population attributable risk (PAR) estimates suggested that a potential decrease of up to 23% of suicide risk could be achieved if interpersonal difficulties were resolved. Bagge et al. (2013) reported that only spouse/ partner negative life events uniquely predicted suicide attempts when controlling for other specific negative life event categories. The study used a case crossover design to demonstrate that interpersonal negative life events are specific triggers for suicide attempts. More importantly, these results showed that interpersonal negative life events, involving a romantic partner, served as a trigger for engaging in suicidal behaviour for individuals not currently planning an attempt (Bagge et al., 2013).
+3.3.	Conflict
+Conflict featured as a predominant theme leading to suicidality within intimate partner relationships. Interpersonal conflict (Runyan et al., 2003), quarrels (Fieldsend and Lowenstein, 1981) and relationship discord (Whisman and Uebelacker, 2006) were
+identified as known risk factors for suicidal thoughts and behaviours. Conflict was often protracted and an experience of a buildup of problems within the intimate partnership.
+Disruption in the partner relationship was frequently reported as a precipitating factor to suicide death (Houston et al., 2001), with participants categorising quarrelling with partner (Zhang and Ma, 2012), interpersonal conflict with boy/girlfriends (Brent et al., 1993), and serious arguments with spouse (Paykel et al., 1975) as significant precipitants of suicide death when compared with control groups. Interpersonal conflict was the second most common precursor to suicide death with conflict predominantly occurring between the victim and her current or former intimate partner (Runyan et al., 2003). With Martin et al. (2013) reporting the odds that a decedent had interpersonal conflict in the 24 hours prior to death were over twice as high among married individuals.
+Interpersonal disputes as a major precipitant of parasuicde were found in all cases interviewed by Edwards et al., (1981), with intimate partner conflict more frequent in the week preceding the attempt (Weyrauch et al., 2001). In addition, Paykel et al. (1975) reported that there was a marked peak of events in the month prior to the suicide attempt suggesting a particularly immediate link between event and reaction. Further, Brent et al. (1993) suggested that in the year before death, suicide completers were significantly more likely to have experienced interpersonal conflict or disruption of a romantic attachment compared to community controls. Findings of particular interest also included the report that conflicting interpersonal relationships were a significant predictor to multiple suicide attempts (Choi et al., 2013).
+Mandal and Zalewska (2012) found that conflicts between partners of a “strong, but emotionally stormy relationship” were the least frequently signalled type of difficulty. However, the participants reported severe developmental trauma and adult experiences of intimate partner violence which could minimise the presence of relational conflict. Hawton and Fagg (1988) also reported that recent disruption of a relationship with a partner was rare in the suicide groups, as was a major row during the period immediately preceding the attempt. They further reported that physical health issues were more persistent and difficult to resolve, compared to relationship difficulties, and more often preceded a suicide attempt (Haw and Hawton, 2008). However, Hawton and Fagg (1988) did acknowledge that the evidence of disruption in the relationship with a partner was age-related and occurred more often in younger participants and more predominantly in males. In comparison, a study examining Iranian females aged 15 to 46 also demonstrated that “conflict between marital partners” and “failure in premarital relationships” emerged as prominent themes in the reasoning for attempting suicide - irrespective of law and traditional values (Keyvanara and Haghshenas, 2010, p.777). Interestingly, only one study examined LGBT intimate partner relationships and suicidality. This study found that intimate partner relationship conflict was a significantly more common precipitant to suicide when compared to non-LGBT cases (Skerrett et al., 2014).
+3.4.	Separation
+Relationship separation featured as the most prominent factor contributing to increased suicidality across the review. The threat of, or actual, separation was the most frequent reason given for a suicide attempt among both genders (Lorensini and Bates, 2002) with Fieldsend and Lowenstein (1981) finding that separation, as a reason for suicide, was more frequent in men. Separation or divorce also showed consistent association with suicide across active duty personnel (Hyman et al., 2012), with younger age samples also attributing the attempted suicide to disappointments such as “being abandoned by a boyfriend” (Krajnc et al., 1998).
+Furthermore, Weyrauch et al. (2001) reported that for all of their participants, a background of interpersonal loss and disruption occurred in the year preceding the suicide attempt. In Australia, the collection of data derived from calls made to a telephone counselling service showed that young males (under 35 years) were 75% more likely to report a relationship breakdown than were young females, with the majority of suicide calls pertaining to broken, strained or inadequate intimate relationships (Barber et al., 2004).
+Additional findings of particular interest included studies exploring the specific impact of relationship separation on subsequent suicidality. Retrospective examination of patient files reported that for both women and men, separation from their partners was one of the main precipitants of suicidal behaviour (Cupina, 2009; Martin et al., 2013). Relationship separation was echoed as a critical precipitant to suicide death within the last three months prior to suicide (Heikkinen et al., 1992b) and if the participant experienced a high burden of adversity (Séguin et al., 2014).
+Of particular note, males were six times more likely to suicide following separation and were more vulnerable to experience associated interpersonal conflict during this phase (Cantor and Slater, 1995; Heikkinen et al., 1992b). Deceased males were reported to be of younger age (Heikkinen et al., 1992a; Shiner et al., 2009), with the risk particularly high for males aged 15 to 24 years (Wyder et al., 2009). Males more likely identified relationship breakdown as the main trigger, rather than as a contributory factor to suicide, whereas females were more evenly divided between these two categories (Shiner et al., 2009). Marriage was reported as a protective factor in regards to suicidality (Cantor and Slater, 1995; Wyder et al., 2009), with divorced men also at a substantially higher risk of suicide, although this decreased with age (Wyder et al., 2009).
+In terms of relationship separation, Batterham et al. (2014) reported that the effects of separation were strongest soon after the separation with nearly a three-fold increase in ideation and an eight-fold increase in plans/attempts in the two years following separation. Kõlves et al. (2012) echoed this finding reporting that the acute stage of marital/de facto separation increased the risk of developing suicidal behaviours. The period up to four years before a separation, when a relationship may be deteriorating, was also found to be a time of increased risk for suicidal thoughts and behaviours (Batterham et al., 2014). Furthermore, Batterham et al. (2014) observed that the “decrease of suicidal ideation and plans/ attempts among individuals more distal from separation suggest that the process of separation likely precipitates elevated risk for suicidality, rather than being a consequence of suicidality” (p.62). Kõlves et al. (2012) also reported that males presented higher levels of suicidality than females following separation. They observed that males who identified as being more affected by stressful experiences, exhibited an increase in suicidality (Kõlves et al., 2012). In support of previous research regarding separation, Houston et al. (2001) reported that the most frequent precipitants within the week prior to death were difficulties in, or the end of a relationship. Separation or disruption of a romantic attachment was significantly more common for young adult suicide victims when compared to controls (Brent et al., 1993). Also, separated males at risk of developing suicidal thoughts and behaviours were found to be more susceptible to the experience of ‘state shame’ (Kõlves et al., 2011).
+4.	Discussion
+The importance of reviewing relationship factors in the context of suicidality was evident across a broad range of cultural contexts,
+clinical settings and retrospective studies. The review highlighted the pervasiveness of relationships issues, regardless of cultural or ethnic background, and the precipitant effect on suicidality. However, it is acknowledged that the reviewed evidence is not sufficiently robust to determine, with confidence, the factors that precede and predict suicide due to the limited number of prospective studies in the specified area.
+The key findings from this review support the assertions made by existing systematic reviews examining suicidality and intimate partner abuse and violence. The associations between relationship separation and conflict, and suicidal ideation and/or behaviours, may be mediated by intimate partner violence with McLaughlin et al. (2012) reporting that irrespective of study design, there was a strong and consistent association between intimate partner abuse and suicidality. The findings that low quality relationships characterised by conflict, problems and arguments contribute to an increased risk of suicidality, is more acutely evidenced by the fact that these factors can quickly create an environment of abuse and violence. The pervasive influence of low quality relationships can render an individual helpless and hopeless, exacerbating vulnerability, particularly in females, which may lead to an increased risk of overall suicidality.
+It is difficult to conclude with certainty the differentiating risk factors that distinguish whether a partner will engage in suicidal ideation, a suicide attempt or completion. The presence of an intimate partner relationship, where two individuals with unique personal backgrounds share an intimate emotional and physical bond, can be argued to be a risk factor if other conditions are met, including trauma history, mental illness, personality and complex attachment patterns. This is further complicated by past suicide history, the presence of intimate partner violence, social networks/support, socio-economic status, age, and sexual orientation. To place this information in context, the Diathesis Stress Model of Suicidal Behaviour can be applied to better formulate biological/genetic traits alongside environmental influences which may increase risk for suicidal behaviours. Yaseen et al., (2012) reported a positive association between “intense feelings of love, particularly in the absence of protective feelings of calm or positive self-view” and a resulting suicide attempt. The results suggesting that the interaction between internalised attachment representations, level of distress and coping mechanisms are all important mechanisms in understanding the interplay between emotional reactivity to intimate partner events and suicidality (Yaseen et al., 2012). It also highlights the way in which a psychosocial crisis, such as a relationship separation, can be a contributing factor to suicidal behaviours, contingent on predispositional vulnerability.
+Due to the rapid changes in composition and size of an individual’s social network following an intimate partner disruption, the Interpersonal Theory of Suicide is a useful theoretical framework to assess impact and suicide risk (Joiner, 2005; Van Orden et al., 2010). Poor relationship quality, increasing problems, chronic conflict and separation are all mechanisms which may exacerbate a lack of belongingness and self-blame which may lead to and/or intensify a sense of perceived burdensomeness. Further, although more studies are needed to investigate intimate relationship quality as a predictor to suicide attempts, Choi et al. (2013) reported that conflicting interpersonal relationships were a significant predictor to multiple suicide attempts which may increase an individuals’ acquired capability for suicide, leading to more lethal attempts and suicide death (Harris and Barraclough, 1997; Owens et al., 2002). It is not news for clinicians that multiple suicide attempts are the primary predictor of suicide completion. However, applying the Interpersonal Theory of Suicide to clinical practice may provide a framework for organising client data and assist in identifying exacerbating factors.
+The likely mechanisms through which intimate partner relationship issues contribute to suicide risk appears to be shaped by
+age, the nature of relationship problems, and time (including length of relationship and proximity to separation). Specifically, the review suggests that a younger cohort (under 35 years) tend to adopt a more impulsive reaction to relationship discord and partners of a separated relationship are also more likely to experience a sense of hopelessness which may lead to suicidality. These findings support research by Stack and Scourfield (2015) who reported that after controlling for psychiatric, social, and economic predictors of suicide completions, the odds of suicide increased by 60% one year after divorce, compared to a 30% increase for distal divorce. The prominence of relationship separation as a risk factor for suicide also supports findings from Ide et al. (2010) systematic review which asserts that the acute stage of separation and relationship difficulties in general, are high risk factors for suicide. This provides a more distinct clinical focus on individuals who no longer identify as being part of a marriage and who have separated, divorced, or are single.
+It is also worthwhile to consider that there is a possibility that the emergence of suicidality in a relationship might impact on quality or lead to conflict and/or separation, that is, a bidirectional effect may exist. However, Batterham et al. (2014) found that the association between separation and suicidality did not reflect a selection effect. They reported that following a period of time post-separation, suicidal ideation and behaviours decreased, emphasising that the process of separation precipitates elevated risk and was not solely as a result of existing suicidality. Further, Sandberg-Thoma and Kamp Dush (2014) observed that individuals, who experienced suicidal ideation, also experienced a decrease in the likelihood of cohabitation dissolution, which suggests that the association between suicidal thoughts and behaviours may be unidirectional. Nevertheless, little additional research has evaluated the impact of suicidal thoughts and behaviours on the initiation, quality or maintenance of intimate partner relationships.
+Associations between gender and relationship impact on suicidality were less clear, with the results inconsistently demonstrating gender differences. Although, more studies found males to be at a heightened risk of suicidal thoughts and behaviours following a recent separation compared to females, it is difficult to make the assertion that males are at an overall heightened risk of suicide as a result of broader relationship issues. The inconclusive gender finding supports a review conducted by Evans et al. (2014), who also stated that no definitive conclusion could be made regarding gender differentials in suicide risk following an intimate relationship breakdown.
+Age appeared to influence the likelihood or emergence of suicidal thoughts and behaviours in response to relationship factors. Younger individuals tended to react to specific and more recent events involving partners, whereas older individuals were more likely to be responding to long-term partner conflicts (Stephens, 1985). Spousal relationship problems tended to become more prominent over the age of 25, with a distinct drop in relationship enquiry and the prevalence of suicidal ideation and behaviours after the age of 60 (Batterham et al., 2014; Davis et al., 2009; Shiner et al., 2009). As young people continue to develop mentally, physically and emotionally, coupled with a lack of relationship experience, a move toward suicidal thoughts and behaviours may be indicative of a lack of support mechanisms or under-developed problem-solving skills.
+4.1.	Mental health
+From the current review it is difficult to draw conclusions regarding the mediating relationship between mental health issues and relationship factors and their independent or interrelated influence on suicidality. The heterogeneous nature of the studies did
+not allow a consistent view to measure the strength of this relationship. However, research suggests that relationship functioning significantly impacts individual mental-health and overall well-being (Whisman and Baucom, 2012).
+A number of the studies suggested that existing mental health issues play an integral role in the increased risk of suicidality with Kõlves et al. (2012) reporting an association between marital and de facto separation, mental illness and suicidality. Busuttil et al. (1994) reported that psychiatric illness accounted for 30.7% of the reasoning for suicide death, ahead of problems in marriage or relationships. Whisman and Uebelacker (2006) found that relationship discord was associated with elevated risk for mood and anxiety disorders and substance use disorders. However, when controlled for psychiatric disorders, the relationship between discord and suicidal ideation was not significant (Whisman and Uebelacker, 2006).
+The idea that culminations of negative life stressors combine to either exacerbate existing mental illness or trigger new illness appears to be a contributory finding arising from this review. Individuals who attempted or completed suicide were observed to have reported higher incidence of external stressors in the year prior to death (Beautrais et al., 1997) with a marked peak of events also culminating in the month prior to the attempt (Paykel et al., 1975). This observation was maintained when compared with control groups (Brent et al., 1993; Zhang and Ma, 2012) and was motivated by the presence of a negative interpersonal life event (Bagge et al., 2013). Furthermore, experiencing an acute negative life event was a trigger for a suicide attempt among individuals not currently planning their attempt, suggesting that clinicians need to be vigilant in monitoring recently assessed ‘non-suicidal’ individuals if their situation precipitates a likely occurrence of a negative interpersonal life event (Bagge et al., 2013).
+Interestingly, Haw and Hawton (2008) observed that for both males and females, those without a psychiatric disorder were more likely to later have a relationship problem compared to those with a psychiatric disorder. This finding was similar to that reported by Logan et al. (2011) who observed that decedents experienced a recent crisis with interpersonal relationship problems in the absence of having a known mental health condition. The occurrence of a psychiatric or personality disorder may engage individuals toward more immediate life issues with a mental health worker already assisting individuals with existing disorders to manage relationship problems. The interrelationship between life stressors and suicide was found to ultimately be dependent on the nature of the stressor and underlying psychiatric disorders (Brent et al., 1993).
+4.2.	Methodological considerations
+The studies reviewed were both heterogeneous and broad in nature. Although study quality was not directly assessed, variations in study design, sample size and measurement meant that not all studies were of high quality, which made it difficult to find consistent and comparative patterns within the studies. The measures of intimate partner relationship factors were varied throughout the studies. The majority of studies focused on interpreting self-report information in relation to the status, quality and level of conflict within the interpersonal relationship. The studies included for review had different definitions of intimate partner relationships and this lack of a clear definition made it difficult to generalise and compare results. The inability to distinctly measure the level of relationship quality and define it as a separate variable from personal and/or situational bias interferes with the quality of association made between relationship factors and suicidality. The lack of an operational definition of an intimate partner relationship can create further difficulties when analysing the risk of acute and long-term relationship factors on suicidality.
+The measures of suicidality also presented a number of limitations. The majority of studies examining presentations to hospital relied on single self-report items with some studies measuring only presenting suicide attempts and not suicidal ideation. Only nine studies used a more formalised measure of suicidal ideation or attempts. The measures of suicidality also recorded varying time periods from recent to lifetime exposure.
+Different inclusion/exclusion criteria were used across the studies making it difficult to isolate intimate partner relationships as a separate factor contributing to suicidality. It is also possible that publication bias was present in the current review, due to the risk that only articles reporting significant results may have been accessible and that the author may have inadvertently not identified all potential papers.
+4.3.	Future research
+As intimate partner relationships are an implicit component in the daily lives of many individuals, further research to understand the association between relationship factors, specific target groups (i.e. males, LGBT community) and suicidality is warranted. The creation of a relationship factors interview schedule would allow for a more rigorous overview of the relationships factors involved in the lead up to suicidal ideation and behaviours. Further research examining the association of relationship factors and suicidality from a longitudinal perspective is also necessary to be able to track changes and behavioural developments across time. Specifically, the coherent categorisation of relationship risk factors which differentiate suicidal ideation, attempts and completion.
+The need for accessible and effective interventions to support individuals’ experiencing suicidal thoughts and/or behaviours following the disruption of an intimate partner relationship, merits further investigation. Due to the proximal nature of a perceived negative intimate partner event and suicidal behaviours, further research aimed at providing more immediate support for individuals experiencing intimate partner relationship problems could also be explored. The integration of new technologies including mobile applications may provide rapid, real-time access to therapeutic interventions.
+4.4.	Clinical implications
+Of clinical significance is the finding that interpersonal negative life events involving a romantic partner served as a trigger for engaging in suicidal behaviour for individuals not currently planning an attempt (Bagge et al., 2013). Furthermore, conflicting interpersonal relationships were found to be a significant predictor to multiple suicide attempts (Choi et al., 2013). These findings indicate an increase in suicide risk for individuals who may not present with a typical suicide risk profile. Additionally, the apparent suicidal impulsivity following a negative intimate partner event for younger people (Stephens, 1985) and a marked peak of negative events in the month prior to the suicide attempt, highlights the critical and immediate link between the event and the reaction (Paykel et al., 1971). The emphasis on a brief solution focused therapy outcome may be of particular clinical relevance due to the recency of triggering interpersonal negative life events and the consequent elevation of suicide risk.
+Although it is acknowledged that only one study involving the LGBT community was identified, it is important to note the potential clinical relevance in the context of this review. Existing research in the area offers mixed conclusions regarding risk for suicide in LGBT individuals. However, recent research has suggested that sexual minorities are at a higher risk for suicidal behaviours (Skerrett et al., 2015). The increased sense of emotional and relational upheaval due to existing stigma, shame, increased
+risk of substance use and perceived obstacles to support, are clinically important factors to consider when working with members of the LGBT community (McDaniel et al., 2001).
+The cross-cultural impact that intimate partner relationships have on suicidality is also of considerable clinical significance. This association transcends cultural and language boundaries in terms of communicating distress, and allows clinicians to have a clear access point to understanding risk factors (separation, disputes, low support etc.
+5.	Conclusion
+Intimate partner relationships play an integral role in influencing the development and exacerbation of suicidal ideation, attempts and completion. Results of the review indicate that relationship separation and poor quality relationships are likely to be important risk factors for suicidal thoughts and behaviours and are frequent triggers for a suicide attempt. The strong association between intimate partner relationships and suicidality that has been demonstrated, further emphasises that those people who have indicated that they are experiencing issues with an intimate partner relationships should additionally be screened for suicidal thoughts and behaviours.
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+D Routledge
+Taylor & Francis Group
+INTRODUCTION
+Self-injurious behaviors (SIB) are defined as any self-directed behaviors which result in physical harm (Muehlenkamp, Claes, Havertape, & Plener, 2012; Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007a). This terminology recognizes that some SIB are performed without a clear intent with respect to the outcome. However, when looking at the self-reported intent, SIB can be divided into two subtypes: non-suicidal self-injury (NSSI) and suicide attempts (SA). NSSI is defined as the “intentional destruction of one’s body without the intent to die” (Nock & Favazza, 2009). In contrast, SA refers to SIB with the intended goal of causing death (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007b, Silverman et al., 2007a).
+NSSI typically has an onset during adolescence (Bergen, Hawton, Waters, Cooper, & Kapur, 2010; Jacobson & Gould, 2007; Klonsky, 2011; Moran et al., 2012), with a prevalence as high as 18-20% (Esposito-Smythers et al., 2010; Muehlenkamp et al., 2012; Swannell, Martin, Page, Hasking, & St John, 2014). Even though NSSI is not accompanied by a desire to die, a lethal outcome is still possible. In fact, it is well documented that NSSI is associated with numerous biopsychosocial repercussions, with the most serious being suicide (Muehlenkamp & Gutierrez, 2007; Whitlock et al., 2013). SA has been also consistently associated with lethal outcomes, with the ratio of attempted suicides to suicide deaths ranging from 50:1 to 100:1, among adolescents (Groholt, Ekeberg, & Haldorsen, 2006). According to the National Center for Health Interview
+Survey 2015 statistics, suicide is the second leading cause of death in individuals 15-19 years old in the United States (Heron, 2017). These findings emphasize the importance of uncovering preceding vulnerabilities toward both NSSI and SA that can be a target for intervention and enhance primary prevention.
+Child and Adolescent Attachment
+Attachment theory was developed by John Bowlby (1969) who proposed that infants are biologically programed to form an emotional bond with their caregivers, most commonly the mother, who represents a source of safety to the infant, through emotional and physical comforting and protection (Bowlby, 1969). Attachment theory was further developed by Mary Ainsworth through experiments that classified infants’ attachment based on their behaviors toward their mothers in times of separation or distress (Ainsworth, Belhar, Water, & Wall, 1978). The infants’ response patterns were classified categorically into: secure, anxious ambivalent, avoidant, and disorganized (Ainsworth et al., 1978; Hesse & Main, 2000; Main & Salomon, 1990). It was noticed that securely attached infants were provided with consistent and responsive care in times of distress, which facilitated the infant to explore their environment comfortably and independently. This safe exploration is thought to promote emotional regulation, self-confidence, problem-solving skills, and increase children’s adaption to the world (Bowlby, 1969). In contrast, when inconsistent or unresponsive care is provided in times of distress, children can develop insecure attachment styles to cope with the perceived rejection or inconsistent care. While the role of biological factors in the development of attachment cannot be denied, the evidence seems to support a larger contribution played by the environmental factors (Mikulincer & Shaver, 2016). For example, monozygotic and dizygotic twins were found to differ negligibly in their genetic contribution compared to the environmental effect (e.g., parental care) on attachment (Oliveira & Fearon, 2019). Further, inborn temperament, (which may impact the expression of emotion) was found to be similar across attachment styles, indicating once again, the environment or parenting style supersedes biological factors (Groh et al., 2017; Mikulincer & Shaver, 2016).
+Children with an anxious ambivalent attachment style employ “hyper-activating” strategies meant to draw the attention and care of caregivers. Anxiously attached individuals (irrespective of age) have a low sense of self but a high sense of others, being preoccupied with maintaining closeness in relationships and having access to support (Ainsworth et al., 1978; Cole-Detke & Kobak, 1996; Mikulincer & Shaver, 2012). In contrast, avoidantly attached individuals are characterized by a high sense of self and a low sense of others, being overly self-reliant, uncomfortable with emotional intimacy in relationships, and dismissive of their own emotional needs (Cole-Detke & Kobak, 1996; Mikulincer & Shaver, 2012). Lastly, the third type of insecure attachment style, with elements of both anxious and avoidant, has been termed as “disorganized.” Considered to be the most impairing of all, disorganized attachment is believed to develop in the context of frightening or severely unavailable or unpredictable caregiving. Disorganized individuals often see themselves and others as unreliable and untrustworthy, and lack an organized system for handling distress (Hesse & Main, 2000; Main, Kaplan, &
+Cassidy, 1985; Main & Salomon, 1990). It has been shown that attachment styles developed early in life are relatively stable over time, shaping peer relationships and self-view in adolescence and onwards (Meeus, Oosterwegel, & Vollebergh, 2002; Stallard, Spears, Montgomery, Phillips, & Sayal, 2013). To date, a broad and very diverse literature on child and adolescent attachment has addressed the categorical as well as dimensional classification of attachment (i.e., attachment anxiety and attachment avoidance) (Baumgarden, 2016).
+Given the wide impact on worldview and self-view, it is understandable why insecure attachment has been studied as a risk factor for NSSI and SA. In fact, insecure attachment has been related to NSSI in adolescent clinical samples (Adam, Sheldon-Keller, & West, 1996; Maimon, Browning, & Brooks-Gunn, 2010; Prinstein, Boergers, Spirito, Little, & Grapentine, 2000). A widely accepted theory has postulated this influence to be mediated by interpersonal and intrapersonal regulation (Nock, 2010). Specifically, individuals with anxious ambivalent attachment are thought to express their upregulated emotions through SIB, resulting in increased support and contact with their attachment figures (Adam et al., 1996; Michelson & Bhugra, 2012). This is in contrast with individuals with avoidant attachment who downregulate their emotions through SIB, meant to decrease support and contact with their attachment figures (Tasca et al., 2009).
+A recent systematic review on the relationship between attachment and SIB in the adult population supports a significant association between attachment styles or dimensions and SIB (Wrath & Adams, 2019). The current review further explores these aspects in children and youth, specifically NSSI and SA. Similar to the literature investigating the adult population, we hypothesized that both attachment dimensions will be associated with NSSI and SA in youth. In addition, we hypothesized that higher attachment anxiety will be associated with higher reporting of NSSI, while higher attachment avoidance will be associated with higher SA reporting.
+MATERIALS AND METHOD
+A computerized search of PubMed, OVID, and PsychINFO for literature published between 1969 and March 31, 2020, was conducted. Google Scholar was further reviewed for relevant articles. The review procedure was completed in accordance with the PRISMA guidelines (Moher, Liberati, Tetzlaff, & Altman, 2009) and was registered with PROSPERO (registration ID: CRD42018103959). The following inclusion criteria were used: articles published in English, examining the relationship between attachment and NSSI and/or SA, investigating child and adolescent populations (up to and including individuals 18 years old), employed a measure of attachment and reported a statistical relationship between attachment and NSSI and/or SA.
+The following search terms were used: “Kid* OR Child* OR Teen* OR Youth OR Adolescent*” AND “attachment” AND “self-harm OR nonsuicidal self-injury OR parasuicide OR self-mutilation OR self-injury OR self-injurious behavior OR suicide related behavior OR suicide attempt OR assisted suicide.” The reference list of selected articles was further reviewed for potential articles not identified in the databases. One reviewer (J.W) reviewed and selected all the titles, and two reviewers (J.W. and A.J.W.) independently reviewed all the selected abstracts. Discrepancies on abstract inclusion/
+exclusion were discussed and resolved by a third reviewer (G.C.A). The following items were extracted from articles selected for full review: author, year of publication, country, study design, sample size, cohort, measure of attachment, measure of NSSI/SA, the major findings along with their statistics (i.e., test statistics, degrees of freedom, 95% Confidence Intervals), level of significance and effects size (e.g., Cohen’s d, odds ratios, etc.). In a few instances, corresponding authors were contacted for further clarification of unreported information (i.e., the age range of samples). Exclusion criteria included: case studies, reviews, theoretical papers, and not having met inclusion criteria.
+In accordance with the proposed nomenclature (Muehlenkamp et al., 2012; Silverman et al., 2007a), articles that defined a behavior as the intentional injuring of one’s body without the intent to die were classified as studies of NSSI. Studies that defined a SIB with the intent to die were classified as studies of SA. Studies that did not specify the intent behind the SIB were included as a heterogeneous group of SA and NSSI. Corresponding authors were contacted on two separate occasions to clarify missing or vaguely stated information. When no clarification was available, the information included in our review kept the wording of the original paper to maximize accuracy.
+As recommended by Khan, Kunz, Kleijnen, and Antes (2011) a quality assessment checklist was developed based on relevant characteristics important to the objectives of this review (i.e., assessing attachment and SIB) and was used to adjudicate the quality of included articles. The checklist was comprised of the following seven characteristics: population sampled (general population vs clinical samples), sample size (whether the sample size was above or below the median size of all included articles), application of longitudinal study design, use of a validated measure of attachment, appropriate statistical analyses, appropriate statistical methods to control for potentially confounding variables (e.g., age, gender), and separation of type of SIB (homogeneous vs heterogeneous groups). Articles were given one point for each of the criteria resulting in a potential total score of 7, with higher scores indicating greater methodological rigor. It was assumed that each characteristic contributed equally to study quality.
+RESULTS
+The search identified 233 potential articles. Upon review of titles, and subsequently abstracts and then full texts, 22 studies met inclusion criteria fully (Figure 1). Three articles reported an upper age limit of 19 or 20. In two studies, only 4 of the 528 participants were 19 years of age (Gandhi et al., 2016, 2019). The remaining study recruited students from high school, and the majority were 18 years of age or younger (Falgares et al., 2017). Given the small percentage of participants over 18, it was unanimously decided to include these articles. Wrath and Adams (2019)made similar concessions. Despite the total article count being 22, one article discussed NSSI and SA separately (Venta & Sharp, 2014), and one article discussed SIB and SA separately (Glazebrook, Townsend, & Sayal, 2015). Therefore, both studies contributed to the count of articles in their respective sections of the results. With respect to study design, 12 studies were retrospective, and 10 studies were longitudinal, ranging in the length from 2 weeks to 6years (Cassels et al., 2019; Fergusson, Woodward, & Horwood, 2000; Gandhi et al., 2019; Glazebrook et al., 2015; Lee, 2016; Mullaley, 2004; Santangelo et al., 2017; Stallard
+Figure 1. Flow chart of article selection process.
+et al., 2013; Tatnell, Kelada, Hasking, & Martin, 2014; Victor, Hipwell, Stepp, & Scott, 2019). The average quality score was 4.2 with a median of 4 indicating that methodologies of included articles are of moderate quality based on the number of evaluation criteria (i.e., 7). Table 1 displays details of the included articles along with additional study results not presented below.
+Most studies used well-validated descriptive measures of attachment, targeting attachment specifically toward parents or peers. The most commonly used measure was the original or revised Inventory of Parent and Peer Attachment (10 studies) (Cerutti, Zuffiano, & Spensieri, 2018; Fergusson et al., 2000; Gandhi et al., 2016, 2019; Jiang, You, Zheng, & Lin, 2017; Lee, 2016; Sari & Kara, 2019; Sheftall, Mathias, Furr, & Dougherty, 2013; Victor et al., 2019; Wang, Zhang, & Chen, 2018). Two studies used the Adolescent Attachment Questionnaire (Tatnell et al., 2014, Tatnell, Hasking, Newman, Taffe, & Martin, 2017), two used the Child Attachment Interview (Glazebrook et al., 2015; Venta & Sharp, 2014), two used the Experiences in Close Relationships scale original (Cassels et al., 2019) or revised version (Sheftall, Schoppe-Sullivan, & Bridge, 2014), two used the Parental Bonding Instrument (Lee, 2016; Zetterqvist et al., 2018), one used the Adult Attachment Interview (Mullaley, 2004), one used the Adult Attachment Projective (Pallini et al., 2020), one study used the Attachment Style Questionnaire (Falgares et al., 2017), and one study used the Attachment Questionnaire for Children (Stallard et al., 2013). The remaining study did not use a specific attachment scale but rather participants rated their attachment to their
+mothers and best friends based on four attachment qualities (Santangelo et al., 2017). Of note, Lee (2016) used both the PBI and IPPA scales. Stallard et al. (2013) only measured peer attachment while the remaining 20 studies measured parental attachment alone or along with peer attachment.
+In regards to study samples, 14 were non-clinical (Cassels et al., 2019; Cerutti et al., 2018; Fergusson et al., 2000; Gandhi et al., 2016, 2019; Jiang et al., 2017; Lee, 2016; Falgares et al., 2017; Stallard et al., 2013; Tatnell et al., 2014, 2017; Victor et al., 2019; Wang et al., 2018; Zetterqvist et al., 2018) although only 10 of these were thought to be a true representation of the general population (Cassels et al., 2019; Fergusson et al., 2000; Gandhi et al., 2016, 2019; Santangelo et al., 2017; Falgares et al., 2017; Tatnell et al., 2014; Victor et al., 2019; Wang et al., 2018; Zetterqvist et al., 2018). The remaining eight studies had heterogeneous clinical samples of individuals with various mental health concerns: five sampled patients following a SA (Mullaley, 2004) or recruited from acute inpatient psychiatric wards (Pallini et al., 2020; Sari & Kara, 2019; Sheftall et al., 2014; Venta & Sharp, 2014), and three studies recruited participants from mood and anxiety clinics/general outpatient clinics (Glazebrook et al., 2015; Santangelo et al., 2017; Sheftall et al., 2014).
+Twenty-one studies reported significant associations between attachment characteristics and NSSI and/or SA or SIB. Venta and Sharp (2014) was the only study which reported non-significant results. As there was significant methodological heterogeneity between studies, as well as a small sample of articles in each category of SIB, based on previous recommendations (Higgins, Thompson, & Spiegelhalter, 2009; Kuipers, van Loenhout, van der Ark, & Bekker, 2016), it was decided that a meta-analysis would likely be neither accurate nor informative and, therefore, not undertaken.
+Attachment and NSSI
+Twelve of the 13 studies exploring the relationship between attachment and NSSI found a positive association between the two variables. Of these, 11 studies were conducted in nonclinical samples within educational systems (junior high or high school) (Cassels et al., 2019; Cerutti et al., 2018; Gandhi et al., 2016, 2019; Jiang et al., 2017; Lee, 2016; Tatnell et al., 2014, 2017; Victor et al., 2019; Wang et al., 2018; Zetterqvist et al., 2018). Two studies used clinical samples (Santangelo et al., 2017; Venta & Sharp, 2014), but only one of these found a significant relationship between NSSI and lower levels of attachment security (Santangelo et al., 2017).
+Attachment anxiety only was measured in 3 studies, all of which found a positive association between attachment anxiety and NSSI (b = 0.28, p = 0.031, Cassels et al., 2019; F(1, 1,714) = 71.69, p < 0.001, Tatnell et al., 2014; and t(2,465) = 15.01, 95% CI = 5.25-6.83, p < 0.001, Tatnell et al., 2017). Further, attachment anxiety was also higher in individuals who maintained their NSSI behavior over the course of the study compared to individuals with no history of NSSI (F(1, 1,714) = 136.55, p < 0.001, Tatnell et al., 2017) and even increased in individuals who had no history of NSSI but began harming themselves over the course of the study (t(66) = —2.53, p = 0.014). When attachment avoidance was specifically measured, Cassels et al. (2019) found that
+avoidant attachment to mother was indirectly associated with NSSI via behavioral problems (b = 0.13, p = 0.045).
+The remaining 10 studies found NSSI behavior to be associated with lower attachment security (Cerutti et al., 2018; Gandhi et al., 2016, 2019; Jiang et al., 2017; Lee, 2016; Victor et al., 2019; Wang et al., 2018; Zetterqvist et al., 2018). Specifically, Lee (2016) found NSSI to be correlated with poorer paternal (r = 0.17, p < 0.01) and maternal bonding (r = 0.15, p < 0.01). Gandhi et al. (2016) found lifetime NSSI to be negatively associated with maternal trust (r = —0.18, p < 0.001) and positively associated with maternal alienation (r = 0.16, p < 0.001). These relationships were found to be mediated by both identity synthesis and identity confusion using multiple statistical approaches to data analysis (see Table 1 for comprehensive statistics for all analyses). Analysis of longitudinal data from the same study (Gandhi et al., 2019) revealed that poor maternal bonding at baseline was related to NSSI at the followup, and again These relationships were again mediated by identity synthesis and identity confusion (see Table 1 for comprehensive statistics for all analyses). Cerutti et al. (2018) found NSSI to be negatively correlated with parental attachment (r = —0.276, p < 0.001), which was found to be predictive of NSSI via alexithymia. Jiang et al. (2017) found the NSSI group had lower scores on maternal (F(3, 521) = 7.77, p < 0.001, g2 = 0.04) and paternal attachment (F(3, 541) = 9.51, p < 0.001, g2 = 0.05), while Santangelo et al. (2017) also found that adolescents engaging in NSSI reported lower levels of attachment to their mother (t(44) = 6.94, p < 0.01, d = 1.50). In a longitudinal study of 2,127 urban youth, Victor et al. (2019) found poor attachment to parents increased the odds of subsequent NSSI onset (OR = 1.07, p = 0.002). Wang et al. (2018) discovered paternal attachment scores were negatively associated with NSSI prevalence (y2 = 25.66, p < 0.001), frequency (F = 7.43, p < 0.01), severity (F = 7.29, p < 0.01) and overall NSSI score (F = 9.08, p < 0.001). Maternal attachment was also negatively associated with NSSI prevalence (y2 = 36.27, p < 0.001), frequency (F = 3.14, p < 0.05), severity (F = 4.44, p < 0.05) and overall NSSI score (F = 6.04, p < 0.01). Finally, Zetterqvist et al. (2018) found that attachment differed between individuals who used different methods of self-injury. Specifically, individuals who engaged in the common methods of NSSI (e.g., cutting, burning, etc.) as well as employed sex as self-injury (SASI), had lower scores of maternal care (p = 0.01) and higher scores of paternal overprotection (p = 0.02) compared to individuals who performed NSSI only. The NSSI + SASI group had lower scores of paternal care compared to those with SASI alone.
+Peer attachment and its relationship to NSSI was investigated in six studies (Cerutti et al., 2018; Gandhi et al., 2016, 2019; Jiang et al., 2017; Lee, 2016; Santangelo et al., 2017). Five of these studies reported a relationship between poorer attachment to peers and reporting NSSI. The NSSI groups reported lower levels of attachment to their best friend (t(44) = 4.36, d = 1.19) (Santangelo et al., 2017), lower levels of peer relations (t = —2.56, p < 0.01) (Lee, 2016) and higher levels of peer alienation (r = 0.28, p < 0.001) (Gandhi et al., 2016). Adolescents’ engagement in NSSI was predicted by lower levels of attachment to peers via the mediator alexithymia (b = —0.04, 95% CI = —0.10 to —0.01, p < 0.05) (Cerutti et al., 2018) and identity synthesis and identity confusion over time, using multiple statistical models (see Table 1 for comprehensive statistics for all
+analyses). Only one study reported non-significant results with respect to peer attachment and NSSI (F(3, 527) = 2.11, ns, g2 = 0.01) (Jiang et al., 2017).
+In summary, lower attachment to parents and/or peers are consistently associated with NSSI. These patterns of association are consistently found across both clinical and non-clinical samples. Further, attachment is also associated with more descriptive features of NSSI, including prevalence, frequency, severity and type. The studies of attachment and NSSI had an average quality score of 4.58, indicating a moderate quality in methodologies.
+Attachment and Suicide Attempts
+Six of the eight studies exploring the relationship between attachment and SA reported significant associations (Fergusson et al., 2000; Mullaley, 2004; Sari & Kara, 2019; Falgares et al., 2017; Sheftall et al., 2013, 2014). Six studies used clinical samples (Glazebrook et al., 2015; Mullaley, 2004; Sari & Kara, 2019; Sheftall et al., 2013, 2014; Venta & Sharp, 2014) and two used a non-clinical sample (Fergusson et al., 2000; Falgares et al., 2017).
+Of the six studies that used a clinical sample (Glazebrook et al., 2015; Mullaley, 2004; Sari & Kara, 2019; Sheftall et al., 2013, 2014; Venta & Sharp, 2014), four reported significant relationships (Mullaley, 2004; Sari & Kara, 2019; Sheftall et al., 2013, 2014). Sheftall et al. (2013) found lower levels of maternal (t = 2.23, p = 0.03) and paternal attachment (t = 2.52, p = 0.01) to be associated with SA. After further analyses, only paternal attachment remained a significant predictor of SA status (b = —0.014, p = 0.01). Sari and Kara (2019) found adolescents who had a SA had a lower maternal (p < 0.001) and paternal attachment security (p < 0.001) compared to adolescents without a SA. Sheftall et al. (2014) found that attachment anxiety (t(39) = 2.79, p = 0.008) and avoidance (t(39) = 3.39, p = 0.002) were associated with SA. After controlling for family alliance and depressive symptoms, only attachment avoidance remained significant a significant predictor (b = 0.72, SE B = 0.35, OR = 2.05, 95% CI = 1.04-4.04, p < 0.01). Mullaley (2004) found participants with preoccupied attachment (i.e., high attachment anxiety and low attachment avoidance) were 3 times more likely to report a SA at follow-up (B = —1.186, B SE = 0.519, Exp(B) = 3.274, p < 0.05). Glazebrook et al. (2015) reported that insecure and secure participants were equally likely to report a SA at baseline (p > 0.05). The remaining study did not find a significant relationship between attachment and SA (Venta & Sharp, 2014).
+Of the non-clinical samples, Fergusson et al. (2000) showed low attachment quality to parents at 15 years old to be a predictor of future SA six years later (b = —0.04, p < 0.01). However, after adolescent mental health and significant life events were included in the model, attachment quality to parents was no longer significant. Falgares et al. (2017) found the relationship between attachment anxiety and SA was mediated by self criticism (point estimate = 0.15, 95% CI = —0.23 to —0.02, p < 0.05) and dependency (point estimate = —0.05, 95% CI = —0.11 to —0.01, p < 0.05). The relationship between attachment avoidance and SA was mediated by self criticism (point estimate = 0.13, 95% CI = 0.04-0.23, p < 0.05), but not dependency (point estimate =
+0.01, 95% CI = —0.01 to 0.03, p > 0.05). Regarding peer attachment, Sheftall et al. (2013) did not find a relationship between peer attachment and SA (t = 0.65, p = 0.52).
+In summary, attachment to parents is lower in individuals who report SA. Moreover, insecure attachment (i.e., the preoccupied attachment style and the avoidant attachment dimension) is predictive of future SA. These findings are present in both clinical and non-clinical samples; however, these relationships are often mediated by personal and familial attributes. The studies of SA had an average quality score of 3.63, indicating a moderate quality in methodologies.
+Attachment in Heterogeneous Groups of Individuals With NSSI and SA (SIB)
+Three studies used heterogeneous samples of individuals who had performed NSSI and/ or SA (Glazebrook et al., 2015; Pallini et al., 2020; Stallard et al., 2013). Two studies used clinical samples (Glazebrook et al., 2015; Pallini et al., 2020), and one used a non-clinical sample (Stallard et al., 2013). All three studies reported positive associations between SIB and attachment. Glazebrook et al. (2015) reported participants insecurely attached to their mother reported more SIB at baseline (%2(1) = 4.44, p = 0.034), were more likely to report SIB at follow up, and insecure maternal attachment independently predicted SIB at 6-months (b = 2.08; adjOR = 7.80, 95% CI = 1.15-52.91, p = 0.035). Pallini et al. (2020) reported healthy controls had a greater prevalence of secure attachment (53%) compared to the SIB group (3%). However, due to the low prevalence of secure attachment in the SIB group, statistical analyses could not be performed.
+Two studies (Glazebrook et al., 2015; Stallard et al., 2013) evaluated peer attachment and SIB, and both found insecure peer attachment correlated with SIB behavior. Stallard et al. (2013) found both anxious (OR = 1.84, 95% CI = 0.94-3.62, p = 0.04) and avoidant (OR = 2.25, 95% CI = 1.12-4.55, p < 0.001) attachment to be associated with SIB. Glazebrook et al. (2015) reported participants insecurely attached to their peers were more likely to report SIB at follow up (v2(1) = 4.62, p = 0.032), and insecure peer attachment independently predicted SIB 6 months later (b = 2.08, adjOR = 8.01, 95% CI = 1.00-64.20, p = 0.05).
+In summary, SIB was associated with general attachment insecurity, and specifically with attachment anxiety and avoidance. These relationships were present in clinical and non-clinical samples when assessing both attachment to mother and attachment to peers. Further, these associations existed both cross-sectionally as well as longitudinally, with baseline attachment to mother predicting SIB at follow-up. The studies of SIB groups had an average quality score of 4.33, indicating a moderate quality in methodologies.
+DISCUSSION
+Our results suggest a significant relationship between attachment and SIB in children and adolescents. Specifically, the findings support a positive relationship between anxious attachment and NSSI, as well as a negative relationship between attachment security and NSSI (Cerutti et al., 2018; Gandhi et al., 2016; Jiang et al., 2017; Lee, 2016; Santangelo et al., 2017; Tatnell et al., 2014, 2017) and SA (Fergusson et al., 2000;
+16 @ J. WOO ET AL.
+Sheftall et al., 2013, 2014). It is difficult to draw conclusions regarding avoidant attachment, as only two studies measured this attachment dimension explicitly, albeit both found positive associations (Sheftall et al., 2013, 2014).
+Three studies (Cassels et al., 2019; Tatnell et al., 2014, 2017) specifically evaluated attachment anxiety and found it to be positively associated with NSSI, while the remaining 10 studies evaluated the relationship between attachment quality and NSSI. Five of six studies found significant associations between low levels of attachment security to peers and NSSI behavior (Cerutti et al., 2018; Gandhi et al., 2016, 2019; Jiang et al., 2017; Santangelo et al., 2017). Most of the NSSI papers in youth measured attachment to one or both parents. The mechanism through which attachment resulting from early interactions with caregivers creates vulnerability for SIB is likely explained by the difficulty in achieving emotional regulation under stress. This maladaptive emotional regulation may lead young individuals to control intense emotions by distracting themselves from emotional pain by inflicting physical pain through NSSI. On the other hand, NSSI may replace verbal communication being used to indirectly express one’s sufferance and need for help through. This interpretation is keeping with previous literature demonstrating SIB as a means of gaining external support and regulating negative affect (Klonsky, 2007). In fact the findings in the child and adolescent population mirror those found in the adult literature (Wrath & Adams, 2019), providing preliminary evidence of a potential life-long association between attachment insecurity and NSSI. Several studies also showed an indirect association between attachment and NSSI, emphasizing the role of mediators such as self-view (i.e., identity synthesis and confusion) (Gandhi et al., 2016, 2019) and alexithymia (Cerutti et al., 2018). This further suggests the importance of building self-esteem, facilitating identity development and communication skills in youth to offset emotional dysregulation and NSSI.
+Our hypothesis that attachment insecurity will be positively associated with SA was supported. Three of the four studies that evaluated the relationship between SA and attachment found a significant relationship (Fergusson et al., 2000; Sheftall et al., 2013, 2014). Fergusson et al. (Fergusson et al., 2000) and Sheftall et al. (2013) found lower levels of parental attachment to be significantly associated with SA. Two studies employing categorical measures had mixed results. Sheftall et al. (2014) found both attachment anxiety and avoidance significantly associated with SA, while Venta and Sharp (2014) found non-significant results. Overall, it appears that general attachment insecurity, attachment avoidance, and attachment anxiety, in particular, are associated with SA. Given that half of these articles measured attachment to parents rather than attachment dimensions, further conclusions are difficult to draw at this time.
+Interestingly, two of the three significant studies investigating SA and attachment found that once psychopathology (depression and other mental health disorders) and family alliance were included in the statistical models, the relationship between lower levels of attachment and SA (Fergusson et al., 2000) and attachment anxiety and SA (Sheftall et al., 2014) was no longer significant. A possible explanation is that attachment insecurity creates a vulnerability toward interpersonal difficulties and future psychopathology that ultimately contribute to SIB. Still, even in this line of research findings, the role of attachment in perpetuating vulnerability for SIB cannot be denied. The importance of the relationship between attachment and SA is further supported by
+similar findings in the adult attachment literature (Wrath & Adams, 2019), which again may be indicative of a life-long association between attachment and SA.
+In the studies with heterogeneous SIB, the classification of NSSI/SA was based on the severity of the behavior rather than its intent, making a clear distinction difficult. Furthermore, three articles used different attachment nomenclature (Glazebrook et al., 2015; Mullaley, 2004; Stallard et al., 2013). However, three studies found positive associations between SIB and insecure attachment. On the other hand, Mullaley (2004) found a significant negative relationship where dismissive attachment (i.e., high attachment avoidance and low attachment anxiety) predicted engagement in repeated SIB. This finding was mirrored in a similar study in the adult population (Stepp et al., 2008). A possible explanation is that individuals with greater attachment avoidance choose suicidal behaviors more carefully, possibly based on the lethality level, rather than resorting to multiple attempts or modalities (Joiner, 2007). Additionally, three of the four studies were longitudinal, providing good evidence of a stable relationship between attachment and SIB.
+Poor peer attachment was associated with both NSSI and heterogeneous SIB in one study (Sheftall et al., 2014). The findings are strongly supported by previous literature, building an argument for the influential role of peers. Arguably, peers’ influence in adolescence is in close competition with parents’ influence when it comes to shaping confidence, self-view, and identity (Rageliene, 2016). Although it is difficult to differentiate the unique contribution of each relationship, peer relationships seem to be an important target for therapeutic intervention. Still, the available literature on this relationship is sparse. Therefore, developing specific interventions meant to buffer against detrimental peer influence in youth might represent an important area of future clinical research.
+Strengths and Limitations
+Strengths of this review include the large proportion of longitudinal studies, relatively large sample sizes, and studies of both clinical and non-clinical populations. The literature evaluating both attachment to parents, but also attachment to peers, also brings a wider perspective. Albeit we did not perform a meta-analysis, we used an assessment tool to quantify the quality of each study and the average score for each section. Generally, studies were of moderate quality based on the evaluating criteria, which increases confidence in their findings, conclusions and ability to develop future hypotheses based upon the current body of literature.
+Limitations of this review include a relatively small number of studies and high levels of heterogeneity with respect to design and measurement tools, making the results hard to generalize. Further, given that a meta-analysis was not conducted, studies with a higher power were not given a stronger weight in the analysis. In addition, the relationship between attachment and suicidal ideation has not been addressed, keeping the focus on behaviors rather than suicidal cognitions.
+CONCLUSIONS
+In summary, this review provides further evidence for a significant positive relationship between insecure attachment and SIB in child and adolescent populations. Since the
+relationship between attachment and SIB in the child and adolescent populations is also reflective of the findings in the adult population (Grunebaum et al., 2010; Wrath & Adams, 2019), this suggests that there may be a life-long relationship between attachment and SIB. Therefore, it appears that evaluating attachment as part of the risk assessments for SIB in these populations might offer valuable information necessary for preventive interventions. In addition, developing therapeutic approaches meant to stabilize attachment security and enhance interpersonal efficacy might have a significant impact on decreasing future SIB. Further research would benefit from longitudinal studies addressing the implications of attachment (to parents, peers and partners) on both suicidal ideations and suicidal behaviors for a thorough understanding of this complex relationship.
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+Introduction
+For more than a century, writers and researchers have considered suicide from two opposite perspectives, invoking broad cultural and societal factors as causes or focusing on uniquely individual characteristics and experiences to explain why people kill themselves. Public health approaches to suicide prevention, however, have to integrate these viewpoints and to develop strategies that will benefit most lives in an effective and measurable way.
+Suicide is a well recognised public health challenge. WHO estimates that the global suicide rate is about 16 per 100 000 individuals per year, which is a 45% increase in the past 45 years.1 Depending on the nation cited by WHO, suicide is one of the top three leading causes of death in people aged 10-24 years or 15-44 years, and often is an especially large burden late in life, when suicide rates are highest in many countries.2 Therefore, suicide causes the loss of many potential years of life and has substantial economic and emotional costs, disrupting families, communities, and society, broadly ramifying sadness and loss.3
+Many countries have initiated suicide prevention programmes,4,5 which use public health strategies that focus on individuals in known high-risk groups and promote population-oriented strategies to broadly reduce risk, in keeping with Rose’s theorem (many people at low risk might give rise to more cases than would a small number at high risk).6 Suicide is not a disease caused by well defined pathological mechanisms, and the occurrence of suicidal behaviour is usually an outcome of complex interactions of socio-environmental, behavioural, and psychiatric factors.6 Identified risk factors, such as severe depression or other mental illnesses, do not have sufficient specificity (ie, high rates of false positives) to guide effective preventive actions.7
+One important population strategy to reduce suicides has been modification of the environment to decrease general access to suicide means. This approach (so-called means restriction) is reported to be one of the intervention measures with strongest empirical support.8,9 Several factors apparently underpin the effectiveness of this approach. Many suicidal people
+cannot be accessed with interventions or restrictions at the time of their greatest risk; indeed, they often seek to avoid detection. The probability of individuals attempting suicide decreases when they are precluded from implementing a preferred method10—ie, suicide attempts are often method-specific. Moreover, if a highly lethal method is not available and some individuals do not defer their attempt, they frequently use less lethal, more common ones (eg, drug overdose). From the perspectives of public health and injury prevention, the choice of a method that is less lethal than are others can be advantageous if the attempt proves to be non-fatal.
+The case fatality of suicide methods varies greatly (appendix).11-14 The potentially fatal moments of suicidal crises are often brief. Strongly felt ambivalence is common, with competing wishes to die and to live.9 The sudden, unplanned (or briefly planned) nature of many suicides implies that individuals tend to use the method most readily accessible to them. When a lethal method is unavailable at the moment of potential action, suicide attempts might be delayed so that (in some cases at least) suicidal impulses will pass without fatal effects.15 Even when individuals have planned, poor access to the most lethal means can be a substantial impediment.
+Search strategy and selection criteria
+We searched PubMed, PsycINFO, ISI Web of Knowledge, and The Cochrane Library, with the terms “suicide” in combination with each method (ie, “jumping”, “hanging”, “charcoal burning”, “carbon monoxide poisoning”, “drowning”, “pesticide*”, “firearm*” and “medicine*” or “drug overdose”) and “restriction”, “availability”, “access”, or “means”. We included reports of epidemiological studies showing change in suicides or suicide rates after method restriction published between January, 2001, and January, 2012. Review articles, case reports, or studies based on clinical populations or those that had non-fatal outcomes (eg, suicidal behaviour or ideation) were excluded. The reference lists of identified reports were also examined for relevant references.
+Although means restriction is considered a generic preventive intervention, few investigators have assessed the relative strength of supporting evidence for different methods. Moreover, the potential effect of decreased access to various methods on overall suicide rates in different countries or regions has not been established. We review the empirical evidence for means restriction from the past decade (figure) and assess its effectiveness and its relation to the dissemination of information about different methods of suicide through various media outlets. We put special emphasis on the difficulties encountered when attempts to measure potential substitution effects are made. Additionally, we draw attention to the potential of socially enacted means restriction (ie, not absolute restriction) as a public health intervention for commonly available products.
+Theory of means restriction
+Suicide is a rare event and high-risk factors are common (eg, depression, other mental disorders). A recurring challenge in suicide prevention is how to accurately identify vulnerable individuals in populations at risk. A prevention strategy that targets the population as a whole, such as means restriction, has many advantages, especially when implemented through so-called distal measures— eg, removal of carbon monoxide from domestic gas or withdrawal of highly lethal pesticides from the market.
+Means restriction entails a community or societal action that (ideally) does not depend on an individual’s intention or volition. Applied to the population as a whole, it typically affects people whose suicide risk is otherwise undetected and who do not seek therapeutic assistance to prevent their crisis or for life-saving interventions when necessary. Removal or restriction of access to a lethal method changes the context of a potential suicide by precluding potentially fatal actions or
+forcing the use of a less lethal method. Because means restriction is broadly applied, detection of its individuallevel effect is often impossible; it is best measured by aggregate findings of method-specific community rates of suicide and related self-harm injuries.
+As a public health measure, means restriction has a long history; removal of the pump handle in Broad Street, London, UK, by John Snow was an early example and a historic landmark in public health practice.16 Similar approaches have been widely applied in criminology, with the label of opportunity-reduction theory (or so-called situational crime prevention) .17 Instead of a focus on individual criminals, an opportunity-reduction approach introduces discreet managerial and environmental changes to reduce the opportunities for crime. Suicide can be affected or forestalled by alteration ofenvironments or access.17 To be successful, this type of strategy depends on committed societal leadership and sustained political will. This approach fits with the notion of context changes to make individuals’ default decisions healthy. The principle of this type of intervention is that individuals would have to expend substantial effort not to benefit.18
+Although means restriction can be broadly applied, related approaches exist for individuals. Clinicians can work with high-risk patients and their kin to remove potentially lethal methods from the immediate environment. By contrast with universal approaches, this strategy necessitates care providers’ vigilance and cooperative participation by people close to the suicidal individual. Such safety planning is not means specific, but is tailored to individuals and situations.
+Suicide rate, method availability, and lethality International variations in common suicide methods suggest that these patterns are linked closely to differences in the availability and lethality of specific approaches.19 Suicides by pesticide poisoning (case fatality up to 75%) have been common in many Asian and Latin American countries where there are large agrarian populations,20 whereas many individuals killing themselves in cities and city states jump from high places (70% lethality).2122 Indeed, jumping has accounted for more than 50% of suicides in Hong Kong and 80% of those in Singapore in the past 20 years.21
+Thomas and colleagues23 described the large increase in suicides in the UK, first in men and later in women, after carbon monoxide gas from coalmines became widely available in the first half of the 20th century. Gas rose to become the primary national method of suicide. The replacement of coal gas with natural gas from North Sea wells between the late 1950s and early 1970s led to a gradual reduction in the carbon monoxide content of domestic gas, which in turn was followed by a steady and prominent decrease in fatal gassing and the overall suicide rate in the UK.23,24 This decline in the overall rate was directly caused by the reduction in suicide with domestic gas. Thomas and colleagues23 showed that the
+number of fatal gas poisonings in the UK rose in the early 1980s, but it later fell after the introduction of catalytic converters into car exhaust systems.25-29
+The increased use of pesticides during the second half of the 20th century was associated with an increase in suicides in many agrarian societies.30 Prevention strategies have sought to substitute less lethal, newer generation compounds,31-33 and to install double-lock boxes34 to remove access to potentially lethal but commonly available chemicals. Enforcement of gun-control policies lowers numbers of firearm suicides.35-52
+An individual’s choice of method is not only dependent on ready access to a specific means of suicide, but also on its socio-cultural acceptability.53 Local norms and traditions, moral attitudes towards suicide, knowledge about past suicides, and personal experience and accessibility all potentially shape a person’s suicidal actions. In turn, means restriction should shape contextual factors, promoting healthy decisions.
+Means substitution after restriction
+A common concern about means restriction has been that individuals will simply switch to other methods of suicide—ie, so-called means substitution. Such concern could be a result of distressed individuals being considered by clinicians as equally at risk of suicide by any method when they are assessed as being very suicidal. However, studies5 have shown that restriction of one method of suicide does not inevitably lead to a compensating rise in the use of others (as shown in the UK in the 1970s), just as the emergence of a new method (eg, domestic gas in the UK in the first half of the 20th century, or the burning of charcoal in confined spaces to generate toxic amounts of carbon monoxide in Hong Kong in the late 1990s) does not result in a substantial decline in the use of long-available means.
+The occurrence of substitution varies between regions and is associated with individual characteristics such as age and sex (appendix).25,46 The effectiveness of means restriction differs between the sexes; women seem to be more responsive than are men, and method substitution is more common in men than in women (appendix).46 Where means restriction has been implemented in Asia—typically of pesticide and charcoal—substitution has been reported rarely.31,33,54,55 The cause of this apparent difference is unknown; characteristics of the populations affected or the restricted methods might play a part.
+At the population level, means restriction proves most effective when the method is common and highly lethal, accounting for a substantial percentage of deaths.15,18 Common methods that have been restricted, such as domestic gas and pesticides, are available in the home. The likelihood that a specific method will lead to death is related to both its lethal properties and its accessibility. When reduction of access to a highly lethal method is possible, people who do attempt suicide with less dangerous means have an increased chance of survival.
+If the overall population rate of suicide is to be substantially reduced by means restriction, the fatality rate of alternative methods should be lower than that of the restricted method of suicide (appendix).15
+The role of the media
+Nowadays, publicly available media—whether in print, on television, or on the internet—might affect the creation or alteration of suicide methods, and hence affect suicide rates. The deaths of celebrities have been publicised.56 Perhaps most importantly, this type of rapid dissemination most often involves members of the public dying in extraordinary circumstances.57 For example, the media introduced and quickly disseminated reports on the burning of charcoal in a confined space in Hong Kong and Taiwan, which then rapidly increased and spread to other Asian regions in the late 1990s.23 An ethnographical investigation in Hong Kong58 established that people chose charcoal burning because they were reminded of the method by newspaper reports. An interview-based study in Taiwan59 showed that 87% of individuals who attempted suicide with charcoal burning reported that the media pointed them towards this method. Suicides by charcoal burning have been recorded in the UK.60 Whether charcoal burning would have spread so quickly had initial graphic reports, pictures, and diagrams not been presented in Hong Kong tabloids in 1998 is unknown. Therefore, in addition to sensationalising suicide, the media can provide precise instructions about how a method can be implemented, further complicating prevention initiatives.
+New online social media can be used to disseminate information within minutes or hours, rather than slow diffusion of models or methods that was the norm previously, such as when domestic gas was introduced.61 As yet, little research has tested whether all forms of today’s media can be used to positively affect vulnerable individuals or populations in a way that promotes good mental health or adaptive help seeking at times of distress.62
+Examples of means restriction
+Implementation of means restriction can be viewed as a continuum, ranging from complete elimination or removal of a potentially fatal substance or compound (eg, changes in the composition of domestic cooking gas), through impeding or interfering with access (eg, barriers to jumping and packaging changes), to promotion of educational and social interventions to enhance safety (eg, education of clinicians to encourage families to remove potentially lethal means from the home). We believe that removal of an agent would have the greatest effects on broadly measured suicide rates, whereas social-educational interventions would be least potent, especially because they necessitate concerted and sustained actions by many individuals.
+Legislation to restrict the quantities of paracetamol and other analgesics (eg, aspirin) sold was enacted in the UK
+in 1998. Early data suggested that mortality and morbidity associated with paracetamol overdose declined as a result,63,64 with little evidence for substitution to other kinds of analgesics, such as ibuprofen (a compound that is safer than is paracetamol).64 Subsequent studies65 have cast doubt on these early findings. Implementation of such legislation does not depend on specific actions of individuals, but is done during manufacture and with widely applied sales regulations. Further research is needed to establish whether people attempting suicide hoard their paracetamol supplies until they have sufficiently lethal amounts, and whether they have the patience to open blister packs to obtain enough pills. Such findings would point to carefully planned suicides and would potentially suggest that other prevention measures are needed.
+In 2010, Yip and colleagues55 described the results of a controlled community experiment in Hong Kong, in which they moved bags of charcoal from easy self-service access on store counters to locked storage, so that customers had to ask store attendants for assistance. This measure did not prohibit purchases, but sales became a source of attention and slightly more time consuming than they had been previously. Compared with a district with a similar population size (500 000 inhabitants), area, and socioeconomic status that had no change in method of shelving, a measurable and significant decline in suicides was reported.55
+Unlike repackaging of paracetamol, agreement of the managers of supermarket chains and day-to-day implementation by store employees was necessary to move the bags of charcoal. Such a high level of cooperation could pose substantial challenges, and many community members might resent or resist such constraints.
+On the island of Cheung Chau in the Islands District of Hong Kong, deaths from poisoning by charcoal burning in holiday houses increased from three to four per year to the high of 14 in 2002.54 Most suicides were of visitors. The community reported negative effects on the island in terms of resort business and general wellbeing after a series of suicides.54 Island residents and businesses developed a self-help organisation to restrict access to holiday flats for distressed or suicidal individuals; owners refused to rent to people on their own. Store employees were alert to visitors who wished to purchase charcoal and beer but no food. The police cycled around the island to identify anyone deemed to be at risk of suicide and irregularities in the community. Of 40 000 residents, the number of suicides on the island declined to two in 2005, without any substantial increase on nearby islands.54 These findings emphasise that means restriction must be embedded into other efforts to modify environments, such as the restriction of access to rental units. Cohesive community action was the central part of this initiative; means restriction—like other elements of the Cheung Chau programme—was a result of concerted and widespread commitment.
+The social dilemma
+Application of universal measures for means restriction might be considered intrusive by many members of the community. Moreover, the benefits for most people will be small or non-existent. Thus, use of widely applied prevention measures could be met with substantial resistance, even though data support large population effects. Many community members express common misunderstandings that, despite data showing powerful populationlevel effects, a seriously suicidal person will inevitably find a way to die and that all methods have roughly equal case fatalities. In many community discussions about means restriction—whether control of access to bags of charcoal safety doors on subway platforms, or bridge barriers54,55,66-73—many participants believe that removal of access to one method of suicide would force people to use another.
+On the basis of the data for relocation of bags of charcoal in supermarket chains in Hong Kong,55 prevention strategies should gain support from senior managers of affected companies, as well as having supporting scientific data. With appropriate media coverage and endorsement by community leaders, means restriction could gain greater acceptance and less resistance from the public than it does presently. The fundamental premise of means restriction is based on the assertion that it is both a community-level intervention and a community-supported initiative.
+We suggest that policy makers and advocates consider several a priori criteria when assessing the potential benefits of means restraint. First, the method in consideration should contribute substantially to the mortality from suicide in the region because of its high lethality. Second, the method should be suitable for elimination or constraint, ideally with broadly applicable policy actions rather than day-to-day implementation by individuals, either alone or collectively. Third, they should assess whether a method is socially important or recognised (eg, suicides from iconic sites or bridges), when the preventive intervention would be noticed by many people, even though the overall contribution to regional rates might be marginal. Fourth, they should be able to monitor the implementation and effects of an intervention.
+Limitations
+Glasgow’s 2011 report74 emphasised that bridge barriers—however effective they might be at individual sites—do not lower regional suicide rates when people jumping from those bridges contributed little to the rates before the barriers were put in place. Although placement of such barriers might not lower regional rates—even when it prevents deaths at specific sites—the action conveys a powerful public message, expressing important community values and serving to promote help-seeking. Such committed political will to save lives could be one potential way to
+counteract media-driven contagion, because it affords opportunities for widespread discussion and collective community action.
+Constraint or elimination of access to commonly used suicide methods of low lethality (eg, fairly non-toxic prescription or over-the-counter drugs) would have a negligible effect on rates and also might inadvertently force individuals attempting suicide in the future to use more lethal methods.75 When high-lethality methods have been constrained, some substitution with low-lethality means has been reported.76 Such findings do not indicate what exactly would happen if low-lethality methods were eliminated.75 For methods of intermediate lethality, such as charcoal burning, the potential gains from constraints that cannot entirely eliminate access should be assessed carefully (appendix).
+Hanging, jumping from heights (particularly from individuals’ own apartments or houses), and fatal shooting with firearms in countries with relatively non-restrictive gun laws such as the USA cannot be readily restricted. However, safety planning for firearm storage is potentially a form of means restriction when effectively applied as part of routine procedures. Similarly to the decision to place bags of charcoal behind shop counters, such changes need committed leadership, corporate cooperation, and consistent individual action to attain sustained, widespread implementation. In clinical practice, physicians and other health professionals should speak with family members about the removal of potentially lethal methods from the reach of vulnerable kin. This type of intervention necessitates an alert clinical provider, a vigilant family, and a cooperative patient, but too often one or several of these components could be absent.
+Conclusion
+Restriction of access to a specific suicide method can have a widespread effect when the method is highly lethal and common, and the means restriction is supported by the community. Newly emerging methods might have large effects as they spread through communities, and in the internet era, the results can be sudden and pronounced. Once a method of suicide has become common, it is especially difficult to eradicate. If faced with similar emerging methods in the future, policy makers should seek support from formal media outlets to restrain spread and lessen the effects, although informal media now makes such interventions even more challenging than previously.
+It is beyond the scope of this report to define elements necessary for promotion of the type of collaborative community discussions that address the balance between the imperative of constraining potentially lethal methods of suicide and the wishes of most community members who are not at risk and might be inconvenienced. But just such discussions are necessary if further, meaningfully broad-based interventions are to be
+implemented. Although we have expressed concerns about the media’s potential to serve as a powerful vector for spreading contagion, these venues of information dissemination can effectively pass on scientific knowledge and protective guidance. As with discussions about means restriction, broad community participation and dynamic social leadership are necessary.
+No one measure, however effective, can sufficiently address the many factors that contribute to regional or national suicide rates. A frank and open discussion of a community’s abiding values, legislative or policy changes, continuing community education, consultation about the challenges posed by suicide and its antecedents, and effective clinical management of individual cases are all necessary for prevention programmes.
+Series
+13	Chen VC, Cheng AT, Tan HK, et al. A community-based study of case fatality proportion among those who carry out suicide acts. Soc Psychiatry Psychiatr Epidemiol 2009; 44: 1005-11.
+14	Yip PSF, Caine ED, Kwok RC, Chen YY. A decompositional analysis of the relative contribution of age, sex and methods of suicide to the changing patterns of suicide in Taipei City, 2004 2006. Inj Prev 2011; published online Oct 25. DOI:10.1136/ injuryprev-2011-040177
+15	Hawton K. Restricting access to methods of suicide: rationale and evaluation of this approach to suicide prevention. Crisis 2007;
+28 (suppl 1): 4-9.
+16	Johnson S. The ghost map: the story of London’s most terrifying epidemic—and how it changed science, cities and the modern world. London: Riverhead Books, 2006.
+17	Clarke RV. Introduction. In: Clarke RV, ed. Situational crime prevention: successful case studies. Monsey, NY: Criminal Justice Press, 1997: 1-43.
+18	Chen YY, Wu KC-C, Yip PSF. Suicide prevention through restricting access to suicide means and hotspots. In: O’Connor RC, Platt S, Gordon J, eds. International handbook of suicide prevention: research, policy and practice. Chichester: John Wiley & Sons, 2011: 545-60.
+19	Ajdacic-Gross V, Weiss MG, Ring M, et al. Methods of suicide: international suicide patterns derived from the WHO mortality database. Bull World Health Organ 2008; 86: 726-32.
+20	Gunnell D, Eddleston M, Phillips MR, Konradsen F. The global distribution of fatal pesticide self-poisoning: systematic review. BMC Public Health 2007; 7: 357
+21	Chen YY, Yip PSF. Prevention of suicide by jumping: experiences from Taipei City (Taiwan), Hong Kong and Singapore. In: Wasserman D, Wasserman C, eds. Oxford textbook of suicidology and suicide prevention. Oxford: Oxford University Press, 2009: 569-72.
+22	Chen YY, Gunnell D, Lu TH. Descriptive epidemiological study of sites of suicide jumps in Taipei, Taiwan. Inj Prev 2009; 15: 41-44.
+23	Thomas K, Chang SS, Gunnell D. Suicide epidemics: the impact of newly emerging methods on overall suicide rates—a time trends study. BMC Public Health 2011; 11: 314.
+24	Kreitman N. The coal gas story: United Kingdom suicide rates, 1960-71. Br J Prev Soc Med 1976; 30: 86-93.
+25	Amos T, Appleby L, Kiernan K. Changes in rates of suicide by car exhaust asphyxiation in England and Wales. Psychol Med 2001; 31: 935-39.
+26	Nordentoft M, Qin P, Helweg-Larsen K, Juel K. Restrictions in means for suicide: an effective tool in preventing suicide: the Danish experience. Suicide Life Threat Behav 2007; 37: 688-97.
+27	Studdert DM, Gurrin LC, Jatkar U, Pirkis J. Relationship between vehicle emissions laws and incidence of suicide by motor vehicle exhaust gas in Australia, 2001-06: an ecological analysis. PLoS Med 2010; 7: e1000210.
+28	Mott JA, Wolfe MI, Alverson CJ, et al. National vehicle emissions policies and practices and declining US carbon monoxide-related mortality. JAMA 2002; 288: 988-95.
+29	Skilling GD, Sclare PD, Watt SJ, Fielding S. The effect of catalytic converter legislation on suicide rates in Grampian and Scotland 1980-2003. Scott Med J 2008; 53: 3-6.
+30	Gunnell D, Eddleston M. Suicide by intentional ingestion of pesticides: a continuing tragedy in developing countries. Int J Epidemiol 2003; 32: 902-09.
+31	Gunnell D, Fernando R, Hewagama M, Priyangika WD, Konradsen F, Eddleston M. The impact of pesticide regulations on suicide in Sri Lanka. Int J Epidemiol 2007; 36: 1235-42.
+32	Roberts DM, Karunarathna A, Buckley NA, Manuweera G, Sheriff MH, Eddleston M. Influence of pesticide regulation on acute poisoning deaths in Sri Lanka. Bull World Health Organ 2003; 81: 789-98.
+33	Vijayakumar L, Satheesh-Babu R. Does ‘no pesticide’ reduce suicides? IntJ Soc Psychiatry 2009; 55: 401-06.
+34	Konradsen F, Pieris R, Weerasinghe M, van der Hoek W, Eddleston M, Dawson AH. Community uptake of safe storage boxes to reduce self-poisoning from pesticides in rural Sri Lanka.
+BMC Public Health 2007; 7: 13.
+35	Beautrais AL, Fergusson DM, Horwood LJ. Firearms legislation and reductions in firearm-related suicide deaths in New Zealand.
+Aust N Z J Psychiatry 2006; 40: 253-59.
+36	Bridges FS. Gun control law (Bill C-17), suicide, and homicide in Canada. Psychol Rep 2004; 94: 819-26.
+37	Caron J. Gun control and suicide: possible impact of Canadian legislation to ensure safe storage of firearms. Arch Suicide Res 2004; 8: 361-74.
+38	Caron J, Julien M, Huang JH. Changes in suicide methods in Quebec between 1987 and 2000: the possible impact of bill C-17 requiring safe storage of firearms. Suicide Life Threat Behav 2008; 38: 195-208.
+39	Chapman S, Alpers P, Agho K, Jones M. Australia’s 1996 gun law reforms: faster falls in firearm deaths, firearm suicides, and a decade without mass shootings. Inj Prev 2006; 12: 365-72.
+40	Cheung AH, Dewa CS. Current trends in youth suicide and firearms regulations. Can J Public Health 2005; 96: 131-35.
+41	Conner KR, Zhong Y. State firearm laws and rates of suicide in men and women. Am J Prev Med 2003; 25: 320-24.
+42	De Leo D, Dwyer J, Firman D, Neulinger K. Trends in hanging and firearm suicide rates in Australia: substitution of method?
+Suicide Life Threat Behav 2003; 33: 151-64.
+43	Marinho de Souza F, Macinko J, Alencar AP, Malta DC, de Morais Neto OL. Reductions in firearm-related mortality and hospitalizations in Brazil after gun control. Health Aff 2007;
+26: 575-84.
+44	Gagne M, Robitaille Y, Hamel D, St-Laurent D. Firearms regulation and declining rates of male suicide in Quebec. Inj Prev 2010;
+16: 247-53.
+45	Kapusta ND, Etzersdorfer E, Krall C, Sonneck G. Firearm legislation reform in the European Union: impact on firearm availability, firearm suicide and homicide rates in Austria.
+BrJ Psychiatry 2007; 191: 253-57
+46	Klieve H, Barnes M, De Leo D. Controlling firearms use in Australia: has the 1996 gun law reform produced the decrease in rates of suicide with this method? Soc Psychiatry Psychiatr Epidemiol 2009; 44: 285-92.
+47	Lee WS, Suardi S. The Australian firearms buyback and its effect on gun deaths. Contemp Econ Policy 2010; 28: 65-79.
+48	Lubin G, Werbeloff N, Halperin D, Shmushkevitch M, Weiser M, Knobler HY. Decrease in suicide rates after a change of policy reducing access to firearms in adolescents: a naturalistic epidemiological study. Suicide Life Threat Behav 2010; 40: 421-24.
+49	Ozanne-Smith J, Ashby K, Newstead S, Stathakis VZ, Clapperton A. Firearm related deaths: the impact of regulatory reform. Inj Prev 2004; 10: 280-86.
+50	Rodriguez Andres A, Hempstead K. Gun control and suicide: the impact of state firearm regulations in the United States, 1995-2004. Health Policy 2011; 101: 95-103.
+51	Rosengart M, Cummings P, Nathens A, Heagerty P, Maier R, Rivara F. An evaluation of state firearm regulations and homicide and suicide death rates. Inj Prev 2005; 11: 77-83.
+52	Webster DW, Vernick JS, Zeoli AM, Manganello JA. Association between youth-focused firearm laws and youth suicides. JAMA 2004; 292: 594-601.
+53	Cantor CH, Baume PJ. Access to methods of suicide: what impact? Aust N Z J Psychiatry 1998; 32: 8-14.
+54	Wong PW, Liu PM, Chan WS, et al. An integrative suicide prevention program for visitor charcoal burning suicide and suicide pact. Suicide Life Threat Behav 2009; 39: 82-90.
+55	Yip PSF, Law CK, Fu KW, Law YW, Wong PW, Xu Y. Restricting the means of suicide by charcoal burning. Br J Psychiatry 2010;
+196: 241-42.
+56	Chen YY, Liao SF, Ten PR, et al. The impact of of media reporting of the suicide of a singer on suicide rates in Taiwan.
+Soc Psychiatry Psychiatr Epidemiol 2012; 47: 215-21.
+57	Chen YY, Chen F, Yip PSF. The impact of media reporting of suicide on actual suicides in Taiwan, 2002-2005.
+J Epidemiol Community Health 2010; 65: 934-40.
+58	Chan KP, Yip PS, Au J, Lee DT. Charcoal-burning suicide in post-transition Hong Kong. Br J Psychiatry 2005; 186: 67-73.
+59	Tsai CW, Gunnell D, Chou YH, Kuo CJ, Lee MB, Chen YY. Why do people choose charcoal burning as a method of suicide? An interview based study of survivors in Taiwan. J Affect Disord 2011; 131: 402-07.
+2398
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+Series
+60	The Age Company. UK records first online suicide pact. Sept 30, 2005. http://www.theage.com.au/news/world/uk-records-first-online-suicide-pact/2005/09/30/1127804638937html (accessed Feb 16, 2012).
+61	Pirkis J, Nordentoft M. Media influences on suicide and attempted suicide. In: O’Connor RC, Platt S, Gordon J, eds. International handbook of suicide prevention: research, policy and practice. Chichester: John Wiley & Sons, 2011: 531-44.
+62	Niederkrotenthaler T, Voracek M, Herberth A, et al. Role of media reports in completed and prevented suicide: Werther v Papageno effects. BrJ Psychiatry 2010; 197: 234 43.
+63	Hawton K, Townsend E, Deeks J, et al. Effects of legislation restricting pack sizes of paracetamol and salicylate on self poisoning in the United Kingdom: before and after study. BMJ 2001; 322: 1203-07
+64	Hawton K, Simkin S, Deeks J, et al. UK legislation on analgesic packs: before and after study of long term effect on poisonings. BMJ 2004; 329: 1076.
+65	Bateman DN. Limiting paracetamol pack size: has it worked in the UK? Clin Toxicol 2009; 47: 536-41.
+66	Beautrais AL. Effectiveness of barriers at suicide jumping sites: a case study. Aust N Z J Psychiatry 2001; 35: 557-62.
+67	Beautrais AL, Gibb SJ, Fergusson DM, Horwood LJ, Larkin GL. Removing bridge barriers stimulates suicides: an unfortunate natural experiment. Aust N Z J Psychiatry 2009; 43: 495-97
+68	Bennewith O, Nowers M, Gunnell D. Effect of barriers on the Clifton suspension bridge, England, on local patterns of suicide: implications for prevention. Br J Psychiatry 2007; 190: 266-67
+69	Law CK, Yip PSF, Chan WS, Fu KW, Wong PW, Law YW Evaluating the effectiveness of barrier installation for preventing railway suicides in Hong Kong. J Affect Disord 2009; 114: 254 62.
+70	Pelletier AR. Preventing suicide by jumping: the effect of a bridge safety fence. Inj Prev 2007; 13: 57-59.
+71	Reisch T, Michel K. Securing a suicide hot spot: effects of a safety net at the Bern Muenster Terrace. Suicide Life Threat Behav 2005; 35: 460-67
+72	Sinyor M, Levitt AJ. Effect of a barrier at Bloor Street Viaduct on suicide rates in Toronto: natural experiment. BMJ 2010; 341: c2884.
+73	Skegg K, Herbison P. Effect of restricting access to a suicide jumping site. Aust N Z J Psychiatry 2009; 43: 498-502.
+74	Glasgow G. Do local landmark bridges increase the suicide rate? An alternative test of the likely effect of means restriction at suicide-jumping sites. Soc Sci Med 2011; 72: 884-89.
+75	Gunnell D, Miller M. Strategies to prevent suicide. BMJ 2010;
+341: c3054.
+76	de Silva VA, Senanayake S, Dias P, Hanwella R. From pesticides to medicinal drugs: time series analyses of methods of self-harm in Sri Lanka. Bull World Health Organ 2012; 90: 40 46.
+www.thelancet.com Vol 379 June 23, 2012
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+Articles
+Neuropsychopharmacology.
+Introduction
+Over 800 000 people worldwide die each year by suicide,1 accounting for 1-4% of deaths worldwide. Suicide can occur at any point in the lifespan, and is the second most frequent, and in some countries the leading, cause of death among young people aged 15-24 years.1 In addition, around 20-30 times as many suicide attempts occur.2
+Suicide occurs because of a convergence of genetic,3 psychological,4 social, and cultural risk factors, combined with experiences of trauma and loss.5 Internal or external risk factors and the relations between them can be explained in models of suicide, such as stress-diathesis,6 gene-environment,7 and gene-environment and timing interactions.8
+The complexity of this multifaceted phenomenon and low base rates, make research on suicide prevention highly challenging.9 However, the recognition of suicide
+prevention as a public health priority10 and national prevention programmes have encouraged research, detection, treatment, and management of people at risk for suicide in many countries.11,12 A major review of the effectiveness of approaches to suicide prevention was done by Mann and colleagues in 2005.13 We did a systematic review using similar methodology to assess progress in suicide prevention research since that influential study.
+Methods
+Search strategy
+We searched PubMed and the Cochrane library for all relevant English language studies published between Jan 1, 2005, and Dec 31, 2014. The initial search used the Medical Subject Headings identifiers for “suicide” (including the subheadings “suicide, attempted”, and
+Research in context
+Evidence before this study
+We reviewed evidence for the effectiveness of suicide prevention interventions published since Mann and colleagues’ review in 2005. We searched PubMed and the Cochrane library systematically for all relevant English language studies published between Jan 1, 2005, and Dec 31, 2014. See Methods for details. We selected studies reporting completed suicide, attempted suicide, and suicidal ideation, as well as studies reporting intermediate outcomes including help-seeking behaviour or identification of at-risk individuals. This produced 164 publications, which were categorised for their level of evidence according to the Oxford criteria.
+Added value of this study
+Research published since 2005 strengthens the evidence base in several areas of suicide prevention. Restricting access to lethal means can clearly prevent suicide. There have been significant results of school-based awareness programmes in reducing suicide attempts and ideation. The anti-suicidal effects of clozapine and
+lithium have been confirmed but may be less specific than previously thought. Effective pharmacological and psychological treatments of depression are important in prevention as well as education of physicians. There is insufficient evidence regarding possible benefits for suicide prevention of screening in primary care, in public education, and in media guidelines.
+Implications of all the available evidence
+Implementation of the evidence-supported methods described in this study via appropriate legislation, public and physician education and awareness has the potential to change public health strategies in suicide prevention plans, and significantly reduce the number of deaths due to suicide. More research using RCTs designed with standardised outcome measures and qualitative methods when applicable is needed to investigate public health approaches such as gatekeeper training, media regulation, internet-based intervention, and helplines, as well as in the two health-care approaches of physician education and screening in primary care.
+“prevention and control”). “Suicide” was then combined with depression, health education, health promotion, public opinion, mass screening, family physicians, medical education, primary health care, antidepressive drugs, psychotherapy, schools, adolescents, methods, firearms, overdose, poisoning, gas poisoning, and mass media.
+Data collection
+RB and CBL reviewed abstracts and retrieved full-text articles that met inclusion criteria. Studies were selected if they reported primary outcomes of interest (completed or attempted suicide, or suicidal ideation), or if they included applicable, intermediate outcomes such as help-seeking behaviour or identification of at-risk individuals. The full text of these papers was retrieved and divided into subcategories of suicide prevention as reported in the medical literature: means restriction; treatment interventions including pharmacotherapy; psychotherapy; community and family-based interventions; follow-up and chain-of-care; education and awareness; media, telephone, or internet-based interventions; screening; and combined prevention interventions. Thereafter, all articles were sent to 18 suicide experts (the authors of this report) divided into eight review working groups. To ensure that key references were included in the review process, the identified references were sent to other known senior researchers in the field who were asked to suggest additional references. A face-to-face consensus meeting was held in Leiden, Netherlands, on Feb 12-14, 2015, at which all reports were categorised for their level of evidence according to the Oxford criteria.14 We excluded some manuscripts because of irrelevance or very low evidence. The results and conclusions of the review were
+agreed by all authors. In total, four face-to-face meetings between the authors were organised to finalise the results and conclusions.
+Role of funding source
+The Expert Platform on Mental Health, Focus on Depression, and the European College of Neuropsychopharmacology supported this project (transportation and accommodation for meetings, English editing, and support for two research assistants) but had no influence on the contents of this report. The authors of this report had full access to all data in the study.
+Results
+Our literature search identified 1797 papers. Another 24 were obtained from other sources (figure). 224 papers were selected because they reported primary outcomes of interest or included applicable intermediate outcomes. These papers were assessed for eligibility, and 80 were excluded because of irrelevance or low evidence.
+Heterogeneity in study methodology and in populations prevented a formal meta-analysis. We therefore present a narrative synthesis of the results in key domains of suicide prevention strategies.
+30 studies addressed suicide prevention by means restriction (table 1). 14 (47%) of these 30 studies examined firearms, a common suicide method in countries where they are readily available. Availability in households increases risk of firearm suicides with pooled odds ratio (OR) of 3-24 (95% CI 2•41-4-40).18,19,28 Mann and colleagues13 concluded that firearm control legislation was associated with reduced suicides; however, more recent findings from the USA show mixed results, indicating that evidence was insufficient to determine the effectiveness of new laws, either alone or in
+combination.15,20 Single studies investigating different populations in the USA, such as general versus high-risk,16 or severely mentally ill populations,17 as well as a combination of laws applied together,21 showed positive effects. Studies on the restriction of firearm availability in other countries also produced mixed results, with some reporting reduced firearm suicide incidence in men aged 20-40 years (Norway,22 Switzerland,23 Israel24) and in men and women of varying ages (New Zealand,25 Austria26), but usually without a major decrease in overall suicide rates or, alternatively, with just a modest methodsubstitution effect.23 Studies in Australia failed to show an association between legislation and suicide in men19 or people aged 20-40 years,27 a general decline in this method of suicide having begun before the legislation (but possibly following earlier firearm legislation).
+Mann and colleagues13 identified changes to packaging of analgesics in the UK as beneficial in reducing deaths by suicide. Three studies in the UK have reinforced the evidence of the beneficial effects of smaller packets29-31 and three studies supported the effectiveness of withdrawing particularly toxic analgesics.45-47 The estimated reduction in number of deaths was 17 (95% CI -25 to -9) every 3 months in the post-intervention period and the overall reduction in number of deaths was about 43% (specific data not available) in the post-legislation period, after lowering the number of analgesic pills per pack.29
+Restrictions on the availability of pesticides contribute to reduced suicides in countries where this method of suicide is prevalent.13 The withdrawal of more toxic pesticides,32 restriction of access to these pesticides,33 and measures related to decreasing absorption of toxic substances34 are likely to reduce suicide in such countries. Safer storage of pesticides is another promising approach to suicide prevention in Sri Lanka35 and India36 but evidence is scarce. Little evidence is available on the prevention of hanging, except in psychiatric hospital inpatient units.37 Potential initiatives have been proposed, such as anti-suicide shower heads in psychiatric units.38
+Three studies reported strong evidence that the erection of barriers at sites popular for jumping is useful, with an overall reduction in deaths by jumping of 86% (95% CI 79-91) and with little evidence of major substitution to other potential jumping sites.39-41
+Mann and colleagues13 concluded that the detoxification of domestic gas and the introduction of catalytic converters in cars were effective prevention methods. We found only one additional study on restricting the ease of purchase of charcoal, which suggested this approach might reduce suicides by carbon monoxide from charcoal burning.42
+Restricting prescriptions and sales of barbiturates,43 and reducing concentration of caffeine tablets44 decreased suicide incidence.
+Since psychiatric disorders are a major risk factor for suicidal behaviour, their pharmacological treatment contributes substantially to the prevention of suicide.48
+Reasonably strong evidence from randomised controlled trials (RCTs) shows that lithium is effective in reducing the risk of suicidal behaviour in people with mood disorders,49-51 which was supported by the findings of a large-scale naturalistic cohort study52 comparing lithium with valproate. A specific anti-suicidal effect of lithium was suggested in a population of people who had attempted suicide and who were treated with lithium, compared with people who had attempted suicide and were treated with placebo,53 although the number of deaths was very small (three deaths vs no deaths on lithium). A large-scale naturalistic study54 suggested that anticonvulsant mood stabilisers might also have protective effects against suicide attempts. Unadjusted rates were greater during treatment with divalproex than during treatment with lithium for suicide attempt that resulted in a visit to the emergency department (31-3 vs 10-8 deaths per 1000 person-years, p<0-001), suicide attempt resulting in hospitalisation (10-5 vs 4-2 suicide attempts per 1000 person-years, p<0-001), and suicide death (1-7 vs 0-7 per 1000 person-years; p=0-04). Precise p values were not reported. After adjustment for age, sex, health plan, year of diagnosis, comorbid medical and psychiatric conditions, and concomitant use of other psychotropic drugs, risk of suicide death was 2-7 times higher (95% CI 1-1-6-3; p=0-03) during treatment with divalproex than during treatment with lithium. Corresponding hazard ratios for non-fatal attempts were 1-7 (95% CI 1-2-2-3; p=0-002) for attempts resulting in hospital admission and 1-8 (1-4-2-2; P<0-001) for attempts diagnosed in the emergency department.54,55
+Clozapine is the only drug indicated in the USA for reduction of suicide risk in psychosis. A meta-analysis56 of the effects of clozapine in comparison with other dopamine and serotonin-receptor antagonists
+(eg, olanzapine and risperidone) demonstrated anti-suicidal effect in schizophrenia.56 However, in another meta-analysis,57 quetiapine showed no specific effect compared with other dopamine antagonists on the occurrence of suicide and attempted suicide.
+Large-scale ecological studies58-60 of antidepressants indicate that initiation of pharmacotherapy is not associated with an increased risk of suicide, while
+continuation of pharmacotherapy for depression is associated with a reduced risk of suicide. SSRIs might increase suicidal thoughts, but not actual suicidal behaviour, in early-phase pharmacotherapy of depression in adults.61 However, emergence of suicidal ideation is low, and the risk-benefit ratio for pharmacotherapy for depression appears to favour its use.62,63 In depression, the administration of sertraline is associated with decreased suicidal ideation and behaviour but not with emergent suicidal thinking or behaviour.64 People aged over 75 years, with depression treated with antidepressants might be at reduced risk of attempting suicide.65 The results of one RCT suggest that SSRIs might exert a stronger effect than would norepinephrine-dopamine reuptake inhibitors on reduction of suicidal thoughts during the initial weeks of pharmacotherapy in high-risk patients with depression.66 Ecological studies do not show increased sales of antidepressants to be associated with an increase in suicide rates.67-70 It remains unclear whether drugs for depression decrease or increase suicidal risk in patients with bipolar disorder.71 However, an evaluation of the relationship between changes in the prescription of antidepressants and changes in suicide prevalence found a clear inverse correlation in 29 European countries.72
+In children and adolescents with depression, evidence does not support avoidance of use of antidepressant medication because of increased risk of suicidal behaviour, although there is evidence to suggest an increased risk of suicidal ideation in this population.73 Adding cognitive behavioural therapy (CBT) to fluoxetine might lead to less suicidal ideation and behaviour than treatment with fluoxetine alone.73
+Ketamine shows promising results as a potentially effective and rapid treatment of suicidal thoughts, independent of improvement in depression, and with minimal side-effects,74 but effects on suicide attempts or death by suicide have not yet been shown, and effects on suicidal ideation longer than a few days have not been demonstrated. Electroconvulsive therapy was shown to rapidly reduce suicide risk in case series,75,76 but no controlled trials have been done.
+In Mann and colleagues’ review,13 CBT, dialectical behavioural therapy (DBT), problem-solving therapy, and intensive outpatient care with outreach were considered to be promising psychotherapies in suicide prevention; however, they had received insufficient support from RCTs or meta-analyses. Table 2 summarises the level of evidence in studies that investigated various treatment strategies.48-124 Eight studies reported that, compared with treatment-as-usual or minimal treatment, cognitive therapies such as CBT and manual-assisted cognitive therapy are effective in reducing suicidal ideation and behaviour in adolescents,86,126 adults (with mixed results),78,84 and patients with schizophrenia,80,87 as well as in patients with borderline personality disorder.81,85 Effects have mostly been found over medium-term follow-up.84 DBT can reduce suicidal ideation and behaviour in adolescents79
+4
+and women with borderline personality disorder.82,83 Preliminary trials have shown effectiveness of low-cost alternatives to classical DBT,90,91 as well as their acceptability for treatment for non-help-seeking suicidal patients.92 Group therapy with elements of CBT, DBT, and problemsolving therapy was effective in reducing self-harm for adolescents in one study79 but showed a negative effect in another.127 Specific elements for which evidence is weak or absent include skill development,83 the creation of a so-called own reasons to live list,88 and the use of modelling in skill development.128 A multi-systemic therapy approach that addresses improving parenting skills, community, school and peer support, and engagement in pro-social activities was associated with a reduction of suicidal attempts when compared with hospitalisation in adolescents.128 A variety of other treatments have shown effectiveness in reducing suicidality,89,93,95 although the level of evidence was relatively low. Psychosocial treatments have not shown clear effectiveness in reducing deaths by suicide, perhaps because of small sample sizes.78 Community, family, and group-based interventions were not identified by Mann and colleagues.13 We found two meta-analyses96,97 that did not show community mentalhealth services for people with serious mental illness to be superior to standard management in preventing suicide. However, results of one study97 showed greater acceptance of treatment, and proposed the possibility of reduced hospital admissions and suicide deaths. A meta-analysis conducted in Japan100 showed a reduction in suicides among people aged over 75 years following a communitybased intervention with screening and follow-up components based on pre-implementation versus postimplementation changes, but with no comparative condition.
+Other studies assessing social support strategies in different populations and settings showed inconsistent effects on suicide attempts and ideation,98,99,101,102 but positive effects on depressive symptoms.101,102 One systematic review103 of family-based interventions for patients with schizophrenia found no effect on suicide. RCTs evaluating family-based interventions in suicidal adolescents have consistently shown a clear decrease in suicidal ideation and suicide risk factors,104-106 and enhanced protective factors105 compared with routine care. Finally, a brief family-based crisis intervention with suicidal adolescents in emergency room settings showed reduced psychiatric hospitalisations and suicide attempts at 3 months of follow-up.107 Table 3 summarises the level of evidence in studies that investigated population-level prevention strategies.121-188
+Mann and colleagues13 reported mixed results on the efficacy of follow-up for people who attempt suicide. Since their report, several more studies have showed conflicting results. Contact interventions through sending regular postcards appeared to be effective in reducing repetition of suicidal behaviour in Iran, but not in high-income countries.112,113 Provision of information
+and support through telephone or face-to-face contacts appeared to reduce suicides in low-income and middle-income countries, where mental health resources in the community are scarce, but paradoxically did not affect repetition of attempted suicide.108,109,111 Structured follow-up of people who attempt suicide decreased the
+number of repeated attempts110,114 and suicides108,115 in some but not all studies.116,117 Collaborative care with the involvement of primary health-care services in follow-up has been shown to be feasible, acceptable, and effective (in terms of ideation) compared with standard care.118,122,124 Positive results were identified in similar programmes targeting depressed and suicidal elderly patients.119-121 Finally, a cohort study190 showed that where mental health services are available, especially with a combination of resources, suicidal behaviour was clearly reduced. On the other hand, no significant association was found between physician density and suicide rates.123
+Mann and colleagues13 concluded that few schoolbased programmes were evidence based or had been assessed in terms of the effectiveness in preventing suicidal behaviour. Over the past decade, evaluation studies involving school-based programmes have been of better quality. Systematic reviews,132-136 although including few RCTs, consistently indicate improved knowledge and attitudes towards suicide but no effect on actual suicidal behaviour. However, three large RCTs129-131 emphasising mental health literacy, suicide risk awareness, and skills training in schools, showed significant effects on suicide attempts and ideation. At 12 month follow-up, there was a significant reduction in suicide attempts (OR 0-45, 95% CI 0-24-0-85; p=0-014) and severe suicidal ideation (0-50, 0-27-0-92; p=0-025), compared with the control group.130 Prospective cohort studies137,139,191 assessing awareness programmes in schools showed inconsistent outcomes linked to suicidal behaviour. The level of evidence was 2c with one study showing positive,137 one showing negative,138 and one showing mixed outcomes.139
+Ecological studies have linked initiation of general public awareness campaigns to a significant increase in calls to helplines140-142 but without reduction in suicides.140 One study143 showed reduced suicidal ideation and plans in a specific population of gay men, and another144 showed reduction in suicides at short-term follow-up.
+Education of primary care physicians targeting depression recognition and treatment was identified as one of the most effective interventions in lowering suicide rates.13 Since 2005, ecological studies in Sweden,145 Hungary,146 and Slovenia,147 investigating programmes for general practitioners (GPs) have shown a significant increase in antidepressant use and decreased suicide rates.145,146 In a multi-component intervention,146 the effectiveness of educational elements could not be separated from other elements of the intervention (free telephone consultations with local psychiatrists, a new depression clinic, and access to cheaper antidepressants). Since 2005, there have been no RCTs on this subject, but results in the former systematic review13 consistently showed the benefit of the GPs’ educational activities.
+Mann and colleagues13 reported that gatekeeper training was helpful in reducing the number of suicides,
+provided that formalised roles and pathways to treatment were readily available.13 Since 2005, gatekeeper training has been studied in several populations, including military personnel,153 public school staff,148 peer helpers,149 youth workers,152 clinicians,145,146,156 depressed persons,154 and Indigenous people.155 However, no RCT has shown that gatekeeper training alone affects suicide rates. Systematic reviews of studies in various populations,150 as well as in Indigenous people in Australia, the USA, Canada, and New Zealand,151 mostly showed positive effects on knowledge, skills, and attitudes of trainees. In some of the studies from New Zealand, a decrease in suicide or suicidal behaviour was reported, but they lacked control groups.151
+Two systematic reviews157,158 have shown an association between the media depiction of suicide and actual suicidal behaviour, although the methodological quality of the studies reviewed was limited. The effects seem to be bi-directional: detrimental in vulnerable populations, such as people who attempt suicide,161 but protective in the general population when emphasising positive coping.160 Media blackouts or better reporting quality have been associated with decreased suicidal behaviour.159,160 Media participation in the development of guidelines assists in successful implementation, but the effectiveness of such guidelines in mitigating imitative suicides varies considerably.157
+Studies of telephone and internet services usually have relatively low levels of evidence. These studies have mainly focused on outcome measures such as acceptability of services by users,178 identification of people at risk and referral to help services,179 and compliance with referrals.180 Other studies have identified specific effective characteristics in these interventions174,181 and service providers,183 as well as service-use barriers.182 Some report reduction in suicidal ideation following interventions such as a brief mobile treatment intervention in Sri Lanka,173 unguided online self-help,175 and a telephone aftercare intervention.176 Intervention is more efficient than wait-listing.177
+Mann and colleagues13 identified the need for assessment of the cost-effectiveness of screening in the general population versus identified at-risk populations in reduction of suicide, the predictive validity and reliability of specific screening tools, and the assessment of standard screening across different cultures. A large systematic review162 concluded that evidence was insufficient to determine the benefits of screening in primary care populations. Eight other studies assessed the screening of a total of 15 244 cases and controls.163,165,166,168-172 One RCT163 showed no iatrogenic effects in youth suicide screening or in high-risk populations. A systematic review167 showed that youth suicide screening programmes improved identification in adolescents at risk; however, the positive predictive value of subsequent suicidal behaviour in school settings was relatively low in some ofthese reports (range 6-33%).
+Screening in both school168 and primary-care settings169 was found to be effective and safe163 in enhancing treatment referrals and service use in high-risk adolescents at long-term follow-up. A large RCT of suicide prevention programmes implemented in Europe189 did not show significant effects of screening in reducing suicidal ideation and attempts.130 However, screening for risk behaviours in addition to psychopathology was shown to add significant value in identifying European pupils with mental health problems.164 In people aged over 75 years in Japan, the use of depression screening and psychiatrist follow-up lowered suicide prevalence by 61%.171
+Some publications since 2005 have addressed the effect of combination and multi-level prevention programmes on suicide and suicide attempts (appendix).
+Discussion
+The heterogeneity of strategies and outcome measures, as well as absence of good standards for evidence level in the literature, limits conclusions about the current effectiveness of suicide prevention strategies. However, there have clearly been major advances since the review by Mann and colleagues in 2005.13
+There is now strong evidence that restricting access to lethal means is associated with a decrease in suicide and that substitution to other methods appears to be limited. This is clearly a major strategy to be integrated in national suicide prevention plans.
+Data support the use of a few pharmacological interventions in suicide prevention. First, antidepressant pharmacotherapy treatment in adults is associated with reduced suicide risk, while initiation of pharmacotherapy does not lead to an exacerbation of suicide risk. In people aged over 75 years with depression, there is a clear beneficial effect of pharmacotherapy on the risk of attempted and completed suicide. In children and adolescents, increased risk of suicidal thoughts has to be taken into account when starting pharmacotherapy for depression. However, given the increased risk of suicide in untreated depression and the absence of an increased risk of suicide associated with pharmacotherapy, currently available evidence does not support the avoidance of initiation and continuation of pharmacotherapy for depression in children and adolescents. Therefore, the ongoing discussion about possible induction of suicidality in minors should not prevent physicians from prescribing SSRIs.77 If a decision is made to use medication, then fluoxetine might be considered, given that it is recommended as first-line medication in guidelines. Second, lithium is effective in reducing the risk of suicide in people with mood disorders, possibly through decreasing aggression and impulsivity. Valproate might have similar efficacy in patients with bipolar disorder. Third, an anti-suicidal effect of clozapine in psychosis has been demonstrated; however, recent studies suggest that clozapine might not differ from risperidone or olanzapine in this respect. Studies of ketamine suggest promising rapid beneficial effects on reducing suicidal thoughts.
+Case series show that electroconvulsive therapy provides a rapid relief of suicidal thoughts. Electroconvulsive therapy should therefore be considered earlier, rather than at its conventional last resort position for patients at risk.
+Data support the efficacy of psychotherapies such as CBT and DBT. Psychodynamic psychotherapies have not been systematically studied. Understanding which treatment components (such as the development of a therapeutic alliance, the role of case-management and of significant others) might be effective in treatment of suicidality is an important line for future investigation.
+The evidence on chain of care and follow-up is scarce and heterogeneous, leading to weak significance of the aggregate data. Follow-up of people who attempt suicide
+is strongly supported by data and should be included in any national suicide prevention strategy. Findings that associate mental health service availability and reduced rates of suicide indicate the need for providing mental health services in national prevention initiatives.
+Community and family-based interventions are not effective in preventing suicide in severely ill mental patients. Nevertheless, the ability of such interventions to promote treatment acceptance and to reduce hospitalisation and suicide should be noted. Although cross-cultural replication is needed, there is evidence that in elderly people, screening for depression combined with community follow-up is effective in reducing suicide risk. Family interventions with suicidal adolescents show a promising effect on suicide ideation.
+RCTs increasingly show reduced suicide attempts and ideation following school-based mental health and suicide awareness programmes, with or without combined screening. While there has been an increase in the evaluation of general public awareness campaigns, a lack of RCTs remains a major limitation, indicating that no statements can be made about the effectiveness of these campaigns in reducing suicide.
+Interventions including training programmes for GPs might be followed by increased prescription of antidepressants, and subsequent decrease in suicides, but a direct association between training and reduced rates is difficult to identify. Future research using RCTs should examine the value of GP training and the efficiency of their diagnostic capabilities.
+No RCT showed that gatekeeper training alone affected suicide rates. Gatekeeper training is usually implemented along with other initiatives, making it difficult to identify the effect of this specific intervention on suicide rates. Intermediate outcome measures, such as referral rates and psychiatric treatment rates, should also be used.13 Future research should review elements of training, such as who is best to lead it, to whom it should be delivered, and with which specific content. Evaluation of the uptake of training in different specific contexts or populations is especially needed.
+Although no controlled studies were done on the effect of media on suicidal behaviour over the last decade, a clear bidirectional effect can be established. Media should be used in collaboration with journalists as a channel for appropriate public education. Further investigation is needed on the effect of the internet and social media on suicidal behaviour.
+Thus far, the evidence of telephone and internet intervention effectiveness is rather scarce and of low quality. Rapidly increasing utilisation of information and communication technologies in suicide prevention requires research assessing their efficacy.
+There is insufficient evidence of the benefits of screening in primary care populations for reducing risk of suicide. If referral to treatment is the outcome measure and not suicidal behaviour, screening might be
+more effective, provided that the chain of care is continuous and useful. Even in high-risk populations, evidence does not justify the cost of expensive screening procedures.
+Combinations of evidence-based strategies should be assessed on the individual and population levels using RCTs with sufficient power and similar methodologies. Future research on the efficacy of combined evidenced-based prevention strategies should focus on specific targeted populations (psychiatric patients, children and adolescents, older people, and ethnic minorities), as well as on cost-effectiveness and effect size. Data suggest that each specific risk group might need a tailored preventive approach. Priority should be given to reaching out to those who fail to seek medical or psychological help, with particular attention paid to older subjects.
+The main limitation of this study is that the final decisions on the level of evidence rely on the investigators’ judgments and therefore reproducibility of
+the findings might be more difficult than in a formal meta-analysis. However, 18 investigators participated in this process, and decisions on disagreements were made based on consensus. There is also an inherent risk of bias at review (eg, incomplete retrieval of identified research, reporting bias). Finally, the literature review goes only to the end of 2014.
+To conclude, sufficient evidence supports effective methods of prevention of suicidal behaviour. Potential interventions using new social media, mobile technologies, and continuous monitoring of large datasets seem to be the next field to explore in the coming decade. It is difficult to standardise methods for evaluation of suicide prevention initiatives. However, wherever possible, randomised trials should be the gold-standard approach. It is also important to look for unexpected untoward effects of initiatives, such as encouragement of suicidal behaviour by greater media attention, or method substitution where access to a common method of suicide is restricted. Evaluative research should be integral to national suicide prevention plans, including access to adequate funding to encourage and permit the necessary studies. Because suicide is a major cause of death and disability, the implementation of proven, evidence-based, and cost-effective strategies are the duty and responsibility of public health policy makers and health-care providers.
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+INTRODUCTION
+The term “transgender” is used to describe individuals whose gender identity differs significantly from the sex they were assigned at birth, and it includes individuals assigned male at birth who identify as female, individuals assigned female at birth who identify as male, and individuals who describe their gender identity outside of the binary categories of male and female (eg, bigender, gen-derqueer).1,2 Limited available research suggests that due to membership in a lower-status social group, transgender individuals experience more violence, discrimination, and victimization compared to cisgender (a term used to
+refer to one’s gender identity and/or expression aligning with one’s birth sex/gender) individuals.2-4 Increased risk of stigma-related stressors has also been documented to increase mental health risk factors, such as depression and anxiety, that heighten vulnerability to suicide .	5—7
+attempts.
+Understanding the relationship between stigma and suicide attempts within transgender populations is particularly relevant given that in the United States, the lifetime prevalence of suicide attempts among this group is estimated to be as high as 41.0%,7 compared to less than 9.0% in the general US population8 and approximately 10-20.0% among lesbian, gay, and bisexual (LGB) adults.9 It is important to note that these estimates are based on data from convenience samples of transgender individuals, given the lack of populationbased data on transgender populations in the US. These disparities highlight the urgent need to better understand
+the factors that heighten transgender adults’ vulnerability to attempted suicide.
+Stigma has been conceptualized in the literature as a multilevel construct, ranging from individual to structural levels. One example of stigma at the individual level is internalized transphobia, which can be understood as one’s internalization of society’s negative attitudes about transgender individuals into the self-concept.3 Although a few studies10-12 have adapted items from an existing measure of internalized homophobia13 for transgender individuals, none, to our knowledge, have measured and assessed associations between internalized transphobia and suicidality.
+Interpersonal forms of stigma, in contrast, refer to microlevel interactions that occur between the stigmatized minority and nonstigmatized majority and can include both overt (eg, victimization, hate crimes) and subtle processes that occur within the context of transgender and cisgender interactions (eg, not using preferred pronoun or social name). Several studies have documented associations between interpersonal stigma and suicidality among transgender populations.4,5,14,15 For instance, in a national sample of transgender adults, chronic stigmatizing experiences as a result of being gender variant, including harassment, discrimination, violence, and rejection, were correlated with increased vulnerability to attempted suicide.7 A study of transgender individuals in San Francisco similarly highlighted gender discrimination and physical gender victimization as key risk factors associated with attempted sui-cide.4 Moreover, a meta-analysis of HIV prevalence among transgender individuals in the United States found that exposure to transphobia was significantly associated with lifetime suicide attempts.16
+Building on research documenting the negative health sequelae of exposure to internalized and interpersonal stigma, researchers have begun to document the ways in which supraindividual forms of stigma—termed structural stigma—can also disadvantage the stigmatized. Structural stigma refers to societal-level conditions, institutional policies and practices, and cultural norms that constrain the opportunities, resources, and well-being of the stigma-tized.17,18 High levels of structural stigma targeting gay and bisexual individuals are significantly associated with a range of adverse health conditions, including substance use19 and all-cause mortality,18 among sexual minority populations. In relation to attempted suicide, lesbian, gay, and bisexual (LGB) youth who live in high-structural stigma environments are more likely to attempt suicide compared to LGB youth who live in low-stigma communities; these relationships are independent of individual-level risk fac-tors.20 To our knowledge, no literature to date has operationalized and statistically modeled the relationship between structural stigma and attempted suicide among transgender individuals. Further, with few exceptions,19,21 studies of the relationship between stigma and health rarely include measures of stigma at both the individual and
+structural level to determine whether these factors independently predict adverse health outcomes.
+To address these current gaps in the literature, this study sought to examine associations between individual and structural forms of stigma with suicide attempts among transgender adults. At the individual level, we hypothesized that those transgender individuals who reported more internalized stigma (ie, internalized transphobia) would be at greater risk for attempting suicide than transgender individuals who report lower levels of internalized stigma. We further hypothesized that transgender individuals living in states with higher structural-level stigma would have increased vulnerability of attempted suicide compared to those living in low-structural stigma states.
+METHODS
+Study Population
+The present study is a secondary data analysis of a 2003 cross-sectional Internet-based health assessment of transgender adults22 living in the United States. Participants self-identified as transgender, and they comprised a spectrum of gender diverse identities, including transgender, transsexual, crossdresser, bigender, and genderqueer. Participants were recruited via banner advertisements and messages posted on transgender community websites and Listservs. The online survey included questions regarding participants’ sociodemographics, transgender identity, sexual behavior, substance use, physical health, and mental health. The survey took approximately 50 to 60 minutes to complete, and participants were compensated with a $30 online gift certificate. To assess participants’ eligibility, inconsistent responding, and survey uniqueness, a computerized duplication and cross-validation protocol was implemented to check a variety of factors, including non-unique IP addresses, ZIP codes, and survey completion time. Surveys that were flagged by this computerized program were further analyzed and, depending on assessment, were removed from the data set (44 individuals were ultimately removed).23
+The study protocol was reviewed and approved by the institutional review board (IRB), human subjects committee, at the University of Minnesota. Further information regarding study methods are described elsewhere.3,22
+Measures
+Lifetime and Past 12-month Suicide Attempts
+Lifetime suicide attempts were assessed with the question, “Have you ever attempted suicide (tried to kill yourself)?” Participants were also asked, “How long ago was the last time you attempted suicide (tried to kill yourself)?” We assessed if participants had attempted
+suicide in the past 12 months by calculating time since last suicide attempt.
+Structural Stigma
+There was very little state-level variation in transgenderspecific policies at the time the study was conducted in 2003, and adequate information on other state-level indicators of structural stigma toward gender minorities (eg, social attitudes) was also lacking. However, states with policies that protected sexual minorities in 2003 later become states that protected gender minorities,24,25 and social attitudes toward gender minorities are correlated with attitudes toward sexual minorities.26 This suggests that structural forms of stigma targeting sexual minorities capture, at least in part, the prejudiced environments surrounding gender minorities. Consequently, we used a previously established measure20,21 of state-level structural stigma related to sexual minorities as a proxy for structural forms of stigma related to gender minorities.
+This measure consists of a 4-item composite index. The first variable was a measure of the density of samesex couples per 1,000 households living in the state, obtained from the 2000 US Census. The second variable was the proportion of public high schools with a gaystraight alliance (GSA) per state. These data were from the Gay, Lesbian, and Straight Education Network (GLSEN) for the year 2006, and the number of public high schools in the state was obtained from the National Center for Education Statistics. The variable was created by dividing the number of gay-straight alliances by the number of public high schools per state. The third variable included five state-level policies related to sexual orientation discrimination, including same-sex marriage; employment nondiscrimination laws that are inclusive of sexual orientation; hate crime statutes that are inclusive of sexual orientation; a nondiscrimination policy that extended to LGB students and/or a statute banning bullying based on sexual orientation; and joint adoption for same-sex couples. States were coded according to policies and legislation that were in place in 2000, and a summary variable was created adding the five policies together. The fourth variable measured public opinion toward citizenship rights for sexual minorities assessed through 41 national opinion polls dating from 19992008. Policy-specific opinions were collected for the following areas: gay adoption, hate crimes, health benefits, discrimination in jobs and housing, marriage, sodomy, and civil unions. We used the mean attitude score for each state. Greater detail to the construction of the index available elsewhere.21
+To create a composite variable for each of these four variables, a z-score was calculated for each dimension, which was then summed. The Cronbach’s alpha for the summary score for the 4 components of the composite
+variable was 0.87, indicating high internal consistency. A principal components analysis resulted in the variables loading onto a single component, with factor loadings ranging from 0.76 to 0.93, providing support for construct validity. The structural stigma variable was then linked to survey responses through participants’ state of residence. There were 1,229 respondents from 48 states (no responses from Montana and South Dakota) and the District of Columbia in the dataset. The composite structural stigma score ranged from a low of —7.56 (North Dakota) to a high of 9.32 (New York), with the lower values indicating less supportive environments (ie, higher levels of structural stigma).
+Demographic Covariates
+Gender identity was coded as male-to-female (MtF) (ie, assigned male at birth but identifying at least in part with the female gender) or female-to-male (FtM) (ie, assigned female at birth but identifying at least in part with the male gender). To assess natal sex, participants were asked the following two questions: “The sex that I was assigned at birth (the sex on my original birth certificate) is: Male or Female” and “When I was born, my external genitals were: Male, female, other.” To assess gender identity, participants were asked “To what extent do you currently identify as a man?” and “To what extent do you currently identify as a woman?,” with response options on a 7-point Likert scale ranging from “Not at all” to “Very strongly.”
+Additional sociodemographic characteristics assessed included age, race (coded as white versus other), education (coded as high school or less, some college, college or higher), self-reported gross annual household income, and urbanicity (coded as rural/small town versus medium-sized/ metropolitan area). Given the skewed distribution of the gross annual income variable, we used the natural logarithm of income for all analyses.
+Internalized Transphobia
+Internalized transphobia was assessed through the 26-item Transgender Identity Survey, which captured four dimensions including pride, passing, alienation, and shame. Participants were asked how they felt about being transgender in the last 3 months, and responses were recorded on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). This measure was specifically developed for the larger transgender health study. The initial item pool was developed by asking a clinical sample of transgender individuals about their thoughts and feelings when they felt down or ashamed of being transgender versus when they felt good or proud. These items were refined by a panel of experts with extensive clinical experience working with the transgender population. The initial item pool was then administered to a transgender community sample (N = 430) and, subsequently, to the online sample that is the
+focus of this study (N = 1,229). Exploratory and subsequent confirmatory factor analysis resulted in a 26-item scale with internal consistency reliability of .94 (Cronbach’s alpha) and test-retest reliability (n = 20) of .93. Furthermore, consistent with moderate to good construct validity, these results indicated that internalized transphobia can be conceptualized as four interrelated dimensions, including, pride, passing, alienation, and shame.27 A higher scale score indicates more internalized transphobia (ie, more negative attitudes about one’s transgender status).
+Statistical Analysis
+Sample characteristics were calculated and reported overall and by lifetime suicide attempts (Table 1). Bivariate logistic generalized estimating equation (GEE) models were used to compare characteristics between participants reporting any suicide attempts (lifetime suicide attempts and/or past 12 months suicide attempts) compared to the full sample.
+Using multivariable logistic generalized estimating equation (GEE) models, we assessed whether (1) individual-level stigma (ie, internalized transphobia) and (2) structural stigma were associated with lifetime and past 12 months suicide attempts among transgender adults. For all analyses, covariates included participant’s age, gender, education, race, income, and urbanicity. Finally, we assessed the interaction between individual (ie, internalized transphobia) and structural stigma to test for joint effects. A complete case analysis was used for all analyses. All analyses were conducted in Stata 12.0 (StataCorp, College Station, TX).
+RESULTS
+Among the sample, self-reported lifetime prevalence of attempted suicide was 32.4%, whereas past 12 month prevalence was 6.41%. Male-to-female transgender participants accounted for slightly less than half (46.8%) of lifetime suicide attempts but the majority (66.67%) of attempted suicide in the past 12 months.
+Table 2 summarizes results from multivariable models assessing factors associated with lifetime and past 12 month suicide attempts. For the lifetime timeframe, in the multivariable model including only sociodemographic factors, male-to-female transgender identity (adjusted odds ratio (AOR): 0.63, 95% confidence interval (CI): 0.46-0.85) and being white (AOR: 0.58, 95% CI: 0.430.80) were each significantly associated with decreased odds of lifetime suicide attempts. Participants who reported a college education or higher had decreased odds (AOR 0.49, 95% CI 0.31-0.75) of lifetime suicide attempts compared to participants who had a high school education or less. Additionally, higher levels of internalized transphobia were significantly associated with increased odds of lifetime suicide attempts (AOR 1.18, 95% CI 1.04-1.33).
+For attempted suicide in the past 12 months, having received a college education or higher was significantly associated with decreased odds (AOR: 0.26, 95% CI: 0.10-0.65) in the multivariable model including only sociodemographic factors. When internalized transphobia was included, a higher level of internalized transphobia was associated with increased odds of attempted
+suicide in the past 12 months, but this did not reach statistical significance (AOR: 1.11, 95% CI: 0.85-1.46).
+Table 3 summarizes the results for the models examining association between state-level structural stigma and lifetime and past 12 month suicide attempts. Lower levels of structural stigma were significantly associated with fewer lifetime suicide attempts (AOR: 0.96, 95% CI: 0.920.997), when adjusting for gender identity, age, race, income, education, urbanicity, and internalized transphobia.
+Other factors that were significantly associated with fewer lifetime suicide attempts included male-to-female transgender identity (AOR: 0.59, 95% CI: 0.43-0.80), being white (AOR: 0.57, 95% CI: 0.41-0.78), attaining college education or higher (AOR: 0.50, 95% CI: 0.32-0.78), and reporting higher levels of internalized transphobia (AOR 1.18, 95% CI 1.04-1.33). For multivariable models assessing the relationship between structural stigma and suicide attempts in past 12 months, neither structural stigma nor internalized
+transphobia remained significantly associated with suicide attempts. In this model, only attaining college education or higher was significantly associated with decreased odds of current suicide attempts (AOR: 0.26, 95% CI: 0.10-0.66).
+DISCUSSION
+The aim of the present study was to assess individual- and structural-level risk factors for suicide attempts among transgender adults. With over 30% of the sample reporting lifetime suicide attempts, these results further underscore the high prevalence of lifetime-attempted suicide among male-to-female as well as female-to-male transgender individuals. Our findings demonstrate that the increased odds of attempted suicide among transgender individuals is associated with internalized transphobia, with racial/ethnic minority status, and with lower levels of educational attainment, paralleling the existing literature.7,28 Previous studies with geographically restricted samples (eg, sampling participants from one state or city) have produced mixed findings with respect to gender differences, with some highlighting elevated lifetime suicide attempts among male-to-female transgender individuals13,16 and others observing no differences between male-to-female and female-to-male transgender individuals.4 While prevalent in both groups, we found that a higher proportion of female-to-male transgender participants attempted suicide across their lifetime compared to male-to-female transgender individuals, similar to results from the recent National Transgender Discrimination Survey.7 Additionally, prior research has shown that female-to-male transgender individuals experience greater victimization and have higher levels of lifetime suicidal ideation compared with their male-to-female counterparts.6 Thus, living in more stigmatizing communities may further facilitate vulnerability toward experiences of stigma-related stressors, which may then lead to increased suicidal ideation and attempts among female-to-male transgender individuals.
+We extend previous literature on risk factors for suicide attempts among transgender populations by addressing sources of stigma at multiple levels of analysis. At the individual level, our results indicated that internalized transphobia was significantly associated with increased odds of suicide attempts in the past 12 months. Although measures to assess interpersonal stigma or expressions of transphobia are available,4,11,29 no work, to our knowledge, has evaluated the relationship between a measure of internalized transphobia and suicide attempts. Our results suggest the need for future research to investigate transgender individuals’ internalized stress and/or discomfort with their own transgenderism to better inform coping and resiliency strategies in existing clinical interventions.
+Beyond individual risk factors, this study employed a novel measure of structural stigma that did not rely on
+participant perceptions in order to assess prejudicial social environments at the state level and linked this measure to suicide attempts. Lower levels of structural stigma were found to reduce the odds of lifetime suicide attempts among transgender adults. Because the timeframes for the exposure (ie, current structural stigma) and outcome (ie, lifetime suicide attempts) differed, we also examined associations between current structural stigma and current (ie, past 12 month) suicide attempts. Although these results did not meet statistical significance, this is likely due to low statistical power, given the small number of past 12 month suicide attempts in this sample (N = 51). Although effect sizes were relatively small, our results suggest that at a population level, a reduction in LGB structural stigma may substantially decrease the odds of attempted suicide among transgender individuals. For example, a 5-unit increase in the structural stigma measure (ie, a 5-unit increase in the supportiveness of the social climate) was associated with a 17% decrease in odds of self-reported lifetime attempted suicide, a significant public health impact.
+Building on the evidence that structural stigma affects the health of sexual minority adults and youth,18,19 future studies assessing health disparities related to gender minority status should prioritize investigating pathways of stigma at multiple levels, including structural. Specifically, researchers may wish to consider additional measures when assessing the impact of state-level policies on the health of transgender and gender-nonconforming individuals, including hate crime laws that list gender identity as a protected class, birth certificate change laws, and attitudes on the rights and legal protections of transgender individuals. Understanding the impact of prejudicial attitudes and policies is crucial to informing structural interventions and legal reforms to improve mental health outcomes among gender minorities.
+Several limitations should be considered when interpreting these results. Because there was very little state-level variation in transgender-specific policies at the time the study was conducted in 2003, policies included in our measure reflected the prejudicial environments surrounding sexual minorities, rather than gender minorities. At the same time, such measures are highly correlated (ie, states with policies that protect sexual minorities later become states that protect gender minorities). For instance, as of 2013, 86% (18/21) of states (plus the District of Columbia) that have passed employment nondiscrimination laws based on sexual orientation also prohibited discrimination based on gender identity.30 Further, currently there exists no state-level law that protects gender identity but not sexual orientation. This highlights that structural forms of stigma targeting sexual minorities largely, though not exclusively, capture the impact of prejudicial environments surrounding gender minorities. Nevertheless, further research is needed to assess the impact of transgender-specific policies on health outcomes, including suicidality.
+Data that comprise the structural stigma measure came from various sources ranging from 2000 to 2008 (given restrictions in data availability), whereas the outcome data were collected in 2003, which may impact the sensitivity of the findings reported here. Despite the discrepancy in time, our state-level construct represents the best possible proxy for anti-LGB prejudicial environments in 2003 when the outcome measure was collected. Nonetheless, to the extent that this measure is not a good proxy for the 2003 climate, our analyses would have suffered from the incorporation of measurement error, which would have reduced our ability to detect significant associations rather than bias our results in the hypothesized direction (ie, leading us to conclude that lower levels of structural stigma is protective against suicide attempts when, in fact, it is not). Research indicates that the effect of legislative action on prejudicial attitudes toward transgender individuals may progress gradually31 highlighting the need for longitudinal studies to assess how changes in community attitudes affect transgender health.
+Additionally, these data were derived from a sample recruited online in 2003, responses were self-reported, and study participants may not be representative of the larger transgender population. Moreover, our sample was predominantly Caucasian, which suggests the need to replicate this study with a more racially and ethnically diverse sample. Nonetheless, this study demonstrates the success of Internet-based recruitment of gender minorities. Moreover, 48 states in the United States and the District of Columbia were represented in this sample, providing sufficient variation of structural stigma surrounding gender minorities.
+This was a cross-sectional study, and, as such, causation cannot necessarily be inferred from observed associations. Finally, our state-level measure of structural stigma may not have adequately captured the differences within states (eg, protective policies varying by county or local ordinances within states). Future studies should examine relationships between structural stigma and suicide attempts across smaller geographic units of analysis. At the same time, our results are particularly striking, given that the state-level measure of structural stigma represents a distal determinant of suicide attempts; the results presented therefore likely underestimate the impact of structural stigma on suicide attempts among transgender individuals.
+CONCLUSIONS
+Due to recent shifts in policies to protect sexual and gender minorities in the United States, the current study represents an important and timely addition to the literature on social determinants of mental health outcomes among transgender populations. These results provide preliminary evidence to suggest that societal-level changes (eg, state-level policy reform) may influence the prevalence of suicide attempts in transgender individuals. Nonetheless, the mechanisms
+underlying the relationship between structural stigma and transgender people’s health remains inadequately understood. Consequently, these results seek to focus attention on the need to further investigate the multilevel relationship between stigma and health among gender minorities in the United States.
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